2025-01-24T01:02:23.4705981Z Current runner version: '2.321.0' 2025-01-24T01:02:23.4712961Z Runner name: 'i-0370698385eeb2f09' 2025-01-24T01:02:23.4713883Z Runner group name: 'Default' 2025-01-24T01:02:23.4714855Z Machine name: 'ip-10-0-14-66' 2025-01-24T01:02:23.4719517Z ##[group]GITHUB_TOKEN Permissions 2025-01-24T01:02:23.4722463Z Actions: read 2025-01-24T01:02:23.4723132Z Attestations: read 2025-01-24T01:02:23.4723810Z Checks: read 2025-01-24T01:02:23.4724449Z Contents: read 2025-01-24T01:02:23.4725071Z Deployments: read 2025-01-24T01:02:23.4725672Z Discussions: read 2025-01-24T01:02:23.4726329Z Issues: read 2025-01-24T01:02:23.4726940Z Metadata: read 2025-01-24T01:02:23.4727663Z Packages: read 2025-01-24T01:02:23.4728281Z Pages: read 2025-01-24T01:02:23.4728858Z PullRequests: read 2025-01-24T01:02:23.4729839Z RepositoryProjects: read 2025-01-24T01:02:23.4730663Z SecurityEvents: read 2025-01-24T01:02:23.4731299Z Statuses: read 2025-01-24T01:02:23.4731965Z ##[endgroup] 2025-01-24T01:02:23.4735579Z Secret source: Actions 2025-01-24T01:02:23.4736771Z Prepare workflow directory 2025-01-24T01:02:23.8490533Z Prepare all required actions 2025-01-24T01:02:23.8533099Z Getting action download info 2025-01-24T01:02:24.0222956Z Download action repository 'pytorch/test-infra@main' (SHA:e83685f710cdb1685e9f12471c30aa36cf09cf13) 2025-01-24T01:02:25.5499245Z Download action repository 'pytorch/pytorch@main' (SHA:b2c89bc115123aea8e075e882ee121537ec92f89) 2025-01-24T01:02:40.1030210Z Download action repository 'aws-actions/configure-aws-credentials@v3' (SHA:50ac8dd1e1b10d09dac7b8727528b91bed831ac0) 2025-01-24T01:02:40.2861911Z Download action repository 'seemethere/upload-artifact-s3@v5' (SHA:baba72d0712b404f646cebe0730933554ebce96a) 2025-01-24T01:02:40.6660244Z Getting action download info 2025-01-24T01:02:40.7818445Z Download action repository 'actions/checkout@v4' (SHA:11bd71901bbe5b1630ceea73d27597364c9af683) 2025-01-24T01:02:41.0127908Z Getting action download info 2025-01-24T01:02:41.1234519Z Download action repository 'nick-fields/retry@v3.0.0' (SHA:7152eba30c6575329ac0576536151aca5a72780e) 2025-01-24T01:02:41.2961928Z Getting action download info 2025-01-24T01:02:41.3909954Z Download action repository 'nick-fields/retry@3e91a01664abd3c5cd539100d10d33b9c5b68482' (SHA:3e91a01664abd3c5cd539100d10d33b9c5b68482) 2025-01-24T01:02:41.5513768Z Getting action download info 2025-01-24T01:02:41.6742895Z Uses: pytorch/pytorch/.github/workflows/_linux-test.yml@refs/pull/145539/merge (74e42e7401e3b57fc188c6d3823585f63c780e87) 2025-01-24T01:02:41.6745074Z ##[group] Inputs 2025-01-24T01:02:41.6745458Z build-environment: linux-focal-py3.13-clang10 2025-01-24T01:02:41.6748435Z test-matrix: {"include": [{"config": "default", "shard": 1, "num_shards": 5, "runner": "lf.linux.4xlarge"}, {"config": "default", "shard": 2, "num_shards": 5, "runner": "lf.linux.4xlarge"}, {"config": "default", "shard": 3, "num_shards": 5, "runner": "lf.linux.4xlarge"}, {"config": "default", "shard": 4, "num_shards": 5, "runner": "lf.linux.4xlarge"}, {"config": "default", "shard": 5, "num_shards": 5, "runner": "lf.linux.4xlarge"}, {"config": "crossref", "shard": 1, "num_shards": 2, "runner": "lf.linux.2xlarge"}, {"config": "crossref", "shard": 2, "num_shards": 2, "runner": "lf.linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 1, "num_shards": 3, "runner": "lf.linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 2, "num_shards": 3, "runner": "lf.linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 3, "num_shards": 3, "runner": "lf.linux.2xlarge"}]} 2025-01-24T01:02:41.6751672Z docker-image: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.13-clang10:bf84073365afe24d4dcffebc142d1c20de914163 2025-01-24T01:02:41.6752463Z sync-tag: 2025-01-24T01:02:41.6753298Z timeout-minutes: 600 2025-01-24T01:02:41.6753594Z use-gha: 2025-01-24T01:02:41.6753849Z dashboard-tag: 2025-01-24T01:02:41.6754115Z s3-bucket: gha-artifacts 2025-01-24T01:02:41.6754418Z aws-role-to-assume: 2025-01-24T01:02:41.6755013Z disable-monitor: false 2025-01-24T01:02:41.6755580Z ##[endgroup] 2025-01-24T01:02:41.6756182Z Complete job name: linux-focal-py3.13-clang10 / test (dynamo_wrapped, 1, 3, lf.linux.2xlarge) 2025-01-24T01:02:41.7204981Z A job started hook has been configured by the self-hosted runner administrator 2025-01-24T01:02:41.7314709Z ##[group]Run '/home/ec2-user/runner-scripts/before_job.sh' 2025-01-24T01:02:41.7323752Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-01-24T01:02:41.7324563Z ##[endgroup] 2025-01-24T01:02:43.3421144Z Runner Type: lf.linux.2xlarge 2025-01-24T01:02:43.3421742Z Instance Type: c5.2xlarge 2025-01-24T01:02:43.3422057Z AMI Name: unknown 2025-01-24T01:02:43.3446153Z AMI ID: ami-05576a079321f21f8 2025-01-24T01:02:48.6478693Z ##[group]Run pytorch/test-infra/.github/actions/setup-ssh@main 2025-01-24T01:02:48.6479492Z with: 2025-01-24T01:02:48.6480187Z github-secret: *** 2025-01-24T01:02:48.6480948Z instructions: All testing is done inside the container, to start an interactive session run: docker exec -it $(docker container ps --format '{{.ID}}') bash 2025-01-24T01:02:48.6481780Z activate-with-label: false 2025-01-24T01:02:48.6482085Z label: with-ssh 2025-01-24T01:02:48.6482344Z remove-existing-keys: true 2025-01-24T01:02:48.6482647Z fail-silently: true 2025-01-24T01:02:48.6482910Z env: 2025-01-24T01:02:48.6483140Z GIT_DEFAULT_BRANCH: main 2025-01-24T01:02:48.6483429Z ##[endgroup] 2025-01-24T01:02:48.7631504Z Please see https://github.com/pytorch/pytorch/wiki/Debugging-using-with-ssh-for-Github-Actions for more info. 2025-01-24T01:02:49.1112875Z Grabbing public ssh keys from https://github.com/facebook-github-bot.keys 2025-01-24T01:02:49.1708763Z No SSH keys found for user facebook-github-bot 2025-01-24T01:02:49.1709362Z Grabbing public ssh keys from https://github.com/yushangdi.keys 2025-01-24T01:02:49.2487369Z ~/.ssh/authorized_keys file found on node, removing ~/.ssh and starting fresh 2025-01-24T01:02:49.2507510Z Public keys pulled and installed to /home/ec2-user/.ssh/authorized_keys 2025-01-24T01:02:49.2542566Z Login using: ssh ec2-user@ec2-54-158-162-233.compute-1.amazonaws.com 2025-01-24T01:02:49.2543211Z All testing is done inside the container, to start an interactive session run: 2025-01-24T01:02:49.2543791Z docker exec -it $(docker container ps --format '{{.ID}}') bash 2025-01-24T01:02:49.2681566Z ##[group]Run pytorch/pytorch/.github/actions/checkout-pytorch@main 2025-01-24T01:02:49.2682197Z with: 2025-01-24T01:02:49.2682576Z no-sudo: true 2025-01-24T01:02:49.2682927Z submodules: recursive 2025-01-24T01:02:49.2683336Z fetch-depth: 0 2025-01-24T01:02:49.2683637Z env: 2025-01-24T01:02:49.2683972Z GIT_DEFAULT_BRANCH: main 2025-01-24T01:02:49.2684383Z ##[endgroup] 2025-01-24T01:02:49.2773457Z ##[group]Run echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT" 2025-01-24T01:02:49.2774549Z echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT" 2025-01-24T01:02:49.2783338Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-01-24T01:02:49.2783908Z env: 2025-01-24T01:02:49.2784319Z GIT_DEFAULT_BRANCH: main 2025-01-24T01:02:49.2784731Z ##[endgroup] 2025-01-24T01:02:49.2877645Z ##[group]Run retry () { 2025-01-24T01:02:49.2878023Z retry () { 2025-01-24T01:02:49.2878373Z  $* || (sleep 1 && $*) || (sleep 2 && $*) || (sleep 4 && $*) || (sleep 8 && $*) 2025-01-24T01:02:49.2878784Z } 2025-01-24T01:02:49.2879038Z echo "${GITHUB_WORKSPACE}" 2025-01-24T01:02:49.2879627Z if [ -z "${NO_SUDO}" ]; then 2025-01-24T01:02:49.2879999Z  retry sudo rm -rf "${GITHUB_WORKSPACE}" 2025-01-24T01:02:49.2880347Z else 2025-01-24T01:02:49.2880622Z  retry rm -rf "${GITHUB_WORKSPACE}" 2025-01-24T01:02:49.2880960Z fi 2025-01-24T01:02:49.2881217Z mkdir "${GITHUB_WORKSPACE}" 2025-01-24T01:02:49.2886718Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-01-24T01:02:49.2887379Z env: 2025-01-24T01:02:49.2887603Z GIT_DEFAULT_BRANCH: main 2025-01-24T01:02:49.2887886Z NO_SUDO: true 2025-01-24T01:02:49.2888128Z ##[endgroup] 2025-01-24T01:02:49.2910360Z /home/ec2-user/actions-runner/_work/pytorch/pytorch 2025-01-24T01:02:51.3891555Z ##[group]Run actions/checkout@v4 2025-01-24T01:02:51.3891886Z with: 2025-01-24T01:02:51.3892150Z ref: d087145b9259b4cfbcb758dc0bca6a6e5d75d08f 2025-01-24T01:02:51.3892504Z fetch-depth: 0 2025-01-24T01:02:51.3892760Z submodules: recursive 2025-01-24T01:02:51.3893021Z show-progress: false 2025-01-24T01:02:51.3893304Z repository: pytorch/pytorch 2025-01-24T01:02:51.3893749Z token: *** 2025-01-24T01:02:51.3893990Z ssh-strict: true 2025-01-24T01:02:51.3894244Z ssh-user: git 2025-01-24T01:02:51.3894492Z persist-credentials: true 2025-01-24T01:02:51.3894781Z clean: true 2025-01-24T01:02:51.3895046Z sparse-checkout-cone-mode: true 2025-01-24T01:02:51.3895363Z fetch-tags: false 2025-01-24T01:02:51.3895610Z lfs: false 2025-01-24T01:02:51.3895853Z set-safe-directory: true 2025-01-24T01:02:51.3896127Z env: 2025-01-24T01:02:51.3896355Z GIT_DEFAULT_BRANCH: main 2025-01-24T01:02:51.3896630Z ##[endgroup] 2025-01-24T01:02:51.5027459Z Syncing repository: pytorch/pytorch 2025-01-24T01:02:51.5028994Z ##[group]Getting Git version info 2025-01-24T01:02:51.5029503Z Working directory is '/home/ec2-user/actions-runner/_work/pytorch/pytorch' 2025-01-24T01:02:51.5030215Z [command]/usr/bin/git version 2025-01-24T01:02:51.5030554Z git version 2.40.1 2025-01-24T01:02:51.5039341Z ##[endgroup] 2025-01-24T01:02:51.5053226Z Temporarily overriding HOME='/home/ec2-user/actions-runner/_work/_temp/c8bb792b-e5f6-4131-963c-defb69a729d7' before making global git config changes 2025-01-24T01:02:51.5054264Z Adding repository directory to the temporary git global config as a safe directory 2025-01-24T01:02:51.5068343Z [command]/usr/bin/git config --global --add safe.directory /home/ec2-user/actions-runner/_work/pytorch/pytorch 2025-01-24T01:02:51.5098177Z Deleting the contents of '/home/ec2-user/actions-runner/_work/pytorch/pytorch' 2025-01-24T01:02:51.5101122Z ##[group]Initializing the repository 2025-01-24T01:02:51.5105059Z [command]/usr/bin/git init /home/ec2-user/actions-runner/_work/pytorch/pytorch 2025-01-24T01:02:51.5130203Z hint: Using 'master' as the name for the initial branch. This default branch name 2025-01-24T01:02:51.5130880Z hint: is subject to change. To configure the initial branch name to use in all 2025-01-24T01:02:51.5131513Z hint: of your new repositories, which will suppress this warning, call: 2025-01-24T01:02:51.5131964Z hint: 2025-01-24T01:02:51.5132297Z hint: git config --global init.defaultBranch 2025-01-24T01:02:51.5132674Z hint: 2025-01-24T01:02:51.5133133Z hint: Names commonly chosen instead of 'master' are 'main', 'trunk' and 2025-01-24T01:02:51.5134115Z hint: 'development'. The just-created branch can be renamed via this command: 2025-01-24T01:02:51.5134569Z hint: 2025-01-24T01:02:51.5134809Z hint: git branch -m 2025-01-24T01:02:51.5135364Z Initialized empty Git repository in /home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/ 2025-01-24T01:02:51.5142130Z [command]/usr/bin/git remote add origin https://github.com/pytorch/pytorch 2025-01-24T01:02:51.5173166Z ##[endgroup] 2025-01-24T01:02:51.5173632Z ##[group]Disabling automatic garbage collection 2025-01-24T01:02:51.5176805Z [command]/usr/bin/git config --local gc.auto 0 2025-01-24T01:02:51.5200124Z ##[endgroup] 2025-01-24T01:02:51.5200545Z ##[group]Setting up auth 2025-01-24T01:02:51.5206209Z [command]/usr/bin/git config --local --name-only --get-regexp core\.sshCommand 2025-01-24T01:02:51.5229687Z [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' || :" 2025-01-24T01:02:51.5489117Z [command]/usr/bin/git config --local --name-only --get-regexp http\.https\:\/\/github\.com\/\.extraheader 2025-01-24T01:02:51.5515976Z [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' || :" 2025-01-24T01:02:51.5773237Z [command]/usr/bin/git config --local http.https://github.com/.extraheader AUTHORIZATION: basic *** 2025-01-24T01:02:51.5814139Z ##[endgroup] 2025-01-24T01:02:51.5814674Z ##[group]Fetching the repository 2025-01-24T01:02:51.5823265Z [command]/usr/bin/git -c protocol.version=2 fetch --prune --no-recurse-submodules origin +refs/heads/*:refs/remotes/origin/* +refs/tags/*:refs/tags/* 2025-01-24T01:03:43.2377412Z From https://github.com/pytorch/pytorch 2025-01-24T01:03:43.2377929Z * [new branch] 144594 -> origin/144594 2025-01-24T01:03:43.2378618Z * [new branch] 2.1-dynamic-doc -> origin/2.1-dynamic-doc 2025-01-24T01:03:43.2379838Z * [new branch] 2.6.0.dev20241004+ -> origin/2.6.0.dev20241004+ 2025-01-24T01:03:43.2382365Z * [new branch] Adjust-Description-for-linux-binary-test-Workflow -> origin/Adjust-Description-for-linux-binary-test-Workflow 2025-01-24T01:03:43.2383889Z * [new branch] Broken-classifications_pending_unstable-Test -> origin/Broken-classifications_pending_unstable-Test 2025-01-24T01:03:43.2385405Z * [new branch] Chillee-patch-5 -> origin/Chillee-patch-5 2025-01-24T01:03:43.2387289Z * [new branch] Cleanup-Previous-Artifacts -> origin/Cleanup-Previous-Artifacts 2025-01-24T01:03:43.2389487Z * [new branch] JackCaoG/add_new_lazy_counter_macro -> origin/JackCaoG/add_new_lazy_counter_macro 2025-01-24T01:03:43.2390946Z * [new branch] JackCaoG/add_openxla_backend_to_list -> origin/JackCaoG/add_openxla_backend_to_list 2025-01-24T01:03:43.2392662Z * [new branch] JackCaoG/dynamo_make_fx_non_core_aten_ops -> origin/JackCaoG/dynamo_make_fx_non_core_aten_ops 2025-01-24T01:03:43.2393996Z * [new branch] JackCaoG/fix_xla_torchbench -> origin/JackCaoG/fix_xla_torchbench 2025-01-24T01:03:43.2396145Z * [new branch] JackCaoG/make_xla_dynamo_backend_boxed -> origin/JackCaoG/make_xla_dynamo_backend_boxed 2025-01-24T01:03:43.2398344Z * [new branch] JackCaoG/test_xla_pin -> origin/JackCaoG/test_xla_pin 2025-01-24T01:03:43.2399394Z * [new branch] JackCaoG/test_xla_pin_update -> origin/JackCaoG/test_xla_pin_update 2025-01-24T01:03:43.2401189Z * [new branch] JackCaoG/update_dynamo_doc -> origin/JackCaoG/update_dynamo_doc 2025-01-24T01:03:43.2402729Z * [new branch] JackCaoG/update_xla_dynamo -> origin/JackCaoG/update_xla_dynamo 2025-01-24T01:03:43.2404736Z * [new branch] JackCaoG/update_xla_pin_to_skip_test -> origin/JackCaoG/update_xla_pin_to_skip_test 2025-01-24T01:03:43.2406062Z * [new branch] JackCaoG/update_xla_pin_to_skip_test2 -> origin/JackCaoG/update_xla_pin_to_skip_test2 2025-01-24T01:03:43.2407327Z * [new branch] NicolasHug-patch-2 -> origin/NicolasHug-patch-2 2025-01-24T01:03:43.2408815Z * [new branch] PR-AOTInductorNoneBug -> origin/PR-AOTInductorNoneBug 2025-01-24T01:03:43.2410435Z * [new branch] PR-AOTInductorNoneBugFix -> origin/PR-AOTInductorNoneBugFix 2025-01-24T01:03:43.2411762Z * [new branch] PR-FixConfigsIssue -> origin/PR-FixConfigsIssue 2025-01-24T01:03:43.2413081Z * [new branch] PR-NoneBugFix-viable -> origin/PR-NoneBugFix-viable 2025-01-24T01:03:43.2414445Z * [new branch] PR-ResetToZero -> origin/PR-ResetToZero 2025-01-24T01:03:43.2416017Z * [new branch] Remove-linux_t4g_2xlarge-Usage -> origin/Remove-linux_t4g_2xlarge-Usage 2025-01-24T01:03:43.2417270Z * [new branch] Revert-PR-110949 -> origin/Revert-PR-110949 2025-01-24T01:03:43.2418870Z * [new branch] Switch-Linux-arm64-Runners -> origin/Switch-Linux-arm64-Runners 2025-01-24T01:03:43.2420308Z * [new branch] Update-Flash-Packaging -> origin/Update-Flash-Packaging 2025-01-24T01:03:43.2422666Z * [new branch] Valentine/flash_attention_bf16 -> origin/Valentine/flash_attention_bf16 2025-01-24T01:03:43.2423871Z * [new branch] WeixiMa/serialize -> origin/WeixiMa/serialize 2025-01-24T01:03:43.2426138Z * [new branch] _tmp-orig/release/2.6 -> origin/_tmp-orig/release/2.6 2025-01-24T01:03:43.2427704Z * [new branch] _tmp-release/2.6 -> origin/_tmp-release/2.6 2025-01-24T01:03:43.2429845Z * [new branch] abock/dort-torch-compile-options -> origin/abock/dort-torch-compile-options 2025-01-24T01:03:43.2431067Z * [new branch] abock/onnx-1.14.1rc1 -> origin/abock/onnx-1.14.1rc1 2025-01-24T01:03:43.2432554Z * [new branch] abock/onnx-1.15.0-validation -> origin/abock/onnx-1.15.0-validation 2025-01-24T01:03:43.2434286Z * [new branch] abock/ort-nightly==1.16.0.dev20230908001 -> origin/abock/ort-nightly==1.16.0.dev20230908001 2025-01-24T01:03:43.2435636Z * [new branch] add-android-build-workflow -> origin/add-android-build-workflow 2025-01-24T01:03:43.2436846Z * [new branch] add-assign -> origin/add-assign 2025-01-24T01:03:43.2438491Z * [new branch] add_broadcast_functional_collective -> origin/add_broadcast_functional_collective 2025-01-24T01:03:43.2439746Z * [new branch] add_mha_to_autocast_policy -> origin/add_mha_to_autocast_policy 2025-01-24T01:03:43.2441312Z * [new branch] add_non_parallel_model_comparison -> origin/add_non_parallel_model_comparison 2025-01-24T01:03:43.2442550Z * [new branch] add_reduce_op -> origin/add_reduce_op 2025-01-24T01:03:43.2444423Z * [new branch] add_torchao_docs -> origin/add_torchao_docs 2025-01-24T01:03:43.2445872Z * [new branch] addmm-heuristic -> origin/addmm-heuristic 2025-01-24T01:03:43.2447110Z * [new branch] addsimde -> origin/addsimde 2025-01-24T01:03:43.2448808Z * [new branch] ah-globalfeedback-hook -> origin/ah-globalfeedback-hook 2025-01-24T01:03:43.2450468Z * [new branch] alanwaketan/pin -> origin/alanwaketan/pin 2025-01-24T01:03:43.2451677Z * [new branch] alanwaketan/pin2 -> origin/alanwaketan/pin2 2025-01-24T01:03:43.2453074Z * [new branch] alt-disable -> origin/alt-disable 2025-01-24T01:03:43.2455066Z * [new branch] angelayi/aot -> origin/angelayi/aot 2025-01-24T01:03:43.2456340Z * [new branch] angelayi/aot_freeze -> origin/angelayi/aot_freeze 2025-01-24T01:03:43.2457977Z * [new branch] angelayi/aot_inductor_bench_comp_time -> origin/angelayi/aot_inductor_bench_comp_time 2025-01-24T01:03:43.2459184Z * [new branch] angelayi/aot_inductor_benchmark -> origin/angelayi/aot_inductor_benchmark 2025-01-24T01:03:43.2460612Z * [new branch] angelayi/aot_inductor_torch -> origin/angelayi/aot_inductor_torch 2025-01-24T01:03:43.2461964Z * [new branch] angelayi/aoti_additional_files -> origin/angelayi/aoti_additional_files 2025-01-24T01:03:43.2463615Z * [new branch] angelayi/aotinductor_const -> origin/angelayi/aotinductor_const 2025-01-24T01:03:43.2465549Z * [new branch] angelayi/aotinductor_const_name -> origin/angelayi/aotinductor_const_name 2025-01-24T01:03:43.2466697Z * [new branch] 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origin/kenjin/call_method_userdefined 2025-01-24T01:03:43.7528079Z * [new branch] kenjin/lambdas -> origin/kenjin/lambdas 2025-01-24T01:03:43.7529314Z * [new branch] kenjin/norefcycles -> origin/kenjin/norefcycles 2025-01-24T01:03:43.7530702Z * [new branch] kineto_warnings_fix -> origin/kineto_warnings_fix 2025-01-24T01:03:43.7532091Z * [new branch] kit1980-patch-2 -> origin/kit1980-patch-2 2025-01-24T01:03:43.7533505Z * [new branch] kleidiai_github_mirror -> origin/kleidiai_github_mirror 2025-01-24T01:03:43.7535131Z * [new branch] klondenberg/cutlass -> origin/klondenberg/cutlass 2025-01-24T01:03:43.7536499Z * [new branch] layernorm_bias_fix -> origin/layernorm_bias_fix 2025-01-24T01:03:43.7538462Z * [new branch] leslie/enable_poc_reduction_fusion -> origin/leslie/enable_poc_reduction_fusion 2025-01-24T01:03:43.7539695Z * [new branch] leslie/test_group_gemm_epilogues -> origin/leslie/test_group_gemm_epilogues 2025-01-24T01:03:43.7541510Z * [new branch] liaoxuan/fuse_attention_pattern -> origin/liaoxuan/fuse_attention_pattern 2025-01-24T01:03:43.7543374Z * [new branch] lts/release/1.8 -> origin/lts/release/1.8 2025-01-24T01:03:43.7544939Z * [new branch] main -> origin/main 2025-01-24T01:03:43.7546543Z * [new branch] main-update-cusparselt-0.6.3 -> origin/main-update-cusparselt-0.6.3 2025-01-24T01:03:43.7547998Z * [new branch] malfet-patch-1 -> origin/malfet-patch-1 2025-01-24T01:03:43.7549395Z * [new branch] malfet-patch-12 -> origin/malfet-patch-12 2025-01-24T01:03:43.7550860Z * [new branch] malfet-patch-14 -> origin/malfet-patch-14 2025-01-24T01:03:43.7552357Z * [new branch] malfet-patch-15 -> origin/malfet-patch-15 2025-01-24T01:03:43.7553876Z * [new branch] malfet-patch-16 -> origin/malfet-patch-16 2025-01-24T01:03:43.7555395Z * [new branch] malfet-patch-17 -> origin/malfet-patch-17 2025-01-24T01:03:43.7557014Z * [new branch] malfet-patch-18 -> origin/malfet-patch-18 2025-01-24T01:03:43.7558560Z * [new branch] malfet-patch-19 -> origin/malfet-patch-19 2025-01-24T01:03:43.7560046Z * [new branch] malfet-patch-2 -> origin/malfet-patch-2 2025-01-24T01:03:43.7561518Z * [new branch] malfet-patch-20 -> origin/malfet-patch-20 2025-01-24T01:03:43.7563008Z * [new branch] malfet-patch-21 -> origin/malfet-patch-21 2025-01-24T01:03:43.7564489Z * [new branch] malfet-patch-22 -> origin/malfet-patch-22 2025-01-24T01:03:43.7565998Z * [new branch] malfet-patch-22-1 -> origin/malfet-patch-22-1 2025-01-24T01:03:43.7567431Z * [new branch] malfet-patch-23 -> origin/malfet-patch-23 2025-01-24T01:03:43.7568989Z * [new branch] malfet-patch-24 -> origin/malfet-patch-24 2025-01-24T01:03:43.7570322Z * [new branch] malfet-patch-25 -> origin/malfet-patch-25 2025-01-24T01:03:43.7571791Z * [new branch] malfet-patch-3 -> origin/malfet-patch-3 2025-01-24T01:03:43.7573136Z * [new branch] malfet-patch-32 -> origin/malfet-patch-32 2025-01-24T01:03:43.7574488Z * [new branch] malfet-patch-4 -> origin/malfet-patch-4 2025-01-24T01:03:43.7576001Z * [new branch] malfet-patch-42 -> origin/malfet-patch-42 2025-01-24T01:03:43.7577449Z * [new branch] malfet-patch-5 -> origin/malfet-patch-5 2025-01-24T01:03:43.7578862Z * [new branch] malfet-patch-6 -> origin/malfet-patch-6 2025-01-24T01:03:43.7583694Z * [new branch] malfet-patch-9 -> origin/malfet-patch-9 2025-01-24T01:03:43.7584564Z * [new branch] malfet/add-ami-option-to-instance-allocation -> origin/malfet/add-ami-option-to-instance-allocation 2025-01-24T01:03:43.7585045Z * [new branch] malfet/add-benchmark-func -> origin/malfet/add-benchmark-func 2025-01-24T01:03:43.7585636Z * [new branch] malfet/add-get-dtype-supported -> origin/malfet/add-get-dtype-supported 2025-01-24T01:03:43.7587070Z * [new branch] malfet/add-mps-inductor-nan -> origin/malfet/add-mps-inductor-nan 2025-01-24T01:03:43.7589298Z * [new branch] malfet/add-nan-to-int-conversion-check-cpu -> origin/malfet/add-nan-to-int-conversion-check-cpu 2025-01-24T01:03:43.7591058Z * [new branch] malfet/cp-143934-and-144055 -> origin/malfet/cp-143934-and-144055 2025-01-24T01:03:43.7592510Z * [new branch] malfet/cp-144716-144698 -> origin/malfet/cp-144716-144698 2025-01-24T01:03:43.7594188Z * [new branch] malfet/delete-find-openmp -> origin/malfet/delete-find-openmp 2025-01-24T01:03:43.7595745Z * [new branch] malfet/ez-cleanup-test -> origin/malfet/ez-cleanup-test 2025-01-24T01:03:43.7597223Z * [new branch] malfet/fix-conv-backward-cl-2 -> origin/malfet/fix-conv-backward-cl-2 2025-01-24T01:03:43.7598725Z * [new branch] malfet/fix-index-printing -> origin/malfet/fix-index-printing 2025-01-24T01:03:43.7600353Z * [new branch] malfet/mps-add-bilineard2d-aa -> origin/malfet/mps-add-bilineard2d-aa 2025-01-24T01:03:43.7601872Z * [new branch] malfet/mps-implement-col2im -> origin/malfet/mps-implement-col2im 2025-01-24T01:03:43.7603575Z * [new branch] malfet/mpsinductor-add-check-bounds -> origin/malfet/mpsinductor-add-check-bounds 2025-01-24T01:03:43.7604861Z * [new branch] malfet/mpsinductor-add-sizevars -> 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ciflow/inductor/127294 2025-01-24T01:03:43.8594006Z * [new tag] ciflow/inductor/128802 -> ciflow/inductor/128802 2025-01-24T01:03:43.8595328Z * [new tag] ciflow/inductor/129352 -> ciflow/inductor/129352 2025-01-24T01:03:43.8596242Z * [new tag] ciflow/inductor/129409 -> ciflow/inductor/129409 2025-01-24T01:03:43.8597199Z * [new tag] ciflow/inductor/129420 -> ciflow/inductor/129420 2025-01-24T01:03:43.8598228Z * [new tag] ciflow/inductor/130141 -> ciflow/inductor/130141 2025-01-24T01:03:43.8599613Z * [new tag] ciflow/inductor/130499 -> ciflow/inductor/130499 2025-01-24T01:03:43.8600464Z * [new tag] ciflow/inductor/130887 -> ciflow/inductor/130887 2025-01-24T01:03:43.8601441Z * [new tag] ciflow/inductor/131354 -> ciflow/inductor/131354 2025-01-24T01:03:43.8602449Z * [new tag] ciflow/inductor/131481 -> ciflow/inductor/131481 2025-01-24T01:03:43.8603686Z * [new tag] ciflow/inductor/131493 -> ciflow/inductor/131493 2025-01-24T01:03:43.8604637Z * [new tag] ciflow/inductor/132021 -> ciflow/inductor/132021 2025-01-24T01:03:43.8605615Z * [new tag] ciflow/inductor/132414 -> ciflow/inductor/132414 2025-01-24T01:03:43.8606753Z * [new tag] ciflow/inductor/132755 -> ciflow/inductor/132755 2025-01-24T01:03:43.8607818Z * [new tag] ciflow/inductor/133044 -> ciflow/inductor/133044 2025-01-24T01:03:43.8608794Z * [new tag] ciflow/inductor/133121 -> ciflow/inductor/133121 2025-01-24T01:03:43.8609801Z * [new tag] ciflow/inductor/133287 -> ciflow/inductor/133287 2025-01-24T01:03:43.8610771Z * [new tag] ciflow/inductor/133289 -> ciflow/inductor/133289 2025-01-24T01:03:43.8611868Z * [new tag] ciflow/inductor/133296 -> ciflow/inductor/133296 2025-01-24T01:03:43.8612820Z * [new tag] ciflow/inductor/133297 -> ciflow/inductor/133297 2025-01-24T01:03:43.8613855Z * [new tag] ciflow/inductor/133307 -> ciflow/inductor/133307 2025-01-24T01:03:43.8614783Z * [new tag] ciflow/inductor/133315 -> ciflow/inductor/133315 2025-01-24T01:03:43.8615804Z * [new tag] ciflow/inductor/133392 -> ciflow/inductor/133392 2025-01-24T01:03:43.8616726Z * [new tag] ciflow/inductor/133419 -> ciflow/inductor/133419 2025-01-24T01:03:43.8617769Z * [new tag] ciflow/inductor/133423 -> ciflow/inductor/133423 2025-01-24T01:03:43.8618748Z * [new tag] ciflow/inductor/133667 -> ciflow/inductor/133667 2025-01-24T01:03:43.8619759Z * [new tag] ciflow/inductor/133753 -> ciflow/inductor/133753 2025-01-24T01:03:43.8620716Z * [new tag] ciflow/inductor/133842 -> ciflow/inductor/133842 2025-01-24T01:03:43.8621707Z * [new tag] ciflow/inductor/134681 -> ciflow/inductor/134681 2025-01-24T01:03:43.8622701Z * [new tag] ciflow/inductor/134881 -> ciflow/inductor/134881 2025-01-24T01:03:43.8624170Z * [new tag] ciflow/inductor/134969 -> ciflow/inductor/134969 2025-01-24T01:03:43.8624879Z * [new tag] ciflow/inductor/135189 -> ciflow/inductor/135189 2025-01-24T01:03:43.8625940Z * [new tag] ciflow/inductor/135337 -> ciflow/inductor/135337 2025-01-24T01:03:43.8627008Z * [new tag] ciflow/inductor/135465 -> ciflow/inductor/135465 2025-01-24T01:03:43.8628017Z * [new tag] ciflow/inductor/135708 -> ciflow/inductor/135708 2025-01-24T01:03:43.8629016Z * [new tag] ciflow/inductor/135719 -> ciflow/inductor/135719 2025-01-24T01:03:43.8629991Z * [new tag] ciflow/inductor/135792 -> ciflow/inductor/135792 2025-01-24T01:03:43.8631643Z * [new tag] ciflow/inductor/136268 -> ciflow/inductor/136268 2025-01-24T01:03:43.8632331Z * [new tag] ciflow/inductor/136355 -> ciflow/inductor/136355 2025-01-24T01:03:43.8633384Z * [new tag] ciflow/inductor/136478 -> ciflow/inductor/136478 2025-01-24T01:03:43.8634348Z * [new tag] ciflow/inductor/136702 -> ciflow/inductor/136702 2025-01-24T01:03:43.8635346Z * [new tag] ciflow/inductor/136753 -> ciflow/inductor/136753 2025-01-24T01:03:43.8636303Z * [new tag] ciflow/inductor/136802 -> ciflow/inductor/136802 2025-01-24T01:03:43.8637350Z * [new tag] ciflow/inductor/137400 -> ciflow/inductor/137400 2025-01-24T01:03:43.8638310Z * [new tag] ciflow/inductor/137493 -> ciflow/inductor/137493 2025-01-24T01:03:43.8639477Z * [new tag] ciflow/inductor/137568 -> ciflow/inductor/137568 2025-01-24T01:03:43.8640455Z * [new tag] ciflow/inductor/137583 -> ciflow/inductor/137583 2025-01-24T01:03:43.8641696Z * [new tag] ciflow/inductor/137658 -> ciflow/inductor/137658 2025-01-24T01:03:43.8642691Z * [new tag] ciflow/inductor/137662 -> ciflow/inductor/137662 2025-01-24T01:03:43.8643666Z * [new tag] ciflow/inductor/137726 -> ciflow/inductor/137726 2025-01-24T01:03:43.8644684Z * [new tag] ciflow/inductor/137808 -> ciflow/inductor/137808 2025-01-24T01:03:43.8645650Z * [new tag] ciflow/inductor/137822 -> ciflow/inductor/137822 2025-01-24T01:03:43.8646971Z * [new tag] ciflow/inductor/137846 -> ciflow/inductor/137846 2025-01-24T01:03:43.8647853Z * [new tag] ciflow/inductor/137861 -> ciflow/inductor/137861 2025-01-24T01:03:43.8648872Z * [new tag] ciflow/inductor/137884 -> ciflow/inductor/137884 2025-01-24T01:03:43.8649884Z * [new tag] ciflow/inductor/137921 -> ciflow/inductor/137921 2025-01-24T01:03:43.8650861Z * [new tag] ciflow/inductor/137959 -> ciflow/inductor/137959 2025-01-24T01:03:43.8651913Z * [new tag] ciflow/inductor/138101 -> ciflow/inductor/138101 2025-01-24T01:03:43.8652891Z * [new tag] ciflow/inductor/138185 -> ciflow/inductor/138185 2025-01-24T01:03:43.8653885Z * [new tag] ciflow/inductor/138202 -> ciflow/inductor/138202 2025-01-24T01:03:43.8654878Z * [new tag] ciflow/inductor/138213 -> ciflow/inductor/138213 2025-01-24T01:03:43.8655874Z * [new tag] ciflow/inductor/138214 -> ciflow/inductor/138214 2025-01-24T01:03:43.8656911Z * [new tag] ciflow/inductor/138282 -> ciflow/inductor/138282 2025-01-24T01:03:43.8657875Z * [new tag] ciflow/inductor/138503 -> ciflow/inductor/138503 2025-01-24T01:03:43.8658879Z * [new tag] ciflow/inductor/138513 -> ciflow/inductor/138513 2025-01-24T01:03:43.8659848Z * [new tag] ciflow/inductor/138519 -> ciflow/inductor/138519 2025-01-24T01:03:43.8661300Z * [new tag] ciflow/inductor/138555 -> ciflow/inductor/138555 2025-01-24T01:03:43.8662345Z * [new tag] ciflow/inductor/138573 -> ciflow/inductor/138573 2025-01-24T01:03:43.8663676Z * [new tag] ciflow/inductor/138626 -> ciflow/inductor/138626 2025-01-24T01:03:43.8664582Z * [new tag] ciflow/inductor/138749 -> ciflow/inductor/138749 2025-01-24T01:03:43.8666045Z * [new tag] ciflow/inductor/138871 -> ciflow/inductor/138871 2025-01-24T01:03:43.8666958Z * [new tag] ciflow/inductor/138889 -> ciflow/inductor/138889 2025-01-24T01:03:43.8667992Z * [new tag] ciflow/inductor/138929 -> ciflow/inductor/138929 2025-01-24T01:03:43.8669002Z * [new tag] ciflow/inductor/138930 -> ciflow/inductor/138930 2025-01-24T01:03:43.8670025Z * [new tag] ciflow/inductor/138938 -> ciflow/inductor/138938 2025-01-24T01:03:43.8671012Z * [new tag] ciflow/inductor/138953 -> ciflow/inductor/138953 2025-01-24T01:03:43.8672022Z * [new tag] ciflow/inductor/138963 -> ciflow/inductor/138963 2025-01-24T01:03:43.8673541Z * [new tag] ciflow/inductor/138966 -> ciflow/inductor/138966 2025-01-24T01:03:43.8674550Z * [new tag] ciflow/inductor/138999 -> ciflow/inductor/138999 2025-01-24T01:03:43.8675553Z * [new tag] ciflow/inductor/139094 -> ciflow/inductor/139094 2025-01-24T01:03:43.8676519Z * [new tag] ciflow/inductor/139098 -> ciflow/inductor/139098 2025-01-24T01:03:43.8677556Z * [new tag] ciflow/inductor/139101 -> ciflow/inductor/139101 2025-01-24T01:03:43.8678606Z * [new tag] ciflow/inductor/139102 -> ciflow/inductor/139102 2025-01-24T01:03:43.8679755Z * [new tag] ciflow/inductor/139105 -> ciflow/inductor/139105 2025-01-24T01:03:43.8681300Z * [new tag] ciflow/inductor/139135 -> ciflow/inductor/139135 2025-01-24T01:03:43.8682346Z * [new tag] ciflow/inductor/139146 -> ciflow/inductor/139146 2025-01-24T01:03:43.8683761Z * [new tag] ciflow/inductor/139171 -> ciflow/inductor/139171 2025-01-24T01:03:43.8684593Z * [new tag] ciflow/inductor/139247 -> ciflow/inductor/139247 2025-01-24T01:03:43.8685582Z * [new tag] ciflow/inductor/139271 -> ciflow/inductor/139271 2025-01-24T01:03:43.8686622Z * [new tag] ciflow/inductor/139308 -> ciflow/inductor/139308 2025-01-24T01:03:43.8687571Z * [new tag] ciflow/inductor/139325 -> ciflow/inductor/139325 2025-01-24T01:03:43.8688601Z * [new tag] ciflow/inductor/139329 -> ciflow/inductor/139329 2025-01-24T01:03:43.8689581Z * [new tag] ciflow/inductor/139338 -> ciflow/inductor/139338 2025-01-24T01:03:43.8690603Z * [new tag] ciflow/inductor/139356 -> ciflow/inductor/139356 2025-01-24T01:03:43.8691604Z * [new tag] ciflow/inductor/139357 -> ciflow/inductor/139357 2025-01-24T01:03:43.8692594Z * [new tag] ciflow/inductor/139379 -> ciflow/inductor/139379 2025-01-24T01:03:43.8693567Z * [new tag] ciflow/inductor/139384 -> ciflow/inductor/139384 2025-01-24T01:03:43.8694577Z * [new tag] ciflow/inductor/139421 -> ciflow/inductor/139421 2025-01-24T01:03:43.8696006Z * [new tag] ciflow/inductor/139437 -> ciflow/inductor/139437 2025-01-24T01:03:43.8697627Z * [new tag] ciflow/inductor/139535 -> ciflow/inductor/139535 2025-01-24T01:03:43.8698463Z * [new tag] ciflow/inductor/139561 -> ciflow/inductor/139561 2025-01-24T01:03:43.8699500Z * [new tag] ciflow/inductor/139672 -> ciflow/inductor/139672 2025-01-24T01:03:43.8700471Z * [new tag] ciflow/inductor/139697 -> ciflow/inductor/139697 2025-01-24T01:03:43.8701614Z * [new tag] ciflow/inductor/139708 -> ciflow/inductor/139708 2025-01-24T01:03:43.8702480Z * [new tag] ciflow/inductor/139796 -> ciflow/inductor/139796 2025-01-24T01:03:43.8703494Z * [new tag] ciflow/inductor/139840 -> ciflow/inductor/139840 2025-01-24T01:03:43.8704920Z * [new tag] ciflow/inductor/139864 -> ciflow/inductor/139864 2025-01-24T01:03:43.8705729Z * [new tag] ciflow/inductor/139928 -> ciflow/inductor/139928 2025-01-24T01:03:43.8706767Z * [new tag] ciflow/inductor/139975 -> ciflow/inductor/139975 2025-01-24T01:03:43.8708097Z * [new tag] ciflow/inductor/139976 -> ciflow/inductor/139976 2025-01-24T01:03:43.8709027Z * [new tag] ciflow/inductor/140032 -> ciflow/inductor/140032 2025-01-24T01:03:43.8710013Z * [new tag] ciflow/inductor/140048 -> ciflow/inductor/140048 2025-01-24T01:03:43.8711038Z * [new tag] ciflow/inductor/140084 -> ciflow/inductor/140084 2025-01-24T01:03:43.8712014Z * [new tag] ciflow/inductor/140132 -> ciflow/inductor/140132 2025-01-24T01:03:43.8713497Z * [new tag] ciflow/inductor/140146 -> ciflow/inductor/140146 2025-01-24T01:03:43.8714471Z * [new tag] ciflow/inductor/140159 -> ciflow/inductor/140159 2025-01-24T01:03:43.8715496Z * [new tag] ciflow/inductor/140195 -> ciflow/inductor/140195 2025-01-24T01:03:43.8716472Z * [new tag] ciflow/inductor/140283 -> ciflow/inductor/140283 2025-01-24T01:03:43.8717503Z * [new tag] ciflow/inductor/140328 -> ciflow/inductor/140328 2025-01-24T01:03:43.8718879Z * [new tag] ciflow/inductor/140362 -> ciflow/inductor/140362 2025-01-24T01:03:43.8719757Z * [new tag] ciflow/inductor/140677 -> ciflow/inductor/140677 2025-01-24T01:03:43.8721100Z * [new tag] ciflow/inductor/140746 -> ciflow/inductor/140746 2025-01-24T01:03:43.8722024Z * [new tag] ciflow/inductor/140747 -> ciflow/inductor/140747 2025-01-24T01:03:43.8723019Z * [new tag] ciflow/inductor/140756 -> ciflow/inductor/140756 2025-01-24T01:03:43.8724364Z * [new tag] ciflow/inductor/140807 -> ciflow/inductor/140807 2025-01-24T01:03:43.8725232Z * [new tag] ciflow/inductor/140858 -> ciflow/inductor/140858 2025-01-24T01:03:43.8726522Z * [new tag] ciflow/inductor/140979 -> ciflow/inductor/140979 2025-01-24T01:03:43.8727424Z * [new tag] ciflow/inductor/141055 -> ciflow/inductor/141055 2025-01-24T01:03:43.8729322Z * [new tag] ciflow/inductor/141082 -> ciflow/inductor/141082 2025-01-24T01:03:43.8730053Z * [new tag] ciflow/inductor/141096 -> ciflow/inductor/141096 2025-01-24T01:03:43.8731060Z * [new tag] ciflow/inductor/141097 -> ciflow/inductor/141097 2025-01-24T01:03:43.8732092Z * [new tag] ciflow/inductor/141131 -> ciflow/inductor/141131 2025-01-24T01:03:43.8733070Z * [new tag] ciflow/inductor/141168 -> ciflow/inductor/141168 2025-01-24T01:03:43.8734072Z * [new tag] ciflow/inductor/141193 -> ciflow/inductor/141193 2025-01-24T01:03:43.8735112Z * [new tag] ciflow/inductor/141195 -> ciflow/inductor/141195 2025-01-24T01:03:43.8736552Z * [new tag] ciflow/inductor/141213 -> ciflow/inductor/141213 2025-01-24T01:03:43.8737608Z * [new tag] ciflow/inductor/141226 -> ciflow/inductor/141226 2025-01-24T01:03:43.8738581Z * [new tag] ciflow/inductor/141309 -> ciflow/inductor/141309 2025-01-24T01:03:43.8740173Z * [new tag] ciflow/inductor/141342 -> ciflow/inductor/141342 2025-01-24T01:03:43.8740889Z * [new tag] ciflow/inductor/141347 -> ciflow/inductor/141347 2025-01-24T01:03:43.8742022Z * [new tag] ciflow/inductor/141393 -> ciflow/inductor/141393 2025-01-24T01:03:43.8743098Z * [new tag] ciflow/inductor/141489 -> ciflow/inductor/141489 2025-01-24T01:03:43.8744127Z * [new tag] ciflow/inductor/141493 -> ciflow/inductor/141493 2025-01-24T01:03:43.8745135Z * [new tag] ciflow/inductor/141496 -> ciflow/inductor/141496 2025-01-24T01:03:43.8746195Z * [new tag] ciflow/inductor/141588 -> ciflow/inductor/141588 2025-01-24T01:03:43.8747228Z * [new tag] ciflow/inductor/141641 -> ciflow/inductor/141641 2025-01-24T01:03:43.8748258Z * [new tag] ciflow/inductor/141659 -> ciflow/inductor/141659 2025-01-24T01:03:43.8749243Z * [new tag] ciflow/inductor/141684 -> ciflow/inductor/141684 2025-01-24T01:03:43.8750280Z * [new tag] ciflow/inductor/141697 -> ciflow/inductor/141697 2025-01-24T01:03:43.8751228Z * [new tag] ciflow/inductor/141700 -> ciflow/inductor/141700 2025-01-24T01:03:43.8752239Z * [new tag] ciflow/inductor/141702 -> ciflow/inductor/141702 2025-01-24T01:03:43.8753212Z * [new tag] ciflow/inductor/141720 -> ciflow/inductor/141720 2025-01-24T01:03:43.8754845Z * [new tag] ciflow/inductor/141730 -> ciflow/inductor/141730 2025-01-24T01:03:43.8755707Z * [new tag] ciflow/inductor/141795 -> ciflow/inductor/141795 2025-01-24T01:03:43.8757156Z * [new tag] ciflow/inductor/141842 -> ciflow/inductor/141842 2025-01-24T01:03:43.8758038Z * [new tag] ciflow/inductor/141889 -> ciflow/inductor/141889 2025-01-24T01:03:43.8759080Z * [new tag] ciflow/inductor/141940 -> ciflow/inductor/141940 2025-01-24T01:03:43.8760152Z * [new tag] ciflow/inductor/141944 -> ciflow/inductor/141944 2025-01-24T01:03:43.8761133Z * [new tag] ciflow/inductor/141947 -> ciflow/inductor/141947 2025-01-24T01:03:43.8762130Z * [new tag] ciflow/inductor/141961 -> ciflow/inductor/141961 2025-01-24T01:03:43.8763135Z * [new tag] ciflow/inductor/142026 -> ciflow/inductor/142026 2025-01-24T01:03:43.8764135Z * [new tag] ciflow/inductor/142046 -> ciflow/inductor/142046 2025-01-24T01:03:43.8765098Z * [new tag] ciflow/inductor/142091 -> ciflow/inductor/142091 2025-01-24T01:03:43.8766890Z * [new tag] ciflow/inductor/142092 -> ciflow/inductor/142092 2025-01-24T01:03:43.8767573Z * [new tag] ciflow/inductor/142105 -> ciflow/inductor/142105 2025-01-24T01:03:43.8769123Z * [new tag] ciflow/inductor/142163 -> ciflow/inductor/142163 2025-01-24T01:03:43.8770493Z * [new tag] ciflow/inductor/142272 -> ciflow/inductor/142272 2025-01-24T01:03:43.8771990Z * [new tag] ciflow/inductor/142273 -> ciflow/inductor/142273 2025-01-24T01:03:43.8772826Z * [new tag] ciflow/inductor/142295 -> ciflow/inductor/142295 2025-01-24T01:03:43.8773883Z * [new tag] ciflow/inductor/142296 -> ciflow/inductor/142296 2025-01-24T01:03:43.8775179Z * [new tag] ciflow/inductor/142309 -> ciflow/inductor/142309 2025-01-24T01:03:43.8776128Z * [new tag] ciflow/inductor/142350 -> ciflow/inductor/142350 2025-01-24T01:03:43.8777080Z * [new tag] ciflow/inductor/142372 -> ciflow/inductor/142372 2025-01-24T01:03:43.8778290Z * [new tag] ciflow/inductor/142407 -> ciflow/inductor/142407 2025-01-24T01:03:43.8779506Z * [new tag] ciflow/inductor/142483 -> ciflow/inductor/142483 2025-01-24T01:03:43.8781769Z * [new tag] ciflow/inductor/142513 -> ciflow/inductor/142513 2025-01-24T01:03:43.8782529Z * [new tag] ciflow/inductor/142830 -> ciflow/inductor/142830 2025-01-24T01:03:43.8783131Z * [new tag] ciflow/inductor/142851 -> ciflow/inductor/142851 2025-01-24T01:03:43.8784089Z * [new tag] ciflow/inductor/142861 -> ciflow/inductor/142861 2025-01-24T01:03:43.8784578Z * [new tag] ciflow/inductor/143044 -> ciflow/inductor/143044 2025-01-24T01:03:43.8785304Z * [new tag] ciflow/inductor/143061 -> ciflow/inductor/143061 2025-01-24T01:03:43.8785962Z * [new tag] ciflow/inductor/143103 -> ciflow/inductor/143103 2025-01-24T01:03:43.8786744Z * [new tag] ciflow/inductor/143220 -> ciflow/inductor/143220 2025-01-24T01:03:43.8787417Z * [new tag] ciflow/inductor/143275 -> ciflow/inductor/143275 2025-01-24T01:03:43.8788450Z * [new tag] ciflow/inductor/143286 -> ciflow/inductor/143286 2025-01-24T01:03:43.8789007Z * [new tag] ciflow/inductor/143296 -> ciflow/inductor/143296 2025-01-24T01:03:43.8789695Z * [new tag] ciflow/inductor/143313 -> ciflow/inductor/143313 2025-01-24T01:03:43.8790366Z * [new tag] ciflow/inductor/143378 -> ciflow/inductor/143378 2025-01-24T01:03:43.8791004Z * [new tag] ciflow/inductor/143387 -> ciflow/inductor/143387 2025-01-24T01:03:43.8791692Z * [new tag] ciflow/inductor/143405 -> ciflow/inductor/143405 2025-01-24T01:03:43.8792368Z * [new tag] ciflow/inductor/143408 -> ciflow/inductor/143408 2025-01-24T01:03:43.8792986Z * [new tag] ciflow/inductor/143411 -> ciflow/inductor/143411 2025-01-24T01:03:43.8793709Z * [new tag] ciflow/inductor/143417 -> ciflow/inductor/143417 2025-01-24T01:03:43.8794559Z * [new tag] ciflow/inductor/143456 -> ciflow/inductor/143456 2025-01-24T01:03:43.8795143Z * [new tag] ciflow/inductor/143457 -> ciflow/inductor/143457 2025-01-24T01:03:43.8796182Z * [new tag] ciflow/inductor/143464 -> ciflow/inductor/143464 2025-01-24T01:03:43.8796838Z * [new tag] ciflow/inductor/143475 -> ciflow/inductor/143475 2025-01-24T01:03:43.8797484Z * [new tag] ciflow/inductor/143525 -> ciflow/inductor/143525 2025-01-24T01:03:43.8798234Z * [new tag] ciflow/inductor/143527 -> ciflow/inductor/143527 2025-01-24T01:03:43.8798942Z * [new tag] ciflow/inductor/143533 -> ciflow/inductor/143533 2025-01-24T01:03:43.8799558Z * [new tag] ciflow/inductor/143534 -> ciflow/inductor/143534 2025-01-24T01:03:43.8800325Z * [new tag] ciflow/inductor/143544 -> ciflow/inductor/143544 2025-01-24T01:03:43.8800992Z * [new tag] ciflow/inductor/143559 -> ciflow/inductor/143559 2025-01-24T01:03:43.8801731Z * [new tag] ciflow/inductor/143622 -> ciflow/inductor/143622 2025-01-24T01:03:43.8802317Z * [new tag] ciflow/inductor/143631 -> ciflow/inductor/143631 2025-01-24T01:03:43.8802976Z * [new tag] ciflow/inductor/143638 -> ciflow/inductor/143638 2025-01-24T01:03:43.8803663Z * [new tag] ciflow/inductor/143671 -> ciflow/inductor/143671 2025-01-24T01:03:43.8804358Z * [new tag] ciflow/inductor/143683 -> ciflow/inductor/143683 2025-01-24T01:03:43.8805012Z * [new tag] ciflow/inductor/143712 -> ciflow/inductor/143712 2025-01-24T01:03:43.8805644Z * [new tag] ciflow/inductor/143808 -> ciflow/inductor/143808 2025-01-24T01:03:43.8806330Z * [new tag] ciflow/inductor/143812 -> ciflow/inductor/143812 2025-01-24T01:03:43.8807147Z * [new tag] ciflow/inductor/143833 -> ciflow/inductor/143833 2025-01-24T01:03:43.8807800Z * [new tag] ciflow/inductor/143872 -> ciflow/inductor/143872 2025-01-24T01:03:43.8808558Z * [new tag] ciflow/inductor/143877 -> ciflow/inductor/143877 2025-01-24T01:03:43.8809142Z * [new tag] ciflow/inductor/143880 -> ciflow/inductor/143880 2025-01-24T01:03:43.8809798Z * [new tag] ciflow/inductor/143961 -> ciflow/inductor/143961 2025-01-24T01:03:43.8810641Z * [new tag] ciflow/inductor/143987 -> ciflow/inductor/143987 2025-01-24T01:03:43.8811303Z * [new tag] ciflow/inductor/144002 -> ciflow/inductor/144002 2025-01-24T01:03:43.8811983Z * [new tag] ciflow/inductor/144008 -> ciflow/inductor/144008 2025-01-24T01:03:43.8812819Z * [new tag] ciflow/inductor/144017 -> ciflow/inductor/144017 2025-01-24T01:03:43.8813477Z * [new tag] ciflow/inductor/144020 -> ciflow/inductor/144020 2025-01-24T01:03:43.8814122Z * [new tag] ciflow/inductor/144073 -> ciflow/inductor/144073 2025-01-24T01:03:43.8814876Z * [new tag] ciflow/inductor/144097 -> ciflow/inductor/144097 2025-01-24T01:03:43.8815547Z * [new tag] ciflow/inductor/144108 -> ciflow/inductor/144108 2025-01-24T01:03:43.8816175Z * [new tag] ciflow/inductor/144114 -> ciflow/inductor/144114 2025-01-24T01:03:43.8816826Z * [new tag] ciflow/inductor/144115 -> ciflow/inductor/144115 2025-01-24T01:03:43.8817494Z * [new tag] ciflow/inductor/144120 -> ciflow/inductor/144120 2025-01-24T01:03:43.8818831Z * [new tag] ciflow/inductor/144172 -> ciflow/inductor/144172 2025-01-24T01:03:43.8819548Z * [new tag] ciflow/inductor/144193 -> ciflow/inductor/144193 2025-01-24T01:03:43.8820330Z * [new tag] ciflow/inductor/144234 -> ciflow/inductor/144234 2025-01-24T01:03:43.8821231Z * [new tag] ciflow/inductor/144272 -> ciflow/inductor/144272 2025-01-24T01:03:43.8821910Z * [new tag] ciflow/inductor/144288 -> ciflow/inductor/144288 2025-01-24T01:03:43.8822602Z * [new tag] ciflow/inductor/144293 -> ciflow/inductor/144293 2025-01-24T01:03:43.8823710Z * [new tag] ciflow/inductor/144294 -> ciflow/inductor/144294 2025-01-24T01:03:43.8824423Z * [new tag] ciflow/inductor/144303 -> ciflow/inductor/144303 2025-01-24T01:03:43.8825092Z * [new tag] ciflow/inductor/144332 -> ciflow/inductor/144332 2025-01-24T01:03:43.8825752Z * [new tag] ciflow/inductor/144333 -> ciflow/inductor/144333 2025-01-24T01:03:43.8826509Z * [new tag] ciflow/inductor/144349 -> ciflow/inductor/144349 2025-01-24T01:03:43.8827243Z * [new tag] ciflow/inductor/144353 -> ciflow/inductor/144353 2025-01-24T01:03:43.8827917Z * [new tag] ciflow/inductor/144365 -> ciflow/inductor/144365 2025-01-24T01:03:43.8828580Z * [new tag] ciflow/inductor/144366 -> ciflow/inductor/144366 2025-01-24T01:03:43.8829257Z * [new tag] ciflow/inductor/144395 -> ciflow/inductor/144395 2025-01-24T01:03:43.8829935Z * [new tag] ciflow/inductor/144405 -> ciflow/inductor/144405 2025-01-24T01:03:43.8830585Z * [new tag] ciflow/inductor/144413 -> ciflow/inductor/144413 2025-01-24T01:03:43.8831228Z * [new tag] ciflow/inductor/144414 -> ciflow/inductor/144414 2025-01-24T01:03:43.8831895Z * [new tag] ciflow/inductor/144420 -> ciflow/inductor/144420 2025-01-24T01:03:43.8832602Z * [new tag] ciflow/inductor/144421 -> ciflow/inductor/144421 2025-01-24T01:03:43.8833255Z * [new tag] ciflow/inductor/144422 -> ciflow/inductor/144422 2025-01-24T01:03:43.8834003Z * [new tag] ciflow/inductor/144423 -> ciflow/inductor/144423 2025-01-24T01:03:43.8834748Z * [new tag] ciflow/inductor/144424 -> ciflow/inductor/144424 2025-01-24T01:03:43.8835393Z * [new tag] ciflow/inductor/144438 -> ciflow/inductor/144438 2025-01-24T01:03:43.8835996Z * [new tag] ciflow/inductor/144452 -> ciflow/inductor/144452 2025-01-24T01:03:43.8836598Z * [new tag] ciflow/inductor/144458 -> ciflow/inductor/144458 2025-01-24T01:03:43.8837311Z * [new tag] ciflow/inductor/144485 -> ciflow/inductor/144485 2025-01-24T01:03:43.8838353Z * [new tag] ciflow/inductor/144492 -> ciflow/inductor/144492 2025-01-24T01:03:43.8838744Z * [new tag] ciflow/inductor/144501 -> ciflow/inductor/144501 2025-01-24T01:03:43.8839499Z * [new tag] ciflow/inductor/144505 -> ciflow/inductor/144505 2025-01-24T01:03:43.8840154Z * [new tag] ciflow/inductor/144507 -> ciflow/inductor/144507 2025-01-24T01:03:43.8840868Z * [new tag] ciflow/inductor/144513 -> ciflow/inductor/144513 2025-01-24T01:03:43.8841486Z * [new tag] ciflow/inductor/144515 -> ciflow/inductor/144515 2025-01-24T01:03:43.8842142Z * [new tag] ciflow/inductor/144516 -> ciflow/inductor/144516 2025-01-24T01:03:43.8842797Z * [new tag] ciflow/inductor/144526 -> ciflow/inductor/144526 2025-01-24T01:03:43.8843470Z * [new tag] ciflow/inductor/144527 -> ciflow/inductor/144527 2025-01-24T01:03:43.8844051Z * [new tag] ciflow/inductor/144528 -> ciflow/inductor/144528 2025-01-24T01:03:43.8844758Z * [new tag] ciflow/inductor/144542 -> ciflow/inductor/144542 2025-01-24T01:03:43.8845440Z * [new tag] ciflow/inductor/144546 -> ciflow/inductor/144546 2025-01-24T01:03:43.8846064Z * [new tag] ciflow/inductor/144547 -> ciflow/inductor/144547 2025-01-24T01:03:43.8846762Z * [new tag] ciflow/inductor/144549 -> ciflow/inductor/144549 2025-01-24T01:03:43.8847449Z * [new tag] ciflow/inductor/144550 -> ciflow/inductor/144550 2025-01-24T01:03:43.8848166Z * [new tag] ciflow/inductor/144551 -> ciflow/inductor/144551 2025-01-24T01:03:43.8848882Z * [new tag] ciflow/inductor/144553 -> ciflow/inductor/144553 2025-01-24T01:03:43.8849824Z * [new tag] ciflow/inductor/144555 -> ciflow/inductor/144555 2025-01-24T01:03:43.8850476Z * [new tag] ciflow/inductor/144556 -> ciflow/inductor/144556 2025-01-24T01:03:43.8851186Z * [new tag] ciflow/inductor/144569 -> ciflow/inductor/144569 2025-01-24T01:03:43.8851845Z * [new tag] ciflow/inductor/144579 -> ciflow/inductor/144579 2025-01-24T01:03:43.8852752Z * [new tag] ciflow/inductor/144598 -> ciflow/inductor/144598 2025-01-24T01:03:43.8853340Z * [new tag] ciflow/inductor/144640 -> ciflow/inductor/144640 2025-01-24T01:03:43.8854234Z * [new tag] ciflow/inductor/144642 -> ciflow/inductor/144642 2025-01-24T01:03:43.8854949Z * [new tag] ciflow/inductor/144646 -> ciflow/inductor/144646 2025-01-24T01:03:43.8855665Z * [new tag] ciflow/inductor/144695 -> ciflow/inductor/144695 2025-01-24T01:03:43.8856414Z * [new tag] ciflow/inductor/144700 -> ciflow/inductor/144700 2025-01-24T01:03:43.8857134Z * [new tag] ciflow/inductor/144707 -> ciflow/inductor/144707 2025-01-24T01:03:43.8857860Z * [new tag] ciflow/inductor/144712 -> ciflow/inductor/144712 2025-01-24T01:03:43.8858559Z * [new tag] ciflow/inductor/144721 -> ciflow/inductor/144721 2025-01-24T01:03:43.8859438Z * [new tag] ciflow/inductor/144724 -> ciflow/inductor/144724 2025-01-24T01:03:43.8860174Z * [new tag] ciflow/inductor/144733 -> ciflow/inductor/144733 2025-01-24T01:03:43.8860901Z * [new tag] ciflow/inductor/144741 -> ciflow/inductor/144741 2025-01-24T01:03:43.8861496Z * [new tag] ciflow/inductor/144765 -> ciflow/inductor/144765 2025-01-24T01:03:43.8862302Z * [new tag] ciflow/inductor/144771 -> ciflow/inductor/144771 2025-01-24T01:03:43.8863105Z * [new tag] ciflow/inductor/144773 -> ciflow/inductor/144773 2025-01-24T01:03:43.8863812Z * [new tag] ciflow/inductor/144829 -> ciflow/inductor/144829 2025-01-24T01:03:43.8865132Z * [new tag] ciflow/inductor/144865 -> ciflow/inductor/144865 2025-01-24T01:03:43.8865936Z * [new tag] ciflow/inductor/144880 -> ciflow/inductor/144880 2025-01-24T01:03:43.8866687Z * [new tag] ciflow/inductor/144894 -> ciflow/inductor/144894 2025-01-24T01:03:43.8867395Z * [new tag] ciflow/inductor/144905 -> ciflow/inductor/144905 2025-01-24T01:03:43.8868064Z * [new tag] ciflow/inductor/144910 -> ciflow/inductor/144910 2025-01-24T01:03:43.8868738Z * [new tag] ciflow/inductor/144925 -> ciflow/inductor/144925 2025-01-24T01:03:43.8869756Z * [new tag] ciflow/inductor/144940 -> ciflow/inductor/144940 2025-01-24T01:03:43.8870307Z * [new tag] ciflow/inductor/144943 -> ciflow/inductor/144943 2025-01-24T01:03:43.8871022Z * [new tag] ciflow/inductor/144953 -> ciflow/inductor/144953 2025-01-24T01:03:43.8871734Z * [new tag] ciflow/inductor/144974 -> ciflow/inductor/144974 2025-01-24T01:03:43.8872420Z * [new tag] ciflow/inductor/144975 -> ciflow/inductor/144975 2025-01-24T01:03:43.8873112Z * [new tag] ciflow/inductor/144979 -> ciflow/inductor/144979 2025-01-24T01:03:43.8873887Z * [new tag] ciflow/inductor/144980 -> ciflow/inductor/144980 2025-01-24T01:03:43.8874584Z * [new tag] ciflow/inductor/144985 -> ciflow/inductor/144985 2025-01-24T01:03:43.8875300Z * [new tag] ciflow/inductor/144986 -> ciflow/inductor/144986 2025-01-24T01:03:43.8876010Z * [new tag] ciflow/inductor/144999 -> ciflow/inductor/144999 2025-01-24T01:03:43.8876721Z * [new tag] ciflow/inductor/145024 -> ciflow/inductor/145024 2025-01-24T01:03:43.8877383Z * [new tag] ciflow/inductor/145047 -> ciflow/inductor/145047 2025-01-24T01:03:43.8878097Z * [new tag] ciflow/inductor/145059 -> ciflow/inductor/145059 2025-01-24T01:03:43.8878732Z * [new tag] ciflow/inductor/145061 -> ciflow/inductor/145061 2025-01-24T01:03:43.8879556Z * [new tag] ciflow/inductor/145082 -> ciflow/inductor/145082 2025-01-24T01:03:43.8880427Z * [new tag] ciflow/inductor/145083 -> ciflow/inductor/145083 2025-01-24T01:03:43.8881175Z * [new tag] ciflow/inductor/145095 -> ciflow/inductor/145095 2025-01-24T01:03:43.8881794Z * [new tag] ciflow/inductor/145103 -> ciflow/inductor/145103 2025-01-24T01:03:43.8882483Z * [new tag] ciflow/inductor/145104 -> ciflow/inductor/145104 2025-01-24T01:03:43.8883178Z * [new tag] ciflow/inductor/145117 -> ciflow/inductor/145117 2025-01-24T01:03:43.8883874Z * [new tag] ciflow/inductor/145119 -> ciflow/inductor/145119 2025-01-24T01:03:43.8884513Z * [new tag] ciflow/inductor/145120 -> ciflow/inductor/145120 2025-01-24T01:03:43.8885209Z * [new tag] ciflow/inductor/145122 -> ciflow/inductor/145122 2025-01-24T01:03:43.8885955Z * [new tag] ciflow/inductor/145128 -> ciflow/inductor/145128 2025-01-24T01:03:43.8887033Z * [new tag] ciflow/inductor/145130 -> ciflow/inductor/145130 2025-01-24T01:03:43.8887679Z * [new tag] ciflow/inductor/145132 -> ciflow/inductor/145132 2025-01-24T01:03:43.8888577Z * [new tag] ciflow/inductor/145148 -> ciflow/inductor/145148 2025-01-24T01:03:43.8889185Z * [new tag] ciflow/inductor/145150 -> ciflow/inductor/145150 2025-01-24T01:03:43.8890277Z * [new tag] ciflow/inductor/145153 -> ciflow/inductor/145153 2025-01-24T01:03:43.8890926Z * [new tag] ciflow/inductor/145156 -> ciflow/inductor/145156 2025-01-24T01:03:43.8891773Z * [new tag] ciflow/inductor/145194 -> ciflow/inductor/145194 2025-01-24T01:03:43.8892542Z * [new tag] ciflow/inductor/145196 -> ciflow/inductor/145196 2025-01-24T01:03:43.8893392Z * [new tag] ciflow/inductor/145204 -> ciflow/inductor/145204 2025-01-24T01:03:43.8894126Z * [new tag] ciflow/inductor/145223 -> ciflow/inductor/145223 2025-01-24T01:03:43.8894879Z * [new tag] ciflow/inductor/145245 -> ciflow/inductor/145245 2025-01-24T01:03:43.8895632Z * [new tag] ciflow/inductor/145248 -> ciflow/inductor/145248 2025-01-24T01:03:43.8896377Z * [new tag] ciflow/inductor/145250 -> ciflow/inductor/145250 2025-01-24T01:03:43.8897100Z * [new tag] ciflow/inductor/145254 -> ciflow/inductor/145254 2025-01-24T01:03:43.8898162Z * [new tag] ciflow/inductor/145303 -> ciflow/inductor/145303 2025-01-24T01:03:43.8898831Z * [new tag] ciflow/inductor/145307 -> ciflow/inductor/145307 2025-01-24T01:03:43.8899575Z * [new tag] ciflow/inductor/145311 -> ciflow/inductor/145311 2025-01-24T01:03:43.8900416Z * [new tag] ciflow/inductor/145318 -> ciflow/inductor/145318 2025-01-24T01:03:43.8901215Z * [new tag] ciflow/inductor/145321 -> ciflow/inductor/145321 2025-01-24T01:03:43.8902033Z * [new tag] ciflow/inductor/145323 -> ciflow/inductor/145323 2025-01-24T01:03:43.8902787Z * [new tag] ciflow/inductor/145331 -> ciflow/inductor/145331 2025-01-24T01:03:43.8903580Z * [new tag] ciflow/inductor/145353 -> ciflow/inductor/145353 2025-01-24T01:03:43.8904324Z * [new tag] ciflow/inductor/145354 -> ciflow/inductor/145354 2025-01-24T01:03:43.8905167Z * [new tag] ciflow/inductor/145373 -> ciflow/inductor/145373 2025-01-24T01:03:43.8905970Z * [new tag] ciflow/inductor/145375 -> ciflow/inductor/145375 2025-01-24T01:03:43.8907125Z * [new tag] ciflow/inductor/145381 -> ciflow/inductor/145381 2025-01-24T01:03:43.8907843Z * [new tag] ciflow/inductor/145400 -> ciflow/inductor/145400 2025-01-24T01:03:43.8908569Z * [new tag] ciflow/inductor/145403 -> ciflow/inductor/145403 2025-01-24T01:03:43.8909315Z * [new tag] ciflow/inductor/145407 -> ciflow/inductor/145407 2025-01-24T01:03:43.8910061Z * [new tag] ciflow/inductor/145415 -> ciflow/inductor/145415 2025-01-24T01:03:43.8910915Z * [new tag] ciflow/inductor/145421 -> ciflow/inductor/145421 2025-01-24T01:03:43.8911702Z * [new tag] ciflow/inductor/145422 -> ciflow/inductor/145422 2025-01-24T01:03:43.8912427Z * [new tag] ciflow/inductor/145426 -> ciflow/inductor/145426 2025-01-24T01:03:43.8913405Z * [new tag] ciflow/inductor/145427 -> ciflow/inductor/145427 2025-01-24T01:03:43.8914552Z * [new tag] ciflow/inductor/145429 -> ciflow/inductor/145429 2025-01-24T01:03:43.8915276Z * [new tag] ciflow/inductor/145431 -> ciflow/inductor/145431 2025-01-24T01:03:43.8915993Z * [new tag] ciflow/inductor/145435 -> ciflow/inductor/145435 2025-01-24T01:03:43.8916765Z * [new tag] ciflow/inductor/145448 -> ciflow/inductor/145448 2025-01-24T01:03:43.8917588Z * [new tag] ciflow/inductor/145455 -> ciflow/inductor/145455 2025-01-24T01:03:43.8918584Z * [new tag] ciflow/inductor/145459 -> ciflow/inductor/145459 2025-01-24T01:03:43.8919234Z * [new tag] ciflow/inductor/145475 -> ciflow/inductor/145475 2025-01-24T01:03:43.8919997Z * [new tag] ciflow/inductor/145479 -> ciflow/inductor/145479 2025-01-24T01:03:43.8920723Z * [new tag] ciflow/inductor/145490 -> ciflow/inductor/145490 2025-01-24T01:03:43.8921489Z * [new tag] ciflow/inductor/145506 -> ciflow/inductor/145506 2025-01-24T01:03:43.8922216Z * [new tag] ciflow/inductor/145509 -> ciflow/inductor/145509 2025-01-24T01:03:43.8922968Z * [new tag] ciflow/inductor/145513 -> ciflow/inductor/145513 2025-01-24T01:03:43.8923725Z * [new tag] ciflow/inductor/145515 -> ciflow/inductor/145515 2025-01-24T01:03:43.8924522Z * [new tag] ciflow/inductor/145518 -> ciflow/inductor/145518 2025-01-24T01:03:43.8925213Z * [new tag] ciflow/inductor/145519 -> ciflow/inductor/145519 2025-01-24T01:03:43.8925974Z * [new tag] ciflow/inductor/145523 -> ciflow/inductor/145523 2025-01-24T01:03:43.8926698Z * [new tag] ciflow/inductor/145527 -> ciflow/inductor/145527 2025-01-24T01:03:43.8927475Z * [new tag] ciflow/inductor/145532 -> ciflow/inductor/145532 2025-01-24T01:03:43.8928190Z * [new tag] ciflow/inductor/145533 -> ciflow/inductor/145533 2025-01-24T01:03:43.8928937Z * [new tag] ciflow/inductor/145537 -> ciflow/inductor/145537 2025-01-24T01:03:43.8929718Z * [new tag] ciflow/inductor/145538 -> ciflow/inductor/145538 2025-01-24T01:03:43.8930594Z * [new tag] ciflow/inductor/145539 -> ciflow/inductor/145539 2025-01-24T01:03:43.8931295Z * [new tag] ciflow/inductor/145540 -> ciflow/inductor/145540 2025-01-24T01:03:43.8932038Z * [new tag] ciflow/inductor/145542 -> ciflow/inductor/145542 2025-01-24T01:03:43.8933081Z * [new tag] ciflow/inductor/145545 -> ciflow/inductor/145545 2025-01-24T01:03:43.8933798Z * [new tag] ciflow/inductor/145546 -> ciflow/inductor/145546 2025-01-24T01:03:43.8934498Z * [new tag] ciflow/inductor/145547 -> ciflow/inductor/145547 2025-01-24T01:03:43.8935285Z * [new tag] ciflow/inductor/145548 -> ciflow/inductor/145548 2025-01-24T01:03:43.8936038Z * [new tag] ciflow/inductor/145549 -> ciflow/inductor/145549 2025-01-24T01:03:43.8936848Z * [new tag] ciflow/inductor/145551 -> ciflow/inductor/145551 2025-01-24T01:03:43.8937536Z * [new tag] ciflow/inductor/145552 -> ciflow/inductor/145552 2025-01-24T01:03:43.8938269Z * [new tag] ciflow/inductor/145553 -> ciflow/inductor/145553 2025-01-24T01:03:43.8939049Z * [new tag] ciflow/inductor/145554 -> ciflow/inductor/145554 2025-01-24T01:03:43.8939831Z * [new tag] ciflow/inductor/145558 -> ciflow/inductor/145558 2025-01-24T01:03:43.8940547Z * [new tag] ciflow/inductor/145559 -> ciflow/inductor/145559 2025-01-24T01:03:43.8941479Z * [new tag] ciflow/inductor/145562 -> ciflow/inductor/145562 2025-01-24T01:03:43.8942196Z * [new tag] ciflow/inductor/145565 -> ciflow/inductor/145565 2025-01-24T01:03:43.8942861Z * [new tag] ciflow/inductor/145569 -> ciflow/inductor/145569 2025-01-24T01:03:43.8943944Z * [new tag] ciflow/inductor/3b9a386 -> ciflow/inductor/3b9a386 2025-01-24T01:03:43.8944783Z * [new tag] ciflow/inductor/3d4b92b -> ciflow/inductor/3d4b92b 2025-01-24T01:03:43.8945771Z * [new tag] ciflow/inductor/88106 -> ciflow/inductor/88106 2025-01-24T01:03:43.8946767Z * [new tag] ciflow/inductor/88196 -> ciflow/inductor/88196 2025-01-24T01:03:43.8947771Z * [new tag] ciflow/inductor/88998 -> ciflow/inductor/88998 2025-01-24T01:03:43.8948609Z * [new tag] ciflow/inductor/d224ac7 -> ciflow/inductor/d224ac7 2025-01-24T01:03:43.8949392Z * [new tag] ciflow/linux-aarch64/125888 -> ciflow/linux-aarch64/125888 2025-01-24T01:03:43.8950038Z * [new tag] ciflow/linux-aarch64/126050 -> ciflow/linux-aarch64/126050 2025-01-24T01:03:43.8950722Z * [new tag] ciflow/linux-aarch64/126054 -> ciflow/linux-aarch64/126054 2025-01-24T01:03:43.8951376Z * [new tag] ciflow/linux-aarch64/133297 -> ciflow/linux-aarch64/133297 2025-01-24T01:03:43.8951948Z * [new tag] ciflow/linux-aarch64/133315 -> ciflow/linux-aarch64/133315 2025-01-24T01:03:43.8952517Z * [new tag] ciflow/linux-aarch64/133392 -> ciflow/linux-aarch64/133392 2025-01-24T01:03:43.8953170Z * [new tag] ciflow/linux-aarch64/133419 -> ciflow/linux-aarch64/133419 2025-01-24T01:03:43.8953855Z * [new tag] ciflow/linux-aarch64/133423 -> ciflow/linux-aarch64/133423 2025-01-24T01:03:43.8954563Z * [new tag] ciflow/linux-aarch64/133667 -> ciflow/linux-aarch64/133667 2025-01-24T01:03:43.8955140Z * [new tag] ciflow/linux-aarch64/133753 -> ciflow/linux-aarch64/133753 2025-01-24T01:03:43.8955899Z * [new tag] ciflow/linux-aarch64/135058 -> ciflow/linux-aarch64/135058 2025-01-24T01:03:43.8956850Z * [new tag] ciflow/linux-aarch64/135333 -> ciflow/linux-aarch64/135333 2025-01-24T01:03:43.8957774Z * [new tag] ciflow/linux-aarch64/135792 -> ciflow/linux-aarch64/135792 2025-01-24T01:03:43.8958532Z * [new tag] ciflow/linux-aarch64/136355 -> ciflow/linux-aarch64/136355 2025-01-24T01:03:43.8959193Z * [new tag] ciflow/linux-aarch64/137562 -> ciflow/linux-aarch64/137562 2025-01-24T01:03:43.8959847Z * [new tag] ciflow/linux-aarch64/137568 -> ciflow/linux-aarch64/137568 2025-01-24T01:03:43.8960581Z * [new tag] ciflow/linux-aarch64/137995 -> ciflow/linux-aarch64/137995 2025-01-24T01:03:43.8961324Z * [new tag] ciflow/linux-aarch64/138388 -> ciflow/linux-aarch64/138388 2025-01-24T01:03:43.8961967Z * [new tag] ciflow/linux-aarch64/138889 -> ciflow/linux-aarch64/138889 2025-01-24T01:03:43.8962620Z * [new tag] ciflow/linux-aarch64/139243 -> ciflow/linux-aarch64/139243 2025-01-24T01:03:43.8963309Z * [new tag] ciflow/linux-aarch64/139973 -> ciflow/linux-aarch64/139973 2025-01-24T01:03:43.8963940Z * [new tag] ciflow/linux-aarch64/140159 -> ciflow/linux-aarch64/140159 2025-01-24T01:03:43.8964601Z * [new tag] ciflow/linux-aarch64/140362 -> ciflow/linux-aarch64/140362 2025-01-24T01:03:43.8965252Z * [new tag] ciflow/linux-aarch64/142501 -> ciflow/linux-aarch64/142501 2025-01-24T01:03:43.8965903Z * [new tag] ciflow/linux-aarch64/142760 -> ciflow/linux-aarch64/142760 2025-01-24T01:03:43.8966686Z * [new tag] ciflow/linux-aarch64/143741 -> ciflow/linux-aarch64/143741 2025-01-24T01:03:43.8967694Z * [new tag] ciflow/mps/102148 -> ciflow/mps/102148 2025-01-24T01:03:43.8968213Z * [new tag] ciflow/mps/119496 -> ciflow/mps/119496 2025-01-24T01:03:43.8968871Z * [new tag] ciflow/mps/120076 -> ciflow/mps/120076 2025-01-24T01:03:43.8969475Z * [new tag] ciflow/mps/133423 -> ciflow/mps/133423 2025-01-24T01:03:43.8970110Z * [new tag] ciflow/mps/133667 -> ciflow/mps/133667 2025-01-24T01:03:43.8970612Z * [new tag] ciflow/mps/137562 -> ciflow/mps/137562 2025-01-24T01:03:43.8971718Z * [new tag] ciflow/mps/138640 -> ciflow/mps/138640 2025-01-24T01:03:43.8972172Z * [new tag] ciflow/mps/139469 -> ciflow/mps/139469 2025-01-24T01:03:43.8972901Z * [new tag] ciflow/mps/140159 -> ciflow/mps/140159 2025-01-24T01:03:43.8973615Z * [new tag] ciflow/mps/140211 -> ciflow/mps/140211 2025-01-24T01:03:43.8974551Z * [new tag] ciflow/mps/140371 -> ciflow/mps/140371 2025-01-24T01:03:43.8975857Z * [new tag] ciflow/mps/140725 -> ciflow/mps/140725 2025-01-24T01:03:43.8976773Z * [new tag] ciflow/mps/140869 -> ciflow/mps/140869 2025-01-24T01:03:43.8977701Z * [new tag] ciflow/mps/141795 -> ciflow/mps/141795 2025-01-24T01:03:43.8978813Z * [new tag] ciflow/mps/142202 -> ciflow/mps/142202 2025-01-24T01:03:43.8979851Z * [new tag] ciflow/mps/142477 -> ciflow/mps/142477 2025-01-24T01:03:43.8980409Z * [new tag] ciflow/mps/143630 -> ciflow/mps/143630 2025-01-24T01:03:43.8981106Z * [new tag] ciflow/mps/143656 -> ciflow/mps/143656 2025-01-24T01:03:43.8981814Z * [new tag] ciflow/mps/143911 -> ciflow/mps/143911 2025-01-24T01:03:43.8982505Z * [new tag] ciflow/mps/143966 -> ciflow/mps/143966 2025-01-24T01:03:43.8983102Z * [new tag] ciflow/mps/144193 -> ciflow/mps/144193 2025-01-24T01:03:43.8983726Z * [new tag] ciflow/mps/144405 -> ciflow/mps/144405 2025-01-24T01:03:43.8984433Z * [new tag] ciflow/mps/144664 -> ciflow/mps/144664 2025-01-24T01:03:43.8985044Z * [new tag] ciflow/mps/145512 -> ciflow/mps/145512 2025-01-24T01:03:43.8985734Z * [new tag] ciflow/mps/145526 -> ciflow/mps/145526 2025-01-24T01:03:43.8986406Z * [new tag] ciflow/mps/145533 -> ciflow/mps/145533 2025-01-24T01:03:43.8987406Z * [new tag] ciflow/mps/145568 -> ciflow/mps/145568 2025-01-24T01:03:43.8988396Z * [new tag] ciflow/periodic/054a2fd -> ciflow/periodic/054a2fd 2025-01-24T01:03:43.8989140Z * [new tag] ciflow/periodic/123020 -> ciflow/periodic/123020 2025-01-24T01:03:43.8989857Z * [new tag] ciflow/periodic/131296 -> ciflow/periodic/131296 2025-01-24T01:03:43.8990546Z * [new tag] ciflow/periodic/131493 -> ciflow/periodic/131493 2025-01-24T01:03:43.8991142Z * [new tag] ciflow/periodic/132742 -> ciflow/periodic/132742 2025-01-24T01:03:43.8991786Z * [new tag] ciflow/periodic/132755 -> ciflow/periodic/132755 2025-01-24T01:03:43.8992529Z * [new tag] ciflow/periodic/134817 -> ciflow/periodic/134817 2025-01-24T01:03:43.8993133Z * [new tag] ciflow/periodic/138503 -> ciflow/periodic/138503 2025-01-24T01:03:43.8993813Z * [new tag] ciflow/periodic/138573 -> ciflow/periodic/138573 2025-01-24T01:03:43.8994523Z * [new tag] ciflow/periodic/139225 -> ciflow/periodic/139225 2025-01-24T01:03:43.8995154Z * [new tag] ciflow/periodic/139576 -> ciflow/periodic/139576 2025-01-24T01:03:43.8996278Z * [new tag] ciflow/periodic/140293 -> ciflow/periodic/140293 2025-01-24T01:03:43.8997118Z * [new tag] ciflow/periodic/140989 -> ciflow/periodic/140989 2025-01-24T01:03:43.8997942Z * [new tag] ciflow/periodic/141168 -> ciflow/periodic/141168 2025-01-24T01:03:43.8998585Z * [new tag] ciflow/periodic/141309 -> ciflow/periodic/141309 2025-01-24T01:03:43.8999290Z * [new tag] ciflow/periodic/141355 -> ciflow/periodic/141355 2025-01-24T01:03:43.8999851Z * [new tag] ciflow/periodic/141588 -> ciflow/periodic/141588 2025-01-24T01:03:43.9000747Z * [new tag] ciflow/periodic/141730 -> ciflow/periodic/141730 2025-01-24T01:03:43.9001314Z * [new tag] ciflow/periodic/142179 -> ciflow/periodic/142179 2025-01-24T01:03:43.9002045Z * [new tag] ciflow/periodic/143959 -> ciflow/periodic/143959 2025-01-24T01:03:43.9002681Z * [new tag] ciflow/periodic/144953 -> ciflow/periodic/144953 2025-01-24T01:03:43.9003331Z * [new tag] ciflow/periodic/145130 -> ciflow/periodic/145130 2025-01-24T01:03:43.9004119Z * [new tag] ciflow/periodic/145416 -> ciflow/periodic/145416 2025-01-24T01:03:43.9004760Z * [new tag] ciflow/periodic/145504 -> ciflow/periodic/145504 2025-01-24T01:03:43.9005610Z * [new tag] ciflow/periodic/2a6d37d -> ciflow/periodic/2a6d37d 2025-01-24T01:03:43.9006373Z * [new tag] ciflow/periodic/317eeb8 -> ciflow/periodic/317eeb8 2025-01-24T01:03:43.9007250Z * [new tag] ciflow/periodic/3c32 -> ciflow/periodic/3c32 2025-01-24T01:03:43.9008107Z * [new tag] ciflow/periodic/3e98831 -> ciflow/periodic/3e98831 2025-01-24T01:03:43.9009123Z * [new tag] ciflow/periodic/94512-point -> ciflow/periodic/94512-point 2025-01-24T01:03:43.9010143Z * [new tag] ciflow/periodic/csl/test87519 -> ciflow/periodic/csl/test87519 2025-01-24T01:03:43.9010911Z * [new tag] ciflow/periodic/csltest88275 -> ciflow/periodic/csltest88275 2025-01-24T01:03:43.9011702Z * [new tag] ciflow/periodic/csltest88761 -> ciflow/periodic/csltest88761 2025-01-24T01:03:43.9012556Z * [new tag] ciflow/periodic/release_1.12 -> ciflow/periodic/release_1.12 2025-01-24T01:03:43.9013501Z * [new tag] ciflow/periodic/release_1.12.0 -> ciflow/periodic/release_1.12.0 2025-01-24T01:03:43.9014572Z * [new tag] ciflow/periodic/sha-ec5b83 -> ciflow/periodic/sha-ec5b83 2025-01-24T01:03:43.9015314Z * [new tag] ciflow/rocm/121885 -> ciflow/rocm/121885 2025-01-24T01:03:43.9016126Z * [new tag] ciflow/rocm/124424 -> ciflow/rocm/124424 2025-01-24T01:03:43.9016766Z * [new tag] ciflow/rocm/129409 -> ciflow/rocm/129409 2025-01-24T01:03:43.9017419Z * [new tag] ciflow/rocm/134817 -> ciflow/rocm/134817 2025-01-24T01:03:43.9018177Z * [new tag] ciflow/rocm/137136 -> ciflow/rocm/137136 2025-01-24T01:03:43.9018911Z * [new tag] ciflow/rocm/138964 -> ciflow/rocm/138964 2025-01-24T01:03:43.9019515Z * [new tag] ciflow/rocm/139469 -> ciflow/rocm/139469 2025-01-24T01:03:43.9020173Z * [new tag] ciflow/rocm/139975 -> ciflow/rocm/139975 2025-01-24T01:03:43.9020777Z * [new tag] ciflow/rocm/140261 -> ciflow/rocm/140261 2025-01-24T01:03:43.9021429Z * [new tag] ciflow/rocm/140293 -> ciflow/rocm/140293 2025-01-24T01:03:43.9022040Z * [new tag] ciflow/rocm/140989 -> ciflow/rocm/140989 2025-01-24T01:03:43.9022937Z * [new tag] ciflow/rocm/141309 -> ciflow/rocm/141309 2025-01-24T01:03:43.9023938Z * [new tag] ciflow/rocm/141355 -> ciflow/rocm/141355 2025-01-24T01:03:43.9024674Z * [new tag] ciflow/rocm/142408 -> ciflow/rocm/142408 2025-01-24T01:03:43.9025375Z * [new tag] ciflow/rocm/143286 -> ciflow/rocm/143286 2025-01-24T01:03:43.9026221Z * [new tag] ciflow/rocm/143416 -> ciflow/rocm/143416 2025-01-24T01:03:43.9026869Z * [new tag] ciflow/rocm/143971 -> ciflow/rocm/143971 2025-01-24T01:03:43.9027523Z * [new tag] ciflow/rocm/144120 -> ciflow/rocm/144120 2025-01-24T01:03:43.9028374Z * [new tag] ciflow/rocm/144441 -> ciflow/rocm/144441 2025-01-24T01:03:43.9029148Z * [new tag] ciflow/rocm/144476 -> ciflow/rocm/144476 2025-01-24T01:03:43.9029906Z * [new tag] ciflow/rocm/144572 -> ciflow/rocm/144572 2025-01-24T01:03:43.9030569Z * [new tag] ciflow/rocm/144664 -> ciflow/rocm/144664 2025-01-24T01:03:43.9031176Z * [new tag] ciflow/rocm/144865 -> ciflow/rocm/144865 2025-01-24T01:03:43.9032074Z * [new tag] ciflow/rocm/144942 -> ciflow/rocm/144942 2025-01-24T01:03:43.9032746Z * [new tag] ciflow/rocm/144985 -> ciflow/rocm/144985 2025-01-24T01:03:43.9033348Z * [new tag] ciflow/rocm/145083 -> ciflow/rocm/145083 2025-01-24T01:03:43.9033970Z * [new tag] ciflow/rocm/145130 -> ciflow/rocm/145130 2025-01-24T01:03:43.9034894Z * [new tag] ciflow/rocm/145227 -> ciflow/rocm/145227 2025-01-24T01:03:43.9035635Z * [new tag] ciflow/rocm/145294 -> ciflow/rocm/145294 2025-01-24T01:03:43.9036234Z * [new tag] ciflow/rocm/145416 -> ciflow/rocm/145416 2025-01-24T01:03:43.9036877Z * [new tag] ciflow/rocm/145475 -> ciflow/rocm/145475 2025-01-24T01:03:43.9037548Z * [new tag] ciflow/rocm/145490 -> ciflow/rocm/145490 2025-01-24T01:03:43.9038120Z * [new tag] ciflow/rocm/145504 -> ciflow/rocm/145504 2025-01-24T01:03:43.9038734Z * [new tag] ciflow/rocm/145518 -> ciflow/rocm/145518 2025-01-24T01:03:43.9039569Z * [new tag] ciflow/s390/137995 -> ciflow/s390/137995 2025-01-24T01:03:43.9040295Z * [new tag] ciflow/s390/142346 -> ciflow/s390/142346 2025-01-24T01:03:43.9040855Z * [new tag] ciflow/s390/143959 -> ciflow/s390/143959 2025-01-24T01:03:43.9041832Z * [new tag] ciflow/slow/01c7106 -> ciflow/slow/01c7106 2025-01-24T01:03:43.9042568Z * [new tag] ciflow/slow/0577043 -> ciflow/slow/0577043 2025-01-24T01:03:43.9043846Z * [new tag] ciflow/slow/0d5b74da0cab798fbfdb9caa53fad816999c8386-sdym -> ciflow/slow/0d5b74da0cab798fbfdb9caa53fad816999c8386-sdym 2025-01-24T01:03:43.9044190Z * [new tag] ciflow/slow/0e81104 -> ciflow/slow/0e81104 2025-01-24T01:03:43.9044791Z * [new tag] ciflow/slow/139975 -> ciflow/slow/139975 2025-01-24T01:03:43.9045521Z * [new tag] ciflow/slow/141588 -> ciflow/slow/141588 2025-01-24T01:03:43.9046110Z * [new tag] ciflow/slow/144865 -> ciflow/slow/144865 2025-01-24T01:03:43.9046768Z * [new tag] ciflow/slow/145206 -> ciflow/slow/145206 2025-01-24T01:03:43.9047885Z * [new tag] ciflow/slow/1732077 -> ciflow/slow/1732077 2025-01-24T01:03:43.9049049Z * [new tag] ciflow/slow/187eb7c -> ciflow/slow/187eb7c 2025-01-24T01:03:43.9050112Z * [new tag] ciflow/slow/1faef89 -> ciflow/slow/1faef89 2025-01-24T01:03:43.9053373Z * [new tag] ciflow/slow/3920ec1 -> ciflow/slow/3920ec1 2025-01-24T01:03:43.9053753Z * [new tag] ciflow/slow/3b7c6b2 -> ciflow/slow/3b7c6b2 2025-01-24T01:03:43.9054088Z * [new tag] ciflow/slow/59a3759 -> ciflow/slow/59a3759 2025-01-24T01:03:43.9054425Z * [new tag] ciflow/slow/70ef0bb -> ciflow/slow/70ef0bb 2025-01-24T01:03:43.9054668Z * [new tag] ciflow/slow/788ff06 -> ciflow/slow/788ff06 2025-01-24T01:03:43.9055772Z * [new tag] ciflow/slow/8751002215790a3a88750faa8f4366933e296693-sdym -> ciflow/slow/8751002215790a3a88750faa8f4366933e296693-sdym 2025-01-24T01:03:43.9056149Z * [new tag] ciflow/slow/9d85864 -> ciflow/slow/9d85864 2025-01-24T01:03:43.9056897Z * [new tag] ciflow/slow/9ffad5b -> ciflow/slow/9ffad5b 2025-01-24T01:03:43.9057543Z * [new tag] ciflow/slow/a206e8b -> ciflow/slow/a206e8b 2025-01-24T01:03:43.9058317Z * [new tag] ciflow/slow/a837609 -> ciflow/slow/a837609 2025-01-24T01:03:43.9059156Z * [new tag] ciflow/slow/af841f3 -> ciflow/slow/af841f3 2025-01-24T01:03:43.9060816Z * [new tag] ciflow/slow/da3aba1e46157c4df504b067477cdf2b3c96b194-sdym -> ciflow/slow/da3aba1e46157c4df504b067477cdf2b3c96b194-sdym 2025-01-24T01:03:43.9061166Z * [new tag] ciflow/trunk/108303 -> ciflow/trunk/108303 2025-01-24T01:03:43.9061607Z * [new tag] ciflow/trunk/113257 -> ciflow/trunk/113257 2025-01-24T01:03:43.9062155Z * [new tag] ciflow/trunk/113258 -> ciflow/trunk/113258 2025-01-24T01:03:43.9062783Z * [new tag] ciflow/trunk/115749 -> ciflow/trunk/115749 2025-01-24T01:03:43.9063437Z * [new tag] ciflow/trunk/120076 -> ciflow/trunk/120076 2025-01-24T01:03:43.9064016Z * [new tag] ciflow/trunk/121445 -> ciflow/trunk/121445 2025-01-24T01:03:43.9064651Z * [new tag] ciflow/trunk/123020 -> ciflow/trunk/123020 2025-01-24T01:03:43.9065266Z * [new tag] ciflow/trunk/124424 -> ciflow/trunk/124424 2025-01-24T01:03:43.9065863Z * [new tag] ciflow/trunk/124490 -> ciflow/trunk/124490 2025-01-24T01:03:43.9066565Z * [new tag] ciflow/trunk/125469 -> ciflow/trunk/125469 2025-01-24T01:03:43.9067209Z * [new tag] ciflow/trunk/125806 -> ciflow/trunk/125806 2025-01-24T01:03:43.9068132Z * [new tag] ciflow/trunk/125888 -> ciflow/trunk/125888 2025-01-24T01:03:43.9069021Z * [new tag] ciflow/trunk/125995 -> ciflow/trunk/125995 2025-01-24T01:03:43.9069939Z * [new tag] ciflow/trunk/126050 -> ciflow/trunk/126050 2025-01-24T01:03:43.9070549Z * [new tag] ciflow/trunk/126054 -> ciflow/trunk/126054 2025-01-24T01:03:43.9071511Z * [new tag] ciflow/trunk/126635 -> ciflow/trunk/126635 2025-01-24T01:03:43.9072255Z * [new tag] ciflow/trunk/127171 -> ciflow/trunk/127171 2025-01-24T01:03:43.9073176Z * [new tag] ciflow/trunk/127370 -> ciflow/trunk/127370 2025-01-24T01:03:43.9073918Z * [new tag] ciflow/trunk/127919 -> ciflow/trunk/127919 2025-01-24T01:03:43.9074528Z * [new tag] ciflow/trunk/128672 -> ciflow/trunk/128672 2025-01-24T01:03:43.9075180Z * [new tag] ciflow/trunk/129352 -> ciflow/trunk/129352 2025-01-24T01:03:43.9075843Z * [new tag] ciflow/trunk/129409 -> ciflow/trunk/129409 2025-01-24T01:03:43.9076480Z * [new tag] ciflow/trunk/129420 -> ciflow/trunk/129420 2025-01-24T01:03:43.9077212Z * [new tag] ciflow/trunk/130140 -> ciflow/trunk/130140 2025-01-24T01:03:43.9077868Z * [new tag] ciflow/trunk/130141 -> ciflow/trunk/130141 2025-01-24T01:03:43.9078529Z * [new tag] ciflow/trunk/130752 -> ciflow/trunk/130752 2025-01-24T01:03:43.9079315Z * [new tag] ciflow/trunk/131296 -> ciflow/trunk/131296 2025-01-24T01:03:43.9079974Z * [new tag] ciflow/trunk/131354 -> ciflow/trunk/131354 2025-01-24T01:03:43.9080639Z * [new tag] ciflow/trunk/131481 -> ciflow/trunk/131481 2025-01-24T01:03:43.9081245Z * [new tag] ciflow/trunk/131493 -> ciflow/trunk/131493 2025-01-24T01:03:43.9081900Z * [new tag] ciflow/trunk/131507 -> ciflow/trunk/131507 2025-01-24T01:03:43.9082552Z * [new tag] ciflow/trunk/132021 -> ciflow/trunk/132021 2025-01-24T01:03:43.9083357Z * [new tag] ciflow/trunk/132742 -> ciflow/trunk/132742 2025-01-24T01:03:43.9083888Z * [new tag] ciflow/trunk/132755 -> ciflow/trunk/132755 2025-01-24T01:03:43.9084549Z * [new tag] ciflow/trunk/133044 -> ciflow/trunk/133044 2025-01-24T01:03:43.9085309Z * [new tag] ciflow/trunk/133222 -> ciflow/trunk/133222 2025-01-24T01:03:43.9085980Z * [new tag] ciflow/trunk/133289 -> ciflow/trunk/133289 2025-01-24T01:03:43.9086612Z * [new tag] ciflow/trunk/133296 -> ciflow/trunk/133296 2025-01-24T01:03:43.9087276Z * [new tag] ciflow/trunk/133297 -> ciflow/trunk/133297 2025-01-24T01:03:43.9087907Z * [new tag] ciflow/trunk/133315 -> ciflow/trunk/133315 2025-01-24T01:03:43.9088560Z * [new tag] ciflow/trunk/133392 -> ciflow/trunk/133392 2025-01-24T01:03:43.9089188Z * [new tag] ciflow/trunk/133419 -> ciflow/trunk/133419 2025-01-24T01:03:43.9089838Z * [new tag] ciflow/trunk/133423 -> ciflow/trunk/133423 2025-01-24T01:03:43.9090468Z * [new tag] ciflow/trunk/133667 -> ciflow/trunk/133667 2025-01-24T01:03:43.9091125Z * [new tag] ciflow/trunk/133753 -> ciflow/trunk/133753 2025-01-24T01:03:43.9092089Z * [new tag] ciflow/trunk/134219 -> ciflow/trunk/134219 2025-01-24T01:03:43.9092770Z * [new tag] ciflow/trunk/134515 -> ciflow/trunk/134515 2025-01-24T01:03:43.9093511Z * [new tag] ciflow/trunk/134667 -> ciflow/trunk/134667 2025-01-24T01:03:43.9094201Z * [new tag] ciflow/trunk/134969 -> ciflow/trunk/134969 2025-01-24T01:03:43.9094891Z * [new tag] ciflow/trunk/135058 -> ciflow/trunk/135058 2025-01-24T01:03:43.9095530Z * [new tag] ciflow/trunk/135189 -> ciflow/trunk/135189 2025-01-24T01:03:43.9096124Z * [new tag] ciflow/trunk/135337 -> ciflow/trunk/135337 2025-01-24T01:03:43.9096890Z * [new tag] ciflow/trunk/135338 -> ciflow/trunk/135338 2025-01-24T01:03:43.9097550Z * [new tag] ciflow/trunk/135465 -> ciflow/trunk/135465 2025-01-24T01:03:43.9098296Z * [new tag] ciflow/trunk/135631 -> ciflow/trunk/135631 2025-01-24T01:03:43.9098968Z * [new tag] ciflow/trunk/135719 -> ciflow/trunk/135719 2025-01-24T01:03:43.9100259Z * [new tag] ciflow/trunk/136241 -> ciflow/trunk/136241 2025-01-24T01:03:43.9100947Z * [new tag] ciflow/trunk/136824 -> ciflow/trunk/136824 2025-01-24T01:03:43.9101709Z * [new tag] ciflow/trunk/136835 -> ciflow/trunk/136835 2025-01-24T01:03:43.9102616Z * [new tag] ciflow/trunk/136993 -> ciflow/trunk/136993 2025-01-24T01:03:43.9103235Z * [new tag] ciflow/trunk/137400 -> ciflow/trunk/137400 2025-01-24T01:03:43.9103877Z * [new tag] ciflow/trunk/137570 -> ciflow/trunk/137570 2025-01-24T01:03:43.9104519Z * [new tag] ciflow/trunk/137580 -> ciflow/trunk/137580 2025-01-24T01:03:43.9105214Z * [new tag] ciflow/trunk/137726 -> ciflow/trunk/137726 2025-01-24T01:03:43.9105858Z * [new tag] ciflow/trunk/137822 -> ciflow/trunk/137822 2025-01-24T01:03:43.9106596Z * [new tag] ciflow/trunk/137921 -> ciflow/trunk/137921 2025-01-24T01:03:43.9107246Z * [new tag] ciflow/trunk/138175 -> ciflow/trunk/138175 2025-01-24T01:03:43.9107916Z * [new tag] ciflow/trunk/138213 -> ciflow/trunk/138213 2025-01-24T01:03:43.9108866Z * [new tag] ciflow/trunk/138618 -> ciflow/trunk/138618 2025-01-24T01:03:43.9109456Z * [new tag] ciflow/trunk/138626 -> ciflow/trunk/138626 2025-01-24T01:03:43.9110325Z * [new tag] ciflow/trunk/138628 -> ciflow/trunk/138628 2025-01-24T01:03:43.9110936Z * [new tag] ciflow/trunk/138672 -> ciflow/trunk/138672 2025-01-24T01:03:43.9111818Z * [new tag] ciflow/trunk/138684 -> ciflow/trunk/138684 2025-01-24T01:03:43.9112428Z * [new tag] ciflow/trunk/138749 -> ciflow/trunk/138749 2025-01-24T01:03:43.9113072Z * [new tag] ciflow/trunk/138871 -> ciflow/trunk/138871 2025-01-24T01:03:43.9113733Z * [new tag] ciflow/trunk/138889 -> ciflow/trunk/138889 2025-01-24T01:03:43.9114410Z * [new tag] ciflow/trunk/138923 -> ciflow/trunk/138923 2025-01-24T01:03:43.9115150Z * [new tag] ciflow/trunk/138938 -> ciflow/trunk/138938 2025-01-24T01:03:43.9115804Z * [new tag] ciflow/trunk/138964 -> ciflow/trunk/138964 2025-01-24T01:03:43.9116461Z * [new tag] ciflow/trunk/138996 -> ciflow/trunk/138996 2025-01-24T01:03:43.9117143Z * [new tag] ciflow/trunk/138998 -> ciflow/trunk/138998 2025-01-24T01:03:43.9117801Z * [new tag] ciflow/trunk/139094 -> ciflow/trunk/139094 2025-01-24T01:03:43.9118416Z * [new tag] ciflow/trunk/139135 -> ciflow/trunk/139135 2025-01-24T01:03:43.9119061Z * [new tag] ciflow/trunk/139171 -> ciflow/trunk/139171 2025-01-24T01:03:43.9119984Z * [new tag] ciflow/trunk/139217 -> ciflow/trunk/139217 2025-01-24T01:03:43.9120557Z * [new tag] ciflow/trunk/139219 -> ciflow/trunk/139219 2025-01-24T01:03:43.9121173Z * [new tag] ciflow/trunk/139225 -> ciflow/trunk/139225 2025-01-24T01:03:43.9121818Z * [new tag] ciflow/trunk/139308 -> ciflow/trunk/139308 2025-01-24T01:03:43.9122496Z * [new tag] ciflow/trunk/139338 -> ciflow/trunk/139338 2025-01-24T01:03:43.9123226Z * [new tag] ciflow/trunk/139380 -> ciflow/trunk/139380 2025-01-24T01:03:43.9123889Z * [new tag] ciflow/trunk/139426 -> ciflow/trunk/139426 2025-01-24T01:03:43.9124551Z * [new tag] ciflow/trunk/139535 -> ciflow/trunk/139535 2025-01-24T01:03:43.9125240Z * [new tag] ciflow/trunk/139576 -> ciflow/trunk/139576 2025-01-24T01:03:43.9125991Z * [new tag] ciflow/trunk/139750 -> ciflow/trunk/139750 2025-01-24T01:03:43.9126673Z * [new tag] ciflow/trunk/139796 -> ciflow/trunk/139796 2025-01-24T01:03:43.9127328Z * [new tag] ciflow/trunk/139820 -> ciflow/trunk/139820 2025-01-24T01:03:43.9128004Z * [new tag] ciflow/trunk/139864 -> ciflow/trunk/139864 2025-01-24T01:03:43.9128593Z * [new tag] ciflow/trunk/139919 -> ciflow/trunk/139919 2025-01-24T01:03:43.9129426Z * [new tag] ciflow/trunk/139932 -> ciflow/trunk/139932 2025-01-24T01:03:43.9130122Z * [new tag] ciflow/trunk/139966 -> ciflow/trunk/139966 2025-01-24T01:03:43.9130797Z * [new tag] ciflow/trunk/139971 -> ciflow/trunk/139971 2025-01-24T01:03:43.9131398Z * [new tag] ciflow/trunk/139975 -> ciflow/trunk/139975 2025-01-24T01:03:43.9132113Z * [new tag] ciflow/trunk/139976 -> ciflow/trunk/139976 2025-01-24T01:03:43.9132799Z * [new tag] ciflow/trunk/139985 -> ciflow/trunk/139985 2025-01-24T01:03:43.9133474Z * [new tag] ciflow/trunk/140061 -> ciflow/trunk/140061 2025-01-24T01:03:43.9134060Z * [new tag] ciflow/trunk/140084 -> ciflow/trunk/140084 2025-01-24T01:03:43.9134712Z * [new tag] ciflow/trunk/140159 -> ciflow/trunk/140159 2025-01-24T01:03:43.9135532Z * [new tag] ciflow/trunk/140200 -> ciflow/trunk/140200 2025-01-24T01:03:43.9136165Z * [new tag] ciflow/trunk/140211 -> ciflow/trunk/140211 2025-01-24T01:03:43.9137127Z * [new tag] ciflow/trunk/140256 -> ciflow/trunk/140256 2025-01-24T01:03:43.9137751Z * [new tag] ciflow/trunk/140298 -> ciflow/trunk/140298 2025-01-24T01:03:43.9138373Z * [new tag] ciflow/trunk/140323 -> ciflow/trunk/140323 2025-01-24T01:03:43.9139056Z * [new tag] ciflow/trunk/140362 -> ciflow/trunk/140362 2025-01-24T01:03:43.9139719Z * [new tag] ciflow/trunk/140365 -> ciflow/trunk/140365 2025-01-24T01:03:43.9140455Z * [new tag] ciflow/trunk/140399 -> ciflow/trunk/140399 2025-01-24T01:03:43.9141114Z * [new tag] ciflow/trunk/140677 -> ciflow/trunk/140677 2025-01-24T01:03:43.9142238Z * [new tag] ciflow/trunk/140734 -> ciflow/trunk/140734 2025-01-24T01:03:43.9142664Z * [new tag] ciflow/trunk/140793 -> ciflow/trunk/140793 2025-01-24T01:03:43.9143414Z * [new tag] ciflow/trunk/140836 -> ciflow/trunk/140836 2025-01-24T01:03:43.9144410Z * [new tag] ciflow/trunk/140868 -> ciflow/trunk/140868 2025-01-24T01:03:43.9145449Z * [new tag] ciflow/trunk/140989 -> ciflow/trunk/140989 2025-01-24T01:03:43.9145981Z * [new tag] ciflow/trunk/141002 -> ciflow/trunk/141002 2025-01-24T01:03:43.9147185Z * [new tag] ciflow/trunk/141038 -> ciflow/trunk/141038 2025-01-24T01:03:43.9147867Z * [new tag] ciflow/trunk/141117 -> ciflow/trunk/141117 2025-01-24T01:03:43.9148557Z * [new tag] ciflow/trunk/141131 -> ciflow/trunk/141131 2025-01-24T01:03:43.9149306Z * [new tag] ciflow/trunk/141136 -> ciflow/trunk/141136 2025-01-24T01:03:43.9149987Z * [new tag] ciflow/trunk/141168 -> ciflow/trunk/141168 2025-01-24T01:03:43.9150647Z * [new tag] ciflow/trunk/141195 -> ciflow/trunk/141195 2025-01-24T01:03:43.9151289Z * [new tag] ciflow/trunk/141226 -> ciflow/trunk/141226 2025-01-24T01:03:43.9152199Z * [new tag] ciflow/trunk/141257 -> ciflow/trunk/141257 2025-01-24T01:03:43.9152739Z * [new tag] ciflow/trunk/141309 -> ciflow/trunk/141309 2025-01-24T01:03:43.9153520Z * [new tag] ciflow/trunk/141468 -> ciflow/trunk/141468 2025-01-24T01:03:43.9154198Z * [new tag] ciflow/trunk/141489 -> ciflow/trunk/141489 2025-01-24T01:03:43.9154859Z * [new tag] ciflow/trunk/141503 -> ciflow/trunk/141503 2025-01-24T01:03:43.9155813Z * [new tag] ciflow/trunk/141508 -> ciflow/trunk/141508 2025-01-24T01:03:43.9156523Z * [new tag] ciflow/trunk/141530 -> ciflow/trunk/141530 2025-01-24T01:03:43.9157165Z * [new tag] ciflow/trunk/141588 -> ciflow/trunk/141588 2025-01-24T01:03:43.9157824Z * [new tag] ciflow/trunk/141697 -> ciflow/trunk/141697 2025-01-24T01:03:43.9158454Z * [new tag] ciflow/trunk/141730 -> ciflow/trunk/141730 2025-01-24T01:03:43.9159491Z * [new tag] ciflow/trunk/141791 -> ciflow/trunk/141791 2025-01-24T01:03:43.9160183Z * [new tag] ciflow/trunk/141796 -> ciflow/trunk/141796 2025-01-24T01:03:43.9161079Z * [new tag] ciflow/trunk/141842 -> ciflow/trunk/141842 2025-01-24T01:03:43.9161719Z * [new tag] ciflow/trunk/141889 -> ciflow/trunk/141889 2025-01-24T01:03:43.9162343Z * [new tag] ciflow/trunk/141910 -> ciflow/trunk/141910 2025-01-24T01:03:43.9163311Z * [new tag] ciflow/trunk/141914 -> ciflow/trunk/141914 2025-01-24T01:03:43.9164127Z * [new tag] ciflow/trunk/141959 -> ciflow/trunk/141959 2025-01-24T01:03:43.9164669Z * [new tag] ciflow/trunk/141961 -> ciflow/trunk/141961 2025-01-24T01:03:43.9165433Z * [new tag] ciflow/trunk/142067 -> ciflow/trunk/142067 2025-01-24T01:03:43.9166099Z * [new tag] ciflow/trunk/142091 -> ciflow/trunk/142091 2025-01-24T01:03:43.9166738Z * [new tag] ciflow/trunk/142092 -> ciflow/trunk/142092 2025-01-24T01:03:43.9167521Z * [new tag] ciflow/trunk/142097 -> ciflow/trunk/142097 2025-01-24T01:03:43.9168158Z * [new tag] ciflow/trunk/142179 -> ciflow/trunk/142179 2025-01-24T01:03:43.9168934Z * [new tag] ciflow/trunk/142263 -> ciflow/trunk/142263 2025-01-24T01:03:43.9169573Z * [new tag] ciflow/trunk/142272 -> ciflow/trunk/142272 2025-01-24T01:03:43.9170248Z * [new tag] ciflow/trunk/142273 -> ciflow/trunk/142273 2025-01-24T01:03:43.9170902Z * [new tag] ciflow/trunk/142346 -> ciflow/trunk/142346 2025-01-24T01:03:43.9171570Z * [new tag] ciflow/trunk/142350 -> ciflow/trunk/142350 2025-01-24T01:03:43.9172211Z * [new tag] ciflow/trunk/142372 -> ciflow/trunk/142372 2025-01-24T01:03:43.9172872Z * [new tag] ciflow/trunk/142477 -> ciflow/trunk/142477 2025-01-24T01:03:43.9173808Z * [new tag] ciflow/trunk/142821 -> ciflow/trunk/142821 2025-01-24T01:03:43.9174508Z * [new tag] ciflow/trunk/142848 -> ciflow/trunk/142848 2025-01-24T01:03:43.9175171Z * [new tag] ciflow/trunk/142859 -> ciflow/trunk/142859 2025-01-24T01:03:43.9175822Z * [new tag] ciflow/trunk/142861 -> ciflow/trunk/142861 2025-01-24T01:03:43.9176590Z * [new tag] ciflow/trunk/142865 -> ciflow/trunk/142865 2025-01-24T01:03:43.9177230Z * [new tag] ciflow/trunk/143082 -> ciflow/trunk/143082 2025-01-24T01:03:43.9178194Z * [new tag] ciflow/trunk/143093 -> ciflow/trunk/143093 2025-01-24T01:03:43.9178815Z * [new tag] ciflow/trunk/143168 -> ciflow/trunk/143168 2025-01-24T01:03:43.9179592Z * [new tag] ciflow/trunk/143220 -> ciflow/trunk/143220 2025-01-24T01:03:43.9180723Z * [new tag] ciflow/trunk/143261 -> ciflow/trunk/143261 2025-01-24T01:03:43.9181729Z * [new tag] ciflow/trunk/143303 -> ciflow/trunk/143303 2025-01-24T01:03:43.9182262Z * [new tag] ciflow/trunk/143313 -> ciflow/trunk/143313 2025-01-24T01:03:43.9183073Z * [new tag] ciflow/trunk/143347 -> ciflow/trunk/143347 2025-01-24T01:03:43.9183761Z * [new tag] ciflow/trunk/143360 -> ciflow/trunk/143360 2025-01-24T01:03:43.9184502Z * [new tag] ciflow/trunk/143378 -> ciflow/trunk/143378 2025-01-24T01:03:43.9185304Z * [new tag] ciflow/trunk/143402 -> ciflow/trunk/143402 2025-01-24T01:03:43.9185950Z * [new tag] ciflow/trunk/143416 -> ciflow/trunk/143416 2025-01-24T01:03:43.9186694Z * [new tag] ciflow/trunk/143417 -> ciflow/trunk/143417 2025-01-24T01:03:43.9187654Z * [new tag] ciflow/trunk/143451 -> ciflow/trunk/143451 2025-01-24T01:03:43.9188248Z * [new tag] ciflow/trunk/143475 -> ciflow/trunk/143475 2025-01-24T01:03:43.9188870Z * [new tag] ciflow/trunk/143622 -> ciflow/trunk/143622 2025-01-24T01:03:43.9189532Z * [new tag] ciflow/trunk/143630 -> ciflow/trunk/143630 2025-01-24T01:03:43.9190184Z * [new tag] ciflow/trunk/143671 -> ciflow/trunk/143671 2025-01-24T01:03:43.9190817Z * [new tag] ciflow/trunk/143712 -> ciflow/trunk/143712 2025-01-24T01:03:43.9191717Z * [new tag] ciflow/trunk/143733 -> ciflow/trunk/143733 2025-01-24T01:03:43.9192863Z * [new tag] ciflow/trunk/143806 -> ciflow/trunk/143806 2025-01-24T01:03:43.9193482Z * [new tag] ciflow/trunk/143808 -> ciflow/trunk/143808 2025-01-24T01:03:43.9194246Z * [new tag] ciflow/trunk/143822 -> ciflow/trunk/143822 2025-01-24T01:03:43.9195044Z * [new tag] ciflow/trunk/143833 -> ciflow/trunk/143833 2025-01-24T01:03:43.9195700Z * [new tag] ciflow/trunk/143879 -> ciflow/trunk/143879 2025-01-24T01:03:43.9196361Z * [new tag] ciflow/trunk/143880 -> ciflow/trunk/143880 2025-01-24T01:03:43.9197317Z * [new tag] ciflow/trunk/143894 -> ciflow/trunk/143894 2025-01-24T01:03:43.9197906Z * [new tag] ciflow/trunk/143896 -> ciflow/trunk/143896 2025-01-24T01:03:43.9198520Z * [new tag] ciflow/trunk/143961 -> ciflow/trunk/143961 2025-01-24T01:03:43.9199180Z * [new tag] ciflow/trunk/143966 -> ciflow/trunk/143966 2025-01-24T01:03:43.9199806Z * [new tag] ciflow/trunk/144011 -> ciflow/trunk/144011 2025-01-24T01:03:43.9200495Z * [new tag] ciflow/trunk/144017 -> ciflow/trunk/144017 2025-01-24T01:03:43.9201238Z * [new tag] ciflow/trunk/144019 -> ciflow/trunk/144019 2025-01-24T01:03:43.9201924Z * [new tag] ciflow/trunk/144020 -> ciflow/trunk/144020 2025-01-24T01:03:43.9202567Z * [new tag] ciflow/trunk/144046 -> ciflow/trunk/144046 2025-01-24T01:03:43.9203241Z * [new tag] ciflow/trunk/144115 -> ciflow/trunk/144115 2025-01-24T01:03:43.9203875Z * [new tag] ciflow/trunk/144120 -> ciflow/trunk/144120 2025-01-24T01:03:43.9204836Z * [new tag] ciflow/trunk/144138 -> ciflow/trunk/144138 2025-01-24T01:03:43.9205358Z * [new tag] ciflow/trunk/144172 -> ciflow/trunk/144172 2025-01-24T01:03:43.9206280Z * [new tag] ciflow/trunk/144177 -> ciflow/trunk/144177 2025-01-24T01:03:43.9207007Z * [new tag] ciflow/trunk/144268 -> ciflow/trunk/144268 2025-01-24T01:03:43.9207775Z * [new tag] ciflow/trunk/144341 -> ciflow/trunk/144341 2025-01-24T01:03:43.9208517Z * [new tag] ciflow/trunk/144354 -> ciflow/trunk/144354 2025-01-24T01:03:43.9209193Z * [new tag] ciflow/trunk/144368 -> ciflow/trunk/144368 2025-01-24T01:03:43.9209795Z * [new tag] ciflow/trunk/144378 -> ciflow/trunk/144378 2025-01-24T01:03:43.9210480Z * [new tag] ciflow/trunk/144441 -> ciflow/trunk/144441 2025-01-24T01:03:43.9211146Z * [new tag] ciflow/trunk/144452 -> ciflow/trunk/144452 2025-01-24T01:03:43.9211911Z * [new tag] ciflow/trunk/144468 -> ciflow/trunk/144468 2025-01-24T01:03:43.9212595Z * [new tag] ciflow/trunk/144485 -> ciflow/trunk/144485 2025-01-24T01:03:43.9213238Z * [new tag] ciflow/trunk/144492 -> ciflow/trunk/144492 2025-01-24T01:03:43.9213926Z * [new tag] ciflow/trunk/144498 -> ciflow/trunk/144498 2025-01-24T01:03:43.9214891Z * [new tag] ciflow/trunk/144508 -> ciflow/trunk/144508 2025-01-24T01:03:43.9215490Z * [new tag] ciflow/trunk/144513 -> ciflow/trunk/144513 2025-01-24T01:03:43.9216392Z * [new tag] ciflow/trunk/144590 -> ciflow/trunk/144590 2025-01-24T01:03:43.9217301Z * [new tag] ciflow/trunk/144616 -> ciflow/trunk/144616 2025-01-24T01:03:43.9217947Z * [new tag] ciflow/trunk/144642 -> ciflow/trunk/144642 2025-01-24T01:03:43.9218782Z * [new tag] ciflow/trunk/144644 -> ciflow/trunk/144644 2025-01-24T01:03:43.9219357Z * [new tag] ciflow/trunk/144646 -> ciflow/trunk/144646 2025-01-24T01:03:43.9219991Z * [new tag] ciflow/trunk/144664 -> ciflow/trunk/144664 2025-01-24T01:03:43.9220680Z * [new tag] ciflow/trunk/144695 -> ciflow/trunk/144695 2025-01-24T01:03:43.9221307Z * [new tag] ciflow/trunk/144700 -> ciflow/trunk/144700 2025-01-24T01:03:43.9221980Z * [new tag] ciflow/trunk/144707 -> ciflow/trunk/144707 2025-01-24T01:03:43.9222771Z * [new tag] ciflow/trunk/144708 -> ciflow/trunk/144708 2025-01-24T01:03:43.9223498Z * [new tag] ciflow/trunk/144721 -> ciflow/trunk/144721 2025-01-24T01:03:43.9224103Z * [new tag] ciflow/trunk/144733 -> ciflow/trunk/144733 2025-01-24T01:03:43.9224896Z * [new tag] ciflow/trunk/144763 -> ciflow/trunk/144763 2025-01-24T01:03:43.9225551Z * [new tag] ciflow/trunk/144771 -> ciflow/trunk/144771 2025-01-24T01:03:43.9226309Z * [new tag] ciflow/trunk/144773 -> ciflow/trunk/144773 2025-01-24T01:03:43.9227000Z * [new tag] ciflow/trunk/144829 -> ciflow/trunk/144829 2025-01-24T01:03:43.9227773Z * [new tag] ciflow/trunk/144841 -> ciflow/trunk/144841 2025-01-24T01:03:43.9228440Z * [new tag] ciflow/trunk/144865 -> ciflow/trunk/144865 2025-01-24T01:03:43.9229123Z * [new tag] ciflow/trunk/144880 -> ciflow/trunk/144880 2025-01-24T01:03:43.9229771Z * [new tag] ciflow/trunk/144894 -> ciflow/trunk/144894 2025-01-24T01:03:43.9230507Z * [new tag] ciflow/trunk/144910 -> ciflow/trunk/144910 2025-01-24T01:03:43.9231180Z * [new tag] ciflow/trunk/144940 -> ciflow/trunk/144940 2025-01-24T01:03:43.9231847Z * [new tag] ciflow/trunk/144953 -> ciflow/trunk/144953 2025-01-24T01:03:43.9232630Z * [new tag] ciflow/trunk/144956 -> ciflow/trunk/144956 2025-01-24T01:03:43.9233309Z * [new tag] ciflow/trunk/144975 -> ciflow/trunk/144975 2025-01-24T01:03:43.9233969Z * [new tag] ciflow/trunk/144985 -> ciflow/trunk/144985 2025-01-24T01:03:43.9234889Z * [new tag] ciflow/trunk/144994 -> ciflow/trunk/144994 2025-01-24T01:03:43.9235524Z * [new tag] ciflow/trunk/144999 -> ciflow/trunk/144999 2025-01-24T01:03:43.9236230Z * [new tag] ciflow/trunk/145031 -> ciflow/trunk/145031 2025-01-24T01:03:43.9236907Z * [new tag] ciflow/trunk/145047 -> ciflow/trunk/145047 2025-01-24T01:03:43.9237673Z * [new tag] ciflow/trunk/145060 -> ciflow/trunk/145060 2025-01-24T01:03:43.9238821Z * [new tag] ciflow/trunk/145061 -> ciflow/trunk/145061 2025-01-24T01:03:43.9239445Z * [new tag] ciflow/trunk/145083 -> ciflow/trunk/145083 2025-01-24T01:03:43.9240084Z * [new tag] ciflow/trunk/145116 -> ciflow/trunk/145116 2025-01-24T01:03:43.9240752Z * [new tag] ciflow/trunk/145119 -> ciflow/trunk/145119 2025-01-24T01:03:43.9241408Z * [new tag] ciflow/trunk/145120 -> ciflow/trunk/145120 2025-01-24T01:03:43.9242251Z * [new tag] ciflow/trunk/145128 -> ciflow/trunk/145128 2025-01-24T01:03:43.9242753Z * [new tag] ciflow/trunk/145130 -> ciflow/trunk/145130 2025-01-24T01:03:43.9243406Z * [new tag] ciflow/trunk/145132 -> ciflow/trunk/145132 2025-01-24T01:03:43.9244294Z * [new tag] ciflow/trunk/145136 -> ciflow/trunk/145136 2025-01-24T01:03:43.9244912Z * [new tag] ciflow/trunk/145148 -> ciflow/trunk/145148 2025-01-24T01:03:43.9245603Z * [new tag] ciflow/trunk/145153 -> ciflow/trunk/145153 2025-01-24T01:03:43.9246293Z * [new tag] ciflow/trunk/145156 -> ciflow/trunk/145156 2025-01-24T01:03:43.9246937Z * [new tag] ciflow/trunk/145204 -> ciflow/trunk/145204 2025-01-24T01:03:43.9247594Z * [new tag] ciflow/trunk/145206 -> ciflow/trunk/145206 2025-01-24T01:03:43.9248238Z * [new tag] ciflow/trunk/145224 -> ciflow/trunk/145224 2025-01-24T01:03:43.9248905Z * [new tag] ciflow/trunk/145227 -> ciflow/trunk/145227 2025-01-24T01:03:43.9249582Z * [new tag] ciflow/trunk/145241 -> ciflow/trunk/145241 2025-01-24T01:03:43.9250231Z * [new tag] ciflow/trunk/145245 -> ciflow/trunk/145245 2025-01-24T01:03:43.9250894Z * [new tag] ciflow/trunk/145250 -> ciflow/trunk/145250 2025-01-24T01:03:43.9251561Z * [new tag] ciflow/trunk/145254 -> ciflow/trunk/145254 2025-01-24T01:03:43.9252370Z * [new tag] ciflow/trunk/145269 -> ciflow/trunk/145269 2025-01-24T01:03:43.9253116Z * [new tag] ciflow/trunk/145288 -> ciflow/trunk/145288 2025-01-24T01:03:43.9253779Z * [new tag] ciflow/trunk/145303 -> ciflow/trunk/145303 2025-01-24T01:03:43.9254449Z * [new tag] ciflow/trunk/145307 -> ciflow/trunk/145307 2025-01-24T01:03:43.9255141Z * [new tag] ciflow/trunk/145311 -> ciflow/trunk/145311 2025-01-24T01:03:43.9255871Z * [new tag] ciflow/trunk/145318 -> ciflow/trunk/145318 2025-01-24T01:03:43.9256450Z * [new tag] ciflow/trunk/145321 -> ciflow/trunk/145321 2025-01-24T01:03:43.9257144Z * [new tag] ciflow/trunk/145323 -> ciflow/trunk/145323 2025-01-24T01:03:43.9257788Z * [new tag] ciflow/trunk/145331 -> ciflow/trunk/145331 2025-01-24T01:03:43.9258451Z * [new tag] ciflow/trunk/145373 -> ciflow/trunk/145373 2025-01-24T01:03:43.9259150Z * [new tag] ciflow/trunk/145375 -> ciflow/trunk/145375 2025-01-24T01:03:43.9259949Z * [new tag] ciflow/trunk/145391 -> ciflow/trunk/145391 2025-01-24T01:03:43.9260616Z * [new tag] ciflow/trunk/145399 -> ciflow/trunk/145399 2025-01-24T01:03:43.9261253Z * [new tag] ciflow/trunk/145407 -> ciflow/trunk/145407 2025-01-24T01:03:43.9261947Z * [new tag] ciflow/trunk/145412 -> ciflow/trunk/145412 2025-01-24T01:03:43.9262579Z * [new tag] ciflow/trunk/145417 -> ciflow/trunk/145417 2025-01-24T01:03:43.9263235Z * [new tag] ciflow/trunk/145421 -> ciflow/trunk/145421 2025-01-24T01:03:43.9263944Z * [new tag] ciflow/trunk/145435 -> ciflow/trunk/145435 2025-01-24T01:03:43.9264944Z * [new tag] ciflow/trunk/145436 -> ciflow/trunk/145436 2025-01-24T01:03:43.9265565Z * [new tag] ciflow/trunk/145443 -> ciflow/trunk/145443 2025-01-24T01:03:43.9266355Z * [new tag] ciflow/trunk/145449 -> ciflow/trunk/145449 2025-01-24T01:03:43.9267291Z * [new tag] ciflow/trunk/145450 -> ciflow/trunk/145450 2025-01-24T01:03:43.9268190Z * [new tag] ciflow/trunk/145465 -> ciflow/trunk/145465 2025-01-24T01:03:43.9268778Z * [new tag] ciflow/trunk/145490 -> ciflow/trunk/145490 2025-01-24T01:03:43.9269588Z * [new tag] ciflow/trunk/145494 -> ciflow/trunk/145494 2025-01-24T01:03:43.9270538Z * [new tag] ciflow/trunk/145495 -> ciflow/trunk/145495 2025-01-24T01:03:43.9271175Z * [new tag] ciflow/trunk/145502 -> ciflow/trunk/145502 2025-01-24T01:03:43.9271951Z * [new tag] ciflow/trunk/145506 -> ciflow/trunk/145506 2025-01-24T01:03:43.9272551Z * [new tag] ciflow/trunk/145509 -> ciflow/trunk/145509 2025-01-24T01:03:43.9273231Z * [new tag] ciflow/trunk/145513 -> ciflow/trunk/145513 2025-01-24T01:03:43.9274177Z * [new tag] ciflow/trunk/145517 -> ciflow/trunk/145517 2025-01-24T01:03:43.9274650Z * [new tag] ciflow/trunk/145519 -> ciflow/trunk/145519 2025-01-24T01:03:43.9275557Z * [new tag] ciflow/trunk/145524 -> ciflow/trunk/145524 2025-01-24T01:03:43.9276276Z * [new tag] ciflow/trunk/145530 -> ciflow/trunk/145530 2025-01-24T01:03:43.9276984Z * [new tag] ciflow/trunk/145531 -> ciflow/trunk/145531 2025-01-24T01:03:43.9277782Z * [new tag] ciflow/trunk/145533 -> ciflow/trunk/145533 2025-01-24T01:03:43.9278519Z * [new tag] ciflow/trunk/145539 -> ciflow/trunk/145539 2025-01-24T01:03:43.9279342Z * [new tag] ciflow/trunk/145542 -> ciflow/trunk/145542 2025-01-24T01:03:43.9280127Z * [new tag] ciflow/trunk/145545 -> ciflow/trunk/145545 2025-01-24T01:03:43.9280755Z * [new tag] ciflow/trunk/145547 -> ciflow/trunk/145547 2025-01-24T01:03:43.9281448Z * [new tag] ciflow/trunk/145550 -> ciflow/trunk/145550 2025-01-24T01:03:43.9282110Z * [new tag] ciflow/trunk/145551 -> ciflow/trunk/145551 2025-01-24T01:03:43.9282809Z * [new tag] ciflow/trunk/145552 -> ciflow/trunk/145552 2025-01-24T01:03:43.9283452Z * [new tag] ciflow/trunk/145553 -> ciflow/trunk/145553 2025-01-24T01:03:43.9284623Z * [new tag] ciflow/trunk/145554 -> ciflow/trunk/145554 2025-01-24T01:03:43.9285167Z * [new tag] ciflow/trunk/145558 -> ciflow/trunk/145558 2025-01-24T01:03:43.9285935Z * [new tag] ciflow/trunk/145559 -> ciflow/trunk/145559 2025-01-24T01:03:43.9286699Z * [new tag] ciflow/trunk/70978 -> ciflow/trunk/70978 2025-01-24T01:03:43.9287585Z * [new tag] ciflow/trunk/70979 -> ciflow/trunk/70979 2025-01-24T01:03:43.9288288Z * [new tag] ciflow/unstable/123 -> ciflow/unstable/123 2025-01-24T01:03:43.9289367Z * [new tag] ciflow/xpu/132945 -> ciflow/xpu/132945 2025-01-24T01:03:43.9289879Z * [new tag] ciflow/xpu/133307 -> ciflow/xpu/133307 2025-01-24T01:03:43.9290462Z * [new tag] ciflow/xpu/135189 -> ciflow/xpu/135189 2025-01-24T01:03:43.9291117Z * [new tag] ciflow/xpu/135337 -> ciflow/xpu/135337 2025-01-24T01:03:43.9291754Z * [new tag] ciflow/xpu/135465 -> ciflow/xpu/135465 2025-01-24T01:03:43.9292348Z * [new tag] ciflow/xpu/137566 -> ciflow/xpu/137566 2025-01-24T01:03:43.9292936Z * [new tag] ciflow/xpu/137570 -> ciflow/xpu/137570 2025-01-24T01:03:43.9293496Z * [new tag] ciflow/xpu/137580 -> ciflow/xpu/137580 2025-01-24T01:03:43.9294151Z * [new tag] ciflow/xpu/138889 -> ciflow/xpu/138889 2025-01-24T01:03:43.9294777Z * [new tag] ciflow/xpu/139171 -> ciflow/xpu/139171 2025-01-24T01:03:43.9295399Z * [new tag] ciflow/xpu/139469 -> ciflow/xpu/139469 2025-01-24T01:03:43.9296410Z * [new tag] ciflow/xpu/139971 -> ciflow/xpu/139971 2025-01-24T01:03:43.9297342Z * [new tag] ciflow/xpu/140362 -> ciflow/xpu/140362 2025-01-24T01:03:43.9298233Z * [new tag] ciflow/xpu/140365 -> ciflow/xpu/140365 2025-01-24T01:03:43.9298850Z * [new tag] ciflow/xpu/140372 -> ciflow/xpu/140372 2025-01-24T01:03:43.9299843Z * [new tag] ciflow/xpu/140389 -> ciflow/xpu/140389 2025-01-24T01:03:43.9300370Z * [new tag] ciflow/xpu/140677 -> ciflow/xpu/140677 2025-01-24T01:03:43.9301013Z * [new tag] ciflow/xpu/140686 -> ciflow/xpu/140686 2025-01-24T01:03:43.9301766Z * [new tag] ciflow/xpu/140972 -> ciflow/xpu/140972 2025-01-24T01:03:43.9302548Z * [new tag] ciflow/xpu/141318 -> ciflow/xpu/141318 2025-01-24T01:03:43.9303183Z * [new tag] ciflow/xpu/142040 -> ciflow/xpu/142040 2025-01-24T01:03:43.9303841Z * [new tag] ciflow/xpu/142067 -> ciflow/xpu/142067 2025-01-24T01:03:43.9304468Z * [new tag] ciflow/xpu/142097 -> ciflow/xpu/142097 2025-01-24T01:03:43.9305116Z * [new tag] ciflow/xpu/143597 -> ciflow/xpu/143597 2025-01-24T01:03:43.9305873Z * [new tag] ciflow/xpu/143784 -> ciflow/xpu/143784 2025-01-24T01:03:43.9306547Z * [new tag] ciflow/xpu/143833 -> ciflow/xpu/143833 2025-01-24T01:03:43.9307313Z * [new tag] ciflow/xpu/144046 -> ciflow/xpu/144046 2025-01-24T01:03:43.9307971Z * [new tag] ciflow/xpu/144368 -> ciflow/xpu/144368 2025-01-24T01:03:43.9308608Z * [new tag] ciflow/xpu/144378 -> ciflow/xpu/144378 2025-01-24T01:03:43.9309381Z * [new tag] ciflow/xpu/144385 -> ciflow/xpu/144385 2025-01-24T01:03:43.9310031Z * [new tag] ciflow/xpu/144452 -> ciflow/xpu/144452 2025-01-24T01:03:43.9310787Z * [new tag] ciflow/xpu/144473 -> ciflow/xpu/144473 2025-01-24T01:03:43.9311446Z * [new tag] ciflow/xpu/144644 -> ciflow/xpu/144644 2025-01-24T01:03:43.9312106Z * [new tag] ciflow/xpu/144664 -> ciflow/xpu/144664 2025-01-24T01:03:43.9312849Z * [new tag] ciflow/xpu/144722 -> ciflow/xpu/144722 2025-01-24T01:03:43.9313491Z * [new tag] ciflow/xpu/145060 -> ciflow/xpu/145060 2025-01-24T01:03:43.9314115Z * [new tag] ciflow/xpu/145083 -> ciflow/xpu/145083 2025-01-24T01:03:43.9314760Z * [new tag] ciflow/xpu/145248 -> ciflow/xpu/145248 2025-01-24T01:03:43.9315398Z * [new tag] ciflow/xpu/145490 -> ciflow/xpu/145490 2025-01-24T01:03:43.9316123Z * [new tag] cslpull75 -> cslpull75 2025-01-24T01:03:43.9316991Z * [new tag] cslpull76 -> cslpull76 2025-01-24T01:03:43.9317688Z * [new tag] cslpull77 -> cslpull77 2025-01-24T01:03:43.9318418Z * [new tag] cslpull78 -> cslpull78 2025-01-24T01:03:43.9319415Z * [new tag] cslpull79 -> cslpull79 2025-01-24T01:03:43.9320407Z * [new tag] cslpull80 -> cslpull80 2025-01-24T01:03:43.9321696Z * [new tag] cslpull81 -> cslpull81 2025-01-24T01:03:43.9322468Z * [new tag] cslpull82 -> cslpull82 2025-01-24T01:03:43.9323217Z * [new tag] cslpull83 -> cslpull83 2025-01-24T01:03:43.9323969Z * [new tag] cslpull84 -> cslpull84 2025-01-24T01:03:43.9324661Z * [new tag] cslpull85 -> cslpull85 2025-01-24T01:03:43.9325548Z * [new tag] cslpull86 -> cslpull86 2025-01-24T01:03:43.9326251Z * [new tag] cslpull87 -> cslpull87 2025-01-24T01:03:43.9327109Z * [new tag] cslpull88 -> cslpull88 2025-01-24T01:03:43.9327770Z * [new tag] cslpull89 -> cslpull89 2025-01-24T01:03:43.9328344Z * [new tag] cslpull90 -> cslpull90 2025-01-24T01:03:43.9329626Z * [new tag] cslpull91 -> cslpull91 2025-01-24T01:03:43.9330281Z * [new tag] cslpull92 -> cslpull92 2025-01-24T01:03:43.9331204Z * [new tag] flight_5 -> flight_5 2025-01-24T01:03:43.9332083Z * [new tag] flight_5.1 -> flight_5.1 2025-01-24T01:03:43.9332847Z * [new tag] flight_5.2 -> flight_5.2 2025-01-24T01:03:43.9333519Z * [new tag] flight_5.3 -> flight_5.3 2025-01-24T01:03:43.9334266Z * [new tag] forpull1 -> forpull1 2025-01-24T01:03:43.9335264Z * [new tag] malfet/tag-2ef5611 -> malfet/tag-2ef5611 2025-01-24T01:03:43.9335994Z * [new tag] malfet/tag-317b1a0 -> malfet/tag-317b1a0 2025-01-24T01:03:43.9336775Z * [new tag] malfet/tag-ec6f767 -> malfet/tag-ec6f767 2025-01-24T01:03:43.9337660Z * [new tag] nightly-binary -> nightly-binary 2025-01-24T01:03:43.9338300Z * [new tag] sqzhang_flight4_plus -> sqzhang_flight4_plus 2025-01-24T01:03:43.9339045Z * [new tag] sqzhang_flight_3 -> sqzhang_flight_3 2025-01-24T01:03:43.9339781Z * [new tag] v0.1.1 -> v0.1.1 2025-01-24T01:03:43.9340642Z * [new tag] v0.1.10 -> v0.1.10 2025-01-24T01:03:43.9341275Z * [new tag] v0.1.11 -> v0.1.11 2025-01-24T01:03:43.9342111Z * [new tag] v0.1.12 -> v0.1.12 2025-01-24T01:03:43.9342743Z * [new tag] v0.1.2 -> v0.1.2 2025-01-24T01:03:43.9343453Z * [new tag] v0.1.3 -> v0.1.3 2025-01-24T01:03:43.9344196Z * [new tag] v0.1.4 -> v0.1.4 2025-01-24T01:03:43.9344925Z * [new tag] v0.1.5 -> v0.1.5 2025-01-24T01:03:43.9345873Z * [new tag] v0.1.6 -> v0.1.6 2025-01-24T01:03:43.9346623Z * [new tag] v0.1.7 -> v0.1.7 2025-01-24T01:03:43.9347356Z * [new tag] v0.1.8 -> v0.1.8 2025-01-24T01:03:43.9348047Z * [new tag] v0.1.9 -> v0.1.9 2025-01-24T01:03:43.9348887Z * [new tag] v0.2.0 -> v0.2.0 2025-01-24T01:03:43.9349583Z * [new tag] v0.3.0 -> v0.3.0 2025-01-24T01:03:43.9350492Z * [new tag] v0.3.1 -> v0.3.1 2025-01-24T01:03:43.9351194Z * [new tag] v0.4.0 -> v0.4.0 2025-01-24T01:03:43.9351932Z * [new tag] v0.4.1 -> v0.4.1 2025-01-24T01:03:43.9352782Z * [new tag] v1.0.0 -> v1.0.0 2025-01-24T01:03:43.9353484Z * [new tag] v1.0.0a0 -> v1.0.0a0 2025-01-24T01:03:43.9354378Z * [new tag] v1.0.1 -> v1.0.1 2025-01-24T01:03:43.9355289Z * [new tag] v1.0rc0 -> v1.0rc0 2025-01-24T01:03:43.9355814Z * [new tag] v1.0rc1 -> v1.0rc1 2025-01-24T01:03:43.9356719Z * [new tag] v1.1.0 -> v1.1.0 2025-01-24T01:03:43.9357359Z * [new tag] v1.1.0a0 -> v1.1.0a0 2025-01-24T01:03:43.9358417Z * [new tag] v1.10.0 -> v1.10.0 2025-01-24T01:03:43.9359219Z * [new tag] v1.10.0-rc1 -> v1.10.0-rc1 2025-01-24T01:03:43.9360168Z * [new tag] v1.10.0-rc2 -> v1.10.0-rc2 2025-01-24T01:03:43.9360645Z * [new tag] v1.10.0-rc3 -> v1.10.0-rc3 2025-01-24T01:03:43.9361507Z * [new tag] v1.10.1 -> v1.10.1 2025-01-24T01:03:43.9362037Z * [new tag] v1.10.1-rc1 -> v1.10.1-rc1 2025-01-24T01:03:43.9362621Z * [new tag] v1.10.2 -> v1.10.2 2025-01-24T01:03:43.9363288Z * [new tag] v1.10.2-rc1 -> v1.10.2-rc1 2025-01-24T01:03:43.9364149Z * [new tag] v1.11.0 -> v1.11.0 2025-01-24T01:03:43.9365028Z * [new tag] v1.11.0-rc1 -> v1.11.0-rc1 2025-01-24T01:03:43.9365910Z * [new tag] v1.11.0-rc2 -> v1.11.0-rc2 2025-01-24T01:03:43.9366642Z * [new tag] v1.11.0-rc3 -> v1.11.0-rc3 2025-01-24T01:03:43.9367593Z * [new tag] v1.11.0-rc4 -> v1.11.0-rc4 2025-01-24T01:03:43.9368315Z * [new tag] v1.11.0-rc5 -> v1.11.0-rc5 2025-01-24T01:03:43.9368913Z * [new tag] v1.11.0-rc6 -> v1.11.0-rc6 2025-01-24T01:03:43.9369557Z * [new tag] v1.11.0-rc7 -> v1.11.0-rc7 2025-01-24T01:03:43.9370533Z * [new tag] v1.12.0 -> v1.12.0 2025-01-24T01:03:43.9371469Z * [new tag] v1.12.0-rc1 -> v1.12.0-rc1 2025-01-24T01:03:43.9372190Z * [new tag] v1.12.0-rc2 -> v1.12.0-rc2 2025-01-24T01:03:43.9373036Z * [new tag] v1.12.0-rc3 -> v1.12.0-rc3 2025-01-24T01:03:43.9374255Z * [new tag] v1.12.0-rc4 -> v1.12.0-rc4 2025-01-24T01:03:43.9374977Z * [new tag] v1.12.0-rc5 -> v1.12.0-rc5 2025-01-24T01:03:43.9375916Z * [new tag] v1.12.0-rc6 -> v1.12.0-rc6 2025-01-24T01:03:43.9376507Z * [new tag] v1.12.0-rc7 -> v1.12.0-rc7 2025-01-24T01:03:43.9377051Z * [new tag] v1.12.0-rc8 -> v1.12.0-rc8 2025-01-24T01:03:43.9377686Z * [new tag] v1.12.1 -> v1.12.1 2025-01-24T01:03:43.9378633Z * [new tag] v1.12.1-rc1 -> v1.12.1-rc1 2025-01-24T01:03:43.9379542Z * [new tag] v1.12.1-rc2 -> v1.12.1-rc2 2025-01-24T01:03:43.9380619Z * [new tag] v1.12.1-rc3 -> v1.12.1-rc3 2025-01-24T01:03:43.9381387Z * [new tag] v1.12.1-rc4 -> v1.12.1-rc4 2025-01-24T01:03:43.9381909Z * [new tag] v1.12.1-rc5 -> v1.12.1-rc5 2025-01-24T01:03:43.9382854Z * [new tag] v1.13.0 -> v1.13.0 2025-01-24T01:03:43.9383616Z * [new tag] v1.13.0-rc1 -> v1.13.0-rc1 2025-01-24T01:03:43.9384354Z * [new tag] v1.13.0-rc2 -> v1.13.0-rc2 2025-01-24T01:03:43.9385216Z * [new tag] v1.13.0-rc3 -> v1.13.0-rc3 2025-01-24T01:03:43.9386160Z * [new tag] v1.13.0-rc4 -> v1.13.0-rc4 2025-01-24T01:03:43.9386849Z * [new tag] v1.13.0-rc5 -> v1.13.0-rc5 2025-01-24T01:03:43.9387375Z * [new tag] v1.13.0-rc6 -> v1.13.0-rc6 2025-01-24T01:03:43.9388318Z * [new tag] v1.13.1 -> v1.13.1 2025-01-24T01:03:43.9388908Z * [new tag] v1.13.1-rc1 -> v1.13.1-rc1 2025-01-24T01:03:43.9389646Z * [new tag] v1.2.0 -> v1.2.0 2025-01-24T01:03:43.9390427Z * [new tag] v1.2.0a0 -> v1.2.0a0 2025-01-24T01:03:43.9391195Z * [new tag] v1.3.0 -> v1.3.0 2025-01-24T01:03:43.9391969Z * [new tag] v1.3.0a0 -> v1.3.0a0 2025-01-24T01:03:43.9392608Z * [new tag] v1.3.1 -> v1.3.1 2025-01-24T01:03:43.9393468Z * [new tag] v1.4.0 -> v1.4.0 2025-01-24T01:03:43.9394150Z * [new tag] v1.4.0a0 -> v1.4.0a0 2025-01-24T01:03:43.9394676Z * [new tag] v1.4.1 -> v1.4.1 2025-01-24T01:03:43.9395735Z * [new tag] v1.5.0 -> v1.5.0 2025-01-24T01:03:43.9396597Z * [new tag] v1.5.0-rc1 -> v1.5.0-rc1 2025-01-24T01:03:43.9397400Z * [new tag] v1.5.0-rc2 -> v1.5.0-rc2 2025-01-24T01:03:43.9398303Z * [new tag] v1.5.0-rc3 -> v1.5.0-rc3 2025-01-24T01:03:43.9398976Z * [new tag] v1.5.0-rc4 -> v1.5.0-rc4 2025-01-24T01:03:43.9399584Z * [new tag] v1.5.0-rc5 -> v1.5.0-rc5 2025-01-24T01:03:43.9400479Z * [new tag] v1.5.1 -> v1.5.1 2025-01-24T01:03:43.9401051Z * [new tag] v1.5.1-rc1 -> v1.5.1-rc1 2025-01-24T01:03:43.9401664Z * [new tag] v1.6.0 -> v1.6.0 2025-01-24T01:03:43.9402554Z * [new tag] v1.6.0-rc1 -> v1.6.0-rc1 2025-01-24T01:03:43.9403323Z * [new tag] v1.6.0-rc2 -> v1.6.0-rc2 2025-01-24T01:03:43.9404227Z * [new tag] v1.6.0-rc3 -> v1.6.0-rc3 2025-01-24T01:03:43.9404963Z * [new tag] v1.6.0-rc4 -> v1.6.0-rc4 2025-01-24T01:03:43.9405740Z * [new tag] v1.6.0-rc5 -> v1.6.0-rc5 2025-01-24T01:03:43.9406609Z * [new tag] v1.6.0-rc6 -> v1.6.0-rc6 2025-01-24T01:03:43.9407185Z * [new tag] v1.6.0-rc7 -> v1.6.0-rc7 2025-01-24T01:03:43.9407937Z * [new tag] v1.7.0 -> v1.7.0 2025-01-24T01:03:43.9408843Z * [new tag] v1.7.0-rc1 -> v1.7.0-rc1 2025-01-24T01:03:43.9409736Z * [new tag] v1.7.0-rc2 -> v1.7.0-rc2 2025-01-24T01:03:43.9410426Z * [new tag] v1.7.0-rc3 -> v1.7.0-rc3 2025-01-24T01:03:43.9411090Z * [new tag] v1.7.0-rc4 -> v1.7.0-rc4 2025-01-24T01:03:43.9412047Z * [new tag] v1.7.1 -> v1.7.1 2025-01-24T01:03:43.9412972Z * [new tag] v1.7.1-rc1 -> v1.7.1-rc1 2025-01-24T01:03:43.9413845Z * [new tag] v1.7.1-rc2 -> v1.7.1-rc2 2025-01-24T01:03:43.9414422Z * [new tag] v1.7.1-rc3 -> v1.7.1-rc3 2025-01-24T01:03:43.9415305Z * [new tag] v1.8.0 -> v1.8.0 2025-01-24T01:03:43.9415868Z * [new tag] v1.8.0-rc1 -> v1.8.0-rc1 2025-01-24T01:03:43.9416794Z * [new tag] v1.8.0-rc2 -> v1.8.0-rc2 2025-01-24T01:03:43.9417456Z * [new tag] v1.8.0-rc3 -> v1.8.0-rc3 2025-01-24T01:03:43.9418195Z * [new tag] v1.8.0-rc4 -> v1.8.0-rc4 2025-01-24T01:03:43.9418759Z * [new tag] v1.8.0-rc5 -> v1.8.0-rc5 2025-01-24T01:03:43.9419415Z * [new tag] v1.8.1 -> v1.8.1 2025-01-24T01:03:43.9420396Z * [new tag] v1.8.1-rc1 -> v1.8.1-rc1 2025-01-24T01:03:43.9420924Z * [new tag] v1.8.1-rc2 -> v1.8.1-rc2 2025-01-24T01:03:43.9421552Z * [new tag] v1.8.1-rc3 -> v1.8.1-rc3 2025-01-24T01:03:43.9422956Z * [new tag] v1.8.2 -> v1.8.2 2025-01-24T01:03:43.9423924Z * [new tag] v1.8.2-rc1 -> v1.8.2-rc1 2025-01-24T01:03:43.9424681Z * [new tag] v1.9.0 -> v1.9.0 2025-01-24T01:03:43.9425578Z * [new tag] v1.9.0-rc1 -> v1.9.0-rc1 2025-01-24T01:03:43.9426640Z * [new tag] v1.9.0-rc2 -> v1.9.0-rc2 2025-01-24T01:03:43.9427344Z * [new tag] v1.9.0-rc3 -> v1.9.0-rc3 2025-01-24T01:03:43.9427926Z * [new tag] v1.9.0-rc4 -> v1.9.0-rc4 2025-01-24T01:03:43.9428850Z * [new tag] v1.9.1 -> v1.9.1 2025-01-24T01:03:43.9429877Z * [new tag] v1.9.1-rc1 -> v1.9.1-rc1 2025-01-24T01:03:43.9430498Z * [new tag] v1.9.1-rc2 -> v1.9.1-rc2 2025-01-24T01:03:43.9431385Z * [new tag] v2.0.0 -> v2.0.0 2025-01-24T01:03:43.9432143Z * [new tag] v2.0.0-rc1 -> v2.0.0-rc1 2025-01-24T01:03:43.9433001Z * [new tag] v2.0.0-rc2 -> v2.0.0-rc2 2025-01-24T01:03:43.9433795Z * [new tag] v2.0.0-rc3 -> v2.0.0-rc3 2025-01-24T01:03:43.9434593Z * [new tag] v2.0.0-rc4 -> v2.0.0-rc4 2025-01-24T01:03:43.9435381Z * [new tag] v2.0.0-rc5 -> v2.0.0-rc5 2025-01-24T01:03:43.9436014Z * [new tag] v2.0.0-rc6 -> v2.0.0-rc6 2025-01-24T01:03:43.9436913Z * [new tag] v2.0.1 -> v2.0.1 2025-01-24T01:03:43.9437686Z * [new tag] v2.0.1-rc1 -> v2.0.1-rc1 2025-01-24T01:03:43.9438329Z * [new tag] v2.0.1-rc2 -> v2.0.1-rc2 2025-01-24T01:03:43.9439068Z * [new tag] v2.0.1-rc3 -> v2.0.1-rc3 2025-01-24T01:03:43.9439695Z * [new tag] v2.0.1-rc4 -> v2.0.1-rc4 2025-01-24T01:03:43.9440964Z * [new tag] v2.1.0 -> v2.1.0 2025-01-24T01:03:43.9441665Z * [new tag] v2.1.0-rc1 -> v2.1.0-rc1 2025-01-24T01:03:43.9442681Z * [new tag] v2.1.0-rc2 -> v2.1.0-rc2 2025-01-24T01:03:43.9443348Z * [new tag] v2.1.0-rc3 -> v2.1.0-rc3 2025-01-24T01:03:43.9444122Z * [new tag] v2.1.0-rc4 -> v2.1.0-rc4 2025-01-24T01:03:43.9444907Z * [new tag] v2.1.0-rc5 -> v2.1.0-rc5 2025-01-24T01:03:43.9445544Z * [new tag] v2.1.0-rc6 -> v2.1.0-rc6 2025-01-24T01:03:43.9446458Z * [new tag] v2.1.1 -> v2.1.1 2025-01-24T01:03:43.9447408Z * [new tag] v2.1.1-rc1 -> v2.1.1-rc1 2025-01-24T01:03:43.9447953Z * [new tag] v2.1.1-rc2 -> v2.1.1-rc2 2025-01-24T01:03:43.9448939Z * [new tag] v2.1.1-rc3 -> v2.1.1-rc3 2025-01-24T01:03:43.9449819Z * [new tag] v2.1.1-rc4 -> v2.1.1-rc4 2025-01-24T01:03:43.9450501Z * [new tag] v2.1.1-rc5 -> v2.1.1-rc5 2025-01-24T01:03:43.9451145Z * [new tag] v2.1.1-rc6 -> v2.1.1-rc6 2025-01-24T01:03:43.9451908Z * [new tag] v2.1.2 -> v2.1.2 2025-01-24T01:03:43.9452860Z * [new tag] v2.1.2-rc1 -> v2.1.2-rc1 2025-01-24T01:03:43.9453474Z * [new tag] v2.1.2-rc2 -> v2.1.2-rc2 2025-01-24T01:03:43.9454127Z * [new tag] v2.1.2-rc3 -> v2.1.2-rc3 2025-01-24T01:03:43.9455022Z * [new tag] v2.2.0 -> v2.2.0 2025-01-24T01:03:43.9455706Z * [new tag] v2.2.0-rc1 -> v2.2.0-rc1 2025-01-24T01:03:43.9456477Z * [new tag] v2.2.0-rc2 -> v2.2.0-rc2 2025-01-24T01:03:43.9457225Z * [new tag] v2.2.0-rc3 -> v2.2.0-rc3 2025-01-24T01:03:43.9458118Z * [new tag] v2.2.0-rc4 -> v2.2.0-rc4 2025-01-24T01:03:43.9458885Z * [new tag] v2.2.0-rc5 -> v2.2.0-rc5 2025-01-24T01:03:43.9459592Z * [new tag] v2.2.0-rc6 -> v2.2.0-rc6 2025-01-24T01:03:43.9460210Z * [new tag] v2.2.0-rc7 -> v2.2.0-rc7 2025-01-24T01:03:43.9460810Z * [new tag] v2.2.0-rc8 -> v2.2.0-rc8 2025-01-24T01:03:43.9461673Z * [new tag] v2.2.1 -> v2.2.1 2025-01-24T01:03:43.9462397Z * [new tag] v2.2.1-rc1 -> v2.2.1-rc1 2025-01-24T01:03:43.9463042Z * [new tag] v2.2.1-rc2 -> v2.2.1-rc2 2025-01-24T01:03:43.9463692Z * [new tag] v2.2.1-rc3 -> v2.2.1-rc3 2025-01-24T01:03:43.9464242Z * [new tag] v2.2.2 -> v2.2.2 2025-01-24T01:03:43.9465237Z * [new tag] v2.2.2-rc1 -> v2.2.2-rc1 2025-01-24T01:03:43.9465808Z * [new tag] v2.2.2-rc2 -> v2.2.2-rc2 2025-01-24T01:03:43.9466552Z * [new tag] v2.2.2-rc3 -> v2.2.2-rc3 2025-01-24T01:03:43.9467502Z * [new tag] v2.3.0 -> v2.3.0 2025-01-24T01:03:43.9468271Z * [new tag] v2.3.0-rc1 -> v2.3.0-rc1 2025-01-24T01:03:43.9469125Z * [new tag] v2.3.0-rc10 -> v2.3.0-rc10 2025-01-24T01:03:43.9470075Z * [new tag] v2.3.0-rc11 -> v2.3.0-rc11 2025-01-24T01:03:43.9470697Z * [new tag] v2.3.0-rc12 -> v2.3.0-rc12 2025-01-24T01:03:43.9471616Z * [new tag] v2.3.0-rc2 -> v2.3.0-rc2 2025-01-24T01:03:43.9472380Z * [new tag] v2.3.0-rc3 -> v2.3.0-rc3 2025-01-24T01:03:43.9473271Z * [new tag] v2.3.0-rc4 -> v2.3.0-rc4 2025-01-24T01:03:43.9474457Z * [new tag] v2.3.0-rc5 -> v2.3.0-rc5 2025-01-24T01:03:43.9474996Z * [new tag] v2.3.0-rc6 -> v2.3.0-rc6 2025-01-24T01:03:43.9475898Z * [new tag] v2.3.0-rc7 -> v2.3.0-rc7 2025-01-24T01:03:43.9476624Z * [new tag] v2.3.0-rc8 -> v2.3.0-rc8 2025-01-24T01:03:43.9477148Z * [new tag] v2.3.0-rc9 -> v2.3.0-rc9 2025-01-24T01:03:43.9477824Z * [new tag] v2.3.1 -> v2.3.1 2025-01-24T01:03:43.9478702Z * [new tag] v2.3.1-rc1 -> v2.3.1-rc1 2025-01-24T01:03:43.9479574Z * [new tag] v2.3.1-rc2 -> v2.3.1-rc2 2025-01-24T01:03:43.9480577Z * [new tag] v2.3.1-rc3 -> v2.3.1-rc3 2025-01-24T01:03:43.9481474Z * [new tag] v2.4.0 -> v2.4.0 2025-01-24T01:03:43.9482226Z * [new tag] v2.4.0-rc1 -> v2.4.0-rc1 2025-01-24T01:03:43.9483137Z * [new tag] v2.4.0-rc2 -> v2.4.0-rc2 2025-01-24T01:03:43.9483874Z * [new tag] v2.4.0-rc3 -> v2.4.0-rc3 2025-01-24T01:03:43.9484746Z * [new tag] v2.4.0-rc4 -> v2.4.0-rc4 2025-01-24T01:03:43.9485632Z * [new tag] v2.4.0-rc5 -> v2.4.0-rc5 2025-01-24T01:03:43.9486405Z * [new tag] v2.4.0-rc6 -> v2.4.0-rc6 2025-01-24T01:03:43.9487309Z * [new tag] v2.4.0-rc7 -> v2.4.0-rc7 2025-01-24T01:03:43.9488050Z * [new tag] v2.4.0-rc8 -> v2.4.0-rc8 2025-01-24T01:03:43.9489041Z * [new tag] v2.4.0-rc9 -> v2.4.0-rc9 2025-01-24T01:03:43.9489676Z * [new tag] v2.4.1 -> v2.4.1 2025-01-24T01:03:43.9490578Z * [new tag] v2.4.1-rc1 -> v2.4.1-rc1 2025-01-24T01:03:43.9491602Z * [new tag] v2.4.1-rc2 -> v2.4.1-rc2 2025-01-24T01:03:43.9492180Z * [new tag] v2.4.1-rc3 -> v2.4.1-rc3 2025-01-24T01:03:43.9493130Z * [new tag] v2.5.0 -> v2.5.0 2025-01-24T01:03:43.9493815Z * [new tag] v2.5.0-rc1 -> v2.5.0-rc1 2025-01-24T01:03:43.9494485Z * [new tag] v2.5.0-rc10 -> v2.5.0-rc10 2025-01-24T01:03:43.9495346Z * [new tag] v2.5.0-rc2 -> v2.5.0-rc2 2025-01-24T01:03:43.9496125Z * [new tag] v2.5.0-rc3 -> v2.5.0-rc3 2025-01-24T01:03:43.9497078Z * [new tag] v2.5.0-rc4 -> v2.5.0-rc4 2025-01-24T01:03:43.9497908Z * [new tag] v2.5.0-rc5 -> v2.5.0-rc5 2025-01-24T01:03:43.9498850Z * [new tag] v2.5.0-rc6 -> v2.5.0-rc6 2025-01-24T01:03:43.9499690Z * [new tag] v2.5.0-rc7 -> v2.5.0-rc7 2025-01-24T01:03:43.9500478Z * [new tag] v2.5.0-rc8 -> v2.5.0-rc8 2025-01-24T01:03:43.9501362Z * [new tag] v2.5.0-rc9 -> v2.5.0-rc9 2025-01-24T01:03:43.9501971Z * [new tag] v2.5.1 -> v2.5.1 2025-01-24T01:03:43.9502549Z * [new tag] v2.5.1-rc1 -> v2.5.1-rc1 2025-01-24T01:03:43.9503471Z * [new tag] v2.6.0-rc1 -> v2.6.0-rc1 2025-01-24T01:03:43.9504395Z * [new tag] v2.6.0-rc2 -> v2.6.0-rc2 2025-01-24T01:03:43.9505139Z * [new tag] v2.6.0-rc3 -> v2.6.0-rc3 2025-01-24T01:03:43.9506019Z * [new tag] v2.6.0-rc4 -> v2.6.0-rc4 2025-01-24T01:03:43.9507197Z * [new tag] v2.6.0-rc5 -> v2.6.0-rc5 2025-01-24T01:03:43.9508112Z * [new tag] v2.6.0-rc6 -> v2.6.0-rc6 2025-01-24T01:03:43.9508716Z * [new tag] v2.6.0-rc7 -> v2.6.0-rc7 2025-01-24T01:03:43.9509485Z * [new tag] whc_flight_1 -> whc_flight_1 2025-01-24T01:03:43.9510094Z * [new tag] whc_flight_2 -> whc_flight_2 2025-01-24T01:03:43.9510777Z * [new tag] whc_flight_4 -> whc_flight_4 2025-01-24T01:03:44.0178518Z [command]/usr/bin/git rev-parse --verify --quiet d087145b9259b4cfbcb758dc0bca6a6e5d75d08f^{object} 2025-01-24T01:03:44.0202522Z d087145b9259b4cfbcb758dc0bca6a6e5d75d08f 2025-01-24T01:03:44.0206294Z ##[endgroup] 2025-01-24T01:03:44.0206615Z ##[group]Determining the checkout info 2025-01-24T01:03:44.0207110Z ##[endgroup] 2025-01-24T01:03:44.0210616Z [command]/usr/bin/git sparse-checkout disable 2025-01-24T01:03:44.0240893Z [command]/usr/bin/git config --local --unset-all extensions.worktreeConfig 2025-01-24T01:03:44.0262641Z ##[group]Checking out the ref 2025-01-24T01:03:44.0265820Z [command]/usr/bin/git checkout --progress --force d087145b9259b4cfbcb758dc0bca6a6e5d75d08f 2025-01-24T01:03:45.0558842Z Updating files: 90% (16292/18011) 2025-01-24T01:03:45.0656362Z Updating files: 91% (16391/18011) 2025-01-24T01:03:45.0774656Z Updating files: 92% (16571/18011) 2025-01-24T01:03:45.0940693Z Updating files: 93% (16751/18011) 2025-01-24T01:03:45.1108360Z Updating files: 94% (16931/18011) 2025-01-24T01:03:45.1281058Z Updating files: 95% (17111/18011) 2025-01-24T01:03:45.1385921Z Updating 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You can look around, make experimental 2025-01-24T01:03:45.2212359Z changes and commit them, and you can discard any commits you make in this 2025-01-24T01:03:45.2212928Z state without impacting any branches by switching back to a branch. 2025-01-24T01:03:45.2213277Z 2025-01-24T01:03:45.2213493Z If you want to create a new branch to retain commits you create, you may 2025-01-24T01:03:45.2214020Z do so (now or later) by using -c with the switch command. Example: 2025-01-24T01:03:45.2214333Z 2025-01-24T01:03:45.2214450Z git switch -c 2025-01-24T01:03:45.2214657Z 2025-01-24T01:03:45.2214779Z Or undo this operation with: 2025-01-24T01:03:45.2214968Z 2025-01-24T01:03:45.2215091Z git switch - 2025-01-24T01:03:45.2215236Z 2025-01-24T01:03:45.2215493Z Turn off this advice by setting config variable advice.detachedHead to false 2025-01-24T01:03:45.2215852Z 2025-01-24T01:03:45.2216102Z HEAD is now at d087145b925 Add accuracy issue support in AOTI Minifier (#145539) 2025-01-24T01:03:45.2298309Z ##[endgroup] 2025-01-24T01:03:45.2298777Z ##[group]Setting up auth for fetching submodules 2025-01-24T01:03:45.2304503Z [command]/usr/bin/git config --global http.https://github.com/.extraheader AUTHORIZATION: basic *** 2025-01-24T01:03:45.2345544Z [command]/usr/bin/git config --global --unset-all url.https://github.com/.insteadOf 2025-01-24T01:03:45.2373027Z [command]/usr/bin/git config --global --add url.https://github.com/.insteadOf git@github.com: 2025-01-24T01:03:45.2399318Z [command]/usr/bin/git config --global --add url.https://github.com/.insteadOf org-21003710@github.com: 2025-01-24T01:03:45.2421461Z ##[endgroup] 2025-01-24T01:03:45.2422200Z ##[group]Fetching submodules 2025-01-24T01:03:45.2425816Z [command]/usr/bin/git submodule sync --recursive 2025-01-24T01:03:45.2700040Z [command]/usr/bin/git -c protocol.version=2 submodule update --init --force --recursive 2025-01-24T01:03:45.2967096Z Submodule 'android/libs/fbjni' (https://github.com/facebookincubator/fbjni.git) registered for path 'android/libs/fbjni' 2025-01-24T01:03:45.2969360Z Submodule 'third_party/NNPACK_deps/FP16' (https://github.com/Maratyszcza/FP16.git) registered for path 'third_party/FP16' 2025-01-24T01:03:45.2972992Z Submodule 'third_party/NNPACK_deps/FXdiv' (https://github.com/Maratyszcza/FXdiv.git) registered for path 'third_party/FXdiv' 2025-01-24T01:03:45.2975882Z Submodule 'third_party/NNPACK' (https://github.com/Maratyszcza/NNPACK.git) registered for path 'third_party/NNPACK' 2025-01-24T01:03:45.2978804Z Submodule 'third_party/NVTX' (https://github.com/NVIDIA/NVTX.git) registered for path 'third_party/NVTX' 2025-01-24T01:03:45.2982638Z Submodule 'third_party/VulkanMemoryAllocator' (https://github.com/GPUOpen-LibrariesAndSDKs/VulkanMemoryAllocator.git) registered for path 'third_party/VulkanMemoryAllocator' 2025-01-24T01:03:45.2985467Z Submodule 'third_party/XNNPACK' (https://github.com/google/XNNPACK.git) registered for path 'third_party/XNNPACK' 2025-01-24T01:03:45.2989091Z Submodule 'third_party/benchmark' (https://github.com/google/benchmark.git) registered for path 'third_party/benchmark' 2025-01-24T01:03:45.2992613Z Submodule 'third_party/composable_kernel' (https://github.com/ROCm/composable_kernel.git) registered for path 'third_party/composable_kernel' 2025-01-24T01:03:45.2996130Z Submodule 'third_party/cpp-httplib' (https://github.com/yhirose/cpp-httplib.git) registered for path 'third_party/cpp-httplib' 2025-01-24T01:03:45.2999710Z Submodule 'third_party/cpuinfo' (https://github.com/pytorch/cpuinfo.git) registered for path 'third_party/cpuinfo' 2025-01-24T01:03:45.3003523Z Submodule 'third_party/cudnn_frontend' (https://github.com/NVIDIA/cudnn-frontend.git) registered for path 'third_party/cudnn_frontend' 2025-01-24T01:03:45.3007193Z Submodule 'third_party/cutlass' (https://github.com/NVIDIA/cutlass.git) registered for path 'third_party/cutlass' 2025-01-24T01:03:45.3011068Z Submodule 'third_party/eigen' (https://gitlab.com/libeigen/eigen.git) registered for path 'third_party/eigen' 2025-01-24T01:03:45.3015275Z Submodule 'third_party/fbgemm' (https://github.com/pytorch/fbgemm) registered for path 'third_party/fbgemm' 2025-01-24T01:03:45.3019753Z Submodule 'third_party/flatbuffers' (https://github.com/google/flatbuffers.git) registered for path 'third_party/flatbuffers' 2025-01-24T01:03:45.3023971Z Submodule 'third_party/fmt' (https://github.com/fmtlib/fmt.git) registered for path 'third_party/fmt' 2025-01-24T01:03:45.3028701Z Submodule 'third_party/gemmlowp/gemmlowp' (https://github.com/google/gemmlowp.git) registered for path 'third_party/gemmlowp/gemmlowp' 2025-01-24T01:03:45.3034455Z Submodule 'third_party/gloo' (https://github.com/facebookincubator/gloo) registered for path 'third_party/gloo' 2025-01-24T01:03:45.3039062Z Submodule 'third_party/googletest' (https://github.com/google/googletest.git) registered for path 'third_party/googletest' 2025-01-24T01:03:45.3043790Z Submodule 'third_party/ideep' (https://github.com/intel/ideep) registered for path 'third_party/ideep' 2025-01-24T01:03:45.3048574Z Submodule 'third_party/ittapi' (https://github.com/intel/ittapi.git) registered for path 'third_party/ittapi' 2025-01-24T01:03:45.3053289Z Submodule 'third_party/kineto' (https://github.com/pytorch/kineto) registered for path 'third_party/kineto' 2025-01-24T01:03:45.3058129Z Submodule 'third_party/kleidiai' (https://github.com/ARM-software/kleidiai.git) registered for path 'third_party/kleidiai' 2025-01-24T01:03:45.3063120Z Submodule 'third_party/mimalloc' (https://github.com/microsoft/mimalloc.git) registered for path 'third_party/mimalloc' 2025-01-24T01:03:45.3068223Z Submodule 'third_party/nccl/nccl' (https://github.com/NVIDIA/nccl) registered for path 'third_party/nccl/nccl' 2025-01-24T01:03:45.3073415Z Submodule 'third_party/nlohmann' (https://github.com/nlohmann/json.git) registered for path 'third_party/nlohmann' 2025-01-24T01:03:45.3078536Z Submodule 'third_party/onnx' (https://github.com/onnx/onnx.git) registered for path 'third_party/onnx' 2025-01-24T01:03:45.3084594Z Submodule 'third_party/opentelemetry-cpp' (https://github.com/open-telemetry/opentelemetry-cpp.git) registered for path 'third_party/opentelemetry-cpp' 2025-01-24T01:03:45.3089831Z Submodule 'third_party/pocketfft' (https://github.com/mreineck/pocketfft) registered for path 'third_party/pocketfft' 2025-01-24T01:03:45.3095368Z Submodule 'third_party/protobuf' (https://github.com/protocolbuffers/protobuf.git) registered for path 'third_party/protobuf' 2025-01-24T01:03:45.3100994Z Submodule 'third_party/NNPACK_deps/psimd' (https://github.com/Maratyszcza/psimd.git) registered for path 'third_party/psimd' 2025-01-24T01:03:45.3106936Z Submodule 'third_party/NNPACK_deps/pthreadpool' (https://github.com/Maratyszcza/pthreadpool.git) registered for path 'third_party/pthreadpool' 2025-01-24T01:03:45.3112675Z Submodule 'third_party/pybind11' (https://github.com/pybind/pybind11.git) registered for path 'third_party/pybind11' 2025-01-24T01:03:45.3118565Z Submodule 'third_party/python-peachpy' (https://github.com/malfet/PeachPy.git) registered for path 'third_party/python-peachpy' 2025-01-24T01:03:45.3126178Z Submodule 'third_party/sleef' (https://github.com/shibatch/sleef) registered for path 'third_party/sleef' 2025-01-24T01:03:45.3132552Z Submodule 'third_party/tensorpipe' (https://github.com/pytorch/tensorpipe.git) registered for path 'third_party/tensorpipe' 2025-01-24T01:03:45.3158845Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/android/libs/fbjni'... 2025-01-24T01:03:45.5795439Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/FP16'... 2025-01-24T01:03:45.7414304Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/FXdiv'... 2025-01-24T01:03:45.9015300Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/NNPACK'... 2025-01-24T01:03:46.1214723Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/NVTX'... 2025-01-24T01:03:46.4395901Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/VulkanMemoryAllocator'... 2025-01-24T01:03:48.5386774Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/XNNPACK'... 2025-01-24T01:04:01.9512251Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/benchmark'... 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'/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/mimalloc'... 2025-01-24T01:04:25.0015379Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/nccl/nccl'... 2025-01-24T01:04:25.3819221Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/nlohmann'... 2025-01-24T01:04:32.4041917Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/onnx'... 2025-01-24T01:04:35.2408816Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp'... 2025-01-24T01:04:43.4247310Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/pocketfft'... 2025-01-24T01:04:43.6472968Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/protobuf'... 2025-01-24T01:04:54.5413065Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/psimd'... 2025-01-24T01:04:54.7180201Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/pthreadpool'... 2025-01-24T01:04:54.9240863Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/pybind11'... 2025-01-24T01:04:56.0699946Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/python-peachpy'... 2025-01-24T01:04:56.3351447Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/sleef'... 2025-01-24T01:04:56.9640066Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/tensorpipe'... 2025-01-24T01:04:57.3634727Z Submodule path 'android/libs/fbjni': checked out '7e1e1fe3858c63c251c637ae41a20de425dde96f' 2025-01-24T01:04:57.3744062Z Submodule path 'third_party/FP16': checked out '4dfe081cf6bcd15db339cf2680b9281b8451eeb3' 2025-01-24T01:04:57.3826766Z Submodule path 'third_party/FXdiv': checked out 'b408327ac2a15ec3e43352421954f5b1967701d1' 2025-01-24T01:04:57.4055121Z Submodule path 'third_party/NNPACK': checked out 'c07e3a0400713d546e0dea2d5466dd22ea389c73' 2025-01-24T01:04:57.4374451Z Submodule path 'third_party/NVTX': checked out 'e170594ac7cf1dac584da473d4ca9301087090c1' 2025-01-24T01:04:57.4715828Z Submodule path 'third_party/VulkanMemoryAllocator': checked out 'a6bfc237255a6bac1513f7c1ebde6d8aed6b5191' 2025-01-24T01:04:58.1363451Z Submodule path 'third_party/XNNPACK': checked out '51a0103656eff6fc9bfd39a4597923c4b542c883' 2025-01-24T01:04:58.1588038Z Submodule path 'third_party/benchmark': checked out '0d98dba29d66e93259db7daa53a9327df767a415' 2025-01-24T01:04:58.3764565Z Submodule path 'third_party/composable_kernel': checked out '50ee4267e27b875d149e642f4cebd47be1dc3b57' 2025-01-24T01:04:58.4226554Z Submodule path 'third_party/cpp-httplib': checked out '3b6597bba913d51161383657829b7e644e59c006' 2025-01-24T01:04:58.5157806Z Submodule path 'third_party/cpuinfo': checked out '1e83a2fdd3102f65c6f1fb602c1b320486218a99' 2025-01-24T01:04:58.5478484Z Submodule path 'third_party/cudnn_frontend': checked out 'ee971b18ee428ff55b444ac93876cfbd377d304f' 2025-01-24T01:04:59.0800382Z Submodule path 'third_party/cutlass': checked out 'bf9da7b76c766d7ee7d536afc77880a4ef1f1156' 2025-01-24T01:04:59.3286051Z Submodule path 'third_party/eigen': checked out '3147391d946bb4b6c68edd901f2add6ac1f31f8c' 2025-01-24T01:04:59.4225119Z Submodule path 'third_party/fbgemm': checked out 'dbc3157bf256f1339b3fa1fef2be89ac4078be0e' 2025-01-24T01:04:59.4241675Z Submodule 'third_party/asmjit' (https://github.com/asmjit/asmjit.git) registered for path 'third_party/fbgemm/third_party/asmjit' 2025-01-24T01:04:59.4244005Z Submodule 'third_party/cpuinfo' (https://github.com/pytorch/cpuinfo) registered for path 'third_party/fbgemm/third_party/cpuinfo' 2025-01-24T01:04:59.4246501Z Submodule 'third_party/cutlass' (https://github.com/NVIDIA/cutlass.git) registered for path 'third_party/fbgemm/third_party/cutlass' 2025-01-24T01:04:59.4248939Z Submodule 'third_party/googletest' (https://github.com/google/googletest) registered for path 'third_party/fbgemm/third_party/googletest' 2025-01-24T01:04:59.4251529Z Submodule 'third_party/hipify_torch' (https://github.com/ROCmSoftwarePlatform/hipify_torch.git) registered for path 'third_party/fbgemm/third_party/hipify_torch' 2025-01-24T01:04:59.4275435Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm/third_party/asmjit'... 2025-01-24T01:05:00.3680146Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm/third_party/cpuinfo'... 2025-01-24T01:05:00.9434125Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm/third_party/cutlass'... 2025-01-24T01:05:03.7326439Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm/third_party/googletest'... 2025-01-24T01:05:04.9163946Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm/third_party/hipify_torch'... 2025-01-24T01:05:05.2562696Z Submodule path 'third_party/fbgemm/third_party/asmjit': checked out 'd3fbf7c9bc7c1d1365a94a45614b91c5a3706b81' 2025-01-24T01:05:05.3485236Z Submodule path 'third_party/fbgemm/third_party/cpuinfo': checked out 'ed8b86a253800bafdb7b25c5c399f91bff9cb1f3' 2025-01-24T01:05:05.7317201Z Submodule path 'third_party/fbgemm/third_party/cutlass': checked out 'fc9ebc645b63f3a6bc80aaefde5c063fb72110d6' 2025-01-24T01:05:05.7919458Z Submodule path 'third_party/fbgemm/third_party/googletest': checked out 'cbf019de22c8dd37b2108da35b2748fd702d1796' 2025-01-24T01:05:05.8036466Z Submodule path 'third_party/fbgemm/third_party/hipify_torch': checked out '23f53b025b466d8ec3c45d52290d3442f7fbe6b1' 2025-01-24T01:05:05.9311970Z Submodule path 'third_party/flatbuffers': checked out '01834de25e4bf3975a9a00e816292b1ad0fe184b' 2025-01-24T01:05:05.9625892Z Submodule path 'third_party/fmt': checked out 'e3ddede6c4ee818825c4e5a6dfa1d384860c27d9' 2025-01-24T01:05:06.0013155Z Submodule path 'third_party/gemmlowp/gemmlowp': checked out '3fb5c176c17c765a3492cd2f0321b0dab712f350' 2025-01-24T01:05:06.0260898Z Submodule path 'third_party/gloo': checked out '5354032ea08eadd7fc4456477f7f7c6308818509' 2025-01-24T01:05:06.0674373Z Submodule path 'third_party/googletest': checked out 'b514bdc898e2951020cbdca1304b75f5950d1f59' 2025-01-24T01:05:06.0790990Z Submodule path 'third_party/ideep': checked out 'e026f3b0318087fe19e2b062e8edf55bfe7a522c' 2025-01-24T01:05:06.0806049Z Submodule 'mkl-dnn' (https://github.com/intel/mkl-dnn.git) registered for path 'third_party/ideep/mkl-dnn' 2025-01-24T01:05:06.0829489Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/ideep/mkl-dnn'... 2025-01-24T01:05:19.0369408Z Submodule path 'third_party/ideep/mkl-dnn': checked out '66f0cb9eb66affd2da3bf5f8d897376f04aae6af' 2025-01-24T01:05:19.0545550Z Submodule path 'third_party/ittapi': checked out '5b8a7d7422611c3a0d799fb5fc5dd4abfae35b42' 2025-01-24T01:05:19.1378626Z Submodule path 'third_party/kineto': checked out 'bc1616a65cfe552516ce62f81ead48e45c85e18c' 2025-01-24T01:05:19.1393570Z Submodule 'libkineto/third_party/dynolog' (https://github.com/facebookincubator/dynolog.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog' 2025-01-24T01:05:19.1395262Z Submodule 'libkineto/third_party/fmt' (https://github.com/fmtlib/fmt.git) registered for path 'third_party/kineto/libkineto/third_party/fmt' 2025-01-24T01:05:19.1397727Z Submodule 'libkineto/third_party/googletest' (https://github.com/google/googletest.git) registered for path 'third_party/kineto/libkineto/third_party/googletest' 2025-01-24T01:05:19.1422592Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog'... 2025-01-24T01:05:20.0134089Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/fmt'... 2025-01-24T01:05:21.3574826Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/googletest'... 2025-01-24T01:05:22.5550030Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog': checked out '7d04a0053a845370ae06ce317a22a48e9edcc74e' 2025-01-24T01:05:22.5565172Z Submodule 'third_party/DCGM' (https://github.com/NVIDIA/DCGM.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM' 2025-01-24T01:05:22.5567404Z Submodule 'third_party/cpr' (https://github.com/libcpr/cpr.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr' 2025-01-24T01:05:22.5570027Z Submodule 'third_party/fmt' (https://github.com/fmtlib/fmt.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt' 2025-01-24T01:05:22.5572517Z Submodule 'third_party/gflags' (https://github.com/gflags/gflags.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags' 2025-01-24T01:05:22.5575115Z Submodule 'third_party/glog' (https://github.com/google/glog.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog' 2025-01-24T01:05:22.5577899Z Submodule 'third_party/googletest' (https://github.com/google/googletest.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest' 2025-01-24T01:05:22.5580904Z Submodule 'third_party/json' (https://github.com/nlohmann/json.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/json' 2025-01-24T01:05:22.5583804Z Submodule 'third_party/pfs' (https://github.com/dtrugman/pfs.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs' 2025-01-24T01:05:22.5609904Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM'... 2025-01-24T01:05:24.0145101Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/cpr'... 2025-01-24T01:05:24.3986395Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/fmt'... 2025-01-24T01:05:25.7652809Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/gflags'... 2025-01-24T01:05:26.0638139Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/glog'... 2025-01-24T01:05:26.5268696Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/googletest'... 2025-01-24T01:05:27.6666541Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/json'... 2025-01-24T01:05:34.7685442Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/pfs'... 2025-01-24T01:05:35.1242363Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM': checked out 'ffde4e54bc7249a6039a5e6b45b395141e1217f9' 2025-01-24T01:05:35.1416082Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr': checked out '871ed52d350214a034f6ef8a3b8f51c5ce1bd400' 2025-01-24T01:05:35.1760305Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt': checked out 'cd4af11efc9c622896a3e4cb599fa28668ca3d05' 2025-01-24T01:05:35.1884515Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags': checked out 'e171aa2d15ed9eb17054558e0b3a6a413bb01067' 2025-01-24T01:05:35.1898190Z Submodule 'doc' (https://github.com/gflags/gflags.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc' 2025-01-24T01:05:35.1921701Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc'... 2025-01-24T01:05:35.4856835Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc': checked out '8411df715cf522606e3b1aca386ddfc0b63d34b4' 2025-01-24T01:05:35.5029176Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog': checked out 'b33e3bad4c46c8a6345525fd822af355e5ef9446' 2025-01-24T01:05:35.5414206Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest': checked out '58d77fa8070e8cec2dc1ed015d66b454c8d78850' 2025-01-24T01:05:35.6367466Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/json': checked out '4f8fba14066156b73f1189a2b8bd568bde5284c5' 2025-01-24T01:05:35.6523452Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs': checked out 'f68a2fa8ea36c783bdd760371411fcb495aa3150' 2025-01-24T01:05:35.6898607Z Submodule path 'third_party/kineto/libkineto/third_party/fmt': checked out '0041a40c1350ba702d475b9c4ad62da77caea164' 2025-01-24T01:05:35.7454993Z Submodule path 'third_party/kineto/libkineto/third_party/googletest': checked out '7aca84427f224eeed3144123d5230d5871e93347' 2025-01-24T01:05:35.7679526Z Submodule path 'third_party/kleidiai': checked out '202603f38a9df9d2ded89f12b41ded621c71d4ea' 2025-01-24T01:05:35.8015085Z Submodule path 'third_party/mimalloc': checked out 'b66e3214d8a104669c2ec05ae91ebc26a8f5ab78' 2025-01-24T01:05:35.8295666Z Submodule path 'third_party/nccl/nccl': checked out 'ab2b89c4c339bd7f816fbc114a4b05d386b66290' 2025-01-24T01:05:35.9297734Z Submodule path 'third_party/nlohmann': checked out '87cda1d6646592ac5866dc703c8e1839046a6806' 2025-01-24T01:05:36.2912324Z Submodule path 'third_party/onnx': checked out 'b8baa8446686496da4cc8fda09f2b6fe65c2a02c' 2025-01-24T01:05:36.2945298Z Submodule 'third_party/pybind11' (https://github.com/pybind/pybind11.git) registered for path 'third_party/onnx/third_party/pybind11' 2025-01-24T01:05:36.2972365Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/onnx/third_party/pybind11'... 2025-01-24T01:05:37.4695012Z Submodule path 'third_party/onnx/third_party/pybind11': checked out '3e9dfa2866941655c56877882565e7577de6fc7b' 2025-01-24T01:05:37.5359495Z Submodule path 'third_party/opentelemetry-cpp': checked out 'a799f4aed9c94b765dcdaabaeab7d5e7e2310878' 2025-01-24T01:05:37.5377852Z Submodule 'third_party/benchmark' (https://github.com/google/benchmark) registered for path 'third_party/opentelemetry-cpp/third_party/benchmark' 2025-01-24T01:05:37.5380651Z Submodule 'third_party/googletest' (https://github.com/google/googletest) registered for path 'third_party/opentelemetry-cpp/third_party/googletest' 2025-01-24T01:05:37.5382809Z Submodule 'third_party/ms-gsl' (https://github.com/microsoft/GSL) registered for path 'third_party/opentelemetry-cpp/third_party/ms-gsl' 2025-01-24T01:05:37.5385225Z Submodule 'third_party/nlohmann-json' (https://github.com/nlohmann/json) registered for path 'third_party/opentelemetry-cpp/third_party/nlohmann-json' 2025-01-24T01:05:37.5388090Z Submodule 'third_party/opentelemetry-proto' (https://github.com/open-telemetry/opentelemetry-proto) registered for path 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto' 2025-01-24T01:05:37.5390595Z Submodule 'third_party/opentracing-cpp' (https://github.com/opentracing/opentracing-cpp.git) registered for path 'third_party/opentelemetry-cpp/third_party/opentracing-cpp' 2025-01-24T01:05:37.5393249Z Submodule 'third_party/prometheus-cpp' (https://github.com/jupp0r/prometheus-cpp) registered for path 'third_party/opentelemetry-cpp/third_party/prometheus-cpp' 2025-01-24T01:05:37.5395919Z Submodule 'tools/vcpkg' (https://github.com/Microsoft/vcpkg) registered for path 'third_party/opentelemetry-cpp/tools/vcpkg' 2025-01-24T01:05:37.5421761Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/third_party/benchmark'... 2025-01-24T01:05:37.9508108Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/third_party/googletest'... 2025-01-24T01:05:39.0782142Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/third_party/ms-gsl'... 2025-01-24T01:05:39.3934188Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/third_party/nlohmann-json'... 2025-01-24T01:05:46.7732296Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/third_party/opentelemetry-proto'... 2025-01-24T01:05:47.0788922Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/third_party/opentracing-cpp'... 2025-01-24T01:05:47.2569965Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/third_party/prometheus-cpp'... 2025-01-24T01:05:47.5150194Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/tools/vcpkg'... 2025-01-24T01:05:54.8441573Z Submodule path 'third_party/opentelemetry-cpp/third_party/benchmark': checked out 'd572f4777349d43653b21d6c2fc63020ab326db2' 2025-01-24T01:05:54.8825523Z Submodule path 'third_party/opentelemetry-cpp/third_party/googletest': checked out 'b796f7d44681514f58a683a3a71ff17c94edb0c1' 2025-01-24T01:05:54.8976648Z Submodule path 'third_party/opentelemetry-cpp/third_party/ms-gsl': checked out '6f4529395c5b7c2d661812257cd6780c67e54afa' 2025-01-24T01:05:54.9942792Z Submodule path 'third_party/opentelemetry-cpp/third_party/nlohmann-json': checked out 'bc889afb4c5bf1c0d8ee29ef35eaaf4c8bef8a5d' 2025-01-24T01:05:55.0062735Z Submodule path 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto': checked out '4ca4f0335c63cda7ab31ea7ed70d6553aee14dce' 2025-01-24T01:05:55.0197949Z Submodule path 'third_party/opentelemetry-cpp/third_party/opentracing-cpp': checked out '06b57f48ded1fa3bdd3d4346f6ef29e40e08eaf5' 2025-01-24T01:05:55.0339420Z Submodule path 'third_party/opentelemetry-cpp/third_party/prometheus-cpp': checked out 'c9ffcdda9086ffd9e1283ea7a0276d831f3c8a8d' 2025-01-24T01:05:55.0353393Z Submodule 'civetweb' (https://github.com/civetweb/civetweb.git) registered for path 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb' 2025-01-24T01:05:55.0355292Z Submodule 'googletest' (https://github.com/google/googletest.git) registered for path 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest' 2025-01-24T01:05:55.0377925Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb'... 2025-01-24T01:05:57.6451298Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest'... 2025-01-24T01:05:59.0879587Z Submodule path 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb': checked out 'eefb26f82b233268fc98577d265352720d477ba4' 2025-01-24T01:05:59.1325329Z Submodule path 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest': checked out 'e2239ee6043f73722e7aa812a459f54a28552929' 2025-01-24T01:05:59.5661609Z Submodule path 'third_party/opentelemetry-cpp/tools/vcpkg': checked out '8eb57355a4ffb410a2e94c07b4dca2dffbee8e50' 2025-01-24T01:05:59.5772210Z Submodule path 'third_party/pocketfft': checked out '9d3ab05a7fffbc71a492bc6a17be034e83e8f0fe' 2025-01-24T01:05:59.8449258Z Submodule path 'third_party/protobuf': checked out 'd1eca4e4b421cd2997495c4b4e65cea6be4e9b8a' 2025-01-24T01:05:59.8469481Z Submodule 'third_party/benchmark' (https://github.com/google/benchmark.git) registered for path 'third_party/protobuf/third_party/benchmark' 2025-01-24T01:05:59.8471604Z Submodule 'third_party/googletest' (https://github.com/google/googletest.git) registered for path 'third_party/protobuf/third_party/googletest' 2025-01-24T01:05:59.8497219Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/protobuf/third_party/benchmark'... 2025-01-24T01:06:00.3092958Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/protobuf/third_party/googletest'... 2025-01-24T01:06:01.5056087Z Submodule path 'third_party/protobuf/third_party/benchmark': checked out '5b7683f49e1e9223cf9927b24f6fd3d6bd82e3f8' 2025-01-24T01:06:01.5734864Z Submodule path 'third_party/protobuf/third_party/googletest': checked out '5ec7f0c4a113e2f18ac2c6cc7df51ad6afc24081' 2025-01-24T01:06:01.5818946Z Submodule path 'third_party/psimd': checked out '072586a71b55b7f8c584153d223e95687148a900' 2025-01-24T01:06:01.5929923Z Submodule path 'third_party/pthreadpool': checked 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file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/composable_kernel/config remote.origin.url 2025-01-24T01:06:07.4291334Z Entering 'third_party/cpp-httplib' 2025-01-24T01:06:07.4337709Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/cpp-httplib/config remote.origin.url 2025-01-24T01:06:07.4353882Z Entering 'third_party/cpuinfo' 2025-01-24T01:06:07.4403993Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/cpuinfo/config remote.origin.url 2025-01-24T01:06:07.4419008Z Entering 'third_party/cudnn_frontend' 2025-01-24T01:06:07.4465890Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/cudnn_frontend/config remote.origin.url 2025-01-24T01:06:07.4481779Z Entering 'third_party/cutlass' 2025-01-24T01:06:07.4529645Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/cutlass/config remote.origin.url 2025-01-24T01:06:07.4553934Z Entering 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'third_party/tensorpipe/third_party/libuv' 2025-01-24T01:06:07.8243427Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/tensorpipe/modules/third_party/libuv/config remote.origin.url 2025-01-24T01:06:07.8256965Z Entering 'third_party/tensorpipe/third_party/pybind11' 2025-01-24T01:06:07.8304391Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/tensorpipe/modules/third_party/pybind11/config remote.origin.url 2025-01-24T01:06:07.8318361Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang' 2025-01-24T01:06:07.8366368Z 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 2025-01-24T01:06:07.9269091Z [command]/usr/bin/git submodule foreach --recursive git config --local --add 'url.https://github.com/.insteadOf' 'git@github.com:' 2025-01-24T01:06:07.9548634Z Entering 'android/libs/fbjni' 2025-01-24T01:06:07.9588638Z Entering 'third_party/FP16' 2025-01-24T01:06:07.9627431Z Entering 'third_party/FXdiv' 2025-01-24T01:06:07.9665844Z Entering 'third_party/NNPACK' 2025-01-24T01:06:07.9704665Z Entering 'third_party/NVTX' 2025-01-24T01:06:07.9743410Z Entering 'third_party/VulkanMemoryAllocator' 2025-01-24T01:06:07.9782723Z Entering 'third_party/XNNPACK' 2025-01-24T01:06:07.9837237Z Entering 'third_party/benchmark' 2025-01-24T01:06:07.9876158Z Entering 'third_party/composable_kernel' 2025-01-24T01:06:07.9922124Z Entering 'third_party/cpp-httplib' 2025-01-24T01:06:07.9960569Z Entering 'third_party/cpuinfo' 2025-01-24T01:06:08.0000909Z Entering 'third_party/cudnn_frontend' 2025-01-24T01:06:08.0039896Z Entering 'third_party/cutlass' 2025-01-24T01:06:08.0086258Z Entering 'third_party/eigen' 2025-01-24T01:06:08.0126995Z Entering 'third_party/fbgemm' 2025-01-24T01:06:08.0166173Z Entering 'third_party/fbgemm/third_party/asmjit' 2025-01-24T01:06:08.0203703Z Entering 'third_party/fbgemm/third_party/cpuinfo' 2025-01-24T01:06:08.0242009Z Entering 'third_party/fbgemm/third_party/cutlass' 2025-01-24T01:06:08.0286233Z Entering 'third_party/fbgemm/third_party/googletest' 2025-01-24T01:06:08.0323316Z Entering 'third_party/fbgemm/third_party/hipify_torch' 2025-01-24T01:06:08.0361812Z Entering 'third_party/flatbuffers' 2025-01-24T01:06:08.0403704Z Entering 'third_party/fmt' 2025-01-24T01:06:08.0442850Z Entering 'third_party/gemmlowp/gemmlowp' 2025-01-24T01:06:08.0484130Z Entering 'third_party/gloo' 2025-01-24T01:06:08.0523220Z Entering 'third_party/googletest' 2025-01-24T01:06:08.0561443Z Entering 'third_party/ideep' 2025-01-24T01:06:08.0599626Z Entering 'third_party/ideep/mkl-dnn' 2025-01-24T01:06:08.0644023Z Entering 'third_party/ittapi' 2025-01-24T01:06:08.0683070Z Entering 'third_party/kineto' 2025-01-24T01:06:08.0722126Z Entering 'third_party/kineto/libkineto/third_party/dynolog' 2025-01-24T01:06:08.0762132Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM' 2025-01-24T01:06:08.0801921Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr' 2025-01-24T01:06:08.0839160Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt' 2025-01-24T01:06:08.0876393Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags' 2025-01-24T01:06:08.0914977Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc' 2025-01-24T01:06:08.0954885Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog' 2025-01-24T01:06:08.0993751Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest' 2025-01-24T01:06:08.1031621Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/json' 2025-01-24T01:06:08.1069938Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs' 2025-01-24T01:06:08.1110396Z Entering 'third_party/kineto/libkineto/third_party/fmt' 2025-01-24T01:06:08.1147890Z Entering 'third_party/kineto/libkineto/third_party/googletest' 2025-01-24T01:06:08.1187038Z Entering 'third_party/kleidiai' 2025-01-24T01:06:08.1225880Z Entering 'third_party/mimalloc' 2025-01-24T01:06:08.1264363Z Entering 'third_party/nccl/nccl' 2025-01-24T01:06:08.1303156Z Entering 'third_party/nlohmann' 2025-01-24T01:06:08.1342826Z Entering 'third_party/onnx' 2025-01-24T01:06:08.1397500Z Entering 'third_party/onnx/third_party/pybind11' 2025-01-24T01:06:08.1437418Z Entering 'third_party/opentelemetry-cpp' 2025-01-24T01:06:08.1478163Z Entering 'third_party/opentelemetry-cpp/third_party/benchmark' 2025-01-24T01:06:08.1518101Z Entering 'third_party/opentelemetry-cpp/third_party/googletest' 2025-01-24T01:06:08.1555406Z Entering 'third_party/opentelemetry-cpp/third_party/ms-gsl' 2025-01-24T01:06:08.1592183Z Entering 'third_party/opentelemetry-cpp/third_party/nlohmann-json' 2025-01-24T01:06:08.1630308Z Entering 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto' 2025-01-24T01:06:08.1666772Z Entering 'third_party/opentelemetry-cpp/third_party/opentracing-cpp' 2025-01-24T01:06:08.1703427Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp' 2025-01-24T01:06:08.1740107Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb' 2025-01-24T01:06:08.1781956Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest' 2025-01-24T01:06:08.1820647Z Entering 'third_party/opentelemetry-cpp/tools/vcpkg' 2025-01-24T01:06:08.1877503Z Entering 'third_party/pocketfft' 2025-01-24T01:06:08.1915731Z Entering 'third_party/protobuf' 2025-01-24T01:06:08.1957165Z Entering 'third_party/protobuf/third_party/benchmark' 2025-01-24T01:06:08.1996519Z Entering 'third_party/protobuf/third_party/googletest' 2025-01-24T01:06:08.2035852Z Entering 'third_party/psimd' 2025-01-24T01:06:08.2073862Z Entering 'third_party/pthreadpool' 2025-01-24T01:06:08.2112339Z Entering 'third_party/pybind11' 2025-01-24T01:06:08.2151062Z Entering 'third_party/python-peachpy' 2025-01-24T01:06:08.2190212Z Entering 'third_party/sleef' 2025-01-24T01:06:08.2228491Z Entering 'third_party/tensorpipe' 2025-01-24T01:06:08.2266912Z Entering 'third_party/tensorpipe/third_party/googletest' 2025-01-24T01:06:08.2304104Z Entering 'third_party/tensorpipe/third_party/libnop' 2025-01-24T01:06:08.2340453Z Entering 'third_party/tensorpipe/third_party/libuv' 2025-01-24T01:06:08.2382554Z Entering 'third_party/tensorpipe/third_party/pybind11' 2025-01-24T01:06:08.2419390Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang' 2025-01-24T01:06:08.2472199Z [command]/usr/bin/git submodule foreach --recursive git config --local --add 'url.https://github.com/.insteadOf' 'org-21003710@github.com:' 2025-01-24T01:06:08.2744180Z Entering 'android/libs/fbjni' 2025-01-24T01:06:08.2782385Z Entering 'third_party/FP16' 2025-01-24T01:06:08.2819875Z Entering 'third_party/FXdiv' 2025-01-24T01:06:08.2857219Z Entering 'third_party/NNPACK' 2025-01-24T01:06:08.2895276Z Entering 'third_party/NVTX' 2025-01-24T01:06:08.2933368Z Entering 'third_party/VulkanMemoryAllocator' 2025-01-24T01:06:08.2970838Z Entering 'third_party/XNNPACK' 2025-01-24T01:06:08.3024003Z Entering 'third_party/benchmark' 2025-01-24T01:06:08.3061534Z Entering 'third_party/composable_kernel' 2025-01-24T01:06:08.3105252Z Entering 'third_party/cpp-httplib' 2025-01-24T01:06:08.3143002Z Entering 'third_party/cpuinfo' 2025-01-24T01:06:08.3181123Z Entering 'third_party/cudnn_frontend' 2025-01-24T01:06:08.3218772Z Entering 'third_party/cutlass' 2025-01-24T01:06:08.3264946Z Entering 'third_party/eigen' 2025-01-24T01:06:08.3304871Z Entering 'third_party/fbgemm' 2025-01-24T01:06:08.3343023Z Entering 'third_party/fbgemm/third_party/asmjit' 2025-01-24T01:06:08.3381065Z Entering 'third_party/fbgemm/third_party/cpuinfo' 2025-01-24T01:06:08.3417062Z Entering 'third_party/fbgemm/third_party/cutlass' 2025-01-24T01:06:08.3460497Z Entering 'third_party/fbgemm/third_party/googletest' 2025-01-24T01:06:08.3497551Z Entering 'third_party/fbgemm/third_party/hipify_torch' 2025-01-24T01:06:08.3535999Z Entering 'third_party/flatbuffers' 2025-01-24T01:06:08.3576163Z Entering 'third_party/fmt' 2025-01-24T01:06:08.3613708Z Entering 'third_party/gemmlowp/gemmlowp' 2025-01-24T01:06:08.3651812Z Entering 'third_party/gloo' 2025-01-24T01:06:08.3689659Z Entering 'third_party/googletest' 2025-01-24T01:06:08.3727194Z Entering 'third_party/ideep' 2025-01-24T01:06:08.3764124Z Entering 'third_party/ideep/mkl-dnn' 2025-01-24T01:06:08.3808800Z Entering 'third_party/ittapi' 2025-01-24T01:06:08.3845776Z Entering 'third_party/kineto' 2025-01-24T01:06:08.3883664Z Entering 'third_party/kineto/libkineto/third_party/dynolog' 2025-01-24T01:06:08.3920804Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM' 2025-01-24T01:06:08.3958919Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr' 2025-01-24T01:06:08.3996620Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt' 2025-01-24T01:06:08.4034380Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags' 2025-01-24T01:06:08.4071065Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc' 2025-01-24T01:06:08.4110325Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog' 2025-01-24T01:06:08.4147582Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest' 2025-01-24T01:06:08.4185478Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/json' 2025-01-24T01:06:08.4223329Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs' 2025-01-24T01:06:08.4262225Z Entering 'third_party/kineto/libkineto/third_party/fmt' 2025-01-24T01:06:08.4299403Z Entering 'third_party/kineto/libkineto/third_party/googletest' 2025-01-24T01:06:08.4338048Z Entering 'third_party/kleidiai' 2025-01-24T01:06:08.4375416Z Entering 'third_party/mimalloc' 2025-01-24T01:06:08.4415384Z Entering 'third_party/nccl/nccl' 2025-01-24T01:06:08.4453234Z Entering 'third_party/nlohmann' 2025-01-24T01:06:08.4493270Z Entering 'third_party/onnx' 2025-01-24T01:06:08.4547786Z Entering 'third_party/onnx/third_party/pybind11' 2025-01-24T01:06:08.4589506Z Entering 'third_party/opentelemetry-cpp' 2025-01-24T01:06:08.4629681Z Entering 'third_party/opentelemetry-cpp/third_party/benchmark' 2025-01-24T01:06:08.4666610Z Entering 'third_party/opentelemetry-cpp/third_party/googletest' 2025-01-24T01:06:08.4705189Z Entering 'third_party/opentelemetry-cpp/third_party/ms-gsl' 2025-01-24T01:06:08.4742037Z Entering 'third_party/opentelemetry-cpp/third_party/nlohmann-json' 2025-01-24T01:06:08.4780361Z Entering 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto' 2025-01-24T01:06:08.4816443Z Entering 'third_party/opentelemetry-cpp/third_party/opentracing-cpp' 2025-01-24T01:06:08.4852800Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp' 2025-01-24T01:06:08.4889589Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb' 2025-01-24T01:06:08.4928298Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest' 2025-01-24T01:06:08.4967455Z Entering 'third_party/opentelemetry-cpp/tools/vcpkg' 2025-01-24T01:06:08.5024406Z Entering 'third_party/pocketfft' 2025-01-24T01:06:08.5063233Z Entering 'third_party/protobuf' 2025-01-24T01:06:08.5105680Z Entering 'third_party/protobuf/third_party/benchmark' 2025-01-24T01:06:08.5144322Z Entering 'third_party/protobuf/third_party/googletest' 2025-01-24T01:06:08.5183467Z Entering 'third_party/psimd' 2025-01-24T01:06:08.5221490Z Entering 'third_party/pthreadpool' 2025-01-24T01:06:08.5259217Z Entering 'third_party/pybind11' 2025-01-24T01:06:08.5298275Z Entering 'third_party/python-peachpy' 2025-01-24T01:06:08.5336233Z Entering 'third_party/sleef' 2025-01-24T01:06:08.5374509Z Entering 'third_party/tensorpipe' 2025-01-24T01:06:08.5413087Z Entering 'third_party/tensorpipe/third_party/googletest' 2025-01-24T01:06:08.5450093Z Entering 'third_party/tensorpipe/third_party/libnop' 2025-01-24T01:06:08.5487040Z Entering 'third_party/tensorpipe/third_party/libuv' 2025-01-24T01:06:08.5525089Z Entering 'third_party/tensorpipe/third_party/pybind11' 2025-01-24T01:06:08.5562111Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang' 2025-01-24T01:06:08.5611582Z ##[endgroup] 2025-01-24T01:06:08.5649099Z [command]/usr/bin/git log -1 --format=%H 2025-01-24T01:06:08.5671219Z d087145b9259b4cfbcb758dc0bca6a6e5d75d08f 2025-01-24T01:06:08.5842365Z Prepare all required actions 2025-01-24T01:06:08.5843069Z Getting action download info 2025-01-24T01:06:08.7239977Z ##[group]Run ./.github/actions/setup-linux 2025-01-24T01:06:08.7240322Z env: 2025-01-24T01:06:08.7240538Z GIT_DEFAULT_BRANCH: main 2025-01-24T01:06:08.7240820Z ##[endgroup] 2025-01-24T01:06:08.7288654Z ##[group]Run set -euo pipefail 2025-01-24T01:06:08.7289026Z set -euo pipefail 2025-01-24T01:06:08.7289329Z function get_ec2_metadata() { 2025-01-24T01:06:08.7289735Z  # Pulled from instance metadata endpoint for EC2 2025-01-24T01:06:08.7290393Z  # see https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/instancedata-data-retrieval.html 2025-01-24T01:06:08.7290984Z  category=$1 2025-01-24T01:06:08.7291364Z  # If it is GCP runner (runner name contains gcp), do not run this 2025-01-24T01:06:08.7291817Z  runner_name_str=i-0370698385eeb2f09 2025-01-24T01:06:08.7292224Z  if [[ -f /.inarc ]]; then 2025-01-24T01:06:08.7292589Z  echo "ARC Runner, no info on ec2 metadata" 2025-01-24T01:06:08.7292987Z  elif [[ $runner_name_str == *"gcp"* ]]; then 2025-01-24T01:06:08.7293478Z  echo "Runner is from Google Cloud Platform, No info on ec2 metadata" 2025-01-24T01:06:08.7293930Z  else 2025-01-24T01:06:08.7294820Z  curl -H "X-aws-ec2-metadata-token: $(curl -s -X PUT "http://169.254.169.254/latest/api/token" -H "X-aws-ec2-metadata-token-ttl-seconds: 30")" -fsSL "http://169.254.169.254/latest/meta-data/${category}" 2025-01-24T01:06:08.7295763Z  fi 2025-01-24T01:06:08.7295995Z } 2025-01-24T01:06:08.7296262Z echo "ami-id: $(get_ec2_metadata ami-id)" 2025-01-24T01:06:08.7296708Z echo "instance-id: $(get_ec2_metadata instance-id)" 2025-01-24T01:06:08.7297207Z echo "instance-type: $(get_ec2_metadata instance-type)" 2025-01-24T01:06:08.7297643Z echo "system info $(uname -a)" 2025-01-24T01:06:08.7304608Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-01-24T01:06:08.7305012Z env: 2025-01-24T01:06:08.7305230Z GIT_DEFAULT_BRANCH: main 2025-01-24T01:06:08.7305505Z ##[endgroup] 2025-01-24T01:06:08.7442679Z ami-id: ami-05576a079321f21f8 2025-01-24T01:06:08.7540606Z instance-id: i-0370698385eeb2f09 2025-01-24T01:06:08.7633372Z instance-type: c5.2xlarge 2025-01-24T01:06:08.7643039Z system info Linux ip-10-0-14-66.ec2.internal 6.1.119-129.201.amzn2023.x86_64 #1 SMP PREEMPT_DYNAMIC Tue Dec 3 21:07:35 UTC 2024 x86_64 x86_64 x86_64 GNU/Linux 2025-01-24T01:06:08.7674222Z ##[group]Run echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT" 2025-01-24T01:06:08.7675203Z echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT" 2025-01-24T01:06:08.7681864Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-01-24T01:06:08.7682311Z env: 2025-01-24T01:06:08.7682560Z GIT_DEFAULT_BRANCH: main 2025-01-24T01:06:08.7682853Z ##[endgroup] 2025-01-24T01:06:08.7741901Z ##[group]Run if systemctl is-active --quiet docker; then 2025-01-24T01:06:08.7742366Z if systemctl is-active --quiet docker; then 2025-01-24T01:06:08.7742774Z  echo "Docker daemon is running..."; 2025-01-24T01:06:08.7743120Z else 2025-01-24T01:06:08.7743492Z  echo "Starting docker deamon..." && sudo systemctl start docker; 2025-01-24T01:06:08.7743938Z fi 2025-01-24T01:06:08.7748927Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-01-24T01:06:08.7749324Z env: 2025-01-24T01:06:08.7749547Z GIT_DEFAULT_BRANCH: main 2025-01-24T01:06:08.7749818Z ##[endgroup] 2025-01-24T01:06:08.7816138Z Docker daemon is running... 2025-01-24T01:06:08.7863164Z ##[group]Run nick-fields/retry@v3.0.0 2025-01-24T01:06:08.7863484Z with: 2025-01-24T01:06:08.7863705Z shell: bash 2025-01-24T01:06:08.7864176Z timeout_minutes: 5 2025-01-24T01:06:08.7864435Z max_attempts: 3 2025-01-24T01:06:08.7864688Z retry_wait_seconds: 30 2025-01-24T01:06:08.7867131Z command: AWS_ACCOUNT_ID=$(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 2025-01-24T01:06:08.7869488Z polling_interval_seconds: 1 2025-01-24T01:06:08.7869797Z warning_on_retry: true 2025-01-24T01:06:08.7870076Z continue_on_error: false 2025-01-24T01:06:08.7870335Z env: 2025-01-24T01:06:08.7870567Z GIT_DEFAULT_BRANCH: main 2025-01-24T01:06:08.7870847Z AWS_RETRY_MODE: standard 2025-01-24T01:06:08.7871127Z AWS_MAX_ATTEMPTS: 5 2025-01-24T01:06:08.7871402Z AWS_DEFAULT_REGION: us-east-1 2025-01-24T01:06:08.7871682Z ##[endgroup] 2025-01-24T01:06:09.9800725Z WARNING! Your password will be stored unencrypted in /home/ec2-user/.docker/config.json. 2025-01-24T01:06:09.9801387Z Configure a credential helper to remove this warning. See 2025-01-24T01:06:09.9802078Z https://docs.docker.com/engine/reference/commandline/login/#credentials-store 2025-01-24T01:06:09.9802497Z 2025-01-24T01:06:09.9802609Z Login Succeeded 2025-01-24T01:06:10.4996250Z WARNING! Your password will be stored unencrypted in /home/ec2-user/.docker/config.json. 2025-01-24T01:06:10.4997133Z Configure a credential helper to remove this warning. See 2025-01-24T01:06:10.4998280Z https://docs.docker.com/engine/reference/commandline/login/#credentials-store 2025-01-24T01:06:10.4998786Z 2025-01-24T01:06:10.4998899Z Login Succeeded 2025-01-24T01:06:10.9353975Z Command completed after 1 attempt(s). 2025-01-24T01:06:10.9419553Z ##[group]Run env | grep '^GITHUB' >> "/tmp/github_env_${GITHUB_RUN_ID}" 2025-01-24T01:06:10.9420134Z env | grep '^GITHUB' >> "/tmp/github_env_${GITHUB_RUN_ID}" 2025-01-24T01:06:10.9420630Z env | grep '^CI' >> "/tmp/github_env_${GITHUB_RUN_ID}" 2025-01-24T01:06:10.9427831Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-01-24T01:06:10.9428237Z env: 2025-01-24T01:06:10.9428480Z GIT_DEFAULT_BRANCH: main 2025-01-24T01:06:10.9428768Z ##[endgroup] 2025-01-24T01:06:10.9511716Z ##[group]Run # ignore expansion of "docker ps -q" since it could be empty 2025-01-24T01:06:10.9512313Z # ignore expansion of "docker ps -q" since it could be empty 2025-01-24T01:06:10.9512760Z # shellcheck disable=SC2046 2025-01-24T01:06:10.9513100Z docker stop $(docker ps -q) || true 2025-01-24T01:06:10.9513464Z # Prune all of the docker images 2025-01-24T01:06:10.9513843Z docker system prune -af 2025-01-24T01:06:10.9518959Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-01-24T01:06:10.9519356Z env: 2025-01-24T01:06:10.9519590Z GIT_DEFAULT_BRANCH: main 2025-01-24T01:06:10.9519867Z ##[endgroup] 2025-01-24T01:06:10.9771135Z "docker stop" requires at least 1 argument. 2025-01-24T01:06:10.9771565Z See 'docker stop --help'. 2025-01-24T01:06:10.9771804Z 2025-01-24T01:06:10.9771992Z Usage: docker stop [OPTIONS] CONTAINER [CONTAINER...] 2025-01-24T01:06:10.9772273Z 2025-01-24T01:06:10.9772390Z Stop one or more running containers 2025-01-24T01:06:10.9921350Z Total reclaimed space: 0B 2025-01-24T01:06:10.9960093Z ##[group]Run set +e 2025-01-24T01:06:10.9960420Z set +e 2025-01-24T01:06:10.9960682Z set -x 2025-01-24T01:06:10.9960942Z  2025-01-24T01:06:10.9961213Z PT_DOMAIN=download.pytorch.org 2025-01-24T01:06:10.9961839Z # TODO: Flaky access to download.pytorch.org https://github.com/pytorch/pytorch/issues/100400, 2025-01-24T01:06:10.9962883Z # cleaning this up once the issue is fixed. There are more than one resolved IP here, the last 2025-01-24T01:06:10.9963456Z # one is returned at random 2025-01-24T01:06:10.9963887Z RESOLVED_IP=$(dig -4 +short "${PT_DOMAIN}" | tail -n1) 2025-01-24T01:06:10.9964302Z  2025-01-24T01:06:10.9964562Z if [ -z "${RESOLVED_IP}" ]; then 2025-01-24T01:06:10.9965019Z  echo "Couldn't resolve ${PT_DOMAIN}, retrying with Google DNS..." 2025-01-24T01:06:10.9965583Z  RESOLVED_IP=$(dig -4 +short "${PT_DOMAIN}" @8.8.8.8 | tail -n1) 2025-01-24T01:06:10.9966015Z  2025-01-24T01:06:10.9966274Z  if [ -z "${RESOLVED_IP}" ]; then 2025-01-24T01:06:10.9966688Z  echo "Couldn't resolve ${PT_DOMAIN}, exiting..." 2025-01-24T01:06:10.9967082Z  exit 1 2025-01-24T01:06:10.9967335Z  fi 2025-01-24T01:06:10.9967578Z fi 2025-01-24T01:06:10.9967812Z  2025-01-24T01:06:10.9968112Z if grep -r "${PT_DOMAIN}" /etc/hosts; then 2025-01-24T01:06:10.9968507Z  # Clean up any old records first 2025-01-24T01:06:10.9968889Z  sudo sed -i "/${PT_DOMAIN}/d" /etc/hosts 2025-01-24T01:06:10.9969243Z fi 2025-01-24T01:06:10.9969481Z  2025-01-24T01:06:10.9969820Z echo "${RESOLVED_IP} ${PT_DOMAIN}" | sudo tee -a /etc/hosts 2025-01-24T01:06:10.9970249Z cat /etc/hosts 2025-01-24T01:06:10.9975847Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-01-24T01:06:10.9976252Z env: 2025-01-24T01:06:10.9976494Z GIT_DEFAULT_BRANCH: main 2025-01-24T01:06:10.9976784Z ##[endgroup] 2025-01-24T01:06:10.9999345Z + PT_DOMAIN=download.pytorch.org 2025-01-24T01:06:11.0004782Z ++ dig -4 +short download.pytorch.org 2025-01-24T01:06:11.0005495Z ++ tail -n1 2025-01-24T01:06:11.0595713Z + RESOLVED_IP=18.160.10.22 2025-01-24T01:06:11.0596066Z + '[' -z 18.160.10.22 ']' 2025-01-24T01:06:11.0596416Z + grep -r download.pytorch.org /etc/hosts 2025-01-24T01:06:11.0607388Z 18.160.10.76 download.pytorch.org 2025-01-24T01:06:11.0608624Z + sudo sed -i /download.pytorch.org/d /etc/hosts 2025-01-24T01:06:11.3655075Z + echo '18.160.10.22 download.pytorch.org' 2025-01-24T01:06:11.3655621Z + sudo tee -a /etc/hosts 2025-01-24T01:06:11.4459444Z 18.160.10.22 download.pytorch.org 2025-01-24T01:06:11.4472459Z + cat /etc/hosts 2025-01-24T01:06:11.4480973Z 127.0.0.1 localhost localhost.localdomain localhost4 localhost4.localdomain4 2025-01-24T01:06:11.4486987Z ::1 localhost6 localhost6.localdomain6 2025-01-24T01:06:11.4487412Z 18.160.10.22 download.pytorch.org 2025-01-24T01:06:11.4669234Z ##[group]Run pytorch/test-infra/.github/actions/calculate-docker-image@main 2025-01-24T01:06:11.4669736Z with: 2025-01-24T01:06:11.4670452Z docker-image-name: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.13-clang10:bf84073365afe24d4dcffebc142d1c20de914163 2025-01-24T01:06:11.4671279Z docker-build-dir: .ci/docker 2025-01-24T01:06:11.4671612Z working-directory: . 2025-01-24T01:06:11.4671995Z docker-registry: 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-01-24T01:06:11.4672426Z force-push: false 2025-01-24T01:06:11.4672675Z env: 2025-01-24T01:06:11.4672912Z GIT_DEFAULT_BRANCH: main 2025-01-24T01:06:11.4673197Z ##[endgroup] 2025-01-24T01:06:11.4706044Z ##[group]Run set -ex 2025-01-24T01:06:11.4706504Z set -ex 2025-01-24T01:06:11.4706753Z  2025-01-24T01:06:11.4707192Z # If the docker build directory or the build script doesn't exist, the action will 2025-01-24T01:06:11.4707968Z # gracefully return the docker image name as it is. Pulling docker image in Linux 2025-01-24T01:06:11.4708587Z # job could then download the pre-built image as usual 2025-01-24T01:06:11.4709128Z if [[ ! -d "${DOCKER_BUILD_DIR}" ]] || [[ ! -f "${DOCKER_BUILD_DIR}/build.sh" ]]; then 2025-01-24T01:06:11.4709643Z  echo "skip=true" >> "${GITHUB_OUTPUT}" 2025-01-24T01:06:11.4710274Z  echo "docker-image=${DOCKER_IMAGE_NAME}" >> "${GITHUB_OUTPUT}" 2025-01-24T01:06:11.4710721Z  2025-01-24T01:06:11.4711113Z  echo "There is no Docker build script in ${REPO_NAME} repo, skipping..." 2025-01-24T01:06:11.4711591Z  exit 0 2025-01-24T01:06:11.4711836Z else 2025-01-24T01:06:11.4712137Z  echo "skip=false" >> "${GITHUB_OUTPUT}" 2025-01-24T01:06:11.4712496Z fi 2025-01-24T01:06:11.4712736Z  2025-01-24T01:06:11.4713105Z if [[ "${DOCKER_IMAGE_NAME}" == *"${DOCKER_REGISTRY}/${REPO_NAME}"* ]]; then 2025-01-24T01:06:11.4713743Z  # The docker image name already includes the ECR prefix and tag, so we can just 2025-01-24T01:06:11.4714319Z  # use it as it is, but first let's extract the tag 2025-01-24T01:06:11.4714844Z  DOCKER_TAG=$(echo "${DOCKER_IMAGE_NAME}" | awk -F '[:,]' '{print $2}') 2025-01-24T01:06:11.4715397Z  echo "docker-tag=${DOCKER_TAG}" >> "${GITHUB_OUTPUT}" 2025-01-24T01:06:11.4715931Z  echo "docker-image=${DOCKER_IMAGE_NAME}" >> "${GITHUB_OUTPUT}" 2025-01-24T01:06:11.4716378Z else 2025-01-24T01:06:11.4716711Z  DOCKER_TAG=$(git rev-parse HEAD:"${DOCKER_BUILD_DIR}") 2025-01-24T01:06:11.4717217Z  echo "docker-tag=${DOCKER_TAG}" >> "${GITHUB_OUTPUT}" 2025-01-24T01:06:11.4717903Z  echo "docker-image=${DOCKER_REGISTRY}/${REPO_NAME}/${DOCKER_IMAGE_NAME}:${DOCKER_TAG}" >> "${GITHUB_OUTPUT}" 2025-01-24T01:06:11.4718502Z fi 2025-01-24T01:06:11.4725465Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-01-24T01:06:11.4725884Z env: 2025-01-24T01:06:11.4726117Z GIT_DEFAULT_BRANCH: main 2025-01-24T01:06:11.4726421Z REPO_NAME: pytorch 2025-01-24T01:06:11.4727169Z DOCKER_IMAGE_NAME: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.13-clang10:bf84073365afe24d4dcffebc142d1c20de914163 2025-01-24T01:06:11.4727977Z DOCKER_BUILD_DIR: .ci/docker 2025-01-24T01:06:11.4728394Z DOCKER_REGISTRY: 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-01-24T01:06:11.4728827Z ##[endgroup] 2025-01-24T01:06:11.4752969Z + [[ ! -d .ci/docker ]] 2025-01-24T01:06:11.4753483Z + [[ ! -f .ci/docker/build.sh ]] 2025-01-24T01:06:11.4753968Z + echo skip=false 2025-01-24T01:06:11.4755530Z + [[ 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.13-clang10:bf84073365afe24d4dcffebc142d1c20de914163 == *\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* ]] 2025-01-24T01:06:11.4760242Z ++ echo 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.13-clang10:bf84073365afe24d4dcffebc142d1c20de914163 2025-01-24T01:06:11.4761115Z ++ awk -F '[:,]' '{print $2}' 2025-01-24T01:06:11.4778873Z + DOCKER_TAG=bf84073365afe24d4dcffebc142d1c20de914163 2025-01-24T01:06:11.4779878Z + echo docker-tag=bf84073365afe24d4dcffebc142d1c20de914163 2025-01-24T01:06:11.4781334Z + echo docker-image=308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.13-clang10:bf84073365afe24d4dcffebc142d1c20de914163 2025-01-24T01:06:11.4818148Z ##[group]Run set +e 2025-01-24T01:06:11.4818520Z set +e 2025-01-24T01:06:11.4818784Z set -x 2025-01-24T01:06:11.4819035Z  2025-01-24T01:06:11.4819259Z login() { 2025-01-24T01:06:11.4819774Z  aws ecr get-login-password --region us-east-1 | docker login -u AWS --password-stdin "$1" 2025-01-24T01:06:11.4820330Z } 2025-01-24T01:06:11.4820563Z  2025-01-24T01:06:11.4820798Z retry () { 2025-01-24T01:06:11.4821097Z  $* || (sleep 1 && $*) || (sleep 2 && $*) 2025-01-24T01:06:11.4821441Z } 2025-01-24T01:06:11.4821674Z  2025-01-24T01:06:11.4821931Z retry login "${DOCKER_REGISTRY}" 2025-01-24T01:06:11.4822268Z  2025-01-24T01:06:11.4822500Z START_TIME=$(date +%s) 2025-01-24T01:06:11.4822821Z # Wait up to 90 minutes 2025-01-24T01:06:11.4823350Z while [[ $(( $(date +%s) - 5400 )) -lt $START_TIME ]]; do 2025-01-24T01:06:11.4823883Z  # Check if image already exists, if it does then skip building it 2025-01-24T01:06:11.4824416Z  if docker manifest inspect "${DOCKER_IMAGE}"; then 2025-01-24T01:06:11.4824815Z  exit 0 2025-01-24T01:06:11.4825065Z  fi 2025-01-24T01:06:11.4825308Z  2025-01-24T01:06:11.4825720Z  # NB: This flag is used by Docker build workflow to push the image to ECR, so we can 2025-01-24T01:06:11.4826523Z  # use this to differentiate between the Docker build and regular build jobs. For the 2025-01-24T01:06:11.4827219Z  # latter, it will wait for the Docker images to become available before continuing 2025-01-24T01:06:11.4827773Z  if [ "${DOCKER_PUSH:-false}" == "true" ]; then 2025-01-24T01:06:11.4828198Z  # It's a Docker build job, let's build the image 2025-01-24T01:06:11.4828585Z  break 2025-01-24T01:06:11.4828841Z  else 2025-01-24T01:06:11.4829209Z  # It's a regular build job, wait for the image to become available 2025-01-24T01:06:11.4829651Z  sleep 300 2025-01-24T01:06:11.4829909Z  fi 2025-01-24T01:06:11.4830151Z done 2025-01-24T01:06:11.4830396Z  2025-01-24T01:06:11.4830772Z # NB: This part requires a full checkout. Otherwise, the merge base will 2025-01-24T01:06:11.4831378Z # be empty. The default action would be to continue rebuild the image 2025-01-24T01:06:11.4831913Z if [[ "$BASE_REVISION" = "$(git rev-parse HEAD)" ]]; then 2025-01-24T01:06:11.4832401Z  # if we're on the base branch then use the parent commit 2025-01-24T01:06:11.4832834Z  MERGE_BASE=$(git rev-parse HEAD~) 2025-01-24T01:06:11.4833179Z else 2025-01-24T01:06:11.4833537Z  # otherwise we're on a PR, so use the most recent base commit 2025-01-24T01:06:11.4834047Z  MERGE_BASE=$(git merge-base HEAD "$BASE_REVISION") 2025-01-24T01:06:11.4834426Z fi 2025-01-24T01:06:11.4834662Z  2025-01-24T01:06:11.4834919Z if [[ -z "${MERGE_BASE}" ]]; then 2025-01-24T01:06:11.4835307Z  echo "rebuild=true" >> "${GITHUB_OUTPUT}" 2025-01-24T01:06:11.4835666Z  2025-01-24T01:06:11.4836153Z  echo "Finding merge base only works with full checkout, please set fetch-depth to 0, continuing ..." 2025-01-24T01:06:11.4836735Z  exit 0 2025-01-24T01:06:11.4837000Z fi 2025-01-24T01:06:11.4837357Z  2025-01-24T01:06:11.4837686Z if ! git rev-parse "${MERGE_BASE}:${DOCKER_BUILD_DIR}"; then 2025-01-24T01:06:11.4838412Z  echo "Directory '${DOCKER_BUILD_DIR}' not found in commit $MERGE_BASE, you should rebase onto a more recent commit" 2025-01-24T01:06:11.4839039Z  exit 1 2025-01-24T01:06:11.4839296Z fi 2025-01-24T01:06:11.4839541Z  2025-01-24T01:06:11.4839925Z PREVIOUS_DOCKER_TAG=$(git rev-parse "${MERGE_BASE}:${DOCKER_BUILD_DIR}") 2025-01-24T01:06:11.4840622Z # If no image exists but the hash is the same as the previous hash then we should error out here 2025-01-24T01:06:11.4841252Z if [[ "${PREVIOUS_DOCKER_TAG}" == "${DOCKER_TAG}" ]]; then 2025-01-24T01:06:11.4841974Z  echo "WARNING: Something has gone wrong and the previous image isn't available for the merge-base of your branch" 2025-01-24T01:06:11.4842789Z  echo " Will re-build docker image to store in local cache, TTS may be longer" 2025-01-24T01:06:11.4843284Z fi 2025-01-24T01:06:11.4843529Z  2025-01-24T01:06:11.4843809Z echo "rebuild=true" >> "${GITHUB_OUTPUT}" 2025-01-24T01:06:11.4849268Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-01-24T01:06:11.4849684Z env: 2025-01-24T01:06:11.4849928Z GIT_DEFAULT_BRANCH: main 2025-01-24T01:06:11.4850326Z DOCKER_BUILD_DIR: .ci/docker 2025-01-24T01:06:11.4850685Z BASE_REVISION: b2c89bc115123aea8e075e882ee121537ec92f89 2025-01-24T01:06:11.4851528Z DOCKER_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.13-clang10:bf84073365afe24d4dcffebc142d1c20de914163 2025-01-24T01:06:11.4852355Z DOCKER_TAG: bf84073365afe24d4dcffebc142d1c20de914163 2025-01-24T01:06:11.4852834Z DOCKER_REGISTRY: 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-01-24T01:06:11.4853253Z DOCKER_PUSH: 2025-01-24T01:06:11.4853504Z ##[endgroup] 2025-01-24T01:06:11.4874793Z + retry login 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-01-24T01:06:11.4875264Z + login 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-01-24T01:06:11.4877596Z + aws ecr get-login-password --region us-east-1 2025-01-24T01:06:11.4878398Z + docker login -u AWS --password-stdin 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-01-24T01:06:11.9971029Z WARNING! Your password will be stored unencrypted in /home/ec2-user/.docker/config.json. 2025-01-24T01:06:11.9971761Z Configure a credential helper to remove this warning. See 2025-01-24T01:06:11.9972504Z https://docs.docker.com/engine/reference/commandline/login/#credentials-store 2025-01-24T01:06:11.9972914Z 2025-01-24T01:06:11.9973033Z Login Succeeded 2025-01-24T01:06:11.9985984Z ++ date +%s 2025-01-24T01:06:11.9994629Z + START_TIME=1737680771 2025-01-24T01:06:11.9997893Z ++ date +%s 2025-01-24T01:06:12.0005819Z + [[ 1737675372 -lt 1737680771 ]] 2025-01-24T01:06:12.0007015Z + docker manifest inspect 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.13-clang10:bf84073365afe24d4dcffebc142d1c20de914163 2025-01-24T01:06:12.2413199Z { 2025-01-24T01:06:12.2413622Z "schemaVersion": 2, 2025-01-24T01:06:12.2414098Z "mediaType": "application/vnd.docker.distribution.manifest.v2+json", 2025-01-24T01:06:12.2414570Z "config": { 2025-01-24T01:06:12.2415051Z "mediaType": "application/vnd.docker.container.image.v1+json", 2025-01-24T01:06:12.2415561Z "size": 41574, 2025-01-24T01:06:12.2416310Z "digest": "sha256:4cf0e421f3ff055d4e789c93110b36c2fff474473cb047622f9ef66777a49f3e" 2025-01-24T01:06:12.2417054Z }, 2025-01-24T01:06:12.2417373Z "layers": [ 2025-01-24T01:06:12.2417693Z { 2025-01-24T01:06:12.2418194Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-01-24T01:06:12.2418827Z "size": 28583948, 2025-01-24T01:06:12.2419513Z "digest": "sha256:86e5016c269355b382c9cabab4f6646d56d75914f20d545289970436dae431b1" 2025-01-24T01:06:12.2420278Z }, 2025-01-24T01:06:12.2420581Z { 2025-01-24T01:06:12.2421509Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-01-24T01:06:12.2422187Z "size": 1824, 2025-01-24T01:06:12.2422879Z "digest": "sha256:06c163cd1bd8b9a058a304d6005129ad0a430fc0ba114ca0b1665f1c2bf96e18" 2025-01-24T01:06:12.2423506Z }, 2025-01-24T01:06:12.2423709Z { 2025-01-24T01:06:12.2424064Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-01-24T01:06:12.2424514Z "size": 313442419, 2025-01-24T01:06:12.2424985Z "digest": 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"sha256:248405dc6ff522744bcb145351351ab471096c0227c3785bbba1d0f3eafb2de3" 2025-01-24T01:06:12.2600297Z }, 2025-01-24T01:06:12.2600504Z { 2025-01-24T01:06:12.2600844Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-01-24T01:06:12.2601264Z "size": 32, 2025-01-24T01:06:12.2601688Z "digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1" 2025-01-24T01:06:12.2602182Z }, 2025-01-24T01:06:12.2602387Z { 2025-01-24T01:06:12.2602726Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-01-24T01:06:12.2603151Z "size": 108, 2025-01-24T01:06:12.2603578Z "digest": "sha256:f9ef2819aef95ee2104e23cb922ddf901a5e06f3163063016b9a0692e0efe67e" 2025-01-24T01:06:12.2604065Z }, 2025-01-24T01:06:12.2604347Z { 2025-01-24T01:06:12.2604692Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-01-24T01:06:12.2605112Z "size": 54145665, 2025-01-24T01:06:12.2605531Z "digest": "sha256:c124231044812c8e90f58a87524551a87338b13f3690a22b45496b77472c1e46" 2025-01-24T01:06:12.2606001Z }, 2025-01-24T01:06:12.2606205Z { 2025-01-24T01:06:12.2606550Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-01-24T01:06:12.2606968Z "size": 32, 2025-01-24T01:06:12.2607391Z "digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1" 2025-01-24T01:06:12.2607881Z } 2025-01-24T01:06:12.2608090Z ] 2025-01-24T01:06:12.2608295Z } 2025-01-24T01:06:12.2654106Z ##[group]Run set -eux 2025-01-24T01:06:12.2654396Z set -eux 2025-01-24T01:06:12.2655309Z aws secretsmanager get-secret-value --secret-id docker_hub_readonly_token | jq --raw-output '.SecretString' | jq -r .docker_hub_readonly_token | docker login --username pytorchbot --password-stdin 2025-01-24T01:06:12.2664239Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-01-24T01:06:12.2664879Z env: 2025-01-24T01:06:12.2665222Z GIT_DEFAULT_BRANCH: main 2025-01-24T01:06:12.2665643Z ##[endgroup] 2025-01-24T01:06:12.2697664Z + aws secretsmanager get-secret-value --secret-id docker_hub_readonly_token 2025-01-24T01:06:12.2698523Z + jq --raw-output .SecretString 2025-01-24T01:06:12.2699363Z + jq -r .docker_hub_readonly_token 2025-01-24T01:06:12.2701266Z + docker login --username pytorchbot --password-stdin 2025-01-24T01:06:12.8425321Z WARNING! Your password will be stored unencrypted in /home/ec2-user/.docker/config.json. 2025-01-24T01:06:12.8426448Z Configure a credential helper to remove this warning. See 2025-01-24T01:06:12.8427391Z https://docs.docker.com/engine/reference/commandline/login/#credentials-store 2025-01-24T01:06:12.8427966Z 2025-01-24T01:06:12.8428132Z Login Succeeded 2025-01-24T01:06:12.8518287Z ##[group]Run tag=${ECR_DOCKER_IMAGE##*/} 2025-01-24T01:06:12.8518675Z tag=${ECR_DOCKER_IMAGE##*/} 2025-01-24T01:06:12.8519096Z echo "docker pull ghcr.io/pytorch/ci-image:${tag/:/-}" 2025-01-24T01:06:12.8524822Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-01-24T01:06:12.8525238Z env: 2025-01-24T01:06:12.8525469Z GIT_DEFAULT_BRANCH: main 2025-01-24T01:06:12.8526223Z ECR_DOCKER_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.13-clang10:bf84073365afe24d4dcffebc142d1c20de914163 2025-01-24T01:06:12.8527020Z ##[endgroup] 2025-01-24T01:06:12.8552051Z docker pull ghcr.io/pytorch/ci-image:pytorch-linux-focal-py3.13-clang10-bf84073365afe24d4dcffebc142d1c20de914163 2025-01-24T01:06:12.8606037Z ##[group]Run pytorch/test-infra/.github/actions/pull-docker-image@main 2025-01-24T01:06:12.8606500Z with: 2025-01-24T01:06:12.8607184Z docker-image: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.13-clang10:bf84073365afe24d4dcffebc142d1c20de914163 2025-01-24T01:06:12.8608035Z docker-registry: 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-01-24T01:06:12.8608448Z env: 2025-01-24T01:06:12.8608672Z GIT_DEFAULT_BRANCH: main 2025-01-24T01:06:12.8608952Z ##[endgroup] 2025-01-24T01:06:12.8634534Z ##[group]Run set -x 2025-01-24T01:06:12.8634829Z set -x 2025-01-24T01:06:12.8635087Z set +e 2025-01-24T01:06:12.8635324Z  2025-01-24T01:06:12.8635583Z login() { 2025-01-24T01:06:12.8636094Z  aws ecr get-login-password --region us-east-1 | docker login -u AWS --password-stdin "$1" 2025-01-24T01:06:12.8636656Z } 2025-01-24T01:06:12.8636892Z  2025-01-24T01:06:12.8637164Z retry () { 2025-01-24T01:06:12.8637464Z  $* || (sleep 1 && $*) || (sleep 2 && $*) 2025-01-24T01:06:12.8637801Z } 2025-01-24T01:06:12.8638038Z  2025-01-24T01:06:12.8638304Z retry login "${DOCKER_REGISTRY}" 2025-01-24T01:06:12.8638647Z  2025-01-24T01:06:12.8638879Z set -e 2025-01-24T01:06:12.8639237Z # ignore output since only exit code is used for conditional 2025-01-24T01:06:12.8639771Z # only pull docker image if it's not available locally 2025-01-24T01:06:12.8640351Z if ! docker inspect --type=image "${DOCKER_IMAGE}" >/dev/null 2>/dev/null; then 2025-01-24T01:06:12.8640891Z  retry docker pull "${DOCKER_IMAGE}" 2025-01-24T01:06:12.8641247Z fi 2025-01-24T01:06:12.8646177Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-01-24T01:06:12.8646573Z env: 2025-01-24T01:06:12.8646813Z GIT_DEFAULT_BRANCH: main 2025-01-24T01:06:12.8647567Z DOCKER_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.13-clang10:bf84073365afe24d4dcffebc142d1c20de914163 2025-01-24T01:06:12.8648445Z DOCKER_REGISTRY: 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-01-24T01:06:12.8648867Z ##[endgroup] 2025-01-24T01:06:12.8669688Z + set +e 2025-01-24T01:06:12.8670042Z + retry login 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-01-24T01:06:12.8670499Z + login 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-01-24T01:06:12.8672893Z + aws ecr get-login-password --region us-east-1 2025-01-24T01:06:12.8674109Z + docker login -u AWS --password-stdin 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-01-24T01:06:13.3804766Z WARNING! Your password will be stored unencrypted in /home/ec2-user/.docker/config.json. 2025-01-24T01:06:13.3805744Z Configure a credential helper to remove this warning. See 2025-01-24T01:06:13.3806593Z https://docs.docker.com/engine/reference/commandline/login/#credentials-store 2025-01-24T01:06:13.3807062Z 2025-01-24T01:06:13.3807162Z Login Succeeded 2025-01-24T01:06:13.3818016Z + set -e 2025-01-24T01:06:13.3819124Z + docker inspect --type=image 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.13-clang10:bf84073365afe24d4dcffebc142d1c20de914163 2025-01-24T01:06:13.3947940Z + retry docker pull 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.13-clang10:bf84073365afe24d4dcffebc142d1c20de914163 2025-01-24T01:06:13.3949176Z + docker pull 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.13-clang10:bf84073365afe24d4dcffebc142d1c20de914163 2025-01-24T01:06:13.6115628Z bf84073365afe24d4dcffebc142d1c20de914163: Pulling from pytorch/pytorch-linux-focal-py3.13-clang10 2025-01-24T01:06:13.6123779Z 86e5016c2693: Pulling fs layer 2025-01-24T01:06:13.6124330Z 06c163cd1bd8: Pulling fs layer 2025-01-24T01:06:13.6124880Z 7b8a63f2b2d5: Pulling fs layer 2025-01-24T01:06:13.6125196Z b22d856095e3: Pulling fs layer 2025-01-24T01:06:13.6125551Z 0ce61b7c5fef: Pulling fs layer 2025-01-24T01:06:13.6126107Z bb0b29a4db57: Pulling fs layer 2025-01-24T01:06:13.6126495Z abdd8d024372: Pulling fs layer 2025-01-24T01:06:13.6126795Z c95f803648aa: Pulling fs layer 2025-01-24T01:06:13.6127164Z 2c3379bfb21f: Pulling fs layer 2025-01-24T01:06:13.6127468Z 0ef3c01caefb: Pulling fs layer 2025-01-24T01:06:13.6127768Z 4fbeac90a1e9: Pulling fs layer 2025-01-24T01:06:13.6128143Z 2b985ebc8fc6: Pulling fs layer 2025-01-24T01:06:13.6128549Z 0cf3ba602a54: Pulling fs layer 2025-01-24T01:06:13.6128905Z bb0b29a4db57: Waiting 2025-01-24T01:06:13.6129187Z 3ed60c0f8e47: Pulling fs layer 2025-01-24T01:06:13.6129551Z abdd8d024372: Waiting 2025-01-24T01:06:13.6129864Z 4f4fb700ef54: Pulling fs layer 2025-01-24T01:06:13.6130209Z c95f803648aa: Waiting 2025-01-24T01:06:13.6130484Z 2c3379bfb21f: Waiting 2025-01-24T01:06:13.6130754Z 6ff598a9bbe6: Pulling fs layer 2025-01-24T01:06:13.6131033Z 0ef3c01caefb: Waiting 2025-01-24T01:06:13.6131302Z 4ef0cce8c397: Pulling fs layer 2025-01-24T01:06:13.6131594Z 4fbeac90a1e9: Waiting 2025-01-24T01:06:13.6131891Z 3c6966040a14: Pulling fs layer 2025-01-24T01:06:13.6132320Z 3ed60c0f8e47: Waiting 2025-01-24T01:06:13.6132718Z 0c5b5fb52278: Pulling fs layer 2025-01-24T01:06:13.6133207Z 0cf3ba602a54: Waiting 2025-01-24T01:06:13.6133674Z 0cf0e6449e6f: Pulling fs layer 2025-01-24T01:06:13.6134138Z 6ff598a9bbe6: Waiting 2025-01-24T01:06:13.6134498Z 71a6ac13df54: Pulling fs layer 2025-01-24T01:06:13.6134794Z 4ef0cce8c397: Waiting 2025-01-24T01:06:13.6135048Z 4f4fb700ef54: Waiting 2025-01-24T01:06:13.6135312Z 68f0ffa222e4: Pulling fs layer 2025-01-24T01:06:13.6135598Z 3c6966040a14: Waiting 2025-01-24T01:06:13.6135844Z 2365d708dd87: Pulling fs layer 2025-01-24T01:06:13.6136220Z 95216ed72149: Pulling fs layer 2025-01-24T01:06:13.6136685Z 2b985ebc8fc6: Waiting 2025-01-24T01:06:13.6137148Z 37ea1a89a768: Pulling fs layer 2025-01-24T01:06:13.6137667Z 102be3878cc2: Pulling fs layer 2025-01-24T01:06:13.6138154Z 1dfa17a00f2e: Pulling fs layer 2025-01-24T01:06:13.6138457Z b42690b7913c: Pulling fs layer 2025-01-24T01:06:13.6138757Z 0c5b5fb52278: Waiting 2025-01-24T01:06:13.6139025Z 2bc346efd3e4: Pulling fs layer 2025-01-24T01:06:13.6139311Z 0cf0e6449e6f: Waiting 2025-01-24T01:06:13.6139621Z b1ce3bd81619: Pulling fs layer 2025-01-24T01:06:13.6139896Z 2365d708dd87: Waiting 2025-01-24T01:06:13.6140152Z 71a6ac13df54: Waiting 2025-01-24T01:06:13.6140418Z c2ab782a54e5: Pulling fs layer 2025-01-24T01:06:13.6140706Z 95216ed72149: Waiting 2025-01-24T01:06:13.6140959Z 68f0ffa222e4: Waiting 2025-01-24T01:06:13.6141210Z 8198e8ec4010: Pulling fs layer 2025-01-24T01:06:13.6141499Z 0ce61b7c5fef: Waiting 2025-01-24T01:06:13.6141766Z eceb2070f454: Pulling fs layer 2025-01-24T01:06:13.6142054Z 102be3878cc2: Waiting 2025-01-24T01:06:13.6142506Z 1dfa17a00f2e: Waiting 2025-01-24T01:06:13.6142788Z 37ea1a89a768: Waiting 2025-01-24T01:06:13.6143147Z be6b3c5a05b0: Pulling fs layer 2025-01-24T01:06:13.6143478Z 2bc346efd3e4: Waiting 2025-01-24T01:06:13.6143737Z b1ce3bd81619: Waiting 2025-01-24T01:06:13.6144022Z b42690b7913c: Waiting 2025-01-24T01:06:13.6144284Z 371bb2852ee7: Pulling fs layer 2025-01-24T01:06:13.6144571Z 8198e8ec4010: Waiting 2025-01-24T01:06:13.6144823Z c2ab782a54e5: Waiting 2025-01-24T01:06:13.6145075Z eceb2070f454: Waiting 2025-01-24T01:06:13.6145332Z b9535c1c7efc: Pulling fs layer 2025-01-24T01:06:13.6145632Z a517ad3c51a9: Pulling fs layer 2025-01-24T01:06:13.6145931Z aa79b39a0302: Pulling fs layer 2025-01-24T01:06:13.6146294Z be6b3c5a05b0: Waiting 2025-01-24T01:06:13.6146580Z cd5d1c1c0d17: Pulling fs layer 2025-01-24T01:06:13.6146852Z 371bb2852ee7: Waiting 2025-01-24T01:06:13.6147117Z d19528ef2167: Pulling fs layer 2025-01-24T01:06:13.6147423Z a517ad3c51a9: Waiting 2025-01-24T01:06:13.6147704Z 79d92c6ee202: Pulling fs layer 2025-01-24T01:06:13.6147998Z cd5d1c1c0d17: Waiting 2025-01-24T01:06:13.6148288Z b1a69b242620: Pulling fs layer 2025-01-24T01:06:13.6148572Z aa79b39a0302: Waiting 2025-01-24T01:06:13.6148862Z d19528ef2167: Waiting 2025-01-24T01:06:13.6149185Z 7d9b845a1ebb: Pulling fs layer 2025-01-24T01:06:13.6149473Z b1a69b242620: Waiting 2025-01-24T01:06:13.6149898Z 8b722fa00db9: Pulling fs layer 2025-01-24T01:06:13.6150197Z e1beae55441c: Pulling fs layer 2025-01-24T01:06:13.6150493Z 8c347ce6dd3c: Pulling fs layer 2025-01-24T01:06:13.6150795Z 29ab4b7be04a: Pulling fs layer 2025-01-24T01:06:13.6151095Z 2fa29189d8c0: Pulling fs layer 2025-01-24T01:06:13.6151368Z 7d9b845a1ebb: Waiting 2025-01-24T01:06:13.6151640Z de56adfd9738: Pulling fs layer 2025-01-24T01:06:13.6151945Z de9fa7771efa: Pulling fs layer 2025-01-24T01:06:13.6152242Z 8b722fa00db9: Waiting 2025-01-24T01:06:13.6152490Z e1beae55441c: Waiting 2025-01-24T01:06:13.6152756Z dd67cf811826: Pulling fs layer 2025-01-24T01:06:13.6153042Z 2fa29189d8c0: Waiting 2025-01-24T01:06:13.6153298Z 8c347ce6dd3c: Waiting 2025-01-24T01:06:13.6153549Z dd67cf811826: Waiting 2025-01-24T01:06:13.6153800Z 38ebbfba335b: Pulling fs layer 2025-01-24T01:06:13.6154124Z 29ab4b7be04a: Waiting 2025-01-24T01:06:13.6154432Z 992bf232f017: Pulling fs layer 2025-01-24T01:06:13.6154737Z c52178e1546d: Pulling fs layer 2025-01-24T01:06:13.6155024Z b22d856095e3: Waiting 2025-01-24T01:06:13.6155302Z 992bf232f017: Waiting 2025-01-24T01:06:13.6155566Z 414c0854fc86: Pulling fs layer 2025-01-24T01:06:13.6155851Z de9fa7771efa: Waiting 2025-01-24T01:06:13.6156101Z a061527ceda8: Pulling fs layer 2025-01-24T01:06:13.6156397Z 8234cf8bfadd: Pulling fs layer 2025-01-24T01:06:13.6156682Z 414c0854fc86: Waiting 2025-01-24T01:06:13.6156931Z a061527ceda8: Waiting 2025-01-24T01:06:13.6157180Z d969083d4080: Pulling fs layer 2025-01-24T01:06:13.6157472Z 043560e5fd1c: Pulling fs layer 2025-01-24T01:06:13.6157770Z 0c2bf67f2676: Pulling fs layer 2025-01-24T01:06:13.6158092Z 043560e5fd1c: Waiting 2025-01-24T01:06:13.6158356Z 8234cf8bfadd: Waiting 2025-01-24T01:06:13.6158627Z 0c2bf67f2676: Waiting 2025-01-24T01:06:13.6158908Z 85f7803cd91b: Pulling fs layer 2025-01-24T01:06:13.6159206Z 23bd84fbc954: Pulling fs layer 2025-01-24T01:06:13.6159494Z 23bd84fbc954: Waiting 2025-01-24T01:06:13.6159758Z 451df874f6a0: Pulling fs layer 2025-01-24T01:06:13.6160045Z cca835738fb0: Pulling fs layer 2025-01-24T01:06:13.6160352Z fa6f5ea46f98: Pulling fs layer 2025-01-24T01:06:13.6160639Z 451df874f6a0: Waiting 2025-01-24T01:06:13.6160888Z cca835738fb0: Waiting 2025-01-24T01:06:13.6161152Z 8c99a92e8840: Pulling fs layer 2025-01-24T01:06:13.6161436Z ef1c3e2f2ce6: Pulling fs layer 2025-01-24T01:06:13.6161720Z 8c99a92e8840: Waiting 2025-01-24T01:06:13.6161983Z 42f5e8389126: Pulling fs layer 2025-01-24T01:06:13.6162278Z 31ba7f9ad583: Pulling fs layer 2025-01-24T01:06:13.6162612Z 248405dc6ff5: Pulling fs layer 2025-01-24T01:06:13.6162886Z 42f5e8389126: Waiting 2025-01-24T01:06:13.6163151Z f9ef2819aef9: Pulling fs layer 2025-01-24T01:06:13.6163438Z 248405dc6ff5: Waiting 2025-01-24T01:06:13.6163865Z c12423104481: Pulling fs layer 2025-01-24T01:06:13.6164138Z 31ba7f9ad583: Waiting 2025-01-24T01:06:13.6164395Z c12423104481: Waiting 2025-01-24T01:06:13.6164649Z f9ef2819aef9: Waiting 2025-01-24T01:06:13.6815076Z 06c163cd1bd8: Verifying Checksum 2025-01-24T01:06:13.6815503Z 06c163cd1bd8: Download complete 2025-01-24T01:06:13.7911584Z b22d856095e3: Verifying Checksum 2025-01-24T01:06:13.7912040Z b22d856095e3: Download complete 2025-01-24T01:06:13.9561616Z 86e5016c2693: Download complete 2025-01-24T01:06:14.0276648Z bb0b29a4db57: Verifying Checksum 2025-01-24T01:06:14.0277255Z bb0b29a4db57: Download complete 2025-01-24T01:06:14.1091170Z abdd8d024372: Download complete 2025-01-24T01:06:14.1659598Z c95f803648aa: Verifying Checksum 2025-01-24T01:06:14.1660242Z c95f803648aa: Download complete 2025-01-24T01:06:14.2685071Z 2c3379bfb21f: Verifying Checksum 2025-01-24T01:06:14.2685647Z 2c3379bfb21f: Download complete 2025-01-24T01:06:14.3435088Z 0ef3c01caefb: Download complete 2025-01-24T01:06:14.4510165Z 4fbeac90a1e9: Verifying Checksum 2025-01-24T01:06:14.4510675Z 4fbeac90a1e9: Download complete 2025-01-24T01:06:14.5291608Z 2b985ebc8fc6: Verifying Checksum 2025-01-24T01:06:14.5292457Z 2b985ebc8fc6: Download complete 2025-01-24T01:06:14.5991344Z 0cf3ba602a54: Verifying Checksum 2025-01-24T01:06:14.5992078Z 0cf3ba602a54: Download complete 2025-01-24T01:06:14.6302584Z 0ce61b7c5fef: Verifying Checksum 2025-01-24T01:06:14.6303127Z 0ce61b7c5fef: Download complete 2025-01-24T01:06:14.6423506Z 4f4fb700ef54: Verifying Checksum 2025-01-24T01:06:14.6424061Z 4f4fb700ef54: Download complete 2025-01-24T01:06:14.7296475Z 6ff598a9bbe6: Verifying Checksum 2025-01-24T01:06:14.7297006Z 6ff598a9bbe6: Download complete 2025-01-24T01:06:14.7981074Z 4ef0cce8c397: Verifying Checksum 2025-01-24T01:06:14.7981698Z 4ef0cce8c397: Download complete 2025-01-24T01:06:14.8229090Z 86e5016c2693: Pull complete 2025-01-24T01:06:14.8440727Z 06c163cd1bd8: Pull complete 2025-01-24T01:06:14.8828888Z 3c6966040a14: Verifying Checksum 2025-01-24T01:06:14.8829491Z 3c6966040a14: Download complete 2025-01-24T01:06:14.9881449Z 0c5b5fb52278: Verifying Checksum 2025-01-24T01:06:14.9881883Z 0c5b5fb52278: Download complete 2025-01-24T01:06:15.0598768Z 0cf0e6449e6f: Verifying Checksum 2025-01-24T01:06:15.0599333Z 0cf0e6449e6f: Download complete 2025-01-24T01:06:15.1463208Z 71a6ac13df54: Verifying Checksum 2025-01-24T01:06:15.1463838Z 71a6ac13df54: Download complete 2025-01-24T01:06:15.2327128Z 68f0ffa222e4: Download complete 2025-01-24T01:06:15.3101334Z 2365d708dd87: Verifying Checksum 2025-01-24T01:06:15.3101720Z 2365d708dd87: Download complete 2025-01-24T01:06:15.4026775Z 95216ed72149: Verifying Checksum 2025-01-24T01:06:15.4027163Z 95216ed72149: Download complete 2025-01-24T01:06:16.6529994Z 37ea1a89a768: Verifying Checksum 2025-01-24T01:06:16.6530547Z 37ea1a89a768: Download complete 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2025-01-24T01:06:43.5835434Z 3ed60c0f8e47: Download complete 2025-01-24T01:07:29.5672181Z 3ed60c0f8e47: Pull complete 2025-01-24T01:07:29.5893151Z 4f4fb700ef54: Pull complete 2025-01-24T01:07:29.6113035Z 6ff598a9bbe6: Pull complete 2025-01-24T01:07:29.6326418Z 4ef0cce8c397: Pull complete 2025-01-24T01:07:29.6542906Z 3c6966040a14: Pull complete 2025-01-24T01:07:29.7196433Z 0c5b5fb52278: Pull complete 2025-01-24T01:07:29.7406454Z 0cf0e6449e6f: Pull complete 2025-01-24T01:07:29.7618436Z 71a6ac13df54: Pull complete 2025-01-24T01:07:29.7826932Z 68f0ffa222e4: Pull complete 2025-01-24T01:07:29.8243560Z 2365d708dd87: Pull complete 2025-01-24T01:07:29.8481495Z 95216ed72149: Pull complete 2025-01-24T01:07:32.6237415Z 37ea1a89a768: Pull complete 2025-01-24T01:07:32.8250547Z 102be3878cc2: Pull complete 2025-01-24T01:07:33.0369813Z 1dfa17a00f2e: Pull complete 2025-01-24T01:07:33.2434513Z b42690b7913c: Pull complete 2025-01-24T01:07:33.4650919Z 2bc346efd3e4: Pull complete 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29ab4b7be04a: Pull complete 2025-01-24T01:07:42.4420768Z 2fa29189d8c0: Pull complete 2025-01-24T01:07:42.4526307Z de56adfd9738: Pull complete 2025-01-24T01:07:42.4741717Z de9fa7771efa: Pull complete 2025-01-24T01:07:42.4960464Z dd67cf811826: Pull complete 2025-01-24T01:07:42.5288876Z 38ebbfba335b: Pull complete 2025-01-24T01:07:42.5533262Z 992bf232f017: Pull complete 2025-01-24T01:07:42.5636924Z c52178e1546d: Pull complete 2025-01-24T01:07:42.5833637Z 414c0854fc86: Pull complete 2025-01-24T01:07:42.6058152Z a061527ceda8: Pull complete 2025-01-24T01:07:42.6240064Z 8234cf8bfadd: Pull complete 2025-01-24T01:07:42.6639138Z d969083d4080: Pull complete 2025-01-24T01:07:42.7083504Z 043560e5fd1c: Pull complete 2025-01-24T01:07:42.7295477Z 0c2bf67f2676: Pull complete 2025-01-24T01:07:42.7709321Z 85f7803cd91b: Pull complete 2025-01-24T01:07:42.7932472Z 23bd84fbc954: Pull complete 2025-01-24T01:07:42.8347808Z 451df874f6a0: Pull complete 2025-01-24T01:07:42.8560414Z cca835738fb0: Pull complete 2025-01-24T01:07:48.3661260Z fa6f5ea46f98: Pull complete 2025-01-24T01:07:48.5861220Z 8c99a92e8840: Pull complete 2025-01-24T01:07:48.7936450Z ef1c3e2f2ce6: Pull complete 2025-01-24T01:07:49.0015416Z 42f5e8389126: Pull complete 2025-01-24T01:07:49.2305929Z 31ba7f9ad583: Pull complete 2025-01-24T01:07:49.4510847Z 248405dc6ff5: Pull complete 2025-01-24T01:07:49.9158892Z f9ef2819aef9: Pull complete 2025-01-24T01:07:51.6596551Z c12423104481: Pull complete 2025-01-24T01:07:51.8988820Z Digest: sha256:b18d6f46bf0b8cb413663001587c0d8163f78580e6b3a5bfd85f7e7cd2b2c872 2025-01-24T01:07:51.9005486Z Status: Downloaded newer image for 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.13-clang10:bf84073365afe24d4dcffebc142d1c20de914163 2025-01-24T01:07:51.9023842Z 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.13-clang10:bf84073365afe24d4dcffebc142d1c20de914163 2025-01-24T01:07:51.9071643Z ##[group]Run echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT" 2025-01-24T01:07:51.9072640Z echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT" 2025-01-24T01:07:51.9080105Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-01-24T01:07:51.9080505Z env: 2025-01-24T01:07:51.9080746Z GIT_DEFAULT_BRANCH: main 2025-01-24T01:07:51.9081031Z ##[endgroup] 2025-01-24T01:07:51.9207737Z Prepare all required actions 2025-01-24T01:07:51.9266718Z ##[group]Run ./.github/actions/get-workflow-job-id 2025-01-24T01:07:51.9267365Z with: 2025-01-24T01:07:51.9268033Z github-token: *** 2025-01-24T01:07:51.9268434Z env: 2025-01-24T01:07:51.9268797Z GIT_DEFAULT_BRANCH: main 2025-01-24T01:07:51.9269269Z ##[endgroup] 2025-01-24T01:07:51.9299815Z ##[group]Run set -eux 2025-01-24T01:07:51.9300301Z set -eux 2025-01-24T01:07:51.9301107Z python3 .github/scripts/get_workflow_job_id.py "${GITHUB_RUN_ID}" "${RUNNER_NAME}" 2025-01-24T01:07:51.9309419Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-01-24T01:07:51.9310141Z env: 2025-01-24T01:07:51.9310521Z GIT_DEFAULT_BRANCH: main 2025-01-24T01:07:51.9311272Z GITHUB_TOKEN: *** 2025-01-24T01:07:51.9311718Z ##[endgroup] 2025-01-24T01:07:51.9339209Z + python3 .github/scripts/get_workflow_job_id.py 12940916563 i-0370698385eeb2f09 2025-01-24T01:07:53.6270236Z setting job-id=36096586663 2025-01-24T01:07:53.6270923Z setting job-name=linux-focal-py3.13-clang10 / test (dynamo_wrapped, 1, 3, lf.linux.2xlarge) 2025-01-24T01:07:53.6391841Z ##[group]Run python3 -m pip install psutil==5.9.1 nvidia-ml-py==11.525.84 dataclasses_json==0.6.7 2025-01-24T01:07:53.6392622Z python3 -m pip install psutil==5.9.1 nvidia-ml-py==11.525.84 dataclasses_json==0.6.7 2025-01-24T01:07:53.6393244Z python3 -m tools.stats.monitor > usage_log.txt 2>&1 & 2025-01-24T01:07:53.6393747Z echo "monitor-script-pid=${!}" >> "${GITHUB_OUTPUT}" 2025-01-24T01:07:53.6399746Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-01-24T01:07:53.6400153Z env: 2025-01-24T01:07:53.6400378Z GIT_DEFAULT_BRANCH: main 2025-01-24T01:07:53.6400659Z JOB_ID: 36096586663 2025-01-24T01:07:53.6401100Z JOB_NAME: linux-focal-py3.13-clang10 / test (dynamo_wrapped, 1, 3, lf.linux.2xlarge) 2025-01-24T01:07:53.6401612Z WORKFLOW_NAME: pull 2025-01-24T01:07:53.6401885Z WORKFLOW_RUN_ID: 12940916563 2025-01-24T01:07:53.6402161Z ##[endgroup] 2025-01-24T01:07:53.8796894Z Defaulting to user installation because normal site-packages is not writeable 2025-01-24T01:07:53.8962008Z Requirement already satisfied: psutil==5.9.1 in /home/ec2-user/.local/lib/python3.9/site-packages (5.9.1) 2025-01-24T01:07:53.8966544Z Requirement already satisfied: nvidia-ml-py==11.525.84 in /home/ec2-user/.local/lib/python3.9/site-packages (11.525.84) 2025-01-24T01:07:54.0524066Z Collecting dataclasses_json==0.6.7 2025-01-24T01:07:54.0897687Z Downloading dataclasses_json-0.6.7-py3-none-any.whl (28 kB) 2025-01-24T01:07:54.1364662Z Collecting typing-inspect<1,>=0.4.0 2025-01-24T01:07:54.1429782Z Downloading typing_inspect-0.9.0-py3-none-any.whl (8.8 kB) 2025-01-24T01:07:54.2521687Z Collecting marshmallow<4.0.0,>=3.18.0 2025-01-24T01:07:54.2558749Z Downloading marshmallow-3.26.0-py3-none-any.whl (50 kB) 2025-01-24T01:07:54.3093627Z Collecting packaging>=17.0 2025-01-24T01:07:54.3154600Z Downloading packaging-24.2-py3-none-any.whl (65 kB) 2025-01-24T01:07:54.3371592Z Collecting mypy-extensions>=0.3.0 2025-01-24T01:07:54.3413050Z Downloading mypy_extensions-1.0.0-py3-none-any.whl (4.7 kB) 2025-01-24T01:07:54.3805137Z Collecting typing-extensions>=3.7.4 2025-01-24T01:07:54.3838821Z Downloading typing_extensions-4.12.2-py3-none-any.whl (37 kB) 2025-01-24T01:07:54.4625091Z Installing collected packages: typing-extensions, packaging, mypy-extensions, typing-inspect, marshmallow, dataclasses-json 2025-01-24T01:07:54.5709408Z Successfully installed dataclasses-json-0.6.7 marshmallow-3.26.0 mypy-extensions-1.0.0 packaging-24.2 typing-extensions-4.12.2 typing-inspect-0.9.0 2025-01-24T01:07:54.6292111Z Prepare all required actions 2025-01-24T01:07:54.6292814Z Getting action download info 2025-01-24T01:07:54.7400802Z Download action repository 'seemethere/download-artifact-s3@v4' (SHA:1da556a7aa0a088e3153970611f6c432d58e80e6) 2025-01-24T01:07:54.9310126Z Download action repository 'actions/download-artifact@v4' (SHA:fa0a91b85d4f404e444e00e005971372dc801d16) 2025-01-24T01:07:55.2031176Z ##[group]Run ./.github/actions/download-build-artifacts 2025-01-24T01:07:55.2031592Z with: 2025-01-24T01:07:55.2031852Z name: linux-focal-py3.13-clang10 2025-01-24T01:07:55.2032188Z s3-bucket: gha-artifacts 2025-01-24T01:07:55.2032471Z env: 2025-01-24T01:07:55.2032700Z GIT_DEFAULT_BRANCH: main 2025-01-24T01:07:55.2032987Z ##[endgroup] 2025-01-24T01:07:55.2068643Z ##[group]Run seemethere/download-artifact-s3@v4 2025-01-24T01:07:55.2069013Z with: 2025-01-24T01:07:55.2069249Z name: linux-focal-py3.13-clang10 2025-01-24T01:07:55.2069579Z s3-bucket: gha-artifacts 2025-01-24T01:07:55.2069926Z region: us-east-1 2025-01-24T01:07:55.2070174Z env: 2025-01-24T01:07:55.2070390Z GIT_DEFAULT_BRANCH: main 2025-01-24T01:07:55.2070671Z ##[endgroup] 2025-01-24T01:07:55.6987975Z (node:291798) NOTE: We are formalizing our plans to enter AWS SDK for JavaScript (v2) into maintenance mode in 2023. 2025-01-24T01:07:55.6988512Z 2025-01-24T01:07:55.6988713Z Please migrate your code to use AWS SDK for JavaScript (v3). 2025-01-24T01:07:55.6989299Z For more information, check the migration guide at https://a.co/7PzMCcy 2025-01-24T01:07:55.8062695Z (Use `node --trace-warnings ...` to show where the warning was created) 2025-01-24T01:07:55.8063405Z Found 1 objects with prefix pytorch/pytorch/12940916563/linux-focal-py3.13-clang10/ 2025-01-24T01:07:55.8064143Z Starting download (1/1): /home/ec2-user/actions-runner/_work/pytorch/pytorch/artifacts.zip 2025-01-24T01:07:59.8166069Z Finished download (1/1): /home/ec2-user/actions-runner/_work/pytorch/pytorch/artifacts.zip 2025-01-24T01:07:59.8172008Z Artifact download has finished successfully 2025-01-24T01:07:59.8335081Z ##[group]Run unzip -o artifacts.zip 2025-01-24T01:07:59.8335465Z unzip -o artifacts.zip 2025-01-24T01:07:59.8342190Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-01-24T01:07:59.8342600Z env: 2025-01-24T01:07:59.8342849Z GIT_DEFAULT_BRANCH: main 2025-01-24T01:07:59.8343144Z ##[endgroup] 2025-01-24T01:07:59.8399742Z Archive: artifacts.zip 2025-01-24T01:07:59.8400937Z creating: dist/ 2025-01-24T01:08:00.8228851Z inflating: dist/torch-2.7.0a0+gitd087145-cp313-cp313-linux_x86_64.whl 2025-01-24T01:08:00.8229732Z creating: build/custom_test_artifacts/ 2025-01-24T01:08:00.8230157Z creating: build/custom_test_artifacts/custom-op-build/ 2025-01-24T01:08:00.8230677Z creating: build/custom_test_artifacts/custom-op-build/CMakeFiles/ 2025-01-24T01:08:00.8231642Z 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inflating: build/bin/memory_format_test 2025-01-24T01:08:06.1569203Z inflating: build/bin/native_test 2025-01-24T01:08:06.1617238Z inflating: build/bin/reduce_ops_test 2025-01-24T01:08:06.1666690Z inflating: build/bin/packedtensoraccessor_test 2025-01-24T01:08:06.1734472Z inflating: build/bin/pow_test 2025-01-24T01:08:06.1788320Z inflating: build/bin/quantized_test 2025-01-24T01:08:06.1839004Z inflating: build/bin/test_edge_op_registration 2025-01-24T01:08:06.1887742Z inflating: build/bin/reportMemoryUsage_test 2025-01-24T01:08:06.1892092Z inflating: build/bin/torch_shm_manager 2025-01-24T01:08:06.1908095Z inflating: build/bin/tutorial_tensorexpr 2025-01-24T01:08:06.2905885Z inflating: build/bin/test_tensorexpr 2025-01-24T01:08:06.3471715Z inflating: build/bin/test_jit 2025-01-24T01:08:06.3472369Z creating: .additional_ci_files/ 2025-01-24T01:08:06.3565124Z inflating: .additional_ci_files/test-times.json 2025-01-24T01:08:06.3929525Z inflating: .additional_ci_files/test-class-times.json 2025-01-24T01:08:06.3961272Z ##[group]Run rm artifacts.zip 2025-01-24T01:08:06.3961613Z rm artifacts.zip 2025-01-24T01:08:06.3967228Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-01-24T01:08:06.3967647Z env: 2025-01-24T01:08:06.3967881Z GIT_DEFAULT_BRANCH: main 2025-01-24T01:08:06.3968158Z ##[endgroup] 2025-01-24T01:08:06.4501834Z ##[group]Run df -H 2025-01-24T01:08:06.4502121Z df -H 2025-01-24T01:08:06.4507624Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-01-24T01:08:06.4508027Z env: 2025-01-24T01:08:06.4508272Z GIT_DEFAULT_BRANCH: main 2025-01-24T01:08:06.4508562Z ##[endgroup] 2025-01-24T01:08:06.4612665Z Filesystem Size Used Avail Use% Mounted on 2025-01-24T01:08:06.4613097Z devtmpfs 4.2M 0 4.2M 0% /dev 2025-01-24T01:08:06.4613444Z tmpfs 8.2G 0 8.2G 0% /dev/shm 2025-01-24T01:08:06.4613793Z tmpfs 3.3G 488k 3.3G 1% /run 2025-01-24T01:08:06.4614134Z /dev/nvme0n1p1 161G 25G 137G 16% / 2025-01-24T01:08:06.4614489Z tmpfs 8.2G 29k 8.2G 1% /tmp 2025-01-24T01:08:06.4615116Z /dev/nvme0n1p128 11M 1.4M 9.2M 13% /boot/efi 2025-01-24T01:08:06.4705755Z Prepare all required actions 2025-01-24T01:08:06.4706406Z Getting action download info 2025-01-24T01:08:06.6302998Z ##[group]Run ./.github/actions/download-td-artifacts 2025-01-24T01:08:06.6303376Z with: 2025-01-24T01:08:06.6303601Z env: 2025-01-24T01:08:06.6303830Z GIT_DEFAULT_BRANCH: main 2025-01-24T01:08:06.6304094Z ##[endgroup] 2025-01-24T01:08:06.6362965Z ##[group]Run seemethere/download-artifact-s3@v4 2025-01-24T01:08:06.6363332Z with: 2025-01-24T01:08:06.6363579Z name: td_results 2025-01-24T01:08:06.6363839Z s3-bucket: gha-artifacts 2025-01-24T01:08:06.6364110Z region: us-east-1 2025-01-24T01:08:06.6364352Z env: 2025-01-24T01:08:06.6364577Z GIT_DEFAULT_BRANCH: main 2025-01-24T01:08:06.6364854Z ##[endgroup] 2025-01-24T01:08:07.0904160Z (node:291817) NOTE: We are formalizing our plans to enter AWS SDK for JavaScript (v2) into maintenance mode in 2023. 2025-01-24T01:08:07.0904679Z 2025-01-24T01:08:07.0904972Z Please migrate your code to use AWS SDK for JavaScript (v3). 2025-01-24T01:08:07.0905530Z For more information, check the migration guide at https://a.co/7PzMCcy 2025-01-24T01:08:07.0906119Z (Use `node --trace-warnings ...` to show where the warning was created) 2025-01-24T01:08:07.1883028Z Found 1 objects with prefix pytorch/pytorch/12940916563/td_results/ 2025-01-24T01:08:07.1883719Z Starting download (1/1): /home/ec2-user/actions-runner/_work/pytorch/pytorch/td_results.json 2025-01-24T01:08:07.3129234Z Finished download (1/1): /home/ec2-user/actions-runner/_work/pytorch/pytorch/td_results.json 2025-01-24T01:08:07.3134874Z Artifact download has finished successfully 2025-01-24T01:08:07.3315919Z ##[group]Run mkdir -p .additional_ci_files 2025-01-24T01:08:07.3316335Z mkdir -p .additional_ci_files 2025-01-24T01:08:07.3316793Z mv td_results.json .additional_ci_files/td_results.json || true 2025-01-24T01:08:07.3323714Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-01-24T01:08:07.3324124Z env: 2025-01-24T01:08:07.3324383Z GIT_DEFAULT_BRANCH: main 2025-01-24T01:08:07.3324666Z ##[endgroup] 2025-01-24T01:08:07.3501882Z ##[group]Run .github/scripts/parse_ref.py 2025-01-24T01:08:07.3502300Z .github/scripts/parse_ref.py 2025-01-24T01:08:07.3508344Z shell: /usr/bin/bash -e {0} 2025-01-24T01:08:07.3508642Z env: 2025-01-24T01:08:07.3508877Z GIT_DEFAULT_BRANCH: main 2025-01-24T01:08:07.3509144Z ##[endgroup] 2025-01-24T01:08:07.3983088Z Prepare all required actions 2025-01-24T01:08:07.3984188Z Getting action download info 2025-01-24T01:08:07.5291067Z ##[group]Run ./.github/actions/filter-test-configs 2025-01-24T01:08:07.5291435Z with: 2025-01-24T01:08:07.5291868Z github-token: *** 2025-01-24T01:08:07.5294606Z test-matrix: {"include": [{"config": "default", "shard": 1, "num_shards": 5, "runner": "lf.linux.4xlarge"}, {"config": "default", "shard": 2, "num_shards": 5, "runner": "lf.linux.4xlarge"}, {"config": "default", "shard": 3, "num_shards": 5, "runner": "lf.linux.4xlarge"}, {"config": "default", "shard": 4, "num_shards": 5, "runner": "lf.linux.4xlarge"}, {"config": "default", "shard": 5, "num_shards": 5, "runner": "lf.linux.4xlarge"}, {"config": "crossref", "shard": 1, "num_shards": 2, "runner": "lf.linux.2xlarge"}, {"config": "crossref", "shard": 2, "num_shards": 2, "runner": "lf.linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 1, "num_shards": 3, "runner": "lf.linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 2, "num_shards": 3, "runner": "lf.linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 3, "num_shards": 3, "runner": "lf.linux.2xlarge"}]} 2025-01-24T01:08:07.5297543Z job-name: linux-focal-py3.13-clang10 / test (dynamo_wrapped, 1, 3, lf.linux.2xlarge) 2025-01-24T01:08:07.5298044Z env: 2025-01-24T01:08:07.5298278Z GIT_DEFAULT_BRANCH: main 2025-01-24T01:08:07.5298557Z ##[endgroup] 2025-01-24T01:08:07.5359223Z ##[group]Run nick-fields/retry@v3.0.0 2025-01-24T01:08:07.5359643Z with: 2025-01-24T01:08:07.5359871Z shell: bash 2025-01-24T01:08:07.5360096Z timeout_minutes: 10 2025-01-24T01:08:07.5360393Z max_attempts: 5 2025-01-24T01:08:07.5360643Z retry_wait_seconds: 30 2025-01-24T01:08:07.5361470Z 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 2025-01-24T01:08:07.5362467Z polling_interval_seconds: 1 2025-01-24T01:08:07.5362773Z warning_on_retry: true 2025-01-24T01:08:07.5363052Z continue_on_error: false 2025-01-24T01:08:07.5363326Z env: 2025-01-24T01:08:07.5363568Z GIT_DEFAULT_BRANCH: main 2025-01-24T01:08:07.5364125Z GITHUB_TOKEN: *** 2025-01-24T01:08:07.5364383Z ##[endgroup] 2025-01-24T01:08:07.6446386Z + python3 -m pip install requests==2.27.1 pyyaml==6.0.1 2025-01-24T01:08:07.8952585Z Defaulting to user installation because normal site-packages is not writeable 2025-01-24T01:08:07.9106740Z Requirement already satisfied: requests==2.27.1 in /home/ec2-user/.local/lib/python3.9/site-packages (2.27.1) 2025-01-24T01:08:07.9112214Z Requirement already satisfied: pyyaml==6.0.1 in /home/ec2-user/.local/lib/python3.9/site-packages (6.0.1) 2025-01-24T01:08:07.9219500Z Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/lib/python3.9/site-packages (from requests==2.27.1) (1.25.10) 2025-01-24T01:08:07.9224308Z Requirement already satisfied: certifi>=2017.4.17 in /home/ec2-user/.local/lib/python3.9/site-packages (from requests==2.27.1) (2024.12.14) 2025-01-24T01:08:07.9235590Z 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) 2025-01-24T01:08:07.9246465Z Requirement already satisfied: idna<4,>=2.5 in /usr/lib/python3.9/site-packages (from requests==2.27.1) (2.10) 2025-01-24T01:08:08.6113545Z Command completed after 1 attempt(s). 2025-01-24T01:08:08.6279026Z ##[group]Run set -x 2025-01-24T01:08:08.6279547Z set -x 2025-01-24T01:08:08.6279800Z  2025-01-24T01:08:08.6280194Z # Use relative path here as this could be checked out anywhere, not necessarily 2025-01-24T01:08:08.6280704Z # in runner workspace 2025-01-24T01:08:08.6281118Z python3 "${GITHUB_ACTION_PATH}/../../scripts/parse_ref.py" 2025-01-24T01:08:08.6288247Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-01-24T01:08:08.6288749Z env: 2025-01-24T01:08:08.6289015Z GIT_DEFAULT_BRANCH: main 2025-01-24T01:08:08.6289631Z ##[endgroup] 2025-01-24T01:08:08.6316245Z + python3 /home/ec2-user/actions-runner/_work/pytorch/pytorch/./.github/actions/filter-test-configs/../../scripts/parse_ref.py 2025-01-24T01:08:08.6555500Z ##[group]Run echo "Workflow: ${GITHUB_WORKFLOW}" 2025-01-24T01:08:08.6555971Z echo "Workflow: ${GITHUB_WORKFLOW}" 2025-01-24T01:08:08.6556349Z echo "Job name: ${JOB_NAME}" 2025-01-24T01:08:08.6556683Z  2025-01-24T01:08:08.6557087Z # Use relative path here as this could be checked out anywhere, not necessarily 2025-01-24T01:08:08.6557610Z # in runner workspace 2025-01-24T01:08:08.6558078Z python3 "${GITHUB_ACTION_PATH}/../../scripts/filter_test_configs.py" \ 2025-01-24T01:08:08.6558596Z  --workflow "${GITHUB_WORKFLOW}" \ 2025-01-24T01:08:08.6558973Z  --job-name "${JOB_NAME}" \ 2025-01-24T01:08:08.6561782Z  --test-matrix "{"include": [{"config": "default", "shard": 1, "num_shards": 5, "runner": "lf.linux.4xlarge"}, {"config": "default", "shard": 2, "num_shards": 5, "runner": "lf.linux.4xlarge"}, {"config": "default", "shard": 3, "num_shards": 5, "runner": "lf.linux.4xlarge"}, {"config": "default", "shard": 4, "num_shards": 5, "runner": "lf.linux.4xlarge"}, {"config": "default", "shard": 5, "num_shards": 5, "runner": "lf.linux.4xlarge"}, {"config": "crossref", "shard": 1, "num_shards": 2, "runner": "lf.linux.2xlarge"}, {"config": "crossref", "shard": 2, "num_shards": 2, "runner": "lf.linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 1, "num_shards": 3, "runner": "lf.linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 2, "num_shards": 3, "runner": "lf.linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 3, "num_shards": 3, "runner": "lf.linux.2xlarge"}]}" \ 2025-01-24T01:08:08.6564628Z  --selected-test-configs "" \ 2025-01-24T01:08:08.6565002Z  --pr-number "${PR_NUMBER}" \ 2025-01-24T01:08:08.6565350Z  --tag "${TAG}" \ 2025-01-24T01:08:08.6565677Z  --event-name "${EVENT_NAME}" \ 2025-01-24T01:08:08.6566019Z  --schedule "${SCHEDULE}" \ 2025-01-24T01:08:08.6566359Z  --branch "${HEAD_BRANCH}" 2025-01-24T01:08:08.6571736Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-01-24T01:08:08.6572149Z env: 2025-01-24T01:08:08.6572392Z GIT_DEFAULT_BRANCH: main 2025-01-24T01:08:08.6572926Z GITHUB_TOKEN: *** 2025-01-24T01:08:08.6573362Z JOB_NAME: linux-focal-py3.13-clang10 / test (dynamo_wrapped, 1, 3, lf.linux.2xlarge) 2025-01-24T01:08:08.6573883Z PR_NUMBER: 145539 2025-01-24T01:08:08.6574147Z TAG: 2025-01-24T01:08:08.6574392Z EVENT_NAME: pull_request 2025-01-24T01:08:08.6574686Z SCHEDULE: 2025-01-24T01:08:08.6574917Z HEAD_BRANCH: 2025-01-24T01:08:08.6575173Z ##[endgroup] 2025-01-24T01:08:08.6597228Z Workflow: pull 2025-01-24T01:08:08.6597681Z Job name: linux-focal-py3.13-clang10 / test (dynamo_wrapped, 1, 3, lf.linux.2xlarge) 2025-01-24T01:08:08.8997566Z INFO:root:Found no test-config label on the PR, so all test configs are included 2025-01-24T01:08:09.0255972Z ##[group]Run echo "Filtered matrix:" 2025-01-24T01:08:09.0256342Z echo "Filtered matrix:" 2025-01-24T01:08:09.0259374Z echo "{"include": [{"config": "default", "shard": 1, "num_shards": 5, "runner": "lf.linux.4xlarge"}, {"config": "default", "shard": 2, "num_shards": 5, "runner": "lf.linux.4xlarge"}, {"config": "default", "shard": 3, "num_shards": 5, "runner": "lf.linux.4xlarge"}, {"config": "default", "shard": 4, "num_shards": 5, "runner": "lf.linux.4xlarge"}, {"config": "default", "shard": 5, "num_shards": 5, "runner": "lf.linux.4xlarge"}, {"config": "crossref", "shard": 1, "num_shards": 2, "runner": "lf.linux.2xlarge"}, {"config": "crossref", "shard": 2, "num_shards": 2, "runner": "lf.linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 1, "num_shards": 3, "runner": "lf.linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 2, "num_shards": 3, "runner": "lf.linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 3, "num_shards": 3, "runner": "lf.linux.2xlarge"}]}" 2025-01-24T01:08:09.0262102Z  2025-01-24T01:08:09.0262329Z echo 2025-01-24T01:08:09.0262620Z echo "Is the current job unstable? False" 2025-01-24T01:08:09.0262983Z  2025-01-24T01:08:09.0263213Z echo 2025-01-24T01:08:09.0263491Z echo "Is keep-going label set? False" 2025-01-24T01:08:09.0263837Z  2025-01-24T01:08:09.0264046Z echo 2025-01-24T01:08:09.0264296Z echo "Renabled issues? " 2025-01-24T01:08:09.0270122Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-01-24T01:08:09.0270554Z env: 2025-01-24T01:08:09.0270796Z GIT_DEFAULT_BRANCH: main 2025-01-24T01:08:09.0271062Z ##[endgroup] 2025-01-24T01:08:09.0293285Z Filtered matrix: 2025-01-24T01:08:09.0296844Z {include: [{config: default, shard: 1, num_shards: 5, runner: lf.linux.4xlarge}, {config: default, shard: 2, num_shards: 5, runner: lf.linux.4xlarge}, {config: default, shard: 3, num_shards: 5, runner: lf.linux.4xlarge}, {config: default, shard: 4, num_shards: 5, runner: lf.linux.4xlarge}, {config: default, shard: 5, num_shards: 5, runner: lf.linux.4xlarge}, {config: crossref, shard: 1, num_shards: 2, runner: lf.linux.2xlarge}, {config: crossref, shard: 2, num_shards: 2, runner: lf.linux.2xlarge}, {config: dynamo_wrapped, shard: 1, num_shards: 3, runner: lf.linux.2xlarge}, {config: dynamo_wrapped, shard: 2, num_shards: 3, runner: lf.linux.2xlarge}, {config: dynamo_wrapped, shard: 3, num_shards: 3, runner: lf.linux.2xlarge}]} 2025-01-24T01:08:09.0299443Z 2025-01-24T01:08:09.0299574Z Is the current job unstable? False 2025-01-24T01:08:09.0299796Z 2025-01-24T01:08:09.0299908Z Is keep-going label set? False 2025-01-24T01:08:09.0300114Z 2025-01-24T01:08:09.0300212Z Renabled issues? 2025-01-24T01:08:09.0342545Z ##[group]Run echo "timeout=$((JOB_TIMEOUT-30))" >> "${GITHUB_OUTPUT}" 2025-01-24T01:08:09.0343084Z echo "timeout=$((JOB_TIMEOUT-30))" >> "${GITHUB_OUTPUT}" 2025-01-24T01:08:09.0348547Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-01-24T01:08:09.0348950Z env: 2025-01-24T01:08:09.0349183Z GIT_DEFAULT_BRANCH: main 2025-01-24T01:08:09.0349480Z JOB_TIMEOUT: 600 2025-01-24T01:08:09.0349715Z ##[endgroup] 2025-01-24T01:08:09.0771055Z ##[group]Run set -x 2025-01-24T01:08:09.0771419Z set -x 2025-01-24T01:08:09.0771663Z  2025-01-24T01:08:09.0771938Z if [[ $TEST_CONFIG == 'multigpu' ]]; then 2025-01-24T01:08:09.0772357Z  TEST_COMMAND=.ci/pytorch/multigpu-test.sh 2025-01-24T01:08:09.0772782Z elif [[ $BUILD_ENVIRONMENT == *onnx* ]]; then 2025-01-24T01:08:09.0773164Z  TEST_COMMAND=.ci/onnx/test.sh 2025-01-24T01:08:09.0773476Z else 2025-01-24T01:08:09.0773748Z  TEST_COMMAND=.ci/pytorch/test.sh 2025-01-24T01:08:09.0774080Z fi 2025-01-24T01:08:09.0774304Z  2025-01-24T01:08:09.0774588Z # Leaving 1GB for the runner and other things 2025-01-24T01:08:09.0775188Z TOTAL_AVAILABLE_MEMORY_IN_GB=$(awk '/MemTotal/ { printf "%.3f \n", $2/1024/1024 - 1 }' /proc/meminfo) 2025-01-24T01:08:09.0776115Z # https://docs.docker.com/engine/containers/resource_constraints/#--memory-swap-details, the 3GB swap 2025-01-24T01:08:09.0776991Z # comes from https://github.com/pytorch/test-infra/pull/6058 2025-01-24T01:08:09.0777543Z TOTAL_MEMORY_WITH_SWAP=$(("${TOTAL_AVAILABLE_MEMORY_IN_GB%.*}" + 3)) 2025-01-24T01:08:09.0777986Z  2025-01-24T01:08:09.0778270Z if [[ ${BUILD_ENVIRONMENT} == *"s390x"* ]]; then 2025-01-24T01:08:09.0778635Z  SHM_OPTS= 2025-01-24T01:08:09.0778904Z  JENKINS_USER= 2025-01-24T01:08:09.0779547Z  # ensure that docker container cleanly exits in 12 hours 2025-01-24T01:08:09.0780058Z  # if for some reason cleanup action doesn't stop container 2025-01-24T01:08:09.0780485Z  # when job is cancelled 2025-01-24T01:08:09.0780824Z  DOCKER_SHELL_CMD="sleep 12h" 2025-01-24T01:08:09.0781145Z  2025-01-24T01:08:09.0781536Z  # since some steps are skipped on s390x, if they are necessary, run them here 2025-01-24T01:08:09.0782102Z  env | grep '^GITHUB' >> "/tmp/github_env_${GITHUB_RUN_ID}" 2025-01-24T01:08:09.0782576Z  env | grep '^CI' >> "/tmp/github_env_${GITHUB_RUN_ID}" 2025-01-24T01:08:09.0782959Z else 2025-01-24T01:08:09.0783232Z  SHM_OPTS="--shm-size=${SHM_SIZE}" 2025-01-24T01:08:09.0783593Z  JENKINS_USER="--user jenkins" 2025-01-24T01:08:09.0783927Z  DOCKER_SHELL_CMD= 2025-01-24T01:08:09.0784195Z fi 2025-01-24T01:08:09.0784419Z  2025-01-24T01:08:09.0784780Z # detached container should get cleaned up by teardown_ec2_linux 2025-01-24T01:08:09.0785342Z # TODO: Stop building test binaries as part of the build phase 2025-01-24T01:08:09.0785982Z # Used for GPU_FLAG, SHM_OPTS, JENKINS_USER and DOCKER_SHELL_CMD since that doesn't play nice 2025-01-24T01:08:09.0786629Z # shellcheck disable=SC2086,SC2090 2025-01-24T01:08:09.0786995Z container_name=$(docker run \ 2025-01-24T01:08:09.0787340Z  ${GPU_FLAG:-} \ 2025-01-24T01:08:09.0787677Z  ${SCCACHE_SERVER_PORT_DOCKER_FLAG:-} \ 2025-01-24T01:08:09.0788049Z  -e BUILD_ENVIRONMENT \ 2025-01-24T01:08:09.0788368Z  -e PR_NUMBER \ 2025-01-24T01:08:09.0788647Z  -e GITHUB_ACTIONS \ 2025-01-24T01:08:09.0788956Z  -e GITHUB_REPOSITORY \ 2025-01-24T01:08:09.0789275Z  -e GITHUB_WORKFLOW \ 2025-01-24T01:08:09.0789583Z  -e GITHUB_JOB \ 2025-01-24T01:08:09.0789873Z  -e GITHUB_RUN_ID \ 2025-01-24T01:08:09.0790159Z  -e GITHUB_RUN_NUMBER \ 2025-01-24T01:08:09.0790476Z  -e GITHUB_RUN_ATTEMPT \ 2025-01-24T01:08:09.0790786Z  -e JOB_ID \ 2025-01-24T01:08:09.0791055Z  -e JOB_NAME \ 2025-01-24T01:08:09.0791332Z  -e BASE_SHA \ 2025-01-24T01:08:09.0791597Z  -e BRANCH \ 2025-01-24T01:08:09.0791863Z  -e SHA1 \ 2025-01-24T01:08:09.0792140Z  -e AWS_DEFAULT_REGION \ 2025-01-24T01:08:09.0792613Z  -e IN_WHEEL_TEST \ 2025-01-24T01:08:09.0792920Z  -e SHARD_NUMBER \ 2025-01-24T01:08:09.0793200Z  -e TEST_CONFIG \ 2025-01-24T01:08:09.0793495Z  -e NUM_TEST_SHARDS \ 2025-01-24T01:08:09.0793807Z  -e REENABLED_ISSUES \ 2025-01-24T01:08:09.0794134Z  -e CONTINUE_THROUGH_ERROR \ 2025-01-24T01:08:09.0794466Z  -e VERBOSE_TEST_LOGS \ 2025-01-24T01:08:09.0794770Z  -e TEST_SHOWLOCALS \ 2025-01-24T01:08:09.0795121Z  -e NO_TEST_TIMEOUT \ 2025-01-24T01:08:09.0795456Z  -e NO_TD \ 2025-01-24T01:08:09.0795732Z  -e TD_DISTRIBUTED \ 2025-01-24T01:08:09.0796034Z  -e PR_LABELS \ 2025-01-24T01:08:09.0796337Z  -e MAX_JOBS="$(nproc --ignore=2)" \ 2025-01-24T01:08:09.0796689Z  -e SCCACHE_BUCKET \ 2025-01-24T01:08:09.0796993Z  -e SCCACHE_REGION \ 2025-01-24T01:08:09.0797293Z  -e XLA_CUDA \ 2025-01-24T01:08:09.0797704Z  -e XLA_CLANG_CACHE_S3_BUCKET_NAME \ 2025-01-24T01:08:09.0798112Z  -e PYTORCH_TEST_CUDA_MEM_LEAK_CHECK \ 2025-01-24T01:08:09.0798517Z  -e PYTORCH_TEST_RERUN_DISABLED_TESTS \ 2025-01-24T01:08:09.0798913Z  -e SKIP_SCCACHE_INITIALIZATION=1 \ 2025-01-24T01:08:09.0799269Z  -e HUGGING_FACE_HUB_TOKEN \ 2025-01-24T01:08:09.0799630Z  -e SCRIBE_GRAPHQL_ACCESS_TOKEN \ 2025-01-24T01:08:09.0799979Z  -e DASHBOARD_TAG \ 2025-01-24T01:08:09.0800284Z  -e IS_A100_RUNNER \ 2025-01-24T01:08:09.0800601Z  -e ARTIFACTS_FILE_SUFFIX \ 2025-01-24T01:08:09.0800978Z  --memory="${TOTAL_AVAILABLE_MEMORY_IN_GB%.*}g" \ 2025-01-24T01:08:09.0801427Z  --memory-swap="${TOTAL_MEMORY_WITH_SWAP}g" \ 2025-01-24T01:08:09.0801872Z  --env-file="/tmp/github_env_${GITHUB_RUN_ID}" \ 2025-01-24T01:08:09.0802298Z  --security-opt seccomp=unconfined \ 2025-01-24T01:08:09.0802674Z  --cap-add=SYS_PTRACE \ 2025-01-24T01:08:09.0802985Z  --ipc=host \ 2025-01-24T01:08:09.0803265Z  ${SHM_OPTS} \ 2025-01-24T01:08:09.0803546Z  --tty \ 2025-01-24T01:08:09.0803804Z  --detach \ 2025-01-24T01:08:09.0804095Z  --name="${container_name}" \ 2025-01-24T01:08:09.0804423Z  ${JENKINS_USER} \ 2025-01-24T01:08:09.0804793Z  -v "${GITHUB_WORKSPACE}:/var/lib/jenkins/workspace" \ 2025-01-24T01:08:09.0805218Z  -w /var/lib/jenkins/workspace \ 2025-01-24T01:08:09.0805560Z  "${DOCKER_IMAGE}" \ 2025-01-24T01:08:09.0805859Z  ${DOCKER_SHELL_CMD} 2025-01-24T01:08:09.0806141Z ) 2025-01-24T01:08:09.0806435Z # Propagate download.pytorch.org IP to container 2025-01-24T01:08:09.0807137Z grep download.pytorch.org /etc/hosts | docker exec -i "${container_name}" sudo bash -c "/bin/cat >> /etc/hosts" 2025-01-24T01:08:09.0807884Z echo "DOCKER_CONTAINER_ID=${container_name}" >> "${GITHUB_ENV}" 2025-01-24T01:08:09.0808330Z  2025-01-24T01:08:09.0808611Z if [[ ${BUILD_ENVIRONMENT} == *"s390x"* ]]; then 2025-01-24T01:08:09.0809217Z  docker exec -t "${container_name}" sh -c "python3 -m pip install -r .ci/docker/requirements-ci.txt" 2025-01-24T01:08:09.0809755Z fi 2025-01-24T01:08:09.0809983Z  2025-01-24T01:08:09.0810499Z docker exec -t "${container_name}" sh -c "python3 -m pip install $(echo dist/*.whl)[opt-einsum] && ${TEST_COMMAND}" 2025-01-24T01:08:09.0816103Z shell: /usr/bin/bash -e {0} 2025-01-24T01:08:09.0816401Z env: 2025-01-24T01:08:09.0816619Z GIT_DEFAULT_BRANCH: main 2025-01-24T01:08:09.0816954Z BUILD_ENVIRONMENT: linux-focal-py3.13-clang10 2025-01-24T01:08:09.0817317Z PR_NUMBER: 145539 2025-01-24T01:08:09.0817591Z GITHUB_REPOSITORY: pytorch/pytorch 2025-01-24T01:08:09.0817917Z GITHUB_WORKFLOW: pull 2025-01-24T01:08:09.0818175Z GITHUB_JOB: test 2025-01-24T01:08:09.0818441Z GITHUB_RUN_ID: 12940916563 2025-01-24T01:08:09.0818850Z GITHUB_RUN_NUMBER: 284905 2025-01-24T01:08:09.0819142Z GITHUB_RUN_ATTEMPT: 1 2025-01-24T01:08:09.0819413Z JOB_ID: 36096586663 2025-01-24T01:08:09.0819856Z JOB_NAME: linux-focal-py3.13-clang10 / test (dynamo_wrapped, 1, 3, lf.linux.2xlarge) 2025-01-24T01:08:09.0820350Z BRANCH: pull/145539 2025-01-24T01:08:09.0820655Z SHA1: d087145b9259b4cfbcb758dc0bca6a6e5d75d08f 2025-01-24T01:08:09.0821055Z BASE_SHA: b2c89bc115123aea8e075e882ee121537ec92f89 2025-01-24T01:08:09.0821423Z TEST_CONFIG: dynamo_wrapped 2025-01-24T01:08:09.0821715Z SHARD_NUMBER: 1 2025-01-24T01:08:09.0821949Z NUM_TEST_SHARDS: 3 2025-01-24T01:08:09.0822209Z REENABLED_ISSUES: 2025-01-24T01:08:09.0822480Z CONTINUE_THROUGH_ERROR: False 2025-01-24T01:08:09.0822781Z VERBOSE_TEST_LOGS: False 2025-01-24T01:08:09.0823067Z TEST_SHOWLOCALS: False 2025-01-24T01:08:09.0823330Z NO_TEST_TIMEOUT: False 2025-01-24T01:08:09.0823598Z NO_TD: False 2025-01-24T01:08:09.0823909Z TD_DISTRIBUTED: False 2025-01-24T01:08:09.0824240Z SCCACHE_BUCKET: ossci-compiler-cache-circleci-v2 2025-01-24T01:08:09.0824626Z SCCACHE_REGION: us-east-1 2025-01-24T01:08:09.0824896Z SHM_SIZE: 1g 2025-01-24T01:08:09.0825581Z DOCKER_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.13-clang10:bf84073365afe24d4dcffebc142d1c20de914163 2025-01-24T01:08:09.0826429Z XLA_CUDA: 2025-01-24T01:08:09.0826807Z XLA_CLANG_CACHE_S3_BUCKET_NAME: ossci-compiler-clang-cache-circleci-xla 2025-01-24T01:08:09.0827290Z PYTORCH_TEST_CUDA_MEM_LEAK_CHECK: 0 2025-01-24T01:08:09.0827616Z PYTORCH_TEST_RERUN_DISABLED_TESTS: 0 2025-01-24T01:08:09.0827943Z DASHBOARD_TAG: 2025-01-24T01:08:09.0828412Z HUGGING_FACE_HUB_TOKEN: *** 2025-01-24T01:08:09.0828847Z SCRIBE_GRAPHQL_ACCESS_TOKEN: *** 2025-01-24T01:08:09.0829160Z IS_A100_RUNNER: 0 2025-01-24T01:08:09.0829568Z ARTIFACTS_FILE_SUFFIX: test-dynamo_wrapped-1-3-lf.linux.2xlarge_36096586663 2025-01-24T01:08:09.0830041Z ##[endgroup] 2025-01-24T01:08:09.0852616Z + [[ dynamo_wrapped == \m\u\l\t\i\g\p\u ]] 2025-01-24T01:08:09.0853027Z + [[ linux-focal-py3.13-clang10 == *onnx* ]] 2025-01-24T01:08:09.0853390Z + TEST_COMMAND=.ci/pytorch/test.sh 2025-01-24T01:08:09.0855641Z ++ awk '/MemTotal/ { printf "%.3f \n", $2/1024/1024 - 1 }' /proc/meminfo 2025-01-24T01:08:09.0901221Z + TOTAL_AVAILABLE_MEMORY_IN_GB='14.244 ' 2025-01-24T01:08:09.0901581Z + TOTAL_MEMORY_WITH_SWAP=17 2025-01-24T01:08:09.0901918Z + [[ linux-focal-py3.13-clang10 == *\s\3\9\0\x* ]] 2025-01-24T01:08:09.0902285Z + SHM_OPTS=--shm-size=1g 2025-01-24T01:08:09.0902566Z + JENKINS_USER='--user jenkins' 2025-01-24T01:08:09.0902847Z + DOCKER_SHELL_CMD= 2025-01-24T01:08:09.0909450Z +++ nproc --ignore=2 2025-01-24T01:08:09.0930255Z ++ 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_REGION -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 -e IS_A100_RUNNER -e ARTIFACTS_FILE_SUFFIX --memory=14g --memory-swap=17g --env-file=/tmp/github_env_12940916563 --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-py3.13-clang10:bf84073365afe24d4dcffebc142d1c20de914163 2025-01-24T01:08:18.4709390Z + container_name=8336c83c461cac917c450a6b582f03790365573c87be1c4764650d0e39a45485 2025-01-24T01:08:18.4711656Z + grep download.pytorch.org /etc/hosts 2025-01-24T01:08:18.4713296Z + docker exec -i 8336c83c461cac917c450a6b582f03790365573c87be1c4764650d0e39a45485 sudo bash -c '/bin/cat >> /etc/hosts' 2025-01-24T01:08:18.6764892Z + echo DOCKER_CONTAINER_ID=8336c83c461cac917c450a6b582f03790365573c87be1c4764650d0e39a45485 2025-01-24T01:08:18.6765508Z + [[ linux-focal-py3.13-clang10 == *\s\3\9\0\x* ]] 2025-01-24T01:08:18.6767121Z ++ echo dist/torch-2.7.0a0+gitd087145-cp313-cp313-linux_x86_64.whl 2025-01-24T01:08:18.6769371Z + docker exec -t 8336c83c461cac917c450a6b582f03790365573c87be1c4764650d0e39a45485 sh -c 'python3 -m pip install dist/torch-2.7.0a0+gitd087145-cp313-cp313-linux_x86_64.whl[opt-einsum] && .ci/pytorch/test.sh' 2025-01-24T01:08:19.4920050Z Processing ./dist/torch-2.7.0a0+gitd087145-cp313-cp313-linux_x86_64.whl (from torch==2.7.0a0+gitd087145) 2025-01-24T01:08:19.9147970Z Requirement already satisfied: filelock in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch==2.7.0a0+gitd087145->torch==2.7.0a0+gitd087145) (3.16.1) 2025-01-24T01:08:19.9151043Z Requirement already satisfied: typing-extensions>=4.10.0 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch==2.7.0a0+gitd087145->torch==2.7.0a0+gitd087145) (4.12.2) 2025-01-24T01:08:19.9154083Z Requirement already satisfied: networkx in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch==2.7.0a0+gitd087145->torch==2.7.0a0+gitd087145) (2.8.8) 2025-01-24T01:08:19.9156996Z Requirement already satisfied: jinja2 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch==2.7.0a0+gitd087145->torch==2.7.0a0+gitd087145) (3.1.5) 2025-01-24T01:08:19.9160001Z Requirement already satisfied: fsspec in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch==2.7.0a0+gitd087145->torch==2.7.0a0+gitd087145) (2024.10.0) 2025-01-24T01:08:19.9171465Z Requirement already satisfied: setuptools in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch==2.7.0a0+gitd087145->torch==2.7.0a0+gitd087145) (75.1.0) 2025-01-24T01:08:19.9176405Z Requirement already satisfied: sympy==1.13.1 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch==2.7.0a0+gitd087145->torch==2.7.0a0+gitd087145) (1.13.1) 2025-01-24T01:08:19.9192498Z Requirement already satisfied: mpmath<1.4,>=1.1.0 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from sympy==1.13.1->torch==2.7.0a0+gitd087145->torch==2.7.0a0+gitd087145) (1.3.0) 2025-01-24T01:08:19.9208870Z Requirement already satisfied: opt-einsum>=3.3 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch==2.7.0a0+gitd087145->torch==2.7.0a0+gitd087145) (3.3.0) 2025-01-24T01:08:19.9229084Z Requirement already satisfied: numpy>=1.7 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from opt-einsum>=3.3->torch==2.7.0a0+gitd087145->torch==2.7.0a0+gitd087145) (2.1.2) 2025-01-24T01:08:19.9337385Z Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from jinja2->torch==2.7.0a0+gitd087145->torch==2.7.0a0+gitd087145) (3.0.2) 2025-01-24T01:08:20.1840081Z Installing collected packages: torch 2025-01-24T01:08:30.0561801Z Successfully installed torch-2.7.0a0+gitd087145 2025-01-24T01:08:30.1341705Z + export TERM=vt100 2025-01-24T01:08:30.1342023Z + TERM=vt100 2025-01-24T01:08:30.1342340Z ++ dirname .ci/pytorch/test.sh 2025-01-24T01:08:30.1361193Z + source .ci/pytorch/common.sh 2025-01-24T01:08:30.1369498Z +++ dirname .ci/pytorch/common.sh 2025-01-24T01:08:30.1375792Z ++ source .ci/pytorch/common_utils.sh 2025-01-24T01:08:30.1382428Z +++ declare -f -t trap_add 2025-01-24T01:08:30.1388122Z ++ set -ex -o pipefail 2025-01-24T01:08:30.1388566Z ++ [[ linux-focal-py3.13-clang10 == *rocm* ]] 2025-01-24T01:08:30.1388999Z ++ BUILD_TEST_LIBTORCH=0 2025-01-24T01:08:30.1389859Z + [[ linux-focal-py3.13-clang10 != *rocm* ]] 2025-01-24T01:08:30.1390291Z + [[ linux-focal-py3.13-clang10 != *s390x* ]] 2025-01-24T01:08:30.1390655Z + [[ -d /var/lib/jenkins/workspace ]] 2025-01-24T01:08:30.1392910Z ++ stat -c %u /var/lib/jenkins/workspace 2025-01-24T01:08:30.1420565Z + WORKSPACE_ORIGINAL_OWNER_ID=1000 2025-01-24T01:08:30.1420913Z + trap_add cleanup_workspace EXIT 2025-01-24T01:08:30.1421254Z + trap_add_cmd=cleanup_workspace 2025-01-24T01:08:30.1421558Z + shift 2025-01-24T01:08:30.1421795Z + for trap_add_name in "$@" 2025-01-24T01:08:30.1427419Z +++ trap -p EXIT 2025-01-24T01:08:30.1430114Z ++ eval 'extract_trap_cmd ' 2025-01-24T01:08:30.1430660Z +++ extract_trap_cmd 2025-01-24T01:08:30.1431132Z +++ printf '%s\n' '' 2025-01-24T01:08:30.1431421Z ++ printf '%s\n' cleanup_workspace 2025-01-24T01:08:30.1432734Z + trap -- ' 2025-01-24T01:08:30.1433177Z cleanup_workspace' EXIT 2025-01-24T01:08:30.1433623Z + sudo chown -R jenkins /var/lib/jenkins/workspace 2025-01-24T01:08:30.6027786Z + git config --global --add safe.directory /var/lib/jenkins/workspace 2025-01-24T01:08:30.6042541Z + echo 'Environment variables:' 2025-01-24T01:08:30.6043142Z Environment variables: 2025-01-24T01:08:30.6043675Z + env 2025-01-24T01:08:30.6062850Z INSTALLED_DB=yes 2025-01-24T01:08:30.6063595Z GITHUB_WORKSPACE=/home/ec2-user/actions-runner/_work/pytorch/pytorch 2025-01-24T01:08:30.6064358Z CONTINUE_THROUGH_ERROR=False 2025-01-24T01:08:30.6064869Z BUILD_ENVIRONMENT=linux-focal-py3.13-clang10 2025-01-24T01:08:30.6065239Z HOSTNAME=8336c83c461c 2025-01-24T01:08:30.6066203Z GITHUB_PATH=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/add_path_371e197a-5858-428d-a0d1-2a5f66bf0667 2025-01-24T01:08:30.6067275Z GITHUB_ACTION=__self 2025-01-24T01:08:30.6067570Z PYTORCH_TEST_CUDA_MEM_LEAK_CHECK=0 2025-01-24T01:08:30.6067897Z GITHUB_RUN_NUMBER=284905 2025-01-24T01:08:30.6068244Z TEST_CONFIG=dynamo_wrapped 2025-01-24T01:08:30.6068647Z GITHUB_REPOSITORY_OWNER_ID=21003710 2025-01-24T01:08:30.6069024Z TORCH_NVCC_FLAGS=-Xfatbin -compress-all 2025-01-24T01:08:30.6069353Z IS_A100_RUNNER=0 2025-01-24T01:08:30.6069860Z SCRIBE_GRAPHQL_ACCESS_TOKEN=*** 2025-01-24T01:08:30.6070198Z GITHUB_TRIGGERING_ACTOR=facebook-github-bot 2025-01-24T01:08:30.6070575Z GITHUB_REF_TYPE=branch 2025-01-24T01:08:30.6070865Z TORCH_CUDA_ARCH_LIST=Maxwell 2025-01-24T01:08:30.6071186Z BASE_SHA=b2c89bc115123aea8e075e882ee121537ec92f89 2025-01-24T01:08:30.6071535Z XLA_CUDA= 2025-01-24T01:08:30.6071873Z HUGGING_FACE_HUB_TOKEN=*** 2025-01-24T01:08:30.6074915Z *** 2025-01-24T01:08:30.6075168Z GITHUB_REPOSITORY_ID=65600975 2025-01-24T01:08:30.6075462Z GITHUB_ACTIONS=true 2025-01-24T01:08:30.6075762Z SHA1=d087145b9259b4cfbcb758dc0bca6a6e5d75d08f 2025-01-24T01:08:30.6076155Z GITHUB_SHA=74e42e7401e3b57fc188c6d3823585f63c780e87 2025-01-24T01:08:30.6076714Z GITHUB_WORKFLOW_REF=pytorch/pytorch/.github/workflows/pull.yml@refs/pull/145539/merge 2025-01-24T01:08:30.6077230Z UCC_HOME=/usr 2025-01-24T01:08:30.6077458Z VERBOSE_TEST_LOGS=False 2025-01-24T01:08:30.6077738Z GITHUB_REF=refs/pull/145539/merge 2025-01-24T01:08:30.6078039Z SHARD_NUMBER=1 2025-01-24T01:08:30.6078292Z GITHUB_REF_PROTECTED=false 2025-01-24T01:08:30.6078575Z HOME=/var/lib/jenkins 2025-01-24T01:08:30.6078876Z GITHUB_API_URL=https://api.github.com 2025-01-24T01:08:30.6079479Z PYTORCH_TEST_RERUN_DISABLED_TESTS=0 2025-01-24T01:08:30.6079798Z UCX_COMMIT= 2025-01-24T01:08:30.6080033Z NUM_TEST_SHARDS=3 2025-01-24T01:08:30.6080277Z UCX_HOME=/usr 2025-01-24T01:08:30.6080867Z GITHUB_STATE=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/save_state_371e197a-5858-428d-a0d1-2a5f66bf0667 2025-01-24T01:08:30.6081694Z JOB_NAME=linux-focal-py3.13-clang10 / test (dynamo_wrapped, 1, 3, lf.linux.2xlarge) 2025-01-24T01:08:30.6093328Z GITHUB_ENV=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/set_env_371e197a-5858-428d-a0d1-2a5f66bf0667 2025-01-24T01:08:30.6094291Z GITHUB_EVENT_PATH=/home/ec2-user/actions-runner/_work/_temp/_github_workflow/event.json 2025-01-24T01:08:30.6094841Z GITHUB_EVENT_NAME=pull_request 2025-01-24T01:08:30.6095135Z DASHBOARD_TAG= 2025-01-24T01:08:30.6095395Z GITHUB_RUN_ID=12940916563 2025-01-24T01:08:30.6096065Z GITHUB_STEP_SUMMARY=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/step_summary_371e197a-5858-428d-a0d1-2a5f66bf0667 2025-01-24T01:08:30.6097503Z GITHUB_ACTOR=facebook-github-bot 2025-01-24T01:08:30.6097834Z PR_NUMBER=145539 2025-01-24T01:08:30.6098072Z DESIRED_CUDA= 2025-01-24T01:08:30.6098326Z GITHUB_RUN_ATTEMPT=1 2025-01-24T01:08:30.6098610Z ANACONDA_PYTHON_VERSION=3.13 2025-01-24T01:08:30.6098978Z GITHUB_GRAPHQL_URL=https://api.github.com/graphql 2025-01-24T01:08:30.6099351Z TERM=vt100 2025-01-24T01:08:30.6099581Z INSTALLED_VISION=yes 2025-01-24T01:08:30.6099850Z BRANCH=pull/145539 2025-01-24T01:08:30.6100109Z SCCACHE_REGION=us-east-1 2025-01-24T01:08:30.6100402Z OPENSSL_ROOT_DIR=/opt/openssl 2025-01-24T01:08:30.6100705Z CUDA_PATH=/usr/local/cuda 2025-01-24T01:08:30.6101242Z GITHUB_ACTION_PATH=/home/ec2-user/actions-runner/_work/pytorch/pytorch/./.github/actions/setup-linux 2025-01-24T01:08:30.6101856Z GITHUB_SERVER_URL=https://github.com 2025-01-24T01:08:30.6102177Z UCC_COMMIT= 2025-01-24T01:08:30.6102440Z REENABLED_ISSUES= 2025-01-24T01:08:30.6102792Z DOCS= 2025-01-24T01:08:30.6102991Z SHLVL=1 2025-01-24T01:08:30.6103211Z MAX_JOBS=6 2025-01-24T01:08:30.6103445Z GITHUB_ACTOR_ID=6422482 2025-01-24T01:08:30.6103798Z GITHUB_WORKFLOW_SHA=74e42e7401e3b57fc188c6d3823585f63c780e87 2025-01-24T01:08:30.6104209Z GITHUB_REF_NAME=145539/merge 2025-01-24T01:08:30.6104623Z XLA_CLANG_CACHE_S3_BUCKET_NAME=ossci-compiler-clang-cache-circleci-xla 2025-01-24T01:08:30.6105078Z GITHUB_JOB=test 2025-01-24T01:08:30.6105327Z NO_TEST_TIMEOUT=False 2025-01-24T01:08:30.6105595Z TD_DISTRIBUTED=False 2025-01-24T01:08:30.6105867Z GITHUB_REPOSITORY=pytorch/pytorch 2025-01-24T01:08:30.6106277Z GITHUB_RETENTION_DAYS=90 2025-01-24T01:08:30.6106568Z OPENSSL_DIR=/opt/openssl 2025-01-24T01:08:30.6106857Z GITHUB_ACTION_REPOSITORY= 2025-01-24T01:08:30.6107675Z PATH=/opt/cache/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/opt/conda/envs/py_3.13/bin:/opt/conda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin 2025-01-24T01:08:30.6108522Z GITHUB_BASE_REF=main 2025-01-24T01:08:30.6108776Z INSTALLED_ACL= 2025-01-24T01:08:30.6109185Z ARTIFACTS_FILE_SUFFIX=test-dynamo_wrapped-1-3-lf.linux.2xlarge_36096586663 2025-01-24T01:08:30.6109654Z CI=true 2025-01-24T01:08:30.6109896Z GITHUB_REPOSITORY_OWNER=pytorch 2025-01-24T01:08:30.6110200Z JOB_ID=36096586663 2025-01-24T01:08:30.6110444Z INSTALLED_PROTOBUF=yes 2025-01-24T01:08:30.6110732Z GITHUB_HEAD_REF=export-D68294638 2025-01-24T01:08:30.6111034Z GITHUB_ACTION_REF= 2025-01-24T01:08:30.6111343Z SCCACHE_BUCKET=ossci-compiler-cache-circleci-v2 2025-01-24T01:08:30.6111711Z TEST_SHOWLOCALS=False 2025-01-24T01:08:30.6111989Z GITHUB_WORKFLOW=pull 2025-01-24T01:08:30.6112269Z DEBIAN_FRONTEND=noninteractive 2025-01-24T01:08:30.6112927Z GITHUB_OUTPUT=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/set_output_371e197a-5858-428d-a0d1-2a5f66bf0667 2025-01-24T01:08:30.6113597Z NO_TD=False 2025-01-24T01:08:30.6113835Z SKIP_SCCACHE_INITIALIZATION=1 2025-01-24T01:08:30.6114134Z _=/usr/bin/env 2025-01-24T01:08:30.6114478Z ++ python -c 'import site; print(site.getsitepackages()[0])' 2025-01-24T01:08:30.6209431Z + TORCH_INSTALL_DIR=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch 2025-01-24T01:08:30.6210504Z + TORCH_BIN_DIR=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/bin 2025-01-24T01:08:30.6211437Z + TORCH_LIB_DIR=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib 2025-01-24T01:08:30.6212298Z + TORCH_TEST_DIR=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/test 2025-01-24T01:08:30.6213004Z + BUILD_DIR=build 2025-01-24T01:08:30.6213413Z + BUILD_RENAMED_DIR=build_renamed 2025-01-24T01:08:30.6213976Z + BUILD_BIN_DIR=build/bin 2025-01-24T01:08:30.6214480Z + SHARD_NUMBER=1 2025-01-24T01:08:30.6214854Z + NUM_TEST_SHARDS=3 2025-01-24T01:08:30.6215315Z + export VALGRIND=ON 2025-01-24T01:08:30.6215722Z + VALGRIND=ON 2025-01-24T01:08:30.6216229Z + [[ linux-focal-py3.13-clang10 == *clang9* ]] 2025-01-24T01:08:30.6216715Z + [[ linux-focal-py3.13-clang10 == *xpu* ]] 2025-01-24T01:08:30.6217084Z + [[ linux-focal-py3.13-clang10 == *s390x* ]] 2025-01-24T01:08:30.6217429Z + [[ 0 == \1 ]] 2025-01-24T01:08:30.6217833Z + [[ False == \1 ]] 2025-01-24T01:08:30.6218120Z + [[ linux-focal-py3.13-clang10 != *bazel* ]] 2025-01-24T01:08:30.6218490Z ++ realpath build/custom_test_artifacts 2025-01-24T01:08:30.6236709Z + CUSTOM_TEST_ARTIFACT_BUILD_DIR=/var/lib/jenkins/workspace/build/custom_test_artifacts 2025-01-24T01:08:30.6237277Z + [[ -n '' ]] 2025-01-24T01:08:30.6237516Z + echo 'Environment variables' 2025-01-24T01:08:30.6237821Z Environment variables 2025-01-24T01:08:30.6238079Z + env 2025-01-24T01:08:30.6244207Z INSTALLED_DB=yes 2025-01-24T01:08:30.6244872Z GITHUB_WORKSPACE=/home/ec2-user/actions-runner/_work/pytorch/pytorch 2025-01-24T01:08:30.6245707Z CONTINUE_THROUGH_ERROR=False 2025-01-24T01:08:30.6246266Z BUILD_ENVIRONMENT=linux-focal-py3.13-clang10 2025-01-24T01:08:30.6246660Z HOSTNAME=8336c83c461c 2025-01-24T01:08:30.6247501Z GITHUB_PATH=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/add_path_371e197a-5858-428d-a0d1-2a5f66bf0667 2025-01-24T01:08:30.6248426Z GITHUB_ACTION=__self 2025-01-24T01:08:30.6248715Z PYTORCH_TEST_CUDA_MEM_LEAK_CHECK=0 2025-01-24T01:08:30.6249040Z GITHUB_RUN_NUMBER=284905 2025-01-24T01:08:30.6249325Z TEST_CONFIG=dynamo_wrapped 2025-01-24T01:08:30.6249629Z GITHUB_REPOSITORY_OWNER_ID=21003710 2025-01-24T01:08:30.6249963Z TORCH_NVCC_FLAGS=-Xfatbin -compress-all 2025-01-24T01:08:30.6250293Z IS_A100_RUNNER=0 2025-01-24T01:08:30.6250760Z SCRIBE_GRAPHQL_ACCESS_TOKEN=*** 2025-01-24T01:08:30.6251099Z GITHUB_TRIGGERING_ACTOR=facebook-github-bot 2025-01-24T01:08:30.6251460Z GITHUB_REF_TYPE=branch 2025-01-24T01:08:30.6251728Z TORCH_CUDA_ARCH_LIST=Maxwell 2025-01-24T01:08:30.6252065Z BASE_SHA=b2c89bc115123aea8e075e882ee121537ec92f89 2025-01-24T01:08:30.6252428Z XLA_CUDA= 2025-01-24T01:08:30.6252780Z HUGGING_FACE_HUB_TOKEN=*** 2025-01-24T01:08:30.6253192Z *** 2025-01-24T01:08:30.6253437Z GITHUB_REPOSITORY_ID=65600975 2025-01-24T01:08:30.6253732Z GITHUB_ACTIONS=true 2025-01-24T01:08:30.6254024Z SHA1=d087145b9259b4cfbcb758dc0bca6a6e5d75d08f 2025-01-24T01:08:30.6254435Z GITHUB_SHA=74e42e7401e3b57fc188c6d3823585f63c780e87 2025-01-24T01:08:30.6254994Z GITHUB_WORKFLOW_REF=pytorch/pytorch/.github/workflows/pull.yml@refs/pull/145539/merge 2025-01-24T01:08:30.6255512Z UCC_HOME=/usr 2025-01-24T01:08:30.6255741Z VERBOSE_TEST_LOGS=False 2025-01-24T01:08:30.6256018Z GITHUB_REF=refs/pull/145539/merge 2025-01-24T01:08:30.6256318Z SHARD_NUMBER=1 2025-01-24T01:08:30.6256570Z GITHUB_REF_PROTECTED=false 2025-01-24T01:08:30.6256851Z HOME=/var/lib/jenkins 2025-01-24T01:08:30.6257140Z GITHUB_API_URL=https://api.github.com 2025-01-24T01:08:30.6257488Z PYTORCH_TEST_RERUN_DISABLED_TESTS=0 2025-01-24T01:08:30.6257796Z UCX_COMMIT= 2025-01-24T01:08:30.6258026Z NUM_TEST_SHARDS=3 2025-01-24T01:08:30.6258269Z UCX_HOME=/usr 2025-01-24T01:08:30.6258844Z GITHUB_STATE=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/save_state_371e197a-5858-428d-a0d1-2a5f66bf0667 2025-01-24T01:08:30.6259693Z JOB_NAME=linux-focal-py3.13-clang10 / test (dynamo_wrapped, 1, 3, lf.linux.2xlarge) 2025-01-24T01:08:30.6260523Z GITHUB_ENV=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/set_env_371e197a-5858-428d-a0d1-2a5f66bf0667 2025-01-24T01:08:30.6261364Z GITHUB_EVENT_PATH=/home/ec2-user/actions-runner/_work/_temp/_github_workflow/event.json 2025-01-24T01:08:30.6261904Z GITHUB_EVENT_NAME=pull_request 2025-01-24T01:08:30.6262196Z DASHBOARD_TAG= 2025-01-24T01:08:30.6262449Z GITHUB_RUN_ID=12940916563 2025-01-24T01:08:30.6263115Z GITHUB_STEP_SUMMARY=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/step_summary_371e197a-5858-428d-a0d1-2a5f66bf0667 2025-01-24T01:08:30.6263843Z GITHUB_ACTOR=facebook-github-bot 2025-01-24T01:08:30.6264158Z PR_NUMBER=145539 2025-01-24T01:08:30.6264400Z DESIRED_CUDA= 2025-01-24T01:08:30.6264649Z GITHUB_RUN_ATTEMPT=1 2025-01-24T01:08:30.6264917Z VALGRIND=ON 2025-01-24T01:08:30.6265167Z ANACONDA_PYTHON_VERSION=3.13 2025-01-24T01:08:30.6265527Z GITHUB_GRAPHQL_URL=https://api.github.com/graphql 2025-01-24T01:08:30.6265894Z TERM=vt100 2025-01-24T01:08:30.6266131Z INSTALLED_VISION=yes 2025-01-24T01:08:30.6266589Z BRANCH=pull/145539 2025-01-24T01:08:30.6266865Z SCCACHE_REGION=us-east-1 2025-01-24T01:08:30.6267164Z OPENSSL_ROOT_DIR=/opt/openssl 2025-01-24T01:08:30.6267464Z CUDA_PATH=/usr/local/cuda 2025-01-24T01:08:30.6268026Z GITHUB_ACTION_PATH=/home/ec2-user/actions-runner/_work/pytorch/pytorch/./.github/actions/setup-linux 2025-01-24T01:08:30.6268645Z GITHUB_SERVER_URL=https://github.com 2025-01-24T01:08:30.6268967Z UCC_COMMIT= 2025-01-24T01:08:30.6269200Z REENABLED_ISSUES= 2025-01-24T01:08:30.6269429Z DOCS= 2025-01-24T01:08:30.6269638Z SHLVL=1 2025-01-24T01:08:30.6269852Z MAX_JOBS=6 2025-01-24T01:08:30.6270082Z GITHUB_ACTOR_ID=6422482 2025-01-24T01:08:30.6270488Z GITHUB_WORKFLOW_SHA=74e42e7401e3b57fc188c6d3823585f63c780e87 2025-01-24T01:08:30.6270923Z GITHUB_REF_NAME=145539/merge 2025-01-24T01:08:30.6271346Z XLA_CLANG_CACHE_S3_BUCKET_NAME=ossci-compiler-clang-cache-circleci-xla 2025-01-24T01:08:30.6271884Z GITHUB_JOB=test 2025-01-24T01:08:30.6272136Z NO_TEST_TIMEOUT=False 2025-01-24T01:08:30.6272408Z TD_DISTRIBUTED=False 2025-01-24T01:08:30.6272684Z GITHUB_REPOSITORY=pytorch/pytorch 2025-01-24T01:08:30.6273003Z GITHUB_RETENTION_DAYS=90 2025-01-24T01:08:30.6273286Z OPENSSL_DIR=/opt/openssl 2025-01-24T01:08:30.6273570Z GITHUB_ACTION_REPOSITORY= 2025-01-24T01:08:30.6274361Z PATH=/opt/cache/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/opt/conda/envs/py_3.13/bin:/opt/conda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin 2025-01-24T01:08:30.6275191Z GITHUB_BASE_REF=main 2025-01-24T01:08:30.6275452Z INSTALLED_ACL= 2025-01-24T01:08:30.6275847Z ARTIFACTS_FILE_SUFFIX=test-dynamo_wrapped-1-3-lf.linux.2xlarge_36096586663 2025-01-24T01:08:30.6276309Z CI=true 2025-01-24T01:08:30.6276544Z GITHUB_REPOSITORY_OWNER=pytorch 2025-01-24T01:08:30.6276826Z JOB_ID=36096586663 2025-01-24T01:08:30.6277078Z INSTALLED_PROTOBUF=yes 2025-01-24T01:08:30.6277359Z GITHUB_HEAD_REF=export-D68294638 2025-01-24T01:08:30.6277664Z GITHUB_ACTION_REF= 2025-01-24T01:08:30.6277963Z SCCACHE_BUCKET=ossci-compiler-cache-circleci-v2 2025-01-24T01:08:30.6278337Z TEST_SHOWLOCALS=False 2025-01-24T01:08:30.6278603Z GITHUB_WORKFLOW=pull 2025-01-24T01:08:30.6278889Z DEBIAN_FRONTEND=noninteractive 2025-01-24T01:08:30.6279734Z GITHUB_OUTPUT=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/set_output_371e197a-5858-428d-a0d1-2a5f66bf0667 2025-01-24T01:08:30.6280401Z NO_TD=False 2025-01-24T01:08:30.6280636Z SKIP_SCCACHE_INITIALIZATION=1 2025-01-24T01:08:30.6280936Z _=/usr/bin/env 2025-01-24T01:08:30.6281273Z + echo 'Testing pytorch' 2025-01-24T01:08:30.6281652Z Testing pytorch 2025-01-24T01:08:30.6281893Z + export LANG=C.UTF-8 2025-01-24T01:08:30.6282151Z + LANG=C.UTF-8 2025-01-24T01:08:30.6330564Z + PR_NUMBER=145539 2025-01-24T01:08:30.6331029Z + [[ dynamo_wrapped == \d\e\f\a\u\l\t ]] 2025-01-24T01:08:30.6331607Z + [[ dynamo_wrapped == \d\i\s\t\r\i\b\u\t\e\d ]] 2025-01-24T01:08:30.6332099Z + [[ dynamo_wrapped == \s\l\o\w ]] 2025-01-24T01:08:30.6332663Z + [[ linux-focal-py3.13-clang10 == *slow-gradcheck* ]] 2025-01-24T01:08:30.6333264Z + [[ linux-focal-py3.13-clang10 == *cuda* ]] 2025-01-24T01:08:30.6333780Z + [[ linux-focal-py3.13-clang10 == *rocm* ]] 2025-01-24T01:08:30.6334258Z + [[ linux-focal-py3.13-clang10 == *xpu* ]] 2025-01-24T01:08:30.6334886Z + [[ dynamo_wrapped == *crossref* ]] 2025-01-24T01:08:30.6335313Z + [[ linux-focal-py3.13-clang10 == *rocm* ]] 2025-01-24T01:08:30.6335676Z + [[ linux-focal-py3.13-clang10 == *xpu* ]] 2025-01-24T01:08:30.6336067Z + [[ linux-focal-py3.13-clang10 != *-bazel-* ]] 2025-01-24T01:08:30.6336444Z + pip_install --user ninja==1.10.2 2025-01-24T01:08:30.6336858Z + pip_install_pkg='python3 -m pip install --progress-bar off' 2025-01-24T01:08:30.6337365Z + python3 -m pip install --progress-bar off --user ninja==1.10.2 2025-01-24T01:08:31.0734617Z Collecting ninja==1.10.2 2025-01-24T01:08:31.0913997Z Downloading ninja-1.10.2-py2.py3-none-manylinux_2_5_x86_64.manylinux1_x86_64.whl.metadata (5.0 kB) 2025-01-24T01:08:31.1033801Z Downloading ninja-1.10.2-py2.py3-none-manylinux_2_5_x86_64.manylinux1_x86_64.whl (108 kB) 2025-01-24T01:08:31.2763786Z Installing collected packages: ninja 2025-01-24T01:08:31.2842161Z  WARNING: The script ninja is installed in '/var/lib/jenkins/.local/bin' which is not on PATH. 2025-01-24T01:08:31.2843178Z Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location. 2025-01-24T01:08:31.2909942Z Successfully installed ninja-1.10.2 2025-01-24T01:08:31.3648645Z + export PATH=/var/lib/jenkins/.local/bin:/opt/cache/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/opt/conda/envs/py_3.13/bin:/opt/conda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin 2025-01-24T01:08:31.3650299Z + PATH=/var/lib/jenkins/.local/bin:/opt/cache/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/opt/conda/envs/py_3.13/bin:/opt/conda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin 2025-01-24T01:08:31.3651302Z + [[ linux-focal-py3.13-clang10 == *aarch64* ]] 2025-01-24T01:08:31.3651974Z + install_tlparse 2025-01-24T01:08:31.3652240Z + pip_install --user tlparse==0.3.30 2025-01-24T01:08:31.3652685Z + pip_install_pkg='python3 -m pip install --progress-bar off' 2025-01-24T01:08:31.3653208Z + python3 -m pip install --progress-bar off --user tlparse==0.3.30 2025-01-24T01:08:31.7474453Z Collecting tlparse==0.3.30 2025-01-24T01:08:31.7644534Z Downloading tlparse-0.3.30-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (1.9 kB) 2025-01-24T01:08:31.7743066Z Downloading tlparse-0.3.30-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.3 MB) 2025-01-24T01:08:31.9521554Z Installing collected packages: tlparse 2025-01-24T01:08:31.9895572Z Successfully installed tlparse-0.3.30 2025-01-24T01:08:32.0625542Z ++ python -m site --user-base 2025-01-24T01:08:32.0772752Z + 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_3.13/bin:/opt/conda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin 2025-01-24T01:08:32.0774449Z + [[ linux-focal-py3.13-clang10 == *asan* ]] 2025-01-24T01:08:32.0774962Z + [[ linux-focal-py3.13-clang10 == *-debug* ]] 2025-01-24T01:08:32.0775344Z + [[ linux-focal-py3.13-clang10 != *-bazel-* ]] 2025-01-24T01:08:32.0775883Z + echo 'We are not in debug mode: linux-focal-py3.13-clang10. Expect the assertion to pass' 2025-01-24T01:08:32.0776587Z We are not in debug mode: linux-focal-py3.13-clang10. Expect the assertion to pass 2025-01-24T01:08:32.0777595Z + cd test 2025-01-24T01:08:32.0778539Z + python -c 'import torch; torch._C._crash_if_debug_asserts_fail(424242)' 2025-01-24T01:08:33.7957494Z + [[ dynamo_wrapped == \n\o\g\p\u\_\N\O\_\A\V\X\2 ]] 2025-01-24T01:08:33.7958278Z + [[ dynamo_wrapped == \n\o\g\p\u\_\A\V\X\5\1\2 ]] 2025-01-24T01:08:33.7963626Z + DYNAMO_BENCHMARK_FLAGS=() 2025-01-24T01:08:33.7965321Z + [[ dynamo_wrapped == *pr_time_benchmarks* ]] 2025-01-24T01:08:33.7966003Z + [[ dynamo_wrapped == *dynamo_eager* ]] 2025-01-24T01:08:33.7966683Z + [[ dynamo_wrapped == *aot_eager* ]] 2025-01-24T01:08:33.7967315Z + [[ dynamo_wrapped == *aot_inductor* ]] 2025-01-24T01:08:33.7967940Z + [[ dynamo_wrapped == *inductor* ]] 2025-01-24T01:08:33.7968540Z + [[ dynamo_wrapped == *dynamic* ]] 2025-01-24T01:08:33.7969116Z + [[ dynamo_wrapped == *cpu* ]] 2025-01-24T01:08:33.7969705Z + DYNAMO_BENCHMARK_FLAGS+=(--device cuda) 2025-01-24T01:08:33.8000813Z + [[ linux-focal-py3.13-clang10 == *libtorch* ]] 2025-01-24T01:08:33.8001272Z + [[ linux-focal-py3.13-clang10 == *-bazel-* ]] 2025-01-24T01:08:33.8004016Z + cd test 2025-01-24T01:08:33.8004446Z + python -c 'import torch; print(torch.__config__.show())' 2025-01-24T01:08:34.9826348Z PyTorch built with: 2025-01-24T01:08:34.9826778Z - GCC 4.2 2025-01-24T01:08:34.9827029Z - C++ Version: 201703 2025-01-24T01:08:34.9827288Z - clang 10.0.0 2025-01-24T01:08:34.9827858Z - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications 2025-01-24T01:08:34.9828930Z - Intel(R) MKL-DNN v3.5.3 (Git Hash 66f0cb9eb66affd2da3bf5f8d897376f04aae6af) 2025-01-24T01:08:34.9829406Z - OpenMP 201511 (a.k.a. OpenMP 4.5) 2025-01-24T01:08:34.9829766Z - LAPACK is enabled (usually provided by MKL) 2025-01-24T01:08:34.9830114Z - NNPACK is enabled 2025-01-24T01:08:34.9830396Z - CPU capability usage: AVX512 2025-01-24T01:08:34.9835798Z - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, COMMIT_SHA=d087145b9259b4cfbcb758dc0bca6a6e5d75d08f, 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-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 -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, TORCH_VERSION=2.7.0, 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, 2025-01-24T01:08:34.9841419Z 2025-01-24T01:08:35.2319935Z + cd test 2025-01-24T01:08:35.2320392Z + python -c 'import torch; print(torch.__config__.parallel_info())' 2025-01-24T01:08:36.3868054Z ATen/Parallel: 2025-01-24T01:08:36.3868460Z at::get_num_threads() : 4 2025-01-24T01:08:36.3868772Z at::get_num_interop_threads() : 4 2025-01-24T01:08:36.3869103Z OpenMP 201511 (a.k.a. OpenMP 4.5) 2025-01-24T01:08:36.3869448Z omp_get_max_threads() : 4 2025-01-24T01:08:36.3870051Z Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications 2025-01-24T01:08:36.3870677Z mkl_get_max_threads() : 4 2025-01-24T01:08:36.3871073Z Intel(R) MKL-DNN v3.5.3 (Git Hash 66f0cb9eb66affd2da3bf5f8d897376f04aae6af) 2025-01-24T01:08:36.3871541Z std::thread::hardware_concurrency() : 8 2025-01-24T01:08:36.3871878Z Environment variables: 2025-01-24T01:08:36.3872156Z OMP_NUM_THREADS : [not set] 2025-01-24T01:08:36.3872452Z MKL_NUM_THREADS : [not set] 2025-01-24T01:08:36.3872753Z ATen parallel backend: OpenMP 2025-01-24T01:08:36.3872962Z 2025-01-24T01:08:36.6074744Z + [[ dynamo_wrapped == *numpy_2* ]] 2025-01-24T01:08:36.6075493Z + [[ linux-focal-py3.13-clang10 == *aarch64* ]] 2025-01-24T01:08:36.6075875Z + [[ dynamo_wrapped == *backward* ]] 2025-01-24T01:08:36.6076206Z + [[ dynamo_wrapped == *xla* ]] 2025-01-24T01:08:36.6076527Z + [[ dynamo_wrapped == *executorch* ]] 2025-01-24T01:08:36.6076909Z + [[ dynamo_wrapped == \j\i\t\_\l\e\g\a\c\y ]] 2025-01-24T01:08:36.6077295Z + [[ linux-focal-py3.13-clang10 == *libtorch* ]] 2025-01-24T01:08:36.6077677Z + [[ dynamo_wrapped == distributed ]] 2025-01-24T01:08:36.6078040Z + [[ dynamo_wrapped == *inductor_distributed* ]] 2025-01-24T01:08:36.6078503Z + [[ dynamo_wrapped == *inductor-halide* ]] 2025-01-24T01:08:36.6078937Z + [[ dynamo_wrapped == *inductor-triton-cpu* ]] 2025-01-24T01:08:36.6079796Z + [[ dynamo_wrapped == *inductor-micro-benchmark* ]] 2025-01-24T01:08:36.6080291Z + [[ dynamo_wrapped == *huggingface* ]] 2025-01-24T01:08:36.6080634Z + [[ dynamo_wrapped == *timm* ]] 2025-01-24T01:08:36.6080954Z + [[ dynamo_wrapped == *torchbench* ]] 2025-01-24T01:08:36.6081303Z + [[ dynamo_wrapped == *inductor_cpp_wrapper* ]] 2025-01-24T01:08:36.6081670Z + [[ dynamo_wrapped == *inductor* ]] 2025-01-24T01:08:36.6082006Z + [[ dynamo_wrapped == *dynamo_wrapped* ]] 2025-01-24T01:08:36.6082340Z + install_torchvision 2025-01-24T01:08:36.6082619Z + local orig_preload 2025-01-24T01:08:36.6083147Z + local commit 2025-01-24T01:08:36.6083418Z ++ get_pinned_commit vision 2025-01-24T01:08:36.6083732Z ++ cat .github/ci_commit_pins/vision.txt 2025-01-24T01:08:36.6099203Z + commit=d23a6e1664d20707c11781299611436e1f0c104f 2025-01-24T01:08:36.6099768Z + orig_preload= 2025-01-24T01:08:36.6100141Z + '[' -n '' ']' 2025-01-24T01:08:36.6101044Z + pip_install --no-use-pep517 --user git+https://github.com/pytorch/vision.git@d23a6e1664d20707c11781299611436e1f0c104f 2025-01-24T01:08:36.6101802Z + pip_install_pkg='python3 -m pip install --progress-bar off' 2025-01-24T01:08:36.6102650Z + python3 -m pip install --progress-bar off --no-use-pep517 --user git+https://github.com/pytorch/vision.git@d23a6e1664d20707c11781299611436e1f0c104f 2025-01-24T01:08:36.9372089Z Collecting git+https://github.com/pytorch/vision.git@d23a6e1664d20707c11781299611436e1f0c104f 2025-01-24T01:08:36.9376185Z Cloning https://github.com/pytorch/vision.git (to revision d23a6e1664d20707c11781299611436e1f0c104f) to /tmp/pip-req-build-oc1uf543 2025-01-24T01:08:36.9394976Z Running command git clone --filter=blob:none --quiet https://github.com/pytorch/vision.git /tmp/pip-req-build-oc1uf543 2025-01-24T01:08:38.3634896Z Running command git rev-parse -q --verify 'sha^d23a6e1664d20707c11781299611436e1f0c104f' 2025-01-24T01:08:38.3652958Z Running command git fetch -q https://github.com/pytorch/vision.git d23a6e1664d20707c11781299611436e1f0c104f 2025-01-24T01:08:39.7308406Z Running command git checkout -q d23a6e1664d20707c11781299611436e1f0c104f 2025-01-24T01:08:40.0511451Z Resolved https://github.com/pytorch/vision.git to commit d23a6e1664d20707c11781299611436e1f0c104f 2025-01-24T01:08:42.1330625Z Preparing metadata (setup.py) ... [?25l- \ done 2025-01-24T01:08:42.1362887Z [?25hRequirement already satisfied: numpy in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torchvision==0.19.0a0+d23a6e1) (2.1.2) 2025-01-24T01:08:42.1366241Z Requirement already satisfied: torch in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torchvision==0.19.0a0+d23a6e1) (2.7.0a0+gitd087145) 2025-01-24T01:08:42.1370395Z Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torchvision==0.19.0a0+d23a6e1) (11.0.0) 2025-01-24T01:08:42.1436304Z Requirement already satisfied: filelock in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch->torchvision==0.19.0a0+d23a6e1) (3.16.1) 2025-01-24T01:08:42.1439902Z Requirement already satisfied: typing-extensions>=4.10.0 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch->torchvision==0.19.0a0+d23a6e1) (4.12.2) 2025-01-24T01:08:42.1442833Z Requirement already satisfied: networkx in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch->torchvision==0.19.0a0+d23a6e1) (2.8.8) 2025-01-24T01:08:42.1445576Z Requirement already satisfied: jinja2 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch->torchvision==0.19.0a0+d23a6e1) (3.1.5) 2025-01-24T01:08:42.1448461Z Requirement already satisfied: fsspec in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch->torchvision==0.19.0a0+d23a6e1) (2024.10.0) 2025-01-24T01:08:42.1459100Z Requirement already satisfied: setuptools in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch->torchvision==0.19.0a0+d23a6e1) (75.1.0) 2025-01-24T01:08:42.1463800Z Requirement already satisfied: sympy==1.13.1 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch->torchvision==0.19.0a0+d23a6e1) (1.13.1) 2025-01-24T01:08:42.1476599Z Requirement already satisfied: mpmath<1.4,>=1.1.0 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from sympy==1.13.1->torch->torchvision==0.19.0a0+d23a6e1) (1.3.0) 2025-01-24T01:08:42.1577589Z Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from jinja2->torch->torchvision==0.19.0a0+d23a6e1) (3.0.2) 2025-01-24T01:08:42.1673337Z Building wheels for collected packages: torchvision 2025-01-24T01:09:48.8784924Z Building wheel for torchvision (setup.py) ... [?25l- \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | done 2025-01-24T01:09:48.8823303Z [?25h Created wheel for torchvision: filename=torchvision-0.19.0a0+d23a6e1-cp313-cp313-linux_x86_64.whl size=1116819 sha256=e142024cc7c726012a14851694a053bf60f8d606799e542a673fb3b1c487472f 2025-01-24T01:09:48.8825291Z Stored in directory: /var/lib/jenkins/.cache/pip/wheels/81/60/73/f2acb628a45eebe28dd9ff5468e774a0d5e194728570f8ff6f 2025-01-24T01:09:48.8859919Z Successfully built torchvision 2025-01-24T01:09:49.0204389Z Installing collected packages: torchvision 2025-01-24T01:09:49.4724684Z Successfully installed torchvision-0.19.0a0+d23a6e1 2025-01-24T01:09:49.5756527Z + '[' -n '' ']' 2025-01-24T01:09:49.5756977Z + test_dynamo_wrapped_shard 1 2025-01-24T01:09:49.5757398Z + [[ -z 3 ]] 2025-01-24T01:09:49.5757765Z + python tools/dynamo/verify_dynamo.py 2025-01-24T01:09:50.7121362Z Python version: 3.13.1 2025-01-24T01:09:50.7121777Z `torch` version: 2.7.0a0+gitd087145 2025-01-24T01:09:50.7122111Z CUDA version: None 2025-01-24T01:09:50.7122369Z ROCM version: None 2025-01-24T01:09:50.7122522Z 2025-01-24T01:09:50.7123076Z /var/lib/jenkins/workspace/tools/dynamo/verify_dynamo.py:220: UserWarning: Dynamo not yet supported in Python 3.13. Skipping check. 2025-01-24T01:09:50.7123977Z warnings.warn("Dynamo not yet supported in Python 3.13. Skipping check.") 2025-01-24T01:09:50.7124459Z All required checks passed 2025-01-24T01:09:50.9379106Z + python test/run_test.py --dynamo --exclude-inductor-tests --exclude-jit-executor --exclude-distributed-tests --exclude-torch-export-tests --exclude-aot-dispatch-tests --shard 1 3 --verbose --upload-artifacts-while-running 2025-01-24T01:09:51.0435643Z /var/lib/jenkins/workspace/test/run_test.py:24: DeprecationWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html 2025-01-24T01:09:51.0436608Z import pkg_resources 2025-01-24T01:09:55.6591968Z Downloading https://ossci-metrics.s3.amazonaws.com/disabled-tests-condensed.json to /var/lib/jenkins/workspace/test/.pytorch-disabled-tests.json 2025-01-24T01:09:55.9503953Z Ignoring disabled issues: [''] 2025-01-24T01:09:55.9662550Z Found test times from artifacts 2025-01-24T01:09:56.0296990Z Found test times from artifacts 2025-01-24T01:09:56.0319819Z Running all tests 2025-01-24T01:09:56.0432707Z Running parallel tests on 3 processes 2025-01-24T01:09:56.0436285Z Name: tests to run (est. time: 61.37min) 2025-01-24T01:09:56.0436877Z Serial tests (24): 2025-01-24T01:09:56.0437319Z test_transformers 1/2 2025-01-24T01:09:56.0437596Z test_utils 1/1 2025-01-24T01:09:56.0437832Z test_torch 1/1 2025-01-24T01:09:56.0438153Z test_nn 2/2 2025-01-24T01:09:56.0438487Z test_show_pickle 1/1 2025-01-24T01:09:56.0438768Z test_fake_tensor 1/1 2025-01-24T01:09:56.0439051Z test_multiprocessing 1/1 2025-01-24T01:09:56.0439364Z test_autocast 1/1 2025-01-24T01:09:56.0439651Z test_tensor_creation_ops 1/1 2025-01-24T01:09:56.0440008Z test_extension_utils 1/1 2025-01-24T01:09:56.0440466Z test_mobile_optimizer 1/1 2025-01-24T01:09:56.0440955Z test_multiprocessing_spawn 1/1 2025-01-24T01:09:56.0441345Z test_spectral_ops 1/1 2025-01-24T01:09:56.0441646Z test_cuda_nvml_based_avail 1/1 2025-01-24T01:09:56.0442249Z test_cuda_primary_ctx 1/1 2025-01-24T01:09:56.0442822Z distributions/test_distributions 2/2 2025-01-24T01:09:56.0443167Z test_reductions 2/3 2025-01-24T01:09:56.0443439Z test_reductions 3/3 2025-01-24T01:09:56.0443705Z test_overrides 1/1 2025-01-24T01:09:56.0443991Z test_cpp_extensions_aot_ninja 1/1 2025-01-24T01:09:56.0444336Z test_cpp_extensions_aot_no_ninja 1/1 2025-01-24T01:09:56.0444651Z doctests 1/1 2025-01-24T01:09:56.0444908Z test_autoload_disable 1/1 2025-01-24T01:09:56.0445218Z test_autoload_enable 1/1 2025-01-24T01:09:56.0445528Z Parallel tests (34): 2025-01-24T01:09:56.0445803Z dynamo/test_minifier 1/1 2025-01-24T01:09:56.0446691Z dynamo/test_modules 1/1 2025-01-24T01:09:56.0446998Z dynamo/test_backends 1/1 2025-01-24T01:09:56.0447299Z dynamo/test_cudagraphs 1/1 2025-01-24T01:09:56.0447605Z dynamo/test_unspec 1/1 2025-01-24T01:09:56.0447892Z dynamo/test_config 1/1 2025-01-24T01:09:56.0448163Z dynamo/test_global 1/1 2025-01-24T01:09:56.0448454Z dynamo/test_subgraphs 1/1 2025-01-24T01:09:56.0448753Z dynamo/test_export 1/1 2025-01-24T01:09:56.0449052Z dynamo/test_verify_correctness 1/1 2025-01-24T01:09:56.0449386Z dynamo/test_subclasses 1/1 2025-01-24T01:09:56.0449675Z dynamo/test_comptime 1/1 2025-01-24T01:09:56.0449970Z dynamo/test_utils 1/1 2025-01-24T01:09:56.0450252Z dynamo/test_debug_utils 1/1 2025-01-24T01:09:56.0450551Z dynamo/test_compile 1/1 2025-01-24T01:09:56.0450857Z dynamo/test_graph_region_tracker 1/1 2025-01-24T01:09:56.0451186Z dynamo/test_structured_trace 1/1 2025-01-24T01:09:56.0451617Z dynamo/test_sources 1/1 2025-01-24T01:09:56.0451926Z dynamo/test_model_output 1/1 2025-01-24T01:09:56.0452276Z dynamo/test_compiler_bisector 1/1 2025-01-24T01:09:56.0452616Z dynamo/test_bytecode_utils 1/1 2025-01-24T01:09:56.0452928Z dynamo/test_torchrec 1/1 2025-01-24T01:09:56.0453234Z dynamo/test_trace_rules 1/1 2025-01-24T01:09:56.0453550Z dynamo/test_guard_manager 1/1 2025-01-24T01:09:56.0453869Z test_tensorboard 1/1 2025-01-24T01:09:56.0454151Z test_view_ops 1/1 2025-01-24T01:09:56.0454405Z test_mkldnn 1/1 2025-01-24T01:09:56.0454696Z test_functional_autograd_benchmark 1/1 2025-01-24T01:09:56.0455047Z test_unary_ufuncs 1/1 2025-01-24T01:09:56.0455340Z test_jiterator 1/1 2025-01-24T01:09:56.0455612Z xpu/test_gemm 1/1 2025-01-24T01:09:56.0455870Z test_matmul_cuda 1/1 2025-01-24T01:09:56.0456158Z optim/test_lrscheduler 1/1 2025-01-24T01:09:56.0456462Z optim/test_optim 1/1 2025-01-24T01:09:56.0456757Z Name: excluded (est. time: 0.0min) 2025-01-24T01:09:56.0457063Z Serial tests (0): 2025-01-24T01:09:56.0457336Z Parallel tests (0): 2025-01-24T01:09:56.0552310Z Running test_transformers 1/2 ... [2025-01-24 01:09:56.054909] 2025-01-24T01:09:57.6687028Z running install 2025-01-24T01:09:57.6688437Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/setuptools/_distutils/cmd.py:66: SetuptoolsDeprecationWarning: setup.py install is deprecated. 2025-01-24T01:09:57.6689849Z !! 2025-01-24T01:09:57.6690069Z 2025-01-24T01:09:57.6690285Z ******************************************************************************** 2025-01-24T01:09:57.6690956Z Please avoid running ``setup.py`` directly. 2025-01-24T01:09:57.6691660Z Instead, use pypa/build, pypa/installer or other 2025-01-24T01:09:57.6692333Z standards-based tools. 2025-01-24T01:09:57.6692663Z 2025-01-24T01:09:57.6693169Z See https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html for details. 2025-01-24T01:09:57.6694157Z ******************************************************************************** 2025-01-24T01:09:57.6694577Z 2025-01-24T01:09:57.6694714Z !! 2025-01-24T01:09:57.6695072Z self.initialize_options() 2025-01-24T01:09:57.6822268Z running build 2025-01-24T01:09:57.6823375Z running build_py 2025-01-24T01:09:57.6902402Z creating build/lib.linux-x86_64-cpython-313/pytorch_openreg 2025-01-24T01:09:57.6904297Z copying pytorch_openreg/__init__.py -> build/lib.linux-x86_64-cpython-313/pytorch_openreg 2025-01-24T01:09:57.6912689Z copying pytorch_openreg/_aten_impl.py -> build/lib.linux-x86_64-cpython-313/pytorch_openreg 2025-01-24T01:09:57.6920858Z copying pytorch_openreg/_device_daemon.py -> build/lib.linux-x86_64-cpython-313/pytorch_openreg 2025-01-24T01:09:57.6929371Z copying pytorch_openreg/_meta_parser.py -> build/lib.linux-x86_64-cpython-313/pytorch_openreg 2025-01-24T01:09:57.6941709Z running build_ext 2025-01-24T01:09:57.8072136Z building 'pytorch_openreg._C' extension 2025-01-24T01:09:57.8074838Z creating /var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/build/temp.linux-x86_64-cpython-313/var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/pytorch_openreg/csrc 2025-01-24T01:09:57.8398812Z Emitting ninja build file /var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/build/temp.linux-x86_64-cpython-313/build.ninja... 2025-01-24T01:09:57.8400396Z Compiling objects... 2025-01-24T01:09:57.8400976Z Using envvar MAX_JOBS (6) as the number of workers... 2025-01-24T01:09:58.3773940Z [1/3] c++ -MMD -MF /var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/build/temp.linux-x86_64-cpython-313/var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/pytorch_openreg/csrc/Module.o.d -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -I/var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/pytorch_openreg/csrc -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/TH -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/THC -I/opt/conda/envs/py_3.13/include/python3.13 -c -c /var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/pytorch_openreg/csrc/Module.cpp -o /var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/build/temp.linux-x86_64-cpython-313/var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/pytorch_openreg/csrc/Module.o -g -Wall -Werror -DTORCH_API_INCLUDE_EXTENSION_H '-DPYBIND11_COMPILER_TYPE="_clang"' '-DPYBIND11_STDLIB="_libstdcpp"' '-DPYBIND11_BUILD_ABI="_cxxabi1002"' -DTORCH_EXTENSION_NAME=_C -D_GLIBCXX_USE_CXX11_ABI=1 -std=c++17 2025-01-24T01:09:58.3853360Z [2/3] c++ -MMD -MF /var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/build/temp.linux-x86_64-cpython-313/var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/pytorch_openreg/csrc/OpenRegHooks.o.d -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -I/var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/pytorch_openreg/csrc -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/TH -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/THC -I/opt/conda/envs/py_3.13/include/python3.13 -c -c /var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/pytorch_openreg/csrc/OpenRegHooks.cpp -o /var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/build/temp.linux-x86_64-cpython-313/var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/pytorch_openreg/csrc/OpenRegHooks.o -g -Wall -Werror -DTORCH_API_INCLUDE_EXTENSION_H '-DPYBIND11_COMPILER_TYPE="_clang"' '-DPYBIND11_STDLIB="_libstdcpp"' '-DPYBIND11_BUILD_ABI="_cxxabi1002"' -DTORCH_EXTENSION_NAME=_C -D_GLIBCXX_USE_CXX11_ABI=1 -std=c++17 2025-01-24T01:09:58.4222923Z [3/3] c++ -MMD -MF /var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/build/temp.linux-x86_64-cpython-313/var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/pytorch_openreg/csrc/OpenRegMem.o.d -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -I/var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/pytorch_openreg/csrc -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/TH -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/THC -I/opt/conda/envs/py_3.13/include/python3.13 -c -c /var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/pytorch_openreg/csrc/OpenRegMem.cpp -o /var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/build/temp.linux-x86_64-cpython-313/var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/pytorch_openreg/csrc/OpenRegMem.o -g -Wall -Werror -DTORCH_API_INCLUDE_EXTENSION_H '-DPYBIND11_COMPILER_TYPE="_clang"' '-DPYBIND11_STDLIB="_libstdcpp"' '-DPYBIND11_BUILD_ABI="_cxxabi1002"' -DTORCH_EXTENSION_NAME=_C -D_GLIBCXX_USE_CXX11_ABI=1 -std=c++17 2025-01-24T01:09:58.4278546Z g++ -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -pthread -B /opt/conda/envs/py_3.13/compiler_compat -shared -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib /var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/build/temp.linux-x86_64-cpython-313/var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/pytorch_openreg/csrc/Module.o /var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/build/temp.linux-x86_64-cpython-313/var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/pytorch_openreg/csrc/OpenRegHooks.o /var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/build/temp.linux-x86_64-cpython-313/var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/pytorch_openreg/csrc/OpenRegMem.o -L/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-313/pytorch_openreg/_C.so 2025-01-24T01:09:58.7119971Z running install_lib 2025-01-24T01:09:58.7204043Z creating install/opt/conda/envs/py_3.13/lib/python3.13/site-packages 2025-01-24T01:09:58.7207619Z creating install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/pytorch_openreg 2025-01-24T01:09:58.7209509Z copying build/lib.linux-x86_64-cpython-313/pytorch_openreg/__init__.py -> ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/pytorch_openreg 2025-01-24T01:09:58.7211728Z copying build/lib.linux-x86_64-cpython-313/pytorch_openreg/_aten_impl.py -> ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/pytorch_openreg 2025-01-24T01:09:58.7215530Z copying build/lib.linux-x86_64-cpython-313/pytorch_openreg/_device_daemon.py -> ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/pytorch_openreg 2025-01-24T01:09:58.7218595Z copying build/lib.linux-x86_64-cpython-313/pytorch_openreg/_meta_parser.py -> ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/pytorch_openreg 2025-01-24T01:09:58.7220899Z copying build/lib.linux-x86_64-cpython-313/pytorch_openreg/_C.so -> ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/pytorch_openreg 2025-01-24T01:09:58.7266580Z byte-compiling ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/pytorch_openreg/__init__.py to __init__.cpython-313.pyc 2025-01-24T01:09:58.7270724Z byte-compiling ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/pytorch_openreg/_aten_impl.py to _aten_impl.cpython-313.pyc 2025-01-24T01:09:58.7291310Z byte-compiling ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/pytorch_openreg/_device_daemon.py to _device_daemon.cpython-313.pyc 2025-01-24T01:09:58.7320675Z byte-compiling ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/pytorch_openreg/_meta_parser.py to _meta_parser.cpython-313.pyc 2025-01-24T01:09:58.7330274Z running install_egg_info 2025-01-24T01:09:58.7501928Z running egg_info 2025-01-24T01:09:58.7502485Z creating pytorch_openreg.egg-info 2025-01-24T01:09:58.7572758Z writing pytorch_openreg.egg-info/PKG-INFO 2025-01-24T01:09:58.7576762Z writing dependency_links to pytorch_openreg.egg-info/dependency_links.txt 2025-01-24T01:09:58.7578252Z writing requirements to pytorch_openreg.egg-info/requires.txt 2025-01-24T01:09:58.7579715Z writing top-level names to pytorch_openreg.egg-info/top_level.txt 2025-01-24T01:09:58.7580963Z writing manifest file 'pytorch_openreg.egg-info/SOURCES.txt' 2025-01-24T01:09:58.7657192Z reading manifest file 'pytorch_openreg.egg-info/SOURCES.txt' 2025-01-24T01:09:58.7665590Z writing manifest file 'pytorch_openreg.egg-info/SOURCES.txt' 2025-01-24T01:09:58.7667144Z Copying pytorch_openreg.egg-info to ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/pytorch_openreg-1.0-py3.13.egg-info 2025-01-24T01:09:58.7673045Z running install_scripts 2025-01-24T01:09:59.1967062Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T01:09:59.1969654Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_transformers.py', '--shard-id=1', '--num-shards=2', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-01-24 01:09:59.196724] 2025-01-24T01:18:53.3465001Z 2025-01-24T01:18:53.3465962Z test_transformers 1/2 was successful, full logs can be found in artifacts with path test/test-reports/test_transformers_1.2_f13409f1a9e61442_.log 2025-01-24T01:18:53.5175665Z Running 1694 items in this shard: test/test_transformers.py::TestSDPAPrivateUse1Only::test_fused_sdp_choice_privateuseone, test/test_transformers.py::TestSDPAPrivateUse1Only::test_scaled_dot_product_fused_attention_overrideable, test/test_transformers.py::TestSDPAPrivateUse1Only::test_scaled_dot_product_fused_attention_overrideable_backward, test/test_transformers.py::TestTransformersCPU::test_bias_is_none_cpu, test/test_transformers.py::TestTransformersCPU::test_decoder_only_layer_cpu, test/test_transformers.py::TestTransformersCPU::test_encoder_padding_and_src_mask_bool_cpu, test/test_transformers.py::TestTransformersCPU::test_is_causal_gpu_cpu, test/test_transformers.py::TestTransformersCPU::test_kpm_mask_trailing_column_with_nested_tensor_cpu, test/test_transformers.py::TestTransformersCPU::test_mask_check_fastpath_cpu, test/test_transformers.py::TestTransformersCPU::test_mha_native_args_nb_heads_1_bias_False_cpu, test/test_transformers.py::TestTransformersCPU::test_mha_native_args_nb_heads_8_bias_True_cpu, test/test_transformers.py::TestTransformersCPU::test_multiheadattention_fastpath_attn_mask_attn_mask_dim2_key_padding_mask_dim1_bool_cpu, test/test_transformers.py::TestTransformersCPU::test_multiheadattention_fastpath_attn_mask_attn_mask_dim2_key_padding_mask_dim_2_float32_cpu, test/test_transformers.py::TestTransformersCPU::test_multiheadattention_fastpath_attn_mask_attn_mask_dim_2_key_padding_mask_dim_2_bool_cpu, test/test_transformers.py::TestTransformersCPU::test_multiheadattention_fastpath_attn_mask_attn_mask_dim_2_key_padding_mask_dim_2_float32_cpu, test/test_transformers.py::TestTransformersCPU::test_multiheadattention_fastpath_attn_mask_attn_mask_dim_3_key_padding_mask_dim1_bool_cpu, test/test_transformers.py::TestTransformersCPU::test_multiheadattention_fastpath_attn_mask_attn_mask_dim_3_key_padding_mask_dim1_float32_cpu, test/test_transformers.py::TestTransformersCPU::test_multiheadattention_fastpath_attn_mask_attn_mask_dim_3_key_padding_mask_dim_2_bool_cpu, test/test_transformers.py::TestTransformersCPU::test_scaled_dot_product_attention_3D_input_dim_2D_attn_mask_dropout_p_0_0_cpu, test/test_transformers.py::TestTransformersCPU::test_scaled_dot_product_attention_3D_input_dim_2D_causal_attn_mask_dropout_p_0_2_cpu, test/test_transformers.py::TestTransformersCPU::test_scaled_dot_product_attention_3D_input_dim_2D_causal_attn_mask_dropout_p_0_5_cpu, test/test_transformers.py::TestTransformersCPU::test_scaled_dot_product_attention_3D_input_dim_3D_attn_mask_dropout_p_0_5_cpu, test/test_transformers.py::TestTransformersCPU::test_scaled_dot_product_attention_3D_input_dim_3D_causal_attn_mask_dropout_p_0_0_cpu, test/test_transformers.py::TestTransformersCPU::test_scaled_dot_product_attention_3D_input_dim_3D_causal_attn_mask_dropout_p_0_5_cpu, test/test_transformers.py::TestTransformersCPU::test_scaled_dot_product_attention_3D_input_dim_no_attn_mask_dropout_p_0_0_cpu, test/test_transformers.py::TestTransformersCPU::test_scaled_dot_product_attention_3D_input_dim_no_attn_mask_dropout_p_0_5_cpu, test/test_transformers.py::TestTransformersCPU::test_scaled_dot_product_attention_4D_input_dim_2D_attn_mask_dropout_p_0_0_cpu, test/test_transformers.py::TestTransformersCPU::test_scaled_dot_product_attention_4D_input_dim_2D_attn_mask_dropout_p_0_5_cpu, test/test_transformers.py::TestTransformersCPU::test_scaled_dot_product_attention_4D_input_dim_2D_causal_attn_mask_dropout_p_0_0_cpu, test/test_transformers.py::TestTransformersCPU::test_scaled_dot_product_attention_4D_input_dim_4D_attn_mask_dropout_p_0_2_cpu, test/test_transformers.py::TestTransformersCPU::test_scaled_dot_product_attention_4D_input_dim_4D_attn_mask_dropout_p_0_5_cpu, test/test_transformers.py::TestTransformersCPU::test_scaled_dot_product_attention_4D_input_dim_4D_causal_attn_mask_dropout_p_0_5_cpu, test/test_transformers.py::TestTransformersCPU::test_scaled_dot_product_attention_4D_input_dim_no_attn_mask_dropout_p_0_2_cpu, test/test_transformers.py::TestTransformersCPU::test_script_mha_in_proj_weight_none_cpu, test/test_transformers.py::TestTransformersCPU::test_self_attn_TxT_attn_mask_cpu, test/test_transformers.py::TestTransformersCPU::test_train_with_pad_and_catch_error_cpu, test/test_transformers.py::TestTransformersCPU::test_transformer_bias_is_none_cpu, test/test_transformers.py::TestTransformersCPU::test_transformerencoder_batch_first_False_training_False_enable_nested_tensor_False_cpu, test/test_transformers.py::TestTransformersCPU::test_transformerencoder_batch_first_False_training_False_enable_nested_tensor_True_cpu, test/test_transformers.py::TestTransformersCPU::test_transformerencoder_batch_first_False_training_True_enable_nested_tensor_False_cpu, test/test_transformers.py::TestTransformersCPU::test_transformerencoder_batch_first_False_training_True_enable_nested_tensor_True_cpu, test/test_transformers.py::TestTransformersCPU::test_transformerencoder_batch_first_True_training_True_enable_nested_tensor_True_cpu, test/test_transformers.py::TestTransformersCPU::test_transformerencoder_fastpath_use_torchscript_False_enable_nested_tensor_False_use_autocast_False_d_model_12_cpu, test/test_transformers.py::TestTransformersCPU::test_transformerencoder_fastpath_use_torchscript_False_enable_nested_tensor_False_use_autocast_False_d_model_256_cpu, test/test_transformers.py::TestTransformersCPU::test_transformerencoder_fastpath_use_torchscript_False_enable_nested_tensor_False_use_autocast_True_d_model_12_cpu, test/test_transformers.py::TestTransformersCPU::test_transformerencoder_fastpath_use_torchscript_False_enable_nested_tensor_False_use_autocast_True_d_model_256_cpu, test/test_transformers.py::TestTransformersCPU::test_transformerencoder_fastpath_use_torchscript_False_enable_nested_tensor_True_use_autocast_False_d_model_256_cpu, test/test_transformers.py::TestTransformersCPU::test_transformerencoder_square_input_with_no_grad_False_training_True_enable_nested_tensor_False_cpu, test/test_transformers.py::TestTransformersCPU::test_transformerencoderlayer_src_mask_nhead_4_cpu, test/test_transformers.py::TestTransformersCPU::test_transformerencoderlayer_subclass_model_cpu, test/test_transformers.py::TestTransformersCPU::test_with_nested_tensor_input_cpu, test/test_transformers.py::TestSDPAFailureModesCPU::test_flash_atteention_large_bf16_nan_values_cpu, test/test_transformers.py::TestSDPAFailureModesCPU::test_flash_attention_fail_with_non_square_causal_attention_cpu, test/test_transformers.py::TestSDPAFailureModesCPU::test_flash_backward_failure_sm86plus_head_dim_193_dropout_p_0_0_cpu, test/test_transformers.py::TestSDPAFailureModesCPU::test_flash_backward_failure_sm86plus_head_dim_256_dropout_p_0_2_cpu, test/test_transformers.py::TestSDPAFailureModesCPU::test_flash_fail_fp32_cpu, test/test_transformers.py::TestSDPAFailureModesCPU::test_fused_kernels_nested_broadcasting_error_cases_cpu, test/test_transformers.py::TestSDPAFailureModesCPU::test_fused_kernels_nested_broadcasting_requires_grad_failure_cpu, test/test_transformers.py::TestSDPAFailureModesCPU::test_fused_kernels_seq_len_0_inputs_fused_kernel0_cpu, test/test_transformers.py::TestSDPAFailureModesCPU::test_invalid_fused_inputs_attn_mask_present_kernel0_cpu, test/test_transformers.py::TestSDPAFailureModesCPU::test_invalid_fused_inputs_broadcast_kernel0_cpu, test/test_transformers.py::TestSDPAFailureModesCPU::test_invalid_fused_inputs_dim_3_kernel0_cpu, test/test_transformers.py::TestSDPAFailureModesCPU::test_invalid_fused_inputs_invalid_dtype_kernel0_cpu, test/test_transformers.py::TestSDPAFailureModesCPU::test_invalid_inputs_1_dimensional_inputs_kernel1_cpu, test/test_transformers.py::TestSDPAFailureModesCPU::test_invalid_inputs_1_dimensional_inputs_kernel2_cpu, test/test_transformers.py::TestSDPAFailureModesCPU::test_invalid_inputs_different_datatypes_kernel1_cpu, test/test_transformers.py::TestSDPAFailureModesCPU::test_invalid_inputs_different_datatypes_kernel2_cpu, test/test_transformers.py::TestSDPAFailureModesCPU::test_invalid_inputs_different_devices_kernel1_cpu, test/test_transformers.py::TestSDPAFailureModesCPU::test_invalid_inputs_different_devices_kernel2_cpu, test/test_transformers.py::TestSDPAFailureModesCPU::test_invalid_last_dim_stride_kernel0_cpu, test/test_transformers.py::TestSDPAFailureModesCPU::test_invalid_sdpa_kernel_grouped_query_attention_cpu_cpu, test/test_transformers.py::TestSDPAFailureModesCPU::test_invalid_sequence_lengths_kernel0_cpu, test/test_transformers.py::TestSDPAFailureModesCPU::test_mem_efficient_fail_bfloat16_less_than_sm80_cpu, test/test_transformers.py::TestSDPAFailureModesCPU::test_nested_fails_on_padding_head_dim_cpu, test/test_transformers.py::TestSDPAFailureModesCPU::test_unaligned_tensors_cpu, test/test_transformers.py::TestSDPACpuOnlyCPU::test_cpu_flash_attn_nan_propagation_float16_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_fully_masked_out_rows_backend0_seq_len_128_head_dim_16_float16_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_fully_masked_out_rows_backend0_seq_len_128_head_dim_16_float32_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_fully_masked_out_rows_backend0_seq_len_128_head_dim_32_float16_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_fully_masked_out_rows_backend0_seq_len_128_head_dim_32_float32_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_fully_masked_out_rows_backend0_seq_len_32_head_dim_32_float16_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_fully_masked_out_rows_backend0_seq_len_32_head_dim_32_float32_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_fully_masked_out_rows_backend0_seq_len_64_head_dim_16_float16_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_fully_masked_out_rows_backend0_seq_len_64_head_dim_16_float32_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_fully_masked_out_rows_backend0_seq_len_64_head_dim_32_float32_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_fully_masked_out_rows_backend1_seq_len_128_head_dim_16_float32_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_fully_masked_out_rows_backend1_seq_len_32_head_dim_16_float32_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_fused_sdp_choice_cpu_type_dense_dropout_0_0_float16_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_fused_sdp_choice_cpu_type_dense_dropout_0_0_float32_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_fused_sdp_choice_cpu_type_dense_dropout_0_0_float64_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_fused_sdp_choice_cpu_type_dense_dropout_0_7_float64_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_fused_sdp_choice_cpu_type_nested_dropout_0_0_bfloat16_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_fused_sdp_choice_cpu_type_nested_dropout_0_7_bfloat16_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_fused_sdp_choice_cpu_type_nested_dropout_0_7_float32_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_fused_sdp_choice_cpu_type_nested_dropout_0_7_float64_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_non_masked_rows_nan_props_float32_fill_val_inf_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_attention_math_with_negative_scale_kernel0_cpu, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_bfloat16_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_bfloat16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float16_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float16, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float32_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float32, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_12_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_1030_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_1030_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_11_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_11_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_514_kv_seq_len_17_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_1_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_False_train_True_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_2_bool_mask_True_train_True_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_False_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_False_train_True_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_False_casual_True_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_False_cpu_float64, test/test_transformers.py::TestSDPACpuOnlyCPU::test_scaled_dot_product_fused_attention_mask_vs_math_cpu_fused_kernel0_float64_batch_size_2_q_seq_len_514_kv_seq_len_514_n_head_3_head_dim_8_mask_dim_4_bool_mask_True_train_True_casual_False_set_attn_mask_True_cpu_float64, test/test_transformers.py::TestAttnBiasCPU::test_causal_variants_causal_variant_1_shape0_cpu, test/test_transformers.py::TestAttnBiasCPU::test_causal_variants_causal_variant_1_shape1_cpu, test/test_transformers.py::TestAttnBiasCPU::test_causal_variants_causal_variant_1_shape2_cpu, test/test_transformers.py::TestAttnBiasCPU::test_causal_variants_causal_variant_2_shape1_cpu, test/test_transformers.py::TestAttnBiasCPU::test_causal_variants_causal_variant_2_shape2_cpu, test/test_transformers.py::TestAttnBiasCPU::test_causal_variants_causal_variant_2_shape3_cpu, test/test_transformers.py::TestAttnBiasCPU::test_causal_variants_compile_causal_variant_1_shape1_cpu, test/test_transformers.py::TestAttnBiasCPU::test_causal_variants_compile_causal_variant_2_shape0_cpu, test/test_transformers.py::TestAttnBiasCPU::test_is_causal_and_mask_fails_cpu, test/test_transformers.py::TestAttnBiasCPU::test_is_causal_equals_upper_left_shape3_cpu 2025-01-24T01:18:53.6821907Z 2025-01-24T01:18:53.6822154Z Running test_utils 1/1 ... [2025-01-24 01:18:53.351068] 2025-01-24T01:18:53.6822547Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T01:18:53.6823579Z Executing ['/opt/conda/envs/py_3.13/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'] ... [2025-01-24 01:18:53.351449] 2025-01-24T01:20:51.2705303Z 2025-01-24T01:20:51.2706221Z test_utils 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_utils_1.1_d2439f25a07aa688_.log 2025-01-24T01:20:51.4836458Z Running 6029 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::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, 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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, 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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, 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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, 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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, 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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, 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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_float8_e4m3fn, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eye_cpu_float8_e4m3fnuz, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eye_cpu_float8_e5m2, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eye_cpu_float8_e5m2fnuz, 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, 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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, 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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, 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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_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_grid_sampler_2d_cpu_float16, 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_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isin_cpu_float16, 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_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logsumexp_cpu_complex64, 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, 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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_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_logsumexp_cpu_complex64, 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_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, 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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, 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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, 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test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_var_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_var_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_var_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_var_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matmul_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matmul_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matmul_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matmul_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matmul_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matmul_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matmul_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matmul_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matmul_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matmul_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matmul_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matrix_exp_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matrix_exp_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matrix_exp_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matrix_exp_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matrix_exp_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matrix_exp_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_binary_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_binary_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_binary_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_binary_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_binary_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_binary_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_binary_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_binary_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_binary_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_binary_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_pool2d_with_indices_backward_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_pool2d_with_indices_backward_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_pool2d_with_indices_backward_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_pool2d_with_indices_backward_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_no_dim_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_no_dim_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_no_dim_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_no_dim_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_no_dim_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_no_dim_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_no_dim_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_no_dim_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_no_dim_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_no_dim_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_with_dim_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_with_dim_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_with_dim_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_with_dim_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_with_dim_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_with_dim_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_with_dim_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_with_dim_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_with_dim_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_with_dim_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_maximum_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_maximum_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_maximum_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_maximum_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_maximum_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_maximum_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_maximum_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_maximum_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_maximum_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_maximum_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mean_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mean_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mean_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mean_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mean_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mean_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_median_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_median_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_median_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_median_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_median_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_median_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_median_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_median_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_median_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_list_of_tensors_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_list_of_tensors_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_list_of_tensors_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_list_of_tensors_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_list_of_tensors_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_list_of_tensors_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_list_of_tensors_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_list_of_tensors_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_list_of_tensors_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_list_of_tensors_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_list_of_tensors_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_list_of_tensors_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_variadic_tensors_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_variadic_tensors_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_variadic_tensors_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_variadic_tensors_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_variadic_tensors_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_variadic_tensors_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_variadic_tensors_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_variadic_tensors_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_variadic_tensors_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_variadic_tensors_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_variadic_tensors_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_variadic_tensors_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_binary_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_binary_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_binary_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_binary_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_binary_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_binary_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_binary_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_binary_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_binary_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_binary_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_no_dim_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_no_dim_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_no_dim_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_no_dim_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_no_dim_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_no_dim_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_no_dim_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_no_dim_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_no_dim_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_no_dim_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_with_dim_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_with_dim_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_with_dim_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_with_dim_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_with_dim_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_with_dim_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_with_dim_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_with_dim_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_with_dim_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_with_dim_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_minimum_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_minimum_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_minimum_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_minimum_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_minimum_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_minimum_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_minimum_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_minimum_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_minimum_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_minimum_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mm_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mm_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mm_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mm_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mm_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mm_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mm_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mm_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mm_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mm_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mm_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mode_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mode_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mode_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mode_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mode_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mode_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mode_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mode_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mode_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mode_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_movedim_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_movedim_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_movedim_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_movedim_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_movedim_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_movedim_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_movedim_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_movedim_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_movedim_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_movedim_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_movedim_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_movedim_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_movedim_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_msort_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_msort_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_msort_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_msort_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_msort_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_msort_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_msort_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_msort_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_msort_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_msort_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mul_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mul_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mul_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mul_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mul_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mul_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mul_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mul_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mul_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mul_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mul_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mul_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mul_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_multinomial_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_multinomial_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_multinomial_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_multinomial_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mv_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mv_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mv_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mv_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mv_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mv_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mv_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mv_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mv_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mv_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mv_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_1_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_1_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_1_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_1_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_1_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_1_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_1_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_1_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_1_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_3_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_3_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_3_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_3_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_3_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_3_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_3_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_3_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_3_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_5_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_5_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_5_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_5_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_5_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_5_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_5_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_5_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_5_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nan_to_num_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nan_to_num_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nan_to_num_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nan_to_num_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nan_to_num_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nan_to_num_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nan_to_num_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nan_to_num_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nan_to_num_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nan_to_num_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nanmean_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nanmean_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nanmean_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nanmean_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nanmedian_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nanmedian_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nanmedian_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nanmedian_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nanmedian_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nanmedian_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nanmedian_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nanmedian_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nanmedian_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nanquantile_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nanquantile_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nansum_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nansum_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nansum_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nansum_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nansum_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nansum_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nansum_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nansum_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nansum_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nansum_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_copy_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_copy_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_copy_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_copy_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_copy_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_copy_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_copy_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_copy_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_copy_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_copy_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_copy_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_copy_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_copy_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_batch_norm_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_batch_norm_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_batch_norm_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_batch_norm_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_dropout_backward_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_dropout_backward_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_dropout_backward_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_dropout_backward_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_dropout_backward_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_dropout_backward_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_dropout_backward_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_dropout_backward_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_dropout_backward_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_dropout_backward_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_layer_norm_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_layer_norm_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_layer_norm_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_layer_norm_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ne_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ne_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ne_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ne_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ne_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ne_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ne_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ne_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ne_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ne_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ne_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ne_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_neg_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_neg_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_neg_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_neg_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_neg_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_neg_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_neg_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_neg_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_neg_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_neg_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_neg_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_neg_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_strided_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_strided_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_strided_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_strided_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_strided_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_strided_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_strided_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_strided_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_strided_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_strided_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_strided_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_strided_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_strided_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_full_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_full_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_full_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_full_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_full_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_full_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_full_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_full_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_full_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_full_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_full_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_full_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_full_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_ones_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_ones_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_ones_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_ones_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_ones_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_ones_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_ones_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_ones_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_ones_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_ones_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_ones_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_ones_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_ones_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_zeros_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_zeros_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_zeros_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_zeros_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_zeros_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_zeros_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_zeros_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_zeros_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_zeros_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_zeros_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_zeros_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_zeros_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_zeros_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nextafter_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nextafter_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nextafter_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nextafter_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_avg_pool1d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_avg_pool1d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_avg_pool1d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_avg_pool1d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_avg_pool2d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_avg_pool2d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_avg_pool2d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_avg_pool2d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_avg_pool3d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_avg_pool3d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_avg_pool3d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_avg_pool3d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_max_pool1d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_max_pool1d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_max_pool1d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_max_pool1d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_max_pool2d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_max_pool2d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_max_pool2d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_max_pool2d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_max_pool3d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_max_pool3d_cpu_float16, 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_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_grid_sample_cpu_float16, 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_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_unpool1d_cpu_float16, 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_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_unpool1d_grad_cpu_float16, 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_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_unpool2d_cpu_float16, 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_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_unpool2d_grad_cpu_float16, 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_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_unpool3d_cpu_float16, 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_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_unpool3d_grad_cpu_float16, 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_bfloat16, 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_float16, 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_float16, 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_float16, 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_bool, 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_complex32, 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_complex32, 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_copy_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_copy_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_copy_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_copy_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_copy_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_copy_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_copy_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_copy_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_copy_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_copy_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_copy_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_copy_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_copy_cpu_uint8, 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, 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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_copy_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_copy_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_copy_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_copy_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_copy_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_copy_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_copy_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_copy_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_copy_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_copy_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_copy_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_copy_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_squeeze_copy_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_copy_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_copy_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_copy_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_copy_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_copy_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_copy_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_copy_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_copy_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_copy_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_copy_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_copy_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_copy_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_copy_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_copy_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_copy_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_copy_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_copy_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_copy_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_copy_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_copy_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_copy_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_copy_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_copy_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_copy_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_copy_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_copy_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 2025-01-24T01:20:51.6919859Z 2025-01-24T01:20:51.6920067Z Running test_torch 1/1 ... [2025-01-24 01:20:51.277626] 2025-01-24T01:20:51.6920470Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T01:20:51.6921507Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_torch.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-01-24 01:20:51.277965] 2025-01-24T01:25:16.6829490Z 2025-01-24T01:25:16.6830490Z test_torch 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_torch_1.1_fff4d454c50ae3ed_.log 2025-01-24T01:25:16.7157009Z Running 1034 items in this shard: test/test_torch.py::TestBasicVitalSigns::test_basic_vitals, test/test_torch.py::TestBasicVitalSigns::test_basic_vitals_read_write, test/test_torch.py::TestBasicVitalSigns::test_dataloader_vitals, test/test_torch.py::TestTorch::test_RNGState, test/test_torch.py::TestTorch::test_RNGStateAliasing, test/test_torch.py::TestTorch::test_RNG_after_pickle, test/test_torch.py::TestTorch::test_Size, test/test_torch.py::TestTorch::test_Size_iter, test/test_torch.py::TestTorch::test_Size_scalar, test/test_torch.py::TestTorch::test_add_meta_scalar, test/test_torch.py::TestTorch::test_allow_tensor_metadata_change, test/test_torch.py::TestTorch::test_apply, test/test_torch.py::TestTorch::test_as_subclass, test/test_torch.py::TestTorch::test_assert_async, test/test_torch.py::TestTorch::test_backward_hooks_traverse, test/test_torch.py::TestTorch::test_batch_norm_cpu_inference, test/test_torch.py::TestTorch::test_bf16_supported_on_cpu, test/test_torch.py::TestTorch::test_bmm_multithreaded, test/test_torch.py::TestTorch::test_boxMullerState, test/test_torch.py::TestTorch::test_cat_neg_dim, test/test_torch.py::TestTorch::test_check, test/test_torch.py::TestTorch::test_chunk_neg_dim, test/test_torch.py::TestTorch::test_conj_neg_tolist, test/test_torch.py::TestTorch::test_conj_physical_meta_stride, test/test_torch.py::TestTorch::test_contains, test/test_torch.py::TestTorch::test_copy_broadcast, test/test_torch.py::TestTorch::test_copy_dtypes, test/test_torch.py::TestTorch::test_copy_float16, test/test_torch.py::TestTorch::test_copy_many_to_one, test/test_torch.py::TestTorch::test_copy_transpose, test/test_torch.py::TestTorch::test_cuda_not_built, test/test_torch.py::TestTorch::test_cummax_neg_dim, test/test_torch.py::TestTorch::test_cummin_neg_dim, test/test_torch.py::TestTorch::test_cumprod_neg_dim, test/test_torch.py::TestTorch::test_cumsum_neg_dim, test/test_torch.py::TestTorch::test_cxx_flags, test/test_torch.py::TestTorch::test_data_ptr_of_empty_tensor_with_storage, test/test_torch.py::TestTorch::test_data_ptr_of_empty_view_with_storage, test/test_torch.py::TestTorch::test_deepcopy_gradient, test/test_torch.py::TestTorch::test_deepcopy_parameter, test/test_torch.py::TestTorch::test_deterministic_fill_uninitialized_memory, test/test_torch.py::TestTorch::test_deterministic_flag, test/test_torch.py::TestTorch::test_device, test/test_torch.py::TestTorch::test_dim_order, test/test_torch.py::TestTorch::test_dir, test/test_torch.py::TestTorch::test_doc, test/test_torch.py::TestTorch::test_doc_template, test/test_torch.py::TestTorch::test_dot_data_use, test/test_torch.py::TestTorch::test_dtype_is_signed, test/test_torch.py::TestTorch::test_element_size, test/test_torch.py::TestTorch::test_empty_meta, test/test_torch.py::TestTorch::test_empty_storage_view, test/test_torch.py::TestTorch::test_equal, test/test_torch.py::TestTorch::test_error_msg_type_translation, test/test_torch.py::TestTorch::test_fill_diagonal, test/test_torch.py::TestTorch::test_format_scalar_meta, test/test_torch.py::TestTorch::test_from_buffer, test/test_torch.py::TestTorch::test_from_file, test/test_torch.py::TestTorch::test_gather_neg_dim, test/test_torch.py::TestTorch::test_generator_cpu, test/test_torch.py::TestTorch::test_get_cpu_capability, test/test_torch.py::TestTorch::test_has_internal_overlap, test/test_torch.py::TestTorch::test_has_storage, test/test_torch.py::TestTorch::test_index_add, test/test_torch.py::TestTorch::test_index_add_all_dtypes, test/test_torch.py::TestTorch::test_index_add_cornercase, test/test_torch.py::TestTorch::test_index_add_correctness, test/test_torch.py::TestTorch::test_index_add_neg_dim, test/test_torch.py::TestTorch::test_index_copy_neg_dim, test/test_torch.py::TestTorch::test_index_fill_neg_dim, test/test_torch.py::TestTorch::test_index_select_neg_dim, test/test_torch.py::TestTorch::test_invalid_arg_error_handling, test/test_torch.py::TestTorch::test_invalid_generator_raises, test/test_torch.py::TestTorch::test_is_nonzero, test/test_torch.py::TestTorch::test_is_same_size, test/test_torch.py::TestTorch::test_iter, test/test_torch.py::TestTorch::test_kthvalue_neg_dim, test/test_torch.py::TestTorch::test_linspace_logspace, test/test_torch.py::TestTorch::test_logcumsumexp_neg_dim, test/test_torch.py::TestTorch::test_manual_seed, test/test_torch.py::TestTorch::test_map, test/test_torch.py::TestTorch::test_map2, test/test_torch.py::TestTorch::test_max_neg_dim, test/test_torch.py::TestTorch::test_mean_neg_dim, test/test_torch.py::TestTorch::test_median_neg_dim, test/test_torch.py::TestTorch::test_memory_format, test/test_torch.py::TestTorch::test_memory_format_contiguous_returns_same_tensor_if_already_satisfies, test/test_torch.py::TestTorch::test_memory_format_empty, test/test_torch.py::TestTorch::test_min_neg_dim, test/test_torch.py::TestTorch::test_mode_neg_dim, test/test_torch.py::TestTorch::test_multinomial_invalid_probs, test/test_torch.py::TestTorch::test_nanmedian_neg_dim, test/test_torch.py::TestTorch::test_narrow_neg_dim, test/test_torch.py::TestTorch::test_nbytes, test/test_torch.py::TestTorch::test_ndim, test/test_torch.py::TestTorch::test_new, test/test_torch.py::TestTorch::test_newaxis_numpy_comparison, test/test_torch.py::TestTorch::test_newindex, test/test_torch.py::TestTorch::test_no_cuda_monkeypatch, test/test_torch.py::TestTorch::test_norm_neg_dim, test/test_torch.py::TestTorch::test_normal_shape, test/test_torch.py::TestTorch::test_numel, test/test_torch.py::TestTorch::test_parallel_info, test/test_torch.py::TestTorch::test_parsing_double, test/test_torch.py::TestTorch::test_parsing_int64, test/test_torch.py::TestTorch::test_parsing_intlist, test/test_torch.py::TestTorch::test_permute, test/test_torch.py::TestTorch::test_pickle, test/test_torch.py::TestTorch::test_pickle_dtype, test/test_torch.py::TestTorch::test_pickle_function, test/test_torch.py::TestTorch::test_pickle_generator, test/test_torch.py::TestTorch::test_pickle_parameter, test/test_torch.py::TestTorch::test_pickle_parameter_no_requires_grad, test/test_torch.py::TestTorch::test_pickle_size, test/test_torch.py::TestTorch::test_pin_memory, test/test_torch.py::TestTorch::test_print, test/test_torch.py::TestTorch::test_prod_neg_dim, test/test_torch.py::TestTorch::test_pyobj_preserved, test/test_torch.py::TestTorch::test_qengine, test/test_torch.py::TestTorch::test_renorm_neg_dim, test/test_torch.py::TestTorch::test_resizable, test/test_torch.py::TestTorch::test_reversed, test/test_torch.py::TestTorch::test_scatter_neg_dim, test/test_torch.py::TestTorch::test_select_neg_dim, test/test_torch.py::TestTorch::test_set_flush_denormal, test/test_torch.py::TestTorch::test_setting_real_imag_to_a_number, test/test_torch.py::TestTorch::test_show_config, test/test_torch.py::TestTorch::test_size_neg_dim, test/test_torch.py::TestTorch::test_size_stride, test/test_torch.py::TestTorch::test_sizeof, test/test_torch.py::TestTorch::test_slice, test/test_torch.py::TestTorch::test_slow_test, test/test_torch.py::TestTorch::test_sobolengine_bounds, test/test_torch.py::TestTorch::test_sobolengine_bounds_scrambled, test/test_torch.py::TestTorch::test_sobolengine_continuing, test/test_torch.py::TestTorch::test_sobolengine_continuing_scrambled, test/test_torch.py::TestTorch::test_sobolengine_default_dtype, test/test_torch.py::TestTorch::test_sobolengine_distribution, test/test_torch.py::TestTorch::test_sobolengine_distribution_scrambled, test/test_torch.py::TestTorch::test_sobolengine_draw, test/test_torch.py::TestTorch::test_sobolengine_draw_base2, test/test_torch.py::TestTorch::test_sobolengine_draw_base2_scrambled, test/test_torch.py::TestTorch::test_sobolengine_draw_scrambled, test/test_torch.py::TestTorch::test_sobolengine_fast_forward, test/test_torch.py::TestTorch::test_sobolengine_fast_forward_scrambled, test/test_torch.py::TestTorch::test_sobolengine_first_point, test/test_torch.py::TestTorch::test_sobolengine_high_dim, test/test_torch.py::TestTorch::test_sobolengine_raise, test/test_torch.py::TestTorch::test_sobolengine_reset, test/test_torch.py::TestTorch::test_sobolengine_reset_scrambled, test/test_torch.py::TestTorch::test_sort_neg_dim, test/test_torch.py::TestTorch::test_split_neg_dim, test/test_torch.py::TestTorch::test_split_with_sizes_copy_out, test/test_torch.py::TestTorch::test_squeeze_neg_dim, test/test_torch.py::TestTorch::test_std_neg_dim, test/test_torch.py::TestTorch::test_storage_base_init, test/test_torch.py::TestTorch::test_storage_base_new, test/test_torch.py::TestTorch::test_storage_byteswap, test/test_torch.py::TestTorch::test_storage_casts, test/test_torch.py::TestTorch::test_storage_cycle_via_dict, test/test_torch.py::TestTorch::test_storage_cycle_via_slots, test/test_torch.py::TestTorch::test_storage_dead_weak_ref, test/test_torch.py::TestTorch::test_storage_dealloc, test/test_torch.py::TestTorch::test_storage_dealloc_resurrected, test/test_torch.py::TestTorch::test_storage_dealloc_subclass_resurrected, test/test_torch.py::TestTorch::test_storage_dealloc_subclass_zombie, test/test_torch.py::TestTorch::test_storage_dict_dealloc, test/test_torch.py::TestTorch::test_storage_error, test/test_torch.py::TestTorch::test_storage_error_no_attribute, test/test_torch.py::TestTorch::test_storage_finalizer_dealloc, test/test_torch.py::TestTorch::test_storage_fix_weakref_no_leak, test/test_torch.py::TestTorch::test_storage_from_tensor_dealloc, test/test_torch.py::TestTorch::test_storage_from_tensor_dealloc_resurrected, test/test_torch.py::TestTorch::test_storage_from_tensor_dealloc_zombie, test/test_torch.py::TestTorch::test_storage_preserve_nonhermetic_in_hermetic_context, test/test_torch.py::TestTorch::test_storage_resurrected_weak_ref, test/test_torch.py::TestTorch::test_storage_slot_dealloc, test/test_torch.py::TestTorch::test_storage_weakref_dealloc, test/test_torch.py::TestTorch::test_structseq_repr, test/test_torch.py::TestTorch::test_subclass_preserved, test/test_torch.py::TestTorch::test_subclass_tensors, test/test_torch.py::TestTorch::test_sum_neg_dim, test/test_torch.py::TestTorch::test_swap_basic, test/test_torch.py::TestTorch::test_swap_fail_slots, test/test_torch.py::TestTorch::test_t_not_2d_error, test/test_torch.py::TestTorch::test_tensor_base_init, test/test_torch.py::TestTorch::test_tensor_base_new, test/test_torch.py::TestTorch::test_tensor_ctor_scalar, test/test_torch.py::TestTorch::test_tensor_cycle_via_dict, test/test_torch.py::TestTorch::test_tensor_cycle_via_slots, test/test_torch.py::TestTorch::test_tensor_dead_weak_ref, test/test_torch.py::TestTorch::test_tensor_dict_dealloc, test/test_torch.py::TestTorch::test_tensor_finalizer_dealloc, test/test_torch.py::TestTorch::test_tensor_fix_weakref_no_leak, test/test_torch.py::TestTorch::test_tensor_ressurecting_clear, test/test_torch.py::TestTorch::test_tensor_resurrected_weak_ref, test/test_torch.py::TestTorch::test_tensor_set, test/test_torch.py::TestTorch::test_tensor_set_errors, test/test_torch.py::TestTorch::test_tensor_slot_dealloc, test/test_torch.py::TestTorch::test_tensor_weakref_dealloc, test/test_torch.py::TestTorch::test_tensor_where_scalar, test/test_torch.py::TestTorch::test_tensoriterator_output_setup, test/test_torch.py::TestTorch::test_terminate_handler_on_crash, test/test_torch.py::TestTorch::test_to, test/test_torch.py::TestTorch::test_to_with_tensor, test/test_torch.py::TestTorch::test_topk_neg_dim, test/test_torch.py::TestTorch::test_torch_from_file, test/test_torch.py::TestTorch::test_transpose_neg_dim, test/test_torch.py::TestTorch::test_type, test/test_torch.py::TestTorch::test_type_alias, test/test_torch.py::TestTorch::test_type_conversion_via_dtype_name, test/test_torch.py::TestTorch::test_typed_storage_deprecation_warning, test/test_torch.py::TestTorch::test_typed_storage_internal_no_warning, test/test_torch.py::TestTorch::test_unbind_neg_dim, test/test_torch.py::TestTorch::test_unflatten, test/test_torch.py::TestTorch::test_unfold_neg_dim, test/test_torch.py::TestTorch::test_unsqueeze_neg_dim, test/test_torch.py::TestTorch::test_upsample_nearest1d_meta, test/test_torch.py::TestTorch::test_upsample_nearest2d_meta, test/test_torch.py::TestTorch::test_var_neg_dim, test/test_torch.py::TestTorch::test_warn_types, test/test_torch.py::TestTorch::test_wildcard_import, test/test_torch.py::TestVitalSignsCudaCPU::test_cuda_vitals_gpu_only_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcdiv_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcdiv_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcdiv_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcdiv_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcdiv_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcdiv_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcdiv_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcdiv_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcdiv_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcmul_cuda_errors_with_cpu_scalars_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcmul_use_cpu_scalar_False_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcmul_use_cpu_scalar_False_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcmul_use_cpu_scalar_False_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcmul_use_cpu_scalar_False_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcmul_use_cpu_scalar_False_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcmul_use_cpu_scalar_False_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcmul_use_cpu_scalar_False_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcmul_use_cpu_scalar_False_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcmul_use_cpu_scalar_False_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcmul_use_cpu_scalar_True_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcmul_use_cpu_scalar_True_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcmul_use_cpu_scalar_True_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcmul_use_cpu_scalar_True_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcmul_use_cpu_scalar_True_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcmul_use_cpu_scalar_True_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcmul_use_cpu_scalar_True_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcmul_use_cpu_scalar_True_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_addcmul_use_cpu_scalar_True_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_assertRaisesRegex_ignore_msg_non_native_device_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_edge_cases_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_edge_cases_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_edge_cases_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_mem_overlap_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_p_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_p_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_p_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_p_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_self_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_self_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_self_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_self_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_self_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_self_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_self_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_self_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_self_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_bfloat16_neg_abs_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_bool_tensor_value_change_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_add_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_addcdiv_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_addcmul_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_atan2_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_copy_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_dist_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_div_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_eq_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_fmod_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_ge_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_gt_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_le_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_lerp_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_lt_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_map2_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_map_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_masked_fill_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_masked_scatter_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_masked_select_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_max_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_min_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_mul_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_ne_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_pow_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_remainder_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_sub_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_bytes_to_scalar_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_bytes_to_scalar_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_bytes_to_scalar_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_bytes_to_scalar_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_bytes_to_scalar_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_bytes_to_scalar_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_bytes_to_scalar_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_bytes_to_scalar_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_bytes_to_scalar_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_bytes_to_scalar_cpu_uint16, test/test_torch.py::TestTorchDeviceTypeCPU::test_bytes_to_scalar_cpu_uint32, test/test_torch.py::TestTorchDeviceTypeCPU::test_bytes_to_scalar_cpu_uint64, test/test_torch.py::TestTorchDeviceTypeCPU::test_bytes_to_scalar_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_cauchy_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_cauchy_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_cauchy_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_cauchy_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_cauchy_kstest_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cauchy_no_inf_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_cauchy_no_inf_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_cdist_cuda_backward_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cdist_empty_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cdist_euclidean_large_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cdist_grad_p_lt_1_no_nan_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cdist_large_batch_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cdist_large_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cdist_non_contiguous_batch_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cdist_non_contiguous_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cdist_norm_batch_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cdist_norm_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cdist_same_inputs_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_check_tensor_all_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_check_tensor_internal_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_clone_all_dtypes_and_devices_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_clone_not_memory_dense_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_clone_zero_stride_dim_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_complex_half_experimental_warning_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_constants_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_conv_transposed_backward_agnostic_to_memory_format_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_conv_transposed_large_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy__cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy__cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy__cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy__cpu_complex32, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy__cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy__cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy__cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy__cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy__cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy__cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy__cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy__cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy__cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy_all_dtypes_and_devices_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy_math_view_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy_mem_overlap_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy_transpose_math_view_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy_transpose_math_view_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_copy_transpose_math_view_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_corrcoef_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_corrcoef_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_corrcoef_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_cov_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cpp_warnings_have_python_context_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cublas_config_nondeterministic_alert_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cummax_cummin_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cummax_discontiguous_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cummin_discontiguous_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cumprod_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cumsum_64bit_indexing_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cumsum_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_deepcopy_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_deepcopy_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_deepcopy_scalar_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_deepcopy_scalar_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_cumsum_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_complex32, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_uint16, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_uint32, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_uint64, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_interpolate_bilinear_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_replication_pad2d_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_resize_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_resize_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_resize_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_resize_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_resize_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_resize_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_resize_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_resize_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_resize_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_resize_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_resize_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_resize_cpu_uint16, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_resize_cpu_uint32, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_resize_cpu_uint64, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_resize_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_device_guard_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_noncontig_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_noncontig_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_noncontig_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_noncontig_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_noncontig_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_noncontig_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_noncontig_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_noncontig_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_noncontig_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_noncontig_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_noncontig_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_dim_function_empty_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_discontiguous_out_cumsum_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_dist_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_dtypetensor_warnings_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_errors_index_copy_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_expected_failure_xla_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_exponential_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_exponential_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_exponential_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_exponential_cpu_float64, 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test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_cpu_uint16, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_cpu_uint32, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_cpu_uint64, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_errors_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_errors_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_errors_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_errors_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_errors_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_errors_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_errors_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_errors_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_errors_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_errors_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_errors_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_errors_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_from_tensor_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_from_tensor_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_from_tensor_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_from_tensor_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_from_tensor_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_from_tensor_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_from_tensor_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_from_tensor_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_from_tensor_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_from_tensor_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_from_tensor_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_from_tensor_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_ok_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_ok_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_ok_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_ok_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_ok_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_ok_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_ok_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_ok_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_ok_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_ok_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_ok_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_ok_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_setitem_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_setitem_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_setitem_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_setitem_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_setitem_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_setitem_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_setitem_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_setitem_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_setitem_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_setitem_cpu_qint32, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_setitem_cpu_qint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_setitem_cpu_quint4x2, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_setitem_cpu_quint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_setitem_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_strides_propagation_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_sync_warning_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_take_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_take_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_take_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_take_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_take_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_take_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_take_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_take_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_take_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_take_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_take_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_take_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_take_empty_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_uint16, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_uint32, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_uint64, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_set_errors_multigpu_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_shape_empty_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_storage_type_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_storage_type_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_storage_type_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_storage_type_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_storage_type_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_storage_type_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_storage_type_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_storage_type_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_storage_type_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_storage_type_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_storage_type_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_storage_type_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_type_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_ternary_op_mem_overlap_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_typed_storage_meta_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_typed_storage_meta_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_typed_storage_meta_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_typed_storage_meta_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_typed_storage_meta_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_typed_storage_meta_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_typed_storage_meta_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_typed_storage_meta_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_typed_storage_meta_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_typed_storage_meta_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_typed_storage_meta_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_typed_storage_meta_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_uniform_kstest_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_uniform_kstest_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_uniform_kstest_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_uniform_kstest_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_untyped_storage_meta_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_warn_always_caught_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_where_scalar_handcrafted_values_cpu 2025-01-24T01:25:16.7471726Z 2025-01-24T01:25:16.7471900Z Running test_nn 2/2 ... [2025-01-24 01:25:16.685266] 2025-01-24T01:25:16.7472300Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T01:25:16.7473306Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_nn.py', '--shard-id=2', '--num-shards=2', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-01-24 01:25:16.685589] 2025-01-24T01:31:12.4601727Z 2025-01-24T01:31:12.4602630Z test_nn 2/2 was successful, full logs can be found in artifacts with path test/test-reports/test_nn_2.2_11fad3592f60cce8_.log 2025-01-24T01:31:12.5219792Z Running 1133 items in this shard: test/test_nn.py::TestNN::test_BCELoss_no_batch_dim_mean, test/test_nn.py::TestNN::test_BCELoss_no_batch_dim_mean_cuda_float, test/test_nn.py::TestNN::test_BCELoss_no_batch_dim_none_cuda_float, 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_reduce, 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_scalar, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_legacy_enum, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_batch_dim_mean, 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_half, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_batch_dim_sum, 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_scalar, 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_Conv1d_circular_stride2_pad2, test/test_nn.py::TestNN::test_Conv1d_cuda, test/test_nn.py::TestNN::test_Conv1d_groups, test/test_nn.py::TestNN::test_Conv1d_pad1size1, test/test_nn.py::TestNN::test_Conv1d_pad2, 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_cuda, test/test_nn.py::TestNN::test_Conv1d_pad_valid, 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_stride_cuda, test/test_nn.py::TestNN::test_Conv1d_zero_batch, 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_depthwise_dilated_cuda, test/test_nn.py::TestNN::test_Conv2d_depthwise_padded, test/test_nn.py::TestNN::test_Conv2d_depthwise_with_multiplier, test/test_nn.py::TestNN::test_Conv2d_dilated_with_long_tensor_cuda, test/test_nn.py::TestNN::test_Conv2d_groups_thnn_with_long_tensor_cuda, test/test_nn.py::TestNN::test_Conv2d_groups_with_long_tensor_cuda, test/test_nn.py::TestNN::test_Conv2d_no_bias_cuda, test/test_nn.py::TestNN::test_Conv2d_no_bias_with_long_tensor_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_cuda, test/test_nn.py::TestNN::test_Conv2d_replicate_stride2_pad2, test/test_nn.py::TestNN::test_Conv2d_strided, 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_zero_batch, test/test_nn.py::TestNN::test_Conv2d_zero_batch_cuda, test/test_nn.py::TestNN::test_Conv2d_zeros_stride2_pad2, 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_with_long_tensor, test/test_nn.py::TestNN::test_Conv3d_1x1x1_no_bias_with_long_tensor_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_groups, test/test_nn.py::TestNN::test_Conv3d_groups_with_long_tensor, test/test_nn.py::TestNN::test_Conv3d_no_bias, test/test_nn.py::TestNN::test_Conv3d_no_bias_with_long_tensor_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_replicate_stride2_pad2, test/test_nn.py::TestNN::test_Conv3d_stride_cuda, test/test_nn.py::TestNN::test_Conv3d_stride_padding_with_long_tensor, 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_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_ConvTranspose1d_cuda, test/test_nn.py::TestNN::test_ConvTranspose1d_groups_cuda, test/test_nn.py::TestNN::test_ConvTranspose2d, test/test_nn.py::TestNN::test_ConvTranspose2d_dilated, 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_no_bias_cuda, test/test_nn.py::TestNN::test_ConvTranspose2d_no_bias_with_long_tensor, test/test_nn.py::TestNN::test_ConvTranspose3d, 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_half, 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_half, 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_half, test/test_nn.py::TestNN::test_CrossMapLRN2d_cuda, test/test_nn.py::TestNN::test_ELU_no_batch_dim, test/test_nn.py::TestNN::test_Embedding, test/test_nn.py::TestNN::test_EmbeddingBag_discontiguous, test/test_nn.py::TestNN::test_EmbeddingBag_max, 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_padding_idx_cuda, 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_sparse_cuda, test/test_nn.py::TestNN::test_Flatten, 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_int_input, test/test_nn.py::TestNN::test_Fold_int_input_cuda, test/test_nn.py::TestNN::test_Fold_no_batch_dim_input_cuda, test/test_nn.py::TestNN::test_GELU_no_batch_dim, 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_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_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_half, 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_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_cuda_double, 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_no_batch_dim_mean, test/test_nn.py::TestNN::test_KLDivLoss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_KLDivLoss_no_batch_dim_none_cuda_float, 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_half, test/test_nn.py::TestNN::test_KLDivLoss_no_reduce, 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_cuda, test/test_nn.py::TestNN::test_KLDivLoss_with_log_target_no_reduce_cuda, 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_half, test/test_nn.py::TestNN::test_L1Loss_no_batch_dim_none_cuda_float, 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_complex, test/test_nn.py::TestNN::test_L1Loss_no_reduce_cuda, test/test_nn.py::TestNN::test_L1Loss_no_reduce_scalar_cuda, test/test_nn.py::TestNN::test_LSTM_cell_forward_hidden_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_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_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_LogSigmoid_no_batch_dim, test/test_nn.py::TestNN::test_MSELoss_no_batch_dim_mean_cuda_float, 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_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_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_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_none, test/test_nn.py::TestNN::test_MarginRankingLoss_no_batch_dim_none_cuda_float, 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_MaxUnpool1d_net, test/test_nn.py::TestNN::test_MaxUnpool2d_net, test/test_nn.py::TestNN::test_MaxUnpool2d_net_no_batch_dim_cuda, test/test_nn.py::TestNN::test_MaxUnpool3d_net_no_batch_dim, test/test_nn.py::TestNN::test_Mish_no_batch_dim, test/test_nn.py::TestNN::test_ModuleDict, 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_cuda, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_index_neg, 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_none, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_no_batch_dim_sum, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_no_batch_dim_sum_cuda_float, 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_cuda_float, 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_half, test/test_nn.py::TestNN::test_MultiMarginLoss_1d_no_reduce_cuda, 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_NLLLoss2d_no_reduce, 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_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_cuda, test/test_nn.py::TestNN::test_NLLLossNd_no_reduce_weights_cuda, test/test_nn.py::TestNN::test_NLLLoss_no_batch_dim_none, 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_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_neg, test/test_nn.py::TestNN::test_PReLU_backward_requires_grad_false, 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_ParameterDict_replication, test/test_nn.py::TestNN::test_ParameterList_replication, test/test_nn.py::TestNN::test_PixelShuffle, 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_reduce_cuda, test/test_nn.py::TestNN::test_RNN_cell, 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_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_with_up_down, 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_ReplicationPad3d, test/test_nn.py::TestNN::test_ReplicationPad3d_complex, test/test_nn.py::TestNN::test_ReplicationPad3d_no_batch_dim, test/test_nn.py::TestNN::test_SELU_no_batch_dim, 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_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_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_Sigmoid_no_batch_dim, test/test_nn.py::TestNN::test_SmoothL1Loss_no_batch_dim_mean, 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_float, test/test_nn.py::TestNN::test_SmoothL1Loss_no_batch_dim_sum_cuda_half, test/test_nn.py::TestNN::test_SmoothL1Loss_no_reduce_scalar, test/test_nn.py::TestNN::test_SmoothL1Loss_zero_beta, test/test_nn.py::TestNN::test_SoftMarginLoss_no_batch_dim_mean, 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_half, test/test_nn.py::TestNN::test_SoftMarginLoss_no_reduce, test/test_nn.py::TestNN::test_Softplus_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_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_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_TransformerEncoderLayer_gelu_activation_cuda, test/test_nn.py::TestNN::test_TransformerEncoderLayer_relu_activation, test/test_nn.py::TestNN::test_TripletMarginLoss_no_batch_dim_mean_cuda_float, 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_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_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_affine_grid, test/test_nn.py::TestNN::test_affine_grid_backward_cl_cf_consistency_device_cpu_nd_3, test/test_nn.py::TestNN::test_batchnorm_half_overflow, test/test_nn.py::TestNN::test_batchnorm_non_contig_cpu_SyncBatchNorm, 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_has_correct_grad_at_zero, 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_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_not_requiring_grad, 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_overwrite, 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_similarity, test/test_nn.py::TestNN::test_cross_entropy_loss, 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_weight_format, 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_missing_get_extra_state, test/test_nn.py::TestNN::test_extra_state_non_dict, test/test_nn.py::TestNN::test_fold_invalid_arg, 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_nearest_neighbor_rounding_mode_consistency, 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_zero_dim_cuda, test/test_nn.py::TestNN::test_interpolate_bicubic_scale_2d_cuda, test/test_nn.py::TestNN::test_interpolate_bicubic_scale_tuple_skewed_2d, 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_tuple_2d, test/test_nn.py::TestNN::test_interpolate_bilinear_tuple_2d_align_corners, 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_cuda, test/test_nn.py::TestNN::test_interpolate_linear_scale_1d, 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_nearest_1d, 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_zero_dim_cuda, test/test_nn.py::TestNN::test_interpolate_nearest_scale_1d, 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_trilinear_3d_cuda, test/test_nn.py::TestNN::test_interpolate_trilinear_3d_zero_dim, test/test_nn.py::TestNN::test_interpolate_trilinear_scale_3d, 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_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_layer_norm_eps, 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_weightStrided, 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_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_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_margin_ranking_loss_margin_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_to_argparse, test/test_nn.py::TestNN::test_mse_loss_size_warning, 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_native_channel_shuffle_return_alias_of_self, 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_pad_scalar_error, 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_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_row, test/test_nn.py::TestNN::test_pdist_large, test/test_nn.py::TestNN::test_pdist_zeros, test/test_nn.py::TestNN::test_pointwise_loss_target_grad_none_reduction, test/test_nn.py::TestNN::test_projections_lstm_check_device, 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_repr, test/test_nn.py::TestNN::test_requires_grad_, test/test_nn.py::TestNN::test_rnn_args_check, test/test_nn.py::TestNN::test_share_memory, test/test_nn.py::TestNN::test_smoothl1loss_negative_beta_not_supported, 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_cuda, test/test_nn.py::TestNN::test_softmax_lastdim_dtype_cuda, 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_special_cuda, 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_train_errors_for_invalid_mode, 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_triplet_margin_loss, test/test_nn.py::TestNN::test_triplet_margin_loss_swap_no_reduce, test/test_nn.py::TestNN::test_unflatten_invalid_arg, test/test_nn.py::TestNN::test_upsamplingLinear1d, test/test_nn.py::TestNN::test_upsamplingTrilinear3d_spatial_invariance, 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::TestConstantPadNd::test_preserves_memory_format, test/test_nn.py::TestAddRelu::test_add_relu_broadcasting, test/test_nn.py::TestFusionUtils::test_fuse_conv_bn_requires_grad, test/test_nn.py::TestNNDeviceTypeCPU::test_BatchNorm_empty_cpu_float64, 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_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_GRU_grad_and_gradgrad_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_GroupNorm_raises_error_if_one_value_per_group_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_LSTM_differentiable_backward_using_oneDNN_cpu_float32, 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_float64, 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_fails_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_ReplicationPad1d_large_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_ReplicationPad3d_large_cpu, 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_Transformer_empty_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_activations_bfloat16_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_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_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_4_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_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_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_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_with_probs_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_large_tensor_reduction_mean_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_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_none_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_cudnn_tensor_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_device_mask_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_fold_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_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_groupnorm_nhwc_cpu_bfloat16, test/test_nn.py::TestNNDeviceTypeCPU::test_hardsigmoid_grad_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_hardswish_grad_corner_cpu_bfloat16, test/test_nn.py::TestNNDeviceTypeCPU::test_hardswish_grad_corner_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_hardswish_grad_corner_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_hardswish_grad_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_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_True_affine_True_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_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_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_mish_inplace_overlap_cpu, 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_empty_tensor_reduction_mean_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_nll_loss_empty_tensor_reduction_none_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_nll_loss_empty_tensor_reduction_sum_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_nll_loss_invalid_target_dim_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_nn_empty_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_nn_scalars_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_pad_cpu_float64, 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_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_smooth_l1_loss_bfloat16_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_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_softmax_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_softmax_forward_64bit_indexing_cpu, 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_transformerencoderlayer_gelu_cpu_float32, 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_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_403_output_size_377_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_format1_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_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_True_mode_bicubic_memory_format0_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_int8_cpu_int8, 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_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_int64_cpu_int64, 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_float64_cpu_float64, 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_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_int64_cpu_int64, 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_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_int8_cpu_int8, 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_int16_cpu_int16, 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_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_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_int64_cpu_int64, 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_int32_cpu_int32, 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_nearest-exact_float64_cpu_float64, 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_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_int8_cpu_int8, 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_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_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_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_uint8_cpu_uint8, 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_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_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_upsamplingBilinear2d_aa_correctness_memory_format0_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest1d_correctness_isize_10_osize_15_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_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_launch_config_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest2d_memory_format0_mode_nearest-exact_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest3d_correctness_memory_format1_isize_20_osize_11_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_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_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_bfloat16, test/test_nn.py::TestNNDeviceTypeCPU::test_warp_softmax_64bit_indexing_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_warp_softmax_64bit_indexing_cpu_float32 2025-01-24T01:31:12.5847350Z 2025-01-24T01:31:12.5847859Z Running test_show_pickle 1/1 ... [2025-01-24 01:31:12.462206] 2025-01-24T01:31:12.5848310Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T01:31:12.5849430Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_show_pickle.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-01-24 01:31:12.462558] 2025-01-24T01:31:16.5324476Z 2025-01-24T01:31:16.5325348Z test_show_pickle 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_show_pickle_1.1_6ed5f46846c5929c_.log 2025-01-24T01:31:16.5326334Z Running 1 items in this shard: test/test_show_pickle.py::TestShowPickle::test_scripted_model 2025-01-24T01:31:16.5326827Z 2025-01-24T01:31:16.5327458Z Running test_fake_tensor 1/1 ... [2025-01-24 01:31:16.532586] 2025-01-24T01:31:16.5327944Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T01:31:16.5331188Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_fake_tensor.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-01-24 01:31:16.532894] 2025-01-24T01:31:33.8699666Z 2025-01-24T01:31:33.8700997Z test_fake_tensor 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_fake_tensor_1.1_30199d38fc7ec6b3_.log 2025-01-24T01:31:33.8815636Z Running 263 items in this shard: test/test_fake_tensor.py::FakeTensorTest::test__adaptive_avg_pool2d_backward, test/test_fake_tensor.py::FakeTensorTest::test_alias_call, test/test_fake_tensor.py::FakeTensorTest::test_allow_meta, test/test_fake_tensor.py::FakeTensorTest::test_aten_copy_multi_device, test/test_fake_tensor.py::FakeTensorTest::test_aten_index_multi_device, test/test_fake_tensor.py::FakeTensorTest::test_aten_slice_scatter_multi_device, test/test_fake_tensor.py::FakeTensorTest::test_basic, test/test_fake_tensor.py::FakeTensorTest::test_batch_tensor, test/test_fake_tensor.py::FakeTensorTest::test_binary_op_type_promotion, test/test_fake_tensor.py::FakeTensorTest::test_constructor, test/test_fake_tensor.py::FakeTensorTest::test_convert_fake_to_real, test/test_fake_tensor.py::FakeTensorTest::test_cpu_fallback, test/test_fake_tensor.py::FakeTensorTest::test_cuda_initialized, test/test_fake_tensor.py::FakeTensorTest::test_cuda_lstm, test/test_fake_tensor.py::FakeTensorTest::test_cudnn_rnn_with_fallback, test/test_fake_tensor.py::FakeTensorTest::test_cudnn_rnn_without_fallback, test/test_fake_tensor.py::FakeTensorTest::test_custom_op_fallback, test/test_fake_tensor.py::FakeTensorTest::test_data_dependent_operator, test/test_fake_tensor.py::FakeTensorTest::test_deepcopy, test/test_fake_tensor.py::FakeTensorTest::test_device_inplace_copy, test/test_fake_tensor.py::FakeTensorTest::test_embedding_bag_meta, test/test_fake_tensor.py::FakeTensorTest::test_export_numpy, test/test_fake_tensor.py::FakeTensorTest::test_fake_dispatch_keys, test/test_fake_tensor.py::FakeTensorTest::test_fake_grad_copy, test/test_fake_tensor.py::FakeTensorTest::test_fake_mode_error, test/test_fake_tensor.py::FakeTensorTest::test_from_numpy, test/test_fake_tensor.py::FakeTensorTest::test_fsdp_flat_param, test/test_fake_tensor.py::FakeTensorTest::test_full, test/test_fake_tensor.py::FakeTensorTest::test_index_cuda_with_cpu_complex128, test/test_fake_tensor.py::FakeTensorTest::test_index_cuda_with_cpu_complex64, test/test_fake_tensor.py::FakeTensorTest::test_index_cuda_with_cpu_float32, test/test_fake_tensor.py::FakeTensorTest::test_index_cuda_with_cpu_float64, test/test_fake_tensor.py::FakeTensorTest::test_index_cuda_with_cpu_float8_e4m3fn, test/test_fake_tensor.py::FakeTensorTest::test_index_cuda_with_cpu_float8_e4m3fnuz, test/test_fake_tensor.py::FakeTensorTest::test_index_cuda_with_cpu_float8_e5m2, test/test_fake_tensor.py::FakeTensorTest::test_index_cuda_with_cpu_float8_e5m2fnuz, test/test_fake_tensor.py::FakeTensorTest::test_index_cuda_with_cpu_int16, test/test_fake_tensor.py::FakeTensorTest::test_index_cuda_with_cpu_int32, test/test_fake_tensor.py::FakeTensorTest::test_index_cuda_with_cpu_int64, test/test_fake_tensor.py::FakeTensorTest::test_index_cuda_with_cpu_int8, test/test_fake_tensor.py::FakeTensorTest::test_index_cuda_with_cpu_uint8, test/test_fake_tensor.py::FakeTensorTest::test_index_put_error, test/test_fake_tensor.py::FakeTensorTest::test_jagged_fake_to_fake_preserved, test/test_fake_tensor.py::FakeTensorTest::test_like_constructor, test/test_fake_tensor.py::FakeTensorTest::test_mixed_real_and_fake_inputs, test/test_fake_tensor.py::FakeTensorTest::test_mode, test/test_fake_tensor.py::FakeTensorTest::test_nan_to_num, test/test_fake_tensor.py::FakeTensorTest::test_new, test/test_fake_tensor.py::FakeTensorTest::test_non_kwarg_device, test/test_fake_tensor.py::FakeTensorTest::test_non_overlapping_stride_zero, test/test_fake_tensor.py::FakeTensorTest::test_non_parameter_grad, test/test_fake_tensor.py::FakeTensorTest::test_normalize_device, test/test_fake_tensor.py::FakeTensorTest::test_out_multi_device, test/test_fake_tensor.py::FakeTensorTest::test_parameter_instantiation, test/test_fake_tensor.py::FakeTensorTest::test_parameter_view, test/test_fake_tensor.py::FakeTensorTest::test_print_in_fake_mode, test/test_fake_tensor.py::FakeTensorTest::test_randperm, test/test_fake_tensor.py::FakeTensorTest::test_recursive_invocation, test/test_fake_tensor.py::FakeTensorTest::test_repr, test/test_fake_tensor.py::FakeTensorTest::test_same_shape_env_preserved, test/test_fake_tensor.py::FakeTensorTest::test_scalar_inputs, test/test_fake_tensor.py::FakeTensorTest::test_scan_reverse_False, test/test_fake_tensor.py::FakeTensorTest::test_scan_reverse_True, test/test_fake_tensor.py::FakeTensorTest::test_setitem, test/test_fake_tensor.py::FakeTensorTest::test_shape_take_not_device, test/test_fake_tensor.py::FakeTensorTest::test_split_return_self, test/test_fake_tensor.py::FakeTensorTest::test_throw, test/test_fake_tensor.py::FakeTensorTest::test_tolist, test/test_fake_tensor.py::FakeTensorTest::test_type_as, test/test_fake_tensor.py::FakeTensorTest::test_unsqueeze_copy, test/test_fake_tensor.py::FakeTensorTest::test_upsample_bilinear_small_channels, test/test_fake_tensor.py::FakeTensorTest::test_zero_dim, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test__adaptive_avg_pool2d_backward_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_alias_call_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_allow_meta_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_aten_copy_multi_device_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_aten_index_multi_device_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_aten_slice_scatter_multi_device_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_basic_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_batch_tensor_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_binary_op_type_promotion_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_constructor_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_convert_fake_to_real_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_cpu_fallback_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_cuda_initialized_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_cuda_lstm_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_cudnn_rnn_with_fallback_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_cudnn_rnn_without_fallback_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_custom_op_fallback_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_data_dependent_operator_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_deepcopy_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_device_inplace_copy_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_embedding_bag_meta_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_export_numpy_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_fake_dispatch_keys_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_fake_grad_copy_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_fake_mode_error_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_from_numpy_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_fsdp_flat_param_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_full_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_index_cuda_with_cpu_complex128_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_index_cuda_with_cpu_complex64_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_index_cuda_with_cpu_float32_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_index_cuda_with_cpu_float64_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_index_cuda_with_cpu_float8_e4m3fn_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_index_cuda_with_cpu_float8_e4m3fnuz_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_index_cuda_with_cpu_float8_e5m2_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_index_cuda_with_cpu_float8_e5m2fnuz_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_index_cuda_with_cpu_int16_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_index_cuda_with_cpu_int32_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_index_cuda_with_cpu_int64_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_index_cuda_with_cpu_int8_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_index_cuda_with_cpu_uint8_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_index_put_error_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_jagged_fake_to_fake_preserved_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_like_constructor_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_mixed_real_and_fake_inputs_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_mode_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_nan_to_num_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_new_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_non_kwarg_device_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_non_overlapping_stride_zero_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_non_parameter_grad_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_normalize_device_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_out_multi_device_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_parameter_instantiation_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_parameter_view_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_print_in_fake_mode_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_randperm_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_recursive_invocation_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_repr_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_same_shape_env_preserved_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_scalar_inputs_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_scan_reverse_False_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_scan_reverse_True_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_setitem_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_shape_take_not_device_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_split_return_self_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_throw_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_tolist_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_type_as_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_unsqueeze_copy_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_upsample_bilinear_small_channels_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_zero_dim_propagate_real_tensors, test/test_fake_tensor.py::FakeTensorConstHandling::test_aliased_const_write, test/test_fake_tensor.py::FakeTensorConstHandling::test_constant_invalidation, test/test_fake_tensor.py::FakeTensorConstHandling::test_constant_propagate_through_functions, test/test_fake_tensor.py::FakeTensorConstHandling::test_fake_tensor_batch_norm_cpu, test/test_fake_tensor.py::FakeTensorConstHandling::test_fake_tensor_in_intlist_repro, test/test_fake_tensor.py::FakeTensorConstHandling::test_inplace_add, test/test_fake_tensor.py::FakeTensorConstHandling::test_inplace_view_invalidation, test/test_fake_tensor.py::FakeTensorConstHandling::test_shared_storage_invalidation, test/test_fake_tensor.py::FakeTensorConstHandling::test_shared_storages, test/test_fake_tensor.py::FakeTensorConstHandling::test_simple, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorConstHandling::test_aliased_const_write_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorConstHandling::test_constant_invalidation_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorConstHandling::test_constant_propagate_through_functions_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorConstHandling::test_fake_tensor_batch_norm_cpu_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorConstHandling::test_fake_tensor_in_intlist_repro_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorConstHandling::test_inplace_add_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorConstHandling::test_inplace_view_invalidation_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorConstHandling::test_shared_storage_invalidation_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorConstHandling::test_shared_storages_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorConstHandling::test_simple_propagate_real_tensors, test/test_fake_tensor.py::FakeTensorOpInfoTestCPU::test_fake_NumpyCatCustomOp_cpu_float32, test/test_fake_tensor.py::FakeTensorOpInfoTestCPU::test_fake_NumpyCubeCustomOp_cpu_float32, test/test_fake_tensor.py::FakeTensorOpInfoTestCPU::test_fake_NumpyMulCustomOp_cpu_float32, test/test_fake_tensor.py::FakeTensorOpInfoTestCPU::test_fake_NumpyMulScalarCustomOp_cpu_float32, test/test_fake_tensor.py::FakeTensorOpInfoTestCPU::test_fake_NumpyNMSCustomOp_cpu_float32, test/test_fake_tensor.py::FakeTensorOpInfoTestCPU::test_fake_NumpyNonzeroCustomOp_cpu_float32, test/test_fake_tensor.py::FakeTensorOpInfoTestCPU::test_fake_NumpySortCustomOp_cpu_float32, test/test_fake_tensor.py::FakeTensorOpInfoTestCPU::test_fake_NumpySplitCopyCustomOp_cpu_float32, test/test_fake_tensor.py::FakeTensorOpInfoTestCPU::test_fake_NumpySplitCopyWithIntCustomOp_cpu_float32, test/test_fake_tensor.py::FakeTensorOpInfoTestCPU::test_fake_NumpyTakeCustomOp_cpu_float32, test/test_fake_tensor.py::FakeTensorOpInfoTestCPU::test_fake_NumpyViewCopyCustomOp_cpu_float32, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOpInfoTestCPU::test_fake_propagate_real_tensors_NumpyCatCustomOp_cpu_float32, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOpInfoTestCPU::test_fake_propagate_real_tensors_NumpyCubeCustomOp_cpu_float32, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOpInfoTestCPU::test_fake_propagate_real_tensors_NumpyMulCustomOp_cpu_float32, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOpInfoTestCPU::test_fake_propagate_real_tensors_NumpyMulScalarCustomOp_cpu_float32, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOpInfoTestCPU::test_fake_propagate_real_tensors_NumpyNMSCustomOp_cpu_float32, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOpInfoTestCPU::test_fake_propagate_real_tensors_NumpyNonzeroCustomOp_cpu_float32, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOpInfoTestCPU::test_fake_propagate_real_tensors_NumpySortCustomOp_cpu_float32, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOpInfoTestCPU::test_fake_propagate_real_tensors_NumpySplitCopyCustomOp_cpu_float32, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOpInfoTestCPU::test_fake_propagate_real_tensors_NumpySplitCopyWithIntCustomOp_cpu_float32, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOpInfoTestCPU::test_fake_propagate_real_tensors_NumpyTakeCustomOp_cpu_float32, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOpInfoTestCPU::test_fake_propagate_real_tensors_NumpyViewCopyCustomOp_cpu_float32, test/test_fake_tensor.py::FakeTensorConverterTest::test_dead_key, test/test_fake_tensor.py::FakeTensorConverterTest::test_dead_weak_ref, test/test_fake_tensor.py::FakeTensorConverterTest::test_memoized_conversion_from_meta, test/test_fake_tensor.py::FakeTensorConverterTest::test_memoized_conversion_to_meta, test/test_fake_tensor.py::FakeTensorConverterTest::test_multiple_modes, test/test_fake_tensor.py::FakeTensorConverterTest::test_no_active_mode, test/test_fake_tensor.py::FakeTensorConverterTest::test_no_ref_cycle, test/test_fake_tensor.py::FakeTensorConverterTest::test_separate_mode_error, test/test_fake_tensor.py::FakeTensorConverterTest::test_separate_tensor_storages_non_view, test/test_fake_tensor.py::FakeTensorConverterTest::test_separate_tensor_storages_view, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorConverterTest::test_dead_key_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorConverterTest::test_dead_weak_ref_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorConverterTest::test_memoized_conversion_from_meta_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorConverterTest::test_memoized_conversion_to_meta_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorConverterTest::test_multiple_modes_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorConverterTest::test_no_active_mode_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorConverterTest::test_no_ref_cycle_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorConverterTest::test_separate_mode_error_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorConverterTest::test_separate_tensor_storages_non_view_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorConverterTest::test_separate_tensor_storages_view_propagate_real_tensors, test/test_fake_tensor.py::FakeTensorOperatorInvariants::test_conv_c1_backward, test/test_fake_tensor.py::FakeTensorOperatorInvariants::test_cross_entropy_loss, test/test_fake_tensor.py::FakeTensorOperatorInvariants::test_embedding_bag_private, test/test_fake_tensor.py::FakeTensorOperatorInvariants::test_fake_gpu_no_init, test/test_fake_tensor.py::FakeTensorOperatorInvariants::test_flash_attention, test/test_fake_tensor.py::FakeTensorOperatorInvariants::test_like_ops, test/test_fake_tensor.py::FakeTensorOperatorInvariants::test_no_dispatch_with_like_function, test/test_fake_tensor.py::FakeTensorOperatorInvariants::test_non_kwarg_only_device, test/test_fake_tensor.py::FakeTensorOperatorInvariants::test_sparse_new, test/test_fake_tensor.py::FakeTensorOperatorInvariants::test_str_storage, test/test_fake_tensor.py::FakeTensorOperatorInvariants::test_tensor_constructors_all_have_kwarg_device, test/test_fake_tensor.py::FakeTensorOperatorInvariants::test_tensor_new, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOperatorInvariants::test_conv_c1_backward_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOperatorInvariants::test_cross_entropy_loss_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOperatorInvariants::test_embedding_bag_private_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOperatorInvariants::test_fake_gpu_no_init_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOperatorInvariants::test_flash_attention_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOperatorInvariants::test_like_ops_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOperatorInvariants::test_no_dispatch_with_like_function_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOperatorInvariants::test_non_kwarg_only_device_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOperatorInvariants::test_sparse_new_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOperatorInvariants::test_str_storage_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOperatorInvariants::test_tensor_constructors_all_have_kwarg_device_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOperatorInvariants::test_tensor_new_propagate_real_tensors, test/test_fake_tensor.py::FakeTensorPropTest::test_fake_tensor_prop_on_nn_module, test/test_fake_tensor.py::FakeTensorPropTest::test_fake_tensor_prop_on_nn_module_with_optional_args, test/test_fake_tensor.py::FakeTensorPropTest::test_torch_load_with_fake_mode, test/test_fake_tensor.py::FakeTensorPropTest::test_unbacked_shape_realloc, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorPropTest::test_fake_tensor_prop_on_nn_module_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorPropTest::test_fake_tensor_prop_on_nn_module_with_optional_args_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorPropTest::test_torch_load_with_fake_mode_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorPropTest::test_unbacked_shape_realloc_propagate_real_tensors, test/test_fake_tensor.py::FakeTensorSerialization::test_serialization, test/test_fake_tensor.py::FakeTensorSerialization::test_serialization_with_tracing, test/test_fake_tensor.py::FakeTensorDispatchCache::test__upsample_bilinear2d_aa_backward_dynamic_shapes, test/test_fake_tensor.py::FakeTensorDispatchCache::test_cache_bypass, test/test_fake_tensor.py::FakeTensorDispatchCache::test_cache_default_device, test/test_fake_tensor.py::FakeTensorDispatchCache::test_cache_default_dtype, test/test_fake_tensor.py::FakeTensorDispatchCache::test_cache_dispatch_key_set, test/test_fake_tensor.py::FakeTensorDispatchCache::test_cache_hit, test/test_fake_tensor.py::FakeTensorDispatchCache::test_cache_inplace_op, test/test_fake_tensor.py::FakeTensorDispatchCache::test_cache_key_constants, test/test_fake_tensor.py::FakeTensorDispatchCache::test_cache_key_device, test/test_fake_tensor.py::FakeTensorDispatchCache::test_cache_key_dtype, test/test_fake_tensor.py::FakeTensorDispatchCache::test_cache_key_is_conj, test/test_fake_tensor.py::FakeTensorDispatchCache::test_cache_key_is_inference, test/test_fake_tensor.py::FakeTensorDispatchCache::test_cache_key_is_neg, test/test_fake_tensor.py::FakeTensorDispatchCache::test_cache_key_memory_format, test/test_fake_tensor.py::FakeTensorDispatchCache::test_cache_key_requires_grad, test/test_fake_tensor.py::FakeTensorDispatchCache::test_cache_key_shape, test/test_fake_tensor.py::FakeTensorDispatchCache::test_cache_key_storage_offset, test/test_fake_tensor.py::FakeTensorDispatchCache::test_cache_key_stride, test/test_fake_tensor.py::FakeTensorDispatchCache::test_cache_tuple_outputs, test/test_fake_tensor.py::FakeTensorDispatchCache::test_cache_view_op, test/test_fake_tensor.py::FakeTensorDispatchCache::test_inference_mode, test/test_fake_tensor.py::FakeTensorDispatchCache::test_shape_env_settings, test/test_fake_tensor.py::FakeTensorDispatchCache::test_wrapper_tensor_subclass_different_device 2025-01-24T01:31:33.8921086Z 2025-01-24T01:31:33.8921313Z Running test_multiprocessing 1/1 ... [2025-01-24 01:31:33.870820] 2025-01-24T01:31:33.8921770Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T01:31:33.8922850Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_multiprocessing.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-01-24 01:31:33.871215] 2025-01-24T01:32:45.5323930Z 2025-01-24T01:32:45.5325267Z test_multiprocessing 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_multiprocessing_1.1_4360ca48f0f3af02_.log 2025-01-24T01:32:45.5340631Z Running 41 items in this shard: test/test_multiprocessing.py::TestMultiprocessing::test_autograd_errors, test/test_multiprocessing.py::TestMultiprocessing::test_autograd_fine_with_spawn, test/test_multiprocessing.py::TestMultiprocessing::test_cuda_bad_call, test/test_multiprocessing.py::TestMultiprocessing::test_cuda_ipc_deadlock, test/test_multiprocessing.py::TestMultiprocessing::test_cuda_memory_allocation, test/test_multiprocessing.py::TestMultiprocessing::test_cuda_parameter_sharing, test/test_multiprocessing.py::TestMultiprocessing::test_cuda_send_many, test/test_multiprocessing.py::TestMultiprocessing::test_cuda_simple, test/test_multiprocessing.py::TestMultiprocessing::test_cuda_small_tensors, test/test_multiprocessing.py::TestMultiprocessing::test_cuda_variable_sharing, test/test_multiprocessing.py::TestMultiprocessing::test_empty_shared, test/test_multiprocessing.py::TestMultiprocessing::test_empty_tensor_sharing, test/test_multiprocessing.py::TestMultiprocessing::test_empty_tensor_sharing_cuda, test/test_multiprocessing.py::TestMultiprocessing::test_empty_tensor_sharing_meta, test/test_multiprocessing.py::TestMultiprocessing::test_event, test/test_multiprocessing.py::TestMultiprocessing::test_event_handle_exporter, test/test_multiprocessing.py::TestMultiprocessing::test_event_handle_importer, test/test_multiprocessing.py::TestMultiprocessing::test_event_handle_multi_gpu, test/test_multiprocessing.py::TestMultiprocessing::test_event_multiprocess, test/test_multiprocessing.py::TestMultiprocessing::test_fd_pool, test/test_multiprocessing.py::TestMultiprocessing::test_fd_preserve_sharing, test/test_multiprocessing.py::TestMultiprocessing::test_fd_sharing, test/test_multiprocessing.py::TestMultiprocessing::test_fs, test/test_multiprocessing.py::TestMultiprocessing::test_fs_is_shared, test/test_multiprocessing.py::TestMultiprocessing::test_fs_pool, test/test_multiprocessing.py::TestMultiprocessing::test_fs_preserve_sharing, test/test_multiprocessing.py::TestMultiprocessing::test_fs_sharing, test/test_multiprocessing.py::TestMultiprocessing::test_inherit_tensor, test/test_multiprocessing.py::TestMultiprocessing::test_integer_parameter_serialization_cpu, test/test_multiprocessing.py::TestMultiprocessing::test_integer_parameter_serialization_cuda, test/test_multiprocessing.py::TestMultiprocessing::test_is_shared, test/test_multiprocessing.py::TestMultiprocessing::test_is_shared_cuda, test/test_multiprocessing.py::TestMultiprocessing::test_leaf_variable_sharing, test/test_multiprocessing.py::TestMultiprocessing::test_meta_simple, test/test_multiprocessing.py::TestMultiprocessing::test_mixed_types_cuda_sharing, test/test_multiprocessing.py::TestMultiprocessing::test_non_leaf_variable_sharing, test/test_multiprocessing.py::TestMultiprocessing::test_parameter_sharing, test/test_multiprocessing.py::TestMultiprocessing::test_set_thread_name, test/test_multiprocessing.py::TestMultiprocessing::test_tensor_sharing_meta, test/test_multiprocessing.py::TestMultiprocessing::test_variable_sharing, test/test_multiprocessing.py::TestMultiprocessing::test_wrong_cuda_fork 2025-01-24T01:32:45.5354461Z 2025-01-24T01:32:45.5354639Z Running test_autocast 1/1 ... [2025-01-24 01:32:45.532631] 2025-01-24T01:32:45.5355054Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T01:32:45.5356078Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_autocast.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-01-24 01:32:45.532940] 2025-01-24T01:33:02.5196040Z 2025-01-24T01:33:02.5196933Z test_autocast 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_autocast_1.1_08ff11df782a9331_.log 2025-01-24T01:33:02.5203722Z Running 20 items in this shard: test/test_autocast.py::TestAutocastCPU::test_autocast_disabled_with_fp32_dtype, test/test_autocast.py::TestAutocastCPU::test_autocast_methods_expect_builtin_promote, test/test_autocast.py::TestAutocastCPU::test_autocast_nn_16, test/test_autocast.py::TestAutocastCPU::test_autocast_nn_fp32, test/test_autocast.py::TestAutocastCPU::test_autocast_rnn, test/test_autocast.py::TestAutocastCPU::test_autocast_torch_16, test/test_autocast.py::TestAutocastCPU::test_autocast_torch_expect_builtin_promote, test/test_autocast.py::TestAutocastCPU::test_autocast_torch_fp32, test/test_autocast.py::TestAutocastCPU::test_autocast_torch_need_autocast_promote, test/test_autocast.py::TestAutocastCPU::test_cpu_autocast_deprecated_warning, test/test_autocast.py::TestAutocastCPU::test_generic_autocast, test/test_autocast.py::TestAutocastGPU::test_autocast_prioritize, test/test_autocast.py::TestAutocastGPU::test_cache_disabled, test/test_autocast.py::TestAutocastGPU::test_cast_cache_is_global, test/test_autocast.py::TestAutocastMPS::test_cast_cache_is_global, test/test_autocast.py::TestAutocastMPS::test_mps_autocast_bfloat16_supported, test/test_autocast.py::TestAutocastMPS::test_mps_autocast_error_message, test/test_autocast.py::TestTorchAutocast::test_autocast_fast_dtype, test/test_autocast.py::TestTorchAutocast::test_invalid_device, test/test_autocast.py::TestTorchAutocast::test_non_string_device 2025-01-24T01:33:02.5209887Z 2025-01-24T01:33:02.5210114Z Running test_tensor_creation_ops 1/1 ... [2025-01-24 01:33:02.519840] 2025-01-24T01:33:02.5210567Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T01:33:02.5211639Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_tensor_creation_ops.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-01-24 01:33:02.520192] 2025-01-24T01:36:36.7569268Z 2025-01-24T01:36:36.7570810Z test_tensor_creation_ops 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_tensor_creation_ops_1.1_c561bc509d2b494d_.log 2025-01-24T01:36:36.7819232Z Running 659 items in this shard: test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_arange_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_arange_device_vs_cpu_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_arange_device_vs_cpu_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_arange_device_vs_cpu_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_arange_device_vs_cpu_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_arange_inference_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_arange_lowp_cpu_bfloat16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_arange_lowp_cpu_float16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_as_strided_neg_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_as_tensor_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_block_diag_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_block_diag_scipy_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cartesian_prod_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat2_cpu_float16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat2_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat2_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_all_dtypes_and_devices_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_big_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_empty_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_empty_legacy_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_in_channels_last_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_mem_overlap_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_out_channels_last_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_out_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_out_fast_path_dim0_dim1_cpu_complex128, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_out_fast_path_dim0_dim1_cpu_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_out_fast_path_dim0_dim1_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_out_fast_path_dim0_dim1_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_out_fast_path_dim0_dim1_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_out_fast_path_dim0_dim1_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_out_fast_path_dim0_dim1_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_out_fast_path_dim0_dim1_cpu_int8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_out_fast_path_dim0_dim1_cpu_uint16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_out_fast_path_dim0_dim1_cpu_uint32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_out_fast_path_dim0_dim1_cpu_uint64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_out_fast_path_dim0_dim1_cpu_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_out_memory_format_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_preserve_channels_last_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_stack_cross_devices_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_combinations_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_complex_type_conversions_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_constructor_device_legacy_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_constructor_dtypes_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_ctor_with_numpy_array_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_device_rounding_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_device_rounding_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_diag_embed_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_diagflat_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_dsplit_cpu_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_dsplit_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_dsplit_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_dstack_cpu_complex128, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_dstack_cpu_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_dstack_cpu_float16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_dstack_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_dstack_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_dstack_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_dstack_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_dstack_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_dstack_cpu_int8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_dstack_cpu_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_empty_full_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_empty_overflow_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_empty_strided_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_empty_tensor_props_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_eye_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_fill_all_dtypes_and_devices_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_float_to_int_conversion_finite_cpu_bool, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_float_to_int_conversion_finite_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_float_to_int_conversion_finite_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_float_to_int_conversion_finite_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_float_to_int_conversion_finite_cpu_int8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_float_to_int_conversion_finite_cpu_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_float_to_int_conversion_nonfinite_cpu_bool, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_float_to_int_conversion_nonfinite_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_float_to_int_conversion_nonfinite_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_float_to_int_conversion_nonfinite_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_float_to_int_conversion_nonfinite_cpu_int8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_float_to_int_conversion_nonfinite_cpu_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_from_file_shared_False_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_from_file_shared_True_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_full_inference_cpu_float16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_full_inference_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_full_inference_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_full_out_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_hsplit_cpu_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_hsplit_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_hsplit_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_hstack_column_stack_cpu_complex128, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_hstack_column_stack_cpu_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_hstack_column_stack_cpu_float16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_hstack_column_stack_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_hstack_column_stack_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_hstack_column_stack_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_hstack_column_stack_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_hstack_column_stack_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_hstack_column_stack_cpu_int8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_hstack_column_stack_cpu_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_kaiser_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_kaiser_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_kaiser_window_cpu_bfloat16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_kaiser_window_cpu_float16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_kaiser_window_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_kaiser_window_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_kaiser_window_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_large_linspace_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_large_linspace_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_like_fn_stride_proparation_vs_tensoriterator_unary_op_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linlogspace_mem_overlap_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_cpu_bfloat16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_cpu_complex128, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_cpu_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_cpu_int8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_cpu_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_deduction_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_device_vs_cpu_cpu_bfloat16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_device_vs_cpu_cpu_complex128, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_device_vs_cpu_cpu_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_device_vs_cpu_cpu_float16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_device_vs_cpu_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_device_vs_cpu_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_special_steps_cpu_bfloat16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_special_steps_cpu_complex128, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_special_steps_cpu_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_special_steps_cpu_float16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_special_steps_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_special_steps_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_vs_numpy_complex_cpu_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_vs_numpy_cpu_complex128, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_vs_numpy_cpu_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_vs_numpy_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_vs_numpy_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_vs_numpy_integral_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_vs_numpy_integral_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_vs_numpy_integral_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_vs_numpy_integral_cpu_int8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_vs_numpy_integral_cpu_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_base2_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_base2_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_cpu_bfloat16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_cpu_int8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_cpu_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_deduction_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_device_vs_cpu_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_device_vs_cpu_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_special_steps_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_special_steps_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_vs_numpy_complex_cpu_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_vs_numpy_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_vs_numpy_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_meshgrid_default_indexing_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_meshgrid_empty_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_meshgrid_ij_indexing_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_meshgrid_ij_indexing_is_default_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_meshgrid_inconsistent_device_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_meshgrid_inconsistent_dtype_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_meshgrid_non_1d_tensor_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_meshgrid_unsupported_indexing_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_meshgrid_vs_numpy_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_meshgrid_warns_if_no_indexing_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_meshgrid_xy_indexing_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_new_empty_strided_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_new_methods_requires_grad_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_new_tensor_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_offset_scalar_cast_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_ones_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_bool_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_cpu_int8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_default_cpu_bfloat16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_default_cpu_float16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_default_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_default_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_default_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_default_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_default_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_default_cpu_int8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_default_cpu_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_from_to_bool_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_from_to_cpu_bfloat16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_from_to_cpu_float16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_from_to_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_from_to_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_from_to_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_from_to_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_from_to_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_from_to_cpu_int8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_from_to_cpu_uint16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_from_to_cpu_uint32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_from_to_cpu_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_full_range_cpu_bfloat16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_full_range_cpu_float16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_full_range_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_full_range_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_full_range_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_full_range_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_full_range_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_full_range_cpu_int8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_full_range_cpu_uint16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_full_range_cpu_uint32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_full_range_cpu_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_to_cpu_bfloat16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_to_cpu_float16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_to_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_to_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_to_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_to_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_to_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_to_cpu_int8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_to_cpu_uint16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_to_cpu_uint32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_to_cpu_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_range_cpu_bfloat16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_range_cpu_float16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_range_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_range_factories_64bit_indexing_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_range_warning_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_refs_tensor_cpu_bfloat16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_refs_tensor_cpu_bool, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_refs_tensor_cpu_complex128, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_refs_tensor_cpu_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_refs_tensor_cpu_float16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_refs_tensor_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_refs_tensor_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_refs_tensor_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_refs_tensor_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_refs_tensor_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_refs_tensor_cpu_int8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_refs_tensor_cpu_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_repeat_interleave_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_roll_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_window_functions_window_bartlett_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_window_functions_window_bartlett_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_window_functions_window_bartlett_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_window_functions_window_blackman_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_window_functions_window_blackman_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_window_functions_window_blackman_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_window_functions_window_hamming_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_window_functions_window_hamming_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_window_functions_window_hamming_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_window_functions_window_hann_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_window_functions_window_hann_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_window_functions_window_hann_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_windows_functions_window_bartlett_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_windows_functions_window_bartlett_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_windows_functions_window_blackman_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_windows_functions_window_blackman_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_windows_functions_window_cosine_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_windows_functions_window_cosine_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_windows_functions_window_hamming_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_windows_functions_window_hamming_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_windows_functions_window_hann_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_windows_functions_window_hann_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_windows_functions_window_nuttall_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_windows_functions_window_nuttall_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_simple_scalar_cast_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_stack_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_stack_out_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_storage_filename_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_strided_mismatched_stride_shape_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_tensor_ctor_device_inference_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_tensor_device_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_tensor_factories_empty_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_tensor_factory_copy_var_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_tensor_factory_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_tensor_factory_gpu_type_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_tensor_factory_gpu_type_inference_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_tensor_factory_type_inference_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_tensor_from_non_writable_numpy_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_tensor_from_sequence_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_torch_complex_cpu_float16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_torch_complex_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_torch_complex_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_torch_complex_floating_dtype_error_cpu_bool, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_torch_complex_floating_dtype_error_cpu_complex128, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_torch_complex_floating_dtype_error_cpu_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_torch_complex_floating_dtype_error_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_torch_complex_floating_dtype_error_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_torch_complex_floating_dtype_error_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_torch_complex_floating_dtype_error_cpu_int8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_torch_complex_floating_dtype_error_cpu_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_torch_complex_out_dtype_error_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_torch_complex_out_dtype_error_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_torch_complex_same_dtype_error_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_torch_complex_same_dtype_error_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_torch_polar_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_torch_polar_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_unpack_double_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_unpack_double_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vander_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vander_types_cpu_bool, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vander_types_cpu_complex128, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vander_types_cpu_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vander_types_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vander_types_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vander_types_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vander_types_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vander_types_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vander_types_cpu_int8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vander_types_cpu_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vsplit_cpu_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vsplit_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vsplit_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vstack_row_stack_cpu_complex128, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vstack_row_stack_cpu_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vstack_row_stack_cpu_float16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vstack_row_stack_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vstack_row_stack_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vstack_row_stack_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vstack_row_stack_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vstack_row_stack_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vstack_row_stack_cpu_int8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vstack_row_stack_cpu_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_zeros_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_zeros_dtype_layout_device_match_cpu_bool, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_zeros_dtype_layout_device_match_cpu_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_zeros_dtype_layout_device_match_cpu_float16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_zeros_dtype_layout_device_match_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_zeros_dtype_layout_device_match_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_zeros_dtype_layout_device_match_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_zeros_dtype_layout_device_match_cpu_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_zeros_out_cpu, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_normal_cpu_float32, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_normal_cpu_float64, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_normal_std_error_cpu, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_rand_cpu_complex128, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_rand_cpu_complex32, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_rand_cpu_complex64, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_rand_cpu_float32, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_rand_cpu_float64, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_rand_like_cpu_complex128, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_rand_like_cpu_complex32, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_rand_like_cpu_complex64, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_rand_like_cpu_float32, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_rand_like_cpu_float64, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_randint_cpu, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_randint_distribution_cpu, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_randint_inference_cpu, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_randint_like_cpu_int16, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_randint_like_cpu_int32, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_randint_like_cpu_int64, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_randint_like_cpu_int8, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_randint_like_cpu_uint16, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_randint_like_cpu_uint32, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_randint_like_cpu_uint64, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_randint_like_cpu_uint8, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_randn_cpu_bfloat16, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_randn_cpu_complex128, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_randn_cpu_complex32, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_randn_cpu_complex64, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_randn_cpu_float16, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_randn_cpu_float32, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_randn_cpu_float64, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_randn_like_cpu_bfloat16, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_randn_like_cpu_complex128, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_randn_like_cpu_complex32, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_randn_like_cpu_complex64, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_randn_like_cpu_float16, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_randn_like_cpu_float32, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_randn_like_cpu_float64, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_random_neg_values_cpu, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_randperm_cpu, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_randperm_device_compatibility_cpu, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_randperm_large_cpu, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_uniform_from_to_cpu_float16, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_uniform_from_to_cpu_float32, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_uniform_from_to_cpu_float64, test/test_tensor_creation_ops.py::TestLikeTensorCreationCPU::test_empty_like_cpu, test/test_tensor_creation_ops.py::TestLikeTensorCreationCPU::test_full_like_inference_cpu, test/test_tensor_creation_ops.py::TestLikeTensorCreationCPU::test_ones_like_cpu, test/test_tensor_creation_ops.py::TestLikeTensorCreationCPU::test_ones_like_multiple_device_cpu, test/test_tensor_creation_ops.py::TestLikeTensorCreationCPU::test_zeros_like_cpu, test/test_tensor_creation_ops.py::TestLikeTensorCreationCPU::test_zeros_like_multiple_device_cpu, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_byte_to_int_cpu, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_invalid_positional_args_cpu_bool, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_invalid_positional_args_cpu_complex128, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_invalid_positional_args_cpu_complex64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_invalid_positional_args_cpu_float16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_invalid_positional_args_cpu_float32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_invalid_positional_args_cpu_float64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_invalid_positional_args_cpu_int16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_invalid_positional_args_cpu_int32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_invalid_positional_args_cpu_int64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_invalid_positional_args_cpu_int8, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_invalid_positional_args_cpu_uint16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_invalid_positional_args_cpu_uint32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_invalid_positional_args_cpu_uint64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_invalid_positional_args_cpu_uint8, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_non_writable_buffer_cpu_bool, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_non_writable_buffer_cpu_complex128, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_non_writable_buffer_cpu_complex64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_non_writable_buffer_cpu_float16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_non_writable_buffer_cpu_float32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_non_writable_buffer_cpu_float64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_non_writable_buffer_cpu_int16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_non_writable_buffer_cpu_int32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_non_writable_buffer_cpu_int64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_non_writable_buffer_cpu_int8, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_non_writable_buffer_cpu_uint16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_non_writable_buffer_cpu_uint32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_non_writable_buffer_cpu_uint64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_non_writable_buffer_cpu_uint8, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_not_a_buffer_cpu_bool, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_not_a_buffer_cpu_complex128, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_not_a_buffer_cpu_complex64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_not_a_buffer_cpu_float16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_not_a_buffer_cpu_float32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_not_a_buffer_cpu_float64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_not_a_buffer_cpu_int16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_not_a_buffer_cpu_int32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_not_a_buffer_cpu_int64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_not_a_buffer_cpu_int8, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_not_a_buffer_cpu_uint16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_not_a_buffer_cpu_uint32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_not_a_buffer_cpu_uint64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_not_a_buffer_cpu_uint8, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_requires_grad_cpu_bool, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_requires_grad_cpu_complex128, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_requires_grad_cpu_complex64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_requires_grad_cpu_float16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_requires_grad_cpu_float32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_requires_grad_cpu_float64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_requires_grad_cpu_int16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_requires_grad_cpu_int32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_requires_grad_cpu_int64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_requires_grad_cpu_int8, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_requires_grad_cpu_uint16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_requires_grad_cpu_uint32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_requires_grad_cpu_uint64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_requires_grad_cpu_uint8, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_same_type_cpu_bool, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_same_type_cpu_complex128, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_same_type_cpu_complex64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_same_type_cpu_float16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_same_type_cpu_float32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_same_type_cpu_float64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_same_type_cpu_int16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_same_type_cpu_int32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_same_type_cpu_int64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_same_type_cpu_int8, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_same_type_cpu_uint16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_same_type_cpu_uint32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_same_type_cpu_uint64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_same_type_cpu_uint8, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_shared_buffer_cpu_bool, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_shared_buffer_cpu_complex128, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_shared_buffer_cpu_complex64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_shared_buffer_cpu_float16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_shared_buffer_cpu_float32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_shared_buffer_cpu_float64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_shared_buffer_cpu_int16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_shared_buffer_cpu_int32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_shared_buffer_cpu_int64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_shared_buffer_cpu_int8, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_shared_buffer_cpu_uint16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_shared_buffer_cpu_uint32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_shared_buffer_cpu_uint64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_shared_buffer_cpu_uint8, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_and_offset_cpu_bool, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_and_offset_cpu_complex128, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_and_offset_cpu_complex64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_and_offset_cpu_float16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_and_offset_cpu_float32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_and_offset_cpu_float64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_and_offset_cpu_int16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_and_offset_cpu_int32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_and_offset_cpu_int64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_and_offset_cpu_int8, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_and_offset_cpu_uint16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_and_offset_cpu_uint32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_and_offset_cpu_uint64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_and_offset_cpu_uint8, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_cpu_bool, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_cpu_complex128, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_cpu_complex64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_cpu_float16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_cpu_float32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_cpu_float64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_cpu_int16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_cpu_int32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_cpu_int64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_cpu_int8, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_cpu_uint16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_cpu_uint32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_cpu_uint64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_count_cpu_uint8, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_offset_cpu_bool, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_offset_cpu_complex128, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_offset_cpu_complex64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_offset_cpu_float16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_offset_cpu_float32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_offset_cpu_float64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_offset_cpu_int16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_offset_cpu_int32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_offset_cpu_int64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_offset_cpu_int8, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_offset_cpu_uint16, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_offset_cpu_uint32, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_offset_cpu_uint64, test/test_tensor_creation_ops.py::TestBufferProtocolCPU::test_with_offset_cpu_uint8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_buffer_cpu_bool, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_buffer_cpu_complex128, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_buffer_cpu_complex64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_buffer_cpu_float16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_buffer_cpu_float32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_buffer_cpu_float64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_buffer_cpu_int16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_buffer_cpu_int32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_buffer_cpu_int64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_buffer_cpu_int8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_buffer_cpu_uint16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_buffer_cpu_uint32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_buffer_cpu_uint64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_buffer_cpu_uint8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_dlpack_cpu_bfloat16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_dlpack_cpu_complex128, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_dlpack_cpu_complex64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_dlpack_cpu_float16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_dlpack_cpu_float32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_dlpack_cpu_float64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_dlpack_cpu_int16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_dlpack_cpu_int32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_dlpack_cpu_int64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_dlpack_cpu_int8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_dlpack_cpu_uint8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_numpy_cpu_bool, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_numpy_cpu_complex128, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_numpy_cpu_complex64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_numpy_cpu_float16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_numpy_cpu_float32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_numpy_cpu_float64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_numpy_cpu_int16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_numpy_cpu_int32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_numpy_cpu_int64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_numpy_cpu_int8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_numpy_cpu_uint16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_numpy_cpu_uint32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_numpy_cpu_uint64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_numpy_cpu_uint8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_tensor_cpu_bfloat16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_tensor_cpu_bool, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_tensor_cpu_complex128, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_tensor_cpu_complex64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_tensor_cpu_float16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_tensor_cpu_float32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_tensor_cpu_float64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_tensor_cpu_int16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_tensor_cpu_int32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_tensor_cpu_int64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_tensor_cpu_int8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_alias_from_tensor_cpu_uint8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_astensor_consistency_cpu, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_buffer_cpu_bool, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_buffer_cpu_complex128, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_buffer_cpu_complex64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_buffer_cpu_float16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_buffer_cpu_float32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_buffer_cpu_float64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_buffer_cpu_int16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_buffer_cpu_int32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_buffer_cpu_int64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_buffer_cpu_int8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_buffer_cpu_uint16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_buffer_cpu_uint32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_buffer_cpu_uint64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_buffer_cpu_uint8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_cpu_bfloat16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_cpu_complex128, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_cpu_complex64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_cpu_float16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_cpu_float32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_cpu_float64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_cpu_int16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_cpu_int32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_cpu_int64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_cpu_int8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_cpu_uint8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_mult_devices_cpu_bfloat16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_mult_devices_cpu_complex128, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_mult_devices_cpu_complex64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_mult_devices_cpu_float16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_mult_devices_cpu_float32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_mult_devices_cpu_float64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_mult_devices_cpu_int16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_mult_devices_cpu_int32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_mult_devices_cpu_int64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_mult_devices_cpu_int8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_dlpack_mult_devices_cpu_uint8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_numpy_cpu_bool, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_numpy_cpu_complex128, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_numpy_cpu_complex64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_numpy_cpu_float16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_numpy_cpu_float32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_numpy_cpu_float64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_numpy_cpu_int16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_numpy_cpu_int32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_numpy_cpu_int64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_numpy_cpu_int8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_numpy_cpu_uint16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_numpy_cpu_uint32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_numpy_cpu_uint64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_numpy_cpu_uint8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_tensor_mult_devices_cpu_bfloat16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_tensor_mult_devices_cpu_complex128, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_tensor_mult_devices_cpu_complex64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_tensor_mult_devices_cpu_float16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_tensor_mult_devices_cpu_float32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_tensor_mult_devices_cpu_float64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_tensor_mult_devices_cpu_int16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_tensor_mult_devices_cpu_int32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_tensor_mult_devices_cpu_int64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_tensor_mult_devices_cpu_int8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_from_tensor_mult_devices_cpu_uint8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_list_cpu_bfloat16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_list_cpu_bool, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_list_cpu_complex128, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_list_cpu_complex64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_list_cpu_float16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_list_cpu_float32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_list_cpu_float64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_list_cpu_int16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_list_cpu_int32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_list_cpu_int64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_list_cpu_int8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_list_cpu_uint8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_tensor_cpu_bfloat16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_tensor_cpu_bool, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_tensor_cpu_complex128, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_tensor_cpu_complex64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_tensor_cpu_float16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_tensor_cpu_float32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_tensor_cpu_float64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_tensor_cpu_int16, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_tensor_cpu_int32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_tensor_cpu_int64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_tensor_cpu_int8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_copy_tensor_cpu_uint8, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_default_device_cpu, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_device_without_index_cpu, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_numpy_scalars_cpu, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_retain_autograd_history_cpu_complex64, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_retain_autograd_history_cpu_float32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_unsupported_alias_cpu_float32, test/test_tensor_creation_ops.py::TestAsArrayCPU::test_unsupported_alias_mult_devices_cpu_float32 2025-01-24T01:36:36.8060760Z 2025-01-24T01:36:36.8061000Z Running test_extension_utils 1/1 ... [2025-01-24 01:36:36.758432] 2025-01-24T01:36:36.8061448Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T01:36:36.8062515Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_extension_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'] ... [2025-01-24 01:36:36.758746] 2025-01-24T01:36:40.4082545Z 2025-01-24T01:36:40.4083459Z test_extension_utils 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_extension_utils_1.1_cbce3092a1c8989c_.log 2025-01-24T01:36:40.4085065Z Running 2 items in this shard: test/test_extension_utils.py::TestExtensionUtils::test_external_module_register, test/test_extension_utils.py::TestExtensionUtils::test_external_module_register_with_renamed_backend 2025-01-24T01:36:40.4086168Z 2025-01-24T01:36:40.4086390Z Running test_mobile_optimizer 1/1 ... [2025-01-24 01:36:40.408434] 2025-01-24T01:36:40.4086879Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T01:36:40.4090376Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_mobile_optimizer.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-01-24 01:36:40.408748] 2025-01-24T01:36:48.4834921Z 2025-01-24T01:36:48.4835901Z test_mobile_optimizer 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_mobile_optimizer_1.1_3474b7a66009c733_.log 2025-01-24T01:36:48.4839527Z Running 7 items in this shard: test/test_mobile_optimizer.py::TestOptimizer::test_clone_module_with_class, test/test_mobile_optimizer.py::TestOptimizer::test_generate_mobile_module_lints, test/test_mobile_optimizer.py::TestOptimizer::test_hoist_conv_packed_params, test/test_mobile_optimizer.py::TestOptimizer::test_mobilenet_optimize_for_mobile, test/test_mobile_optimizer.py::TestOptimizer::test_optimize_for_mobile, test/test_mobile_optimizer.py::TestOptimizer::test_preserve_bundled_inputs_methods, test/test_mobile_optimizer.py::TestOptimizer::test_quantized_conv_no_asan_failures 2025-01-24T01:36:48.4842549Z 2025-01-24T01:36:48.4842849Z Running test_multiprocessing_spawn 1/1 ... [2025-01-24 01:36:48.483662] 2025-01-24T01:36:48.4843325Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T01:36:48.4844526Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_multiprocessing_spawn.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-01-24 01:36:48.483973] 2025-01-24T01:39:10.2383129Z 2025-01-24T01:39:10.2384719Z test_multiprocessing_spawn 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_multiprocessing_spawn_1.1_fbac800e756698fb_.log 2025-01-24T01:39:10.2405122Z Running 31 items in this shard: test/test_multiprocessing_spawn.py::SpawnTest::test_exception_all, test/test_multiprocessing_spawn.py::SpawnTest::test_exception_raises, test/test_multiprocessing_spawn.py::SpawnTest::test_exception_single, test/test_multiprocessing_spawn.py::SpawnTest::test_first_argument_index, test/test_multiprocessing_spawn.py::SpawnTest::test_signal_raises, test/test_multiprocessing_spawn.py::SpawnTest::test_success, test/test_multiprocessing_spawn.py::SpawnTest::test_success_first_then_exception, test/test_multiprocessing_spawn.py::SpawnTest::test_success_non_blocking, test/test_multiprocessing_spawn.py::SpawnTest::test_terminate_exit_grace_period0, test/test_multiprocessing_spawn.py::SpawnTest::test_terminate_exit_grace_period_5, test/test_multiprocessing_spawn.py::SpawnTest::test_terminate_signal, test/test_multiprocessing_spawn.py::ForkTest::test_exception_all, test/test_multiprocessing_spawn.py::ForkTest::test_exception_single, test/test_multiprocessing_spawn.py::ForkTest::test_first_argument_index, test/test_multiprocessing_spawn.py::ForkTest::test_success, test/test_multiprocessing_spawn.py::ForkTest::test_success_first_then_exception, test/test_multiprocessing_spawn.py::ForkTest::test_success_non_blocking, test/test_multiprocessing_spawn.py::ForkTest::test_terminate_exit_grace_period0, test/test_multiprocessing_spawn.py::ForkTest::test_terminate_exit_grace_period_5, test/test_multiprocessing_spawn.py::ForkTest::test_terminate_signal, test/test_multiprocessing_spawn.py::ParallelForkServerShouldWorkTest::test_exception_all, test/test_multiprocessing_spawn.py::ParallelForkServerShouldWorkTest::test_exception_single, test/test_multiprocessing_spawn.py::ParallelForkServerShouldWorkTest::test_first_argument_index, test/test_multiprocessing_spawn.py::ParallelForkServerShouldWorkTest::test_success, test/test_multiprocessing_spawn.py::ParallelForkServerShouldWorkTest::test_success_first_then_exception, test/test_multiprocessing_spawn.py::ParallelForkServerShouldWorkTest::test_success_non_blocking, test/test_multiprocessing_spawn.py::ParallelForkServerShouldWorkTest::test_terminate_exit_grace_period0, test/test_multiprocessing_spawn.py::ParallelForkServerShouldWorkTest::test_terminate_exit_grace_period_5, test/test_multiprocessing_spawn.py::ParallelForkServerShouldWorkTest::test_terminate_signal, test/test_multiprocessing_spawn.py::ParallelForkServerPerfTest::test_forkserver_perf, test/test_multiprocessing_spawn.py::ErrorTest::test_errors_pickleable 2025-01-24T01:39:10.2425473Z 2025-01-24T01:39:10.2425854Z Running test_spectral_ops 1/1 ... [2025-01-24 01:39:10.238703] 2025-01-24T01:39:10.2426650Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T01:39:10.2428402Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_spectral_ops.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-01-24 01:39:10.239109] 2025-01-24T01:39:42.7955792Z 2025-01-24T01:39:42.7957994Z test_spectral_ops 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_spectral_ops_1.1_49b2f06c88d8e893_.log 2025-01-24T01:39:42.8055382Z Running 280 items in this shard: test/test_spectral_ops.py::TestFFTCPU::test_batch_istft_cpu, test/test_spectral_ops.py::TestFFTCPU::test_complex_istft_real_equiv_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_complex_stft_definition_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_complex_stft_onesided_cpu, test/test_spectral_ops.py::TestFFTCPU::test_complex_stft_real_equiv_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_complex_stft_roundtrip_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_complex_stft_roundtrip_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_cufft_context_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_cufft_context_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_cufft_plan_cache_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_fft2_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_fft2_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_fft_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_fft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_fftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_fftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_hfft2_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_hfft2_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_hfft_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_hfft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_hfftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_hfftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_ifft2_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_ifft2_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_ifft_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_ifft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_ifftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_ifftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_ihfft2_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_ihfft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_ihfftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_irfft2_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_irfft2_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_irfft_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_irfft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_irfftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_irfftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_rfft2_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_rfft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_rfftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_fft2_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_fft2_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_fft_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_fft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_fftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_fftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_hfft2_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_hfft2_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_hfft_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_hfft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_hfftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_hfftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_ifft2_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_ifft2_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_ifft_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_ifft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_ifftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_ifftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_ihfft2_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_ihfft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_ihfftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_irfft2_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_irfft2_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_irfft_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_irfft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_irfftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_irfftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_rfft2_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_rfft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_rfftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_ifft_cpu, test/test_spectral_ops.py::TestFFTCPU::test_fft2_fftn_equivalence_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_fft2_fftn_equivalence_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fft2_invalid_cpu, test/test_spectral_ops.py::TestFFTCPU::test_fft2_numpy_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_fft2_numpy_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_fft2_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_fft2_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_fft_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_fft_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_fftn_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_fftn_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_hfft2_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_hfft2_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_hfft_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_hfft_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_hfftn_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_hfftn_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_ifft2_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_ifft2_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_ifft_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_ifft_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_ifftn_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_ifftn_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_ihfft2_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_ihfft2_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_ihfft_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_ihfft_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_ihfftn_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_ihfftn_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_irfft2_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_irfft2_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_irfft_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_irfft_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_irfftn_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_irfftn_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_rfft2_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_rfft2_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_rfft_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_rfft_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_rfftn_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_rfftn_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_fft2_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_fft2_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_fft_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_fft_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_fftn_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_fftn_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_hfft2_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_hfft2_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_hfft_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_hfft_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_hfftn_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_hfftn_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_ifft2_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_ifft2_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_ifft_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_ifft_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_ifftn_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_ifftn_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_ihfft2_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_ihfft2_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_ihfft_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_ihfft_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_ihfftn_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_ihfftn_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_irfft2_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_irfft2_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_irfft_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_irfft_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_irfftn_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_irfftn_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_rfft2_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_rfft2_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_rfft_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_rfft_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_rfftn_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_rfftn_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_ifft_rfft_irfft_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_fft_input_modification_cpu, test/test_spectral_ops.py::TestFFTCPU::test_fft_invalid_dtypes_cpu, test/test_spectral_ops.py::TestFFTCPU::test_fft_plan_repeatable_cpu, test/test_spectral_ops.py::TestFFTCPU::test_fft_round_trip_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_fft_round_trip_cpu_complex32, test/test_spectral_ops.py::TestFFTCPU::test_fft_round_trip_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_fft_round_trip_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_round_trip_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fft_round_trip_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_fft_type_promotion_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_fft_type_promotion_cpu_complex32, test/test_spectral_ops.py::TestFFTCPU::test_fft_type_promotion_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_fft_type_promotion_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_type_promotion_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fft_type_promotion_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_fft_type_promotion_cpu_int8, test/test_spectral_ops.py::TestFFTCPU::test_fftfreq_numpy_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fftfreq_numpy_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_fftfreq_out_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fftfreq_out_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_fftn_invalid__refs_fft_fftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_fftn_invalid__refs_fft_fftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fftn_invalid__refs_fft_hfftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_fftn_invalid__refs_fft_hfftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fftn_invalid__refs_fft_ifftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_fftn_invalid__refs_fft_ifftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fftn_invalid__refs_fft_ihfftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fftn_invalid__refs_fft_irfftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_fftn_invalid__refs_fft_irfftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fftn_invalid__refs_fft_rfftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fftn_invalid_fft_fftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_fftn_invalid_fft_fftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fftn_invalid_fft_hfftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_fftn_invalid_fft_hfftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fftn_invalid_fft_ifftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_fftn_invalid_fft_ifftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fftn_invalid_fft_ihfftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fftn_invalid_fft_irfftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_fftn_invalid_fft_irfftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fftn_invalid_fft_rfftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fftn_noop_transform_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_fftn_noop_transform_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_fftn_noop_transform_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fftn_noop_transform_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fftn_noop_transform_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_fftn_round_trip_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_fftn_round_trip_cpu_complex32, test/test_spectral_ops.py::TestFFTCPU::test_fftn_round_trip_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_fftn_round_trip_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fftn_round_trip_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fftn_round_trip_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_fftshift_frequencies_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fftshift_frequencies_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_fftshift_numpy_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_fftshift_numpy_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_fftshift_numpy_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fftshift_numpy_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_hfftn_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_hfftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_hfftn_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_ihfftn_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_ihfftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_ihfftn_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_istft_against_librosa_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_istft_linearity_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_istft_of_sine_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_istft_requires_window_cpu, test/test_spectral_ops.py::TestFFTCPU::test_istft_round_trip_simple_cases_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_istft_round_trip_various_params_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_istft_round_trip_with_padding_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_istft_throws_cpu, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d__refs_fft_fft_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d__refs_fft_fft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d__refs_fft_hfft_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d__refs_fft_hfft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d__refs_fft_ifft_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d__refs_fft_ifft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d__refs_fft_ihfft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d__refs_fft_irfft_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d__refs_fft_irfft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d__refs_fft_rfft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d_fft_fft_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d_fft_fft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d_fft_hfft_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d_fft_hfft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d_fft_ifft_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d_fft_ifft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d_fft_ihfft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d_fft_irfft_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d_fft_irfft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d_fft_rfft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_reference_nd__refs_fft_fftn_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_reference_nd__refs_fft_fftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_reference_nd__refs_fft_hfftn_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_reference_nd__refs_fft_hfftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_reference_nd__refs_fft_ifftn_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_reference_nd__refs_fft_ifftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_reference_nd__refs_fft_irfftn_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_reference_nd__refs_fft_irfftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_reference_nd_fft_fftn_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_reference_nd_fft_fftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_reference_nd_fft_hfftn_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_reference_nd_fft_hfftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_reference_nd_fft_ifftn_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_reference_nd_fft_ifftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_reference_nd_fft_irfftn_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_reference_nd_fft_irfftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_stft_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_stft_requires_complex_cpu, test/test_spectral_ops.py::TestFFTCPU::test_stft_requires_window_cpu, test/test_spectral_ops.py::TestFFTCPU::test_stft_roundtrip_complex_window_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_stft_roundtrip_complex_window_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_stft_window_device_cpu, test/test_spectral_ops.py::TestFFTDocExamplesCPU::test_fft2_cpu, test/test_spectral_ops.py::TestFFTDocExamplesCPU::test_fft_cpu, test/test_spectral_ops.py::TestFFTDocExamplesCPU::test_fftfreq_cpu, test/test_spectral_ops.py::TestFFTDocExamplesCPU::test_fftn_cpu, test/test_spectral_ops.py::TestFFTDocExamplesCPU::test_fftshift_cpu, test/test_spectral_ops.py::TestFFTDocExamplesCPU::test_hfft_cpu, test/test_spectral_ops.py::TestFFTDocExamplesCPU::test_ifft2_cpu, test/test_spectral_ops.py::TestFFTDocExamplesCPU::test_ifft_cpu, test/test_spectral_ops.py::TestFFTDocExamplesCPU::test_ifftn_cpu, test/test_spectral_ops.py::TestFFTDocExamplesCPU::test_ifftshift_cpu, test/test_spectral_ops.py::TestFFTDocExamplesCPU::test_ihfft_cpu, test/test_spectral_ops.py::TestFFTDocExamplesCPU::test_irfft2_cpu, test/test_spectral_ops.py::TestFFTDocExamplesCPU::test_irfft_cpu, test/test_spectral_ops.py::TestFFTDocExamplesCPU::test_irfftn_cpu, test/test_spectral_ops.py::TestFFTDocExamplesCPU::test_rfft2_cpu, test/test_spectral_ops.py::TestFFTDocExamplesCPU::test_rfft_cpu, test/test_spectral_ops.py::TestFFTDocExamplesCPU::test_rfftfreq_cpu, test/test_spectral_ops.py::TestFFTDocExamplesCPU::test_rfftn_cpu 2025-01-24T01:39:42.8145563Z 2025-01-24T01:39:42.8145803Z Running test_cuda_nvml_based_avail 1/1 ... [2025-01-24 01:39:42.796396] 2025-01-24T01:39:42.8146388Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T01:39:42.8147527Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_cuda_nvml_based_avail.py', '--shard-id=1', '--num-shards=1', '-v', '--subprocess', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-01-24 01:39:42.796797] 2025-01-24T01:39:45.9262272Z 2025-01-24T01:39:45.9263751Z test_cuda_nvml_based_avail 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_cuda_nvml_based_avail_1.1_51e5e582e7312031_.log 2025-01-24T01:39:45.9265058Z Running 0 items in this shard: 2025-01-24T01:39:45.9265389Z 2025-01-24T01:39:45.9266598Z Running test_cuda_primary_ctx 1/1 ... [2025-01-24 01:39:45.926466] 2025-01-24T01:39:45.9267265Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T01:39:45.9272278Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_cuda_primary_ctx.py', '--shard-id=1', '--num-shards=1', '-v', '--subprocess', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-01-24 01:39:45.926889] 2025-01-24T01:39:49.0501522Z 2025-01-24T01:39:49.0502561Z test_cuda_primary_ctx 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_cuda_primary_ctx_1.1_cc2a1a78ffbc4075_.log 2025-01-24T01:39:49.0503419Z Running 0 items in this shard: 2025-01-24T01:39:49.0503648Z 2025-01-24T01:39:49.0505572Z Running distributions/test_distributions 2/2 ... [2025-01-24 01:39:49.050338] 2025-01-24T01:39:49.0506125Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T01:39:49.0509117Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'distributions/test_distributions.py', '--shard-id=2', '--num-shards=2', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-01-24 01:39:49.050685] 2025-01-24T01:46:03.1471407Z 2025-01-24T01:46:03.1472621Z distributions/test_distributions 2/2 was successful, full logs can be found in artifacts with path test/test-reports/distributions.test_distributions_2.2_d4f889b69fb745ae_.log 2025-01-24T01:46:03.1514036Z Running 95 items in this shard: test/distributions/test_distributions.py::TestDistributions::test_beta_sample, test/distributions/test_distributions.py::TestDistributions::test_beta_shape, test/distributions/test_distributions.py::TestDistributions::test_beta_underflow_gpu, test/distributions/test_distributions.py::TestDistributions::test_binomial_bfloat16, test/distributions/test_distributions.py::TestDistributions::test_binomial_enumerate_support, test/distributions/test_distributions.py::TestDistributions::test_binomial_extreme_vals, test/distributions/test_distributions.py::TestDistributions::test_binomial_log_prob_and_entropy, test/distributions/test_distributions.py::TestDistributions::test_binomial_sample, test/distributions/test_distributions.py::TestDistributions::test_binomial_stable, test/distributions/test_distributions.py::TestDistributions::test_chi2_sample, test/distributions/test_distributions.py::TestDistributions::test_dirichlet_log_prob, test/distributions/test_distributions.py::TestDistributions::test_dirichlet_log_prob_zero, test/distributions/test_distributions.py::TestDistributions::test_dirichlet_mode, test/distributions/test_distributions.py::TestDistributions::test_dirichlet_sample, test/distributions/test_distributions.py::TestDistributions::test_dirichlet_shape, test/distributions/test_distributions.py::TestDistributions::test_distribution_subclass_expand, test/distributions/test_distributions.py::TestDistributions::test_fishersnedecor_sample, test/distributions/test_distributions.py::TestDistributions::test_gamma_gpu_sample, test/distributions/test_distributions.py::TestDistributions::test_gamma_gpu_shape, test/distributions/test_distributions.py::TestDistributions::test_gamma_log_prob_at_boundary, test/distributions/test_distributions.py::TestDistributions::test_gumbel, test/distributions/test_distributions.py::TestDistributions::test_halfnormal_sample, test/distributions/test_distributions.py::TestDistributions::test_laplace, test/distributions/test_distributions.py::TestDistributions::test_laplace_sample, test/distributions/test_distributions.py::TestDistributions::test_lazy_property_grad, test/distributions/test_distributions.py::TestDistributions::test_logisticnormal, test/distributions/test_distributions.py::TestDistributions::test_lognormal, test/distributions/test_distributions.py::TestDistributions::test_lowrank_multivariate_normal_properties, test/distributions/test_distributions.py::TestDistributions::test_lowrank_multivariate_normal_sample, test/distributions/test_distributions.py::TestDistributions::test_mixture_same_family_sample, test/distributions/test_distributions.py::TestDistributions::test_mixture_same_family_shape, test/distributions/test_distributions.py::TestDistributions::test_mode, test/distributions/test_distributions.py::TestDistributions::test_multinomial_1d, test/distributions/test_distributions.py::TestDistributions::test_multivariate_normal_sample, test/distributions/test_distributions.py::TestDistributions::test_multivariate_normal_stable_with_precision_matrix, test/distributions/test_distributions.py::TestDistributions::test_negative_binomial_log_prob, test/distributions/test_distributions.py::TestDistributions::test_negative_binomial_log_prob_vectorized_count, test/distributions/test_distributions.py::TestDistributions::test_one_hot_categorical_1d, test/distributions/test_distributions.py::TestDistributions::test_one_hot_categorical_enumerate_support, test/distributions/test_distributions.py::TestDistributions::test_poisson_gpu_sample, test/distributions/test_distributions.py::TestDistributions::test_poisson_sample, test/distributions/test_distributions.py::TestDistributions::test_poisson_shape, test/distributions/test_distributions.py::TestDistributions::test_relaxed_bernoulli, test/distributions/test_distributions.py::TestDistributions::test_relaxed_one_hot_categorical_1d, test/distributions/test_distributions.py::TestDistributions::test_relaxed_one_hot_categorical_2d, test/distributions/test_distributions.py::TestDistributions::test_rsample_requires_grad, test/distributions/test_distributions.py::TestDistributions::test_sample_detached, test/distributions/test_distributions.py::TestDistributions::test_uniform, test/distributions/test_distributions.py::TestDistributions::test_valid_parameter_broadcasting, test/distributions/test_distributions.py::TestDistributions::test_wishart_shape, test/distributions/test_distributions.py::TestRsample::test_chi2, test/distributions/test_distributions.py::TestRsample::test_dirichlet_multivariate, test/distributions/test_distributions.py::TestRsample::test_gamma, test/distributions/test_distributions.py::TestDistributionShapes::test_bernoulli_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_beta_shape_tensor_params, test/distributions/test_distributions.py::TestDistributionShapes::test_chi2_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_continuous_bernoulli_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_continuous_bernoulli_shape_tensor_params, test/distributions/test_distributions.py::TestDistributionShapes::test_dirichlet_shape, test/distributions/test_distributions.py::TestDistributionShapes::test_entropy_shape, test/distributions/test_distributions.py::TestDistributionShapes::test_geometric_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_geometric_shape_tensor_params, test/distributions/test_distributions.py::TestDistributionShapes::test_halfcauchy_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_halfcauchy_shape_tensor_params, test/distributions/test_distributions.py::TestDistributionShapes::test_kumaraswamy_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_laplace_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_mixture_same_family_shape, test/distributions/test_distributions.py::TestDistributionShapes::test_normal_shape_tensor_params, test/distributions/test_distributions.py::TestDistributionShapes::test_pareto_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_vonmises_shape_tensor_params, test/distributions/test_distributions.py::TestDistributionShapes::test_weibull_scale_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_wishart_shape_tensor_params, test/distributions/test_distributions.py::TestKL::test_entropy_exponential_family, test/distributions/test_distributions.py::TestKL::test_kl_edgecases, test/distributions/test_distributions.py::TestKL::test_kl_infinite, test/distributions/test_distributions.py::TestKL::test_kl_monte_carlo, test/distributions/test_distributions.py::TestKL::test_kl_multivariate_normal, test/distributions/test_distributions.py::TestKL::test_kl_shape, test/distributions/test_distributions.py::TestConstraints::test_params_constraints, test/distributions/test_distributions.py::TestNumericalStability::test_bernoulli_with_logits_overflow, test/distributions/test_distributions.py::TestNumericalStability::test_bernoulli_with_logits_underflow, test/distributions/test_distributions.py::TestNumericalStability::test_categorical_log_prob, test/distributions/test_distributions.py::TestNumericalStability::test_continuous_bernoulli_with_logits_underflow, test/distributions/test_distributions.py::TestNumericalStability::test_multinomial_log_prob_with_logits, test/distributions/test_distributions.py::TestLazyLogitsInitialization::test_lazy_probs_initialization, test/distributions/test_distributions.py::TestAgainstScipy::test_cdf, test/distributions/test_distributions.py::TestAgainstScipy::test_variance_stddev, test/distributions/test_distributions.py::TestFunctors::test_cat_transform, test/distributions/test_distributions.py::TestFunctors::test_cat_transform_non_uniform, test/distributions/test_distributions.py::TestValidation::test_invalid, test/distributions/test_distributions.py::TestJit::test_entropy, test/distributions/test_distributions.py::TestJit::test_enumerate_support, test/distributions/test_distributions.py::TestJit::test_log_prob, test/distributions/test_distributions.py::TestJit::test_rsample, test/distributions/test_distributions.py::TestJit::test_variance 2025-01-24T01:46:03.1551850Z 2025-01-24T01:46:03.1552051Z Running test_reductions 2/3 ... [2025-01-24 01:46:03.147388] 2025-01-24T01:46:03.1552477Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T01:46:03.1553526Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_reductions.py', '--shard-id=2', '--num-shards=3', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-01-24 01:46:03.147737] 2025-01-24T01:48:04.3187461Z 2025-01-24T01:48:04.3188528Z test_reductions 2/3 was successful, full logs can be found in artifacts with path test/test-reports/test_reductions_2.3_74cb5c123362990e_.log 2025-01-24T01:48:04.3764437Z Running 1530 items in this shard: test/test_reductions.py::TestReductionsCPU::test_accreal_type_cpu, test/test_reductions.py::TestReductionsCPU::test_all_any_cpu, test/test_reductions.py::TestReductionsCPU::test_all_any_vs_numpy_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_all_any_vs_numpy_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_all_any_vs_numpy_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_all_any_vs_numpy_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_all_any_with_dim_cpu, test/test_reductions.py::TestReductionsCPU::test_amax_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_amax_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_amax_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_amax_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_amin_amax_some_dims_cpu, test/test_reductions.py::TestReductionsCPU::test_amin_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_amin_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_aminmax_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_aminmax_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_argminmax_axis_with_dim_one_cpu, test/test_reductions.py::TestReductionsCPU::test_argminmax_multiple_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_argminmax_multiple_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_argminmax_multiple_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_argminmax_multiple_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_bucketization_cpu, test/test_reductions.py::TestReductionsCPU::test_count_nonzero_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_cumprod_integer_upcast_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_arg_reduction_scalar_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_dim_arg_reduction_scalar_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_dim_arg_reduction_scalar_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_dim_arg_reduction_scalar_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_dim_arg_reduction_scalar_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_dim_arg_reduction_scalar_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_dim_default__refs_sum_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default_any_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default_count_nonzero_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default_keepdim__refs_all_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default_keepdim__refs_prod_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default_keepdim_any_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default_keepdim_argmin_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default_keepdim_prod_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default_linalg_vector_norm_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default_masked_amax_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default_masked_amin_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default_masked_var_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default_nansum_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default_prod_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default_sum_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default_var_unbiased_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_empty__refs_all_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_empty__refs_any_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_empty__refs_linalg_vector_norm_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_empty__refs_mean_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_empty__refs_prod_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_empty__refs_std_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_empty__refs_sum_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_empty_all_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_empty_amax_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_empty_amin_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_empty_count_nonzero_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_empty_keepdim__refs_all_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_empty_keepdim__refs_sum_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_empty_keepdim_amin_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_empty_keepdim_linalg_vector_norm_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_empty_keepdim_masked_std_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_empty_keepdim_masked_sum_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_empty_keepdim_mean_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_empty_keepdim_nanmean_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_empty_keepdim_sum_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_empty_keepdim_var_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_empty_mean_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_empty_var_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_empty_var_unbiased_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi__refs_count_nonzero_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi__refs_linalg_vector_norm_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi__refs_mean_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi__refs_prod_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_all_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_count_nonzero_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_duplicate__refs_count_nonzero_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_duplicate__refs_std_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_duplicate__refs_var_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_duplicate_amax_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_duplicate_any_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_duplicate_count_nonzero_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_duplicate_masked_amin_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_duplicate_masked_mean_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_duplicate_masked_norm_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_duplicate_masked_std_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_duplicate_masked_sum_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_duplicate_std_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_duplicate_std_unbiased_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_duplicate_var_unbiased_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_keepdim__refs_all_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_keepdim__refs_amax_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_keepdim__refs_amin_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_keepdim__refs_any_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_keepdim__refs_prod_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_keepdim__refs_std_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_keepdim_count_nonzero_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_keepdim_linalg_vector_norm_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_keepdim_masked_amin_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_keepdim_masked_logsumexp_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_keepdim_masked_std_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_keepdim_masked_var_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_keepdim_mean_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_keepdim_nansum_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_keepdim_std_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_keepdim_std_unbiased_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_keepdim_var_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_linalg_vector_norm_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_masked_mean_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_mean_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_std_unbiased_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_sum_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted__refs_any_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted__refs_std_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted__refs_sum_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_keepdim__refs_count_nonzero_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_keepdim__refs_mean_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_keepdim_count_nonzero_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_keepdim_linalg_vector_norm_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_keepdim_masked_amin_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_keepdim_masked_logsumexp_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_keepdim_masked_mean_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_keepdim_nansum_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_keepdim_std_unbiased_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_masked_mean_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_masked_var_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_nansum_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_unsorted_std_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_unsupported_argmin_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_var_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_multi_var_unbiased_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_ndim_limit__refs_amax_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_ndim_limit__refs_count_nonzero_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_ndim_limit__refs_linalg_vector_norm_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_ndim_limit__refs_std_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_ndim_limit__refs_sum_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_ndim_limit__refs_var_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_ndim_limit_amin_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_ndim_limit_any_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_ndim_limit_argmax_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_ndim_limit_count_nonzero_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_ndim_limit_masked_amax_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_ndim_limit_masked_std_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_none__refs_amax_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_none__refs_linalg_vector_norm_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_none__refs_mean_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_none__refs_sum_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_none__refs_var_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_none_all_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_none_keepdim__refs_amin_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_none_keepdim__refs_std_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_none_keepdim__refs_var_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_none_keepdim_any_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_none_keepdim_count_nonzero_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_none_keepdim_linalg_vector_norm_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_none_keepdim_masked_amin_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_none_keepdim_masked_logsumexp_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_none_keepdim_masked_sum_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_none_keepdim_std_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_none_keepdim_std_unbiased_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_none_linalg_vector_norm_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_none_masked_argmin_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_none_masked_mean_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_none_masked_std_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_none_masked_sum_cpu, 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test/test_reductions.py::TestReductionsCPU::test_result_dtype_linalg_vector_norm_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_result_dtype_linalg_vector_norm_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_linalg_vector_norm_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_amax_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_amax_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_amax_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_amax_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_amin_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_amin_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_argmax_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_argmax_cpu_float16, 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test/test_reductions.py::TestReductionsCPU::test_result_dtype_var_unbiased_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_std_correction_vs_numpy_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_std_dim_cpu, test/test_reductions.py::TestReductionsCPU::test_std_mean_correction_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_std_mean_cpu, test/test_reductions.py::TestReductionsCPU::test_std_vs_numpy_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_sum_noncontig_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_sum_parallel_cpu, test/test_reductions.py::TestReductionsCPU::test_sum_vs_numpy_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_tensor_compare_ops_empty_cpu, test/test_reductions.py::TestReductionsCPU::test_var_correction_vs_numpy_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_var_correction_vs_numpy_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_var_cpu, test/test_reductions.py::TestReductionsCPU::test_var_dim_cpu, test/test_reductions.py::TestReductionsCPU::test_var_mean_correction_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_var_mean_correction_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_var_mean_correction_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_var_mean_some_dims_cpu, test/test_reductions.py::TestReductionsCPU::test_var_stability2_cpu, test/test_reductions.py::TestReductionsCPU::test_var_vs_numpy_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_warn_invalid_degrees_of_freedom_cpu_complex128 2025-01-24T01:48:04.4321869Z 2025-01-24T01:48:04.4322143Z Running test_reductions 3/3 ... [2025-01-24 01:48:04.321176] 2025-01-24T01:48:04.4322574Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T01:48:04.4323633Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_reductions.py', '--shard-id=3', '--num-shards=3', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-01-24 01:48:04.321524] 2025-01-24T01:49:58.2794406Z 2025-01-24T01:49:58.2795325Z test_reductions 3/3 was successful, full logs can be found in artifacts with path test/test-reports/test_reductions_3.3_63fc9a4db332c664_.log 2025-01-24T01:49:58.3370815Z Running 1522 items in this shard: test/test_reductions.py::TestReductionsCPU::test_all_any_vs_numpy_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_all_any_vs_numpy_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_all_any_vs_numpy_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_all_any_vs_numpy_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_all_issue117215_cpu, test/test_reductions.py::TestReductionsCPU::test_amax_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_amax_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_amin_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_amin_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_amin_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_aminmax_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_argminmax_multiple_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_argminmax_multiple_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_count_nonzero_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_count_nonzero_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_count_nonzero_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_count_nonzero_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_count_nonzero_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_count_nonzero_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_cumsum_integer_upcast_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_arg_reduction_scalar_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_dim_arg_reduction_scalar_cpu_uint8, 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test/test_reductions.py::TestReductionsCPU::test_result_dtype__refs_var_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_all_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_all_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_result_dtype_all_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_all_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_all_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_all_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_result_dtype_amax_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_amax_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_result_dtype_amin_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_amin_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_amin_cpu_int8, 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test/test_reductions.py::TestReductionsCPU::test_result_dtype_var_unbiased_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_std_correction_vs_numpy_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_std_correction_vs_numpy_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_std_mean_correction_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_std_mean_correction_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_std_vs_numpy_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_sum_dim_cpu, test/test_reductions.py::TestReductionsCPU::test_sum_integer_upcast_cpu, test/test_reductions.py::TestReductionsCPU::test_sum_noncontig_lowp_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_sum_noncontig_lowp_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_sum_out_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_sum_vs_numpy_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_sum_vs_numpy_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_tensor_reduce_ops_empty_cpu, test/test_reductions.py::TestReductionsCPU::test_var_correction_vs_numpy_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_var_mean_all_dims_cpu, test/test_reductions.py::TestReductionsCPU::test_var_mean_cpu, test/test_reductions.py::TestReductionsCPU::test_var_unbiased_cpu, test/test_reductions.py::TestReductionsCPU::test_var_vs_numpy_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_var_vs_numpy_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_var_vs_numpy_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_warn_invalid_degrees_of_freedom_cpu_complex64 2025-01-24T01:49:58.3931859Z 2025-01-24T01:49:58.3932105Z Running test_overrides 1/1 ... [2025-01-24 01:49:58.281730] 2025-01-24T01:49:58.3932554Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T01:49:58.3933631Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_overrides.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-01-24 01:49:58.282068] 2025-01-24T01:51:17.8471730Z 2025-01-24T01:51:17.8473069Z test_overrides 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_overrides_1.1_12d9bedecf8f8f40_.log 2025-01-24T01:51:17.8967551Z Running 1467 items in this shard: test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_H___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_T___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase__backward_hooks___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase__base___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase__cdata___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase__grad___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase__grad_fn___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase__post_accumulate_grad_hooks___get__, 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test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_igammac_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_index_add, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_index_add_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_index_copy, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_index_copy_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_index_fill, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_index_fill_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_index_put, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_index_put_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_index_reduce, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_index_reduce_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_index_select, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_indices, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_inner, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_int, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_int_repr, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_inverse, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_ipu, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_is_coalesced, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_is_complex, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_is_conj, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_is_contiguous, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_is_distributed, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_is_floating_point, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_is_inference, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_is_neg, 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test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_log_softmax, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_logaddexp, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_logaddexp2, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_logcumsumexp, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_logdet, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_logical_and, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_logical_and_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_logical_not, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_logical_not_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_logical_or, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_logical_or_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_logical_xor, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_logical_xor_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_logit, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_logit_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_logsumexp, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_long, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_lt, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_lt_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_lu, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_lu_solve, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_map2_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_map_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_masked_fill, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_masked_fill_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_masked_scatter, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_masked_scatter_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_masked_select, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_matmul, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_matrix_exp, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_matrix_power, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_max, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_maximum, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_mean, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_median, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_min, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_minimum, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_mm, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_mode, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_module_load, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_moveaxis, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_movedim, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_msort, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_mtia, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_mul, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_mul_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_multinomial, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_multiply, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_multiply_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_mv, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_mvlgamma, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_mvlgamma_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_nan_to_num, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_nan_to_num_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_nanmean, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_nanmedian, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_nanquantile, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_nansum, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_narrow, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_narrow_copy, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_ndimension, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_ne, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_ne_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_neg, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_neg_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_negative, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_negative_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_nelement, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_nextafter, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_nextafter_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_nonzero, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_nonzero_static, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_norm, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_normal_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_not_equal, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_not_equal_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_numel, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_numpy, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_orgqr, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_ormqr, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_outer, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_permute, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_pin_memory, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_pinverse, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_polygamma, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_polygamma_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_positive, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_pow, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_pow_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_prelu, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_prod, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_put, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_put_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_q_per_channel_axis, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_q_per_channel_scales, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_q_per_channel_zero_points, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_q_scale, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_q_zero_point, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_qr, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_qscheme, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_quantile, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_rad2deg, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_rad2deg_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_random_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_ravel, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_reciprocal, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_reciprocal_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_record_stream, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_refine_names, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_register_hook, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_register_post_accumulate_grad_hook, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_relu, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_relu_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_remainder, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_remainder_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_rename, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_rename_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_renorm, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_renorm_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_repeat, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_repeat_interleave, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_requires_grad_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_reshape, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_reshape_as, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_resize, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_resize_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_resize_as, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_resize_as_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_resize_as_sparse_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_resolve_conj, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_resolve_neg, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_retain_grad, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_roll, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_rot90, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_round, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_round_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_row_indices, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_rsqrt, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_rsqrt_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_scatter, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_scatter_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_scatter_add, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_scatter_add_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_scatter_reduce, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_scatter_reduce_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_select, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_select_scatter, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_set_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sgn, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sgn_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_share_memory_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_short, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sigmoid, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sigmoid_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sign, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sign_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_signbit, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sin, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sin_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sinc, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sinc_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sinh, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sinh_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_size, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_slice_inverse, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_slice_scatter, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_slogdet, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_smm, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_softmax, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sort, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sparse_dim, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sparse_mask, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sparse_resize_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sparse_resize_and_clear_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_split, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_split_with_sizes, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sqrt, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sqrt_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_square, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_square_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_squeeze, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_squeeze_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sspaddmm, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_std, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_stft, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_storage, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_storage_offset, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_storage_type, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sub, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sub_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_subtract, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_subtract_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sum, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sum_to_size, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_svd, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_swapaxes, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_swapaxes_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_swapdims, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_swapdims_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_t, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_t_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_take, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_take_along_dim, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_tan, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_tan_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_tanh, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_tanh_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_tensor_split, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_tile, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_to, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_to_dense, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_to_mkldnn, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_to_sparse, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_tolist, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_topk, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_trace, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_transpose, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_transpose_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_triangular_solve, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_tril, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_tril_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_triu, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_triu_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_true_divide, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_true_divide_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_trunc, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_trunc_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_type, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_type_as, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_unbind, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_unfold, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_uniform_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_unique, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_unique_consecutive, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_unsafe_chunk, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_unsafe_split, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_unsafe_split_with_sizes, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_unsqueeze, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_unsqueeze_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_untyped_storage, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_values, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_var, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_vdot, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_view, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_view_as, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_vsplit, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_where, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_xlogy, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_xlogy_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_xpu, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_zero_, test/test_overrides.py::TestTorchFunctionOverride::test_base, test/test_overrides.py::TestTorchFunctionOverride::test_dtype_override, test/test_overrides.py::TestTorchFunctionOverride::test_grad, test/test_overrides.py::TestTorchFunctionOverride::test_has_torch_function_non_sequence, test/test_overrides.py::TestTorchFunctionOverride::test_mean_semantics, test/test_overrides.py::TestTorchFunctionOverride::test_mm_semantics, test/test_overrides.py::TestTorchFunctionOverride::test_pow_rpow, test/test_overrides.py::TestTorchFunctionOverride::test_precedence_semantics, test/test_overrides.py::TestTorchFunctionOverride::test_tensor_subclass_propagation, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__fft_fft_fft, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__fft_fft_fft2, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__fft_fft_fftn, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__fft_fft_fftshift, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__fft_fft_hfft, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__fft_fft_hfft2, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__fft_fft_hfftn, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__fft_fft_ifft, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__fft_fft_ifft2, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__fft_fft_ifftn, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__fft_fft_ifftshift, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__fft_fft_ihfft, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__fft_fft_ihfft2, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__fft_fft_ihfftn, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__fft_fft_irfft, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__fft_fft_irfft2, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__fft_fft_irfftn, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__fft_fft_rfft, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__fft_fft_rfft2, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__fft_fft_rfftn, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__linalg_linalg_cholesky, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__linalg_linalg_cholesky_ex, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__linalg_linalg_cond, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__linalg_linalg_cross, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__linalg_linalg_det, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__linalg_linalg_diagonal, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__linalg_linalg_eig, 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test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_psi, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_round, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_scaled_modified_bessel_k0, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_scaled_modified_bessel_k1, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_shifted_chebyshev_polynomial_t, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_shifted_chebyshev_polynomial_u, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_shifted_chebyshev_polynomial_v, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_shifted_chebyshev_polynomial_w, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_sinc, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_softmax, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_spherical_bessel_j0, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_xlog1py, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_xlogy, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_zeta, test/test_overrides.py::TestTorchFunctionOverride::test_torch__assert_async, test/test_overrides.py::TestTorchFunctionOverride::test_torch__conj_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch__functional_assert_async, test/test_overrides.py::TestTorchFunctionOverride::test_torch__fw_primal_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch__indices_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch__lobpcg_lobpcg, test/test_overrides.py::TestTorchFunctionOverride::test_torch__lowrank_pca_lowrank, test/test_overrides.py::TestTorchFunctionOverride::test_torch__lowrank_svd_lowrank, test/test_overrides.py::TestTorchFunctionOverride::test_torch__make_dual_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch__native_batch_norm_legit, test/test_overrides.py::TestTorchFunctionOverride::test_torch__neg_view_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch__reshape_alias_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch__rowwise_prune, test/test_overrides.py::TestTorchFunctionOverride::test_torch__sparse_broadcast_to_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch__sym_acos, test/test_overrides.py::TestTorchFunctionOverride::test_torch__sym_asin, test/test_overrides.py::TestTorchFunctionOverride::test_torch__sym_atan, test/test_overrides.py::TestTorchFunctionOverride::test_torch__sym_cos, test/test_overrides.py::TestTorchFunctionOverride::test_torch__sym_cosh, test/test_overrides.py::TestTorchFunctionOverride::test_torch__sym_sin, test/test_overrides.py::TestTorchFunctionOverride::test_torch__sym_sinh, test/test_overrides.py::TestTorchFunctionOverride::test_torch__sym_sqrt, test/test_overrides.py::TestTorchFunctionOverride::test_torch__sym_tan, test/test_overrides.py::TestTorchFunctionOverride::test_torch__sym_tanh, test/test_overrides.py::TestTorchFunctionOverride::test_torch__values_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch__wrapped_linear_prepack, test/test_overrides.py::TestTorchFunctionOverride::test_torch__wrapped_quantized_linear_prepacked, test/test_overrides.py::TestTorchFunctionOverride::test_torch_abs, test/test_overrides.py::TestTorchFunctionOverride::test_torch_absolute, test/test_overrides.py::TestTorchFunctionOverride::test_torch_acos, test/test_overrides.py::TestTorchFunctionOverride::test_torch_acosh, test/test_overrides.py::TestTorchFunctionOverride::test_torch_adaptive_avg_pool1d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_adaptive_max_pool1d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_add, test/test_overrides.py::TestTorchFunctionOverride::test_torch_addbmm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_addcdiv, test/test_overrides.py::TestTorchFunctionOverride::test_torch_addcmul, test/test_overrides.py::TestTorchFunctionOverride::test_torch_addmm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_addmv, test/test_overrides.py::TestTorchFunctionOverride::test_torch_addr, test/test_overrides.py::TestTorchFunctionOverride::test_torch_adjoint, test/test_overrides.py::TestTorchFunctionOverride::test_torch_affine_grid_generator, test/test_overrides.py::TestTorchFunctionOverride::test_torch_alias_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_all, test/test_overrides.py::TestTorchFunctionOverride::test_torch_allclose, test/test_overrides.py::TestTorchFunctionOverride::test_torch_alpha_dropout, test/test_overrides.py::TestTorchFunctionOverride::test_torch_amax, test/test_overrides.py::TestTorchFunctionOverride::test_torch_amin, test/test_overrides.py::TestTorchFunctionOverride::test_torch_aminmax, test/test_overrides.py::TestTorchFunctionOverride::test_torch_angle, test/test_overrides.py::TestTorchFunctionOverride::test_torch_any, test/test_overrides.py::TestTorchFunctionOverride::test_torch_arccos, test/test_overrides.py::TestTorchFunctionOverride::test_torch_arccosh, test/test_overrides.py::TestTorchFunctionOverride::test_torch_arcsin, test/test_overrides.py::TestTorchFunctionOverride::test_torch_arcsinh, test/test_overrides.py::TestTorchFunctionOverride::test_torch_arctan, test/test_overrides.py::TestTorchFunctionOverride::test_torch_arctan2, test/test_overrides.py::TestTorchFunctionOverride::test_torch_arctanh, test/test_overrides.py::TestTorchFunctionOverride::test_torch_argmax, test/test_overrides.py::TestTorchFunctionOverride::test_torch_argmin, test/test_overrides.py::TestTorchFunctionOverride::test_torch_argsort, test/test_overrides.py::TestTorchFunctionOverride::test_torch_argwhere, test/test_overrides.py::TestTorchFunctionOverride::test_torch_as_strided_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_as_strided_scatter, test/test_overrides.py::TestTorchFunctionOverride::test_torch_asin, test/test_overrides.py::TestTorchFunctionOverride::test_torch_asinh, test/test_overrides.py::TestTorchFunctionOverride::test_torch_atan, test/test_overrides.py::TestTorchFunctionOverride::test_torch_atan2, test/test_overrides.py::TestTorchFunctionOverride::test_torch_atanh, test/test_overrides.py::TestTorchFunctionOverride::test_torch_avg_pool1d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_baddbmm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_batch_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_batch_norm_backward_elemt, test/test_overrides.py::TestTorchFunctionOverride::test_torch_batch_norm_backward_reduce, test/test_overrides.py::TestTorchFunctionOverride::test_torch_batch_norm_elemt, test/test_overrides.py::TestTorchFunctionOverride::test_torch_batch_norm_gather_stats, test/test_overrides.py::TestTorchFunctionOverride::test_torch_batch_norm_gather_stats_with_counts, test/test_overrides.py::TestTorchFunctionOverride::test_torch_batch_norm_stats, test/test_overrides.py::TestTorchFunctionOverride::test_torch_batch_norm_update_stats, test/test_overrides.py::TestTorchFunctionOverride::test_torch_bernoulli, test/test_overrides.py::TestTorchFunctionOverride::test_torch_bilinear, test/test_overrides.py::TestTorchFunctionOverride::test_torch_binary_cross_entropy_with_logits, test/test_overrides.py::TestTorchFunctionOverride::test_torch_bincount, test/test_overrides.py::TestTorchFunctionOverride::test_torch_binomial, test/test_overrides.py::TestTorchFunctionOverride::test_torch_bitwise_and, test/test_overrides.py::TestTorchFunctionOverride::test_torch_bitwise_left_shift, test/test_overrides.py::TestTorchFunctionOverride::test_torch_bitwise_not, test/test_overrides.py::TestTorchFunctionOverride::test_torch_bitwise_or, test/test_overrides.py::TestTorchFunctionOverride::test_torch_bitwise_right_shift, test/test_overrides.py::TestTorchFunctionOverride::test_torch_bitwise_xor, test/test_overrides.py::TestTorchFunctionOverride::test_torch_bmm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_broadcast_to, test/test_overrides.py::TestTorchFunctionOverride::test_torch_bucketize, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cat, test/test_overrides.py::TestTorchFunctionOverride::test_torch_ccol_indices_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_ceil, test/test_overrides.py::TestTorchFunctionOverride::test_torch_celu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_channel_shuffle, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cholesky, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cholesky_inverse, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cholesky_solve, test/test_overrides.py::TestTorchFunctionOverride::test_torch_choose_qparams_optimized, test/test_overrides.py::TestTorchFunctionOverride::test_torch_chunk, test/test_overrides.py::TestTorchFunctionOverride::test_torch_clamp, test/test_overrides.py::TestTorchFunctionOverride::test_torch_clamp_max, test/test_overrides.py::TestTorchFunctionOverride::test_torch_clamp_min, test/test_overrides.py::TestTorchFunctionOverride::test_torch_clip, test/test_overrides.py::TestTorchFunctionOverride::test_torch_clone, test/test_overrides.py::TestTorchFunctionOverride::test_torch_col_indices_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_column_stack, test/test_overrides.py::TestTorchFunctionOverride::test_torch_combinations, test/test_overrides.py::TestTorchFunctionOverride::test_torch_complex, test/test_overrides.py::TestTorchFunctionOverride::test_torch_concat, test/test_overrides.py::TestTorchFunctionOverride::test_torch_concatenate, test/test_overrides.py::TestTorchFunctionOverride::test_torch_conj, test/test_overrides.py::TestTorchFunctionOverride::test_torch_conj_physical, test/test_overrides.py::TestTorchFunctionOverride::test_torch_constant_pad_nd, test/test_overrides.py::TestTorchFunctionOverride::test_torch_conv1d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_conv2d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_conv3d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_conv_tbc, test/test_overrides.py::TestTorchFunctionOverride::test_torch_conv_transpose1d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_conv_transpose2d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_conv_transpose3d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_convolution, test/test_overrides.py::TestTorchFunctionOverride::test_torch_copysign, test/test_overrides.py::TestTorchFunctionOverride::test_torch_corrcoef, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cos, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cosh, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cosine_embedding_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cosine_similarity, test/test_overrides.py::TestTorchFunctionOverride::test_torch_count_nonzero, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cov, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cross, test/test_overrides.py::TestTorchFunctionOverride::test_torch_crow_indices_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_ctc_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cummax, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cummin, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cumprod, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cumsum, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cumulative_trapezoid, test/test_overrides.py::TestTorchFunctionOverride::test_torch_deg2rad, test/test_overrides.py::TestTorchFunctionOverride::test_torch_dequantize, test/test_overrides.py::TestTorchFunctionOverride::test_torch_det, test/test_overrides.py::TestTorchFunctionOverride::test_torch_detach, test/test_overrides.py::TestTorchFunctionOverride::test_torch_detach_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_diag, test/test_overrides.py::TestTorchFunctionOverride::test_torch_diag_embed, test/test_overrides.py::TestTorchFunctionOverride::test_torch_diagflat, test/test_overrides.py::TestTorchFunctionOverride::test_torch_diagonal, test/test_overrides.py::TestTorchFunctionOverride::test_torch_diagonal_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_diagonal_scatter, test/test_overrides.py::TestTorchFunctionOverride::test_torch_diff, test/test_overrides.py::TestTorchFunctionOverride::test_torch_digamma, test/test_overrides.py::TestTorchFunctionOverride::test_torch_dist, test/test_overrides.py::TestTorchFunctionOverride::test_torch_div, test/test_overrides.py::TestTorchFunctionOverride::test_torch_divide, test/test_overrides.py::TestTorchFunctionOverride::test_torch_dot, test/test_overrides.py::TestTorchFunctionOverride::test_torch_dropout, test/test_overrides.py::TestTorchFunctionOverride::test_torch_dsmm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_dsplit, test/test_overrides.py::TestTorchFunctionOverride::test_torch_dstack, test/test_overrides.py::TestTorchFunctionOverride::test_torch_embedding, test/test_overrides.py::TestTorchFunctionOverride::test_torch_embedding_bag, test/test_overrides.py::TestTorchFunctionOverride::test_torch_empty_like, test/test_overrides.py::TestTorchFunctionOverride::test_torch_eq, test/test_overrides.py::TestTorchFunctionOverride::test_torch_equal, test/test_overrides.py::TestTorchFunctionOverride::test_torch_erf, test/test_overrides.py::TestTorchFunctionOverride::test_torch_erfc, test/test_overrides.py::TestTorchFunctionOverride::test_torch_erfinv, test/test_overrides.py::TestTorchFunctionOverride::test_torch_exp, test/test_overrides.py::TestTorchFunctionOverride::test_torch_exp2, test/test_overrides.py::TestTorchFunctionOverride::test_torch_expand_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_expm1, test/test_overrides.py::TestTorchFunctionOverride::test_torch_fake_quantize_per_channel_affine, test/test_overrides.py::TestTorchFunctionOverride::test_torch_fake_quantize_per_tensor_affine, test/test_overrides.py::TestTorchFunctionOverride::test_torch_fbgemm_linear_fp16_weight, test/test_overrides.py::TestTorchFunctionOverride::test_torch_fbgemm_linear_fp16_weight_fp32_activation, test/test_overrides.py::TestTorchFunctionOverride::test_torch_fbgemm_linear_int8_weight, test/test_overrides.py::TestTorchFunctionOverride::test_torch_fbgemm_linear_int8_weight_fp32_activation, test/test_overrides.py::TestTorchFunctionOverride::test_torch_fbgemm_linear_quantize_weight, test/test_overrides.py::TestTorchFunctionOverride::test_torch_fbgemm_pack_gemm_matrix_fp16, test/test_overrides.py::TestTorchFunctionOverride::test_torch_fbgemm_pack_quantized_matrix, test/test_overrides.py::TestTorchFunctionOverride::test_torch_feature_alpha_dropout, test/test_overrides.py::TestTorchFunctionOverride::test_torch_feature_dropout, test/test_overrides.py::TestTorchFunctionOverride::test_torch_fix, test/test_overrides.py::TestTorchFunctionOverride::test_torch_flatten, test/test_overrides.py::TestTorchFunctionOverride::test_torch_flip, test/test_overrides.py::TestTorchFunctionOverride::test_torch_fliplr, test/test_overrides.py::TestTorchFunctionOverride::test_torch_flipud, test/test_overrides.py::TestTorchFunctionOverride::test_torch_float_power, test/test_overrides.py::TestTorchFunctionOverride::test_torch_floor, test/test_overrides.py::TestTorchFunctionOverride::test_torch_floor_divide, test/test_overrides.py::TestTorchFunctionOverride::test_torch_fmax, test/test_overrides.py::TestTorchFunctionOverride::test_torch_fmin, test/test_overrides.py::TestTorchFunctionOverride::test_torch_fmod, test/test_overrides.py::TestTorchFunctionOverride::test_torch_frac, test/test_overrides.py::TestTorchFunctionOverride::test_torch_frexp, test/test_overrides.py::TestTorchFunctionOverride::test_torch_frobenius_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_full_like, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_atleast_1d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_atleast_2d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_atleast_3d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_block_diag, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_broadcast_tensors, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_cartesian_prod, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_cdist, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_chain_matmul, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_einsum, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_lu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_meshgrid, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_split, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_stft, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_tensordot, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_unique, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_unique_consecutive, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_unravel_index, test/test_overrides.py::TestTorchFunctionOverride::test_torch_fused_moving_avg_obs_fake_quant, test/test_overrides.py::TestTorchFunctionOverride::test_torch_gather, test/test_overrides.py::TestTorchFunctionOverride::test_torch_gcd, test/test_overrides.py::TestTorchFunctionOverride::test_torch_ge, test/test_overrides.py::TestTorchFunctionOverride::test_torch_geqrf, test/test_overrides.py::TestTorchFunctionOverride::test_torch_ger, test/test_overrides.py::TestTorchFunctionOverride::test_torch_get_device, test/test_overrides.py::TestTorchFunctionOverride::test_torch_gradient, test/test_overrides.py::TestTorchFunctionOverride::test_torch_greater, test/test_overrides.py::TestTorchFunctionOverride::test_torch_greater_equal, test/test_overrides.py::TestTorchFunctionOverride::test_torch_grid_sampler, test/test_overrides.py::TestTorchFunctionOverride::test_torch_grid_sampler_2d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_grid_sampler_3d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_group_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_gru, test/test_overrides.py::TestTorchFunctionOverride::test_torch_gru_cell, test/test_overrides.py::TestTorchFunctionOverride::test_torch_gt, test/test_overrides.py::TestTorchFunctionOverride::test_torch_hardshrink, test/test_overrides.py::TestTorchFunctionOverride::test_torch_heaviside, test/test_overrides.py::TestTorchFunctionOverride::test_torch_hinge_embedding_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_histc, test/test_overrides.py::TestTorchFunctionOverride::test_torch_histogram, test/test_overrides.py::TestTorchFunctionOverride::test_torch_histogramdd, test/test_overrides.py::TestTorchFunctionOverride::test_torch_hsmm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_hsplit, test/test_overrides.py::TestTorchFunctionOverride::test_torch_hstack, test/test_overrides.py::TestTorchFunctionOverride::test_torch_hypot, test/test_overrides.py::TestTorchFunctionOverride::test_torch_i0, test/test_overrides.py::TestTorchFunctionOverride::test_torch_igamma, test/test_overrides.py::TestTorchFunctionOverride::test_torch_igammac, test/test_overrides.py::TestTorchFunctionOverride::test_torch_imag, test/test_overrides.py::TestTorchFunctionOverride::test_torch_index_add, 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test/test_overrides.py::TestTorchFunctionOverride::test_torch_is_floating_point, test/test_overrides.py::TestTorchFunctionOverride::test_torch_is_inference, test/test_overrides.py::TestTorchFunctionOverride::test_torch_is_neg, test/test_overrides.py::TestTorchFunctionOverride::test_torch_is_nonzero, test/test_overrides.py::TestTorchFunctionOverride::test_torch_is_same_size, test/test_overrides.py::TestTorchFunctionOverride::test_torch_is_signed, test/test_overrides.py::TestTorchFunctionOverride::test_torch_isclose, test/test_overrides.py::TestTorchFunctionOverride::test_torch_isfinite, test/test_overrides.py::TestTorchFunctionOverride::test_torch_isin, test/test_overrides.py::TestTorchFunctionOverride::test_torch_isinf, test/test_overrides.py::TestTorchFunctionOverride::test_torch_isnan, test/test_overrides.py::TestTorchFunctionOverride::test_torch_isneginf, test/test_overrides.py::TestTorchFunctionOverride::test_torch_isposinf, test/test_overrides.py::TestTorchFunctionOverride::test_torch_isreal, test/test_overrides.py::TestTorchFunctionOverride::test_torch_istft, test/test_overrides.py::TestTorchFunctionOverride::test_torch_kl_div, test/test_overrides.py::TestTorchFunctionOverride::test_torch_kron, test/test_overrides.py::TestTorchFunctionOverride::test_torch_kthvalue, test/test_overrides.py::TestTorchFunctionOverride::test_torch_layer_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_lcm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_ldexp, test/test_overrides.py::TestTorchFunctionOverride::test_torch_le, test/test_overrides.py::TestTorchFunctionOverride::test_torch_lerp, test/test_overrides.py::TestTorchFunctionOverride::test_torch_less, test/test_overrides.py::TestTorchFunctionOverride::test_torch_less_equal, test/test_overrides.py::TestTorchFunctionOverride::test_torch_lgamma, test/test_overrides.py::TestTorchFunctionOverride::test_torch_log, 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test/test_overrides.py::TestTorchFunctionOverride::test_torch_logsumexp, test/test_overrides.py::TestTorchFunctionOverride::test_torch_lstm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_lstm_cell, test/test_overrides.py::TestTorchFunctionOverride::test_torch_lt, test/test_overrides.py::TestTorchFunctionOverride::test_torch_lu_solve, test/test_overrides.py::TestTorchFunctionOverride::test_torch_lu_unpack, test/test_overrides.py::TestTorchFunctionOverride::test_torch_margin_ranking_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_masked_fill, test/test_overrides.py::TestTorchFunctionOverride::test_torch_masked_scatter, test/test_overrides.py::TestTorchFunctionOverride::test_torch_masked_select, test/test_overrides.py::TestTorchFunctionOverride::test_torch_matmul, test/test_overrides.py::TestTorchFunctionOverride::test_torch_matrix_exp, test/test_overrides.py::TestTorchFunctionOverride::test_torch_matrix_power, test/test_overrides.py::TestTorchFunctionOverride::test_torch_max, test/test_overrides.py::TestTorchFunctionOverride::test_torch_max_pool1d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_max_pool1d_with_indices, test/test_overrides.py::TestTorchFunctionOverride::test_torch_max_pool2d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_max_pool3d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_maximum, test/test_overrides.py::TestTorchFunctionOverride::test_torch_mean, test/test_overrides.py::TestTorchFunctionOverride::test_torch_median, test/test_overrides.py::TestTorchFunctionOverride::test_torch_min, test/test_overrides.py::TestTorchFunctionOverride::test_torch_minimum, test/test_overrides.py::TestTorchFunctionOverride::test_torch_miopen_batch_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_miopen_convolution, test/test_overrides.py::TestTorchFunctionOverride::test_torch_miopen_convolution_add_relu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_miopen_convolution_relu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_miopen_convolution_transpose, test/test_overrides.py::TestTorchFunctionOverride::test_torch_miopen_depthwise_convolution, test/test_overrides.py::TestTorchFunctionOverride::test_torch_miopen_rnn, test/test_overrides.py::TestTorchFunctionOverride::test_torch_mode, test/test_overrides.py::TestTorchFunctionOverride::test_torch_moveaxis, test/test_overrides.py::TestTorchFunctionOverride::test_torch_movedim, test/test_overrides.py::TestTorchFunctionOverride::test_torch_msort, test/test_overrides.py::TestTorchFunctionOverride::test_torch_mul, test/test_overrides.py::TestTorchFunctionOverride::test_torch_multinomial, test/test_overrides.py::TestTorchFunctionOverride::test_torch_multiply, test/test_overrides.py::TestTorchFunctionOverride::test_torch_mv, test/test_overrides.py::TestTorchFunctionOverride::test_torch_mvlgamma, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nan_to_num, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nanmean, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nanmedian, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nanquantile, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nansum, test/test_overrides.py::TestTorchFunctionOverride::test_torch_narrow, test/test_overrides.py::TestTorchFunctionOverride::test_torch_narrow_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_native_batch_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_native_channel_shuffle, test/test_overrides.py::TestTorchFunctionOverride::test_torch_native_dropout, test/test_overrides.py::TestTorchFunctionOverride::test_torch_native_group_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_native_layer_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_native_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_ne, test/test_overrides.py::TestTorchFunctionOverride::test_torch_neg, test/test_overrides.py::TestTorchFunctionOverride::test_torch_negative, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nextafter, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional__threshold, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_adaptive_avg_pool2d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_adaptive_avg_pool3d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_adaptive_max_pool1d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_adaptive_max_pool1d_with_indices, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_adaptive_max_pool2d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_adaptive_max_pool2d_with_indices, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_adaptive_max_pool3d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_adaptive_max_pool3d_with_indices, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_affine_grid, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_alpha_dropout, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_batch_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_binary_cross_entropy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_binary_cross_entropy_with_logits, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_celu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_cosine_embedding_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_cross_entropy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_ctc_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_dropout, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_dropout1d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_dropout2d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_dropout3d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_elu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_embedding, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_embedding_bag, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_feature_alpha_dropout, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_fold, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_fractional_max_pool2d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_fractional_max_pool2d_with_indices, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_fractional_max_pool3d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_fractional_max_pool3d_with_indices, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_gaussian_nll_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_glu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_grid_sample, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_group_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_gumbel_softmax, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_hardtanh, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_hinge_embedding_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_huber_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_instance_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_interpolate, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_kl_div, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_l1_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_layer_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_leaky_relu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_local_response_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_log_softmax, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_lp_pool1d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_lp_pool2d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_lp_pool3d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_margin_ranking_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_max_pool1d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_max_pool1d_with_indices, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_max_pool2d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_max_pool2d_with_indices, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_max_pool3d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_max_pool3d_with_indices, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_max_unpool1d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_max_unpool2d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_max_unpool3d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_mish, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_mse_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_multi_head_attention_forward, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_multi_margin_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_multilabel_margin_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_multilabel_soft_margin_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_nll_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_normalize, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_pad, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_poisson_nll_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_relu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_relu6, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_rms_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_rrelu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_selu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_silu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_smooth_l1_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_soft_margin_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_softmax, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_softmin, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_softsign, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_tanhshrink, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_triplet_margin_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_triplet_margin_with_distance_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_unfold, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_init_constant_, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_init_kaiming_uniform_, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_init_normal_, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_init_uniform_, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nonzero, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nonzero_static, test/test_overrides.py::TestTorchFunctionOverride::test_torch_norm_except_dim, test/test_overrides.py::TestTorchFunctionOverride::test_torch_not_equal, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nuclear_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_numel, test/test_overrides.py::TestTorchFunctionOverride::test_torch_ones_like, test/test_overrides.py::TestTorchFunctionOverride::test_torch_orgqr, test/test_overrides.py::TestTorchFunctionOverride::test_torch_ormqr, test/test_overrides.py::TestTorchFunctionOverride::test_torch_outer, test/test_overrides.py::TestTorchFunctionOverride::test_torch_pairwise_distance, test/test_overrides.py::TestTorchFunctionOverride::test_torch_pdist, test/test_overrides.py::TestTorchFunctionOverride::test_torch_permute, test/test_overrides.py::TestTorchFunctionOverride::test_torch_permute_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_pinverse, test/test_overrides.py::TestTorchFunctionOverride::test_torch_pixel_shuffle, test/test_overrides.py::TestTorchFunctionOverride::test_torch_pixel_unshuffle, test/test_overrides.py::TestTorchFunctionOverride::test_torch_poisson, test/test_overrides.py::TestTorchFunctionOverride::test_torch_poisson_nll_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_polar, test/test_overrides.py::TestTorchFunctionOverride::test_torch_polygamma, test/test_overrides.py::TestTorchFunctionOverride::test_torch_positive, test/test_overrides.py::TestTorchFunctionOverride::test_torch_pow, test/test_overrides.py::TestTorchFunctionOverride::test_torch_prelu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_prod, test/test_overrides.py::TestTorchFunctionOverride::test_torch_put, test/test_overrides.py::TestTorchFunctionOverride::test_torch_q_per_channel_axis, test/test_overrides.py::TestTorchFunctionOverride::test_torch_q_per_channel_scales, test/test_overrides.py::TestTorchFunctionOverride::test_torch_q_per_channel_zero_points, test/test_overrides.py::TestTorchFunctionOverride::test_torch_q_scale, test/test_overrides.py::TestTorchFunctionOverride::test_torch_q_zero_point, test/test_overrides.py::TestTorchFunctionOverride::test_torch_qr, test/test_overrides.py::TestTorchFunctionOverride::test_torch_quantile, test/test_overrides.py::TestTorchFunctionOverride::test_torch_quantize_per_channel, test/test_overrides.py::TestTorchFunctionOverride::test_torch_quantize_per_tensor, test/test_overrides.py::TestTorchFunctionOverride::test_torch_quantize_per_tensor_dynamic, test/test_overrides.py::TestTorchFunctionOverride::test_torch_quantized_batch_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_quantized_gru_cell, test/test_overrides.py::TestTorchFunctionOverride::test_torch_quantized_lstm_cell, test/test_overrides.py::TestTorchFunctionOverride::test_torch_quantized_max_pool1d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_quantized_max_pool2d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_quantized_max_pool3d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_quantized_rnn_relu_cell, test/test_overrides.py::TestTorchFunctionOverride::test_torch_quantized_rnn_tanh_cell, test/test_overrides.py::TestTorchFunctionOverride::test_torch_rad2deg, test/test_overrides.py::TestTorchFunctionOverride::test_torch_rand_like, test/test_overrides.py::TestTorchFunctionOverride::test_torch_randint_like, test/test_overrides.py::TestTorchFunctionOverride::test_torch_randn_like, test/test_overrides.py::TestTorchFunctionOverride::test_torch_ravel, test/test_overrides.py::TestTorchFunctionOverride::test_torch_real, test/test_overrides.py::TestTorchFunctionOverride::test_torch_reciprocal, test/test_overrides.py::TestTorchFunctionOverride::test_torch_relu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_remainder, test/test_overrides.py::TestTorchFunctionOverride::test_torch_renorm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_repeat_interleave, test/test_overrides.py::TestTorchFunctionOverride::test_torch_reshape, test/test_overrides.py::TestTorchFunctionOverride::test_torch_resolve_conj, test/test_overrides.py::TestTorchFunctionOverride::test_torch_resolve_neg, test/test_overrides.py::TestTorchFunctionOverride::test_torch_rms_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_rnn_relu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_rnn_relu_cell, test/test_overrides.py::TestTorchFunctionOverride::test_torch_rnn_tanh, test/test_overrides.py::TestTorchFunctionOverride::test_torch_rnn_tanh_cell, test/test_overrides.py::TestTorchFunctionOverride::test_torch_roll, test/test_overrides.py::TestTorchFunctionOverride::test_torch_rot90, test/test_overrides.py::TestTorchFunctionOverride::test_torch_round, test/test_overrides.py::TestTorchFunctionOverride::test_torch_row_indices_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_row_stack, test/test_overrides.py::TestTorchFunctionOverride::test_torch_rrelu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_rsqrt, test/test_overrides.py::TestTorchFunctionOverride::test_torch_rsub, test/test_overrides.py::TestTorchFunctionOverride::test_torch_saddmm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_scatter, test/test_overrides.py::TestTorchFunctionOverride::test_torch_scatter_add, test/test_overrides.py::TestTorchFunctionOverride::test_torch_scatter_reduce, test/test_overrides.py::TestTorchFunctionOverride::test_torch_searchsorted, test/test_overrides.py::TestTorchFunctionOverride::test_torch_segment_reduce, test/test_overrides.py::TestTorchFunctionOverride::test_torch_select, test/test_overrides.py::TestTorchFunctionOverride::test_torch_select_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_select_scatter, test/test_overrides.py::TestTorchFunctionOverride::test_torch_selu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_sgn, test/test_overrides.py::TestTorchFunctionOverride::test_torch_sigmoid, test/test_overrides.py::TestTorchFunctionOverride::test_torch_sign, test/test_overrides.py::TestTorchFunctionOverride::test_torch_signbit, test/test_overrides.py::TestTorchFunctionOverride::test_torch_sin, test/test_overrides.py::TestTorchFunctionOverride::test_torch_sinc, test/test_overrides.py::TestTorchFunctionOverride::test_torch_sinh, test/test_overrides.py::TestTorchFunctionOverride::test_torch_slice_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_slice_inverse, test/test_overrides.py::TestTorchFunctionOverride::test_torch_slice_scatter, test/test_overrides.py::TestTorchFunctionOverride::test_torch_slogdet, test/test_overrides.py::TestTorchFunctionOverride::test_torch_smm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_softmax, test/test_overrides.py::TestTorchFunctionOverride::test_torch_sort, test/test_overrides.py::TestTorchFunctionOverride::test_torch_split_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_split_with_sizes, test/test_overrides.py::TestTorchFunctionOverride::test_torch_split_with_sizes_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_sqrt, test/test_overrides.py::TestTorchFunctionOverride::test_torch_square, test/test_overrides.py::TestTorchFunctionOverride::test_torch_squeeze, test/test_overrides.py::TestTorchFunctionOverride::test_torch_squeeze_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_stack, test/test_overrides.py::TestTorchFunctionOverride::test_torch_std, test/test_overrides.py::TestTorchFunctionOverride::test_torch_std_mean, test/test_overrides.py::TestTorchFunctionOverride::test_torch_sub, test/test_overrides.py::TestTorchFunctionOverride::test_torch_subtract, test/test_overrides.py::TestTorchFunctionOverride::test_torch_sum, test/test_overrides.py::TestTorchFunctionOverride::test_torch_svd, test/test_overrides.py::TestTorchFunctionOverride::test_torch_swapaxes, test/test_overrides.py::TestTorchFunctionOverride::test_torch_swapdims, test/test_overrides.py::TestTorchFunctionOverride::test_torch_sym_float, test/test_overrides.py::TestTorchFunctionOverride::test_torch_sym_int, test/test_overrides.py::TestTorchFunctionOverride::test_torch_sym_ite, test/test_overrides.py::TestTorchFunctionOverride::test_torch_sym_max, test/test_overrides.py::TestTorchFunctionOverride::test_torch_sym_min, test/test_overrides.py::TestTorchFunctionOverride::test_torch_sym_not, test/test_overrides.py::TestTorchFunctionOverride::test_torch_sym_sum, test/test_overrides.py::TestTorchFunctionOverride::test_torch_t, test/test_overrides.py::TestTorchFunctionOverride::test_torch_t_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_take, test/test_overrides.py::TestTorchFunctionOverride::test_torch_take_along_dim, test/test_overrides.py::TestTorchFunctionOverride::test_torch_tan, test/test_overrides.py::TestTorchFunctionOverride::test_torch_tanh, test/test_overrides.py::TestTorchFunctionOverride::test_torch_tensor_split, test/test_overrides.py::TestTorchFunctionOverride::test_torch_threshold, test/test_overrides.py::TestTorchFunctionOverride::test_torch_tile, test/test_overrides.py::TestTorchFunctionOverride::test_torch_topk, test/test_overrides.py::TestTorchFunctionOverride::test_torch_trace, test/test_overrides.py::TestTorchFunctionOverride::test_torch_transpose, test/test_overrides.py::TestTorchFunctionOverride::test_torch_transpose_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_trapezoid, test/test_overrides.py::TestTorchFunctionOverride::test_torch_trapz, test/test_overrides.py::TestTorchFunctionOverride::test_torch_triangular_solve, test/test_overrides.py::TestTorchFunctionOverride::test_torch_tril, test/test_overrides.py::TestTorchFunctionOverride::test_torch_triplet_margin_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_triu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_true_divide, test/test_overrides.py::TestTorchFunctionOverride::test_torch_trunc, test/test_overrides.py::TestTorchFunctionOverride::test_torch_unbind, test/test_overrides.py::TestTorchFunctionOverride::test_torch_unbind_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_unflatten, test/test_overrides.py::TestTorchFunctionOverride::test_torch_unfold_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_unsafe_chunk, test/test_overrides.py::TestTorchFunctionOverride::test_torch_unsafe_split, test/test_overrides.py::TestTorchFunctionOverride::test_torch_unsafe_split_with_sizes, test/test_overrides.py::TestTorchFunctionOverride::test_torch_unsqueeze, test/test_overrides.py::TestTorchFunctionOverride::test_torch_unsqueeze_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_values_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_var, test/test_overrides.py::TestTorchFunctionOverride::test_torch_var_mean, test/test_overrides.py::TestTorchFunctionOverride::test_torch_vdot, test/test_overrides.py::TestTorchFunctionOverride::test_torch_view_as_complex, test/test_overrides.py::TestTorchFunctionOverride::test_torch_view_as_complex_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_view_as_real, test/test_overrides.py::TestTorchFunctionOverride::test_torch_view_as_real_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_view_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_vsplit, test/test_overrides.py::TestTorchFunctionOverride::test_torch_vstack, test/test_overrides.py::TestTorchFunctionOverride::test_torch_where, test/test_overrides.py::TestTorchFunctionOverride::test_torch_xlogy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_zeros_like, test/test_overrides.py::TestTorchFunctionOverride::test_user_implementation_raises, test/test_overrides.py::TestEinsumOverride::test_wrapper, test/test_overrides.py::TestGradCheckOverride::test_gradcheck, test/test_overrides.py::TestNamedTuple::test_max, test/test_overrides.py::TestGradNewOnesOverride::test_newones, test/test_overrides.py::TestPickle::test_pickle, test/test_overrides.py::TestBroadcastAllOverride::test_broadcast_all, test/test_overrides.py::TestWrapTorchFunction::test_wrap_torch_function, test/test_overrides.py::TestIndexing::test_getitem, test/test_overrides.py::TestIndexing::test_getitem_subclass, test/test_overrides.py::TestIndexing::test_setitem, test/test_overrides.py::TestIndexing::test_setitem_subclass, test/test_overrides.py::TestIndexing::test_setitem_val, test/test_overrides.py::TestIterator::test_iterator, test/test_overrides.py::TestRNN::test_rnn, test/test_overrides.py::TestDisabledTorchFunction::test_parameter_does_not_prevent_dispatch, test/test_overrides.py::TestResolveName::test_resolve_name, test/test_overrides.py::TestTorchFunctionWarning::test_warn_on_invalid_torch_function_standalone_class, test/test_overrides.py::TestTorchFunctionWarning::test_warn_on_invalid_torch_function_tensor_subclass, test/test_overrides.py::TestDisabledUserWarnings::test_no_implicit_user_warning_for_deprecated_functions, test/test_overrides.py::TestTorchFunctionMode::test_all_same_mode, test/test_overrides.py::TestTorchFunctionMode::test_basic, test/test_overrides.py::TestTorchFunctionMode::test_custom_device_type, test/test_overrides.py::TestTorchFunctionMode::test_device_context_semantics, test/test_overrides.py::TestTorchFunctionMode::test_disable_enable_subclass, test/test_overrides.py::TestTorchFunctionMode::test_disable_enable_torch_function_ctx, test/test_overrides.py::TestTorchFunctionMode::test_disable_subclass_mode, test/test_overrides.py::TestTorchFunctionMode::test_disable_subclass_not_mode, test/test_overrides.py::TestTorchFunctionMode::test_distributions_bernoulli, test/test_overrides.py::TestTorchFunctionMode::test_error_using_class_method_on_mode, test/test_overrides.py::TestTorchFunctionMode::test_factory_override, test/test_overrides.py::TestTorchFunctionMode::test_get_cur_mode, test/test_overrides.py::TestTorchFunctionMode::test_get_mode_stack, test/test_overrides.py::TestTorchFunctionMode::test_getitem_call, test/test_overrides.py::TestTorchFunctionMode::test_mode_notimplemented_loop, test/test_overrides.py::TestTorchFunctionMode::test_modes_handle_first, test/test_overrides.py::TestTorchFunctionMode::test_modes_return_notimplemented, test/test_overrides.py::TestTorchFunctionMode::test_nested_modes_with_python_has_torch_function, test/test_overrides.py::TestTorchFunctionMode::test_nested_same_mode, test/test_overrides.py::TestTorchFunctionMode::test_nn_parse_to, test/test_overrides.py::TestTorchFunctionMode::test_reentrant_mode_idiom, test/test_overrides.py::TestTorchFunctionMode::test_restacking_with_ancestor, test/test_overrides.py::TestTorchFunctionMode::test_subclass_hash, test/test_overrides.py::TestTorchFunctionMode::test_torch_function_all_disabled_api, test/test_overrides.py::TestTorchFunctionMode::test_with_mode, test/test_overrides.py::TestTorchFunctionMode::test_with_mode_created_separately, test/test_overrides.py::TestTorchFunctionMode::test_with_nested_modes 2025-01-24T01:51:17.9444790Z 2025-01-24T01:51:17.9445070Z Running test_cpp_extensions_aot_ninja 1/1 ... [2025-01-24 01:51:17.849333] 2025-01-24T01:51:20.3900109Z running install 2025-01-24T01:51:20.3901353Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/setuptools/_distutils/cmd.py:66: SetuptoolsDeprecationWarning: setup.py install is deprecated. 2025-01-24T01:51:20.3902227Z !! 2025-01-24T01:51:20.3902349Z 2025-01-24T01:51:20.3902544Z ******************************************************************************** 2025-01-24T01:51:20.3903140Z Please avoid running ``setup.py`` directly. 2025-01-24T01:51:20.3903813Z Instead, use pypa/build, pypa/installer or other 2025-01-24T01:51:20.3904400Z standards-based tools. 2025-01-24T01:51:20.3904613Z 2025-01-24T01:51:20.3904927Z See https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html for details. 2025-01-24T01:51:20.3905494Z ******************************************************************************** 2025-01-24T01:51:20.3905742Z 2025-01-24T01:51:20.3905849Z !! 2025-01-24T01:51:20.3906098Z self.initialize_options() 2025-01-24T01:51:20.4033700Z running build 2025-01-24T01:51:20.4034195Z running build_py 2025-01-24T01:51:20.4119607Z creating build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension 2025-01-24T01:51:20.4137506Z copying torch_test_cpp_extension/__init__.py -> build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension 2025-01-24T01:51:20.4150058Z running build_ext 2025-01-24T01:51:20.5255616Z building 'torch_test_cpp_extension.cpp' extension 2025-01-24T01:51:20.5256696Z creating /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-313 2025-01-24T01:51:20.5570501Z Emitting ninja build file /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-313/build.ninja... 2025-01-24T01:51:20.5571407Z Compiling objects... 2025-01-24T01:51:20.5571804Z Using envvar MAX_JOBS (6) as the number of workers... 2025-01-24T01:51:21.5278078Z [1/1] c++ -MMD -MF /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-313/extension.o.d -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/TH -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/THC -I/var/lib/jenkins/workspace/test/cpp_extensions/self_compiler_include_dirs_test -I/opt/conda/envs/py_3.13/include/python3.13 -c -c /var/lib/jenkins/workspace/test/cpp_extensions/extension.cpp -o /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-313/extension.o -g -DTORCH_API_INCLUDE_EXTENSION_H '-DPYBIND11_COMPILER_TYPE="_clang"' '-DPYBIND11_STDLIB="_libstdcpp"' '-DPYBIND11_BUILD_ABI="_cxxabi1002"' -DTORCH_EXTENSION_NAME=cpp -D_GLIBCXX_USE_CXX11_ABI=1 -std=c++17 2025-01-24T01:51:21.5375267Z g++ -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -pthread -B /opt/conda/envs/py_3.13/compiler_compat -shared -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-313/extension.o -L/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension/cpp.cpython-313-x86_64-linux-gnu.so 2025-01-24T01:51:21.7855971Z building 'torch_test_cpp_extension.maia' extension 2025-01-24T01:51:21.8199797Z Emitting ninja build file /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-313/build.ninja... 2025-01-24T01:51:21.8206071Z Compiling objects... 2025-01-24T01:51:21.8206438Z Using envvar MAX_JOBS (6) as the number of workers... 2025-01-24T01:51:22.5645113Z [1/1] c++ -MMD -MF /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-313/maia_extension.o.d -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/TH -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/THC -I/var/lib/jenkins/workspace/test/cpp_extensions/self_compiler_include_dirs_test -I/opt/conda/envs/py_3.13/include/python3.13 -c -c /var/lib/jenkins/workspace/test/cpp_extensions/maia_extension.cpp -o /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-313/maia_extension.o -g -DTORCH_API_INCLUDE_EXTENSION_H '-DPYBIND11_COMPILER_TYPE="_clang"' '-DPYBIND11_STDLIB="_libstdcpp"' '-DPYBIND11_BUILD_ABI="_cxxabi1002"' -DTORCH_EXTENSION_NAME=maia -D_GLIBCXX_USE_CXX11_ABI=1 -std=c++17 2025-01-24T01:51:22.5706996Z g++ -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -pthread -B /opt/conda/envs/py_3.13/compiler_compat -shared -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-313/maia_extension.o -L/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension/maia.cpython-313-x86_64-linux-gnu.so 2025-01-24T01:51:22.8135737Z building 'torch_test_cpp_extension.rng' extension 2025-01-24T01:51:22.8472413Z Emitting ninja build file /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-313/build.ninja... 2025-01-24T01:51:22.8473758Z Compiling objects... 2025-01-24T01:51:22.8474304Z Using envvar MAX_JOBS (6) as the number of workers... 2025-01-24T01:51:23.8079525Z [1/1] c++ -MMD -MF /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-313/rng_extension.o.d -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/TH -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/THC -I/var/lib/jenkins/workspace/test/cpp_extensions/self_compiler_include_dirs_test -I/opt/conda/envs/py_3.13/include/python3.13 -c -c /var/lib/jenkins/workspace/test/cpp_extensions/rng_extension.cpp -o /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-313/rng_extension.o -g -DTORCH_API_INCLUDE_EXTENSION_H '-DPYBIND11_COMPILER_TYPE="_clang"' '-DPYBIND11_STDLIB="_libstdcpp"' '-DPYBIND11_BUILD_ABI="_cxxabi1002"' -DTORCH_EXTENSION_NAME=rng -D_GLIBCXX_USE_CXX11_ABI=1 -std=c++17 2025-01-24T01:51:23.8134973Z g++ -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -pthread -B /opt/conda/envs/py_3.13/compiler_compat -shared -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-313/rng_extension.o -L/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension/rng.cpython-313-x86_64-linux-gnu.so 2025-01-24T01:51:24.0640080Z running install_lib 2025-01-24T01:51:24.0720220Z creating install/opt/conda/envs/py_3.13/lib/python3.13/site-packages 2025-01-24T01:51:24.0723717Z creating install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_test_cpp_extension 2025-01-24T01:51:24.0725631Z copying build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension/__init__.py -> ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_test_cpp_extension 2025-01-24T01:51:24.0727525Z copying build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension/cpp.cpython-313-x86_64-linux-gnu.so -> ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_test_cpp_extension 2025-01-24T01:51:24.0761966Z copying build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension/maia.cpython-313-x86_64-linux-gnu.so -> ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_test_cpp_extension 2025-01-24T01:51:24.0796993Z copying build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension/rng.cpython-313-x86_64-linux-gnu.so -> ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_test_cpp_extension 2025-01-24T01:51:24.0835864Z byte-compiling ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_test_cpp_extension/__init__.py to __init__.cpython-313.pyc 2025-01-24T01:51:24.0838031Z running install_egg_info 2025-01-24T01:51:24.1015195Z running egg_info 2025-01-24T01:51:24.1015798Z creating torch_test_cpp_extension.egg-info 2025-01-24T01:51:24.1091132Z writing torch_test_cpp_extension.egg-info/PKG-INFO 2025-01-24T01:51:24.1094174Z writing dependency_links to torch_test_cpp_extension.egg-info/dependency_links.txt 2025-01-24T01:51:24.1095908Z writing entry points to torch_test_cpp_extension.egg-info/entry_points.txt 2025-01-24T01:51:24.1097657Z writing top-level names to torch_test_cpp_extension.egg-info/top_level.txt 2025-01-24T01:51:24.1099077Z writing manifest file 'torch_test_cpp_extension.egg-info/SOURCES.txt' 2025-01-24T01:51:24.1173400Z reading manifest file 'torch_test_cpp_extension.egg-info/SOURCES.txt' 2025-01-24T01:51:24.1181006Z writing manifest file 'torch_test_cpp_extension.egg-info/SOURCES.txt' 2025-01-24T01:51:24.1182397Z Copying torch_test_cpp_extension.egg-info to ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_test_cpp_extension-0.0.0-py3.13.egg-info 2025-01-24T01:51:24.1188150Z running install_scripts 2025-01-24T01:51:26.1928183Z running install 2025-01-24T01:51:26.1929575Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/setuptools/_distutils/cmd.py:66: SetuptoolsDeprecationWarning: setup.py install is deprecated. 2025-01-24T01:51:26.1931053Z !! 2025-01-24T01:51:26.1931248Z 2025-01-24T01:51:26.1931480Z ******************************************************************************** 2025-01-24T01:51:26.1932234Z Please avoid running ``setup.py`` directly. 2025-01-24T01:51:26.1932988Z Instead, use pypa/build, pypa/installer or other 2025-01-24T01:51:26.1933674Z standards-based tools. 2025-01-24T01:51:26.1934032Z 2025-01-24T01:51:26.1934581Z See https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html for details. 2025-01-24T01:51:26.1935564Z ******************************************************************************** 2025-01-24T01:51:26.1936017Z 2025-01-24T01:51:26.1936184Z !! 2025-01-24T01:51:26.1936560Z self.initialize_options() 2025-01-24T01:51:26.2062514Z running build 2025-01-24T01:51:26.2062971Z running build_ext 2025-01-24T01:51:26.3149783Z building 'no_python_abi_suffix_test' extension 2025-01-24T01:51:26.3152143Z creating /var/lib/jenkins/workspace/test/cpp_extensions/no_python_abi_suffix_test/build/temp.linux-x86_64-cpython-313 2025-01-24T01:51:26.3473349Z Emitting ninja build file /var/lib/jenkins/workspace/test/cpp_extensions/no_python_abi_suffix_test/build/temp.linux-x86_64-cpython-313/build.ninja... 2025-01-24T01:51:26.3474975Z Compiling objects... 2025-01-24T01:51:26.3475521Z Using envvar MAX_JOBS (6) as the number of workers... 2025-01-24T01:51:26.4216859Z [1/1] c++ -MMD -MF /var/lib/jenkins/workspace/test/cpp_extensions/no_python_abi_suffix_test/build/temp.linux-x86_64-cpython-313/no_python_abi_suffix_test.o.d -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/TH -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/THC -I/opt/conda/envs/py_3.13/include/python3.13 -c -c /var/lib/jenkins/workspace/test/cpp_extensions/no_python_abi_suffix_test/no_python_abi_suffix_test.cpp -o /var/lib/jenkins/workspace/test/cpp_extensions/no_python_abi_suffix_test/build/temp.linux-x86_64-cpython-313/no_python_abi_suffix_test.o -DTORCH_API_INCLUDE_EXTENSION_H '-DPYBIND11_COMPILER_TYPE="_clang"' '-DPYBIND11_STDLIB="_libstdcpp"' '-DPYBIND11_BUILD_ABI="_cxxabi1002"' -DTORCH_EXTENSION_NAME=no_python_abi_suffix_test -D_GLIBCXX_USE_CXX11_ABI=1 -std=c++17 2025-01-24T01:51:26.4257948Z creating build/lib.linux-x86_64-cpython-313 2025-01-24T01:51:26.4262842Z g++ -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -pthread -B /opt/conda/envs/py_3.13/compiler_compat -shared -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib /var/lib/jenkins/workspace/test/cpp_extensions/no_python_abi_suffix_test/build/temp.linux-x86_64-cpython-313/no_python_abi_suffix_test.o -L/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-313/no_python_abi_suffix_test.so 2025-01-24T01:51:26.4834017Z running install_lib 2025-01-24T01:51:26.4912611Z creating install/opt/conda/envs/py_3.13/lib/python3.13/site-packages 2025-01-24T01:51:26.4917003Z copying build/lib.linux-x86_64-cpython-313/no_python_abi_suffix_test.so -> ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages 2025-01-24T01:51:26.4922142Z running install_egg_info 2025-01-24T01:51:26.5095649Z running egg_info 2025-01-24T01:51:26.5096373Z creating no_python_abi_suffix_test.egg-info 2025-01-24T01:51:26.5165770Z writing no_python_abi_suffix_test.egg-info/PKG-INFO 2025-01-24T01:51:26.5169311Z writing dependency_links to no_python_abi_suffix_test.egg-info/dependency_links.txt 2025-01-24T01:51:26.5171366Z writing top-level names to no_python_abi_suffix_test.egg-info/top_level.txt 2025-01-24T01:51:26.5172657Z writing manifest file 'no_python_abi_suffix_test.egg-info/SOURCES.txt' 2025-01-24T01:51:26.5245428Z reading manifest file 'no_python_abi_suffix_test.egg-info/SOURCES.txt' 2025-01-24T01:51:26.5252333Z writing manifest file 'no_python_abi_suffix_test.egg-info/SOURCES.txt' 2025-01-24T01:51:26.5254172Z Copying no_python_abi_suffix_test.egg-info to ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/no_python_abi_suffix_test-0.0.0-py3.13.egg-info 2025-01-24T01:51:26.5265314Z running install_scripts 2025-01-24T01:51:26.9204944Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T01:51:26.9207617Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_cpp_extensions_aot_ninja.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-01-24 01:51:26.920494] 2025-01-24T01:51:32.7711858Z 2025-01-24T01:51:32.7713100Z test_cpp_extensions_aot_ninja 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_cpp_extensions_aot_ninja_1.1_b240f4503e3046c6_.log 2025-01-24T01:51:32.7720368Z Running 18 items in this shard: test/test_cpp_extensions_aot_ninja.py::TestCppExtensionAOT::test_backward, test/test_cpp_extensions_aot_ninja.py::TestCppExtensionAOT::test_cublas_extension, test/test_cpp_extensions_aot_ninja.py::TestCppExtensionAOT::test_cuda_dlink_libs, test/test_cpp_extensions_aot_ninja.py::TestCppExtensionAOT::test_cuda_extension, test/test_cpp_extensions_aot_ninja.py::TestCppExtensionAOT::test_cusolver_extension, test/test_cpp_extensions_aot_ninja.py::TestCppExtensionAOT::test_extension_function, test/test_cpp_extensions_aot_ninja.py::TestCppExtensionAOT::test_extension_module, test/test_cpp_extensions_aot_ninja.py::TestCppExtensionAOT::test_mps_extension, test/test_cpp_extensions_aot_ninja.py::TestCppExtensionAOT::test_no_python_abi_suffix_sets_the_correct_library_name, test/test_cpp_extensions_aot_ninja.py::TestCppExtensionAOT::test_optional, test/test_cpp_extensions_aot_ninja.py::TestCppExtensionAOT::test_python_agnostic, test/test_cpp_extensions_aot_ninja.py::TestPybindTypeCasters::test_pybind_return_types, test/test_cpp_extensions_aot_ninja.py::TestMAIATensor::test_add, test/test_cpp_extensions_aot_ninja.py::TestMAIATensor::test_conv_backend_override, test/test_cpp_extensions_aot_ninja.py::TestMAIATensor::test_unregistered, test/test_cpp_extensions_aot_ninja.py::TestMAIATensor::test_zeros, test/test_cpp_extensions_aot_ninja.py::TestRNGExtension::test_rng, test/test_cpp_extensions_aot_ninja.py::TestTorchLibrary::test_torch_library 2025-01-24T01:51:32.7726737Z 2025-01-24T01:51:32.7727007Z Running test_cpp_extensions_aot_no_ninja 1/1 ... [2025-01-24 01:51:32.771423] 2025-01-24T01:51:35.2675302Z running install 2025-01-24T01:51:35.2676477Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/setuptools/_distutils/cmd.py:66: SetuptoolsDeprecationWarning: setup.py install is deprecated. 2025-01-24T01:51:35.2677941Z !! 2025-01-24T01:51:35.2678080Z 2025-01-24T01:51:35.2678226Z ******************************************************************************** 2025-01-24T01:51:35.2678630Z Please avoid running ``setup.py`` directly. 2025-01-24T01:51:35.2679052Z Instead, use pypa/build, pypa/installer or other 2025-01-24T01:51:35.2679617Z standards-based tools. 2025-01-24T01:51:35.2679825Z 2025-01-24T01:51:35.2680136Z See https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html for details. 2025-01-24T01:51:35.2680684Z ******************************************************************************** 2025-01-24T01:51:35.2680928Z 2025-01-24T01:51:35.2681032Z !! 2025-01-24T01:51:35.2681265Z self.initialize_options() 2025-01-24T01:51:35.2807651Z running build 2025-01-24T01:51:35.2807978Z running build_py 2025-01-24T01:51:35.2885436Z creating build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension 2025-01-24T01:51:35.2888012Z copying torch_test_cpp_extension/__init__.py -> build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension 2025-01-24T01:51:35.2891318Z running build_ext 2025-01-24T01:51:35.3675048Z building 'torch_test_cpp_extension.cpp' extension 2025-01-24T01:51:35.3675662Z creating build/temp.linux-x86_64-cpython-313 2025-01-24T01:51:35.3682070Z g++ -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/TH -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/THC -Iself_compiler_include_dirs_test -I/opt/conda/envs/py_3.13/include/python3.13 -c extension.cpp -o build/temp.linux-x86_64-cpython-313/extension.o -g -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\"_clang\" -DPYBIND11_STDLIB=\"_libstdcpp\" -DPYBIND11_BUILD_ABI=\"_cxxabi1002\" -DTORCH_EXTENSION_NAME=cpp -D_GLIBCXX_USE_CXX11_ABI=1 -std=c++17 2025-01-24T01:51:36.4204400Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/Exceptions.h:12, 2025-01-24T01:51:36.4205491Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/python.h:11, 2025-01-24T01:51:36.4206481Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:9, 2025-01-24T01:51:36.4207109Z from extension.cpp:1: 2025-01-24T01:51:36.4225317Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/pybind11/pybind11.h: In instantiation of ‘class pybind11::class_’: 2025-01-24T01:51:36.4226405Z extension.cpp:45:53: required from here 2025-01-24T01:51:36.4228423Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/pybind11/pybind11.h:1539:7: warning: ‘pybind11::class_’ declared with greater visibility than its base ‘pybind11::detail::generic_type’ [-Wattributes] 2025-01-24T01:51:36.4229930Z 1539 | class class_ : public detail::generic_type { 2025-01-24T01:51:36.4230361Z | ^~~~~~ 2025-01-24T01:51:36.4232137Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/pybind11/pybind11.h: In instantiation of ‘pybind11::class_< , >::class_(pybind11::handle, const char*, const Extra& ...) [with Extra = {}; type_ = MatrixMultiplier; options = {}]’: 2025-01-24T01:51:36.4233660Z extension.cpp:45:53: required from here 2025-01-24T01:51:36.4237236Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/pybind11/pybind11.h:1599:28: warning: ‘pybind11::class_< , >::class_(pybind11::handle, const char*, const Extra& ...) [with Extra = {}; type_ = MatrixMultiplier; options = {}]::’ declared with greater visibility than the type of its field ‘pybind11::class_< , >::class_(pybind11::handle, const char*, const Extra& ...) [with Extra = {}; type_ = MatrixMultiplier; options = {}]::::’ [-Wattributes] 2025-01-24T01:51:36.4240183Z 1599 | with_internals([&](internals &internals) { 2025-01-24T01:51:36.4240657Z | ^~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-01-24T01:51:36.4241176Z 1600 | auto &instances = record.module_local ? get_local_internals().registered_types_cpp 2025-01-24T01:51:36.4241816Z | ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-01-24T01:51:36.4242283Z 1601 | : internals.registered_types_cpp; 2025-01-24T01:51:36.4242709Z | ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-01-24T01:51:36.4243145Z 1602 | instances[std::type_index(typeid(type_alias))] 2025-01-24T01:51:36.4243563Z | ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-01-24T01:51:36.4244039Z 1603 | = instances[std::type_index(typeid(type))]; 2025-01-24T01:51:36.4244433Z | ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-01-24T01:51:36.4244777Z 1604 | }); 2025-01-24T01:51:36.4245041Z | ~ 2025-01-24T01:51:36.4248849Z g++ -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -pthread -B /opt/conda/envs/py_3.13/compiler_compat -shared -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib build/temp.linux-x86_64-cpython-313/extension.o -L/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension/cpp.cpython-313-x86_64-linux-gnu.so 2025-01-24T01:51:36.7969390Z building 'torch_test_cpp_extension.maia' extension 2025-01-24T01:51:36.7974159Z g++ -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/TH -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/THC -Iself_compiler_include_dirs_test -I/opt/conda/envs/py_3.13/include/python3.13 -c maia_extension.cpp -o build/temp.linux-x86_64-cpython-313/maia_extension.o -g -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\"_clang\" -DPYBIND11_STDLIB=\"_libstdcpp\" -DPYBIND11_BUILD_ABI=\"_cxxabi1002\" -DTORCH_EXTENSION_NAME=maia -D_GLIBCXX_USE_CXX11_ABI=1 -std=c++17 2025-01-24T01:51:37.9146866Z g++ -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -pthread -B /opt/conda/envs/py_3.13/compiler_compat -shared -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib build/temp.linux-x86_64-cpython-313/maia_extension.o -L/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension/maia.cpython-313-x86_64-linux-gnu.so 2025-01-24T01:51:38.2879805Z building 'torch_test_cpp_extension.rng' extension 2025-01-24T01:51:38.2884354Z g++ -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/TH -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/THC -Iself_compiler_include_dirs_test -I/opt/conda/envs/py_3.13/include/python3.13 -c rng_extension.cpp -o build/temp.linux-x86_64-cpython-313/rng_extension.o -g -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\"_clang\" -DPYBIND11_STDLIB=\"_libstdcpp\" -DPYBIND11_BUILD_ABI=\"_cxxabi1002\" -DTORCH_EXTENSION_NAME=rng -D_GLIBCXX_USE_CXX11_ABI=1 -std=c++17 2025-01-24T01:51:39.5548048Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec256/vec256.h:8, 2025-01-24T01:51:39.5549724Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec.h:7, 2025-01-24T01:51:39.5551089Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/native/cpu/Loops.h:37, 2025-01-24T01:51:39.5552653Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/native/cpu/DistributionTemplates.h:9, 2025-01-24T01:51:39.5553765Z from rng_extension.cpp:6: 2025-01-24T01:51:39.5554748Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_base.h:1155: warning: ignoring #pragma unroll [-Wunknown-pragmas] 2025-01-24T01:51:39.5555576Z 1155 | # pragma unroll 2025-01-24T01:51:39.5555845Z | 2025-01-24T01:51:39.5556432Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_base.h:1195, 2025-01-24T01:51:39.5557342Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec256/vec256.h:8, 2025-01-24T01:51:39.5558161Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec.h:7, 2025-01-24T01:51:39.5558965Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/native/cpu/Loops.h:37, 2025-01-24T01:51:39.5559882Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/native/cpu/DistributionTemplates.h:9, 2025-01-24T01:51:39.5560567Z from rng_extension.cpp:6: 2025-01-24T01:51:39.5561374Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_n.h:59: warning: ignoring #pragma unroll [-Wunknown-pragmas] 2025-01-24T01:51:39.5562137Z 59 | #pragma unroll 2025-01-24T01:51:39.5562559Z | 2025-01-24T01:51:39.5563353Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_n.h:72: warning: ignoring #pragma unroll [-Wunknown-pragmas] 2025-01-24T01:51:39.5564132Z 72 | #pragma unroll 2025-01-24T01:51:39.5564395Z | 2025-01-24T01:51:39.5565084Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_n.h:87: warning: ignoring #pragma unroll [-Wunknown-pragmas] 2025-01-24T01:51:39.5565845Z 87 | #pragma unroll 2025-01-24T01:51:39.5566107Z | 2025-01-24T01:51:39.5566668Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_base.h:1196, 2025-01-24T01:51:39.5567617Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec256/vec256.h:8, 2025-01-24T01:51:39.5568444Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec.h:7, 2025-01-24T01:51:39.5569303Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/native/cpu/Loops.h:37, 2025-01-24T01:51:39.5570211Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/native/cpu/DistributionTemplates.h:9, 2025-01-24T01:51:39.5570902Z from rng_extension.cpp:6: 2025-01-24T01:51:39.5571714Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_mask.h:153: warning: ignoring #pragma unroll [-Wunknown-pragmas] 2025-01-24T01:51:39.5572515Z 153 | #pragma unroll 2025-01-24T01:51:39.5572780Z | 2025-01-24T01:51:39.5575882Z g++ -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -pthread -B /opt/conda/envs/py_3.13/compiler_compat -shared -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib build/temp.linux-x86_64-cpython-313/rng_extension.o -L/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension/rng.cpython-313-x86_64-linux-gnu.so 2025-01-24T01:51:39.9427783Z running install_lib 2025-01-24T01:51:39.9515136Z copying build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension/cpp.cpython-313-x86_64-linux-gnu.so -> ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_test_cpp_extension 2025-01-24T01:51:39.9608858Z copying build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension/maia.cpython-313-x86_64-linux-gnu.so -> ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_test_cpp_extension 2025-01-24T01:51:39.9694464Z copying build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension/rng.cpython-313-x86_64-linux-gnu.so -> ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_test_cpp_extension 2025-01-24T01:51:39.9793528Z running install_egg_info 2025-01-24T01:51:39.9974789Z running egg_info 2025-01-24T01:51:40.0049765Z writing torch_test_cpp_extension.egg-info/PKG-INFO 2025-01-24T01:51:40.0052936Z writing dependency_links to torch_test_cpp_extension.egg-info/dependency_links.txt 2025-01-24T01:51:40.0061237Z writing entry points to torch_test_cpp_extension.egg-info/entry_points.txt 2025-01-24T01:51:40.0070838Z writing top-level names to torch_test_cpp_extension.egg-info/top_level.txt 2025-01-24T01:51:40.0153652Z reading manifest file 'torch_test_cpp_extension.egg-info/SOURCES.txt' 2025-01-24T01:51:40.0162046Z writing manifest file 'torch_test_cpp_extension.egg-info/SOURCES.txt' 2025-01-24T01:51:40.0172157Z removing './install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_test_cpp_extension-0.0.0-py3.13.egg-info' (and everything under it) 2025-01-24T01:51:40.0173834Z Copying torch_test_cpp_extension.egg-info to ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_test_cpp_extension-0.0.0-py3.13.egg-info 2025-01-24T01:51:40.0179972Z running install_scripts 2025-01-24T01:51:42.1199008Z running install 2025-01-24T01:51:42.1200230Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/setuptools/_distutils/cmd.py:66: SetuptoolsDeprecationWarning: setup.py install is deprecated. 2025-01-24T01:51:42.1201043Z !! 2025-01-24T01:51:42.1201162Z 2025-01-24T01:51:42.1201289Z ******************************************************************************** 2025-01-24T01:51:42.1201692Z Please avoid running ``setup.py`` directly. 2025-01-24T01:51:42.1202127Z Instead, use pypa/build, pypa/installer or other 2025-01-24T01:51:42.1202517Z standards-based tools. 2025-01-24T01:51:42.1202713Z 2025-01-24T01:51:42.1203037Z See https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html for details. 2025-01-24T01:51:42.1203586Z ******************************************************************************** 2025-01-24T01:51:42.1204070Z 2025-01-24T01:51:42.1204172Z !! 2025-01-24T01:51:42.1204411Z self.initialize_options() 2025-01-24T01:51:42.1333547Z running build 2025-01-24T01:51:42.1334027Z running build_ext 2025-01-24T01:51:42.2443262Z building 'no_python_abi_suffix_test' extension 2025-01-24T01:51:42.2761324Z Emitting ninja build file /var/lib/jenkins/workspace/test/cpp_extensions/no_python_abi_suffix_test/build/temp.linux-x86_64-cpython-313/build.ninja... 2025-01-24T01:51:42.2762256Z Compiling objects... 2025-01-24T01:51:42.2762603Z Using envvar MAX_JOBS (6) as the number of workers... 2025-01-24T01:51:42.3040648Z ninja: no work to do. 2025-01-24T01:51:42.3086980Z g++ -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -pthread -B /opt/conda/envs/py_3.13/compiler_compat -shared -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib /var/lib/jenkins/workspace/test/cpp_extensions/no_python_abi_suffix_test/build/temp.linux-x86_64-cpython-313/no_python_abi_suffix_test.o -L/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-313/no_python_abi_suffix_test.so 2025-01-24T01:51:42.3675929Z running install_lib 2025-01-24T01:51:42.3756544Z copying build/lib.linux-x86_64-cpython-313/no_python_abi_suffix_test.so -> ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages 2025-01-24T01:51:42.3761498Z running install_egg_info 2025-01-24T01:51:42.3936924Z running egg_info 2025-01-24T01:51:42.4008252Z writing no_python_abi_suffix_test.egg-info/PKG-INFO 2025-01-24T01:51:42.4011776Z writing dependency_links to no_python_abi_suffix_test.egg-info/dependency_links.txt 2025-01-24T01:51:42.4022694Z writing top-level names to no_python_abi_suffix_test.egg-info/top_level.txt 2025-01-24T01:51:42.4104381Z reading manifest file 'no_python_abi_suffix_test.egg-info/SOURCES.txt' 2025-01-24T01:51:42.4112221Z writing manifest file 'no_python_abi_suffix_test.egg-info/SOURCES.txt' 2025-01-24T01:51:42.4122537Z removing './install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/no_python_abi_suffix_test-0.0.0-py3.13.egg-info' (and everything under it) 2025-01-24T01:51:42.4124164Z Copying no_python_abi_suffix_test.egg-info to ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/no_python_abi_suffix_test-0.0.0-py3.13.egg-info 2025-01-24T01:51:42.4129225Z running install_scripts 2025-01-24T01:51:42.8109257Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T01:51:42.8111839Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_cpp_extensions_aot_no_ninja.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-01-24 01:51:42.810939] 2025-01-24T01:51:48.6728951Z 2025-01-24T01:51:48.6729985Z test_cpp_extensions_aot_no_ninja 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_cpp_extensions_aot_no_ninja_1.1_369950f173425791_.log 2025-01-24T01:51:48.6737126Z Running 18 items in this shard: test/test_cpp_extensions_aot_no_ninja.py::TestCppExtensionAOT::test_backward, test/test_cpp_extensions_aot_no_ninja.py::TestCppExtensionAOT::test_cublas_extension, test/test_cpp_extensions_aot_no_ninja.py::TestCppExtensionAOT::test_cuda_dlink_libs, test/test_cpp_extensions_aot_no_ninja.py::TestCppExtensionAOT::test_cuda_extension, test/test_cpp_extensions_aot_no_ninja.py::TestCppExtensionAOT::test_cusolver_extension, test/test_cpp_extensions_aot_no_ninja.py::TestCppExtensionAOT::test_extension_function, test/test_cpp_extensions_aot_no_ninja.py::TestCppExtensionAOT::test_extension_module, test/test_cpp_extensions_aot_no_ninja.py::TestCppExtensionAOT::test_mps_extension, test/test_cpp_extensions_aot_no_ninja.py::TestCppExtensionAOT::test_no_python_abi_suffix_sets_the_correct_library_name, test/test_cpp_extensions_aot_no_ninja.py::TestCppExtensionAOT::test_optional, test/test_cpp_extensions_aot_no_ninja.py::TestCppExtensionAOT::test_python_agnostic, test/test_cpp_extensions_aot_no_ninja.py::TestPybindTypeCasters::test_pybind_return_types, test/test_cpp_extensions_aot_no_ninja.py::TestMAIATensor::test_add, test/test_cpp_extensions_aot_no_ninja.py::TestMAIATensor::test_conv_backend_override, test/test_cpp_extensions_aot_no_ninja.py::TestMAIATensor::test_unregistered, test/test_cpp_extensions_aot_no_ninja.py::TestMAIATensor::test_zeros, test/test_cpp_extensions_aot_no_ninja.py::TestRNGExtension::test_rng, test/test_cpp_extensions_aot_no_ninja.py::TestTorchLibrary::test_torch_library 2025-01-24T01:51:48.6743650Z 2025-01-24T01:51:48.6743836Z Running doctests 1/1 ... [2025-01-24 01:51:48.673113] 2025-01-24T01:51:48.7795270Z Start doctest_module('/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch') 2025-01-24T01:51:49.1244702Z Listing tests 2025-01-24T01:51:49.1245967Z msg = Cannot scrape callname=Tensor.dim_order in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py line=1498. 2025-01-24T01:51:49.1247628Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:51:49.1248392Z 2025-01-24T01:51:49.1248627Z dim_order(ambiguity_check=False) -> tuple 2025-01-24T01:51:49.1249075Z 2025-01-24T01:51:49.1249513Z Returns the uniquely determined tuple of int describing the dim order or 2025-01-24T01:51:49.1250386Z physical layout of :attr:`self`. 2025-01-24T01:51:49.1250751Z 2025-01-24T01:51:49.1251121Z The dim order represents how dimensions are laid out in memory, 2025-01-24T01:51:49.1252022Z starting from the outermost to the innermost dimension. 2025-01-24T01:51:49.1252531Z 2025-01-24T01:51:49.1252869Z Note that the dim order may not always be uniquely determined. 2025-01-24T01:51:49.1254182Z If `ambiguity_check` is True, this function raises a RuntimeError when the dim order cannot be uniquely determined; 2025-01-24T01:51:49.1255896Z If `ambiguity_check` is a list of memory formats, this function raises a RuntimeError when tensor can not be interpreted 2025-01-24T01:51:49.1257393Z into exactly one of the given memory formats, or it cannot be uniquely determined. 2025-01-24T01:51:49.1258739Z If `ambiguity_check` is False, it will return one of legal dim order(s) without checking its uniqueness. 2025-01-24T01:51:49.1259802Z Otherwise, it will raise TypeError. 2025-01-24T01:51:49.1260183Z 2025-01-24T01:51:49.1260347Z Args: 2025-01-24T01:51:49.1261162Z ambiguity_check (bool or List[torch.memory_format]): The check method for ambiguity of dim order. 2025-01-24T01:51:49.1262010Z 2025-01-24T01:51:49.1262213Z >>> torch.empty((2, 3, 5, 7)).dim_order() 2025-01-24T01:51:49.1262790Z (0, 1, 2, 3) 2025-01-24T01:51:49.1263325Z >>> torch.empty((2, 3, 5, 7)).transpose(1, 2).dim_order() 2025-01-24T01:51:49.1263994Z (0, 2, 1, 3) 2025-01-24T01:51:49.1265016Z >>> torch.empty((2, 3, 5, 7), memory_format=torch.channels_last).dim_order() 2025-01-24T01:51:49.1265775Z (0, 2, 3, 1) 2025-01-24T01:51:49.1266327Z >>> torch.empty((1, 2, 3, 4)).dim_order() 2025-01-24T01:51:49.1266908Z (0, 1, 2, 3) 2025-01-24T01:51:49.1267465Z >>> try: 2025-01-24T01:51:49.1268009Z ... torch.empty((1, 2, 3, 4)).dim_order(ambiguity_check=True) 2025-01-24T01:51:49.1268757Z ... except RuntimeError as e: 2025-01-24T01:51:49.1269299Z ... print(e) 2025-01-24T01:51:49.1270169Z The tensor does not have unique dim order, or cannot map to exact one of the given memory formats. 2025-01-24T01:51:49.1271208Z >>> torch.empty((1, 2, 3, 4)).dim_order( 2025-01-24T01:51:49.1272013Z ... ambiguity_check=[torch.contiguous_format, torch.channels_last] 2025-01-24T01:51:49.1272880Z ... ) # It can be mapped to contiguous format 2025-01-24T01:51:49.1273493Z (0, 1, 2, 3) 2025-01-24T01:51:49.1274049Z >>> try: 2025-01-24T01:51:49.1274639Z ... torch.empty((1, 2, 3, 4)).dim_order(ambiguity_check="ILLEGAL") 2025-01-24T01:51:49.1275380Z ... except TypeError as e: 2025-01-24T01:51:49.1275899Z ... print(e) 2025-01-24T01:51:49.1276617Z The ambiguity_check argument must be a bool or a list of memory formats. 2025-01-24T01:51:49.1279542Z .. warning:: 2025-01-24T01:51:49.1280206Z The dim_order tensor API is experimental and subject to change. 2025-01-24T01:51:49.1280804Z 2025-01-24T01:51:49.1281308Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:51:49.1282026Z 2025-01-24T01:51:49.1800398Z msg = Cannot scrape callname=meshgrid in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py line=432. 2025-01-24T01:51:49.1801288Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:51:49.1801925Z Creates grids of coordinates specified by the 1D inputs in `attr`:tensors. 2025-01-24T01:51:49.1802312Z 2025-01-24T01:51:49.1802509Z This is helpful when you want to visualize data over some 2025-01-24T01:51:49.1802965Z range of inputs. See below for a plotting example. 2025-01-24T01:51:49.1803237Z 2025-01-24T01:51:49.1803425Z Given :math:`N` 1D tensors :math:`T_0 \ldots T_{N-1}` as 2025-01-24T01:51:49.1803900Z inputs with corresponding sizes :math:`S_0 \ldots S_{N-1}`, 2025-01-24T01:51:49.1804398Z this creates :math:`N` N-dimensional tensors :math:`G_0 \ldots 2025-01-24T01:51:49.1804871Z G_{N-1}`, each with shape :math:`(S_0, ..., S_{N-1})` where 2025-01-24T01:51:49.1805340Z the output :math:`G_i` is constructed by expanding :math:`T_i` 2025-01-24T01:51:49.1805759Z to the result shape. 2025-01-24T01:51:49.1805942Z 2025-01-24T01:51:49.1806124Z .. note:: 2025-01-24T01:51:49.1806425Z 0D inputs are treated equivalently to 1D inputs of a 2025-01-24T01:51:49.1806813Z single element. 2025-01-24T01:51:49.1807012Z 2025-01-24T01:51:49.1807110Z .. warning:: 2025-01-24T01:51:49.1807464Z `torch.meshgrid(*tensors)` currently has the same behavior 2025-01-24T01:51:49.1807943Z as calling `numpy.meshgrid(*arrays, indexing='ij')`. 2025-01-24T01:51:49.1808219Z 2025-01-24T01:51:49.1808385Z In the future `torch.meshgrid` will transition to 2025-01-24T01:51:49.1808763Z `indexing='xy'` as the default. 2025-01-24T01:51:49.1809027Z 2025-01-24T01:51:49.1809232Z https://github.com/pytorch/pytorch/issues/50276 tracks 2025-01-24T01:51:49.1809701Z this issue with the goal of migrating to NumPy's behavior. 2025-01-24T01:51:49.1810011Z 2025-01-24T01:51:49.1810112Z .. seealso:: 2025-01-24T01:51:49.1810275Z 2025-01-24T01:51:49.1810449Z :func:`torch.cartesian_prod` has the same effect but it 2025-01-24T01:51:49.1810877Z collects the data in a tensor of vectors. 2025-01-24T01:51:49.1811122Z 2025-01-24T01:51:49.1811226Z Args: 2025-01-24T01:51:49.1811899Z tensors (list of Tensor): list of scalars or 1 dimensional tensors. Scalars will be 2025-01-24T01:51:49.1812447Z treated as tensors of size :math:`(1,)` automatically 2025-01-24T01:51:49.1812744Z 2025-01-24T01:51:49.1812923Z indexing: (str, optional): the indexing mode, either "xy" 2025-01-24T01:51:49.1813411Z or "ij", defaults to "ij". See warning for future changes. 2025-01-24T01:51:49.1813710Z 2025-01-24T01:51:49.1813870Z If "xy" is selected, the first dimension corresponds 2025-01-24T01:51:49.1814322Z to the cardinality of the second input and the second 2025-01-24T01:51:49.1814786Z dimension corresponds to the cardinality of the first 2025-01-24T01:51:49.1815181Z input. 2025-01-24T01:51:49.1815337Z 2025-01-24T01:51:49.1815505Z If "ij" is selected, the dimensions are in the same 2025-01-24T01:51:49.1815902Z order as the cardinality of the inputs. 2025-01-24T01:51:49.1816211Z 2025-01-24T01:51:49.1816315Z Returns: 2025-01-24T01:51:49.1816635Z seq (sequence of Tensors): If the input has :math:`N` 2025-01-24T01:51:49.1817084Z tensors of size :math:`S_0 \ldots S_{N-1}``, then the 2025-01-24T01:51:49.1817560Z output will also have :math:`N` tensors, where each tensor 2025-01-24T01:51:49.1817997Z is of shape :math:`(S_0, ..., S_{N-1})`. 2025-01-24T01:51:49.1818233Z 2025-01-24T01:51:49.1818330Z Example:: 2025-01-24T01:51:49.1818491Z 2025-01-24T01:51:49.1818604Z >>> x = torch.tensor([1, 2, 3]) 2025-01-24T01:51:49.1818948Z >>> y = torch.tensor([4, 5, 6]) 2025-01-24T01:51:49.1819166Z 2025-01-24T01:51:49.1819401Z Observe the element-wise pairings across the grid, (1, 4), 2025-01-24T01:51:49.1820051Z (1, 5), ..., (3, 6). This is the same thing as the 2025-01-24T01:51:49.1820519Z cartesian product. 2025-01-24T01:51:49.1821069Z >>> grid_x, grid_y = torch.meshgrid(x, y, indexing='ij') 2025-01-24T01:51:49.1821579Z >>> grid_x 2025-01-24T01:51:49.1821870Z tensor([[1, 1, 1], 2025-01-24T01:51:49.1822281Z [2, 2, 2], 2025-01-24T01:51:49.1822661Z [3, 3, 3]]) 2025-01-24T01:51:49.1823056Z >>> grid_y 2025-01-24T01:51:49.1823530Z tensor([[4, 5, 6], 2025-01-24T01:51:49.1823915Z [4, 5, 6], 2025-01-24T01:51:49.1824189Z [4, 5, 6]]) 2025-01-24T01:51:49.1824365Z 2025-01-24T01:51:49.1824550Z This correspondence can be seen when these grids are 2025-01-24T01:51:49.1824930Z stacked properly. 2025-01-24T01:51:49.1825325Z >>> torch.equal(torch.cat(tuple(torch.dstack([grid_x, grid_y]))), 2025-01-24T01:51:49.1825784Z ... torch.cartesian_prod(x, y)) 2025-01-24T01:51:49.1826126Z True 2025-01-24T01:51:49.1826345Z 2025-01-24T01:51:49.1826566Z `torch.meshgrid` is commonly used to produce a grid for 2025-01-24T01:51:49.1826963Z plotting. 2025-01-24T01:51:49.1827263Z >>> # xdoctest: +REQUIRES(module:matplotlib) 2025-01-24T01:51:49.1827648Z >>> # xdoctest: +REQUIRES(env:DOCTEST_SHOW) 2025-01-24T01:51:49.1828026Z >>> import matplotlib.pyplot as plt 2025-01-24T01:51:49.1828400Z >>> xs = torch.linspace(-5, 5, steps=100) 2025-01-24T01:51:49.1828767Z >>> ys = torch.linspace(-5, 5, steps=100) 2025-01-24T01:51:49.1829140Z >>> x, y = torch.meshgrid(xs, ys, indexing='xy') 2025-01-24T01:51:49.1829528Z >>> z = torch.sin(torch.sqrt(x * x + y * y)) 2025-01-24T01:51:49.1829899Z >>> ax = plt.axes(projection='3d') 2025-01-24T01:51:49.1830293Z >>> ax.plot_surface(x.numpy(), y.numpy(), z.numpy()) 2025-01-24T01:51:49.1830664Z >>> plt.show() 2025-01-24T01:51:49.1830835Z 2025-01-24T01:51:49.1830977Z .. image:: ../_static/img/meshgrid.png 2025-01-24T01:51:49.1831480Z :width: 512 2025-01-24T01:51:49.1831655Z 2025-01-24T01:51:49.1831744Z 2025-01-24T01:51:49.1832216Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:51:49.1832587Z 2025-01-24T01:51:49.1833138Z msg = Cannot scrape callname=_unique_impl in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py line=821. 2025-01-24T01:51:49.1834013Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:51:49.1834784Z unique(input, sorted=True, return_inverse=False, return_counts=False, dim=None) -> tuple[Tensor, Tensor, Tensor] 2025-01-24T01:51:49.1835281Z 2025-01-24T01:51:49.1835449Z Returns the unique elements of the input tensor. 2025-01-24T01:51:49.1835713Z 2025-01-24T01:51:49.1836022Z .. note:: This function is different from :func:`torch.unique_consecutive` in the sense that 2025-01-24T01:51:49.1836650Z this function also eliminates non-consecutive duplicate values. 2025-01-24T01:51:49.1837016Z 2025-01-24T01:51:49.1837250Z .. note:: Currently in the CUDA implementation and the CPU implementation, 2025-01-24T01:51:49.1837895Z `torch.unique` always sort the tensor at the beginning regardless of the `sort` argument. 2025-01-24T01:51:49.1838592Z Sorting could be slow, so if your input tensor is already sorted, it is recommended to use 2025-01-24T01:51:49.1839176Z :func:`torch.unique_consecutive` which avoids the sorting. 2025-01-24T01:51:49.1839471Z 2025-01-24T01:51:49.1839580Z Args: 2025-01-24T01:51:49.1839827Z input (Tensor): the input tensor 2025-01-24T01:51:49.1840260Z sorted (bool): Whether to sort the unique elements in ascending order 2025-01-24T01:51:49.1840713Z before returning as output. 2025-01-24T01:51:49.1841149Z return_inverse (bool): Whether to also return the indices for where 2025-01-24T01:51:49.1841704Z elements in the original input ended up in the returned unique list. 2025-01-24T01:51:49.1842278Z return_counts (bool): Whether to also return the counts for each unique 2025-01-24T01:51:49.1842717Z element. 2025-01-24T01:51:49.1843072Z dim (int, optional): the dimension to operate upon. If ``None``, the 2025-01-24T01:51:49.1843614Z unique of the flattened input is returned. Otherwise, each of the 2025-01-24T01:51:49.1844149Z tensors indexed by the given dimension is treated as one of the 2025-01-24T01:51:49.1844689Z elements to apply the unique operation upon. See examples for more 2025-01-24T01:51:49.1845137Z details. Default: ``None`` 2025-01-24T01:51:49.1845346Z 2025-01-24T01:51:49.1845436Z Returns: 2025-01-24T01:51:49.1845861Z (Tensor, Tensor (optional), Tensor (optional)): A tensor or a tuple of tensors containing 2025-01-24T01:51:49.1846277Z 2025-01-24T01:51:49.1846472Z - **output** (*Tensor*): the output list of unique scalar elements. 2025-01-24T01:51:49.1846947Z - **inverse_indices** (*Tensor*): (optional) if 2025-01-24T01:51:49.1847401Z :attr:`return_inverse` is True, there will be an additional 2025-01-24T01:51:49.1847918Z returned tensor (same shape as input) representing the indices 2025-01-24T01:51:49.1848435Z for where elements in the original input map to in the output; 2025-01-24T01:51:49.1848950Z otherwise, this function will only return a single tensor. 2025-01-24T01:51:49.1849393Z - **counts** (*Tensor*): (optional) if 2025-01-24T01:51:49.1849815Z :attr:`return_counts` is True, there will be an additional 2025-01-24T01:51:49.1850307Z returned tensor (same shape as output or output.size(dim), 2025-01-24T01:51:49.1850812Z if dim was specified) representing the number of occurrences 2025-01-24T01:51:49.1851232Z for each unique value or tensor. 2025-01-24T01:51:49.1851478Z 2025-01-24T01:51:49.1851576Z Example:: 2025-01-24T01:51:49.1851761Z 2025-01-24T01:51:49.1852036Z >>> output = torch.unique(torch.tensor([1, 3, 2, 3], dtype=torch.long)) 2025-01-24T01:51:49.1852473Z >>> output 2025-01-24T01:51:49.1852720Z tensor([1, 2, 3]) 2025-01-24T01:51:49.1852889Z 2025-01-24T01:51:49.1853034Z >>> output, inverse_indices = torch.unique( 2025-01-24T01:51:49.1853512Z ... torch.tensor([1, 3, 2, 3], dtype=torch.long), sorted=True, return_inverse=True) 2025-01-24T01:51:49.1853967Z >>> output 2025-01-24T01:51:49.1854219Z tensor([1, 2, 3]) 2025-01-24T01:51:49.1854509Z >>> inverse_indices 2025-01-24T01:51:49.1854797Z tensor([0, 2, 1, 2]) 2025-01-24T01:51:49.1854974Z 2025-01-24T01:51:49.1855104Z >>> output, inverse_indices = torch.unique( 2025-01-24T01:51:49.1855597Z ... torch.tensor([[1, 3], [2, 3]], dtype=torch.long), sorted=True, return_inverse=True) 2025-01-24T01:51:49.1856056Z >>> output 2025-01-24T01:51:49.1856335Z tensor([1, 2, 3]) 2025-01-24T01:51:49.1856622Z >>> inverse_indices 2025-01-24T01:51:49.1856891Z tensor([[0, 2], 2025-01-24T01:51:49.1857154Z [1, 2]]) 2025-01-24T01:51:49.1857336Z 2025-01-24T01:51:49.1857439Z >>> a = torch.tensor([ 2025-01-24T01:51:49.1857723Z ... [ 2025-01-24T01:51:49.1857967Z ... [1, 1, 0, 0], 2025-01-24T01:51:49.1858237Z ... [1, 1, 0, 0], 2025-01-24T01:51:49.1858524Z ... [0, 0, 1, 1], 2025-01-24T01:51:49.1858806Z ... ], 2025-01-24T01:51:49.1859038Z ... [ 2025-01-24T01:51:49.1859273Z ... [0, 0, 1, 1], 2025-01-24T01:51:49.1859548Z ... [0, 0, 1, 1], 2025-01-24T01:51:49.1859831Z ... [1, 1, 1, 1], 2025-01-24T01:51:49.1860106Z ... ], 2025-01-24T01:51:49.1860343Z ... [ 2025-01-24T01:51:49.1860581Z ... [1, 1, 0, 0], 2025-01-24T01:51:49.1860848Z ... [1, 1, 0, 0], 2025-01-24T01:51:49.1861134Z ... [0, 0, 1, 1], 2025-01-24T01:51:49.1861415Z ... ], 2025-01-24T01:51:49.1861646Z ... ]) 2025-01-24T01:51:49.1861781Z 2025-01-24T01:51:49.1862008Z >>> # If we call `torch.unique(a, dim=0)`, each of the tensors `a[idx, :, :]` 2025-01-24T01:51:49.1862542Z >>> # will be compared. We can see that `a[0, :, :]` and `a[2, :, :]` match 2025-01-24T01:51:49.1863016Z >>> # each other, so one of them will be removed. 2025-01-24T01:51:49.1863380Z >>> (a[0, :, :] == a[2, :, :]).all() 2025-01-24T01:51:49.1863693Z tensor(True) 2025-01-24T01:51:49.1863982Z >>> a_unique_dim0 = torch.unique(a, dim=0) 2025-01-24T01:51:49.1864317Z >>> a_unique_dim0 2025-01-24T01:51:49.1864590Z tensor([[[0, 0, 1, 1], 2025-01-24T01:51:49.1864879Z [0, 0, 1, 1], 2025-01-24T01:51:49.1865155Z [1, 1, 1, 1]], 2025-01-24T01:51:49.1865429Z [[1, 1, 0, 0], 2025-01-24T01:51:49.1865707Z [1, 1, 0, 0], 2025-01-24T01:51:49.1865987Z [0, 0, 1, 1]]]) 2025-01-24T01:51:49.1866183Z 2025-01-24T01:51:49.1866507Z >>> # Notice which sub-tensors from `a` match with the sub-tensors from 2025-01-24T01:51:49.1866957Z >>> # `a_unique_dim0`: 2025-01-24T01:51:49.1867280Z >>> (a_unique_dim0[0, :, :] == a[1, :, :]).all() 2025-01-24T01:51:49.1867607Z tensor(True) 2025-01-24T01:51:49.1867893Z >>> (a_unique_dim0[1, :, :] == a[0, :, :]).all() 2025-01-24T01:51:49.1868229Z tensor(True) 2025-01-24T01:51:49.1868382Z 2025-01-24T01:51:49.1868605Z >>> # For `torch.unique(a, dim=1)`, each of the tensors `a[:, idx, :]` are 2025-01-24T01:51:49.1869128Z >>> # compared. `a[:, 0, :]` and `a[:, 1, :]` match each other, so one of 2025-01-24T01:51:49.1869544Z >>> # them will be removed. 2025-01-24T01:51:49.1869848Z >>> (a[:, 0, :] == a[:, 1, :]).all() 2025-01-24T01:51:49.1870156Z tensor(True) 2025-01-24T01:51:49.1870481Z >>> torch.unique(a, dim=1) 2025-01-24T01:51:49.1870873Z tensor([[[0, 0, 1, 1], 2025-01-24T01:51:49.1871158Z [1, 1, 0, 0]], 2025-01-24T01:51:49.1871423Z [[1, 1, 1, 1], 2025-01-24T01:51:49.1871712Z [0, 0, 1, 1]], 2025-01-24T01:51:49.1871988Z [[0, 0, 1, 1], 2025-01-24T01:51:49.1872264Z [1, 1, 0, 0]]]) 2025-01-24T01:51:49.1872446Z 2025-01-24T01:51:49.1872670Z >>> # For `torch.unique(a, dim=2)`, the tensors `a[:, :, idx]` are compared. 2025-01-24T01:51:49.1873157Z >>> # `a[:, :, 0]` and `a[:, :, 1]` match each other. Also, `a[:, :, 2]` and 2025-01-24T01:51:49.1873615Z >>> # `a[:, :, 3]` match each other as well. So in this case, two of the 2025-01-24T01:51:49.1874029Z >>> # sub-tensors will be removed. 2025-01-24T01:51:49.1874365Z >>> (a[:, :, 0] == a[:, :, 1]).all() 2025-01-24T01:51:49.1874680Z tensor(True) 2025-01-24T01:51:49.1874973Z >>> (a[:, :, 2] == a[:, :, 3]).all() 2025-01-24T01:51:49.1875284Z tensor(True) 2025-01-24T01:51:49.1875549Z >>> torch.unique(a, dim=2) 2025-01-24T01:51:49.1875850Z tensor([[[0, 1], 2025-01-24T01:51:49.1876111Z [0, 1], 2025-01-24T01:51:49.1876356Z [1, 0]], 2025-01-24T01:51:49.1876616Z [[1, 0], 2025-01-24T01:51:49.1876871Z [1, 0], 2025-01-24T01:51:49.1877135Z [1, 1]], 2025-01-24T01:51:49.1877393Z [[0, 1], 2025-01-24T01:51:49.1877670Z [0, 1], 2025-01-24T01:51:49.1877930Z [1, 0]]]) 2025-01-24T01:51:49.1878188Z 2025-01-24T01:51:49.1878571Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:51:49.1878943Z 2025-01-24T01:51:49.1989467Z msg = Cannot scrape callname=load in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/hub.py line=560. 2025-01-24T01:51:49.1990301Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:51:49.1990709Z 2025-01-24T01:51:49.1990870Z Load a model from a github repo or a local directory. 2025-01-24T01:51:49.1991155Z 2025-01-24T01:51:49.1991386Z Note: Loading a model is the typical use case, but this can also be used to 2025-01-24T01:51:49.1991954Z for loading other objects such as tokenizers, loss functions, etc. 2025-01-24T01:51:49.1992295Z 2025-01-24T01:51:49.1992465Z If ``source`` is 'github', ``repo_or_dir`` is expected to be 2025-01-24T01:51:49.1992930Z of the form ``repo_owner/repo_name[:ref]`` with an optional 2025-01-24T01:51:49.1993324Z ref (a tag or a branch). 2025-01-24T01:51:49.1993493Z 2025-01-24T01:51:49.1993664Z If ``source`` is 'local', ``repo_or_dir`` is expected to be a 2025-01-24T01:51:49.1994070Z path to a local directory. 2025-01-24T01:51:49.1994263Z 2025-01-24T01:51:49.1994352Z Args: 2025-01-24T01:51:49.1994615Z repo_or_dir (str): If ``source`` is 'github', 2025-01-24T01:51:49.1995163Z this should correspond to a github repo with format ``repo_owner/repo_name[:ref]`` with 2025-01-24T01:51:49.1996007Z an optional ref (tag or branch), for example 'pytorch/vision:0.10'. If ``ref`` is not specified, 2025-01-24T01:51:49.1996784Z the default branch is assumed to be ``main`` if it exists, and otherwise ``master``. 2025-01-24T01:51:49.1997427Z If ``source`` is 'local' then it should be a path to a local directory. 2025-01-24T01:51:49.1997957Z model (str): the name of a callable (entrypoint) defined in the 2025-01-24T01:51:49.1998387Z repo/dir's ``hubconf.py``. 2025-01-24T01:51:49.1998805Z *args (optional): the corresponding args for callable ``model``. 2025-01-24T01:51:49.1999297Z source (str, optional): 'github' or 'local'. Specifies how 2025-01-24T01:51:49.1999777Z ``repo_or_dir`` is to be interpreted. Default is 'github'. 2025-01-24T01:51:49.2000292Z trust_repo (bool, str or None): ``"check"``, ``True``, ``False`` or ``None``. 2025-01-24T01:51:49.2001104Z This parameter was introduced in v1.12 and helps ensuring that users 2025-01-24T01:51:49.2001739Z only run code from repos that they trust. 2025-01-24T01:51:49.2002036Z 2025-01-24T01:51:49.2002302Z - If ``False``, a prompt will ask the user whether the repo should 2025-01-24T01:51:49.2002748Z be trusted. 2025-01-24T01:51:49.2003109Z - If ``True``, the repo will be added to the trusted list and loaded 2025-01-24T01:51:49.2003547Z without requiring explicit confirmation. 2025-01-24T01:51:49.2003978Z - If ``"check"``, the repo will be checked against the list of 2025-01-24T01:51:49.2004491Z trusted repos in the cache. If it is not present in that list, the 2025-01-24T01:51:49.2005029Z behaviour will fall back onto the ``trust_repo=False`` option. 2025-01-24T01:51:49.2005536Z - If ``None``: this will raise a warning, inviting the user to set 2025-01-24T01:51:49.2006025Z ``trust_repo`` to either ``False``, ``True`` or ``"check"``. This 2025-01-24T01:51:49.2006611Z is only present for backward compatibility and will be removed in 2025-01-24T01:51:49.2007037Z v2.0. 2025-01-24T01:51:49.2007188Z 2025-01-24T01:51:49.2007398Z Default is ``None`` and will eventually change to ``"check"`` in v2.0. 2025-01-24T01:51:49.2007946Z force_reload (bool, optional): whether to force a fresh download of 2025-01-24T01:51:49.2008476Z the github repo unconditionally. Does not have any effect if 2025-01-24T01:51:49.2008913Z ``source = 'local'``. Default is ``False``. 2025-01-24T01:51:49.2009365Z verbose (bool, optional): If ``False``, mute messages about hitting 2025-01-24T01:51:49.2009906Z local caches. Note that the message about first download cannot be 2025-01-24T01:51:49.2010416Z muted. Does not have any effect if ``source = 'local'``. 2025-01-24T01:51:49.2010813Z Default is ``True``. 2025-01-24T01:51:49.2011289Z skip_validation (bool, optional): if ``False``, torchhub will check that the branch or commit 2025-01-24T01:51:49.2012091Z specified by the ``github`` argument properly belongs to the repo owner. This will make 2025-01-24T01:51:49.2012780Z requests to the GitHub API; you can specify a non-default GitHub token by setting the 2025-01-24T01:51:49.2013363Z ``GITHUB_TOKEN`` environment variable. Default is ``False``. 2025-01-24T01:51:49.2013890Z **kwargs (optional): the corresponding kwargs for callable ``model``. 2025-01-24T01:51:49.2014224Z 2025-01-24T01:51:49.2014326Z Returns: 2025-01-24T01:51:49.2014633Z The output of the ``model`` callable when called with the given 2025-01-24T01:51:49.2015044Z ``*args`` and ``**kwargs``. 2025-01-24T01:51:49.2015243Z 2025-01-24T01:51:49.2015333Z Example: 2025-01-24T01:51:49.2015604Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_HUB) 2025-01-24T01:51:49.2015961Z >>> # from a github repo 2025-01-24T01:51:49.2016242Z >>> repo = "pytorch/vision" 2025-01-24T01:51:49.2016556Z >>> model = torch.hub.load( 2025-01-24T01:51:49.2016944Z ... repo, "resnet50", weights="ResNet50_Weights.IMAGENET1K_V1" 2025-01-24T01:51:49.2017340Z ... ) 2025-01-24T01:51:49.2017574Z >>> # from a local directory 2025-01-24T01:51:49.2017891Z >>> path = "/some/local/path/pytorch/vision" 2025-01-24T01:51:49.2018237Z >>> # xdoctest: +SKIP 2025-01-24T01:51:49.2018658Z >>> model = torch.hub.load(path, "resnet50", weights="ResNet50_Weights.DEFAULT") 2025-01-24T01:51:49.2019035Z 2025-01-24T01:51:49.2019290Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:51:49.2019659Z 2025-01-24T01:51:49.2020156Z msg = Cannot scrape callname=download_url_to_file in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/hub.py line=687. 2025-01-24T01:51:49.2021022Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:51:49.2021554Z Download object at the given URL to a local path. 2025-01-24T01:51:49.2021814Z 2025-01-24T01:51:49.2021954Z Args: 2025-01-24T01:51:49.2022274Z url (str): URL of the object to download 2025-01-24T01:51:49.2022864Z dst (str): Full path where object will be saved, e.g. ``/tmp/temporary_file`` 2025-01-24T01:51:49.2023567Z hash_prefix (str, optional): If not None, the SHA256 downloaded file should start with ``hash_prefix``. 2025-01-24T01:51:49.2024123Z Default: None 2025-01-24T01:51:49.2024538Z progress (bool, optional): whether or not to display a progress bar to stderr 2025-01-24T01:51:49.2025020Z Default: True 2025-01-24T01:51:49.2025202Z 2025-01-24T01:51:49.2025293Z Example: 2025-01-24T01:51:49.2025569Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_HUB) 2025-01-24T01:51:49.2025937Z >>> # xdoctest: +REQUIRES(POSIX) 2025-01-24T01:51:49.2026365Z >>> torch.hub.download_url_to_file( 2025-01-24T01:51:49.2026818Z ... "https://s3.amazonaws.com/pytorch/models/resnet18-5c106cde.pth", 2025-01-24T01:51:49.2027319Z ... "/tmp/temporary_file", 2025-01-24T01:51:49.2027631Z ... ) 2025-01-24T01:51:49.2027761Z 2025-01-24T01:51:49.2027864Z 2025-01-24T01:51:49.2028241Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:51:49.2028612Z 2025-01-24T01:51:49.2029107Z msg = Cannot scrape callname=load_state_dict_from_url in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/hub.py line=812. 2025-01-24T01:51:49.2029992Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:51:49.2030529Z Loads the Torch serialized object at the given URL. 2025-01-24T01:51:49.2030808Z 2025-01-24T01:51:49.2030991Z If downloaded file is a zip file, it will be automatically 2025-01-24T01:51:49.2031399Z decompressed. 2025-01-24T01:51:49.2031546Z 2025-01-24T01:51:49.2031778Z If the object is already present in `model_dir`, it's deserialized and 2025-01-24T01:51:49.2032198Z returned. 2025-01-24T01:51:49.2032562Z The default value of ``model_dir`` is ``/checkpoints`` where 2025-01-24T01:51:49.2033102Z ``hub_dir`` is the directory returned by :func:`~torch.hub.get_dir`. 2025-01-24T01:51:49.2033429Z 2025-01-24T01:51:49.2033519Z Args: 2025-01-24T01:51:49.2033774Z url (str): URL of the object to download 2025-01-24T01:51:49.2034216Z model_dir (str, optional): directory in which to save the object 2025-01-24T01:51:49.2034874Z map_location (optional): a function or a dict specifying how to remap storage locations (see torch.load) 2025-01-24T01:51:49.2035609Z progress (bool, optional): whether or not to display a progress bar to stderr. 2025-01-24T01:51:49.2036087Z Default: True 2025-01-24T01:51:49.2036590Z check_hash(bool, optional): If True, the filename part of the URL should follow the naming convention 2025-01-24T01:51:49.2037260Z ``filename-.ext`` where ```` is the first eight or more 2025-01-24T01:51:49.2037841Z digits of the SHA256 hash of the contents of the file. The hash is used to 2025-01-24T01:51:49.2038379Z ensure unique names and to verify the contents of the file. 2025-01-24T01:51:49.2038798Z Default: False 2025-01-24T01:51:49.2039317Z file_name (str, optional): name for the downloaded file. Filename from ``url`` will be used if not set. 2025-01-24T01:51:49.2040118Z weights_only(bool, optional): If True, only weights will be loaded and no complex pickled objects. 2025-01-24T01:51:49.2040833Z Recommended for untrusted sources. See :func:`~torch.load` for more details. 2025-01-24T01:51:49.2041214Z 2025-01-24T01:51:49.2041321Z Example: 2025-01-24T01:51:49.2041589Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_HUB) 2025-01-24T01:51:49.2042002Z >>> state_dict = torch.hub.load_state_dict_from_url( 2025-01-24T01:51:49.2042496Z ... "https://s3.amazonaws.com/pytorch/models/resnet18-5c106cde.pth" 2025-01-24T01:51:49.2042940Z ... ) 2025-01-24T01:51:49.2043107Z 2025-01-24T01:51:49.2043272Z 2025-01-24T01:51:49.2043636Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:51:49.2044024Z 2025-01-24T01:51:49.2078624Z msg = Cannot scrape callname=Library.fallback in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py line=373. 2025-01-24T01:51:49.2079658Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-01-24T01:51:49.2080280Z Registers the function implementation as the fallback for the given key. 2025-01-24T01:51:49.2080642Z 2025-01-24T01:51:49.2080882Z This function only works for a library with global namespace ("_"). 2025-01-24T01:51:49.2081219Z 2025-01-24T01:51:49.2081325Z Args: 2025-01-24T01:51:49.2081744Z fn: function used as fallback for the given dispatch key or :func:`~fallthrough_kernel` 2025-01-24T01:51:49.2082262Z to register a fallthrough. 2025-01-24T01:51:49.2082916Z dispatch_key: dispatch key that the input function should be registered for. By default, it uses 2025-01-24T01:51:49.2083546Z the dispatch key that the library was created with. 2025-01-24T01:51:49.2084208Z with_keyset: flag controlling if the current dispatcher call keyset should be passed as the first argument 2025-01-24T01:51:49.2085027Z to :attr:`fn` when calling. This should be used to create the appropriate keyset for redispatch calls. 2025-01-24T01:51:49.2085464Z 2025-01-24T01:51:49.2085585Z Example:: 2025-01-24T01:51:49.2085836Z >>> my_lib = Library("_", "IMPL") 2025-01-24T01:51:49.2086210Z >>> def fallback_kernel(op, *args, **kwargs): 2025-01-24T01:51:49.2086595Z >>> # Handle all autocast ops generically 2025-01-24T01:51:49.2086942Z >>> # ... 2025-01-24T01:51:49.2087258Z >>> my_lib.fallback(fallback_kernel, "Autocast") 2025-01-24T01:51:49.2087615Z 2025-01-24T01:51:49.2088373Z Original Error: IndentationError('expected an indented block after function definition on line 2', ('', 5, 1, 'my_lib.fallback(fallback_kernel, "Autocast")\n', 5, 7)) 2025-01-24T01:51:49.2089124Z 2025-01-24T01:51:49.2089257Z my_lib.fallback(fallback_kernel, "Autocast") 2025-01-24T01:51:49.2089594Z ^ 2025-01-24T01:51:49.2151226Z msg = Cannot scrape callname=register_fake in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py line=834. 2025-01-24T01:51:49.2152217Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-01-24T01:51:49.2152823Z Register a FakeTensor implementation ("fake impl") for this operator. 2025-01-24T01:51:49.2153176Z 2025-01-24T01:51:49.2153372Z Also sometimes known as a "meta kernel", "abstract impl". 2025-01-24T01:51:49.2153666Z 2025-01-24T01:51:49.2153912Z An "FakeTensor implementation" specifies the behavior of this operator on 2025-01-24T01:51:49.2154519Z Tensors that carry no data ("FakeTensor"). Given some input Tensors with 2025-01-24T01:51:49.2155116Z certain properties (sizes/strides/storage_offset/device), it specifies 2025-01-24T01:51:49.2155620Z what the properties of the output Tensors are. 2025-01-24T01:51:49.2155876Z 2025-01-24T01:51:49.2156128Z The FakeTensor implementation has the same signature as the operator. 2025-01-24T01:51:49.2156700Z It is run for both FakeTensors and meta tensors. To write a FakeTensor 2025-01-24T01:51:49.2157242Z implementation, assume that all Tensor inputs to the operator are 2025-01-24T01:51:49.2157805Z regular CPU/CUDA/Meta tensors, but they do not have storage, and 2025-01-24T01:51:49.2158345Z you are trying to return regular CPU/CUDA/Meta tensor(s) as output. 2025-01-24T01:51:49.2158915Z The FakeTensor implementation must consist of only PyTorch operations 2025-01-24T01:51:49.2159470Z (and may not directly access the storage or data of any input or 2025-01-24T01:51:49.2159917Z intermediate Tensors). 2025-01-24T01:51:49.2160222Z 2025-01-24T01:51:49.2160479Z This API may be used as a decorator (see examples). 2025-01-24T01:51:49.2160819Z 2025-01-24T01:51:49.2161015Z For a detailed guide on custom ops, please see 2025-01-24T01:51:49.2161797Z https://pytorch.org/tutorials/advanced/custom_ops_landing_page.html 2025-01-24T01:51:49.2162332Z 2025-01-24T01:51:49.2162497Z Examples: 2025-01-24T01:51:49.2162875Z >>> import torch 2025-01-24T01:51:49.2163390Z >>> import numpy as np 2025-01-24T01:51:49.2163750Z >>> from torch import Tensor 2025-01-24T01:51:49.2164053Z >>> 2025-01-24T01:51:49.2164381Z >>> # Example 1: an operator without data-dependent output shape 2025-01-24T01:51:49.2164928Z >>> @torch.library.custom_op("mylib::custom_linear", mutates_args=()) 2025-01-24T01:51:49.2165498Z >>> def custom_linear(x: Tensor, weight: Tensor, bias: Tensor) -> Tensor: 2025-01-24T01:51:49.2166029Z >>> raise NotImplementedError("Implementation goes here") 2025-01-24T01:51:49.2166469Z >>> 2025-01-24T01:51:49.2166783Z >>> @torch.library.register_fake("mylib::custom_linear") 2025-01-24T01:51:49.2167189Z >>> def _(x, weight, bias): 2025-01-24T01:51:49.2167508Z >>> assert x.dim() == 2 2025-01-24T01:51:49.2167835Z >>> assert weight.dim() == 2 2025-01-24T01:51:49.2168160Z >>> assert bias.dim() == 1 2025-01-24T01:51:49.2168512Z >>> assert x.shape[1] == weight.shape[1] 2025-01-24T01:51:49.2168894Z >>> assert weight.shape[0] == bias.shape[0] 2025-01-24T01:51:49.2169272Z >>> assert x.device == weight.device 2025-01-24T01:51:49.2169604Z >>> 2025-01-24T01:51:49.2169844Z >>> return (x @ weight.t()) + bias 2025-01-24T01:51:49.2170172Z >>> 2025-01-24T01:51:49.2170484Z >>> with torch._subclasses.fake_tensor.FakeTensorMode(): 2025-01-24T01:51:49.2170889Z >>> x = torch.randn(2, 3) 2025-01-24T01:51:49.2171218Z >>> w = torch.randn(3, 3) 2025-01-24T01:51:49.2171535Z >>> b = torch.randn(3) 2025-01-24T01:51:49.2171889Z >>> y = torch.ops.mylib.custom_linear(x, w, b) 2025-01-24T01:51:49.2172244Z >>> 2025-01-24T01:51:49.2172484Z >>> assert y.shape == (2, 3) 2025-01-24T01:51:49.2172791Z >>> 2025-01-24T01:51:49.2173086Z >>> # Example 2: an operator with data-dependent output shape 2025-01-24T01:51:49.2173605Z >>> @torch.library.custom_op("mylib::custom_nonzero", mutates_args=()) 2025-01-24T01:51:49.2174079Z >>> def custom_nonzero(x: Tensor) -> Tensor: 2025-01-24T01:51:49.2174440Z >>> x_np = x.numpy(force=True) 2025-01-24T01:51:49.2174800Z >>> res = np.stack(np.nonzero(x_np), axis=1) 2025-01-24T01:51:49.2175188Z >>> return torch.tensor(res, device=x.device) 2025-01-24T01:51:49.2175526Z >>> 2025-01-24T01:51:49.2175833Z >>> @torch.library.register_fake("mylib::custom_nonzero") 2025-01-24T01:51:49.2176227Z >>> def _(x): 2025-01-24T01:51:49.2176552Z >>> # Number of nonzero-elements is data-dependent. 2025-01-24T01:51:49.2176985Z >>> # Since we cannot peek at the data in an fake impl, 2025-01-24T01:51:49.2177410Z >>> # we use the ctx object to construct a new symint that 2025-01-24T01:51:49.2177826Z >>> # represents the data-dependent size. 2025-01-24T01:51:49.2178200Z >>> ctx = torch.library.get_ctx() 2025-01-24T01:51:49.2178556Z >>> nnz = ctx.new_dynamic_size() 2025-01-24T01:51:49.2178897Z >>> shape = [nnz, x.dim()] 2025-01-24T01:51:49.2179458Z >>> result = x.new_empty(shape, dtype=torch.int64) 2025-01-24T01:51:49.2179839Z >>> return result 2025-01-24T01:51:49.2180121Z >>> 2025-01-24T01:51:49.2180442Z >>> from torch.fx.experimental.proxy_tensor import make_fx 2025-01-24T01:51:49.2180838Z >>> 2025-01-24T01:51:49.2181072Z >>> x = torch.tensor([0, 1, 2, 3, 4, 0]) 2025-01-24T01:51:49.2181728Z >>> trace = make_fx(torch.ops.mylib.custom_nonzero, tracing_mode="symbolic")(x) 2025-01-24T01:51:49.2182217Z >>> trace.print_readable() 2025-01-24T01:51:49.2182525Z >>> 2025-01-24T01:51:49.2182888Z >>> assert torch.allclose(trace(x), torch.ops.mylib.custom_nonzero(x)) 2025-01-24T01:51:49.2183236Z 2025-01-24T01:51:49.2183321Z 2025-01-24T01:51:49.2183976Z Original Error: IndentationError('expected an indented block after function definition on line 37', ('', 38, 1, '_._ = None\n', 38, 2)) 2025-01-24T01:51:49.2184631Z 2025-01-24T01:51:49.2184720Z _._ = None 2025-01-24T01:51:49.2184936Z ^ 2025-01-24T01:51:49.2185582Z msg = Cannot scrape callname=register_autograd in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py line=955. 2025-01-24T01:51:49.2186536Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:51:49.2187074Z Register a backward formula for this custom op. 2025-01-24T01:51:49.2187374Z 2025-01-24T01:51:49.2187596Z In order for an operator to work with autograd, you need to register 2025-01-24T01:51:49.2188012Z a backward formula: 2025-01-24T01:51:49.2188388Z 1. You must tell us how to compute gradients during the backward pass 2025-01-24T01:51:49.2188839Z by providing us a "backward" function. 2025-01-24T01:51:49.2189290Z 2. If you need any values from the forward to compute gradients, you can 2025-01-24T01:51:49.2189777Z use `setup_context` to save values for backward. 2025-01-24T01:51:49.2190034Z 2025-01-24T01:51:49.2190275Z ``backward`` runs during the backward pass. It accepts ``(ctx, *grads)``: 2025-01-24T01:51:49.2190811Z - ``grads`` is one or more gradients. The number of gradients matches 2025-01-24T01:51:49.2191261Z the number of outputs of the operator. 2025-01-24T01:51:49.2191716Z The ``ctx`` object is `the same ctx object `_ used by 2025-01-24T01:51:49.2192301Z :class:`torch.autograd.Function`. The semantics of ``backward_fn`` are the 2025-01-24T01:51:49.2192838Z same as :meth:`torch.autograd.Function.backward`. 2025-01-24T01:51:49.2193104Z 2025-01-24T01:51:49.2193316Z ``setup_context(ctx, inputs, output)`` runs during the forward pass. 2025-01-24T01:51:49.2193886Z Please save quantities needed for backward onto the ``ctx`` object via 2025-01-24T01:51:49.2194481Z either :meth:`torch.autograd.function.FunctionCtx.save_for_backward` 2025-01-24T01:51:49.2195041Z or assigning them as attributes of ``ctx``. If your custom op has 2025-01-24T01:51:49.2195578Z kwarg-only arguments, we expect the signature of ``setup_context`` 2025-01-24T01:51:49.2196112Z to be ``setup_context(ctx, inputs, keyword_only_inputs, output)``. 2025-01-24T01:51:49.2196419Z 2025-01-24T01:51:49.2196639Z Both ``setup_context_fn`` and ``backward_fn`` must be traceable. That is, 2025-01-24T01:51:49.2197212Z they may not directly access :meth:`torch.Tensor.data_ptr` and they must 2025-01-24T01:51:49.2197812Z not depend on or mutate global state. If you need a non-traceable backward, 2025-01-24T01:51:49.2198396Z you can make it a separate custom_op that you call inside ``backward_fn``. 2025-01-24T01:51:49.2198736Z 2025-01-24T01:51:49.2198967Z If you need different autograd behavior on different devices, then we 2025-01-24T01:51:49.2199550Z recommend creating two different custom operators, one for each device 2025-01-24T01:51:49.2200118Z that needs different behavior, and switching between them at runtime. 2025-01-24T01:51:49.2200469Z 2025-01-24T01:51:49.2200563Z Examples: 2025-01-24T01:51:49.2200800Z >>> import torch 2025-01-24T01:51:49.2201075Z >>> import numpy as np 2025-01-24T01:51:49.2201375Z >>> from torch import Tensor 2025-01-24T01:51:49.2201675Z >>> 2025-01-24T01:51:49.2202002Z >>> @torch.library.custom_op("mylib::numpy_sin", mutates_args=()) 2025-01-24T01:51:49.2202457Z >>> def numpy_sin(x: Tensor) -> Tensor: 2025-01-24T01:51:49.2202842Z >>> x_np = x.cpu().numpy() 2025-01-24T01:51:49.2203218Z >>> y_np = np.sin(x_np) 2025-01-24T01:51:49.2203589Z >>> return torch.from_numpy(y_np).to(device=x.device) 2025-01-24T01:51:49.2203941Z >>> 2025-01-24T01:51:49.2204226Z >>> def setup_context(ctx, inputs, output) -> Tensor: 2025-01-24T01:51:49.2204600Z >>> x, = inputs 2025-01-24T01:51:49.2204892Z >>> ctx.save_for_backward(x) 2025-01-24T01:51:49.2205201Z >>> 2025-01-24T01:51:49.2205425Z >>> def backward(ctx, grad): 2025-01-24T01:51:49.2205742Z >>> x, = ctx.saved_tensors 2025-01-24T01:51:49.2206062Z >>> return grad * x.cos() 2025-01-24T01:51:49.2206359Z >>> 2025-01-24T01:51:49.2206613Z >>> torch.library.register_autograd( 2025-01-24T01:51:49.2207022Z ... "mylib::numpy_sin", backward, setup_context=setup_context 2025-01-24T01:51:49.2207458Z ... ) 2025-01-24T01:51:49.2207826Z >>> 2025-01-24T01:51:49.2208193Z >>> x = torch.randn(3, requires_grad=True) 2025-01-24T01:51:49.2208706Z >>> y = numpy_sin(x) 2025-01-24T01:51:49.2209068Z >>> (grad_x,) = torch.autograd.grad(y, x, torch.ones_like(y)) 2025-01-24T01:51:49.2209697Z >>> assert torch.allclose(grad_x, x.cos()) 2025-01-24T01:51:49.2210298Z >>> 2025-01-24T01:51:49.2210565Z >>> # Example with a keyword-only arg 2025-01-24T01:51:49.2211014Z >>> @torch.library.custom_op("mylib::numpy_mul", mutates_args=()) 2025-01-24T01:51:49.2211484Z >>> def numpy_mul(x: Tensor, *, val: float) -> Tensor: 2025-01-24T01:51:49.2211875Z >>> x_np = x.cpu().numpy() 2025-01-24T01:51:49.2212196Z >>> y_np = x_np * val 2025-01-24T01:51:49.2212563Z >>> return torch.from_numpy(y_np).to(device=x.device) 2025-01-24T01:51:49.2212937Z >>> 2025-01-24T01:51:49.2213285Z >>> def setup_context(ctx, inputs, keyword_only_inputs, output) -> Tensor: 2025-01-24T01:51:49.2213776Z >>> ctx.val = keyword_only_inputs["val"] 2025-01-24T01:51:49.2214186Z >>> 2025-01-24T01:51:49.2214429Z >>> def backward(ctx, grad): 2025-01-24T01:51:49.2214751Z >>> return grad * ctx.val 2025-01-24T01:51:49.2215042Z >>> 2025-01-24T01:51:49.2215299Z >>> torch.library.register_autograd( 2025-01-24T01:51:49.2215725Z ... "mylib::numpy_mul", backward, setup_context=setup_context 2025-01-24T01:51:49.2216123Z ... ) 2025-01-24T01:51:49.2216350Z >>> 2025-01-24T01:51:49.2216604Z >>> x = torch.randn(3, requires_grad=True) 2025-01-24T01:51:49.2216965Z >>> y = numpy_mul(x, val=3.14) 2025-01-24T01:51:49.2217360Z >>> (grad_x,) = torch.autograd.grad(y, x, torch.ones_like(y)) 2025-01-24T01:51:49.2217839Z >>> assert torch.allclose(grad_x, torch.full_like(x, 3.14)) 2025-01-24T01:51:49.2218132Z 2025-01-24T01:51:49.2218237Z 2025-01-24T01:51:49.2218623Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:51:49.2219000Z 2025-01-24T01:51:49.2219510Z msg = Cannot scrape callname=opcheck in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py line=1367. 2025-01-24T01:51:49.2220363Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:51:49.2220965Z Given an operator and some sample arguments, tests if the operator is 2025-01-24T01:51:49.2221417Z registered correctly. 2025-01-24T01:51:49.2221666Z 2025-01-24T01:51:49.2221881Z That is, when you use the torch.library/TORCH_LIBRARY APIs to create a 2025-01-24T01:51:49.2222470Z custom op, you specified metadata (e.g. mutability info) about the custom op 2025-01-24T01:51:49.2223072Z and these APIs require that the functions you pass them satisfy certain 2025-01-24T01:51:49.2223655Z properties (e.g. no data pointer access in the fake/meta/abstract kernel) 2025-01-24T01:51:49.2224156Z ``opcheck`` tests these metadata and properties. 2025-01-24T01:51:49.2224498Z 2025-01-24T01:51:49.2224681Z Concretely, we test the following: 2025-01-24T01:51:49.2224917Z 2025-01-24T01:51:49.2225098Z - test_schema: If the schema matches the implementation of 2025-01-24T01:51:49.2225627Z the operator. For example: if the schema specifies a Tensor is mutated, 2025-01-24T01:51:49.2226188Z then we check the implementation mutates the Tensor. If the schema 2025-01-24T01:51:49.2226795Z specifies that we return a new Tensor, then we check that the 2025-01-24T01:51:49.2227320Z implementation returns a new Tensor (instead of an existing one or 2025-01-24T01:51:49.2227780Z a view of an existing one). 2025-01-24T01:51:49.2228196Z - test_autograd_registration: If the operator supports training 2025-01-24T01:51:49.2228726Z (autograd): we check that its autograd formula is registered via 2025-01-24T01:51:49.2229262Z torch.library.register_autograd or a manual registration to one 2025-01-24T01:51:49.2229846Z or more DispatchKey::Autograd keys. Any other DispatchKey-based 2025-01-24T01:51:49.2230321Z registrations may lead to undefined behavior. 2025-01-24T01:51:49.2230770Z - test_faketensor: If the operator has a FakeTensor kernel 2025-01-24T01:51:49.2231252Z (and if it is correct). The FakeTensor kernel is necessary ( 2025-01-24T01:51:49.2231780Z but not sufficient) for the operator to work with PyTorch compilation 2025-01-24T01:51:49.2232346Z APIs (torch.compile/export/FX). We check that a FakeTensor kernel 2025-01-24T01:51:49.2232877Z (also sometimes known as a meta kernel) was registered for the 2025-01-24T01:51:49.2233390Z operator and that it is correct. This test takes the result of 2025-01-24T01:51:49.2233906Z running the operator on real tensors and the result of running 2025-01-24T01:51:49.2234423Z the operator on FakeTensors and checks that they have the same 2025-01-24T01:51:49.2234893Z Tensor metadata (sizes/strides/dtype/device/etc). 2025-01-24T01:51:49.2235365Z - test_aot_dispatch_dynamic: If the operator has correct behavior 2025-01-24T01:51:49.2235879Z with PyTorch compilation APIs (torch.compile/export/FX). 2025-01-24T01:51:49.2236395Z This checks that the outputs (and gradients, if applicable) are the 2025-01-24T01:51:49.2236883Z same under eager-mode PyTorch and torch.compile. 2025-01-24T01:51:49.2237355Z This test is a superset of ``test_faketensor`` and is an e2e test; 2025-01-24T01:51:49.2237839Z other things it tests are that the operator supports 2025-01-24T01:51:49.2238331Z functionalization and that the backward pass (if it exists) also 2025-01-24T01:51:49.2238811Z supports FakeTensor and functionalization. 2025-01-24T01:51:49.2239071Z 2025-01-24T01:51:49.2239273Z For best results, please call ``opcheck`` multiple times with a 2025-01-24T01:51:49.2239778Z representative set of inputs. If your operator supports 2025-01-24T01:51:49.2240315Z autograd, please use ``opcheck`` with inputs with ``requires_grad = True``; 2025-01-24T01:51:49.2240906Z if your operator supports multiple devices (e.g. CPU and CUDA), please 2025-01-24T01:51:49.2241405Z use ``opcheck`` with inputs on all supported devices. 2025-01-24T01:51:49.2241687Z 2025-01-24T01:51:49.2241776Z Args: 2025-01-24T01:51:49.2242086Z op: The operator. Must either be a function decorated with 2025-01-24T01:51:49.2242605Z :func:`torch.library.custom_op` or an OpOverload/OpOverloadPacket 2025-01-24T01:51:49.2243160Z found in torch.ops.* (e.g. torch.ops.aten.sin, torch.ops.mylib.foo) 2025-01-24T01:51:49.2243596Z args: The args to the operator 2025-01-24T01:51:49.2243937Z kwargs: The kwargs to the operator 2025-01-24T01:51:49.2244350Z test_utils: Tests that we should run. Default: all of them. 2025-01-24T01:51:49.2244792Z Example: ("test_schema", "test_faketensor") 2025-01-24T01:51:49.2245239Z raise_exception: If we should raise an exception on the first 2025-01-24T01:51:49.2245812Z error. If False, we will return a dict with information 2025-01-24T01:51:49.2246208Z on if each test passed or not. 2025-01-24T01:51:49.2246446Z 2025-01-24T01:51:49.2246541Z .. warning:: 2025-01-24T01:51:49.2246696Z 2025-01-24T01:51:49.2246919Z opcheck and :func:`torch.autograd.gradcheck` test different things; 2025-01-24T01:51:49.2247482Z opcheck tests if your usage of torch.library APIs is correct while 2025-01-24T01:51:49.2248030Z :func:`torch.autograd.gradcheck` tests if your autograd formula is 2025-01-24T01:51:49.2248588Z mathematically correct. Use both to test custom ops that support 2025-01-24T01:51:49.2249029Z gradient computation. 2025-01-24T01:51:49.2249231Z 2025-01-24T01:51:49.2249322Z Example: 2025-01-24T01:51:49.2249463Z 2025-01-24T01:51:49.2249603Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2025-01-24T01:51:49.2250066Z >>> @torch.library.custom_op("mylib::numpy_mul", mutates_args=()) 2025-01-24T01:51:49.2250573Z >>> def numpy_mul(x: Tensor, y: float) -> Tensor: 2025-01-24T01:51:49.2250935Z >>> x_np = x.numpy(force=True) 2025-01-24T01:51:49.2251264Z >>> z_np = x_np * y 2025-01-24T01:51:49.2251600Z >>> return torch.from_numpy(z_np).to(x.device) 2025-01-24T01:51:49.2251950Z >>> 2025-01-24T01:51:49.2252191Z >>> @numpy_mul.register_fake 2025-01-24T01:51:49.2252491Z >>> def _(x, y): 2025-01-24T01:51:49.2252780Z >>> return torch.empty_like(x) 2025-01-24T01:51:49.2253099Z >>> 2025-01-24T01:51:49.2253358Z >>> def setup_context(ctx, inputs, output): 2025-01-24T01:51:49.2253710Z >>> y, = inputs 2025-01-24T01:51:49.2253968Z >>> ctx.y = y 2025-01-24T01:51:49.2254231Z >>> 2025-01-24T01:51:49.2254468Z >>> def backward(ctx, grad): 2025-01-24T01:51:49.2254797Z >>> return grad * ctx.y, None 2025-01-24T01:51:49.2255113Z >>> 2025-01-24T01:51:49.2255466Z >>> numpy_mul.register_autograd(backward, setup_context=setup_context) 2025-01-24T01:51:49.2255906Z >>> 2025-01-24T01:51:49.2256140Z >>> sample_inputs = [ 2025-01-24T01:51:49.2256448Z >>> (torch.randn(3), 3.14), 2025-01-24T01:51:49.2256803Z >>> (torch.randn(2, 3, device='cuda'), 2.718), 2025-01-24T01:51:49.2257194Z >>> (torch.randn(1, 10, requires_grad=True), 1.234), 2025-01-24T01:51:49.2257661Z >>> (torch.randn(64, 64, device='cuda', requires_grad=True), 90.18), 2025-01-24T01:51:49.2258069Z >>> ] 2025-01-24T01:51:49.2258302Z >>> 2025-01-24T01:51:49.2258547Z >>> for args in sample_inputs: 2025-01-24T01:51:49.2258899Z >>> torch.library.opcheck(numpy_mul, args) 2025-01-24T01:51:49.2259165Z 2025-01-24T01:51:49.2259261Z 2025-01-24T01:51:49.2259644Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:51:49.2260028Z 2025-01-24T01:51:49.2632699Z msg = Cannot scrape callname=load in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/serialization.py line=1238. 2025-01-24T01:51:49.2634224Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:51:49.2634953Z load(f, map_location=None, pickle_module=pickle, *, weights_only=True, mmap=None, **pickle_load_args) 2025-01-24T01:51:49.2635404Z 2025-01-24T01:51:49.2635601Z Loads an object saved with :func:`torch.save` from a file. 2025-01-24T01:51:49.2635893Z 2025-01-24T01:51:49.2636145Z :func:`torch.load` uses Python's unpickling facilities but treats storages, 2025-01-24T01:51:49.2636737Z which underlie tensors, specially. They are first deserialized on the 2025-01-24T01:51:49.2637294Z CPU and are then moved to the device they were saved from. If this fails 2025-01-24T01:51:49.2637867Z (e.g. because the run time system doesn't have certain devices), an exception 2025-01-24T01:51:49.2638466Z is raised. However, storages can be dynamically remapped to an alternative 2025-01-24T01:51:49.2639216Z set of devices using the :attr:`map_location` argument. 2025-01-24T01:51:49.2639497Z 2025-01-24T01:51:49.2639753Z If :attr:`map_location` is a callable, it will be called once for each serialized 2025-01-24T01:51:49.2640361Z storage with two arguments: storage and location. The storage argument 2025-01-24T01:51:49.2640932Z will be the initial deserialization of the storage, residing on the CPU. 2025-01-24T01:51:49.2641508Z Each serialized storage has a location tag associated with it which 2025-01-24T01:51:49.2642066Z identifies the device it was saved from, and this tag is the second 2025-01-24T01:51:49.2642659Z argument passed to :attr:`map_location`. The builtin location tags are ``'cpu'`` 2025-01-24T01:51:49.2643262Z for CPU tensors and ``'cuda:device_id'`` (e.g. ``'cuda:2'``) for CUDA tensors. 2025-01-24T01:51:49.2643825Z :attr:`map_location` should return either ``None`` or a storage. If 2025-01-24T01:51:49.2644450Z :attr:`map_location` returns a storage, it will be used as the final deserialized 2025-01-24T01:51:49.2645083Z object, already moved to the right device. Otherwise, :func:`torch.load` will 2025-01-24T01:51:49.2645701Z fall back to the default behavior, as if :attr:`map_location` wasn't specified. 2025-01-24T01:51:49.2646065Z 2025-01-24T01:51:49.2646316Z If :attr:`map_location` is a :class:`torch.device` object or a string containing 2025-01-24T01:51:49.2646920Z a device tag, it indicates the location where all tensors should be loaded. 2025-01-24T01:51:49.2647270Z 2025-01-24T01:51:49.2647543Z Otherwise, if :attr:`map_location` is a dict, it will be used to remap location tags 2025-01-24T01:51:49.2648126Z appearing in the file (keys), to ones that specify where to put the 2025-01-24T01:51:49.2648570Z storages (values). 2025-01-24T01:51:49.2648744Z 2025-01-24T01:51:49.2648969Z User extensions can register their own location tags and tagging and 2025-01-24T01:51:49.2649590Z deserialization methods using :func:`torch.serialization.register_package`. 2025-01-24T01:51:49.2649974Z 2025-01-24T01:51:49.2650077Z Args: 2025-01-24T01:51:49.2650528Z f: a file-like object (has to implement :meth:`read`, :meth:`readline`, :meth:`tell`, and :meth:`seek`), 2025-01-24T01:51:49.2651136Z or a string or os.PathLike object containing a file name 2025-01-24T01:51:49.2651762Z map_location: a function, :class:`torch.device`, string or a dict specifying how to remap storage 2025-01-24T01:51:49.2652307Z locations 2025-01-24T01:51:49.2652702Z pickle_module: module used for unpickling metadata and objects (has to 2025-01-24T01:51:49.2653234Z match the :attr:`pickle_module` used to serialize file) 2025-01-24T01:51:49.2653743Z weights_only: Indicates whether unpickler should be restricted to 2025-01-24T01:51:49.2654237Z loading only tensors, primitive types, dictionaries 2025-01-24T01:51:49.2654748Z and any types added via :func:`torch.serialization.add_safe_globals`. 2025-01-24T01:51:49.2655232Z See :ref:`weights-only` for more details. 2025-01-24T01:51:49.2655827Z mmap: Indicates whether the file should be mmaped rather than loading all the storages into memory. 2025-01-24T01:51:49.2656621Z Typically, tensor storages in the file will first be moved from disk to CPU memory, after which they 2025-01-24T01:51:49.2657425Z are moved to the location that they were tagged with when saving, or specified by ``map_location``. This 2025-01-24T01:51:49.2658209Z second step is a no-op if the final location is CPU. When the ``mmap`` flag is set, instead of copying the 2025-01-24T01:51:49.2658901Z tensor storages from disk to CPU memory in the first step, ``f`` is mmaped. 2025-01-24T01:51:49.2659510Z pickle_load_args: (Python 3 only) optional keyword arguments passed over to 2025-01-24T01:51:49.2660108Z :func:`pickle_module.load` and :func:`pickle_module.Unpickler`, e.g., 2025-01-24T01:51:49.2660652Z :attr:`errors=...`. 2025-01-24T01:51:49.2660840Z 2025-01-24T01:51:49.2660972Z .. warning:: 2025-01-24T01:51:49.2661336Z :func:`torch.load()` unless `weights_only` parameter is set to `True`, 2025-01-24T01:51:49.2661889Z uses ``pickle`` module implicitly, which is known to be insecure. 2025-01-24T01:51:49.2662505Z It is possible to construct malicious pickle data which will execute arbitrary code 2025-01-24T01:51:49.2663162Z during unpickling. Never load data that could have come from an untrusted 2025-01-24T01:51:49.2663829Z source in an unsafe mode, or that could have been tampered with. **Only load data you trust**. 2025-01-24T01:51:49.2664237Z 2025-01-24T01:51:49.2664351Z .. note:: 2025-01-24T01:51:49.2664746Z When you call :func:`torch.load()` on a file which contains GPU tensors, those tensors 2025-01-24T01:51:49.2665398Z will be loaded to GPU by default. You can call ``torch.load(.., map_location='cpu')`` 2025-01-24T01:51:49.2666102Z and then :meth:`load_state_dict` to avoid GPU RAM surge when loading a model checkpoint. 2025-01-24T01:51:49.2666590Z 2025-01-24T01:51:49.2666692Z .. note:: 2025-01-24T01:51:49.2667090Z By default, we decode byte strings as ``utf-8``. This is to avoid a common error 2025-01-24T01:51:49.2667696Z case ``UnicodeDecodeError: 'ascii' codec can't decode byte 0x...`` 2025-01-24T01:51:49.2668245Z when loading files saved by Python 2 in Python 3. If this default 2025-01-24T01:51:49.2668849Z is incorrect, you may use an extra :attr:`encoding` keyword argument to specify how 2025-01-24T01:51:49.2669489Z these objects should be loaded, e.g., :attr:`encoding='latin1'` decodes them 2025-01-24T01:51:49.2670109Z to strings using ``latin1`` encoding, and :attr:`encoding='bytes'` keeps them 2025-01-24T01:51:49.2670711Z as byte arrays which can be decoded later with ``byte_array.decode(...)``. 2025-01-24T01:51:49.2671066Z 2025-01-24T01:51:49.2671177Z Example: 2025-01-24T01:51:49.2671437Z >>> # xdoctest: +SKIP("undefined filepaths") 2025-01-24T01:51:49.2671838Z >>> torch.load("tensors.pt", weights_only=True) 2025-01-24T01:51:49.2672209Z # Load all tensors onto the CPU 2025-01-24T01:51:49.2672684Z >>> torch.load("tensors.pt", map_location=torch.device("cpu"), weights_only=True) 2025-01-24T01:51:49.2673215Z # Load all tensors onto the CPU, using a function 2025-01-24T01:51:49.2673564Z >>> torch.load( 2025-01-24T01:51:49.2673978Z ... "tensors.pt", map_location=lambda storage, loc: storage, weights_only=True 2025-01-24T01:51:49.2674437Z ... ) 2025-01-24T01:51:49.2674687Z # Load all tensors onto GPU 1 2025-01-24T01:51:49.2675006Z >>> torch.load( 2025-01-24T01:51:49.2675265Z ... "tensors.pt", 2025-01-24T01:51:49.2675628Z ... map_location=lambda storage, loc: storage.cuda(1), 2025-01-24T01:51:49.2676026Z ... weights_only=True, 2025-01-24T01:51:49.2676357Z ... ) # type: ignore[attr-defined] 2025-01-24T01:51:49.2676706Z # Map tensors from GPU 1 to GPU 0 2025-01-24T01:51:49.2688906Z >>> torch.load("tensors.pt", map_location={"cuda:1": "cuda:0"}, weights_only=True) 2025-01-24T01:51:49.2689430Z # Load tensor from io.BytesIO object 2025-01-24T01:51:49.2689936Z # Loading from a buffer setting weights_only=False, warning this can be unsafe 2025-01-24T01:51:49.2690445Z >>> with open("tensor.pt", "rb") as f: 2025-01-24T01:51:49.2690794Z ... buffer = io.BytesIO(f.read()) 2025-01-24T01:51:49.2691162Z >>> torch.load(buffer, weights_only=False) 2025-01-24T01:51:49.2691574Z # Load a module with 'ascii' encoding for unpickling 2025-01-24T01:51:49.2692106Z # Loading from a module setting weights_only=False, warning this can be unsafe 2025-01-24T01:51:49.2692685Z >>> torch.load("module.pt", encoding="ascii", weights_only=False) 2025-01-24T01:51:49.2693323Z 2025-01-24T01:51:49.2693715Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:51:49.2694102Z 2025-01-24T01:51:49.3686816Z msg = Cannot scrape callname=is_available in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/accelerator/__init__.py line=37. 2025-01-24T01:51:49.3687759Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-01-24T01:51:49.3688346Z Check if there is an available :ref:`accelerator`. 2025-01-24T01:51:49.3688678Z 2025-01-24T01:51:49.3688775Z Returns: 2025-01-24T01:51:49.3689203Z bool: A boolean indicating if there is an available :ref:`accelerator`. 2025-01-24T01:51:49.3689602Z 2025-01-24T01:51:49.3689740Z Example:: 2025-01-24T01:51:49.3689875Z 2025-01-24T01:51:49.3690151Z >>> assert torch.accelerator.is_available() "No available accelerators detected." 2025-01-24T01:51:49.3690799Z 2025-01-24T01:51:49.3691509Z Original Error: SyntaxError('invalid syntax', ('', 1, 41, 'assert torch.accelerator.is_available() "No available accelerators detected."\n', 1, 78)) 2025-01-24T01:51:49.3692206Z 2025-01-24T01:51:49.3692474Z assert torch.accelerator.is_available() "No available accelerators detected." 2025-01-24T01:51:49.3692969Z ^ 2025-01-24T01:51:49.3705225Z msg = Cannot scrape callname=synchronize in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/accelerator/__init__.py line=138. 2025-01-24T01:51:49.3706157Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-01-24T01:51:49.3706831Z Wait for all kernels in all streams on the given device to complete. 2025-01-24T01:51:49.3707157Z 2025-01-24T01:51:49.3707250Z Args: 2025-01-24T01:51:49.3707717Z device (:class:`torch.device`, str, int, optional): device for which to synchronize. It must match 2025-01-24T01:51:49.3708417Z the current :ref:`accelerator` device type. If not given, 2025-01-24T01:51:49.3708987Z use :func:`torch.accelerator.current_device_index` by default. 2025-01-24T01:51:49.3709302Z 2025-01-24T01:51:49.3709651Z .. note:: This function is a no-op if the current :ref:`accelerator` is not initialized. 2025-01-24T01:51:49.3710079Z 2025-01-24T01:51:49.3710195Z Example:: 2025-01-24T01:51:49.3710332Z 2025-01-24T01:51:49.3710477Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2025-01-24T01:51:49.3711014Z >>> assert torch.accelerator.is_available() "No available accelerators detected." 2025-01-24T01:51:49.3711567Z >>> start_event = torch.Event(enable_timing=True) 2025-01-24T01:51:49.3711985Z >>> end_event = torch.Event(enable_timing=True) 2025-01-24T01:51:49.3712352Z >>> start_event.record() 2025-01-24T01:51:49.3712799Z >>> tensor = torch.randn(100, device=torch.accelerator.current_accelerator()) 2025-01-24T01:51:49.3713270Z >>> sum = torch.sum(tensor) 2025-01-24T01:51:49.3713597Z >>> end_event.record() 2025-01-24T01:51:49.3713924Z >>> torch.accelerator.synchronize() 2025-01-24T01:51:49.3714342Z >>> elapsed_time_ms = start_event.elapsed_time(end_event) 2025-01-24T01:51:49.3714724Z 2025-01-24T01:51:49.3715396Z Original Error: SyntaxError('invalid syntax', ('', 2, 41, 'assert torch.accelerator.is_available() "No available accelerators detected."\n', 2, 78)) 2025-01-24T01:51:49.3716079Z 2025-01-24T01:51:49.3716350Z assert torch.accelerator.is_available() "No available accelerators detected." 2025-01-24T01:51:49.3716844Z ^ 2025-01-24T01:51:49.3944004Z msg = Cannot scrape callname=cudart in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/__init__.py line=344. 2025-01-24T01:51:49.3945039Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-01-24T01:51:49.3945560Z Retrieves the CUDA runtime API module. 2025-01-24T01:51:49.3945923Z 2025-01-24T01:51:49.3946035Z 2025-01-24T01:51:49.3946368Z This function initializes the CUDA runtime environment if it is not already 2025-01-24T01:51:49.3946980Z initialized and returns the CUDA runtime API module (_cudart). The CUDA 2025-01-24T01:51:49.3947574Z runtime API module provides access to various CUDA runtime functions. 2025-01-24T01:51:49.3947939Z 2025-01-24T01:51:49.3948032Z Args: 2025-01-24T01:51:49.3948261Z ``None`` 2025-01-24T01:51:49.3948403Z 2025-01-24T01:51:49.3948514Z Returns: 2025-01-24T01:51:49.3948809Z module: The CUDA runtime API module (_cudart). 2025-01-24T01:51:49.3949067Z 2025-01-24T01:51:49.3949158Z Raises: 2025-01-24T01:51:49.3949532Z RuntimeError: If CUDA cannot be re-initialized in a forked subprocess. 2025-01-24T01:51:49.3950260Z AssertionError: If PyTorch is not compiled with CUDA support or if libcudart functions are unavailable. 2025-01-24T01:51:49.3950791Z 2025-01-24T01:51:49.3950940Z Example of CUDA operations with profiling: 2025-01-24T01:51:49.3951303Z >>> import torch 2025-01-24T01:51:49.3951621Z >>> from torch.cuda import cudart, check_error 2025-01-24T01:51:49.3951980Z >>> import os 2025-01-24T01:51:49.3952239Z >>> 2025-01-24T01:51:49.3952480Z >>> os.environ['CUDA_PROFILE'] = '1' 2025-01-24T01:51:49.3952808Z >>> 2025-01-24T01:51:49.3953102Z >>> def perform_cuda_operations_with_streams(): 2025-01-24T01:51:49.3953487Z >>> stream = torch.cuda.Stream() 2025-01-24T01:51:49.3953850Z >>> with torch.cuda.stream(stream): 2025-01-24T01:51:49.3954226Z >>> x = torch.randn(100, 100, device='cuda') 2025-01-24T01:51:49.3954596Z >>> y = torch.randn(100, 100, device='cuda') 2025-01-24T01:51:49.3954960Z >>> z = torch.mul(x, y) 2025-01-24T01:51:49.3955425Z >>> return z 2025-01-24T01:51:49.3955693Z >>> 2025-01-24T01:51:49.3955951Z >>> torch.cuda.synchronize() 2025-01-24T01:51:49.3956304Z >>> print("====== Start nsys profiling ======") 2025-01-24T01:51:49.3956704Z >>> check_error(cudart().cudaProfilerStart()) 2025-01-24T01:51:49.3957116Z >>> with torch.autograd.profiler.emit_nvtx(): 2025-01-24T01:51:49.3957537Z >>> result = perform_cuda_operations_with_streams() 2025-01-24T01:51:49.3957946Z >>> print("CUDA operations completed.") 2025-01-24T01:51:49.3958364Z >>> check_error(torch.cuda.cudart().cudaProfilerStop()) 2025-01-24T01:51:49.3958767Z >>> print("====== End nsys profiling ======") 2025-01-24T01:51:49.3959021Z 2025-01-24T01:51:49.3959227Z To run this example and save the profiling information, execute: 2025-01-24T01:51:49.3959933Z >>> $ nvprof --profile-from-start off --csv --print-summary -o trace_name.prof -f -- python cudart_test.py 2025-01-24T01:51:49.3960406Z 2025-01-24T01:51:49.3960678Z This command profiles the CUDA operations in the provided script and saves 2025-01-24T01:51:49.3961269Z the profiling information to a file named `trace_name.prof`. 2025-01-24T01:51:49.3961845Z The `--profile-from-start off` option ensures that profiling starts only 2025-01-24T01:51:49.3962349Z after the `cudaProfilerStart` call in the script. 2025-01-24T01:51:49.3962847Z The `--csv` and `--print-summary` options format the profiling output as a 2025-01-24T01:51:49.3963337Z CSV file and print a summary, respectively. 2025-01-24T01:51:49.3963838Z The `-o` option specifies the output file name, and the `-f` option forces the 2025-01-24T01:51:49.3964376Z overwrite of the output file if it already exists. 2025-01-24T01:51:49.3964724Z 2025-01-24T01:51:49.3965499Z 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)) 2025-01-24T01:51:49.3966273Z 2025-01-24T01:51:49.3966724Z $ nvprof --profile-from-start off --csv --print-summary -o trace_name.prof -f -- python cudart_test.py 2025-01-24T01:51:49.3967335Z ^ 2025-01-24T01:51:49.4103329Z msg = Cannot scrape callname=Future.then in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/futures/__init__.py line=105. 2025-01-24T01:51:49.4104267Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:51:49.4104653Z 2025-01-24T01:51:49.4104902Z Append the given callback function to this ``Future``, which will be run 2025-01-24T01:51:49.4105474Z when the ``Future`` is completed. Multiple callbacks can be added to 2025-01-24T01:51:49.4106004Z the same ``Future``, but the order in which they will be executed cannot 2025-01-24T01:51:49.4106595Z be guaranteed (to enforce a certain order consider chaining: 2025-01-24T01:51:49.4107112Z ``fut.then(cb1).then(cb2)``). The callback must take one argument, which 2025-01-24T01:51:49.4107656Z is the reference to this ``Future``. The callback function can use the 2025-01-24T01:51:49.4108369Z :meth:`value` method to get the value. Note that if this ``Future`` is 2025-01-24T01:51:49.4108936Z already completed, the given callback will be run immediately inline. 2025-01-24T01:51:49.4109279Z 2025-01-24T01:51:49.4109479Z If the ``Future``'s value contains tensors that reside on GPUs, the 2025-01-24T01:51:49.4110025Z callback might be invoked while the async kernels that are populating 2025-01-24T01:51:49.4110604Z those tensors haven't yet finished executing on the device. However, the 2025-01-24T01:51:49.4111172Z callback will be invoked with some dedicated streams set as current 2025-01-24T01:51:49.4111714Z (fetched from a global pool) which will be synchronized with those 2025-01-24T01:51:49.4112257Z kernels. Hence any operation performed by the callback on these tensors 2025-01-24T01:51:49.4112933Z will be scheduled on the device after the kernels complete. In other 2025-01-24T01:51:49.4113489Z words, as long as the callback doesn't switch streams, it can safely 2025-01-24T01:51:49.4114354Z manipulate the result without any additional synchronization. This is 2025-01-24T01:51:49.4115058Z similar to the non-blocking behavior of :meth:`wait`. 2025-01-24T01:51:49.4115334Z 2025-01-24T01:51:49.4115564Z Similarly, if the callback returns a value that contains tensors that 2025-01-24T01:51:49.4116088Z reside on a GPU, it can do so even if the kernels that are producing 2025-01-24T01:51:49.4116635Z these tensors are still running on the device, as long as the callback 2025-01-24T01:51:49.4117186Z didn't change streams during its execution. If one wants to change 2025-01-24T01:51:49.4117729Z streams, one must be careful to re-synchronize them with the original 2025-01-24T01:51:49.4118286Z streams, that is, those that were current when the callback was invoked. 2025-01-24T01:51:49.4118618Z 2025-01-24T01:51:49.4118720Z Args: 2025-01-24T01:51:49.4119042Z callback(``Callable``): a ``Callable`` that takes this ``Future`` as 2025-01-24T01:51:49.4119494Z the only argument. 2025-01-24T01:51:49.4119735Z 2025-01-24T01:51:49.4119825Z Returns: 2025-01-24T01:51:49.4120129Z A new ``Future`` object that holds the return value of the 2025-01-24T01:51:49.4120603Z ``callback`` and will be marked as completed when the given 2025-01-24T01:51:49.4120993Z ``callback`` finishes. 2025-01-24T01:51:49.4121184Z 2025-01-24T01:51:49.4121375Z .. note:: Note that if the callback function throws, either 2025-01-24T01:51:49.4121887Z through the original future being completed with an exception and 2025-01-24T01:51:49.4122422Z calling ``fut.wait()``, or through other code in the callback, the 2025-01-24T01:51:49.4122952Z future returned by ``then`` will be marked appropriately with the 2025-01-24T01:51:49.4123510Z encountered error. However, if this callback later completes 2025-01-24T01:51:49.4124052Z additional futures, those futures are not marked as completed with 2025-01-24T01:51:49.4124588Z an error and the user is responsible for handling completion/waiting 2025-01-24T01:51:49.4125155Z on those futures independently. 2025-01-24T01:51:49.4125474Z 2025-01-24T01:51:49.4125572Z Example:: 2025-01-24T01:51:49.4125867Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_FUTURES) 2025-01-24T01:51:49.4126240Z >>> def callback(fut): 2025-01-24T01:51:49.4126551Z ... print(f"RPC return value is {fut.wait()}.") 2025-01-24T01:51:49.4126929Z >>> fut = torch.futures.Future() 2025-01-24T01:51:49.4127326Z >>> # The inserted callback will print the return value when 2025-01-24T01:51:49.4127747Z >>> # receiving the response from "worker1" 2025-01-24T01:51:49.4128105Z >>> cb_fut = fut.then(callback) 2025-01-24T01:51:49.4128413Z >>> chain_cb_fut = cb_fut.then( 2025-01-24T01:51:49.4128786Z ... lambda x : print(f"Chained cb done. {x.wait()}") 2025-01-24T01:51:49.4129153Z ... ) 2025-01-24T01:51:49.4129391Z >>> fut.set_result(5) 2025-01-24T01:51:49.4129679Z RPC return value is 5. 2025-01-24T01:51:49.4129952Z Chained cb done. None 2025-01-24T01:51:49.4130191Z 2025-01-24T01:51:49.4130454Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:51:49.4130844Z 2025-01-24T01:51:49.4131380Z msg = Cannot scrape callname=Future.set_result in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/futures/__init__.py line=213. 2025-01-24T01:51:49.4132303Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:51:49.4132686Z 2025-01-24T01:51:49.4132913Z Set the result for this ``Future``, which will mark this ``Future`` as 2025-01-24T01:51:49.4133479Z completed and trigger all attached callbacks. Note that a ``Future`` 2025-01-24T01:51:49.4133942Z cannot be marked completed twice. 2025-01-24T01:51:49.4134150Z 2025-01-24T01:51:49.4134373Z If the result contains tensors that reside on GPUs, this method can be 2025-01-24T01:51:49.4134937Z called even if the asynchronous kernels that are populating those 2025-01-24T01:51:49.4135495Z tensors haven't yet completed running on the device, provided that the 2025-01-24T01:51:49.4136083Z streams on which those kernels were enqueued are set as the current ones 2025-01-24T01:51:49.4136661Z when this method is called. Put simply, it's safe to call this method 2025-01-24T01:51:49.4137208Z immediately after launching those kernels, without any additional 2025-01-24T01:51:49.4137786Z synchronization, as long as one doesn't change streams in between. This 2025-01-24T01:51:49.4138377Z method will record events on all the relevant current streams and will 2025-01-24T01:51:49.4138930Z use them to ensure proper scheduling for all the consumers of this 2025-01-24T01:51:49.4139351Z ``Future``. 2025-01-24T01:51:49.4139480Z 2025-01-24T01:51:49.4139582Z Args: 2025-01-24T01:51:49.4139866Z result (object): the result object of this ``Future``. 2025-01-24T01:51:49.4140153Z 2025-01-24T01:51:49.4140256Z Example:: 2025-01-24T01:51:49.4140540Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_FUTURES) 2025-01-24T01:51:49.4140908Z >>> import threading 2025-01-24T01:51:49.4141191Z >>> import time 2025-01-24T01:51:49.4141451Z >>> def slow_set_future(fut, value): 2025-01-24T01:51:49.4141780Z ... time.sleep(0.5) 2025-01-24T01:51:49.4142071Z ... fut.set_result(value) 2025-01-24T01:51:49.4142388Z >>> fut = torch.futures.Future() 2025-01-24T01:51:49.4142713Z >>> t = threading.Thread( 2025-01-24T01:51:49.4143002Z ... target=slow_set_future, 2025-01-24T01:51:49.4143325Z ... args=(fut, torch.ones(2) * 3) 2025-01-24T01:51:49.4143640Z ... ) 2025-01-24T01:51:49.4143864Z >>> t.start() 2025-01-24T01:51:49.4144114Z >>> print(fut.wait()) 2025-01-24T01:51:49.4144377Z tensor([3., 3.]) 2025-01-24T01:51:49.4144634Z >>> t.join() 2025-01-24T01:51:49.4144787Z 2025-01-24T01:51:49.4145045Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:51:49.4145415Z 2025-01-24T01:51:49.4247586Z msg = Cannot scrape callname=_compile_shader in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/mps/__init__.py line=144. 2025-01-24T01:51:49.4248714Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:51:49.4249335Z Compiles compute shader from source and allows one to invoke kernels 2025-01-24T01:51:49.4249825Z defined there from the comfort of Python runtime 2025-01-24T01:51:49.4250312Z Example:: 2025-01-24T01:51:49.4250465Z 2025-01-24T01:51:49.4250608Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_MPS) 2025-01-24T01:51:49.4250990Z >>> lib = torch.mps._compile_shader( 2025-01-24T01:51:49.4251614Z ... "kernel void full(device float* out, constant float& val, uint idx [[thread_position_in_grid]]) { out[idx] = val; }" 2025-01-24T01:51:49.4252216Z ... ) 2025-01-24T01:51:49.4252474Z >>> x = torch.zeros(16, device="mps") 2025-01-24T01:51:49.4252820Z >>> lib.full(x, 3.14) 2025-01-24T01:51:49.4253107Z 2025-01-24T01:51:49.4253497Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:51:49.4253921Z 2025-01-24T01:51:49.4354551Z msg = Cannot scrape callname=masked_select in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nested/__init__.py line=469. 2025-01-24T01:51:49.4355482Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:51:49.4355868Z 2025-01-24T01:51:49.4356286Z Constructs a nested tensor given a strided tensor input and a strided mask, the resulting jagged layout nested tensor 2025-01-24T01:51:49.4357206Z will have values retain values where the mask is equal to True. The dimensionality of the mask is preserved and is 2025-01-24T01:51:49.4358102Z represented with the offsets, this is unlike :func:`masked_select` where the output is collapsed to a 1D tensor. 2025-01-24T01:51:49.4358610Z 2025-01-24T01:51:49.4358707Z Args: 2025-01-24T01:51:49.4359216Z tensor (:class:`torch.Tensor`): a strided tensor from which the jagged layout nested tensor is constructed from. 2025-01-24T01:51:49.4360010Z mask (:class:`torch.Tensor`): a strided mask tensor which is applied to the tensor input 2025-01-24T01:51:49.4360407Z 2025-01-24T01:51:49.4360531Z Example:: 2025-01-24T01:51:49.4360656Z 2025-01-24T01:51:49.4360771Z >>> tensor = torch.randn(3, 3) 2025-01-24T01:51:49.4361077Z >>> mask = torch.tensor( 2025-01-24T01:51:49.4361458Z ... [[False, False, True], [True, False, True], [False, False, True]] 2025-01-24T01:51:49.4361873Z ... ) 2025-01-24T01:51:49.4362146Z >>> nt = torch.nested.masked_select(tensor, mask) 2025-01-24T01:51:49.4362492Z >>> nt.shape 2025-01-24T01:51:49.4362739Z torch.Size([3, j4]) 2025-01-24T01:51:49.4363014Z >>> # Length of each item in the batch: 2025-01-24T01:51:49.4363343Z >>> nt.offsets().diff() 2025-01-24T01:51:49.4363606Z tensor([1, 2, 1]) 2025-01-24T01:51:49.4363748Z 2025-01-24T01:51:49.4363854Z >>> tensor = torch.randn(6, 5) 2025-01-24T01:51:49.4364159Z >>> mask = torch.tensor([False]) 2025-01-24T01:51:49.4364498Z >>> nt = torch.nested.masked_select(tensor, mask) 2025-01-24T01:51:49.4364849Z >>> nt.shape 2025-01-24T01:51:49.4365088Z torch.Size([6, j5]) 2025-01-24T01:51:49.4365349Z >>> # Length of each item in the batch: 2025-01-24T01:51:49.4365675Z >>> nt.offsets().diff() 2025-01-24T01:51:49.4365949Z tensor([0, 0, 0, 0, 0, 0]) 2025-01-24T01:51:49.4366119Z 2025-01-24T01:51:49.4366389Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:51:49.4366766Z 2025-01-24T01:51:49.4472654Z msg = Cannot scrape callname=sum in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/sparse/__init__.py line=202. 2025-01-24T01:51:49.4473600Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:51:49.4474142Z Return the sum of each row of the given sparse tensor. 2025-01-24T01:51:49.4474434Z 2025-01-24T01:51:49.4474690Z Returns the sum of each row of the sparse tensor :attr:`input` in the given 2025-01-24T01:51:49.4475248Z dimensions :attr:`dim`. If :attr:`dim` is a list of dimensions, 2025-01-24T01:51:49.4476022Z reduce over all of them. When sum over all ``sparse_dim``, this method 2025-01-24T01:51:49.4476530Z returns a dense tensor instead of a sparse tensor. 2025-01-24T01:51:49.4476794Z 2025-01-24T01:51:49.4477070Z All summed :attr:`dim` are squeezed (see :func:`torch.squeeze`), resulting an output 2025-01-24T01:51:49.4477658Z tensor having :attr:`dim` fewer dimensions than :attr:`input`. 2025-01-24T01:51:49.4477971Z 2025-01-24T01:51:49.4478199Z During backward, only gradients at ``nnz`` locations of :attr:`input` 2025-01-24T01:51:49.4478798Z will propagate back. Note that the gradients of :attr:`input` is coalesced. 2025-01-24T01:51:49.4479340Z 2025-01-24T01:51:49.4479436Z Args: 2025-01-24T01:51:49.4479712Z input (Tensor): the input sparse tensor 2025-01-24T01:51:49.4480244Z dim (int or tuple of ints): a dimension or a list of dimensions to reduce. Default: reduce 2025-01-24T01:51:49.4480756Z over all dims. 2025-01-24T01:51:49.4481244Z dtype (:class:`torch.dtype`, optional): the desired data type of returned Tensor. 2025-01-24T01:51:49.4481757Z Default: dtype of :attr:`input`. 2025-01-24T01:51:49.4482001Z 2025-01-24T01:51:49.4482106Z Example:: 2025-01-24T01:51:49.4482241Z 2025-01-24T01:51:49.4482351Z >>> nnz = 3 2025-01-24T01:51:49.4482605Z >>> dims = [5, 5, 2, 3] 2025-01-24T01:51:49.4482956Z >>> I = torch.cat([torch.randint(0, dims[0], size=(nnz,)), 2025-01-24T01:51:49.4483438Z torch.randint(0, dims[1], size=(nnz,))], 0).reshape(2, nnz) 2025-01-24T01:51:49.4483888Z >>> V = torch.randn(nnz, dims[2], dims[3]) 2025-01-24T01:51:49.4484252Z >>> size = torch.Size(dims) 2025-01-24T01:51:49.4484618Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2025-01-24T01:51:49.4485020Z >>> S = torch.sparse_coo_tensor(I, V, size) 2025-01-24T01:51:49.4485353Z >>> S 2025-01-24T01:51:49.4485614Z tensor(indices=tensor([[2, 0, 3], 2025-01-24T01:51:49.4485958Z [2, 4, 1]]), 2025-01-24T01:51:49.4486318Z values=tensor([[[-0.6438, -1.6467, 1.4004], 2025-01-24T01:51:49.4486693Z [ 0.3411, 0.0918, -0.2312]], 2025-01-24T01:51:49.4486921Z 2025-01-24T01:51:49.4487037Z [[ 0.5348, 0.0634, -2.0494], 2025-01-24T01:51:49.4487385Z [-0.7125, -1.0646, 2.1844]], 2025-01-24T01:51:49.4487625Z 2025-01-24T01:51:49.4487741Z [[ 0.1276, 0.1874, -0.6334], 2025-01-24T01:51:49.4488092Z [-1.9682, -0.5340, 0.7483]]]), 2025-01-24T01:51:49.4488479Z size=(5, 5, 2, 3), nnz=3, layout=torch.sparse_coo) 2025-01-24T01:51:49.4488738Z 2025-01-24T01:51:49.4488944Z # when sum over only part of sparse_dims, return a sparse tensor 2025-01-24T01:51:49.4489370Z >>> torch.sparse.sum(S, [1, 3]) 2025-01-24T01:51:49.4489714Z tensor(indices=tensor([[0, 2, 3]]), 2025-01-24T01:51:49.4490067Z values=tensor([[-1.4512, 0.4073], 2025-01-24T01:51:49.4490409Z [-0.8901, 0.2017], 2025-01-24T01:51:49.4490739Z [-0.3183, -1.7539]]), 2025-01-24T01:51:49.4491093Z size=(5, 2), nnz=3, layout=torch.sparse_coo) 2025-01-24T01:51:49.4491358Z 2025-01-24T01:51:49.4491515Z # when sum over all sparse dim, return a dense tensor 2025-01-24T01:51:49.4491902Z # with summed dims squeezed 2025-01-24T01:51:49.4492232Z >>> torch.sparse.sum(S, [0, 1, 3]) 2025-01-24T01:51:49.4492572Z tensor([-2.6596, -1.1450]) 2025-01-24T01:51:49.4492865Z 2025-01-24T01:51:49.4493234Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:51:49.4493624Z 2025-01-24T01:51:49.9937138Z msg = Cannot scrape callname=vmap in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/apis.py line=39. 2025-01-24T01:51:49.9938396Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:51:49.9938919Z 2025-01-24T01:51:49.9939162Z vmap is the vectorizing map; ``vmap(func)`` returns a new function that 2025-01-24T01:51:49.9939784Z maps ``func`` over some dimension of the inputs. Semantically, vmap 2025-01-24T01:51:49.9940380Z pushes the map into PyTorch operations called by ``func``, effectively 2025-01-24T01:51:49.9940863Z vectorizing those operations. 2025-01-24T01:51:49.9941122Z 2025-01-24T01:51:49.9941344Z vmap is useful for handling batch dimensions: one can write a function 2025-01-24T01:51:49.9941965Z ``func`` that runs on examples and then lift it to a function that can 2025-01-24T01:51:49.9942577Z take batches of examples with ``vmap(func)``. vmap can also be used to 2025-01-24T01:51:49.9943101Z compute batched gradients when composed with autograd. 2025-01-24T01:51:49.9943454Z 2025-01-24T01:51:49.9943566Z .. note:: 2025-01-24T01:51:49.9943894Z :func:`torch.vmap` is aliased to :func:`torch.func.vmap` for 2025-01-24T01:51:49.9944455Z convenience. Use whichever one you'd like. 2025-01-24T01:51:49.9944732Z 2025-01-24T01:51:49.9944861Z Args: 2025-01-24T01:51:49.9945212Z func (function): A Python function that takes one or more arguments. 2025-01-24T01:51:49.9945738Z Must return one or more Tensors. 2025-01-24T01:51:49.9946163Z in_dims (int or nested structure): Specifies which dimension of the 2025-01-24T01:51:49.9946807Z inputs should be mapped over. ``in_dims`` should have a 2025-01-24T01:51:49.9947372Z structure like the inputs. If the ``in_dim`` for a particular 2025-01-24T01:51:49.9947952Z input is None, then that indicates there is no map dimension. 2025-01-24T01:51:49.9948376Z Default: 0. 2025-01-24T01:51:49.9948794Z out_dims (int or Tuple[int]): Specifies where the mapped dimension 2025-01-24T01:51:49.9949335Z should appear in the outputs. If ``out_dims`` is a Tuple, then 2025-01-24T01:51:49.9949879Z it should have one element per output. Default: 0. 2025-01-24T01:51:49.9950420Z randomness (str): Specifies whether the randomness in this 2025-01-24T01:51:49.9950995Z vmap should be the same or different across batches. If 'different', 2025-01-24T01:51:49.9951594Z the randomness for each batch will be different. If 'same', the 2025-01-24T01:51:49.9952212Z randomness will be the same across batches. If 'error', any calls to 2025-01-24T01:51:49.9952823Z random functions will error. Default: 'error'. WARNING: this flag 2025-01-24T01:51:49.9953440Z only applies to random PyTorch operations and does not apply to 2025-01-24T01:51:49.9953920Z Python's random module or numpy randomness. 2025-01-24T01:51:49.9954480Z chunk_size (None or int): If None (default), apply a single vmap over inputs. 2025-01-24T01:51:49.9955126Z If not None, then compute the vmap :attr:`chunk_size` samples at a time. 2025-01-24T01:51:49.9955798Z Note that :attr:`chunk_size=1` is equivalent to computing the vmap with a for-loop. 2025-01-24T01:51:49.9956524Z If you run into memory issues computing the vmap, please try a non-None chunk_size. 2025-01-24T01:51:49.9956930Z 2025-01-24T01:51:49.9957050Z Returns: 2025-01-24T01:51:49.9957417Z Returns a new "batched" function. It takes the same inputs as 2025-01-24T01:51:49.9957981Z ``func``, except each input has an extra dimension at the index 2025-01-24T01:51:49.9958491Z specified by ``in_dims``. It takes returns the same outputs as 2025-01-24T01:51:49.9959063Z ``func``, except each output has an extra dimension at the index 2025-01-24T01:51:49.9959530Z specified by ``out_dims``. 2025-01-24T01:51:49.9959738Z 2025-01-24T01:51:49.9959829Z .. warning: 2025-01-24T01:51:49.9960228Z :func:`vmap` works best with functional-style code. Please do not 2025-01-24T01:51:49.9960754Z perform any side-effects in ``func``, with the exception of 2025-01-24T01:51:49.9961383Z in-place PyTorch operations. Examples of side-effects include mutating 2025-01-24T01:51:49.9962199Z Python data structures and assigning values to variables not captured 2025-01-24T01:51:49.9962732Z in ``func``. 2025-01-24T01:51:49.9962879Z 2025-01-24T01:51:49.9963120Z One example of using :func:`vmap` is to compute batched dot products. PyTorch 2025-01-24T01:51:49.9963782Z doesn't provide a batched ``torch.dot`` API; instead of unsuccessfully 2025-01-24T01:51:49.9964428Z rummaging through docs, use :func:`vmap` to construct a new function. 2025-01-24T01:51:49.9964828Z 2025-01-24T01:51:49.9965020Z >>> torch.dot # [D], [D] -> [] 2025-01-24T01:51:49.9965547Z >>> batched_dot = torch.func.vmap(torch.dot) # [N, D], [N, D] -> [N] 2025-01-24T01:51:49.9966041Z >>> x, y = torch.randn(2, 5), torch.randn(2, 5) 2025-01-24T01:51:49.9966447Z >>> batched_dot(x, y) 2025-01-24T01:51:49.9966640Z 2025-01-24T01:51:49.9966877Z :func:`vmap` can be helpful in hiding batch dimensions, leading to a simpler 2025-01-24T01:51:49.9967466Z model authoring experience. 2025-01-24T01:51:49.9967661Z 2025-01-24T01:51:49.9967822Z >>> batch_size, feature_size = 3, 5 2025-01-24T01:51:49.9968270Z >>> weights = torch.randn(feature_size, requires_grad=True) 2025-01-24T01:51:49.9968707Z >>> 2025-01-24T01:51:49.9968951Z >>> def model(feature_vec): 2025-01-24T01:51:49.9969293Z >>> # Very simple linear model with activation 2025-01-24T01:51:49.9969742Z >>> return feature_vec.dot(weights).relu() 2025-01-24T01:51:49.9970106Z >>> 2025-01-24T01:51:49.9970424Z >>> examples = torch.randn(batch_size, feature_size) 2025-01-24T01:51:49.9970820Z >>> result = torch.vmap(model)(examples) 2025-01-24T01:51:49.9971128Z 2025-01-24T01:51:49.9971381Z :func:`vmap` can also help vectorize computations that were previously difficult 2025-01-24T01:51:49.9972069Z or impossible to batch. One example is higher-order gradient computation. 2025-01-24T01:51:49.9972722Z The PyTorch autograd engine computes vjps (vector-Jacobian products). 2025-01-24T01:51:49.9973324Z Computing a full Jacobian matrix for some function f: R^N -> R^N usually 2025-01-24T01:51:49.9973991Z requires N calls to ``autograd.grad``, one per Jacobian row. Using :func:`vmap`, 2025-01-24T01:51:49.9974608Z we can vectorize the whole computation, computing the Jacobian in a single 2025-01-24T01:51:49.9975150Z call to ``autograd.grad``. 2025-01-24T01:51:49.9975333Z 2025-01-24T01:51:49.9975438Z >>> # Setup 2025-01-24T01:51:49.9975676Z >>> N = 5 2025-01-24T01:51:49.9975905Z >>> f = lambda x: x ** 2 2025-01-24T01:51:49.9976220Z >>> x = torch.randn(N, requires_grad=True) 2025-01-24T01:51:49.9976557Z >>> y = f(x) 2025-01-24T01:51:49.9976808Z >>> I_N = torch.eye(N) 2025-01-24T01:51:49.9977145Z >>> 2025-01-24T01:51:49.9977371Z >>> # Sequential approach 2025-01-24T01:51:49.9977792Z >>> jacobian_rows = [torch.autograd.grad(y, x, v, retain_graph=True)[0] 2025-01-24T01:51:49.9978254Z >>> for v in I_N.unbind()] 2025-01-24T01:51:49.9978620Z >>> jacobian = torch.stack(jacobian_rows) 2025-01-24T01:51:49.9978958Z >>> 2025-01-24T01:51:49.9979389Z >>> # vectorized gradient computation 2025-01-24T01:51:49.9979893Z >>> def get_vjp(v): 2025-01-24T01:51:49.9980332Z >>> return torch.autograd.grad(y, x, v) 2025-01-24T01:51:49.9980834Z >>> jacobian = torch.vmap(get_vjp)(I_N) 2025-01-24T01:51:49.9981068Z 2025-01-24T01:51:49.9981351Z :func:`vmap` can also be nested, producing an output with multiple batched dimensions 2025-01-24T01:51:49.9981741Z 2025-01-24T01:51:49.9981907Z >>> torch.dot # [D], [D] -> [] 2025-01-24T01:51:49.9982437Z >>> batched_dot = torch.vmap(torch.vmap(torch.dot)) # [N1, N0, D], [N1, N0, D] -> [N1, N0] 2025-01-24T01:51:49.9982978Z >>> x, y = torch.randn(2, 3, 5), torch.randn(2, 3, 5) 2025-01-24T01:51:49.9983367Z >>> batched_dot(x, y) # tensor of size [2, 3] 2025-01-24T01:51:49.9983606Z 2025-01-24T01:51:49.9983866Z If the inputs are not batched along the first dimension, ``in_dims`` specifies 2025-01-24T01:51:49.9984595Z the dimension that each inputs are batched along as 2025-01-24T01:51:49.9984905Z 2025-01-24T01:51:49.9985107Z >>> torch.dot # [N], [N] -> [] 2025-01-24T01:51:49.9985588Z >>> batched_dot = torch.vmap(torch.dot, in_dims=1) # [N, D], [N, D] -> [D] 2025-01-24T01:51:49.9986073Z >>> x, y = torch.randn(2, 5), torch.randn(2, 5) 2025-01-24T01:51:49.9986656Z >>> batched_dot(x, y) # output is [5] instead of [2] if batched along the 0th dimension 2025-01-24T01:51:49.9987017Z 2025-01-24T01:51:49.9987297Z If there are multiple inputs each of which is batched along different dimensions, 2025-01-24T01:51:49.9987954Z ``in_dims`` must be a tuple with the batch dimension for each input as 2025-01-24T01:51:49.9988273Z 2025-01-24T01:51:49.9988433Z >>> torch.dot # [D], [D] -> [] 2025-01-24T01:51:49.9988920Z >>> batched_dot = torch.vmap(torch.dot, in_dims=(0, None)) # [N, D], [D] -> [N] 2025-01-24T01:51:49.9989476Z >>> x, y = torch.randn(2, 5), torch.randn(5) 2025-01-24T01:51:49.9989964Z >>> batched_dot(x, y) # second arg doesn't have a batch dim because in_dim[1] was None 2025-01-24T01:51:49.9990340Z 2025-01-24T01:51:49.9990581Z If the input is a Python struct, ``in_dims`` must be a tuple containing a struct 2025-01-24T01:51:49.9991066Z matching the shape of the input: 2025-01-24T01:51:49.9991270Z 2025-01-24T01:51:49.9991428Z >>> f = lambda dict: torch.dot(dict['x'], dict['y']) 2025-01-24T01:51:49.9991797Z >>> x, y = torch.randn(2, 5), torch.randn(5) 2025-01-24T01:51:49.9992146Z >>> input = {'x': x, 'y': y} 2025-01-24T01:51:49.9992525Z >>> batched_dot = torch.vmap(f, in_dims=({'x': 0, 'y': None},)) 2025-01-24T01:51:49.9992930Z >>> batched_dot(input) 2025-01-24T01:51:49.9993105Z 2025-01-24T01:51:49.9993394Z By default, the output is batched along the first dimension. However, it can be batched 2025-01-24T01:51:49.9993911Z along any dimension by using ``out_dims`` 2025-01-24T01:51:49.9994156Z 2025-01-24T01:51:49.9994264Z >>> f = lambda x: x ** 2 2025-01-24T01:51:49.9994556Z >>> x = torch.randn(2, 5) 2025-01-24T01:51:49.9994878Z >>> batched_pow = torch.vmap(f, out_dims=1) 2025-01-24T01:51:49.9995235Z >>> batched_pow(x) # [5, 2] 2025-01-24T01:51:49.9995422Z 2025-01-24T01:51:49.9995838Z For any function that uses kwargs, the returned function will not batch the kwargs but will 2025-01-24T01:51:49.9996343Z accept kwargs 2025-01-24T01:51:49.9996496Z 2025-01-24T01:51:49.9996600Z >>> x = torch.randn([2, 5]) 2025-01-24T01:51:49.9996900Z >>> def fn(x, scale=4.): 2025-01-24T01:51:49.9997193Z >>> return x * scale 2025-01-24T01:51:49.9997470Z >>> 2025-01-24T01:51:49.9997694Z >>> batched_pow = torch.vmap(fn) 2025-01-24T01:51:49.9998067Z >>> assert torch.allclose(batched_pow(x), x * 4) 2025-01-24T01:51:49.9998562Z >>> batched_pow(x, scale=x) # scale is not batched, output has shape [2, 2, 5] 2025-01-24T01:51:49.9998907Z 2025-01-24T01:51:49.9999024Z .. note:: 2025-01-24T01:51:49.9999387Z vmap does not provide general autobatching or handle variable-length 2025-01-24T01:51:49.9999827Z sequences out of the box. 2025-01-24T01:51:50.0000035Z 2025-01-24T01:51:50.0000289Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:51:50.0000676Z 2025-01-24T01:51:51.3086600Z msg = Cannot scrape callname=triton_op in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/triton.py line=21. 2025-01-24T01:51:51.3087588Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:51:51.3088303Z Create a custom operator whose implementation is backed by 1+ triton kernels. 2025-01-24T01:51:51.3088716Z 2025-01-24T01:51:51.3088964Z This is a more structured way of using triton kernels with PyTorch. 2025-01-24T01:51:51.3089630Z Prefer using triton kernels with no ``torch.library`` custom operator wrappers 2025-01-24T01:51:51.3090673Z (like :func:`torch.library.custom_op`, :func:`torch.library.triton_op`) because 2025-01-24T01:51:51.3091298Z that is simpler; 2025-01-24T01:51:51.3091759Z only use :func:`torch.library.custom_op`/:func:`torch.library.triton_op` if you 2025-01-24T01:51:51.3092421Z want to create an operator that behaves like PyTorch built-in operators. 2025-01-24T01:51:51.3093171Z For example, you may use a ``torch.library`` wrapper API to define the 2025-01-24T01:51:51.3093802Z behavior of the triton kernel when passed a tensor subclass or under 2025-01-24T01:51:51.3094347Z a TorchDispatchMode. 2025-01-24T01:51:51.3094540Z 2025-01-24T01:51:51.3094795Z Use :func:`torch.library.triton_op` instead of :func:`torch.library.custom_op` 2025-01-24T01:51:51.3095283Z when the implementation 2025-01-24T01:51:51.3095697Z consists of 1+ triton kernels. :func:`torch.library.custom_op` treats 2025-01-24T01:51:51.3096211Z custom operators as opaque (:func:`torch.compile` and 2025-01-24T01:51:51.3096804Z :func:`torch.export.export` will never trace into them), but ``triton_op`` 2025-01-24T01:51:51.3097393Z makes the implementation visible to these subsystems, allowing them 2025-01-24T01:51:51.3097862Z to optimize the triton kernel(s). 2025-01-24T01:51:51.3098078Z 2025-01-24T01:51:51.3098284Z Note that ``fn`` must only consist of calls to PyTorch-understood 2025-01-24T01:51:51.3098830Z operators and triton kernels. Any triton kernels called inside ``fn`` 2025-01-24T01:51:51.3099383Z must be wrapped in a call to :func:`torch._library.wrap_triton``. 2025-01-24T01:51:51.3099697Z 2025-01-24T01:51:51.3099786Z Args: 2025-01-24T01:51:51.3100167Z name (str): A name for the custom op that looks like "{namespace}::{name}", 2025-01-24T01:51:51.3100734Z e.g. "mylib::my_linear". The name is used as the op's stable identifier 2025-01-24T01:51:51.3101241Z in PyTorch subsystems (e.g. torch.export, FX graphs). 2025-01-24T01:51:51.3101774Z To avoid name collisions, please use your project name as the namespace; 2025-01-24T01:51:51.3102355Z e.g. all custom ops in pytorch/fbgemm use "fbgemm" as the namespace. 2025-01-24T01:51:51.3102950Z mutates_args (Iterable[str] or "unknown"): The names of args that the function mutates. 2025-01-24T01:51:51.3103590Z This MUST be accurate, otherwise, the behavior is undefined. If "unknown", 2025-01-24T01:51:51.3104223Z it pessimistically assumes that all inputs to the operator are being mutated. 2025-01-24T01:51:51.3104801Z schema (None | str): A schema string for the operator. If None 2025-01-24T01:51:51.3105326Z (recommended) we'll infer a schema for the operator from its type 2025-01-24T01:51:51.3105867Z annotations. We recommend letting us infer a schema unless you 2025-01-24T01:51:51.3106383Z have a specific reason not to. 2025-01-24T01:51:51.3106778Z Example: "(Tensor x, int y) -> (Tensor, Tensor)". 2025-01-24T01:51:51.3107063Z 2025-01-24T01:51:51.3107158Z Example:: 2025-01-24T01:51:51.3107295Z 2025-01-24T01:51:51.3107451Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2025-01-24T01:51:51.3107813Z >>> import torch 2025-01-24T01:51:51.3108131Z >>> from torch.library import triton_op, wrap_triton 2025-01-24T01:51:51.3108509Z >>> 2025-01-24T01:51:51.3108738Z >>> import triton 2025-01-24T01:51:51.3109033Z >>> from triton import language as tl 2025-01-24T01:51:51.3109360Z >>> 2025-01-24T01:51:51.3109576Z >>> @triton.jit 2025-01-24T01:51:51.3109836Z >>> def add_kernel( 2025-01-24T01:51:51.3110109Z >>> in_ptr0, 2025-01-24T01:51:51.3110375Z >>> in_ptr1, 2025-01-24T01:51:51.3110633Z >>> out_ptr, 2025-01-24T01:51:51.3110884Z >>> n_elements, 2025-01-24T01:51:51.3111183Z >>> BLOCK_SIZE: "tl.constexpr", 2025-01-24T01:51:51.3111504Z >>> ): 2025-01-24T01:51:51.3111761Z >>> pid = tl.program_id(axis=0) 2025-01-24T01:51:51.3112253Z >>> block_start = pid * BLOCK_SIZE 2025-01-24T01:51:51.3112634Z >>> offsets = block_start + tl.arange(0, BLOCK_SIZE) 2025-01-24T01:51:51.3113023Z >>> mask = offsets < n_elements 2025-01-24T01:51:51.3113388Z >>> x = tl.load(in_ptr0 + offsets, mask=mask) 2025-01-24T01:51:51.3113780Z >>> y = tl.load(in_ptr1 + offsets, mask=mask) 2025-01-24T01:51:51.3114133Z >>> output = x + y 2025-01-24T01:51:51.3114457Z >>> tl.store(out_ptr + offsets, output, mask=mask) 2025-01-24T01:51:51.3114822Z >>> 2025-01-24T01:51:51.3115129Z >>> @triton_op("mylib::add", mutates_args={}) 2025-01-24T01:51:51.3115569Z >>> def add(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor: 2025-01-24T01:51:51.3115998Z >>> output = torch.empty_like(x) 2025-01-24T01:51:51.3116350Z >>> n_elements = output.numel() 2025-01-24T01:51:51.3116669Z >>> 2025-01-24T01:51:51.3116925Z >>> def grid(meta): 2025-01-24T01:51:51.3117293Z >>> return (triton.cdiv(n_elements, meta["BLOCK_SIZE"]),) 2025-01-24T01:51:51.3117679Z >>> 2025-01-24T01:51:51.3117992Z >>> # NB: we need to wrap the triton kernel in a call to wrap_triton 2025-01-24T01:51:51.3118485Z >>> wrap_triton(add_kernel)[grid](x, y, output, n_elements, 16) 2025-01-24T01:51:51.3118885Z >>> return output 2025-01-24T01:51:51.3119162Z >>> 2025-01-24T01:51:51.3119390Z >>> @torch.compile 2025-01-24T01:51:51.3119669Z >>> def f(x, y): 2025-01-24T01:51:51.3119941Z >>> return add(x, y) 2025-01-24T01:51:51.3120212Z >>> 2025-01-24T01:51:51.3120462Z >>> x = torch.randn(3, device="cuda") 2025-01-24T01:51:51.3120812Z >>> y = torch.randn(3, device="cuda") 2025-01-24T01:51:51.3121130Z >>> 2025-01-24T01:51:51.3121353Z >>> z = f(x, y) 2025-01-24T01:51:51.3121623Z >>> assert torch.allclose(z, x + y) 2025-01-24T01:51:51.3121867Z 2025-01-24T01:51:51.3121957Z 2025-01-24T01:51:51.3122337Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:51:51.3122708Z 2025-01-24T01:51:51.3123229Z msg = Cannot scrape callname=wrap_triton in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/triton.py line=202. 2025-01-24T01:51:51.3124120Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:51:51.3124702Z Allows capture of a triton kernel into a graph via make_fx or 2025-01-24T01:51:51.3125130Z non-strict ``torch.export``. 2025-01-24T01:51:51.3125341Z 2025-01-24T01:51:51.3125529Z These technologies perform Dispatcher-based tracing (via 2025-01-24T01:51:51.3126063Z ``__torch_dispatch__``) and cannot see calls to raw triton kernels. 2025-01-24T01:51:51.3126590Z The ``wrap_triton`` API wraps a triton kernel into a callable that 2025-01-24T01:51:51.3127043Z can actually be traced into a graph. 2025-01-24T01:51:51.3127272Z 2025-01-24T01:51:51.3127490Z Please use this API together with :func:`torch.library.triton_op`. 2025-01-24T01:51:51.3127833Z 2025-01-24T01:51:51.3127929Z Examples: 2025-01-24T01:51:51.3128083Z 2025-01-24T01:51:51.3128187Z >>> # xdoctest: +SKIP 2025-01-24T01:51:51.3128482Z >>> import torch 2025-01-24T01:51:51.3128758Z >>> import triton 2025-01-24T01:51:51.3129046Z >>> from triton import language as tl 2025-01-24T01:51:51.3129473Z >>> from torch.fx.experimental.proxy_tensor import make_fx 2025-01-24T01:51:51.3129917Z >>> from torch.library import wrap_triton 2025-01-24T01:51:51.3130261Z >>> 2025-01-24T01:51:51.3130491Z >>> @triton.jit 2025-01-24T01:51:51.3130745Z >>> def add_kernel( 2025-01-24T01:51:51.3131017Z >>> in_ptr0, 2025-01-24T01:51:51.3131285Z >>> in_ptr1, 2025-01-24T01:51:51.3131550Z >>> out_ptr, 2025-01-24T01:51:51.3131815Z >>> n_elements, 2025-01-24T01:51:51.3132209Z >>> BLOCK_SIZE: "tl.constexpr", 2025-01-24T01:51:51.3132539Z >>> ): 2025-01-24T01:51:51.3132795Z >>> pid = tl.program_id(axis=0) 2025-01-24T01:51:51.3133144Z >>> block_start = pid * BLOCK_SIZE 2025-01-24T01:51:51.3133532Z >>> offsets = block_start + tl.arange(0, BLOCK_SIZE) 2025-01-24T01:51:51.3133903Z >>> mask = offsets < n_elements 2025-01-24T01:51:51.3134261Z >>> x = tl.load(in_ptr0 + offsets, mask=mask) 2025-01-24T01:51:51.3134641Z >>> y = tl.load(in_ptr1 + offsets, mask=mask) 2025-01-24T01:51:51.3134988Z >>> output = x + y 2025-01-24T01:51:51.3135327Z >>> tl.store(out_ptr + offsets, output, mask=mask) 2025-01-24T01:51:51.3135670Z >>> 2025-01-24T01:51:51.3135898Z >>> def add(x, y): 2025-01-24T01:51:51.3136195Z >>> output = torch.empty_like(x) 2025-01-24T01:51:51.3136545Z >>> n_elements = output.numel() 2025-01-24T01:51:51.3136898Z >>> 2025-01-24T01:51:51.3137125Z >>> def grid_fn(meta): 2025-01-24T01:51:51.3137508Z >>> return (triton.cdiv(n_elements, meta["BLOCK_SIZE"]),) 2025-01-24T01:51:51.3137897Z >>> 2025-01-24T01:51:51.3138228Z >>> wrap_triton(add_kernel)[grid_fn](x, y, output, n_elements, 16) 2025-01-24T01:51:51.3138650Z >>> return output 2025-01-24T01:51:51.3138914Z >>> 2025-01-24T01:51:51.3139171Z >>> x = torch.randn(3, device="cuda") 2025-01-24T01:51:51.3139518Z >>> y = torch.randn(3, device="cuda") 2025-01-24T01:51:51.3139855Z >>> gm = make_fx(add)(x, y) 2025-01-24T01:51:51.3140164Z >>> print(gm.code) 2025-01-24T01:51:51.3140447Z >>> # def forward(self, x_1, y_1): 2025-01-24T01:51:51.3140912Z >>> # empty_like = torch.ops.aten.empty_like.default(x_1, pin_memory = False) 2025-01-24T01:51:51.3141524Z >>> # triton_kernel_wrapper_mutation_proxy = triton_kernel_wrapper_mutation( 2025-01-24T01:51:51.3142037Z >>> # kernel_idx = 0, constant_args_idx = 0, 2025-01-24T01:51:51.3142408Z >>> # grid = [(1, 1, 1)], kwargs = { 2025-01-24T01:51:51.3142801Z >>> # 'in_ptr0': x_1, 'in_ptr1': y_1, 'out_ptr': empty_like, 2025-01-24T01:51:51.3143197Z >>> # 'n_elements': 3, 'BLOCK_SIZE': 16 2025-01-24T01:51:51.3143538Z >>> # }) 2025-01-24T01:51:51.3143809Z >>> # return empty_like 2025-01-24T01:51:51.3144009Z 2025-01-24T01:51:51.3144106Z 2025-01-24T01:51:51.3144481Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:51:51.3144852Z 2025-01-24T01:51:51.3801418Z msg = Cannot scrape callname=assert_almost_equal in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py line=326. 2025-01-24T01:51:51.3802402Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:51:51.3802819Z 2025-01-24T01:51:51.3803061Z Raises an AssertionError if two items are not equal up to desired 2025-01-24T01:51:51.3803584Z precision. 2025-01-24T01:51:51.3803777Z 2025-01-24T01:51:51.3804007Z .. note:: It is recommended to use one of `assert_allclose`, 2025-01-24T01:51:51.3804576Z `assert_array_almost_equal_nulp` or `assert_array_max_ulp` 2025-01-24T01:51:51.3805068Z instead of this function for more consistent floating point 2025-01-24T01:51:51.3805481Z comparisons. 2025-01-24T01:51:51.3805655Z 2025-01-24T01:51:51.3805873Z The test verifies that the elements of `actual` and `desired` satisfy. 2025-01-24T01:51:51.3806213Z 2025-01-24T01:51:51.3806388Z ``abs(desired-actual) < float64(1.5 * 10**(-decimal))`` 2025-01-24T01:51:51.3806660Z 2025-01-24T01:51:51.3806903Z That is a looser test than originally documented, but agrees with what the 2025-01-24T01:51:51.3807497Z actual implementation in `assert_array_almost_equal` did up to rounding 2025-01-24T01:51:51.3808091Z vagaries. An exception is raised at conflicting values. For ndarrays this 2025-01-24T01:51:51.3809102Z delegates to assert_array_almost_equal 2025-01-24T01:51:51.3809415Z 2025-01-24T01:51:51.3809626Z Parameters 2025-01-24T01:51:51.3809892Z ---------- 2025-01-24T01:51:51.3810135Z actual : array_like 2025-01-24T01:51:51.3810398Z The object to check. 2025-01-24T01:51:51.3810676Z desired : array_like 2025-01-24T01:51:51.3810952Z The expected object. 2025-01-24T01:51:51.3811216Z decimal : int, optional 2025-01-24T01:51:51.3811505Z Desired precision, default is 7. 2025-01-24T01:51:51.3811828Z err_msg : str, optional 2025-01-24T01:51:51.3812151Z The error message to be printed in case of failure. 2025-01-24T01:51:51.3812515Z verbose : bool, optional 2025-01-24T01:51:51.3812905Z If True, the conflicting values are appended to the error message. 2025-01-24T01:51:51.3813241Z 2025-01-24T01:51:51.3813371Z Raises 2025-01-24T01:51:51.3813662Z ------ 2025-01-24T01:51:51.3813979Z AssertionError 2025-01-24T01:51:51.3814454Z If actual and desired are not equal up to specified precision. 2025-01-24T01:51:51.3815002Z 2025-01-24T01:51:51.3815177Z See Also 2025-01-24T01:51:51.3815556Z -------- 2025-01-24T01:51:51.3815958Z assert_allclose: Compare two array_like objects for equality with desired 2025-01-24T01:51:51.3816459Z relative and/or absolute precision. 2025-01-24T01:51:51.3816922Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2025-01-24T01:51:51.3817248Z 2025-01-24T01:51:51.3817338Z Examples 2025-01-24T01:51:51.3817561Z -------- 2025-01-24T01:51:51.3817856Z >>> from torch._numpy.testing import assert_almost_equal 2025-01-24T01:51:51.3818278Z >>> assert_almost_equal(2.3333333333333, 2.33333334) 2025-01-24T01:51:51.3818715Z >>> assert_almost_equal(2.3333333333333, 2.33333334, decimal=10) 2025-01-24T01:51:51.3819116Z Traceback (most recent call last): 2025-01-24T01:51:51.3819429Z ... 2025-01-24T01:51:51.3819655Z AssertionError: 2025-01-24T01:51:51.3819938Z Arrays are not almost equal to 10 decimals 2025-01-24T01:51:51.3820289Z ACTUAL: 2.3333333333333 2025-01-24T01:51:51.3820554Z DESIRED: 2.33333334 2025-01-24T01:51:51.3820723Z 2025-01-24T01:51:51.3820874Z >>> assert_almost_equal(np.array([1.0,2.3333333333333]), 2025-01-24T01:51:51.3821279Z ... np.array([1.0,2.33333334]), decimal=9) 2025-01-24T01:51:51.3821643Z Traceback (most recent call last): 2025-01-24T01:51:51.3821949Z ... 2025-01-24T01:51:51.3822159Z AssertionError: 2025-01-24T01:51:51.3822433Z Arrays are not almost equal to 9 decimals 2025-01-24T01:51:51.3822762Z 2025-01-24T01:51:51.3823006Z Mismatched elements: 1 / 2 (50%) 2025-01-24T01:51:51.3823342Z Max absolute difference: 6.666699636781459e-09 2025-01-24T01:51:51.3823707Z Max relative difference: 2.8571569790287484e-09 2025-01-24T01:51:51.3824096Z x: torch.ndarray([1.0000, 2.3333], dtype=float64) 2025-01-24T01:51:51.3824491Z y: torch.ndarray([1.0000, 2.3333], dtype=float64) 2025-01-24T01:51:51.3824739Z 2025-01-24T01:51:51.3824746Z 2025-01-24T01:51:51.3825021Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:51:51.3825393Z 2025-01-24T01:51:51.3825970Z msg = Cannot scrape callname=assert_approx_equal in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py line=451. 2025-01-24T01:51:51.3827023Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:51:51.3827405Z 2025-01-24T01:51:51.3827633Z Raises an AssertionError if two items are not equal up to significant 2025-01-24T01:51:51.3828071Z digits. 2025-01-24T01:51:51.3828205Z 2025-01-24T01:51:51.3828389Z .. note:: It is recommended to use one of `assert_allclose`, 2025-01-24T01:51:51.3828876Z `assert_array_almost_equal_nulp` or `assert_array_max_ulp` 2025-01-24T01:51:51.3829372Z instead of this function for more consistent floating point 2025-01-24T01:51:51.3829779Z comparisons. 2025-01-24T01:51:51.3829941Z 2025-01-24T01:51:51.3830130Z Given two numbers, check that they are approximately equal. 2025-01-24T01:51:51.3830821Z Approximately equal is defined as the number of significant digits 2025-01-24T01:51:51.3831261Z that agree. 2025-01-24T01:51:51.3831389Z 2025-01-24T01:51:51.3831494Z Parameters 2025-01-24T01:51:51.3831719Z ---------- 2025-01-24T01:51:51.3831934Z actual : scalar 2025-01-24T01:51:51.3832188Z The object to check. 2025-01-24T01:51:51.3832464Z desired : scalar 2025-01-24T01:51:51.3832719Z The expected object. 2025-01-24T01:51:51.3833011Z significant : int, optional 2025-01-24T01:51:51.3833307Z Desired precision, default is 7. 2025-01-24T01:51:51.3833636Z err_msg : str, optional 2025-01-24T01:51:51.3833963Z The error message to be printed in case of failure. 2025-01-24T01:51:51.3834346Z verbose : bool, optional 2025-01-24T01:51:51.3834739Z If True, the conflicting values are appended to the error message. 2025-01-24T01:51:51.3835061Z 2025-01-24T01:51:51.3835149Z Raises 2025-01-24T01:51:51.3835395Z ------ 2025-01-24T01:51:51.3835622Z AssertionError 2025-01-24T01:51:51.3835971Z If actual and desired are not equal up to specified precision. 2025-01-24T01:51:51.3836277Z 2025-01-24T01:51:51.3836380Z See Also 2025-01-24T01:51:51.3836586Z -------- 2025-01-24T01:51:51.3836963Z assert_allclose: Compare two array_like objects for equality with desired 2025-01-24T01:51:51.3837465Z relative and/or absolute precision. 2025-01-24T01:51:51.3837930Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2025-01-24T01:51:51.3838256Z 2025-01-24T01:51:51.3838361Z Examples 2025-01-24T01:51:51.3838566Z -------- 2025-01-24T01:51:51.3838970Z >>> np.testing.assert_approx_equal(0.12345677777777e-20, 0.1234567e-20) # doctest: +SKIP 2025-01-24T01:51:51.3839625Z >>> np.testing.assert_approx_equal(0.12345670e-20, 0.12345671e-20, # doctest: +SKIP 2025-01-24T01:51:51.3840124Z ... significant=8) 2025-01-24T01:51:51.3840614Z >>> np.testing.assert_approx_equal(0.12345670e-20, 0.12345672e-20, # doctest: +SKIP 2025-01-24T01:51:51.3841121Z ... significant=8) 2025-01-24T01:51:51.3841464Z Traceback (most recent call last): 2025-01-24T01:51:51.3841768Z ... 2025-01-24T01:51:51.3841992Z AssertionError: 2025-01-24T01:51:51.3842283Z Items are not equal to 8 significant digits: 2025-01-24T01:51:51.3842643Z ACTUAL: 1.234567e-21 2025-01-24T01:51:51.3842891Z DESIRED: 1.2345672e-21 2025-01-24T01:51:51.3843064Z 2025-01-24T01:51:51.3843227Z the evaluated condition that raises the exception is 2025-01-24T01:51:51.3843514Z 2025-01-24T01:51:51.3843689Z >>> abs(0.12345670e-20/1e-21 - 0.12345672e-20/1e-21) >= 10**-(8-1) 2025-01-24T01:51:51.3844068Z True 2025-01-24T01:51:51.3844185Z 2025-01-24T01:51:51.3844189Z 2025-01-24T01:51:51.3844457Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:51:51.3844829Z 2025-01-24T01:51:51.3845405Z msg = Cannot scrape callname=assert_array_equal in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py line=730. 2025-01-24T01:51:51.3846342Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:51:51.3846736Z 2025-01-24T01:51:51.3846947Z Raises an AssertionError if two array_like objects are not equal. 2025-01-24T01:51:51.3847282Z 2025-01-24T01:51:51.3847488Z Given two array_like objects, check that the shape is equal and all 2025-01-24T01:51:51.3848041Z elements of these objects are equal (but see the Notes for the special 2025-01-24T01:51:51.3848587Z handling of a scalar). An exception is raised at shape mismatch or 2025-01-24T01:51:51.3849143Z conflicting values. In contrast to the standard usage in numpy, NaNs 2025-01-24T01:51:51.3849711Z are compared like numbers, no assertion is raised if both objects have 2025-01-24T01:51:51.3850166Z NaNs in the same positions. 2025-01-24T01:51:51.3850362Z 2025-01-24T01:51:51.3850593Z The usual caution for verifying equality with floating point numbers is 2025-01-24T01:51:51.3851042Z advised. 2025-01-24T01:51:51.3851206Z 2025-01-24T01:51:51.3851380Z Parameters 2025-01-24T01:51:51.3851616Z ---------- 2025-01-24T01:51:51.3851846Z x : array_like 2025-01-24T01:51:51.3852089Z The actual object to check. 2025-01-24T01:51:51.3852390Z y : array_like 2025-01-24T01:51:51.3852654Z The desired, expected object. 2025-01-24T01:51:51.3852969Z err_msg : str, optional 2025-01-24T01:51:51.3853300Z The error message to be printed in case of failure. 2025-01-24T01:51:51.3853669Z verbose : bool, optional 2025-01-24T01:51:51.3854064Z If True, the conflicting values are appended to the error message. 2025-01-24T01:51:51.3854503Z strict : bool, optional 2025-01-24T01:51:51.3854885Z If True, raise an AssertionError when either the shape or the data 2025-01-24T01:51:51.3855395Z type of the array_like objects does not match. The special 2025-01-24T01:51:51.3855903Z handling for scalars mentioned in the Notes section is disabled. 2025-01-24T01:51:51.3856238Z 2025-01-24T01:51:51.3856358Z Raises 2025-01-24T01:51:51.3856579Z ------ 2025-01-24T01:51:51.3856811Z AssertionError 2025-01-24T01:51:51.3857095Z If actual and desired objects are not equal. 2025-01-24T01:51:51.3857340Z 2025-01-24T01:51:51.3857443Z See Also 2025-01-24T01:51:51.3857650Z -------- 2025-01-24T01:51:51.3858021Z assert_allclose: Compare two array_like objects for equality with desired 2025-01-24T01:51:51.3858521Z relative and/or absolute precision. 2025-01-24T01:51:51.3858983Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2025-01-24T01:51:51.3859309Z 2025-01-24T01:51:51.3859407Z Notes 2025-01-24T01:51:51.3859605Z ----- 2025-01-24T01:51:51.3859915Z When one of `x` and `y` is a scalar and the other is array_like, the 2025-01-24T01:51:51.3860451Z function checks that each element of the array_like object is equal to 2025-01-24T01:51:51.3861026Z the scalar. This behaviour can be disabled with the `strict` parameter. 2025-01-24T01:51:51.3861368Z 2025-01-24T01:51:51.3861473Z Examples 2025-01-24T01:51:51.3861681Z -------- 2025-01-24T01:51:51.3861954Z The first assert does not raise an exception: 2025-01-24T01:51:51.3862212Z 2025-01-24T01:51:51.3862367Z >>> np.testing.assert_array_equal([1.0,2.33333,np.nan], 2025-01-24T01:51:51.3862771Z ... [np.exp(0),2.33333, np.nan]) 2025-01-24T01:51:51.3863012Z 2025-01-24T01:51:51.3863254Z Use `assert_allclose` or one of the nulp (number of floating point values) 2025-01-24T01:51:51.3863722Z functions for these cases instead: 2025-01-24T01:51:51.3863930Z 2025-01-24T01:51:51.3864077Z >>> np.testing.assert_allclose([1.0,np.pi,np.nan], 2025-01-24T01:51:51.3864473Z ... [1, np.sqrt(np.pi)**2, np.nan], 2025-01-24T01:51:51.3864839Z ... rtol=1e-10, atol=0) 2025-01-24T01:51:51.3865064Z 2025-01-24T01:51:51.3865312Z As mentioned in the Notes section, `assert_array_equal` has special 2025-01-24T01:51:51.3866124Z handling for scalars. Here the test checks that each value in `x` is 3: 2025-01-24T01:51:51.3866624Z 2025-01-24T01:51:51.3866804Z >>> x = np.full((2, 5), fill_value=3) 2025-01-24T01:51:51.3867245Z >>> np.testing.assert_array_equal(x, 3) 2025-01-24T01:51:51.3867487Z 2025-01-24T01:51:51.3867706Z Use `strict` to raise an AssertionError when comparing a scalar with an 2025-01-24T01:51:51.3868135Z array: 2025-01-24T01:51:51.3868254Z 2025-01-24T01:51:51.3868420Z >>> np.testing.assert_array_equal(x, 3, strict=True) 2025-01-24T01:51:51.3868806Z Traceback (most recent call last): 2025-01-24T01:51:51.3869099Z ... 2025-01-24T01:51:51.3869329Z AssertionError: 2025-01-24T01:51:51.3869579Z Arrays are not equal 2025-01-24T01:51:51.3869838Z 2025-01-24T01:51:51.3870080Z (shapes (2, 5), () mismatch) 2025-01-24T01:51:51.3870363Z x: torch.ndarray([[3, 3, 3, 3, 3], 2025-01-24T01:51:51.3870669Z [3, 3, 3, 3, 3]]) 2025-01-24T01:51:51.3870939Z y: torch.ndarray(3) 2025-01-24T01:51:51.3871095Z 2025-01-24T01:51:51.3871326Z The `strict` parameter also ensures that the array data types match: 2025-01-24T01:51:51.3871738Z 2025-01-24T01:51:51.3871915Z >>> x = np.array([2, 2, 2]) 2025-01-24T01:51:51.3872219Z >>> y = np.array([2., 2., 2.], dtype=np.float32) 2025-01-24T01:51:51.3872621Z >>> np.testing.assert_array_equal(x, y, strict=True) 2025-01-24T01:51:51.3873020Z Traceback (most recent call last): 2025-01-24T01:51:51.3873325Z ... 2025-01-24T01:51:51.3873552Z AssertionError: 2025-01-24T01:51:51.3873789Z Arrays are not equal 2025-01-24T01:51:51.3874050Z 2025-01-24T01:51:51.3874328Z (dtypes dtype("int64"), dtype("float32") mismatch) 2025-01-24T01:51:51.3874701Z x: torch.ndarray([2, 2, 2]) 2025-01-24T01:51:51.3874999Z y: torch.ndarray([2., 2., 2.]) 2025-01-24T01:51:51.3875186Z 2025-01-24T01:51:51.3875444Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:51:51.3875833Z 2025-01-24T01:51:51.3876477Z msg = Cannot scrape callname=assert_array_almost_equal in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py line=836. 2025-01-24T01:51:51.3877490Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:51:51.3877868Z 2025-01-24T01:51:51.3878086Z Raises an AssertionError if two objects are not equal up to desired 2025-01-24T01:51:51.3878518Z precision. 2025-01-24T01:51:51.3878664Z 2025-01-24T01:51:51.3878846Z .. note:: It is recommended to use one of `assert_allclose`, 2025-01-24T01:51:51.3879522Z `assert_array_almost_equal_nulp` or `assert_array_max_ulp` 2025-01-24T01:51:51.3880024Z instead of this function for more consistent floating point 2025-01-24T01:51:51.3880423Z comparisons. 2025-01-24T01:51:51.3880600Z 2025-01-24T01:51:51.3880842Z The test verifies identical shapes and that the elements of ``actual`` and 2025-01-24T01:51:51.3881308Z ``desired`` satisfy. 2025-01-24T01:51:51.3881478Z 2025-01-24T01:51:51.3881610Z ``abs(desired-actual) < 1.5 * 10**(-decimal)`` 2025-01-24T01:51:51.3881858Z 2025-01-24T01:51:51.3882109Z That is a looser test than originally documented, but agrees with what the 2025-01-24T01:51:51.3882718Z actual implementation did up to rounding vagaries. An exception is raised 2025-01-24T01:51:51.3883315Z at shape mismatch or conflicting values. In contrast to the standard usage 2025-01-24T01:51:51.3883914Z in numpy, NaNs are compared like numbers, no assertion is raised if both 2025-01-24T01:51:51.3884390Z objects have NaNs in the same positions. 2025-01-24T01:51:51.3884631Z 2025-01-24T01:51:51.3884726Z Parameters 2025-01-24T01:51:51.3884951Z ---------- 2025-01-24T01:51:51.3885183Z x : array_like 2025-01-24T01:51:51.3885423Z The actual object to check. 2025-01-24T01:51:51.3885713Z y : array_like 2025-01-24T01:51:51.3885967Z The desired, expected object. 2025-01-24T01:51:51.3886288Z decimal : int, optional 2025-01-24T01:51:51.3886565Z Desired precision, default is 6. 2025-01-24T01:51:51.3886890Z err_msg : str, optional 2025-01-24T01:51:51.3887218Z The error message to be printed in case of failure. 2025-01-24T01:51:51.3887603Z verbose : bool, optional 2025-01-24T01:51:51.3888004Z If True, the conflicting values are appended to the error message. 2025-01-24T01:51:51.3888328Z 2025-01-24T01:51:51.3888431Z Raises 2025-01-24T01:51:51.3888633Z ------ 2025-01-24T01:51:51.3888861Z AssertionError 2025-01-24T01:51:51.3889217Z If actual and desired are not equal up to specified precision. 2025-01-24T01:51:51.3889529Z 2025-01-24T01:51:51.3889636Z See Also 2025-01-24T01:51:51.3889846Z -------- 2025-01-24T01:51:51.3890221Z assert_allclose: Compare two array_like objects for equality with desired 2025-01-24T01:51:51.3890727Z relative and/or absolute precision. 2025-01-24T01:51:51.3900366Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2025-01-24T01:51:51.3900720Z 2025-01-24T01:51:51.3900835Z Examples 2025-01-24T01:51:51.3901051Z -------- 2025-01-24T01:51:51.3901326Z the first assert does not raise an exception 2025-01-24T01:51:51.3901582Z 2025-01-24T01:51:51.3901774Z >>> np.testing.assert_array_almost_equal([1.0,2.333,np.nan], 2025-01-24T01:51:51.3902445Z ... [1.0,2.333,np.nan]) 2025-01-24T01:51:51.3902681Z 2025-01-24T01:51:51.3902887Z >>> np.testing.assert_array_almost_equal([1.0,2.33333,np.nan], 2025-01-24T01:51:51.3903320Z ... [1.0,2.33339,np.nan], decimal=5) 2025-01-24T01:51:51.3903686Z Traceback (most recent call last): 2025-01-24T01:51:51.3904009Z ... 2025-01-24T01:51:51.3904241Z AssertionError: 2025-01-24T01:51:51.3904521Z Arrays are not almost equal to 5 decimals 2025-01-24T01:51:51.3904859Z 2025-01-24T01:51:51.3905094Z Mismatched elements: 1 / 3 (33.3%) 2025-01-24T01:51:51.3905443Z Max absolute difference: 5.999999999994898e-05 2025-01-24T01:51:51.3905828Z Max relative difference: 2.5713661239633743e-05 2025-01-24T01:51:51.3906357Z x: torch.ndarray([1.0000, 2.3333, nan], dtype=float64) 2025-01-24T01:51:51.3906816Z y: torch.ndarray([1.0000, 2.3334, nan], dtype=float64) 2025-01-24T01:51:51.3907145Z 2025-01-24T01:51:51.3907331Z >>> np.testing.assert_array_almost_equal([1.0,2.33333,np.nan], 2025-01-24T01:51:51.3907756Z ... [1.0,2.33333, 5], decimal=5) 2025-01-24T01:51:51.3908122Z Traceback (most recent call last): 2025-01-24T01:51:51.3908426Z ... 2025-01-24T01:51:51.3908656Z AssertionError: 2025-01-24T01:51:51.3908924Z Arrays are not almost equal to 5 decimals 2025-01-24T01:51:51.3909257Z 2025-01-24T01:51:51.3909502Z x and y nan location mismatch: 2025-01-24T01:51:51.3909872Z x: torch.ndarray([1.0000, 2.3333, nan], dtype=float64) 2025-01-24T01:51:51.3910314Z y: torch.ndarray([1.0000, 2.3333, 5.0000], dtype=float64) 2025-01-24T01:51:51.3910583Z 2025-01-24T01:51:51.3910586Z 2025-01-24T01:51:51.3910889Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:51:51.3911262Z 2025-01-24T01:51:51.3911857Z msg = Cannot scrape callname=clear_and_catch_warnings in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py line=1786. 2025-01-24T01:51:51.3912831Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:51:51.3913439Z Context manager that resets warning registry for catching warnings 2025-01-24T01:51:51.3913788Z 2025-01-24T01:51:51.3914035Z Warnings can be slippery, because, whenever a warning is triggered, Python 2025-01-24T01:51:51.3914636Z adds a ``__warningregistry__`` member to the *calling* module. This makes 2025-01-24T01:51:51.3915234Z it impossible to retrigger the warning in this module, whatever you put in 2025-01-24T01:51:51.3915848Z the warnings filters. This context manager accepts a sequence of `modules` 2025-01-24T01:51:51.3916349Z as a keyword argument to its constructor and: 2025-01-24T01:51:51.3916613Z 2025-01-24T01:51:51.3916847Z * stores and removes any ``__warningregistry__`` entries in given `modules` 2025-01-24T01:51:51.3917297Z on entry; 2025-01-24T01:51:51.3917650Z * resets ``__warningregistry__`` to its previous state on exit. 2025-01-24T01:51:51.3917955Z 2025-01-24T01:51:51.3918203Z This makes it possible to trigger any warning afresh inside the context 2025-01-24T01:51:51.3918729Z manager without disturbing the state of warnings outside. 2025-01-24T01:51:51.3919042Z 2025-01-24T01:51:51.3919281Z For compatibility with Python 3.0, please consider all arguments to be 2025-01-24T01:51:51.3919741Z keyword-only. 2025-01-24T01:51:51.3919903Z 2025-01-24T01:51:51.3919997Z Parameters 2025-01-24T01:51:51.3920239Z ---------- 2025-01-24T01:51:51.3920492Z record : bool, optional 2025-01-24T01:51:51.3920865Z Specifies whether warnings should be captured by a custom 2025-01-24T01:51:51.3921421Z implementation of ``warnings.showwarning()`` and be appended to a list 2025-01-24T01:51:51.3921995Z returned by the context manager. Otherwise None is returned by the 2025-01-24T01:51:51.3922567Z context manager. The objects appended to the list are arguments whose 2025-01-24T01:51:51.3923201Z attributes mirror the arguments to ``showwarning()``. 2025-01-24T01:51:51.3923597Z modules : sequence, optional 2025-01-24T01:51:51.3924033Z Sequence of modules for which to reset warnings registry on entry and 2025-01-24T01:51:51.3924581Z restore on exit. To work correctly, all 'ignore' filters should 2025-01-24T01:51:51.3925020Z filter by one of these modules. 2025-01-24T01:51:51.3925236Z 2025-01-24T01:51:51.3925344Z Examples 2025-01-24T01:51:51.3925595Z -------- 2025-01-24T01:51:51.3925821Z >>> import warnings 2025-01-24T01:51:51.3926184Z >>> with np.testing.clear_and_catch_warnings( # doctest: +SKIP 2025-01-24T01:51:51.3926629Z ... modules=[np.core.fromnumeric]): 2025-01-24T01:51:51.3927000Z ... warnings.simplefilter('always') 2025-01-24T01:51:51.3927472Z ... warnings.filterwarnings('ignore', module='np.core.fromnumeric') 2025-01-24T01:51:51.3928024Z ... # do something that raises a warning but ignore those in 2025-01-24T01:51:51.3928436Z ... # np.core.fromnumeric 2025-01-24T01:51:51.3928733Z 2025-01-24T01:51:51.3929120Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:51:51.3929493Z 2025-01-24T01:51:51.6848122Z msg = Cannot scrape callname=Conv1d in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/conv.py line=354. 2025-01-24T01:51:51.6849116Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:51:51.6849726Z Applies a 1D convolution over a quantized input signal composed of 2025-01-24T01:51:51.6850169Z several quantized input planes. 2025-01-24T01:51:51.6850404Z 2025-01-24T01:51:51.6850621Z For details on input arguments, parameters, and implementation see 2025-01-24T01:51:51.6851074Z :class:`~torch.nn.Conv1d`. 2025-01-24T01:51:51.6851266Z 2025-01-24T01:51:51.6851428Z .. note:: 2025-01-24T01:51:51.6851793Z Only `zeros` is supported for the :attr:`padding_mode` argument. 2025-01-24T01:51:51.6852112Z 2025-01-24T01:51:51.6852205Z .. note:: 2025-01-24T01:51:51.6852535Z Only `torch.quint8` is supported for the input data type. 2025-01-24T01:51:51.6852847Z 2025-01-24T01:51:51.6852851Z 2025-01-24T01:51:51.6852948Z Attributes: 2025-01-24T01:51:51.6853317Z weight (Tensor): packed tensor derived from the learnable weight 2025-01-24T01:51:51.6853770Z parameter. 2025-01-24T01:51:51.6854138Z scale (Tensor): scalar for the output scale 2025-01-24T01:51:51.6854559Z zero_point (Tensor): scalar for the output zero point 2025-01-24T01:51:51.6854853Z 2025-01-24T01:51:51.6855011Z See :class:`~torch.nn.Conv1d` for other attributes. 2025-01-24T01:51:51.6855293Z 2025-01-24T01:51:51.6855390Z Examples:: 2025-01-24T01:51:51.6855544Z 2025-01-24T01:51:51.6855698Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_QENGINE) 2025-01-24T01:51:51.6856122Z >>> m = nn.quantized.Conv1d(16, 33, 3, stride=2) 2025-01-24T01:51:51.6856497Z >>> input = torch.randn(20, 16, 100) 2025-01-24T01:51:51.6856830Z >>> # quantize input to quint8 2025-01-24T01:51:51.6857166Z >>> # xdoctest: +SKIP 2025-01-24T01:51:51.6857579Z >>> q_input = torch.quantize_per_tensor(input, scale=1.0, zero_point=0, 2025-01-24T01:51:51.6858052Z ... dtype=torch.quint8) 2025-01-24T01:51:51.6858415Z >>> output = m(q_input) 2025-01-24T01:51:51.6858604Z 2025-01-24T01:51:51.6858691Z 2025-01-24T01:51:51.6859069Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:51:51.6859457Z 2025-01-24T01:51:51.7037742Z msg = Cannot scrape callname=LSTM in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/rnn.py line=11. 2025-01-24T01:51:51.7038680Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:51:51.7039543Z A quantized long short-term memory (LSTM). 2025-01-24T01:51:51.7039788Z 2025-01-24T01:51:51.7040081Z For the description and the argument types, please, refer to :class:`~torch.nn.LSTM` 2025-01-24T01:51:51.7040482Z 2025-01-24T01:51:51.7040594Z Attributes: 2025-01-24T01:51:51.7040855Z layers : instances of the `_LSTMLayer` 2025-01-24T01:51:51.7041104Z 2025-01-24T01:51:51.7041205Z .. note:: 2025-01-24T01:51:51.7041560Z To access the weights and biases, you need to access them per layer. 2025-01-24T01:51:51.7042075Z See examples in :class:`~torch.ao.nn.quantizable.LSTM` 2025-01-24T01:51:51.7042363Z 2025-01-24T01:51:51.7042472Z Examples:: 2025-01-24T01:51:51.7042715Z >>> # xdoctest: +SKIP 2025-01-24T01:51:51.7043035Z >>> custom_module_config = { 2025-01-24T01:51:51.7043392Z ... 'float_to_observed_custom_module_class': { 2025-01-24T01:51:51.7043780Z ... nn.LSTM: nn.quantizable.LSTM, 2025-01-24T01:51:51.7044181Z ... }, 2025-01-24T01:51:51.7044491Z ... 'observed_to_quantized_custom_module_class': { 2025-01-24T01:51:51.7044900Z ... nn.quantizable.LSTM: nn.quantized.LSTM, 2025-01-24T01:51:51.7045256Z ... } 2025-01-24T01:51:51.7045496Z ... } 2025-01-24T01:51:51.7045863Z >>> tq.prepare(model, prepare_custom_module_class=custom_module_config) 2025-01-24T01:51:51.7046428Z >>> tq.convert(model, convert_custom_module_class=custom_module_config) 2025-01-24T01:51:51.7046853Z 2025-01-24T01:51:51.7047238Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:51:51.7047625Z 2025-01-24T01:51:51.7864691Z msg = Cannot scrape callname=BaseSparsifier.squash_mask in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/pruning/sparsifier/base_sparsifier.py line=227. 2025-01-24T01:51:51.7865800Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:51:51.7866494Z Squashes the sparse masks into the appropriate tensors. 2025-01-24T01:51:51.7866782Z 2025-01-24T01:51:51.7867011Z If either the `params_to_keep` or `params_to_keep_per_layer` is set, 2025-01-24T01:51:51.7867544Z the module will have a `sparse_params` dict attached to it. 2025-01-24T01:51:51.7867849Z 2025-01-24T01:51:51.7867942Z Args: 2025-01-24T01:51:51.7868279Z params_to_keep: List of keys to save in the module or a dict 2025-01-24T01:51:51.7868759Z representing the modules and keys that will have 2025-01-24T01:51:51.7869180Z sparsity parameters saved 2025-01-24T01:51:51.7869646Z params_to_keep_per_layer: Dict to specify the params that should be 2025-01-24T01:51:51.7870150Z saved for specific layers. The keys in the dict 2025-01-24T01:51:51.7870585Z should be the module fqn, while the values should 2025-01-24T01:51:51.7871050Z be a list of strings with the names of the variables 2025-01-24T01:51:51.7871469Z to save in the `sparse_params` 2025-01-24T01:51:51.7871707Z 2025-01-24T01:51:51.7871815Z Examples: 2025-01-24T01:51:51.7872106Z >>> # xdoctest: +SKIP("locals are undefined") 2025-01-24T01:51:51.7872468Z >>> # Don't save any sparse params 2025-01-24T01:51:51.7872824Z >>> sparsifier.squash_mask() 2025-01-24T01:51:51.7873196Z >>> hasattr(model.submodule1, 'sparse_params') 2025-01-24T01:51:51.7873548Z False 2025-01-24T01:51:51.7873690Z 2025-01-24T01:51:51.7873826Z >>> # Keep sparse params per layer 2025-01-24T01:51:51.7874183Z >>> sparsifier.squash_mask( 2025-01-24T01:51:51.7874512Z ... params_to_keep_per_layer={ 2025-01-24T01:51:51.7874909Z ... 'submodule1.linear1': ('foo', 'bar'), 2025-01-24T01:51:51.7875299Z ... 'submodule2.linear42': ('baz',) 2025-01-24T01:51:51.7875879Z ... }) 2025-01-24T01:51:51.7876206Z >>> print(model.submodule1.linear1.sparse_params) 2025-01-24T01:51:51.7876575Z {'foo': 42, 'bar': 24} 2025-01-24T01:51:51.7876945Z >>> print(model.submodule2.linear42.sparse_params) 2025-01-24T01:51:51.7877331Z {'baz': 0.1} 2025-01-24T01:51:51.7877511Z 2025-01-24T01:51:51.7877636Z >>> # Keep sparse params for all layers 2025-01-24T01:51:51.7878060Z >>> sparsifier.squash_mask(params_to_keep=('foo', 'bar')) 2025-01-24T01:51:51.7878520Z >>> print(model.submodule1.linear1.sparse_params) 2025-01-24T01:51:51.7878885Z {'foo': 42, 'bar': 24} 2025-01-24T01:51:51.7879447Z >>> print(model.submodule2.linear42.sparse_params) 2025-01-24T01:51:51.7879845Z {'foo': 42, 'bar': 24} 2025-01-24T01:51:51.7880040Z 2025-01-24T01:51:51.7880254Z >>> # Keep some sparse params for all layers, and specific ones for 2025-01-24T01:51:51.7880753Z >>> # some other layers 2025-01-24T01:51:51.7881066Z >>> sparsifier.squash_mask( 2025-01-24T01:51:51.7881418Z ... params_to_keep=('foo', 'bar'), 2025-01-24T01:51:51.7881780Z ... params_to_keep_per_layer={ 2025-01-24T01:51:51.7882151Z ... 'submodule2.linear42': ('baz',) 2025-01-24T01:51:51.7882496Z ... }) 2025-01-24T01:51:51.7882809Z >>> print(model.submodule1.linear1.sparse_params) 2025-01-24T01:51:51.7883195Z {'foo': 42, 'bar': 24} 2025-01-24T01:51:51.7883565Z >>> print(model.submodule2.linear42.sparse_params) 2025-01-24T01:51:51.7883971Z {'foo': 42, 'bar': 24, 'baz': 0.1} 2025-01-24T01:51:51.7884312Z 2025-01-24T01:51:51.7884686Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:51:51.7885080Z 2025-01-24T01:51:51.8786835Z msg = Cannot scrape callname=DTypeConfig in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/backend_config/backend_config.py line=181. 2025-01-24T01:51:51.8787947Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:51:51.8788334Z 2025-01-24T01:51:51.8788615Z Config object that specifies the supported data types passed as arguments to 2025-01-24T01:51:51.8789263Z quantize ops in the reference model spec, for input and output activations, 2025-01-24T01:51:51.8789734Z weights, and biases. 2025-01-24T01:51:51.8789895Z 2025-01-24T01:51:51.8790072Z For example, consider the following reference model: 2025-01-24T01:51:51.8790344Z 2025-01-24T01:51:51.8790510Z quant1 - [dequant1 - fp32_linear - quant2] - dequant2 2025-01-24T01:51:51.8790789Z 2025-01-24T01:51:51.8791006Z The pattern in the square brackets refers to the reference pattern of 2025-01-24T01:51:51.8791589Z statically quantized linear. Setting the input dtype as `torch.quint8` 2025-01-24T01:51:51.8792183Z in the DTypeConfig means we pass in `torch.quint8` as the dtype argument 2025-01-24T01:51:51.8792775Z to the first quantize op (quant1). Similarly, setting the output dtype as 2025-01-24T01:51:51.8793350Z `torch.quint8` means we pass in `torch.quint8` as the dtype argument to 2025-01-24T01:51:51.8793796Z the second quantize op (quant2). 2025-01-24T01:51:51.8794008Z 2025-01-24T01:51:51.8794231Z Note that the dtype here does not refer to the interface dtypes of the 2025-01-24T01:51:51.8794783Z op. For example, the "input dtype" here is not the dtype of the input 2025-01-24T01:51:51.8795329Z tensor passed to the quantized linear op. Though it can still be the 2025-01-24T01:51:51.8795877Z same as the interface dtype, this is not always the case, e.g. the 2025-01-24T01:51:51.8796419Z interface dtype is fp32 in dynamic quantization but the "input dtype" 2025-01-24T01:51:51.8796966Z specified in the DTypeConfig would still be quint8. The semantics of 2025-01-24T01:51:51.8797520Z dtypes here are the same as the semantics of the dtypes specified in 2025-01-24T01:51:51.8798102Z the observers. 2025-01-24T01:51:51.8798376Z 2025-01-24T01:51:51.8798604Z These dtypes are matched against the ones specified in the user's 2025-01-24T01:51:51.8799153Z QConfig. If there is a match, and the QConfig satisfies the constraints 2025-01-24T01:51:51.8799726Z specified in the DTypeConfig (if any), then we will quantize the given 2025-01-24T01:51:51.8800289Z pattern using this DTypeConfig. Otherwise, the QConfig is ignored and 2025-01-24T01:51:51.8800759Z the pattern will not be quantized. 2025-01-24T01:51:51.8800984Z 2025-01-24T01:51:51.8801099Z Example usage:: 2025-01-24T01:51:51.8801241Z 2025-01-24T01:51:51.8801370Z >>> # xdoctest: +SKIP(failing) 2025-01-24T01:51:51.8801691Z >>> dtype_config1 = DTypeConfig( 2025-01-24T01:51:51.8802012Z ... input_dtype=torch.quint8, 2025-01-24T01:51:51.8802345Z ... output_dtype=torch.quint8, 2025-01-24T01:51:51.8802677Z ... weight_dtype=torch.qint8, 2025-01-24T01:51:51.8803066Z ... bias_dtype=torch.float) 2025-01-24T01:51:51.8803271Z 2025-01-24T01:51:51.8803406Z >>> dtype_config2 = DTypeConfig( 2025-01-24T01:51:51.8803740Z ... input_dtype=DTypeWithConstraints( 2025-01-24T01:51:51.8804098Z ... dtype=torch.quint8, 2025-01-24T01:51:51.8804423Z ... quant_min_lower_bound=0, 2025-01-24T01:51:51.8804763Z ... quant_max_upper_bound=255, 2025-01-24T01:51:51.8805082Z ... ), 2025-01-24T01:51:51.8805342Z ... output_dtype=DTypeWithConstraints( 2025-01-24T01:51:51.8805697Z ... dtype=torch.quint8, 2025-01-24T01:51:51.8806012Z ... quant_min_lower_bound=0, 2025-01-24T01:51:51.8806348Z ... quant_max_upper_bound=255, 2025-01-24T01:51:51.8806664Z ... ), 2025-01-24T01:51:51.8806924Z ... weight_dtype=DTypeWithConstraints( 2025-01-24T01:51:51.8807278Z ... dtype=torch.qint8, 2025-01-24T01:51:51.8807600Z ... quant_min_lower_bound=-128, 2025-01-24T01:51:51.8807945Z ... quant_max_upper_bound=127, 2025-01-24T01:51:51.8808262Z ... ), 2025-01-24T01:51:51.8808498Z ... bias_dtype=torch.float) 2025-01-24T01:51:51.8808713Z 2025-01-24T01:51:51.8808827Z >>> dtype_config1.input_dtype 2025-01-24T01:51:51.8809128Z torch.quint8 2025-01-24T01:51:51.8809270Z 2025-01-24T01:51:51.8809390Z >>> dtype_config2.input_dtype 2025-01-24T01:51:51.8809694Z torch.quint8 2025-01-24T01:51:51.8809836Z 2025-01-24T01:51:51.8809987Z >>> dtype_config2.input_dtype_with_constraints 2025-01-24T01:51:51.8810771Z DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=0, quant_max_upper_bound=255, scale_min_lower_bound=None, scale_max_upper_bound=None) 2025-01-24T01:51:51.8811435Z 2025-01-24T01:51:51.8811692Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:51:51.8812075Z 2025-01-24T01:51:52.0018014Z msg = Cannot scrape callname=ModelReportVisualizer.generate_filtered_tables in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fx/_model_report/model_report_visualizer.py line=301. 2025-01-24T01:51:52.0019614Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:51:52.0020018Z 2025-01-24T01:51:52.0020290Z Takes in optional filter values and generates two tables with desired information. 2025-01-24T01:51:52.0020683Z 2025-01-24T01:51:52.0020910Z The generated tables are presented in both a list-of-lists format 2025-01-24T01:51:52.0021233Z 2025-01-24T01:51:52.0021458Z The reason for the two tables are that they handle different things: 2025-01-24T01:51:52.0021945Z 1.) the first table handles all tensor level information 2025-01-24T01:51:52.0022450Z 2.) the second table handles and displays all channel based information 2025-01-24T01:51:52.0022860Z 2025-01-24T01:51:52.0023235Z The reasoning for this is that having all the info in one table can make it ambiguous which collected 2025-01-24T01:51:52.0024177Z statistics are global, and which are actually per-channel, so it's better to split it up into two 2025-01-24T01:51:52.0025288Z tables. This also makes the information much easier to digest given the plethora of statistics collected 2025-01-24T01:51:52.0025769Z 2025-01-24T01:51:52.0025890Z Tensor table columns: 2025-01-24T01:51:52.0026439Z idx layer_fqn feature_1 feature_2 feature_3 .... feature_n 2025-01-24T01:51:52.0027036Z ---- --------- --------- --------- --------- --------- 2025-01-24T01:51:52.0027380Z 2025-01-24T01:51:52.0027498Z Per-Channel table columns: 2025-01-24T01:51:52.0027915Z idx layer_fqn channel feature_1 feature_2 feature_3 .... feature_n 2025-01-24T01:51:52.0028435Z ---- --------- ------- --------- --------- --------- --------- 2025-01-24T01:51:52.0028721Z 2025-01-24T01:51:52.0028823Z Args: 2025-01-24T01:51:52.0029222Z feature_filter (str, optional): Filters the features presented to only those that 2025-01-24T01:51:52.0029721Z contain this filter substring 2025-01-24T01:51:52.0030596Z Default = "", results in all the features being printed 2025-01-24T01:51:52.0031157Z module_fqn_filter (str, optional): Only includes modules that contains this string 2025-01-24T01:51:52.0031805Z Default = "", results in all the modules in the reports to be visible in the table 2025-01-24T01:51:52.0032173Z 2025-01-24T01:51:52.0032306Z Returns a dictionary with two keys: 2025-01-24T01:51:52.0032694Z (Dict[str, Tuple[List, List]]) A dict containing two keys: 2025-01-24T01:51:52.0033122Z "tensor_level_info", "channel_level_info" 2025-01-24T01:51:52.0033486Z Each key maps to a tuple with: 2025-01-24T01:51:52.0033842Z A list of the headers of each table 2025-01-24T01:51:52.0034271Z A list of lists containing the table information row by row 2025-01-24T01:51:52.0034800Z The 0th index row will contain the headers of the columns 2025-01-24T01:51:52.0035233Z The rest of the rows will contain data 2025-01-24T01:51:52.0035476Z 2025-01-24T01:51:52.0035573Z Example Use: 2025-01-24T01:51:52.0035852Z >>> # xdoctest: +SKIP("undefined variables") 2025-01-24T01:51:52.0036257Z >>> mod_report_visualizer.generate_filtered_tables( 2025-01-24T01:51:52.0036656Z ... feature_filter = "per_channel_min", 2025-01-24T01:51:52.0037015Z ... module_fqn_filter = "block1" 2025-01-24T01:51:52.0037517Z ... ) # generates table with per_channel_min info for all modules in block 1 of the model 2025-01-24T01:51:52.0037911Z 2025-01-24T01:51:52.0038166Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:51:52.0038548Z 2025-01-24T01:51:52.0039467Z msg = Cannot scrape callname=ModelReportVisualizer.generate_table_visualization in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fx/_model_report/model_report_visualizer.py line=400. 2025-01-24T01:51:52.0040751Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:51:52.0041147Z 2025-01-24T01:51:52.0041426Z Takes in optional filter values and prints out formatted tables of the information. 2025-01-24T01:51:52.0041834Z 2025-01-24T01:51:52.0042185Z The reason for the two tables printed out instead of one large one are that they handle different things: 2025-01-24T01:51:52.0042822Z 1.) the first table handles all tensor level information 2025-01-24T01:51:52.0043320Z 2.) the second table handles and displays all channel based information 2025-01-24T01:51:52.0043655Z 2025-01-24T01:51:52.0043975Z The reasoning for this is that having all the info in one table can make it ambiguous which collected 2025-01-24T01:51:52.0044750Z statistics are global, and which are actually per-channel, so it's better to split it up into two 2025-01-24T01:51:52.0045560Z tables. This also makes the information much easier to digest given the plethora of statistics collected 2025-01-24T01:51:52.0046050Z 2025-01-24T01:51:52.0046215Z Tensor table columns: 2025-01-24T01:51:52.0046663Z idx layer_fqn feature_1 feature_2 feature_3 .... feature_n 2025-01-24T01:51:52.0047172Z ---- --------- --------- --------- --------- --------- 2025-01-24T01:51:52.0047438Z 2025-01-24T01:51:52.0047566Z Per-Channel table columns: 2025-01-24T01:51:52.0047754Z 2025-01-24T01:51:52.0047977Z idx layer_fqn channel feature_1 feature_2 feature_3 .... feature_n 2025-01-24T01:51:52.0048486Z ---- --------- ------- --------- --------- --------- --------- 2025-01-24T01:51:52.0048782Z 2025-01-24T01:51:52.0048870Z Args: 2025-01-24T01:51:52.0049267Z feature_filter (str, optional): Filters the features presented to only those that 2025-01-24T01:51:52.0049784Z contain this filter substring 2025-01-24T01:51:52.0050159Z Default = "", results in all the features being printed 2025-01-24T01:51:52.0050714Z module_fqn_filter (str, optional): Only includes modules that contains this string 2025-01-24T01:51:52.0051361Z Default = "", results in all the modules in the reports to be visible in the table 2025-01-24T01:51:52.0051784Z 2025-01-24T01:51:52.0051881Z Example Use: 2025-01-24T01:51:52.0052151Z >>> # xdoctest: +SKIP("undefined variables") 2025-01-24T01:51:52.0052568Z >>> mod_report_visualizer.generate_table_visualization( 2025-01-24T01:51:52.0052971Z ... feature_filter = "per_channel_min", 2025-01-24T01:51:52.0053329Z ... module_fqn_filter = "block1" 2025-01-24T01:51:52.0053648Z ... ) 2025-01-24T01:51:52.0053976Z >>> # prints out neatly formatted table with per_channel_min info 2025-01-24T01:51:52.0054419Z >>> # for all modules in block 1 of the model 2025-01-24T01:51:52.0054655Z 2025-01-24T01:51:52.0054910Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:51:52.0055298Z 2025-01-24T01:51:52.0056204Z msg = Cannot scrape callname=ModelReportVisualizer.generate_plot_visualization in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fx/_model_report/model_report_visualizer.py line=566. 2025-01-24T01:51:52.0057497Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:51:52.0057890Z 2025-01-24T01:51:52.0058131Z Takes in a feature and optional module_filter and plots of the desired data. 2025-01-24T01:51:52.0058501Z 2025-01-24T01:51:52.0058774Z For per channel features, it averages the value across the channels and plots a point 2025-01-24T01:51:52.0059441Z per module. The reason for this is that for models with hundreds of channels, it can 2025-01-24T01:51:52.0060105Z be hard to differentiate one channel line from another, and so the point of generating 2025-01-24T01:51:52.0060777Z a single average point per module is to give a sense of general trends that encourage 2025-01-24T01:51:52.0061261Z further deep dives. 2025-01-24T01:51:52.0061426Z 2025-01-24T01:51:52.0061513Z Note: 2025-01-24T01:51:52.0061904Z Only features in the report that have tensor value data are plottable by this class 2025-01-24T01:51:52.0062477Z When the tensor information is plotted, it will plot: 2025-01-24T01:51:52.0062903Z idx as the x val, feature value as the y_val 2025-01-24T01:51:52.0063316Z When the channel information is plotted, it will plot: 2025-01-24T01:51:52.0063866Z the first idx of each module as the x val, feature value as the y_val [for each channel] 2025-01-24T01:51:52.0064488Z The reason for this is that we want to be able to compare values across the 2025-01-24T01:51:52.0065081Z channels for same layer, and it will be hard if values are staggered by idx 2025-01-24T01:51:52.0065619Z This means each module is represented by only 1 x value 2025-01-24T01:51:52.0066000Z Args: 2025-01-24T01:51:52.0066456Z feature_filter (str): Filters the features presented to only those that 2025-01-24T01:51:52.0066934Z contain this filter substring 2025-01-24T01:51:52.0067422Z module_fqn_filter (str, optional): Only includes modules that contains this string 2025-01-24T01:51:52.0068189Z Default = "", results in all the modules in the reports to be visible in the table 2025-01-24T01:51:52.0068554Z 2025-01-24T01:51:52.0068662Z Example Use: 2025-01-24T01:51:52.0068918Z >>> # xdoctest: +SKIP("undefined variables") 2025-01-24T01:51:52.0069333Z >>> mod_report_visualizer.generate_plot_visualization( 2025-01-24T01:51:52.0069744Z ... feature_filter = "per_channel_min", 2025-01-24T01:51:52.0070096Z ... module_fqn_filter = "block1" 2025-01-24T01:51:52.0070411Z ... ) 2025-01-24T01:51:52.0070710Z >>> # outputs line plot of per_channel_min information for all 2025-01-24T01:51:52.0071203Z >>> # modules in block1 of model each channel gets it's own line, 2025-01-24T01:51:52.0071690Z >>> # and it's plotted across the in-order modules on the x-axis 2025-01-24T01:51:52.0071995Z 2025-01-24T01:51:52.0072252Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:51:52.0072621Z 2025-01-24T01:51:52.0073603Z msg = Cannot scrape callname=ModelReportVisualizer.generate_histogram_visualization in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fx/_model_report/model_report_visualizer.py line=646. 2025-01-24T01:51:52.0074912Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:51:52.0075292Z 2025-01-24T01:51:52.0075589Z Takes in a feature and optional module_filter and plots the histogram of desired data. 2025-01-24T01:51:52.0075992Z 2025-01-24T01:51:52.0076095Z Note: 2025-01-24T01:51:52.0076481Z Only features in the report that have tensor value data can be viewed as a histogram 2025-01-24T01:51:52.0077144Z If you want to plot a histogram from all the channel values of a specific feature for 2025-01-24T01:51:52.0077790Z a specific model, make sure to specify both the model and the feature properly 2025-01-24T01:51:52.0078416Z in the filters and you should be able to see a distribution of the channel data 2025-01-24T01:51:52.0078785Z 2025-01-24T01:51:52.0078886Z Args: 2025-01-24T01:51:52.0079487Z feature_filter (str, optional): Filters the features presented to only those that 2025-01-24T01:51:52.0080036Z contain this filter substring 2025-01-24T01:51:52.0080432Z Default = "", results in all the features being printed 2025-01-24T01:51:52.0080973Z module_fqn_filter (str, optional): Only includes modules that contains this string 2025-01-24T01:51:52.0081618Z Default = "", results in all the modules in the reports to be visible in the table 2025-01-24T01:51:52.0082224Z num_bins (int, optional): The number of bins to create the histogram with 2025-01-24T01:51:52.0082768Z Default = 10, the values will be split into 10 equal sized bins 2025-01-24T01:51:52.0083075Z 2025-01-24T01:51:52.0083186Z Example Use: 2025-01-24T01:51:52.0083448Z >>> # xdoctest: +SKIP 2025-01-24T01:51:52.0083925Z >>> mod_report_visualizer.generategenerate_histogram_visualization_plot_visualization( 2025-01-24T01:51:52.0084497Z ... feature_filter = "per_channel_min", 2025-01-24T01:51:52.0084862Z ... module_fqn_filter = "block1" 2025-01-24T01:51:52.0085181Z ... ) 2025-01-24T01:51:52.0085596Z # outputs histogram of per_channel_min information for all modules in block1 of model 2025-01-24T01:51:52.0086253Z information is gathered across all channels for all modules in block 1 for the 2025-01-24T01:51:52.0086872Z per_channel_min and is displayed in a histogram of equally sized bins 2025-01-24T01:51:52.0087230Z 2025-01-24T01:51:52.0087487Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:51:52.0087876Z 2025-01-24T01:51:52.3039287Z msg = Cannot scrape callname=DeviceMesh.__getitem__ in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/device_mesh.py line=662. 2025-01-24T01:51:52.3040299Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-01-24T01:51:52.3040703Z 2025-01-24T01:51:52.3041345Z Slice the current DeviceMesh based on the mesh_dim_names given to create a submesh. 2025-01-24T01:51:52.3042038Z The submesh created consists of the dimensions and the communicators indicated by 2025-01-24T01:51:52.3042535Z ``mesh_dim_names`` 2025-01-24T01:51:52.3042708Z 2025-01-24T01:51:52.3042807Z Args: 2025-01-24T01:51:52.3043195Z mesh_dim_names (Union[str, Tuple[str]]): the name or the tuple of names of the 2025-01-24T01:51:52.3043766Z mesh dimension of the DeviceMesh to create the submesh for. 2025-01-24T01:51:52.3044181Z Returns: 2025-01-24T01:51:52.3044423Z A :class:`DeviceMesh` object 2025-01-24T01:51:52.3044642Z 2025-01-24T01:51:52.3044940Z The following program runs on each process/rank in an SPMD manner in a world size of 8. 2025-01-24T01:51:52.3045471Z In the first example: 2025-01-24T01:51:52.3045908Z Calling mesh_2d["tp"] on rank 0, 1, 2, 3 returns a 1D submesh of DeviceMesh:([0, 1, 2, 3]). 2025-01-24T01:51:52.3046551Z Calling mesh_2d["tp"] on rank 4, 5, 6, 7 returns a 1D submesh of DeviceMesh:([4, 5, 6, 7]). 2025-01-24T01:51:52.3047274Z Calling mesh_2d["dp"] on rank 0, 4 returns a 1D submesh of DeviceMesh:([0, 4]). 2025-01-24T01:51:52.3047890Z Calling mesh_2d["dp"] on rank 1, 5 returns a 1D submesh of DeviceMesh:([1, 5]). 2025-01-24T01:51:52.3048472Z Calling mesh_2d["dp"] on rank 2, 6 returns a 1D submesh of DeviceMesh:([2, 6]). 2025-01-24T01:51:52.3049068Z Calling mesh_2d["dp"] on rank 3, 7 returns a 1D submesh of DeviceMesh:([3, 7]). 2025-01-24T01:51:52.3049434Z 2025-01-24T01:51:52.3049547Z In the second example: 2025-01-24T01:51:52.3050015Z Calling mesh_3d["dp", "cp"] on rank 0, 1, 4, 5 returns a 2D submesh of DeviceMesh:([[0, 1], [4, 5]]). 2025-01-24T01:51:52.3050694Z Calling mesh_3d["dp", "cp"] on rank 2, 3, 6, 7 returns a 2D submesh of DeviceMesh:([[2, 3], [6, 7]]). 2025-01-24T01:51:52.3051364Z Calling mesh_3d["cp", "dp"] on rank 0, 1, 4, 5 returns a 2D submesh of DeviceMesh:([[0, 4], [1, 5]]). 2025-01-24T01:51:52.3052022Z Calling mesh_3d["cp", "dp"] on rank 2, 3, 6, 7 returns a 2D submesh of DeviceMesh:([[2, 6], [3, 7]]). 2025-01-24T01:51:52.3052423Z 2025-01-24T01:51:52.3052541Z Example:: 2025-01-24T01:51:52.3052803Z >>> # xdoctest: +SKIP("no rank") 2025-01-24T01:51:52.3053213Z >>> from torch.distributed.device_mesh import DeviceMesh 2025-01-24T01:51:52.3053609Z >>> 2025-01-24T01:51:52.3053942Z >>> # Initialize a 2D device mesh as (2, 4) to represent the topology 2025-01-24T01:51:52.3054435Z >>> # of cross-host(dim 0), and within-host (dim 1). 2025-01-24T01:51:52.3054974Z >>> mesh_2d = init_device_mesh(device_type="cuda", (2,4), mesh_dim_names=("dp", "tp")) 2025-01-24T01:51:52.3055480Z >>> tp_mesh = mesh_2d["tp"] 2025-01-24T01:51:52.3055798Z >>> dp_mesh = mesh_2d["dp"] 2025-01-24T01:51:52.3056093Z >>> 2025-01-24T01:51:52.3056329Z >>> # Initialize a 3D mesh. 2025-01-24T01:51:52.3056826Z >>> mesh_3d = init_device_mesh(device_type="cuda", (2,2,2), mesh_dim_names=("dp", "pp", "cp")) 2025-01-24T01:51:52.3057558Z >>> # The order of the mesh_dim_names provided deteremines the order of dimensions in the submesh. 2025-01-24T01:51:52.3058120Z >>> dp_cp_mesh = mesh_3d["dp", "cp"] 2025-01-24T01:51:52.3058474Z >>> cp_dp_mesh = mesh_3d["cp", "dp"] 2025-01-24T01:51:52.3058697Z 2025-01-24T01:51:52.3059390Z 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)) 2025-01-24T01:51:52.3060175Z 2025-01-24T01:51:52.3060427Z mesh_2d = init_device_mesh(device_type="cuda", (2,4), mesh_dim_names=("dp", "tp")) 2025-01-24T01:51:52.3060934Z ^ 2025-01-24T01:51:52.3411977Z msg = Cannot scrape callname=gather_object in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py line=3062. 2025-01-24T01:51:52.3413015Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:51:52.3413786Z 2025-01-24T01:51:52.3414042Z Gathers picklable objects from the whole group in a single process. 2025-01-24T01:51:52.3414396Z 2025-01-24T01:51:52.3414658Z Similar to :func:`gather`, but Python objects can be passed in. Note that the 2025-01-24T01:51:52.3415194Z object must be picklable in order to be gathered. 2025-01-24T01:51:52.3415461Z 2025-01-24T01:51:52.3415559Z Args: 2025-01-24T01:51:52.3415836Z obj (Any): Input object. Must be picklable. 2025-01-24T01:51:52.3416319Z object_gather_list (list[Any]): Output list. On the ``dst`` rank, it 2025-01-24T01:51:52.3416858Z should be correctly sized as the size of the group for this 2025-01-24T01:51:52.3417396Z collective and will contain the output. Must be ``None`` on non-dst 2025-01-24T01:51:52.3417845Z ranks. (default is ``None``) 2025-01-24T01:51:52.3418404Z dst (int, optional): Destination rank on global process group (regardless of ``group`` argument). 2025-01-24T01:51:52.3419131Z (If both ``dst`` and ``group_dst`` are None, default is global rank 0) 2025-01-24T01:51:52.3419703Z group: (ProcessGroup, optional): The process group to work on. If None, 2025-01-24T01:51:52.3420272Z the default process group will be used. Default is ``None``. 2025-01-24T01:51:52.3420946Z group_dst (int, optional): Destination rank on ``group``. Invalid to specify both ``dst`` and ``group_dst`` 2025-01-24T01:51:52.3421407Z 2025-01-24T01:51:52.3421504Z Returns: 2025-01-24T01:51:52.3421836Z None. On the ``dst`` rank, ``object_gather_list`` will contain the 2025-01-24T01:51:52.3422261Z output of the collective. 2025-01-24T01:51:52.3422457Z 2025-01-24T01:51:52.3422708Z .. note:: Note that this API differs slightly from the gather collective 2025-01-24T01:51:52.3423278Z since it does not provide an async_op handle and thus will be a blocking 2025-01-24T01:51:52.3423731Z call. 2025-01-24T01:51:52.3423862Z 2025-01-24T01:51:52.3424113Z .. note:: For NCCL-based processed groups, internal tensor representations 2025-01-24T01:51:52.3424701Z of objects must be moved to the GPU device before communication takes 2025-01-24T01:51:52.3425228Z place. In this case, the device used is given by 2025-01-24T01:51:52.3425734Z ``torch.cuda.current_device()`` and it is the user's responsiblity to 2025-01-24T01:51:52.3426397Z ensure that this is set so that each rank has an individual GPU, via 2025-01-24T01:51:52.3426861Z ``torch.cuda.set_device()``. 2025-01-24T01:51:52.3427065Z 2025-01-24T01:51:52.3427164Z .. warning:: 2025-01-24T01:51:52.3427524Z :func:`gather_object` uses ``pickle`` module implicitly, which is 2025-01-24T01:51:52.3428087Z known to be insecure. It is possible to construct malicious pickle data 2025-01-24T01:51:52.3428674Z which will execute arbitrary code during unpickling. Only call this 2025-01-24T01:51:52.3429145Z function with data you trust. 2025-01-24T01:51:52.3429355Z 2025-01-24T01:51:52.3429477Z .. warning:: 2025-01-24T01:51:52.3429848Z Calling :func:`gather_object` with GPU tensors is not well supported 2025-01-24T01:51:52.3430438Z and inefficient as it incurs GPU -> CPU transfer since tensors would be 2025-01-24T01:51:52.3430986Z pickled. Please consider using :func:`gather` instead. 2025-01-24T01:51:52.3431276Z 2025-01-24T01:51:52.3431393Z Example:: 2025-01-24T01:51:52.3431683Z >>> # xdoctest: +SKIP("need process group init") 2025-01-24T01:51:52.3432130Z >>> # Note: Process group initialization omitted on each rank. 2025-01-24T01:51:52.3432574Z >>> import torch.distributed as dist 2025-01-24T01:51:52.3432935Z >>> # Assumes world_size of 3. 2025-01-24T01:51:52.3433337Z >>> gather_objects = ["foo", 12, {1: 2}] # any picklable object 2025-01-24T01:51:52.3433777Z >>> output = [None for _ in gather_objects] 2025-01-24T01:51:52.3434122Z >>> dist.gather_object( 2025-01-24T01:51:52.3434450Z ... gather_objects[dist.get_rank()], 2025-01-24T01:51:52.3434845Z ... output if dist.get_rank() == 0 else None, 2025-01-24T01:51:52.3435355Z ... dst=0 2025-01-24T01:51:52.3435611Z ... ) 2025-01-24T01:51:52.3435831Z >>> # On rank 0 2025-01-24T01:51:52.3436091Z >>> output 2025-01-24T01:51:52.3436348Z ['foo', 12, {1: 2}] 2025-01-24T01:51:52.3436510Z 2025-01-24T01:51:52.3436788Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:51:52.3437169Z 2025-01-24T01:51:52.3600988Z msg = Cannot scrape callname=__doc__ in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/launch.py line=2. 2025-01-24T01:51:52.3602109Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:51:52.3602505Z 2025-01-24T01:51:52.3602647Z Module ``torch.distributed.launch``. 2025-01-24T01:51:52.3602877Z 2025-01-24T01:51:52.3603140Z ``torch.distributed.launch`` is a module that spawns up multiple distributed 2025-01-24T01:51:52.3603688Z training processes on each of the training nodes. 2025-01-24T01:51:52.3604197Z 2025-01-24T01:51:52.3604320Z .. warning:: 2025-01-24T01:51:52.3604480Z 2025-01-24T01:51:52.3604747Z This module is going to be deprecated in favor of :ref:`torchrun `. 2025-01-24T01:51:52.3605130Z 2025-01-24T01:51:52.3605396Z The utility can be used for single-node distributed training, in which one or 2025-01-24T01:51:52.3606016Z more processes per node will be spawned. The utility can be used for either 2025-01-24T01:51:52.3606619Z CPU training or GPU training. If the utility is used for GPU training, 2025-01-24T01:51:52.3607214Z each distributed process will be operating on a single GPU. This can achieve 2025-01-24T01:51:52.3607838Z well-improved single-node training performance. It can also be used in 2025-01-24T01:51:52.3608481Z multi-node distributed training, by spawning up multiple processes on each node 2025-01-24T01:51:52.3609126Z for well-improved multi-node distributed training performance as well. 2025-01-24T01:51:52.3609737Z This will especially be beneficial for systems with multiple Infiniband 2025-01-24T01:51:52.3610360Z interfaces that have direct-GPU support, since all of them can be utilized for 2025-01-24T01:51:52.3610875Z aggregated communication bandwidth. 2025-01-24T01:51:52.3611112Z 2025-01-24T01:51:52.3611357Z In both cases of single-node distributed training or multi-node distributed 2025-01-24T01:51:52.3611973Z training, this utility will launch the given number of processes per node 2025-01-24T01:51:52.3612584Z (``--nproc-per-node``). If used for GPU training, this number needs to be less 2025-01-24T01:51:52.3613176Z or equal to the number of GPUs on the current system (``nproc_per_node``), 2025-01-24T01:51:52.3613734Z and each process will be operating on a single GPU from *GPU 0 to 2025-01-24T01:51:52.3614194Z GPU (nproc_per_node - 1)*. 2025-01-24T01:51:52.3614401Z 2025-01-24T01:51:52.3614514Z **How to use this module:** 2025-01-24T01:51:52.3614719Z 2025-01-24T01:51:52.3614883Z 1. Single-Node multi-process distributed training 2025-01-24T01:51:52.3615158Z 2025-01-24T01:51:52.3615279Z :: 2025-01-24T01:51:52.3615398Z 2025-01-24T01:51:52.3615663Z python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE 2025-01-24T01:51:52.3616223Z YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3 and all other 2025-01-24T01:51:52.3616681Z arguments of your training script) 2025-01-24T01:51:52.3616938Z 2025-01-24T01:51:52.3617159Z 2. Multi-Node multi-process distributed training: (e.g. two nodes) 2025-01-24T01:51:52.3617505Z 2025-01-24T01:51:52.3617509Z 2025-01-24T01:51:52.3617665Z Node 1: *(IP: 192.168.1.1, and has a free port: 1234)* 2025-01-24T01:51:52.3617930Z 2025-01-24T01:51:52.3618042Z :: 2025-01-24T01:51:52.3618161Z 2025-01-24T01:51:52.3618424Z python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE 2025-01-24T01:51:52.3618966Z --nnodes=2 --node-rank=0 --master-addr="192.168.1.1" 2025-01-24T01:51:52.3619466Z --master-port=1234 YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3 2025-01-24T01:51:52.3620168Z and all other arguments of your training script) 2025-01-24T01:51:52.3620459Z 2025-01-24T01:51:52.3620558Z Node 2: 2025-01-24T01:51:52.3620689Z 2025-01-24T01:51:52.3620807Z :: 2025-01-24T01:51:52.3620927Z 2025-01-24T01:51:52.3621193Z python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE 2025-01-24T01:51:52.3621720Z --nnodes=2 --node-rank=1 --master-addr="192.168.1.1" 2025-01-24T01:51:52.3622228Z --master-port=1234 YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3 2025-01-24T01:51:52.3622737Z and all other arguments of your training script) 2025-01-24T01:51:52.3623066Z 2025-01-24T01:51:52.3623236Z 3. To look up what optional arguments this module offers: 2025-01-24T01:51:52.3623531Z 2025-01-24T01:51:52.3623627Z :: 2025-01-24T01:51:52.3623763Z 2025-01-24T01:51:52.3623910Z python -m torch.distributed.launch --help 2025-01-24T01:51:52.3624203Z 2025-01-24T01:51:52.3624209Z 2025-01-24T01:51:52.3624333Z **Important Notices:** 2025-01-24T01:51:52.3624511Z 2025-01-24T01:51:52.3624724Z 1. This utility and multi-process distributed (single-node or 2025-01-24T01:51:52.3625305Z multi-node) GPU training currently only achieves the best performance using 2025-01-24T01:51:52.3625937Z the NCCL distributed backend. Thus NCCL backend is the recommended backend to 2025-01-24T01:51:52.3626575Z use for GPU training. 2025-01-24T01:51:52.3626759Z 2025-01-24T01:51:52.3626985Z 2. In your training program, you must parse the command-line argument: 2025-01-24T01:51:52.3627577Z ``--local-rank=LOCAL_PROCESS_RANK``, which will be provided by this module. 2025-01-24T01:51:52.3628189Z If your training program uses GPUs, you should ensure that your code only 2025-01-24T01:51:52.3628744Z runs on the GPU device of LOCAL_PROCESS_RANK. This can be done by: 2025-01-24T01:51:52.3629075Z 2025-01-24T01:51:52.3629194Z Parsing the local_rank argument 2025-01-24T01:51:52.3629415Z 2025-01-24T01:51:52.3629512Z :: 2025-01-24T01:51:52.3629644Z 2025-01-24T01:51:52.3629754Z >>> # xdoctest: +SKIP 2025-01-24T01:51:52.3630049Z >>> import argparse 2025-01-24T01:51:52.3630358Z >>> parser = argparse.ArgumentParser() 2025-01-24T01:51:52.3630799Z >>> parser.add_argument("--local-rank", "--local_rank", type=int) 2025-01-24T01:51:52.3631242Z >>> args = parser.parse_args() 2025-01-24T01:51:52.3631472Z 2025-01-24T01:51:52.3631605Z Set your device to local rank using either 2025-01-24T01:51:52.3631844Z 2025-01-24T01:51:52.3631974Z :: 2025-01-24T01:51:52.3632095Z 2025-01-24T01:51:52.3632323Z >>> torch.cuda.set_device(args.local_rank) # before your code runs 2025-01-24T01:51:52.3632649Z 2025-01-24T01:51:52.3632748Z or 2025-01-24T01:51:52.3632886Z 2025-01-24T01:51:52.3632980Z :: 2025-01-24T01:51:52.3633114Z 2025-01-24T01:51:52.3633260Z >>> with torch.cuda.device(args.local_rank): 2025-01-24T01:51:52.3633633Z >>> # your code to run 2025-01-24T01:51:52.3633940Z >>> ... 2025-01-24T01:51:52.3634086Z 2025-01-24T01:51:52.3634207Z .. versionchanged:: 2.0.0 2025-01-24T01:51:52.3634406Z 2025-01-24T01:51:52.3634664Z The launcher will passes the ``--local-rank=`` argument to your script. 2025-01-24T01:51:52.3635301Z From PyTorch 2.0.0 onwards, the dashed ``--local-rank`` is preferred over the 2025-01-24T01:51:52.3635831Z previously used underscored ``--local_rank``. 2025-01-24T01:51:52.3636092Z 2025-01-24T01:51:52.3636355Z For backward compatibility, it may be necessary for users to handle both 2025-01-24T01:51:52.3637007Z cases in their argument parsing code. This means including both ``"--local-rank"`` 2025-01-24T01:51:52.3637627Z and ``"--local_rank"`` in the argument parser. If only ``"--local_rank"`` is 2025-01-24T01:51:52.3638250Z provided, the launcher will trigger an error: "error: unrecognized arguments: 2025-01-24T01:51:52.3638887Z --local-rank=". For training code that only supports PyTorch 2.0.0+, 2025-01-24T01:51:52.3639420Z including ``"--local-rank"`` should be sufficient. 2025-01-24T01:51:52.3639818Z 2025-01-24T01:51:52.3640082Z 3. In your training program, you are supposed to call the following function 2025-01-24T01:51:52.3640705Z at the beginning to start the distributed backend. It is strongly recommended 2025-01-24T01:51:52.3641315Z that ``init_method=env://``. Other init methods (e.g. ``tcp://``) may work, 2025-01-24T01:51:52.3641870Z but ``env://`` is the one that is officially supported by this module. 2025-01-24T01:51:52.3642198Z 2025-01-24T01:51:52.3642299Z :: 2025-01-24T01:51:52.3642434Z 2025-01-24T01:51:52.3642649Z >>> torch.distributed.init_process_group(backend='YOUR BACKEND', 2025-01-24T01:51:52.3643136Z >>> init_method='env://') 2025-01-24T01:51:52.3643382Z 2025-01-24T01:51:52.3643644Z 4. In your training program, you can either use regular distributed functions 2025-01-24T01:51:52.3644264Z or use :func:`torch.nn.parallel.DistributedDataParallel` module. If your 2025-01-24T01:51:52.3644880Z training program uses GPUs for training and you would like to use 2025-01-24T01:51:52.3645425Z :func:`torch.nn.parallel.DistributedDataParallel` module, 2025-01-24T01:51:52.3645859Z here is how to configure it. 2025-01-24T01:51:52.3646053Z 2025-01-24T01:51:52.3646165Z :: 2025-01-24T01:51:52.3646286Z 2025-01-24T01:51:52.3646502Z >>> model = torch.nn.parallel.DistributedDataParallel(model, 2025-01-24T01:51:52.3646961Z >>> device_ids=[args.local_rank], 2025-01-24T01:51:52.3647390Z >>> output_device=args.local_rank) 2025-01-24T01:51:52.3647671Z 2025-01-24T01:51:52.3647925Z Please ensure that ``device_ids`` argument is set to be the only GPU device id 2025-01-24T01:51:52.3648557Z that your code will be operating on. This is generally the local rank of the 2025-01-24T01:51:52.3649166Z process. In other words, the ``device_ids`` needs to be ``[args.local_rank]``, 2025-01-24T01:51:52.3649777Z and ``output_device`` needs to be ``args.local_rank`` in order to use this 2025-01-24T01:51:52.3650225Z utility 2025-01-24T01:51:52.3650365Z 2025-01-24T01:51:52.3650620Z 5. Another way to pass ``local_rank`` to the subprocesses via environment variable 2025-01-24T01:51:52.3651229Z ``LOCAL_RANK``. This behavior is enabled when you launch the script with 2025-01-24T01:51:52.3651811Z ``--use-env=True``. You must adjust the subprocess example above to replace 2025-01-24T01:51:52.3652374Z ``args.local_rank`` with ``os.environ['LOCAL_RANK']``; the launcher 2025-01-24T01:51:52.3652887Z will not pass ``--local-rank`` when you specify this flag. 2025-01-24T01:51:52.3653182Z 2025-01-24T01:51:52.3653282Z .. warning:: 2025-01-24T01:51:52.3653438Z 2025-01-24T01:51:52.3653653Z ``local_rank`` is NOT globally unique: it is only unique per process 2025-01-24T01:51:52.3654190Z on a machine. Thus, don't use it to decide if you should, e.g., 2025-01-24T01:51:52.3654640Z write to a networked filesystem. See 2025-01-24T01:51:52.3655134Z https://github.com/pytorch/pytorch/issues/12042 for an example of 2025-01-24T01:51:52.3655666Z how things can go wrong if you don't do this correctly. 2025-01-24T01:51:52.3655952Z 2025-01-24T01:51:52.3655956Z 2025-01-24T01:51:52.3655960Z 2025-01-24T01:51:52.3655963Z 2025-01-24T01:51:52.3656243Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:51:52.3656626Z 2025-01-24T01:51:52.4268985Z msg = Cannot scrape callname=init_from_local_shards in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/_shard/sharded_tensor/__init__.py line=361. 2025-01-24T01:51:52.4270074Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:51:52.4270488Z 2025-01-24T01:51:52.4270742Z Creates an :class:`ShardedTensor` from local shards and the global metadata. 2025-01-24T01:51:52.4271273Z Needs to be called on all ranks in an SPMD fashion. 2025-01-24T01:51:52.4271576Z 2025-01-24T01:51:52.4271851Z Args: 2025-01-24T01:51:52.4272375Z local_shards (List[:class `torch.distributed._shard.sharded_tensor.Shard`]): A list 2025-01-24T01:51:52.4272969Z of shards that represent the local shards on this rank. 2025-01-24T01:51:52.4273497Z global_size (int...): a list, tuple, or `torch.Size` of integers defining the 2025-01-24T01:51:52.4273984Z shape of the overall sharded tensor. 2025-01-24T01:51:52.4274234Z 2025-01-24T01:51:52.4274334Z Keyword args: 2025-01-24T01:51:52.4274760Z process_group (ProcessGroup, optional): The process group to work on. If None, 2025-01-24T01:51:52.4275294Z the default process group will be used. 2025-01-24T01:51:52.4275732Z init_rrefs (bool, optional): Whether or not to initialize 2025-01-24T01:51:52.4276241Z :class:`torch.distributed.rpc.RRef`s pointing to remote shards. 2025-01-24T01:51:52.4276788Z Need to initialize the RPC Framework if specified as ``True``. 2025-01-24T01:51:52.4277270Z Default: ``False``. 2025-01-24T01:51:52.4277458Z 2025-01-24T01:51:52.4277570Z Returns: 2025-01-24T01:51:52.4277864Z A :class:`ShardedTensor` object handle on this rank 2025-01-24T01:51:52.4278134Z 2025-01-24T01:51:52.4278138Z 2025-01-24T01:51:52.4278248Z Examples: 2025-01-24T01:51:52.4278632Z Suppose we want construct a sharded tensor on two ranks, global size = (10, 5), 2025-01-24T01:51:52.4279505Z each shard have a (5, 5) local tensor, we can do it like below: 2025-01-24T01:51:52.4279898Z 2025-01-24T01:51:52.4279999Z on rank 0: 2025-01-24T01:51:52.4280278Z >>> # xdoctest: +SKIP("not distributed") 2025-01-24T01:51:52.4280656Z >>> local_shard_metadata = ShardMetadata( 2025-01-24T01:51:52.4281002Z >>> shard_offsets=[0, 0], 2025-01-24T01:51:52.4281320Z >>> shard_lengths=[5, 5], 2025-01-24T01:51:52.4281648Z >>> placement="rank:0/cuda:0" 2025-01-24T01:51:52.4281972Z >>> ) 2025-01-24T01:51:52.4282310Z >>> local_shards = [Shard(torch.randn(5, 5), local_shard_metadata)] 2025-01-24T01:51:52.4282844Z >>> sharded_tensor = init_from_local_shards(local_shards, [10, 5]) 2025-01-24T01:51:52.4283179Z 2025-01-24T01:51:52.4283301Z on rank 1: 2025-01-24T01:51:52.4283579Z >>> # xdoctest: +SKIP("not distributed") 2025-01-24T01:51:52.4283956Z >>> local_shard_metadata = ShardMetadata( 2025-01-24T01:51:52.4284311Z >>> shard_offsets=[5, 0], 2025-01-24T01:51:52.4284628Z >>> shard_lengths=[5, 5], 2025-01-24T01:51:52.4284942Z >>> placement="rank:1/cuda:1" 2025-01-24T01:51:52.4285261Z >>> ) 2025-01-24T01:51:52.4285597Z >>> local_shards = [Shard(torch.randn(5, 5), local_shard_metadata)] 2025-01-24T01:51:52.4286125Z >>> sharded_tensor = init_from_local_shards(local_shards, [10, 5]) 2025-01-24T01:51:52.4286437Z 2025-01-24T01:51:52.4286712Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:51:52.4287091Z 2025-01-24T01:51:52.4399671Z msg = Cannot scrape callname=ShardedTensor._init_from_local_tensor in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/_shard/sharded_tensor/api.py line=797. 2025-01-24T01:51:52.4400808Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:51:52.4401231Z 2025-01-24T01:51:52.4401502Z Initialize a ShardedTensor given only one local tensor, global sharded tensor 2025-01-24T01:51:52.4402024Z size and sharding spec on each rank. 2025-01-24T01:51:52.4402263Z 2025-01-24T01:51:52.4402360Z Args: 2025-01-24T01:51:52.4402732Z local_tensor (Tensor): Single tensor of local shard stored in each rank. 2025-01-24T01:51:52.4403366Z sharding_spec (:class:`torch.distributed._shard.sharding_spec.ShardingSpec`): 2025-01-24T01:51:52.4403944Z The specification describing how to shard the Tensor. 2025-01-24T01:51:52.4404413Z global_size (Sequence[int]): Size of the sharded tensor. 2025-01-24T01:51:52.4404983Z process_group (ProcessGroup, optional): The process group to aggregate on. 2025-01-24T01:51:52.4405481Z Default: None 2025-01-24T01:51:52.4406263Z init_rrefs (bool, optional): Whether or not to initialize 2025-01-24T01:51:52.4406949Z :class:`torch.distributed.rpc.RRef`s pointing to remote shards. 2025-01-24T01:51:52.4407665Z Need to initialize the RPC Framework if specified as ``True``. 2025-01-24T01:51:52.4408453Z Default: ``False``. 2025-01-24T01:51:52.4408664Z 2025-01-24T01:51:52.4408765Z Returns: 2025-01-24T01:51:52.4409163Z A :class:`ShardedTensor` sharded based on the given sharding_spec with local 2025-01-24T01:51:52.4409667Z tensor stored in the current rank. 2025-01-24T01:51:52.4409896Z 2025-01-24T01:51:52.4410007Z Examples: 2025-01-24T01:51:52.4410238Z >>> # xdoctest: +SKIP 2025-01-24T01:51:52.4410566Z >>> # All tensors below are of torch.int64 type. 2025-01-24T01:51:52.4410955Z >>> # We have 2 process groups, 2 ranks. 2025-01-24T01:51:52.4411382Z >>> tensor = torch.arange(2, dtype=torch.int64) + 1 + 2 * rank 2025-01-24T01:51:52.4411974Z >>> local_tensor = torch.unsqueeze(torch.cat([tensor, tensor + 2])) 2025-01-24T01:51:52.4412399Z >>> local_tensor 2025-01-24T01:51:52.4412677Z tensor([[1, 2, 3, 4]]) # Rank 0 2025-01-24T01:51:52.4412995Z tensor([[3, 4, 5, 6]]) # Rank 1 2025-01-24T01:51:52.4413309Z >>> sharding_dim = 0 2025-01-24T01:51:52.4413624Z >>> sharding_spec = ChunkShardingSpec( 2025-01-24T01:51:52.4413960Z dim=sharding_dim, 2025-01-24T01:51:52.4414261Z placements=[ 2025-01-24T01:51:52.4414546Z "rank:0/cuda:0", 2025-01-24T01:51:52.4414853Z "rank:1/cuda:1", 2025-01-24T01:51:52.4415146Z ], 2025-01-24T01:51:52.4415372Z ) 2025-01-24T01:51:52.4415781Z >>> st = ShardedTensor._init_from_local_tensor(local_tensor, sharding_spec, [2, 4]) 2025-01-24T01:51:52.4416275Z >>> st 2025-01-24T01:51:52.4416521Z ShardedTensor( 2025-01-24T01:51:52.4416786Z ShardedTensorMetadata( 2025-01-24T01:51:52.4417099Z shards_metadata=[ 2025-01-24T01:51:52.4417587Z ShardMetadata(shard_offsets=[0, 0], shard_sizes=[1, 4], placement=rank:0/cuda:0), 2025-01-24T01:51:52.4418254Z ShardMetadata(shard_offsets=[1, 0], shard_sizes=[1, 4], placement=rank:1/cuda:1), 2025-01-24T01:51:52.4418742Z ], 2025-01-24T01:51:52.4419005Z size=torch.Size([2, 4]) 2025-01-24T01:51:52.4419305Z ) 2025-01-24T01:51:52.4419542Z >>> st.local_tensor() 2025-01-24T01:51:52.4419836Z tensor([1, 2, 3, 4]) # Rank 0 2025-01-24T01:51:52.4420149Z tensor([3, 4, 5, 6]) # Rank 1 2025-01-24T01:51:52.4420345Z 2025-01-24T01:51:52.4420636Z Warning: This API is experimental and subject to change. It lacks of a fully across 2025-01-24T01:51:52.4421281Z rank validations, and we only validate the local shard on the current rank. 2025-01-24T01:51:52.4421879Z We fully rely on the user to ensure local tensor is sharded based on the 2025-01-24T01:51:52.4422331Z sharding spec. 2025-01-24T01:51:52.4422503Z 2025-01-24T01:51:52.4422780Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:51:52.4423158Z 2025-01-24T01:51:52.4423892Z msg = Cannot scrape callname=ShardedTensor.reshard in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/_shard/sharded_tensor/api.py line=1036. 2025-01-24T01:51:52.4424963Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:51:52.4425349Z 2025-01-24T01:51:52.4425625Z Reshard a sharded tensor given the ``resharding_spec``. For now, we only support 2025-01-24T01:51:52.4426116Z single local shard. 2025-01-24T01:51:52.4426369Z 2025-01-24T01:51:52.4426603Z If ``resharding_spec`` is same as the original one, this becomes a no-op. 2025-01-24T01:51:52.4427212Z If only ``resharding_spec`` shares the same sharding dim with the original one, 2025-01-24T01:51:52.4427709Z we swap local shards directly. 2025-01-24T01:51:52.4428188Z For more generic cases, we merge different shards across different ranks and split 2025-01-24T01:51:52.4428982Z the local shards based on the ``resharding_spec`` via `all_to_all` collective API. 2025-01-24T01:51:52.4429359Z 2025-01-24T01:51:52.4429467Z Args: 2025-01-24T01:51:52.4429887Z resharding_spec (:class:`torch.distributed._shard.sharding_spec.ShardingSpec`): The 2025-01-24T01:51:52.4430485Z specification describing how the tensor is sharded. 2025-01-24T01:51:52.4430786Z 2025-01-24T01:51:52.4430882Z Returns: 2025-01-24T01:51:52.4431232Z A :class:`ShardedTensor` object whose local shards are resharded. 2025-01-24T01:51:52.4431555Z 2025-01-24T01:51:52.4431665Z Examples: 2025-01-24T01:51:52.4431920Z >>> # xdoctest: +SKIP 2025-01-24T01:51:52.4432217Z >>> # We have 2 process groups, 2 ranks. 2025-01-24T01:51:52.4432641Z >>> tensor = torch.arange(4, dtype=torch.int64) + 1 + 2 * rank 2025-01-24T01:51:52.4433078Z >>> tensor = torch.stack([tensor, tensor]) 2025-01-24T01:51:52.4433418Z >>> tensor 2025-01-24T01:51:52.4433727Z tensor([[1, 2, 3, 4], [1, 2, 3, 4]]) # Rank 0 2025-01-24T01:51:52.4434090Z tensor([[3, 4, 5, 6], [3, 4, 5, 6]]) # Rank 1 2025-01-24T01:51:52.4434455Z tensor([[5, 6, 7, 8], [5, 6, 7, 8]]) # Rank 2 2025-01-24T01:51:52.4434848Z tensor([[7, 8, 9, 10], [7, 8, 9, 10]]) # Rank 3 2025-01-24T01:51:52.4435200Z >>> sharding_dim = 0 2025-01-24T01:51:52.4435503Z >>> spec = ChunkShardingSpec( 2025-01-24T01:51:52.4435818Z dim=sharding_dim, 2025-01-24T01:51:52.4436127Z placements=[ 2025-01-24T01:51:52.4436422Z "rank:0/cuda:0", 2025-01-24T01:51:52.4436726Z "rank:1/cuda:1", 2025-01-24T01:51:52.4437030Z "rank:2/cuda:2", 2025-01-24T01:51:52.4437321Z "rank:3/cuda:3", 2025-01-24T01:51:52.4437613Z ], 2025-01-24T01:51:52.4437852Z ) 2025-01-24T01:51:52.4438103Z >>> current_offsets = [0] * 2 2025-01-24T01:51:52.4438420Z >>> current_offsets[0] = rank * 2 2025-01-24T01:51:52.4438771Z >>> shard_metadata = ShardMetadata( 2025-01-24T01:51:52.4439187Z shard_offsets=copy.deepcopy(current_offsets), 2025-01-24T01:51:52.4439591Z shard_sizes=tensor.size(), 2025-01-24T01:51:52.4439959Z placement=spec.placements[rank], 2025-01-24T01:51:52.4440304Z ) 2025-01-24T01:51:52.4440530Z >>> local_shards = [ 2025-01-24T01:51:52.4440813Z Shard( 2025-01-24T01:51:52.4441081Z tensor=tensor, 2025-01-24T01:51:52.4441401Z metadata=shard_metadata, 2025-01-24T01:51:52.4441722Z ) 2025-01-24T01:51:52.4441968Z ] 2025-01-24T01:51:52.4442349Z >>> st = ShardedTensor._init_from_local_shards(local_shards, tensor.size()) 2025-01-24T01:51:52.4442821Z >>> sharding_dim = 1 2025-01-24T01:51:52.4443142Z >>> resharding_spec = ChunkShardingSpec( 2025-01-24T01:51:52.4443494Z dim=sharding_dim, 2025-01-24T01:51:52.4443802Z placements=[ 2025-01-24T01:51:52.4444097Z "rank:0/cuda:0", 2025-01-24T01:51:52.4444408Z "rank:1/cuda:1", 2025-01-24T01:51:52.4444719Z "rank:2/cuda:2", 2025-01-24T01:51:52.4445018Z "rank:3/cuda:3", 2025-01-24T01:51:52.4445316Z ], 2025-01-24T01:51:52.4445568Z ) 2025-01-24T01:51:52.4445819Z >>> st.reshard(resharding_spec) 2025-01-24T01:51:52.4446170Z >>> tensor = st.local_shards()[0].tensor 2025-01-24T01:51:52.4446490Z >>> tensor 2025-01-24T01:51:52.4446784Z tensor([[1], [1], [3], [3], [5], [5], [7], [7]]) # Rank 0 2025-01-24T01:51:52.4447200Z tensor([[2], [2], [4], [4], [6], [6], [8], [8]]) # Rank 1 2025-01-24T01:51:52.4447612Z tensor([[3], [3], [5], [5], [7], [7], [9], [9]]) # Rank 2 2025-01-24T01:51:52.4448025Z tensor([[4], [4], [6], [6], [8], [8], [10], [10]]) # Rank 3 2025-01-24T01:51:52.4448283Z 2025-01-24T01:51:52.4448546Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:51:52.4448936Z 2025-01-24T01:51:52.4619151Z msg = Cannot scrape callname=ShardingPlan in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/_shard/sharding_plan/api.py line=12. 2025-01-24T01:51:52.4621135Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:51:52.4621861Z 2025-01-24T01:51:52.4622270Z Representation of a sharding plan, describes how to shard a module 2025-01-24T01:51:52.4623393Z across hosts. `plan` is used to shard module parameters according to the spec provided, 2025-01-24T01:51:52.4624640Z `output_plan` and `return_local_tensor` are optional, they are used to specify the output 2025-01-24T01:51:52.4625855Z layout of a module with a spec, and when to convert back to data parallel fashion. 2025-01-24T01:51:52.4626603Z 2025-01-24T01:51:52.4626783Z Args: 2025-01-24T01:51:52.4627456Z plan (Dict[str, Union[:class:`torch.distributed._shard.sharding_spec.ShardingSpec`, 2025-01-24T01:51:52.4628489Z :class:`torch.distributed._shard.sharder.Sharder`]): 2025-01-24T01:51:52.4629662Z a dict describes how to shard a module, there're currently two ways to shard a module: 2025-01-24T01:51:52.4630853Z 1. directly shard a module parameter by a `ShardingSpec`, keyed by the name of 2025-01-24T01:51:52.4631765Z a parameter to a `ShardingSpec`. 2025-01-24T01:51:52.4632664Z 2. shard a submodule by applying a `Sharder` on it, keyed by the name of a module 2025-01-24T01:51:52.4633547Z to a `Sharder` object. 2025-01-24T01:51:52.4634540Z output_plan (Dict[str, :class:`torch.distributed._shard.sharding_spec.ShardingSpec`), optional): 2025-01-24T01:51:52.4635848Z a dict specifies the layout of a module's output which produces a ShardedTensor, 2025-01-24T01:51:52.4636992Z keyed by the name of module to ShardingSpec("" in key means the root module). 2025-01-24T01:51:52.4637836Z Default: `None` 2025-01-24T01:51:52.4638595Z return_local_tensor (List[str], optional): a list of string, each element enables 2025-01-24T01:51:52.4639735Z a module's sharded output to be returned as a Tensor from its local shards to 2025-01-24T01:51:52.4640868Z ensure further processing in a data parallel fashion. ("" in list means the 2025-01-24T01:51:52.4641731Z root module). 2025-01-24T01:51:52.4642189Z Default: None 2025-01-24T01:51:52.4642635Z Example: 2025-01-24T01:51:52.4643363Z Suppose we want to shard a module with two linear layers and then run it with DDP, we also 2025-01-24T01:51:52.4644632Z want to convert the output of the second linear layer back to DDP, we can do it as follows: 2025-01-24T01:51:52.4645405Z 2025-01-24T01:51:52.4645713Z >>> # xdoctest: +REQUIRES(module:torch._C._distributed_c10d) 2025-01-24T01:51:52.4646434Z >>> class MyModule(nn.Module): 2025-01-24T01:51:52.4647002Z >>> def __init__(self) -> None: 2025-01-24T01:51:52.4647586Z >>> super().__init__() 2025-01-24T01:51:52.4648103Z >>> self.fc1 = nn.Linear() 2025-01-24T01:51:52.4648680Z >>> self.gelu = nn.GELU() 2025-01-24T01:51:52.4649245Z >>> self.fc2 = nn.Linear() 2025-01-24T01:51:52.4649820Z >>> self.relu = nn.Linear() 2025-01-24T01:51:52.4650348Z >>> 2025-01-24T01:51:52.4650760Z >>> def forward(self, input): 2025-01-24T01:51:52.4651458Z >>> return self.relu(self.fc2(self.gelu(self.fc1(input)))) 2025-01-24T01:51:52.4652017Z 2025-01-24T01:51:52.4652057Z 2025-01-24T01:51:52.4652288Z >>> # xdoctest: +SKIP("Undefined spec1, spec2) 2025-01-24T01:51:52.4652948Z >>> sharding_plan = ShardingPlan( 2025-01-24T01:51:52.4653508Z >>> plan={ 2025-01-24T01:51:52.4653933Z >>> "fc1.weight": spec1, 2025-01-24T01:51:52.4654483Z >>> "fc2.weight": spec2 2025-01-24T01:51:52.4654999Z >>> }, 2025-01-24T01:51:52.4655400Z >>> output_plan={ 2025-01-24T01:51:52.4655885Z >>> "fc2": output_spec 2025-01-24T01:51:52.4656371Z >>> }, 2025-01-24T01:51:52.4656785Z >>> return_local_tensor=["fc2"] 2025-01-24T01:51:52.4657429Z >>> ) 2025-01-24T01:51:52.4657750Z 2025-01-24T01:51:52.4658236Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:51:52.4658916Z 2025-01-24T01:51:52.5467496Z msg = Cannot scrape callname=post_localSGD_hook in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/algorithms/ddp_comm_hooks/post_localSGD_hook.py line=72. 2025-01-24T01:51:52.5468694Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:51:52.5469086Z 2025-01-24T01:51:52.5469227Z Run post-localSGD algorithm. 2025-01-24T01:51:52.5469428Z 2025-01-24T01:51:52.5469680Z This DDP communication hook is used for running post-localSGD algorithm, 2025-01-24T01:51:52.5470221Z by combining with a model averaging component (e.g., 2025-01-24T01:51:52.5470845Z :class:`~torch.distributed.algorithms.model_averaging.averagers.PeriodicModelAverager`) 2025-01-24T01:51:52.5471434Z that runs after the optimizer step. 2025-01-24T01:51:52.5471895Z 2025-01-24T01:51:52.5472007Z Args: 2025-01-24T01:51:52.5472389Z state (PostLocalSGDState): State information to run post-localSGD. 2025-01-24T01:51:52.5473026Z Users mainly need to tune ``start_localSGD_iter`` to determine when to start local SGD. 2025-01-24T01:51:52.5473868Z bucket (dist.GradBucket): Bucket that stores a 1D flattened gradient tensor that batches multiple per-variable tensors. 2025-01-24T01:51:52.5474678Z Note that since DDP comm hook only supports single process single device mode, 2025-01-24T01:51:52.5475224Z only exactly one tensor is stored in this bucket. 2025-01-24T01:51:52.5475500Z 2025-01-24T01:51:52.5475612Z Returns: 2025-01-24T01:51:52.5476007Z Future handler of the communication, which updates the gradients in place. 2025-01-24T01:51:52.5476379Z 2025-01-24T01:51:52.5476499Z Example:: 2025-01-24T01:51:52.5476755Z >>> # xdoctest: +SKIP 2025-01-24T01:51:52.5477215Z >>> state = PostLocalSGDState(process_group=process_group, subgroup=subgroup, 2025-01-24T01:51:52.5477754Z start_localSGD_iter=10) 2025-01-24T01:51:52.5478194Z >>> ddp_model.register_comm_hook(state, post_localSGD_hook) 2025-01-24T01:51:52.5490052Z >>> # Also need to establish a model averaging module and run model averaging after ``optimizer.step()``. 2025-01-24T01:51:52.5491118Z >>> # Please refer to the examples in ``torch.distributed.algorithms.model_averaging.averagers`` module. 2025-01-24T01:51:52.5491617Z 2025-01-24T01:51:52.5491909Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:51:52.5492305Z 2025-01-24T01:51:52.5524743Z msg = Cannot scrape callname=powerSGD_hook in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/algorithms/ddp_comm_hooks/powerSGD_hook.py line=342. 2025-01-24T01:51:52.5525864Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:51:52.5526272Z 2025-01-24T01:51:52.5526413Z Implement PowerSGD algorithm. 2025-01-24T01:51:52.5526625Z 2025-01-24T01:51:52.5526890Z This DDP communication hook implements PowerSGD gradient compression 2025-01-24T01:51:52.5527517Z algorithm described in the `paper `_. 2025-01-24T01:51:52.5528123Z Once gradient tensors are aggregated across all workers, this hook applies 2025-01-24T01:51:52.5528616Z compression as follows: 2025-01-24T01:51:52.5528806Z 2025-01-24T01:51:52.5529257Z 1. Views the input flattened 1D gradient tensor as a list of per-parameter tensors, and divides all the tensors into two groups: 2025-01-24T01:51:52.5529834Z 2025-01-24T01:51:52.5530265Z 1.1 The tensors that should be compressed before allreduce, because the compression can give enough saving in bandwidth. 2025-01-24T01:51:52.5530824Z 2025-01-24T01:51:52.5531242Z 1.2 Rest of the tensors will be directly allreduced without compression, including all the vector tensors (for biases). 2025-01-24T01:51:52.5531778Z 2025-01-24T01:51:52.5531913Z 2. Handles uncompressed tensors: 2025-01-24T01:51:52.5532442Z 2025-01-24T01:51:52.5532981Z 2.1. Allocate contiguous memory for those uncompressed tensors, and allreduces all the uncompressed tensors as a batch, without compression; 2025-01-24T01:51:52.5533618Z 2025-01-24T01:51:52.5533985Z 2.2. Copies the individual uncompressed tensors from the contiguous memory back to the input tensor. 2025-01-24T01:51:52.5534454Z 2025-01-24T01:51:52.5534712Z 3. Handles the tensors that should be compressed by PowerSGD compression: 2025-01-24T01:51:52.5535073Z 2025-01-24T01:51:52.5535325Z 3.1. For each tensor M, creates two low-rank tensors P and Q for decomposing M, 2025-01-24T01:51:52.5536032Z such that M = PQ^T, where Q is initialized from a standard normal distribution and orthogonalized; 2025-01-24T01:51:52.5536477Z 2025-01-24T01:51:52.5536634Z 3.2. Computes each P in Ps, which is equal to MQ; 2025-01-24T01:51:52.5536914Z 2025-01-24T01:51:52.5537034Z 3.3. Allreduces Ps as a batch; 2025-01-24T01:51:52.5537301Z 2025-01-24T01:51:52.5537447Z 3.4. Orthogonalizes each P in Ps; 2025-01-24T01:51:52.5537668Z 2025-01-24T01:51:52.5537890Z 3.5. Computes each Q in Qs, which is approximately equal to M^TP; 2025-01-24T01:51:52.5538208Z 2025-01-24T01:51:52.5538324Z 3.6. Allreduces Qs as a batch; 2025-01-24T01:51:52.5538549Z 2025-01-24T01:51:52.5538856Z 3.7. Computes each M among all the compressed tensors, which is approximately equal to PQ^T. 2025-01-24T01:51:52.5539289Z 2025-01-24T01:51:52.5539704Z Note that this communication hook enforces vanilla allreduce for the first ``state.start_powerSGD_iter`` iterations. 2025-01-24T01:51:52.5540537Z This not only gives the user more control over the tradeoff between speedup and accuracy, 2025-01-24T01:51:52.5541393Z but also helps abstract away some complexity of the internal optimization of DDP for future communication hook developers. 2025-01-24T01:51:52.5541946Z 2025-01-24T01:51:52.5542056Z Args: 2025-01-24T01:51:52.5542659Z state (PowerSGDState): State information to configure the compression rate and support error feedback, warm start, etc. 2025-01-24T01:51:52.5543591Z To tune the compression configs, mainly need to tune ``matrix_approximation_rank``, ``start_powerSGD_iter`` 2025-01-24T01:51:52.5544190Z and ``min_compression_rate``. 2025-01-24T01:51:52.5544844Z bucket (dist.GradBucket): Bucket that stores a 1D flattened gradient tensor that batches multiple per-variable tensors. 2025-01-24T01:51:52.5545654Z Note that since DDP comm hook only supports single process single device mode, 2025-01-24T01:51:52.5546206Z only exactly one tensor is stored in this bucket. 2025-01-24T01:51:52.5546566Z 2025-01-24T01:51:52.5546681Z Returns: 2025-01-24T01:51:52.5547078Z Future handler of the communication, which updates the gradients in place. 2025-01-24T01:51:52.5547451Z 2025-01-24T01:51:52.5547566Z Example:: 2025-01-24T01:51:52.5547822Z >>> # xdoctest: +SKIP 2025-01-24T01:51:52.5548301Z >>> state = PowerSGDState(process_group=process_group, matrix_approximation_rank=1, 2025-01-24T01:51:52.5548886Z start_powerSGD_iter=10, min_compression_rate=0.5) 2025-01-24T01:51:52.5549355Z >>> ddp_model.register_comm_hook(state, powerSGD_hook) 2025-01-24T01:51:52.5549634Z 2025-01-24T01:51:52.5549912Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:51:52.5550293Z 2025-01-24T01:51:52.5575796Z msg = Cannot scrape callname=PeriodicModelAverager in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/algorithms/model_averaging/averagers.py line=38. 2025-01-24T01:51:52.5576930Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:51:52.5577333Z 2025-01-24T01:51:52.5577534Z Averages parameters periodically after the warm-up stage. 2025-01-24T01:51:52.5577855Z 2025-01-24T01:51:52.5578122Z This can be used for running `post-local SGD `_, 2025-01-24T01:51:52.5578877Z by running :class:`~torch.nn.DistributedDataParallel` (DDP) 2025-01-24T01:51:52.5579646Z using the subgroups created by :meth:`~torch.distributed.new_subgroups`. 2025-01-24T01:51:52.5580007Z 2025-01-24T01:51:52.5580121Z Args: 2025-01-24T01:51:52.5580421Z period (int): The number of steps per model averaging. 2025-01-24T01:51:52.5580994Z Usually the period should be greater than ``1`` to reduce the communication cost. 2025-01-24T01:51:52.5581536Z Otherwise, only DDP needs to be used. 2025-01-24T01:51:52.5582019Z warmup_steps (int): The number of warm-up steps. During this stage, 2025-01-24T01:51:52.5582490Z model averaging is skipped. 2025-01-24T01:51:52.5582937Z process_group: The process group to be used for all-reduce. 2025-01-24T01:51:52.5583395Z If ``None``, the default process group, which 2025-01-24T01:51:52.5583873Z is created by :func:`torch.distributed.init_process_group`, 2025-01-24T01:51:52.5584412Z will be used. (default: ``None``) 2025-01-24T01:51:52.5584652Z 2025-01-24T01:51:52.5584775Z Example:: 2025-01-24T01:51:52.5584907Z 2025-01-24T01:51:52.5585058Z >>> # xdoctest: +SKIP("undefined variables") 2025-01-24T01:51:52.5585439Z >>> import torch 2025-01-24T01:51:52.5585729Z >>> import torch.distributed as dist 2025-01-24T01:51:52.5586431Z >>> import torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook as post_localSGD 2025-01-24T01:51:52.5587172Z >>> import torch.distributed.algorithms.model_averaging.averagers as averagers 2025-01-24T01:51:52.5587696Z >>> import torch.nn as nn 2025-01-24T01:51:52.5587999Z >>> 2025-01-24T01:51:52.5588315Z >>> dist.init_process_group("nccl", rank=rank, world_size=16) 2025-01-24T01:51:52.5588740Z >>> torch.cuda.set_device(rank) 2025-01-24T01:51:52.5589101Z >>> module = nn.Linear(1, 1, bias=False).cuda() 2025-01-24T01:51:52.5589532Z >>> model = nn.parallel.DistributedDataParallel( 2025-01-24T01:51:52.5589957Z >>> module, device_ids=[rank], output_device=rank 2025-01-24T01:51:52.5590322Z >>> ) 2025-01-24T01:51:52.5590620Z >>> # Register a post-localSGD communication hook. 2025-01-24T01:51:52.5591196Z >>> state = PostLocalSGDState(process_group=None, subgroup=None, start_localSGD_iter=100) 2025-01-24T01:51:52.5591800Z >>> model.register_comm_hook(state, post_localSGD_hook) 2025-01-24T01:51:52.5592187Z >>> 2025-01-24T01:51:52.5592585Z >>> # In the first 100 steps, run global gradient averaging like normal DDP at every step. 2025-01-24T01:51:52.5593156Z >>> # After 100 steps, run model averaging every 4 steps. 2025-01-24T01:51:52.5593766Z >>> # Note that ``warmup_steps`` must be the same as ``start_localSGD_iter`` used in ``PostLocalSGDState``. 2025-01-24T01:51:52.5594475Z >>> averager = averagers.PeriodicModelAverager(period=4, warmup_steps=100) 2025-01-24T01:51:52.5594972Z >>> for step in range(0, 200): 2025-01-24T01:51:52.5595290Z >>> optimizer.zero_grad() 2025-01-24T01:51:52.5595639Z >>> loss = loss_fn(output, labels) 2025-01-24T01:51:52.5595987Z >>> loss.backward() 2025-01-24T01:51:52.5596288Z >>> optimizer.step() 2025-01-24T01:51:52.5596680Z >>> # Will average model parameters globally every 4 steps. Thus, 2025-01-24T01:51:52.5597206Z >>> # inter-node communication only occurs every 4 iterations after 2025-01-24T01:51:52.5597679Z >>> # the initial ``warmup_steps`` period. 2025-01-24T01:51:52.5598195Z >>> averager.average_parameters(model.parameters()) 2025-01-24T01:51:52.5598594Z 2025-01-24T01:51:52.5598987Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:51:52.5599516Z 2025-01-24T01:51:52.5601286Z msg = Cannot scrape callname=HierarchicalModelAverager in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/algorithms/model_averaging/hierarchical_model_averager.py line=19. 2025-01-24T01:51:52.5602754Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:51:52.5603190Z 2025-01-24T01:51:52.5603553Z Runs hierarchical model averaging (`hierarchical SGD `_). 2025-01-24T01:51:52.5604010Z 2025-01-24T01:51:52.5604337Z Process groups of different sizes are organized in a hierarchy, and they average parameters 2025-01-24T01:51:52.5604969Z by using different periods concurrently after the warm-up stage. 2025-01-24T01:51:52.5605704Z This is an extension of :class:`~torch.distributed.algorithms.model_averaging.averagers.PeriodicModelAverager` 2025-01-24T01:51:52.5606575Z that supports `post-local SGD `_, which essentially only supports 2025-01-24T01:51:52.5607350Z a two-level hierarchy: the intra-machine level and the global level, where the intra-machine 2025-01-24T01:51:52.5608164Z level is usually embedded in :meth:`~torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook`. 2025-01-24T01:51:52.5608987Z Similarly, the process groups within this class do not have such an intra-machine process 2025-01-24T01:51:52.5609700Z subgroup, which should be embedded by the post-local SGD communication hook instead. 2025-01-24T01:51:52.5610099Z 2025-01-24T01:51:52.5610201Z Args: 2025-01-24T01:51:52.5610587Z period_group_size_dict: An ordered dict mapping keys of model averaging period to 2025-01-24T01:51:52.5611189Z process group size, used for initializing process groups of 2025-01-24T01:51:52.5611749Z different sizes in a hierarchy to average parameters concurrently. 2025-01-24T01:51:52.5612317Z Particularly, at each iteration, there will be at most a single 2025-01-24T01:51:52.5612891Z process group that runs averaging -- the period of such group should 2025-01-24T01:51:52.5613465Z have the largest period which the current step can be divided by. 2025-01-24T01:51:52.5613973Z For example, if the dict has three keys: 2, 4, and 8, 2025-01-24T01:51:52.5614482Z then this means totally three process groups will be created to 2025-01-24T01:51:52.5615041Z average parameters every 2, 4, and 8 iterations, respectively. 2025-01-24T01:51:52.5615573Z At the 4th iteration, only the second process group will run 2025-01-24T01:51:52.5616070Z averaging, because the first process group should be a 2025-01-24T01:51:52.5616603Z subset of the second process group, and no need to execute the first 2025-01-24T01:51:52.5617082Z process group redundantly. 2025-01-24T01:51:52.5617516Z On the other hand, the third process group can only be triggered 2025-01-24T01:51:52.5618072Z every 8 iterations, so it will not be triggered at the 4th iteration. 2025-01-24T01:51:52.5618741Z warmup_steps (int): The number of warm-up steps. During this stage, model averaging is skipped. 2025-01-24T01:51:52.5619622Z process_group (ProcessGroup, optional): The overall process group containing all the processes that runs model averaging. 2025-01-24T01:51:52.5620372Z If ``None``, the default process group, which is created 2025-01-24T01:51:52.5620890Z by :func:`torch.distributed.init_process_group`, will be used. 2025-01-24T01:51:52.5621338Z (default: ``None``) 2025-01-24T01:51:52.5621590Z 2025-01-24T01:51:52.5621692Z Example:: 2025-01-24T01:51:52.5621952Z >>> # xdoctest: +SKIP('undefined rank') 2025-01-24T01:51:52.5622319Z >>> from collections import OrderedDict 2025-01-24T01:51:52.5622653Z >>> import torch 2025-01-24T01:51:52.5622922Z >>> import torch.distributed as dist 2025-01-24T01:51:52.5623445Z >>> from torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook import ( 2025-01-24T01:51:52.5624085Z >>> PostLocalSGDState, 2025-01-24T01:51:52.5624390Z >>> post_localSGD_hook, 2025-01-24T01:51:52.5624681Z >>> ) 2025-01-24T01:51:52.5625185Z >>> import torch.distributed.algorithms.model_averaging.hierarchical_model_averager as hierarchicalSGD 2025-01-24T01:51:52.5625793Z >>> import torch.nn as nn 2025-01-24T01:51:52.5626079Z >>> 2025-01-24T01:51:52.5626492Z >>> dist.init_process_group("nccl", rank=rank, world_size=16) 2025-01-24T01:51:52.5626911Z >>> torch.cuda.set_device(rank) 2025-01-24T01:51:52.5627259Z >>> module = nn.Linear(1, 1, bias=False).to(rank) 2025-01-24T01:51:52.5627675Z >>> model = nn.parallel.DistributedDataParallel( 2025-01-24T01:51:52.5628095Z >>> module, device_ids=[rank], output_device=rank 2025-01-24T01:51:52.5628442Z >>> ) 2025-01-24T01:51:52.5628722Z >>> # Register a post-localSGD communication hook. 2025-01-24T01:51:52.5629305Z >>> # Assume that each machine has 4 GPUs, then each intra-machine subgroup has a size of 4. 2025-01-24T01:51:52.5629819Z >>> subgroup, _ = dist.new_subgroups() 2025-01-24T01:51:52.5630366Z >>> state = PostLocalSGDState(process_group=None, subgroup=subgroup, start_localSGD_iter=100) 2025-01-24T01:51:52.5630980Z >>> model.register_comm_hook(state, post_localSGD_hook) 2025-01-24T01:51:52.5631352Z >>> 2025-01-24T01:51:52.5631762Z >>> # Average parameters among each group of 8 processes every 4 iterations, and among all 2025-01-24T01:51:52.5632284Z >>> # the 16 processes every 16 iterations. 2025-01-24T01:51:52.5632712Z >>> averager = hierarchicalSGD.HierarchicalModelAverager( 2025-01-24T01:51:52.5633256Z >>> period_group_size_dict=OrderedDict([(4, 8), (16, 16)]), warmup_steps=100) 2025-01-24T01:51:52.5633932Z >>> # Note that ``warmup_steps`` must be the same as ``start_localSGD_iter`` used in ``PostLocalSGDState``. 2025-01-24T01:51:52.5634644Z >>> # In the first 100 steps, run global gradient averaging like normal DDP at every step. 2025-01-24T01:51:52.5635207Z >>> # After 100 steps, run model averaging at two levels. 2025-01-24T01:51:52.5635583Z >>> for step in range(0, 200): 2025-01-24T01:51:52.5635906Z >>> optimizer.zero_grad() 2025-01-24T01:51:52.5636233Z >>> loss = loss_fn(output, labels) 2025-01-24T01:51:52.5636566Z >>> loss.backward() 2025-01-24T01:51:52.5636851Z >>> optimizer.step() 2025-01-24T01:51:52.5637185Z >>> # Average parameters after ``optimizer.step()``. 2025-01-24T01:51:52.5637748Z >>> # Thus, the inter-node communication only occurs periodically after ``warmup_steps``. 2025-01-24T01:51:52.5638346Z >>> averager.average_parameters(model.parameters()) 2025-01-24T01:51:52.5638621Z 2025-01-24T01:51:52.5638743Z .. warning :: 2025-01-24T01:51:52.5639145Z The last group size in the dict must be the size of the provided ``process_group``, 2025-01-24T01:51:52.5639751Z which indicates model averaging at the highest level of the hierarchy. 2025-01-24T01:51:52.5640431Z If ``process_group`` is not provided, then the last group size should be equal to the world size. 2025-01-24T01:51:52.5640874Z 2025-01-24T01:51:52.5640970Z .. warning :: 2025-01-24T01:51:52.5641355Z `HierarchicalModelAverager` is experimental and subject to change. 2025-01-24T01:51:52.5641712Z 2025-01-24T01:51:52.5641989Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:51:52.5642362Z 2025-01-24T01:51:52.5934804Z msg = Cannot scrape callname=BroadcastingTorchSaveReader in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/checkpoint/format_utils.py line=40. 2025-01-24T01:51:52.5936019Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:51:52.5936406Z 2025-01-24T01:51:52.5936718Z StorageReader for reading a Torch Save file. This reader will read the entire checkpoint 2025-01-24T01:51:52.5937639Z on the coordinator rank, and then broadcast and shard each tensor to all ranks. 2025-01-24T01:51:52.5938714Z 2025-01-24T01:51:52.5938898Z . N.B. Intended to be used with DynamicMetaLoadPlanner 2025-01-24T01:51:52.5939195Z 2025-01-24T01:51:52.5939303Z .. warning:: 2025-01-24T01:51:52.5939625Z Current implementation only supports loading Tensors. 2025-01-24T01:51:52.5939912Z 2025-01-24T01:51:52.5940048Z >>> # xdoctest: +SKIP("undefined vars") 2025-01-24T01:51:52.5940385Z >>> sd = {"mode": model} 2025-01-24T01:51:52.5940640Z >>> dcp.load( 2025-01-24T01:51:52.5940870Z >>> sd, 2025-01-24T01:51:52.5941161Z >>> storage_reader=BroadcastingTorchSaveReader(), 2025-01-24T01:51:52.5941559Z >>> planner=DynamicMetaLoadPlanner(), 2025-01-24T01:51:52.5941919Z >>> checkpoint_id="path_to_model.pt" 2025-01-24T01:51:52.5942221Z >>> ) 2025-01-24T01:51:52.5942348Z 2025-01-24T01:51:52.5942605Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:51:52.5942996Z 2025-01-24T01:51:52.5943759Z msg = Cannot scrape callname=DynamicMetaLoadPlanner in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/checkpoint/format_utils.py line=151. 2025-01-24T01:51:52.5944832Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:51:52.5945211Z 2025-01-24T01:51:52.5945597Z Extension of DefaultLoadPlanner, which creates a new Metadata object based on the passed in state dict, 2025-01-24T01:51:52.5946513Z avoiding the need to read metadata from disk. This is useful when reading formats which don't have a 2025-01-24T01:51:52.5947091Z metadata file, like Torch Save files. 2025-01-24T01:51:52.5947309Z 2025-01-24T01:51:52.5947498Z . N.B. Intended to be used with BroadcastingTorchSaveReader 2025-01-24T01:51:52.5947810Z 2025-01-24T01:51:52.5947906Z .. warning:: 2025-01-24T01:51:52.5948225Z Current implementation only supports loading Tensors. 2025-01-24T01:51:52.5948524Z 2025-01-24T01:51:52.5948641Z >>> # xdoctest: +SKIP("undefined vars") 2025-01-24T01:51:52.5948976Z >>> sd = {"mode": model} 2025-01-24T01:51:52.5949250Z >>> dcp.load( 2025-01-24T01:51:52.5949472Z >>> sd, 2025-01-24T01:51:52.5949765Z >>> storage_reader=BroadcastingTorchSaveReader(), 2025-01-24T01:51:52.5950165Z >>> planner=DynamicMetaLoadPlanner(), 2025-01-24T01:51:52.5950524Z >>> checkpoint_id="path_to_model.pt" 2025-01-24T01:51:52.5950828Z >>> ) 2025-01-24T01:51:52.5950955Z 2025-01-24T01:51:52.5951210Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:51:52.5951596Z 2025-01-24T01:51:52.6010030Z msg = Cannot scrape callname=load_sharded_optimizer_state_dict in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/checkpoint/optimizer.py line=221. 2025-01-24T01:51:52.6011133Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:51:52.6011533Z 2025-01-24T01:51:52.6011746Z Load a state_dict in conjunction with FSDP sharded optimizer state. 2025-01-24T01:51:52.6012096Z 2025-01-24T01:51:52.6012272Z This is the current recommended way to checkpoint FSDP. 2025-01-24T01:51:52.6012664Z >>> # xdoctest: +SKIP 2025-01-24T01:51:52.6012988Z >>> import torch.distributed.checkpoint as dist_cp 2025-01-24T01:51:52.6013340Z >>> # Save 2025-01-24T01:51:52.6013585Z >>> model: torch.nn.Model 2025-01-24T01:51:52.6013887Z >>> optim_params = model.parameters() 2025-01-24T01:51:52.6014257Z >>> optim = torch.optim.SGD(optim_params, lr=0.01) 2025-01-24T01:51:52.6014608Z >>> # Save 2025-01-24T01:51:52.6014947Z >>> with FSDP.state_dict_type(model, StateDictType.SHARDED_STATE_DICT): 2025-01-24T01:51:52.6015391Z >>> state_dict = { 2025-01-24T01:51:52.6015716Z >>> "optimizer": FSDP.optim_state_dict(model, optim), 2025-01-24T01:51:52.6016106Z >>> "model": model.state_dict() 2025-01-24T01:51:52.6016417Z >>> } 2025-01-24T01:51:52.6016642Z >>> dist_cp.save_state_dict( 2025-01-24T01:51:52.6016948Z >>> state_dict=optim_state, 2025-01-24T01:51:52.6017343Z >>> storage_writer=dist_cp.FileSystemWriter("checkpoint"), 2025-01-24T01:51:52.6017988Z >>> planner=dist_cp.DefaultSavePlanner(), 2025-01-24T01:51:52.6018336Z >>> ) 2025-01-24T01:51:52.6018539Z >>> 2025-01-24T01:51:52.6018748Z >>> # Load 2025-01-24T01:51:52.6019114Z >>> with FSDP.state_dict_type(model_tp, StateDictType.SHARDED_STATE_DICT): 2025-01-24T01:51:52.6019613Z >>> model_state_dict = model_tp.state_dict() 2025-01-24T01:51:52.6019960Z >>> checkpoint = { 2025-01-24T01:51:52.6020224Z >>> "model": model_state_dict 2025-01-24T01:51:52.6020530Z >>> } 2025-01-24T01:51:52.6020761Z >>> dist_cp.load_state_dict( 2025-01-24T01:51:52.6021067Z >>> state_dict=checkpoint, 2025-01-24T01:51:52.6021469Z >>> storage_reader=dist_cp.FileSystemReader(checkpoint_file), 2025-01-24T01:51:52.6021908Z >>> planner=dist_cp.DefaultLoadPlanner(), 2025-01-24T01:51:52.6022266Z >>> ) 2025-01-24T01:51:52.6022557Z >>> model.load_state_dict(checkpoint["model_state"]) 2025-01-24T01:51:52.6022974Z >>> 2025-01-24T01:51:52.6023285Z >>> optim_state = dist_cp.load_sharded_optimizer_state_dict( 2025-01-24T01:51:52.6023673Z >>> model_state_dict, 2025-01-24T01:51:52.6023982Z >>> optimizer_key="optimizer", 2025-01-24T01:51:52.6024396Z >>> storage_reader=dist_cp.FileSystemReader("checkpoint"), 2025-01-24T01:51:52.6024787Z >>> ) 2025-01-24T01:51:52.6025001Z >>> 2025-01-24T01:51:52.6025259Z >>> flattened_osd = FSDP.optim_state_dict_to_load( 2025-01-24T01:51:52.6025654Z >>> model, optim, optim_state["optimizer"] 2025-01-24T01:51:52.6025989Z >>> ) 2025-01-24T01:51:52.6026293Z >>> 2025-01-24T01:51:52.6026548Z >>> optim.load_state_dict(flattened_osd) 2025-01-24T01:51:52.6026780Z 2025-01-24T01:51:52.6027035Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:51:52.6027430Z 2025-01-24T01:51:52.6040419Z msg = Cannot scrape callname=SavePlanner in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/checkpoint/planner.py line=110. 2025-01-24T01:51:52.6041418Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:51:52.6041822Z 2025-01-24T01:51:52.6042113Z Abstract class defining the protocol used by save_state_dict to plan the save process. 2025-01-24T01:51:52.6042539Z 2025-01-24T01:51:52.6042839Z SavePlanners are stateful objects that can be used to customize the whole save process. 2025-01-24T01:51:52.6043277Z 2025-01-24T01:51:52.6043560Z SavePlanner acts as an access proxy to the state_dict, so any transformation done to it 2025-01-24T01:51:52.6044092Z will be visible to the whole process. 2025-01-24T01:51:52.6044313Z 2025-01-24T01:51:52.6044609Z A planner subclass can expect the following sequence of calls during save_state_dict: 2025-01-24T01:51:52.6045008Z 2025-01-24T01:51:52.6045151Z 1) set_up_planner - called on all ranks. 2025-01-24T01:51:52.6045506Z Signals the start of a checkpoint save. 2025-01-24T01:51:52.6045759Z 2025-01-24T01:51:52.6045890Z 2) create_local_plan - called on all ranks. 2025-01-24T01:51:52.6046688Z Process the state_dict and produces a `SavePlan` that will be sent for global planning. 2025-01-24T01:51:52.6047311Z 2025-01-24T01:51:52.6047691Z 3) create_global_plan - called on the coordinator rank only. 2025-01-24T01:51:52.6048348Z Takes the SavePlan from all ranks and make any global decision. 2025-01-24T01:51:52.6048665Z 2025-01-24T01:51:52.6048801Z 4) finish_plan - called on all ranks. 2025-01-24T01:51:52.6049236Z This gives each rank a chance to adjust to global planning decisions. 2025-01-24T01:51:52.6049587Z 2025-01-24T01:51:52.6049746Z 5) resolve_data - called multiple times on each rank 2025-01-24T01:51:52.6050236Z Lookups a value on the `state_dict` for the storage layer to write. 2025-01-24T01:51:52.6050572Z 2025-01-24T01:51:52.6050879Z Users are recommended to extend DefaultSavePlanner instead of this interface directly as 2025-01-24T01:51:52.6051506Z most changes can be expressed by changes in a single method. 2025-01-24T01:51:52.6051898Z 2025-01-24T01:51:52.6052111Z There are 3 usual patterns of extension: 2025-01-24T01:51:52.6052343Z 2025-01-24T01:51:52.6052598Z Rewriting state_dict. This is the simplest way to extend the save process as it 2025-01-24T01:51:52.6053220Z doesn't requite understanding the intrincacies of how SavePlan works: 2025-01-24T01:51:52.6053575Z 2025-01-24T01:51:52.6053693Z >>> # xdoctest: +SKIP("undefined vars") 2025-01-24T01:51:52.6054053Z >>> class RenamePlanner(DefaultSavePlanner): 2025-01-24T01:51:52.6054403Z >>> def set_up_planner( 2025-01-24T01:51:52.6054682Z >>> self, 2025-01-24T01:51:52.6054927Z >>> state_dict: STATE_DICT_TYPE, 2025-01-24T01:51:52.6055279Z >>> storage_meta: Optional[StorageMeta], 2025-01-24T01:51:52.6055629Z >>> is_coordinator: bool, 2025-01-24T01:51:52.6055929Z >>> ) -> None: 2025-01-24T01:51:52.6056198Z >>> # prefix all keys with `foo_`` 2025-01-24T01:51:52.6056757Z >>> super().set_up_planner({"foo_" + k: v for k, v in state_dict.items()}, storage_meta, is_coordinator) 2025-01-24T01:51:52.6057198Z 2025-01-24T01:51:52.6057549Z Modifying local plan and lookup in tandem. This is useful when fine control of how data is persisted 2025-01-24T01:51:52.6057998Z 2025-01-24T01:51:52.6058130Z >>> # xdoctest: +SKIP("undefined vars") 2025-01-24T01:51:52.6058485Z >>> class FP16Planner(DefaultSavePlanner): 2025-01-24T01:51:52.6058851Z >>> def create_local_plan(self): 2025-01-24T01:51:52.6059197Z >>> plan = super().create_local_plan() 2025-01-24T01:51:52.6059540Z >>> for p in plan: 2025-01-24T01:51:52.6059849Z >>> if p.tensor_data is not None: 2025-01-24T01:51:52.6060232Z >>> p.tensor_data.properties.dtype = torch.float16 2025-01-24T01:51:52.6060612Z >>> return plan 2025-01-24T01:51:52.6060871Z >>> 2025-01-24T01:51:52.6061111Z >>> def resolve_data(self, write_item): 2025-01-24T01:51:52.6061473Z >>> item = super().resolve_data(write_item) 2025-01-24T01:51:52.6061994Z >>> return item if write_item.type == WriteItemType.BYTE_IO else item.to(torch.float16) 2025-01-24T01:51:52.6062398Z 2025-01-24T01:51:52.6062744Z Using the global planning step to make central decisions that can't be made individually by each rank 2025-01-24T01:51:52.6063220Z 2025-01-24T01:51:52.6063339Z >>> # xdoctest: +SKIP("undefined vars") 2025-01-24T01:51:52.6063685Z >>> from itertools import zip_longest 2025-01-24T01:51:52.6064024Z >>> from dataclasses import replace 2025-01-24T01:51:52.6064418Z >>> class DDPLoadBalancingPlanner(DefaultSavePlanner): 2025-01-24T01:51:52.6064972Z >>> # This uses the default local plan behavior of having all non-sharded writes in rank 0 2025-01-24T01:51:52.6065508Z >>> # This sample doesn't handle ShardedTensors 2025-01-24T01:51:52.6065900Z >>> def create_global_plan(self, all_plans): 2025-01-24T01:51:52.6066413Z >>> iters = [iter(all_plans[0].items)] * len(all_plans) 2025-01-24T01:51:52.6066796Z >>> items_per_rank = [ 2025-01-24T01:51:52.6067142Z >>> [item for item in items if item is not None] 2025-01-24T01:51:52.6067552Z >>> for items in zip(*zip_longest(*iters), strict=True) 2025-01-24T01:51:52.6067927Z >>> ] 2025-01-24T01:51:52.6068164Z >>> all_plans = [ 2025-01-24T01:51:52.6068453Z >>> replace(plan, items=items) 2025-01-24T01:51:52.6068873Z >>> for plan, items in zip(all_plans, items_per_rank, strict=True) 2025-01-24T01:51:52.6069268Z >>> ] 2025-01-24T01:51:52.6069554Z >>> return super().create_global_plan(all_plans) 2025-01-24T01:51:52.6069819Z 2025-01-24T01:51:52.6070089Z Finally, some planners need to save additional metadata in the checkpoint, this is 2025-01-24T01:51:52.6070761Z accomplished by having each rank contribute their data items in the local plan and 2025-01-24T01:51:52.6071276Z the global planner aggregate them: 2025-01-24T01:51:52.6071484Z 2025-01-24T01:51:52.6071602Z >>> # xdoctest: +SKIP("undefined vars") 2025-01-24T01:51:52.6072159Z >>> class SaveExtraDataPlanner(DefaultSavePlanner): 2025-01-24T01:51:52.6072571Z >>> def create_local_plan(self) -> SavePlan: 2025-01-24T01:51:52.6072950Z >>> plan = super().create_local_plan() 2025-01-24T01:51:52.6073354Z >>> return replace(plan, planner_data="per-rank-data") 2025-01-24T01:51:52.6073726Z >>> 2025-01-24T01:51:52.6074135Z >>> def create_global_plan(self, all_plans: List[SavePlan]) -> Tuple[List[SavePlan], Metadata]: 2025-01-24T01:51:52.6074760Z >>> global_plan, metadata = super().create_global_plan(all_plans) 2025-01-24T01:51:52.6075245Z >>> merged_data = [p.planner_data for p in global_plan] 2025-01-24T01:51:52.6075982Z >>> metadata = replace(metadata, planner_data=merged_data) 2025-01-24T01:51:52.6076391Z >>> return global_plan, metadata 2025-01-24T01:51:52.6076609Z 2025-01-24T01:51:52.6076864Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:51:52.6077294Z 2025-01-24T01:51:52.6077898Z msg = Cannot scrape callname=LoadPlanner in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/checkpoint/planner.py line=272. 2025-01-24T01:51:52.6078876Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:51:52.6079477Z 2025-01-24T01:51:52.6079776Z Abstract class defining the protocol used by load_state_dict to plan the load process. 2025-01-24T01:51:52.6080201Z 2025-01-24T01:51:52.6080491Z LoadPlanner are stateful objects that can be used to customize the whole load process. 2025-01-24T01:51:52.6080903Z 2025-01-24T01:51:52.6081200Z LoadPlanner acts as an access proxy to the state_dict, so any transformation done to it 2025-01-24T01:51:52.6081736Z will be visible to the whole process. 2025-01-24T01:51:52.6081954Z 2025-01-24T01:51:52.6082247Z A planner subclass can expect the following sequence of calls during load_state_dict: 2025-01-24T01:51:52.6082646Z 2025-01-24T01:51:52.6082768Z 1) set_up_planner - called on all ranks. 2025-01-24T01:51:52.6083149Z Signals the start of loading a checkpoint. 2025-01-24T01:51:52.6083404Z 2025-01-24T01:51:52.6083531Z 2) create_local_plan - called on all ranks. 2025-01-24T01:51:52.6084058Z Process the state_dict and produces a `LoadPlan` that will be sent for global planning. 2025-01-24T01:51:52.6084464Z 2025-01-24T01:51:52.6084661Z 3) create_global_plan - called on the coordinator rank only. 2025-01-24T01:51:52.6085185Z Takes the LoadPlan from all ranks and make any global decision. 2025-01-24T01:51:52.6085499Z 2025-01-24T01:51:52.6085647Z 4) load_bytes - called multiple times on each rank 2025-01-24T01:51:52.6086099Z This is called once per non-tensor value in state_dict. 2025-01-24T01:51:52.6086395Z 2025-01-24T01:51:52.6086621Z 5) resolve_tensor and commit_tensor - called multiple times on each rank 2025-01-24T01:51:52.6087160Z They are called in pair for each Tensor value in state_dict. 2025-01-24T01:51:52.6087455Z 2025-01-24T01:51:52.6087782Z Users are recommended to extend DefaultLoadPlanner instead of this interface directly as 2025-01-24T01:51:52.6088413Z most changes can be expressed by changes in a single method. 2025-01-24T01:51:52.6088713Z 2025-01-24T01:51:52.6088852Z There are two usual patterns of extension: 2025-01-24T01:51:52.6089107Z 2025-01-24T01:51:52.6089366Z Rewriting state_dict. This is the simplest way to extend the load process as it 2025-01-24T01:51:52.6090014Z doesn't requite understanding the intrincacies of how LoadPlan works. We need 2025-01-24T01:51:52.6090636Z to keep a reference to the original state_dict as load happens in place so 2025-01-24T01:51:52.6091119Z we need to be able to perform it in place 2025-01-24T01:51:52.6091351Z 2025-01-24T01:51:52.6091485Z >>> # xdoctest: +SKIP("undefined vars") 2025-01-24T01:51:52.6091841Z >>> class RenamePlanner(DefaultLoadPlanner): 2025-01-24T01:51:52.6092201Z >>> def set_up_planner( 2025-01-24T01:51:52.6092488Z >>> self, 2025-01-24T01:51:52.6092757Z >>> state_dict: STATE_DICT_TYPE, 2025-01-24T01:51:52.6093180Z >>> metadata: Metadata, 2025-01-24T01:51:52.6093558Z >>> is_coordinator: bool, 2025-01-24T01:51:52.6093856Z >>> ) -> None: 2025-01-24T01:51:52.6094136Z >>> self.original_state_dict = state_dict 2025-01-24T01:51:52.6094560Z >>> state_dict = {"foo_" + k: v for k, v in state_dict.items()} 2025-01-24T01:51:52.6094945Z >>> 2025-01-24T01:51:52.6095168Z >>> if self.flatten_sharded_tensors: 2025-01-24T01:51:52.6095563Z >>> state_dict = _flatten_sharded_tensors(state_dict) 2025-01-24T01:51:52.6095924Z >>> 2025-01-24T01:51:52.6096155Z >>> if self.flatten_state_dict: 2025-01-24T01:51:52.6096568Z >>> state_dict, self.mappings = flatten_state_dict(state_dict) 2025-01-24T01:51:52.6096954Z >>> 2025-01-24T01:51:52.6097186Z >>> self.state_dict = state_dict 2025-01-24T01:51:52.6097517Z >>> self.metadata = metadata 2025-01-24T01:51:52.6097854Z >>> self.is_coordinator = is_coordinator 2025-01-24T01:51:52.6098226Z >>> 2025-01-24T01:51:52.6098464Z >>> def load_bytes(self, read_item, value): 2025-01-24T01:51:52.6098817Z >>> # Remove the "foo_" prefix 2025-01-24T01:51:52.6099361Z >>> self.original_state_dict[read_item.dest_index.fqn[4:]] = torch.load(value, weights_only=False) 2025-01-24T01:51:52.6099807Z 2025-01-24T01:51:52.6099827Z 2025-01-24T01:51:52.6100089Z Modifying resolve_tensor and commit_tensor to handle load time transformation. 2025-01-24T01:51:52.6100472Z 2025-01-24T01:51:52.6100606Z >>> # xdoctest: +SKIP("undefined vars") 2025-01-24T01:51:52.6100991Z >>> class MetaModelMaterialize(DefaultSavePlanner): 2025-01-24T01:51:52.6101375Z >>> def resolve_tensor(self, read_item): 2025-01-24T01:51:52.6101746Z >>> tensor = super().resolve_tensor(read_item) 2025-01-24T01:51:52.6102154Z >>> return torch.empty_like(tensor, device="cpu") 2025-01-24T01:51:52.6102506Z >>> 2025-01-24T01:51:52.6102759Z >>> def commit_tensor(self, read_item, tensor): 2025-01-24T01:51:52.6103167Z >>> self.state_dict[read_item.dest_index.fqn] = tensor 2025-01-24T01:51:52.6103451Z 2025-01-24T01:51:52.6103706Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:51:52.6104090Z 2025-01-24T01:51:52.6255807Z msg = Cannot scrape callname=load in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/checkpoint/state_dict_loader.py line=61. 2025-01-24T01:51:52.6256815Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:51:52.6257211Z 2025-01-24T01:51:52.6257358Z Load a distributed ``state_dict`` in SPMD style. 2025-01-24T01:51:52.6257615Z 2025-01-24T01:51:52.6257835Z Each rank will try to read the least amount of data necessary 2025-01-24T01:51:52.6258384Z to fullfill the requested `state_dict`. When loading :class:`ShardedTensor` 2025-01-24T01:51:52.6259003Z or :class:`DTensor` instances, each rank only reads data for their local shards. 2025-01-24T01:51:52.6259382Z 2025-01-24T01:51:52.6259666Z For each ``Stateful`` object (having both a ``state_dict`` and a ``load_state_dict``), 2025-01-24T01:51:52.6260299Z load will first call ``state_dict`` before attempting deserialization, followed by 2025-01-24T01:51:52.6260853Z ``load_state_dict`` once the deserialization is complete. 2025-01-24T01:51:52.6261399Z For each non-``Stateful`` object, load will deserailize the object, and then replace 2025-01-24T01:51:52.6261945Z it in the ``state_dict`` with the deserialized object. 2025-01-24T01:51:52.6262209Z 2025-01-24T01:51:52.6262337Z .. warning:: 2025-01-24T01:51:52.6262646Z All tensors in ``state_dict`` must be allocated on their 2025-01-24T01:51:52.6263091Z destination device *prior to* calling this function. 2025-01-24T01:51:52.6263382Z 2025-01-24T01:51:52.6263613Z All non-tensor data is loaded using `torch.load()` and modified in place 2025-01-24T01:51:52.6264061Z on state_dict. 2025-01-24T01:51:52.6264218Z 2025-01-24T01:51:52.6264309Z .. warning:: 2025-01-24T01:51:52.6264809Z Users must call `load_state_dict` on the root module to ensure load 2025-01-24T01:51:52.6265377Z pos-processing and non-tensor data properly propagates. 2025-01-24T01:51:52.6265673Z 2025-01-24T01:51:52.6265765Z .. note: 2025-01-24T01:51:52.6266129Z If no process group is initialized, this function will assume the intent 2025-01-24T01:51:52.6266791Z is to load a checkpoint into the local process. This can be useful in the 2025-01-24T01:51:52.6267393Z case of local inference, and when using regular Tensors (as opposed to DTensor 2025-01-24T01:51:52.6267866Z or ShardedTensor) 2025-01-24T01:51:52.6268030Z 2025-01-24T01:51:52.6268117Z .. note: 2025-01-24T01:51:52.6268390Z Rank 0 is assumed to be the coordinator rank. 2025-01-24T01:51:52.6268651Z 2025-01-24T01:51:52.6268736Z Args: 2025-01-24T01:51:52.6269017Z state_dict (Dict[str, Any]): The state_dict to save. 2025-01-24T01:51:52.6269449Z checkpoint_id (Union[str, os.PathLike, None]): 2025-01-24T01:51:52.6269983Z The ID of this checkpoint instance. The meaning of the checkpoint_id 2025-01-24T01:51:52.6270516Z depends on the storage. It can be a path to a folder or to a file. 2025-01-24T01:51:52.6271019Z It can also be a key if the storage is a key-value store. 2025-01-24T01:51:52.6271412Z (Default: ``None``) 2025-01-24T01:51:52.6271732Z storage_reader (Optional[StorageReader]): 2025-01-24T01:51:52.6272200Z Instance of StorageWriter used to perform reads. If this is not 2025-01-24T01:51:52.6272733Z specified, DCP will automatically infer the reader based on the 2025-01-24T01:51:52.6273269Z checkpoint_id. If checkpoint_id is also None, an exception will 2025-01-24T01:51:52.6273714Z be raised. (Default: ``None``) 2025-01-24T01:51:52.6274158Z planner (Optional[LoadPlanner]): 2025-01-24T01:51:52.6274592Z Instance of LoadPlanner. If this is not specificed, the default 2025-01-24T01:51:52.6275048Z planner will be used. (Default: ``None``) 2025-01-24T01:51:52.6275634Z process_group (Optional[ProcessGroup]): 2025-01-24T01:51:52.6276082Z ProcessGroup to be used for cross-rank synchronization. 2025-01-24T01:51:52.6276498Z (Default: ``None``) 2025-01-24T01:51:52.6276677Z 2025-01-24T01:51:52.6276792Z Returns: 2025-01-24T01:51:52.6277022Z None. 2025-01-24T01:51:52.6277144Z 2025-01-24T01:51:52.6277244Z Examples 2025-01-24T01:51:52.6277483Z >>> # xdoctest: +SKIP 2025-01-24T01:51:52.6277783Z >>> my_model = MyModule() 2025-01-24T01:51:52.6278127Z >>> optimizer = Adagrad(my_model.parameters()) 2025-01-24T01:51:52.6278526Z >>> model_state_dict = my_model.state_dict() 2025-01-24T01:51:52.6279072Z >>> fs_storage_reader = torch.distributed.checkpoint.FileSystemReader("/checkpoint/1") 2025-01-24T01:51:52.6279709Z 2025-01-24T01:51:52.6279883Z >>> torch.distributed.checkpoint.load_state_dict( 2025-01-24T01:51:52.6280285Z >>> state_dict=model_state_dict, 2025-01-24T01:51:52.6280649Z >>> storage_reader=fs_storage_reader, 2025-01-24T01:51:52.6280996Z >>> ) 2025-01-24T01:51:52.6281122Z 2025-01-24T01:51:52.6281332Z >>> # module.load_state_dict() function might have customized steps 2025-01-24T01:51:52.6281798Z >>> # to flush the state_dict, must call it to 2025-01-24T01:51:52.6282165Z >>> # ensure correct behavior. 2025-01-24T01:51:52.6282515Z >>> my_model.load_state_dict(model_state_dict) 2025-01-24T01:51:52.6282759Z 2025-01-24T01:51:52.6282879Z .. note:: 2025-01-24T01:51:52.6283239Z load_state_dict uses collectives to coordinate reads across ranks. 2025-01-24T01:51:52.6283789Z For NCCL-based process groups, internal tensor representations of 2025-01-24T01:51:52.6284362Z objects must be moved to the GPU device before communication takes place. 2025-01-24T01:51:52.6284944Z In this case, the device used is given by ``torch.cuda.current_device()`` 2025-01-24T01:51:52.6285517Z and it is the user's responsibility to ensure that this is set so that each 2025-01-24T01:51:52.6286223Z rank has an individual GPU, via ``torch.cuda.set_device()``. 2025-01-24T01:51:52.6286523Z 2025-01-24T01:51:52.6286790Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:51:52.6287160Z 2025-01-24T01:51:52.6289421Z msg = Cannot scrape callname=save in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/checkpoint/state_dict_saver.py line=67. 2025-01-24T01:51:52.6290641Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:51:52.6291037Z 2025-01-24T01:51:52.6291159Z Save a distributed model in SPMD style. 2025-01-24T01:51:52.6291401Z 2025-01-24T01:51:52.6291594Z This function is different from ``torch.save()`` as it handles 2025-01-24T01:51:52.6292169Z ``ShardedTensor`` , and ``DTensor`` by having each rank only save their local shards. 2025-01-24T01:51:52.6292551Z 2025-01-24T01:51:52.6292823Z For each ``Stateful`` object (having both a ``state_dict`` and a ``load_state_dict``), 2025-01-24T01:51:52.6293491Z save will call ``state_dict`` before serialization. 2025-01-24T01:51:52.6293754Z 2025-01-24T01:51:52.6293853Z .. warning:: 2025-01-24T01:51:52.6294364Z There is no guarantees of Backwards Compatibility across PyTorch versions 2025-01-24T01:51:52.6294908Z for saved state_dicts. 2025-01-24T01:51:52.6295101Z 2025-01-24T01:51:52.6295204Z .. warning:: 2025-01-24T01:51:52.6295797Z If using the `process_group` argument, make sure that only its ranks 2025-01-24T01:51:52.6296530Z call `save_state_dict` and that all data in state_dict belong to it. 2025-01-24T01:51:52.6296855Z 2025-01-24T01:51:52.6297024Z .. note:: 2025-01-24T01:51:52.6297401Z When saving checkpoint for FSDP's `ShardingStrategy.HYBRID_SHARD`, only one of 2025-01-24T01:51:52.6298051Z the shard_group should be calling `save_state_dict` and the corresponding process 2025-01-24T01:51:52.6298557Z group needs to be passed in. 2025-01-24T01:51:52.6298758Z 2025-01-24T01:51:52.6298867Z .. note:: 2025-01-24T01:51:52.6299273Z If no process group is available, this function assumes the intention is to save the 2025-01-24T01:51:52.6299770Z state_dict in the local process. 2025-01-24T01:51:52.6300002Z 2025-01-24T01:51:52.6300092Z .. note: 2025-01-24T01:51:52.6300369Z Rank 0 is assumed to be the coordinator rank. 2025-01-24T01:51:52.6300617Z 2025-01-24T01:51:52.6300634Z 2025-01-24T01:51:52.6300722Z Args: 2025-01-24T01:51:52.6301004Z state_dict (Dict[str, Any]): The state_dict to save. 2025-01-24T01:51:52.6301429Z checkpoint_id (Union[str, os.PathLike, None]): 2025-01-24T01:51:52.6301894Z The ID of this checkpoint instance. The meaning of the checkpoint_id 2025-01-24T01:51:52.6302436Z depends on the storage. It can be a path to a folder or to a file. 2025-01-24T01:51:52.6302939Z It can also be a key if the storage is a key-value store. 2025-01-24T01:51:52.6303328Z (Default: ``None``) 2025-01-24T01:51:52.6303648Z storage_writer (Optional[StorageWriter]): 2025-01-24T01:51:52.6304110Z Instance of StorageWriter used to perform writes. If this is not 2025-01-24T01:51:52.6304652Z specified, DCP will automatically infer the writer based on the 2025-01-24T01:51:52.6305188Z checkpoint_id. If checkpoint_id is also None, an exception will 2025-01-24T01:51:52.6305625Z be raised. (Default: ``None``) 2025-01-24T01:51:52.6305960Z planner (Optional[SavePlanner]): 2025-01-24T01:51:52.6306468Z Instance of SavePlanner. If this is not specificed, the default 2025-01-24T01:51:52.6306929Z planner will be used. (Default: ``None``) 2025-01-24T01:51:52.6307313Z process_group (Optional[ProcessGroup]): 2025-01-24T01:51:52.6307748Z ProcessGroup to be used for cross-rank synchronization. 2025-01-24T01:51:52.6308152Z (Default: ``None``) 2025-01-24T01:51:52.6308328Z 2025-01-24T01:51:52.6308432Z Returns: 2025-01-24T01:51:52.6308704Z Metadata: Metadata object for the saved checkpoint. 2025-01-24T01:51:52.6308986Z 2025-01-24T01:51:52.6309149Z Example: 2025-01-24T01:51:52.6309444Z >>> # xdoctest: +SKIP 2025-01-24T01:51:52.6309728Z >>> my_model = MyModule() 2025-01-24T01:51:52.6309910Z 2025-01-24T01:51:52.6310033Z >>> state_dict = {"model": my_model} 2025-01-24T01:51:52.6310249Z 2025-01-24T01:51:52.6310550Z >>> fs_storage_writer = torch.distributed.checkpoint.FileSystemWriter("/checkpoint/1") 2025-01-24T01:51:52.6311114Z >>> torch.distributed.checkpoint.save( 2025-01-24T01:51:52.6311467Z >>> state_dict=state_dict, 2025-01-24T01:51:52.6311795Z >>> storage_writer=fs_storage_writer, 2025-01-24T01:51:52.6312117Z >>> ) 2025-01-24T01:51:52.6312237Z 2025-01-24T01:51:52.6312341Z .. note:: 2025-01-24T01:51:52.6312673Z save_state_dict uses collectives to coordinate writes across ranks. 2025-01-24T01:51:52.6313231Z For NCCL-based process groups, internal tensor representations of 2025-01-24T01:51:52.6313810Z objects must be moved to the GPU device before communication takes place. 2025-01-24T01:51:52.6314443Z In this case, the device used is given by ``torch.cuda.current_device()`` 2025-01-24T01:51:52.6315004Z and it is the user's responsibility to ensure that this is set so that 2025-01-24T01:51:52.6315530Z each rank has an individual GPU, via ``torch.cuda.set_device()``. 2025-01-24T01:51:52.6315863Z 2025-01-24T01:51:52.6316116Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:51:52.6316502Z 2025-01-24T01:51:52.6317155Z msg = Cannot scrape callname=async_save in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/checkpoint/state_dict_saver.py line=170. 2025-01-24T01:51:52.6318159Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:51:52.6318818Z Asynchronous version of ``save``. This code first de-stages the state_dict on to the 2025-01-24T01:51:52.6319506Z staging storage (defaults to CPU memory), and then calls the `save` in a separate thread. 2025-01-24T01:51:52.6319918Z 2025-01-24T01:51:52.6320029Z .. warning:: 2025-01-24T01:51:52.6320347Z This feature is experimental and subject to change. 2025-01-24T01:51:52.6320636Z 2025-01-24T01:51:52.6320726Z Args: 2025-01-24T01:51:52.6321018Z state_dict (Dict[str, Any]): The state_dict to save. 2025-01-24T01:51:52.6321455Z checkpoint_id (Union[str, os.PathLike, None]): 2025-01-24T01:51:52.6321941Z The ID of this checkpoint instance. The meaning of the checkpoint_id 2025-01-24T01:51:52.6322496Z depends on the storage. It can be a path to a folder or to a file. 2025-01-24T01:51:52.6322988Z It can also be a key if the storage is a key-value store. 2025-01-24T01:51:52.6323384Z (Default: ``None``) 2025-01-24T01:51:52.6323718Z storage_writer (Optional[StorageWriter]): 2025-01-24T01:51:52.6324193Z Instance of StorageWriter used to perform 'stage' and 'save'. If 2025-01-24T01:51:52.6324776Z this is not specified, DCP will automatically infer the writer based on the 2025-01-24T01:51:52.6325368Z checkpoint_id. If checkpoint_id is also None, an exception will 2025-01-24T01:51:52.6325804Z be raised. (Default: ``None``) 2025-01-24T01:51:52.6326160Z planner (Optional[SavePlanner]): 2025-01-24T01:51:52.6326611Z Instance of SavePlanner. If this is not specificed, the default 2025-01-24T01:51:52.6327084Z planner will be used. (Default: ``None``) 2025-01-24T01:51:52.6327480Z process_group (Optional[ProcessGroup]): 2025-01-24T01:51:52.6327909Z ProcessGroup to be used for cross-rank synchronization. 2025-01-24T01:51:52.6328318Z (Default: ``None``) 2025-01-24T01:51:52.6328517Z 2025-01-24T01:51:52.6328611Z Returns: 2025-01-24T01:51:52.6328965Z Future: A future holding the resultant Metadata object from `save`. 2025-01-24T01:51:52.6329295Z 2025-01-24T01:51:52.6329399Z Example: 2025-01-24T01:51:52.6329644Z >>> # xdoctest: +SKIP 2025-01-24T01:51:52.6329967Z >>> my_model = MyModule() 2025-01-24T01:51:52.6330231Z 2025-01-24T01:51:52.6330345Z >>> state_dict = {"model": my_model} 2025-01-24T01:51:52.6330579Z 2025-01-24T01:51:52.6330881Z >>> fs_storage_writer = torch.distributed.checkpoint.FileSystemWriter("/checkpoint/1") 2025-01-24T01:51:52.6331522Z >>> checkpoint_future = torch.distributed.checkpoint.async_save( 2025-01-24T01:51:52.6331964Z >>> state_dict=state_dict, 2025-01-24T01:51:52.6332295Z >>> storage_writer=fs_storage_writer, 2025-01-24T01:51:52.6332622Z >>> ) 2025-01-24T01:51:52.6332851Z >>> 2025-01-24T01:51:52.6333083Z >>> # ... do some work ... 2025-01-24T01:51:52.6333372Z >>> 2025-01-24T01:51:52.6333607Z >>> checkpoint_future.result() 2025-01-24T01:51:52.6333835Z 2025-01-24T01:51:52.6333922Z 2025-01-24T01:51:52.6334297Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:51:52.6334713Z 2025-01-24T01:51:52.6416645Z msg = Cannot scrape callname=construct_and_record_rdzv_event in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/elastic/events/__init__.py line=91. 2025-01-24T01:51:52.6417726Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:51:52.6418108Z 2025-01-24T01:51:52.6418327Z Initialize rendezvous event object and record its operations. 2025-01-24T01:51:52.6418647Z 2025-01-24T01:51:52.6418750Z Args: 2025-01-24T01:51:52.6418993Z run_id (str): The run id of the rendezvous. 2025-01-24T01:51:52.6419395Z message (str): The message describing the event. 2025-01-24T01:51:52.6419917Z node_state (NodeState): The state of the node (INIT, RUNNING, SUCCEEDED, FAILED). 2025-01-24T01:51:52.6420494Z name (str): Event name. (E.g. Current action being performed). 2025-01-24T01:51:52.6420924Z hostname (str): Hostname of the node. 2025-01-24T01:51:52.6421301Z pid (Optional[int]): The process id of the node. 2025-01-24T01:51:52.6421810Z master_endpoint (str): The master endpoint for the rendezvous store, if known. 2025-01-24T01:51:52.6422469Z local_id (Optional[int]): The local_id of the node, if defined in dynamic_rendezvous.py 2025-01-24T01:51:52.6423028Z rank (Optional[int]): The rank of the node, if known. 2025-01-24T01:51:52.6423399Z Returns: 2025-01-24T01:51:52.6423618Z None 2025-01-24T01:51:52.6423820Z Example: 2025-01-24T01:51:52.6424076Z >>> # See DynamicRendezvousHandler class 2025-01-24T01:51:52.6424413Z >>> def _record( 2025-01-24T01:51:52.6424661Z ... self, 2025-01-24T01:51:52.6424908Z ... message: str, 2025-01-24T01:51:52.6425214Z ... node_state: NodeState = NodeState.RUNNING, 2025-01-24T01:51:52.6425581Z ... rank: Optional[int] = None, 2025-01-24T01:51:52.6425898Z ... ) -> None: 2025-01-24T01:51:52.6426168Z ... construct_and_record_rdzv_event( 2025-01-24T01:51:52.6426665Z ... name=f"{self.__class__.__name__}.{get_method_name()}", 2025-01-24T01:51:52.6427073Z ... run_id=self._settings.run_id, 2025-01-24T01:51:52.6427414Z ... message=message, 2025-01-24T01:51:52.6427718Z ... node_state=node_state, 2025-01-24T01:51:52.6428064Z ... hostname=self._this_node.addr, 2025-01-24T01:51:52.6428413Z ... pid=self._this_node.pid, 2025-01-24T01:51:52.6428749Z ... local_id=self._this_node.local_id, 2025-01-24T01:51:52.6429087Z ... rank=rank, 2025-01-24T01:51:52.6429350Z ... ) 2025-01-24T01:51:52.6429478Z 2025-01-24T01:51:52.6429743Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:51:52.6430116Z 2025-01-24T01:51:52.8351378Z msg = Cannot scrape callname=MixedPrecision in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/fsdp/api.py line=114. 2025-01-24T01:51:52.8352356Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:51:52.8352758Z 2025-01-24T01:51:52.8353256Z This configures FSDP-native mixed precision training. 2025-01-24T01:51:52.8353540Z 2025-01-24T01:51:52.8353636Z Attributes: 2025-01-24T01:51:52.8354018Z param_dtype (Optional[torch.dtype]): This specifies the dtype for model 2025-01-24T01:51:52.8354591Z parameters during forward and backward and thus the dtype for 2025-01-24T01:51:52.8355146Z forward and backward computation. Outside forward and backward, the 2025-01-24T01:51:52.8355695Z *sharded* parameters are kept in full precision (e.g. for the 2025-01-24T01:51:52.8356229Z optimizer step), and for model checkpointing, the parameters are 2025-01-24T01:51:52.8356712Z always saved in full precision. (Default: ``None``) 2025-01-24T01:51:52.8357215Z reduce_dtype (Optional[torch.dtype]): This specifies the dtype for 2025-01-24T01:51:52.8357788Z gradient reduction (i.e. reduce-scatter or all-reduce). If this is 2025-01-24T01:51:52.8358315Z ``None`` but ``param_dtype`` is not ``None``, then this takes on 2025-01-24T01:51:52.8358890Z the ``param_dtype`` value, still running gradient reduction in low 2025-01-24T01:51:52.8359435Z precision. This is permitted to differ from ``param_dtype``, e.g. 2025-01-24T01:51:52.8359963Z to force gradient reduction to run in full precision. (Default: 2025-01-24T01:51:52.8360391Z ``None``) 2025-01-24T01:51:52.8360760Z buffer_dtype (Optional[torch.dtype]): This specifies the dtype for 2025-01-24T01:51:52.8361306Z buffers. FSDP does not shard buffers. Rather, FSDP casts them to 2025-01-24T01:51:52.8361834Z ``buffer_dtype`` in the first forward pass and keeps them in that 2025-01-24T01:51:52.8362360Z dtype thereafter. For model checkpointing, the buffers are saved 2025-01-24T01:51:52.8362885Z in full precision except for ``LOCAL_STATE_DICT``. (Default: 2025-01-24T01:51:52.8363283Z ``None``) 2025-01-24T01:51:52.8363634Z keep_low_precision_grads (bool): If ``False``, then FSDP upcasts 2025-01-24T01:51:52.8364188Z gradients to full precision after the backward pass in preparation 2025-01-24T01:51:52.8364746Z for the optimizer step. If ``True``, then FSDP keeps the gradients 2025-01-24T01:51:52.8365272Z in the dtype used for gradient reduction, which can save memory if 2025-01-24T01:51:52.8365821Z using a custom optimizer that supports running in low precision. 2025-01-24T01:51:52.8366258Z (Default: ``False``) 2025-01-24T01:51:52.8366660Z cast_forward_inputs (bool): If ``True``, then this FSDP module casts 2025-01-24T01:51:52.8367205Z its forward args and kwargs to ``param_dtype``. This is to ensure 2025-01-24T01:51:52.8367738Z that parameter and input dtypes match for forward computation, as 2025-01-24T01:51:52.8368290Z required by many ops. This may need to be set to ``True`` when only 2025-01-24T01:51:52.8368854Z applying mixed precision to some but not all FSDP modules, in which 2025-01-24T01:51:52.8369423Z case a mixed-precision FSDP submodule needs to recast its inputs. 2025-01-24T01:51:52.8369865Z (Default: ``False``) 2025-01-24T01:51:52.8370282Z cast_root_forward_inputs (bool): If ``True``, then the root FSDP module 2025-01-24T01:51:52.8370817Z casts its forward args and kwargs to ``param_dtype``, overriding 2025-01-24T01:51:52.8371335Z the value of ``cast_forward_inputs``. For non-root FSDP modules, 2025-01-24T01:51:52.8371795Z this does not do anything. (Default: ``True``) 2025-01-24T01:51:52.8372279Z _module_classes_to_ignore: (Sequence[Type[nn.Module]]): This specifies 2025-01-24T01:51:52.8372810Z module classes to ignore for mixed precision when using an 2025-01-24T01:51:52.8373294Z ``auto_wrap_policy``: Modules of these classes will have FSDP 2025-01-24T01:51:52.8373858Z applied to them separately with mixed precision disabled (meaning 2025-01-24T01:51:52.8374390Z that the final FSDP construction would deviate from the specified 2025-01-24T01:51:52.8375055Z policy). If ``auto_wrap_policy`` is not specified, then this does 2025-01-24T01:51:52.8375828Z not do anything. This API is experimental and subject to change. 2025-01-24T01:51:52.8376277Z (Default: ``(_BatchNorm,)``) 2025-01-24T01:51:52.8376486Z 2025-01-24T01:51:52.8376685Z .. note:: This API is experimental and subject to change. 2025-01-24T01:51:52.8376964Z 2025-01-24T01:51:52.8377201Z .. note:: Only floating point tensors are cast to their specified dtypes. 2025-01-24T01:51:52.8377540Z 2025-01-24T01:51:52.8377725Z .. note:: In ``summon_full_params``, parameters are forced to full 2025-01-24T01:51:52.8378147Z precision, but buffers are not. 2025-01-24T01:51:52.8378371Z 2025-01-24T01:51:52.8378580Z .. note:: Layer norm and batch norm accumulate in ``float32`` even when 2025-01-24T01:51:52.8379125Z their inputs are in a low precision like ``float16`` or ``bfloat16``. 2025-01-24T01:51:52.8379916Z Disabling FSDP's mixed precision for those norm modules only means that 2025-01-24T01:51:52.8380500Z the affine parameters are kept in ``float32``. However, this incurs 2025-01-24T01:51:52.8381072Z separate all-gathers and reduce-scatters for those norm modules, which 2025-01-24T01:51:52.8381659Z may be inefficient, so if the workload permits, the user should prefer 2025-01-24T01:51:52.8382159Z to still apply mixed precision to those modules. 2025-01-24T01:51:52.8382428Z 2025-01-24T01:51:52.8382636Z .. note:: By default, if the user passes a model with any ``_BatchNorm`` 2025-01-24T01:51:52.8383174Z modules and specifies an ``auto_wrap_policy``, then the batch norm 2025-01-24T01:51:52.8383737Z modules will have FSDP applied to them separately with mixed precision 2025-01-24T01:51:52.8384256Z disabled. See the ``_module_classes_to_ignore`` argument. 2025-01-24T01:51:52.8384557Z 2025-01-24T01:51:52.8384770Z .. note:: ``MixedPrecision`` has ``cast_root_forward_inputs=True`` and 2025-01-24T01:51:52.8385328Z ``cast_forward_inputs=False`` by default. For the root FSDP instance, 2025-01-24T01:51:52.8385848Z its ``cast_root_forward_inputs`` takes precedence over its 2025-01-24T01:51:52.8386402Z ``cast_forward_inputs``. For non-root FSDP instances, their 2025-01-24T01:51:52.8386927Z ``cast_root_forward_inputs`` values are ignored. The default setting is 2025-01-24T01:51:52.8387480Z sufficient for the typical case where each FSDP instance has the same 2025-01-24T01:51:52.8388058Z ``MixedPrecision`` configuration and only needs to cast inputs to the 2025-01-24T01:51:52.8388603Z ``param_dtype`` at the beginning of the model's forward pass. 2025-01-24T01:51:52.8388901Z 2025-01-24T01:51:52.8389127Z .. note:: For nested FSDP instances with different ``MixedPrecision`` 2025-01-24T01:51:52.8389702Z configurations, we recommend setting individual ``cast_forward_inputs`` 2025-01-24T01:51:52.8390278Z values to configure casting inputs or not before each instance's 2025-01-24T01:51:52.8390799Z forward. In such a case, since the casts happen before each FSDP 2025-01-24T01:51:52.8391344Z instance's forward, a parent FSDP instance should have its non-FSDP 2025-01-24T01:51:52.8391920Z submodules run before its FSDP submodules to avoid the activation dtype 2025-01-24T01:51:52.8392495Z being changed due to a different ``MixedPrecision`` configuration. 2025-01-24T01:51:52.8392821Z 2025-01-24T01:51:52.8392937Z Example:: 2025-01-24T01:51:52.8393071Z 2025-01-24T01:51:52.8393218Z >>> # xdoctest: +SKIP("undefined variables") 2025-01-24T01:51:52.8393627Z >>> model = nn.Sequential(nn.Linear(3, 3), nn.Linear(3, 3)) 2025-01-24T01:51:52.8394022Z >>> model[1] = FSDP( 2025-01-24T01:51:52.8394307Z >>> model[1], 2025-01-24T01:51:52.8394794Z >>> mixed_precision=MixedPrecision(param_dtype=torch.float16, cast_forward_inputs=True), 2025-01-24T01:51:52.8395316Z >>> ) 2025-01-24T01:51:52.8395538Z >>> model = FSDP( 2025-01-24T01:51:52.8395887Z >>> model, 2025-01-24T01:51:52.8396445Z >>> mixed_precision=MixedPrecision(param_dtype=torch.bfloat16, cast_forward_inputs=True), 2025-01-24T01:51:52.8396974Z >>> ) 2025-01-24T01:51:52.8397104Z 2025-01-24T01:51:52.8397337Z The above shows a working example. On the other hand, if ``model[1]`` 2025-01-24T01:51:52.8397868Z were replaced with ``model[0]``, meaning that the submodule using 2025-01-24T01:51:52.8398418Z different ``MixedPrecision`` ran its forward first, then ``model[1]`` 2025-01-24T01:51:52.8398997Z would incorrectly see ``float16`` activations instead of ``bfloat16`` 2025-01-24T01:51:52.8399435Z ones. 2025-01-24T01:51:52.8399556Z 2025-01-24T01:51:52.8399561Z 2025-01-24T01:51:52.8399827Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:51:52.8400201Z 2025-01-24T01:51:52.8500204Z msg = Cannot scrape callname=FullyShardedDataParallel.set_state_dict_type in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py line=639. 2025-01-24T01:51:52.8501569Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:51:52.8502224Z Set the ``state_dict_type`` of all the descendant FSDP modules of the target module. 2025-01-24T01:51:52.8502599Z 2025-01-24T01:51:52.8502888Z Also takes (optional) configuration for the model's and optimizer's state dict. 2025-01-24T01:51:52.8503652Z The target module does not have to be a FSDP module. If the target 2025-01-24T01:51:52.8504195Z module is a FSDP module, its ``state_dict_type`` will also be changed. 2025-01-24T01:51:52.8504598Z 2025-01-24T01:51:52.8504813Z .. note:: This API should be called for only the top-level (root) 2025-01-24T01:51:52.8505230Z module. 2025-01-24T01:51:52.8505393Z 2025-01-24T01:51:52.8505610Z .. note:: This API enables users to transparently use the conventional 2025-01-24T01:51:52.8506156Z ``state_dict`` API to take model checkpoints in cases where the 2025-01-24T01:51:52.8506795Z root FSDP module is wrapped by another ``nn.Module``. For example, 2025-01-24T01:51:52.8507335Z the following will ensure ``state_dict`` is called on all non-FSDP 2025-01-24T01:51:52.8507919Z instances, while dispatching into `sharded_state_dict` implementation 2025-01-24T01:51:52.8508387Z for FSDP: 2025-01-24T01:51:52.8508560Z 2025-01-24T01:51:52.8508658Z Example:: 2025-01-24T01:51:52.8508804Z 2025-01-24T01:51:52.8508958Z >>> # xdoctest: +SKIP("undefined variables") 2025-01-24T01:51:52.8509338Z >>> model = DDP(FSDP(...)) 2025-01-24T01:51:52.8509662Z >>> FSDP.set_state_dict_type( 2025-01-24T01:51:52.8509992Z >>> model, 2025-01-24T01:51:52.8510308Z >>> StateDictType.SHARDED_STATE_DICT, 2025-01-24T01:51:52.8510789Z >>> state_dict_config = ShardedStateDictConfig(offload_to_cpu=True), 2025-01-24T01:51:52.8511376Z >>> optim_state_dict_config = OptimStateDictConfig(offload_to_cpu=True), 2025-01-24T01:51:52.8511812Z >>> ) 2025-01-24T01:51:52.8512091Z >>> param_state_dict = model.state_dict() 2025-01-24T01:51:52.8512515Z >>> optim_state_dict = FSDP.optim_state_dict(model, optim) 2025-01-24T01:51:52.8512804Z 2025-01-24T01:51:52.8512905Z Args: 2025-01-24T01:51:52.8513167Z module (torch.nn.Module): Root module. 2025-01-24T01:51:52.8513653Z state_dict_type (StateDictType): the desired ``state_dict_type`` to set. 2025-01-24T01:51:52.8514248Z state_dict_config (Optional[StateDictConfig]): the configuration for the 2025-01-24T01:51:52.8514728Z target ``state_dict_type``. 2025-01-24T01:51:52.8515216Z optim_state_dict_config (Optional[OptimStateDictConfig]): the configuration 2025-01-24T01:51:52.8515716Z for the optimizer state dict. 2025-01-24T01:51:52.8515947Z 2025-01-24T01:51:52.8516161Z Returns: 2025-01-24T01:51:52.8516628Z A StateDictSettings that include the previous state_dict type and 2025-01-24T01:51:52.8517098Z configuration for the module. 2025-01-24T01:51:52.8517423Z 2025-01-24T01:51:52.8517807Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:51:52.8518178Z 2025-01-24T01:51:52.8519017Z msg = Cannot scrape callname=FullyShardedDataParallel.state_dict_type in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py line=795. 2025-01-24T01:51:52.8520206Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:51:52.8520856Z Set the ``state_dict_type`` of all the descendant FSDP modules of the target module. 2025-01-24T01:51:52.8521232Z 2025-01-24T01:51:52.8521553Z This context manager has the same functions as :meth:`set_state_dict_type`. Read the document of 2025-01-24T01:51:52.8522174Z :meth:`set_state_dict_type` for the detail. 2025-01-24T01:51:52.8522428Z 2025-01-24T01:51:52.8522522Z Example:: 2025-01-24T01:51:52.8522681Z 2025-01-24T01:51:52.8522815Z >>> # xdoctest: +SKIP("undefined variables") 2025-01-24T01:51:52.8523178Z >>> model = DDP(FSDP(...)) 2025-01-24T01:51:52.8523515Z >>> with FSDP.state_dict_type( 2025-01-24T01:51:52.8523827Z >>> model, 2025-01-24T01:51:52.8524148Z >>> StateDictType.SHARDED_STATE_DICT, 2025-01-24T01:51:52.8524487Z >>> ): 2025-01-24T01:51:52.8524761Z >>> checkpoint = model.state_dict() 2025-01-24T01:51:52.8524999Z 2025-01-24T01:51:52.8525103Z Args: 2025-01-24T01:51:52.8525357Z module (torch.nn.Module): Root module. 2025-01-24T01:51:52.8525841Z state_dict_type (StateDictType): the desired ``state_dict_type`` to set. 2025-01-24T01:51:52.8526438Z state_dict_config (Optional[StateDictConfig]): the model ``state_dict`` 2025-01-24T01:51:52.8526970Z configuration for the target ``state_dict_type``. 2025-01-24T01:51:52.8527491Z optim_state_dict_config (Optional[OptimStateDictConfig]): the optimizer 2025-01-24T01:51:52.8528064Z ``state_dict`` configuration for the target ``state_dict_type``. 2025-01-24T01:51:52.8528469Z 2025-01-24T01:51:52.8528854Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:51:52.8529235Z 2025-01-24T01:51:52.8560174Z msg = Cannot scrape callname=FullyShardedDataParallel.optim_state_dict in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py line=1808. 2025-01-24T01:51:52.8561382Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:51:52.8561763Z 2025-01-24T01:51:52.8562019Z Transform the state-dict of an optimizer corresponding to a sharded model. 2025-01-24T01:51:52.8562389Z 2025-01-24T01:51:52.8562610Z The given state-dict can be transformed to one of three types: 2025-01-24T01:51:52.8563349Z 1) full optimizer state_dict, 2) sharded optimizer state_dict, 3) local optimizer state_dict. 2025-01-24T01:51:52.8563840Z 2025-01-24T01:51:52.8564079Z For full optimizer state_dict, all states are unflattened and not sharded. 2025-01-24T01:51:52.8564655Z Rank0 only and CPU only can be specified via :meth:`state_dict_type` to 2025-01-24T01:51:52.8565090Z avoid OOM. 2025-01-24T01:51:52.8565229Z 2025-01-24T01:51:52.8565465Z For sharded optimizer state_dict, all states are unflattened but sharded. 2025-01-24T01:51:52.8566031Z CPU only can be specified via :meth:`state_dict_type` to further save 2025-01-24T01:51:52.8566447Z memory. 2025-01-24T01:51:52.8566564Z 2025-01-24T01:51:52.8566785Z For local state_dict, no transformation will be performed. But a state 2025-01-24T01:51:52.8567367Z will be converted from nn.Tensor to ShardedTensor to represent its sharding 2025-01-24T01:51:52.8567845Z nature (this is not supported yet). 2025-01-24T01:51:52.8568163Z 2025-01-24T01:51:52.8568357Z Example:: 2025-01-24T01:51:52.8568502Z 2025-01-24T01:51:52.8568639Z >>> # xdoctest: +SKIP("undefined variables") 2025-01-24T01:51:52.8569123Z >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP 2025-01-24T01:51:52.8569646Z >>> from torch.distributed.fsdp import StateDictType 2025-01-24T01:51:52.8570093Z >>> from torch.distributed.fsdp import FullStateDictConfig 2025-01-24T01:51:52.8570597Z >>> from torch.distributed.fsdp import FullOptimStateDictConfig 2025-01-24T01:51:52.8571025Z >>> # Save a checkpoint 2025-01-24T01:51:52.8571319Z >>> model, optim = ... 2025-01-24T01:51:52.8571610Z >>> FSDP.set_state_dict_type( 2025-01-24T01:51:52.8571902Z >>> model, 2025-01-24T01:51:52.8572185Z >>> StateDictType.FULL_STATE_DICT, 2025-01-24T01:51:52.8572552Z >>> FullStateDictConfig(rank0_only=False), 2025-01-24T01:51:52.8572955Z >>> FullOptimStateDictConfig(rank0_only=False), 2025-01-24T01:51:52.8573391Z >>> ) 2025-01-24T01:51:52.8573623Z >>> state_dict = model.state_dict() 2025-01-24T01:51:52.8574021Z >>> optim_state_dict = FSDP.optim_state_dict(model, optim) 2025-01-24T01:51:52.8574459Z >>> save_a_checkpoint(state_dict, optim_state_dict) 2025-01-24T01:51:52.8574827Z >>> # Load a checkpoint 2025-01-24T01:51:52.8575125Z >>> model, optim = ... 2025-01-24T01:51:52.8575698Z >>> state_dict, optim_state_dict = load_a_checkpoint() 2025-01-24T01:51:52.8576077Z >>> FSDP.set_state_dict_type( 2025-01-24T01:51:52.8576380Z >>> model, 2025-01-24T01:51:52.8576659Z >>> StateDictType.FULL_STATE_DICT, 2025-01-24T01:51:52.8577031Z >>> FullStateDictConfig(rank0_only=False), 2025-01-24T01:51:52.8577434Z >>> FullOptimStateDictConfig(rank0_only=False), 2025-01-24T01:51:52.8577778Z >>> ) 2025-01-24T01:51:52.8578029Z >>> model.load_state_dict(state_dict) 2025-01-24T01:51:52.8578425Z >>> optim_state_dict = FSDP.optim_state_dict_to_load( 2025-01-24T01:51:52.8578825Z >>> model, optim, optim_state_dict 2025-01-24T01:51:52.8579348Z >>> ) 2025-01-24T01:51:52.8579646Z >>> optim.load_state_dict(optim_state_dict) 2025-01-24T01:51:52.8592500Z 2025-01-24T01:51:52.8592658Z Args: 2025-01-24T01:51:52.8593024Z model (torch.nn.Module): Root module (which may or may not be a 2025-01-24T01:51:52.8593571Z :class:`FullyShardedDataParallel` instance) whose parameters 2025-01-24T01:51:52.8594040Z were passed into the optimizer ``optim``. 2025-01-24T01:51:52.8594471Z optim (torch.optim.Optimizer): Optimizer for ``model`` 's 2025-01-24T01:51:52.8594879Z parameters. 2025-01-24T01:51:52.8595258Z optim_state_dict (Dict[str, Any]): the target optimizer state_dict to 2025-01-24T01:51:52.8595818Z transform. If the value is None, optim.state_dict() will be used. ( 2025-01-24T01:51:52.8596263Z Default: ``None``) 2025-01-24T01:51:52.8596694Z group (dist.ProcessGroup): Model's process group across which parameters 2025-01-24T01:51:52.8597266Z are sharded or ``None`` if using the default process group. ( 2025-01-24T01:51:52.8597681Z Default: ``None``) 2025-01-24T01:51:52.8597868Z 2025-01-24T01:51:52.8597959Z Returns: 2025-01-24T01:51:52.8598292Z Dict[str, Any]: A :class:`dict` containing the optimizer state for 2025-01-24T01:51:52.8598789Z ``model``. The sharding of the optimizer state is based on 2025-01-24T01:51:52.8599168Z ``state_dict_type``. 2025-01-24T01:51:52.8599355Z 2025-01-24T01:51:52.8599613Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:51:52.8599995Z 2025-01-24T01:51:52.8600842Z msg = Cannot scrape callname=FullyShardedDataParallel.optim_state_dict_to_load in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py line=1906. 2025-01-24T01:51:52.8602059Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:51:52.8602589Z 2025-01-24T01:51:52.8603057Z Convert an optimizer state-dict so that it can be loaded into the optimizer associated with the FSDP model. 2025-01-24T01:51:52.8603535Z 2025-01-24T01:51:52.8603726Z Given a ``optim_state_dict`` that is transformed through 2025-01-24T01:51:52.8604233Z :meth:`optim_state_dict`, it gets converted to the flattened optimizer 2025-01-24T01:51:52.8604774Z state_dict that can be loaded to ``optim`` which is the optimizer for 2025-01-24T01:51:52.8605313Z ``model``. ``model`` must be sharded by FullyShardedDataParallel. 2025-01-24T01:51:52.8605634Z 2025-01-24T01:51:52.8605769Z >>> # xdoctest: +SKIP("undefined variables") 2025-01-24T01:51:52.8606264Z >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP 2025-01-24T01:51:52.8606788Z >>> from torch.distributed.fsdp import StateDictType 2025-01-24T01:51:52.8607251Z >>> from torch.distributed.fsdp import FullStateDictConfig 2025-01-24T01:51:52.8607790Z >>> from torch.distributed.fsdp import FullOptimStateDictConfig 2025-01-24T01:51:52.8608227Z >>> # Save a checkpoint 2025-01-24T01:51:52.8608522Z >>> model, optim = ... 2025-01-24T01:51:52.8608814Z >>> FSDP.set_state_dict_type( 2025-01-24T01:51:52.8609119Z >>> model, 2025-01-24T01:51:52.8609384Z >>> StateDictType.FULL_STATE_DICT, 2025-01-24T01:51:52.8609755Z >>> FullStateDictConfig(rank0_only=False), 2025-01-24T01:51:52.8610159Z >>> FullOptimStateDictConfig(rank0_only=False), 2025-01-24T01:51:52.8610514Z >>> ) 2025-01-24T01:51:52.8610763Z >>> state_dict = model.state_dict() 2025-01-24T01:51:52.8611096Z >>> original_osd = optim.state_dict() 2025-01-24T01:51:52.8611460Z >>> optim_state_dict = FSDP.optim_state_dict( 2025-01-24T01:51:52.8611799Z >>> model, 2025-01-24T01:51:52.8612040Z >>> optim, 2025-01-24T01:51:52.8612310Z >>> optim_state_dict=original_osd 2025-01-24T01:51:52.8612614Z >>> ) 2025-01-24T01:51:52.8612894Z >>> save_a_checkpoint(state_dict, optim_state_dict) 2025-01-24T01:51:52.8613276Z >>> # Load a checkpoint 2025-01-24T01:51:52.8613565Z >>> model, optim = ... 2025-01-24T01:51:52.8613906Z >>> state_dict, optim_state_dict = load_a_checkpoint() 2025-01-24T01:51:52.8614277Z >>> FSDP.set_state_dict_type( 2025-01-24T01:51:52.8614575Z >>> model, 2025-01-24T01:51:52.8614845Z >>> StateDictType.FULL_STATE_DICT, 2025-01-24T01:51:52.8615217Z >>> FullStateDictConfig(rank0_only=False), 2025-01-24T01:51:52.8615616Z >>> FullOptimStateDictConfig(rank0_only=False), 2025-01-24T01:51:52.8615965Z >>> ) 2025-01-24T01:51:52.8616216Z >>> model.load_state_dict(state_dict) 2025-01-24T01:51:52.8616605Z >>> optim_state_dict = FSDP.optim_state_dict_to_load( 2025-01-24T01:51:52.8616999Z >>> model, optim, optim_state_dict 2025-01-24T01:51:52.8617318Z >>> ) 2025-01-24T01:51:52.8617563Z >>> optim.load_state_dict(optim_state_dict) 2025-01-24T01:51:52.8617815Z 2025-01-24T01:51:52.8617905Z Args: 2025-01-24T01:51:52.8618242Z model (torch.nn.Module): Root module (which may or may not be a 2025-01-24T01:51:52.8618782Z :class:`FullyShardedDataParallel` instance) whose parameters 2025-01-24T01:51:52.8619246Z were passed into the optimizer ``optim``. 2025-01-24T01:51:52.8619673Z optim (torch.optim.Optimizer): Optimizer for ``model`` 's 2025-01-24T01:51:52.8620078Z parameters. 2025-01-24T01:51:52.8620453Z optim_state_dict (Dict[str, Any]): The optimizer states to be loaded. 2025-01-24T01:51:52.8620993Z is_named_optimizer (bool): Is this optimizer a NamedOptimizer or 2025-01-24T01:51:52.8621529Z KeyedOptimizer. Only set to True if ``optim`` is TorchRec's 2025-01-24T01:51:52.8622028Z KeyedOptimizer or torch.distributed's NamedOptimizer. 2025-01-24T01:51:52.8622516Z load_directly (bool): If this is set to True, this API will also 2025-01-24T01:51:52.8623041Z call optim.load_state_dict(result) before returning the result. 2025-01-24T01:51:52.8623707Z Otherwise, users are responsible to call ``optim.load_state_dict()`` 2025-01-24T01:51:52.8624166Z (Default: ``False``) 2025-01-24T01:51:52.8624603Z group (dist.ProcessGroup): Model's process group across which parameters 2025-01-24T01:51:52.8625160Z are sharded or ``None`` if using the default process group. ( 2025-01-24T01:51:52.8625561Z Default: ``None``) 2025-01-24T01:51:52.8625757Z 2025-01-24T01:51:52.8626011Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:51:52.8626491Z 2025-01-24T01:51:52.9088946Z msg = Cannot scrape callname=_RemoteModule.__init__ in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/nn/api/remote_module.py line=125. 2025-01-24T01:51:52.9090007Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:51:52.9090408Z 2025-01-24T01:51:52.9090641Z RemoteModule instance can only be created after RPC initialization. 2025-01-24T01:51:52.9091177Z 2025-01-24T01:51:52.9091402Z It creates a user-specified module on a specified remote node. 2025-01-24T01:51:52.9091956Z It behaves like a regular ``nn.Module`` except that the ``forward`` method is 2025-01-24T01:51:52.9092428Z executed on the remote node. 2025-01-24T01:51:52.9092849Z It takes care of autograd recording to ensure the backward pass propagates 2025-01-24T01:51:52.9093369Z gradients back to the corresponding remote module. 2025-01-24T01:51:52.9094004Z It can be shared across processors using `RPC framework `__, 2025-01-24T01:51:52.9094679Z without incurring any overheads of copying the actual module, 2025-01-24T01:51:52.9095195Z which is equivalent to an :class:`~torch.distributed.rpc.RRef` 2025-01-24T01:51:52.9095616Z pointing to the remote module. 2025-01-24T01:51:52.9095820Z 2025-01-24T01:51:52.9096022Z The arguments of ``forward_async`` and ``forward`` are the same as 2025-01-24T01:51:52.9096555Z the ``forward`` method of the module returned by the ``module_cls``. 2025-01-24T01:51:52.9096997Z 2025-01-24T01:51:52.9097446Z Apart from ``forward_async`` and ``forward``, no other methods are supported from nn.Module for now. 2025-01-24T01:51:52.9097951Z 2025-01-24T01:51:52.9098286Z Particularly, to create a hybrid model, typically the local modules should be 2025-01-24T01:51:52.9099038Z created outside of remote modules, rather than as submodules of any remote module (by calling ``add_module``). 2025-01-24T01:51:52.9099634Z Hybrid Example: 2025-01-24T01:51:52.9099887Z >>> class HybridModel(nn.Module): 2025-01-24T01:51:52.9100238Z >>> def __init__(self) -> None: 2025-01-24T01:51:52.9100582Z >>> nn.Module.__init__(self) 2025-01-24T01:51:52.9100952Z >>> self.remote_embedding = RemoteModule(...) 2025-01-24T01:51:52.9101345Z >>> self.local_linear = nn.Linear(...) 2025-01-24T01:51:52.9101581Z 2025-01-24T01:51:52.9101780Z For example, if ``module_cls`` returns an instance of ``nn.Linear``, 2025-01-24T01:51:52.9102363Z that has ``forward`` method signature, ``def forward(input: Tensor) -> Tensor:``, 2025-01-24T01:51:52.9102957Z the generated ``RemoteModule`` will have 2 methods in signature of 2025-01-24T01:51:52.9103419Z ``def forward(input: Tensor) -> Tensor:`` and 2025-01-24T01:51:52.9103835Z ``def forward_async(input: Tensor) -> Future[Tensor]:``. 2025-01-24T01:51:52.9104111Z 2025-01-24T01:51:52.9104230Z .. note:: 2025-01-24T01:51:52.9104495Z If the remote module is placed on a cuda device, 2025-01-24T01:51:52.9105005Z any input CPU tensors will be automatically moved to the same cuda device, 2025-01-24T01:51:52.9105967Z and GPU tensors are returned over the wire according to the device map of the remote worker on TensorPipe RPC backend. 2025-01-24T01:51:52.9106668Z 2025-01-24T01:51:52.9106773Z Args: 2025-01-24T01:51:52.9107262Z remote_device (str): Device on the destination worker where we'd like to place this module. 2025-01-24T01:51:52.9108143Z The device can be a local device or a remote device specified by one of the following remote 2025-01-24T01:51:52.9108709Z formats: 2025-01-24T01:51:52.9108867Z 2025-01-24T01:51:52.9109011Z 1. "rank:/" (ex: "rank:0/cuda:0"). 2025-01-24T01:51:52.9109424Z 2. "/" (ex: "trainer0/cuda:0"). 2025-01-24T01:51:52.9109689Z 2025-01-24T01:51:52.9109957Z In addition, the device field can be optional and the default value is "cpu". 2025-01-24T01:51:52.9110458Z module_cls (nn.Module): For example, 2025-01-24T01:51:52.9110791Z >>> class MyModule(nn.Module): 2025-01-24T01:51:52.9111120Z >>> def forward(input): 2025-01-24T01:51:52.9111431Z >>> return input + 1 2025-01-24T01:51:52.9111730Z >>> 2025-01-24T01:51:52.9115027Z >>> module_cls = MyModule 2025-01-24T01:51:52.9115485Z args (Sequence, optional): args to be passed to ``module_cls``. 2025-01-24T01:51:52.9116018Z kwargs (Dict, optional): kwargs to be passed to ``module_cls``. 2025-01-24T01:51:52.9116688Z _module_interface_cls (type, optional): The TorchScript interface type for the module 2025-01-24T01:51:52.9117345Z to be created. The type object should be decorated by @torch.jit.interface. 2025-01-24T01:51:52.9117937Z If not provided, the generated RemoteModule is not torchscript-able. 2025-01-24T01:51:52.9118508Z Warning, this is an experimental API and susceptible to frequent changes. 2025-01-24T01:51:52.9118922Z 2025-01-24T01:51:52.9119027Z Returns: 2025-01-24T01:51:52.9119411Z A remote module instance which wraps the :class:`~nn.Module` created by the 2025-01-24T01:51:52.9120001Z user-provided ``module_cls``, it has a blocking ``forward`` method and an 2025-01-24T01:51:52.9120669Z asynchronous ``forward_async`` method that returns a future of the ``forward`` call 2025-01-24T01:51:52.9121214Z on the user-provided module on the remote side. 2025-01-24T01:51:52.9121472Z 2025-01-24T01:51:52.9121588Z Example:: 2025-01-24T01:51:52.9121874Z Run the following code in two different processes: 2025-01-24T01:51:52.9122159Z 2025-01-24T01:51:52.9122277Z >>> # xdoctest: +SKIP("distributed") 2025-01-24T01:51:52.9122608Z >>> # On worker 0: 2025-01-24T01:51:52.9122878Z >>> import torch 2025-01-24T01:51:52.9123168Z >>> import torch.distributed.rpc as rpc 2025-01-24T01:51:52.9123508Z >>> from torch import nn, Tensor 2025-01-24T01:51:52.9123948Z >>> from torch.distributed.nn.api.remote_module import RemoteModule 2025-01-24T01:51:52.9124384Z >>> 2025-01-24T01:51:52.9124652Z >>> rpc.init_rpc("worker0", rank=0, world_size=2) 2025-01-24T01:51:52.9125036Z >>> remote_linear_module = RemoteModule( 2025-01-24T01:51:52.9125389Z >>> "worker1/cpu", nn.Linear, args=(20, 30), 2025-01-24T01:51:52.9125724Z >>> ) 2025-01-24T01:51:52.9125967Z >>> input = torch.randn(128, 20) 2025-01-24T01:51:52.9126341Z >>> ret_fut = remote_linear_module.forward_async(input) 2025-01-24T01:51:52.9126726Z >>> ret = ret_fut.wait() 2025-01-24T01:51:52.9127001Z >>> rpc.shutdown() 2025-01-24T01:51:52.9127177Z 2025-01-24T01:51:52.9127270Z >>> # On worker 1: 2025-01-24T01:51:52.9127527Z >>> import torch 2025-01-24T01:51:52.9127809Z >>> import torch.distributed.rpc as rpc 2025-01-24T01:51:52.9128140Z >>> 2025-01-24T01:51:52.9128400Z >>> rpc.init_rpc("worker1", rank=1, world_size=2) 2025-01-24T01:51:52.9128749Z >>> rpc.shutdown() 2025-01-24T01:51:52.9128936Z 2025-01-24T01:51:52.9129191Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:51:52.9129579Z 2025-01-24T01:51:52.9130285Z msg = Cannot scrape callname=_RemoteModule.init_from_module_rref in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/nn/api/remote_module.py line=502. 2025-01-24T01:51:52.9131372Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:51:52.9131767Z 2025-01-24T01:51:52.9132144Z Besides the constructor, a RemoteModule instance can also be initialized given a module RRef. 2025-01-24T01:51:52.9132630Z 2025-01-24T01:51:52.9132973Z This alternate initialization method can be particularly useful if we want to create multiple 2025-01-24T01:51:52.9133747Z RemoteModule instances that share the same underlying module and reduce memory consumption. 2025-01-24T01:51:52.9134182Z 2025-01-24T01:51:52.9134480Z Moreover, this also provides a workaround for passing script RemoteModule over RPC, 2025-01-24T01:51:52.9135059Z which is not supported. The recommended way is as follows: 2025-01-24T01:51:52.9135371Z 2025-01-24T01:51:52.9135494Z 1. the sender creates a RemoteModule; 2025-01-24T01:51:52.9135886Z 2. the sender sends its ``module_rref`` over RPC; 2025-01-24T01:51:52.9136574Z 3. the receiver calls this method to initialize another RemoteModule using the same ``module_rref``. 2025-01-24T01:51:52.9137032Z 2025-01-24T01:51:52.9137152Z Example:: 2025-01-24T01:51:52.9137499Z Run the following code in two different processes: 2025-01-24T01:51:52.9137785Z 2025-01-24T01:51:52.9137911Z >>> # xdoctest: +SKIP("distributed") 2025-01-24T01:51:52.9138251Z >>> # On worker 0: 2025-01-24T01:51:52.9138527Z >>> import torch 2025-01-24T01:51:52.9138830Z >>> import torch.distributed.rpc as rpc 2025-01-24T01:51:52.9139193Z >>> from torch import nn, Tensor 2025-01-24T01:51:52.9139628Z >>> from torch.distributed.nn.api.remote_module import RemoteModule 2025-01-24T01:51:52.9140067Z >>> 2025-01-24T01:51:52.9140339Z >>> rpc.init_rpc("worker0", rank=0, world_size=2) 2025-01-24T01:51:52.9140724Z >>> remote_module = RemoteModule( 2025-01-24T01:51:52.9141082Z >>> "worker1/cpu", nn.Linear, args=(20, 30), 2025-01-24T01:51:52.9141410Z >>> ) 2025-01-24T01:51:52.9141632Z >>> 2025-01-24T01:51:52.9141875Z >>> remote_module1 = rpc.rpc_sync( 2025-01-24T01:51:52.9142203Z >>> "worker1/cpu", 2025-01-24T01:51:52.9142517Z >>> RemoteModule.init_from_module_rref, 2025-01-24T01:51:52.9142923Z >>> ("worker1/cpu", remote_module1.get_module_rref()), 2025-01-24T01:51:52.9143297Z >>> ) 2025-01-24T01:51:52.9143534Z >>> rpc.shutdown() 2025-01-24T01:51:52.9143693Z 2025-01-24T01:51:52.9143798Z >>> # On worker 1: 2025-01-24T01:51:52.9144059Z >>> import torch 2025-01-24T01:51:52.9144332Z >>> import torch.distributed.rpc as rpc 2025-01-24T01:51:52.9144669Z >>> 2025-01-24T01:51:52.9144946Z >>> rpc.init_rpc("worker1", rank=1, world_size=2) 2025-01-24T01:51:52.9145306Z >>> rpc.shutdown() 2025-01-24T01:51:52.9145465Z 2025-01-24T01:51:52.9145570Z Args: 2025-01-24T01:51:52.9145979Z remote_device (str): Device on the destination worker where we'd like to place this module. 2025-01-24T01:51:52.9146816Z The device can be a local device or a remote device specified by one of the following remote 2025-01-24T01:51:52.9147329Z formats: 2025-01-24T01:51:52.9147473Z 2025-01-24T01:51:52.9147632Z 1. "rank:/" (ex: "rank:0/cuda:0"). 2025-01-24T01:51:52.9148052Z 2. "/" (ex: "trainer0/cuda:0"). 2025-01-24T01:51:52.9148317Z 2025-01-24T01:51:52.9148581Z In addition, the device field can be optional and the default value is "cpu". 2025-01-24T01:51:52.9149201Z module_rref (RRef[nn.Module]): The module reference shared by both the caller and 2025-01-24T01:51:52.9149699Z the created remote module. 2025-01-24T01:51:52.9150197Z _module_interface_cls (type, optional): The TorchScript interface type for the module 2025-01-24T01:51:52.9150844Z to be created. The type object should be decorated by @torch.jit.interface. 2025-01-24T01:51:52.9151431Z If not provided, the generated RemoteModule is not torchscript-able. 2025-01-24T01:51:52.9152006Z Warning, this is an experimental API and susceptible to frequent changes. 2025-01-24T01:51:52.9152372Z 2025-01-24T01:51:52.9152463Z Returns: 2025-01-24T01:51:52.9152837Z A remote module instance which wraps the :class:`~nn.Module` created by the 2025-01-24T01:51:52.9153533Z user-provided ``module_rref``, it has a blocking ``forward`` method and an 2025-01-24T01:51:52.9154177Z asynchronous ``forward_async`` method that returns a future of the ``forward`` call 2025-01-24T01:51:52.9154729Z on the user-provided module on the remote side. 2025-01-24T01:51:52.9154987Z 2025-01-24T01:51:52.9155239Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:51:52.9155619Z 2025-01-24T01:51:52.9156231Z msg = Cannot scrape callname=RemoteModule in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/nn/api/remote_module.py line=594. 2025-01-24T01:51:52.9157223Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:51:52.9157615Z 2025-01-24T01:51:52.9157900Z A RemoteModule instance can only be created after RPC initialization. 2025-01-24T01:51:52.9158264Z 2025-01-24T01:51:52.9158462Z It creates a user-specified module on a specified remote node. 2025-01-24T01:51:52.9159065Z It behaves like a regular ``nn.Module`` except that the ``forward`` method is 2025-01-24T01:51:52.9159541Z executed on the remote node. 2025-01-24T01:51:52.9159973Z It takes care of autograd recording to ensure the backward pass propagates 2025-01-24T01:51:52.9160493Z gradients back to the corresponding remote module. 2025-01-24T01:51:52.9160775Z 2025-01-24T01:51:52.9160996Z It generates two methods ``forward_async`` and ``forward`` based on the 2025-01-24T01:51:52.9161556Z signature of the ``forward`` method of ``module_cls``. ``forward_async`` 2025-01-24T01:51:52.9162154Z runs asynchronously and returns a Future. The arguments of ``forward_async`` 2025-01-24T01:51:52.9162731Z and ``forward`` are the same as the ``forward`` method of the module 2025-01-24T01:51:52.9163159Z returned by the ``module_cls``. 2025-01-24T01:51:52.9163384Z 2025-01-24T01:51:52.9163586Z For example, if ``module_cls`` returns an instance of ``nn.Linear``, 2025-01-24T01:51:52.9164172Z that has ``forward`` method signature: ``def forward(input: Tensor) -> Tensor:``, 2025-01-24T01:51:52.9164784Z the generated ``RemoteModule`` will have 2 methods with the signatures: 2025-01-24T01:51:52.9165127Z 2025-01-24T01:51:52.9165268Z | ``def forward(input: Tensor) -> Tensor:`` 2025-01-24T01:51:52.9165680Z | ``def forward_async(input: Tensor) -> Future[Tensor]:`` 2025-01-24T01:51:52.9165956Z 2025-01-24T01:51:52.9166044Z Args: 2025-01-24T01:51:52.9166461Z remote_device (str): Device on the destination worker where we'd like to place this module. 2025-01-24T01:51:52.9167227Z The format should be "/", where the device field can be parsed as torch.device type. 2025-01-24T01:51:52.9167845Z E.g., "trainer0/cpu", "trainer0", "ps0/cuda:0". 2025-01-24T01:51:52.9168349Z In addition, the device field can be optional and the default value is "cpu". 2025-01-24T01:51:52.9168983Z module_cls (nn.Module): Class for the module to be created remotely. For example, 2025-01-24T01:51:52.9169357Z 2025-01-24T01:51:52.9169472Z >>> class MyModule(nn.Module): 2025-01-24T01:51:52.9169805Z >>> def forward(input): 2025-01-24T01:51:52.9170117Z >>> return input + 1 2025-01-24T01:51:52.9170422Z >>> 2025-01-24T01:51:52.9170655Z >>> module_cls = MyModule 2025-01-24T01:51:52.9170849Z 2025-01-24T01:51:52.9171051Z args (Sequence, optional): args to be passed to ``module_cls``. 2025-01-24T01:51:52.9171571Z kwargs (Dict, optional): kwargs to be passed to ``module_cls``. 2025-01-24T01:51:52.9171895Z 2025-01-24T01:51:52.9171986Z Returns: 2025-01-24T01:51:52.9172363Z A remote module instance which wraps the :class:`~nn.Module` created by the 2025-01-24T01:51:52.9172967Z user-provided ``module_cls``, it has a blocking ``forward`` method and an 2025-01-24T01:51:52.9173602Z asynchronous ``forward_async`` method that returns a future of the ``forward`` call 2025-01-24T01:51:52.9174208Z on the user-provided module on the remote side. 2025-01-24T01:51:52.9174480Z 2025-01-24T01:51:52.9174577Z Example:: 2025-01-24T01:51:52.9174871Z Run the following code in two different processes: 2025-01-24T01:51:52.9175137Z 2025-01-24T01:51:52.9175269Z >>> # xdoctest: +SKIP("distributed") 2025-01-24T01:51:52.9175869Z >>> # On worker 0: 2025-01-24T01:51:52.9176184Z >>> import torch 2025-01-24T01:51:52.9176480Z >>> import torch.distributed.rpc as rpc 2025-01-24T01:51:52.9176838Z >>> from torch import nn, Tensor 2025-01-24T01:51:52.9177285Z >>> from torch.distributed.nn.api.remote_module import RemoteModule 2025-01-24T01:51:52.9177729Z >>> 2025-01-24T01:51:52.9177989Z >>> rpc.init_rpc("worker0", rank=0, world_size=2) 2025-01-24T01:51:52.9178421Z >>> remote_linear_module = RemoteModule( 2025-01-24T01:51:52.9178799Z >>> "worker1/cpu", nn.Linear, args=(20, 30), 2025-01-24T01:51:52.9179320Z >>> ) 2025-01-24T01:51:52.9179685Z >>> input = torch.randn(128, 20) 2025-01-24T01:51:52.9180054Z >>> ret_fut = remote_linear_module.forward_async(input) 2025-01-24T01:51:52.9180434Z >>> ret = ret_fut.wait() 2025-01-24T01:51:52.9180729Z >>> rpc.shutdown() 2025-01-24T01:51:52.9180906Z 2025-01-24T01:51:52.9181006Z >>> # On worker 1: 2025-01-24T01:51:52.9181274Z >>> import torch 2025-01-24T01:51:52.9181569Z >>> import torch.distributed.rpc as rpc 2025-01-24T01:51:52.9181892Z >>> 2025-01-24T01:51:52.9182165Z >>> rpc.init_rpc("worker1", rank=1, world_size=2) 2025-01-24T01:51:52.9182527Z >>> rpc.shutdown() 2025-01-24T01:51:52.9182683Z 2025-01-24T01:51:52.9182904Z Furthermore, a more practical example that is combined with 2025-01-24T01:51:52.9183722Z `DistributedDataParallel `__ (DDP) 2025-01-24T01:51:52.9184643Z can be found in this `tutorial `__. 2025-01-24T01:51:52.9185111Z 2025-01-24T01:51:52.9185377Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:51:52.9185760Z 2025-01-24T01:51:52.9336410Z msg = Cannot scrape callname=DistributedOptimizer in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/optim/optimizer.py line=130. 2025-01-24T01:51:52.9337444Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:51:52.9337844Z 2025-01-24T01:51:52.9338097Z DistributedOptimizer takes remote references to parameters scattered 2025-01-24T01:51:52.9338719Z across workers and applies the given optimizer locally for each parameter. 2025-01-24T01:51:52.9339076Z 2025-01-24T01:51:52.9339341Z This class uses :meth:`~torch.distributed.autograd.get_gradients` in order 2025-01-24T01:51:52.9339878Z to retrieve the gradients for specific parameters. 2025-01-24T01:51:52.9340147Z 2025-01-24T01:51:52.9340249Z Concurrent calls to 2025-01-24T01:51:52.9340625Z :meth:`~torch.distributed.optim.DistributedOptimizer.step`, 2025-01-24T01:51:52.9341106Z either from the same or different clients, will 2025-01-24T01:51:52.9341602Z be serialized on each worker -- as each worker's optimizer can only work 2025-01-24T01:51:52.9342160Z on one set of gradients at a time. However, there is no guarantee that 2025-01-24T01:51:52.9342728Z the full forward-backward-optimizer sequence will execute for one client 2025-01-24T01:51:52.9343322Z at a time. This means that the gradients being applied may not correspond 2025-01-24T01:51:52.9343895Z to the latest forward pass executed on a given worker. Also, there is no 2025-01-24T01:51:52.9344368Z guaranteed ordering across workers. 2025-01-24T01:51:52.9344582Z 2025-01-24T01:51:52.9344859Z `DistributedOptimizer` creates the local optimizer with TorchScript enabled 2025-01-24T01:51:52.9345490Z by default, so that optimizer updates are not blocked by the Python Global 2025-01-24T01:51:52.9346080Z Interpreter Lock (GIL) in the case of multithreaded training (e.g. Distributed 2025-01-24T01:51:52.9346937Z Model Parallel). This feature is currently enabled for most optimizers. You 2025-01-24T01:51:52.9347569Z can also follow `the recipe`__ in PyTorch tutorials to enable TorchScript support 2025-01-24T01:51:52.9348070Z for your own custom optimizers. 2025-01-24T01:51:52.9348268Z 2025-01-24T01:51:52.9348399Z Args: 2025-01-24T01:51:52.9348728Z optimizer_class (optim.Optimizer): the class of optimizer to 2025-01-24T01:51:52.9349169Z instantiate on each worker. 2025-01-24T01:51:52.9349588Z params_rref (list[RRef]): list of RRefs to local or remote parameters 2025-01-24T01:51:52.9350029Z to optimize. 2025-01-24T01:51:52.9350412Z args: arguments to pass to the optimizer constructor on each worker. 2025-01-24T01:51:52.9351045Z kwargs: arguments to pass to the optimizer constructor on each worker. 2025-01-24T01:51:52.9351390Z 2025-01-24T01:51:52.9351516Z Example:: 2025-01-24T01:51:52.9351765Z >>> # xdoctest: +SKIP("distributed") 2025-01-24T01:51:52.9352223Z >>> import torch.distributed.autograd as dist_autograd 2025-01-24T01:51:52.9352645Z >>> import torch.distributed.rpc as rpc 2025-01-24T01:51:52.9353053Z >>> from torch import optim 2025-01-24T01:51:52.9353576Z >>> from torch.distributed.optim import DistributedOptimizer 2025-01-24T01:51:52.9353975Z >>> 2025-01-24T01:51:52.9354246Z >>> with dist_autograd.context() as context_id: 2025-01-24T01:51:52.9354677Z >>> # Forward pass. 2025-01-24T01:51:52.9355068Z >>> rref1 = rpc.remote("worker1", torch.add, args=(torch.ones(2), 3)) 2025-01-24T01:51:52.9355600Z >>> rref2 = rpc.remote("worker1", torch.add, args=(torch.ones(2), 1)) 2025-01-24T01:51:52.9356048Z >>> loss = rref1.to_here() + rref2.to_here() 2025-01-24T01:51:52.9356391Z >>> 2025-01-24T01:51:52.9356629Z >>> # Backward pass. 2025-01-24T01:51:52.9356972Z >>> dist_autograd.backward(context_id, [loss.sum()]) 2025-01-24T01:51:52.9357339Z >>> 2025-01-24T01:51:52.9357555Z >>> # Optimizer. 2025-01-24T01:51:52.9357863Z >>> dist_optim = DistributedOptimizer( 2025-01-24T01:51:52.9358209Z >>> optim.SGD, 2025-01-24T01:51:52.9358483Z >>> [rref1, rref2], 2025-01-24T01:51:52.9358772Z >>> lr=0.05, 2025-01-24T01:51:52.9359018Z >>> ) 2025-01-24T01:51:52.9359275Z >>> dist_optim.step(context_id) 2025-01-24T01:51:52.9359510Z 2025-01-24T01:51:52.9359680Z __ https://github.com/pytorch/tutorials/pull/1465 2025-01-24T01:51:52.9359946Z 2025-01-24T01:51:52.9360216Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:51:52.9360586Z 2025-01-24T01:51:52.9361337Z msg = Cannot scrape callname=PostLocalSGDOptimizer in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/optim/post_localSGD_optimizer.py line=9. 2025-01-24T01:51:52.9362424Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:51:52.9362806Z 2025-01-24T01:51:52.9363211Z Wraps an arbitrary :class:`torch.optim.Optimizer` and runs `post-local SGD `_, 2025-01-24T01:51:52.9363888Z This optimizer runs local optimizer at every step. 2025-01-24T01:51:52.9364480Z After the warm-up stage, it averages parameters periodically afer the local optimizer is applied. 2025-01-24T01:51:52.9364947Z 2025-01-24T01:51:52.9365034Z Args: 2025-01-24T01:51:52.9365271Z optim: The local optimizer. 2025-01-24T01:51:52.9365711Z averager: A model averager instance to run post-localSGD algorithm. 2025-01-24T01:51:52.9366049Z 2025-01-24T01:51:52.9366160Z Example:: 2025-01-24T01:51:52.9366289Z 2025-01-24T01:51:52.9366418Z >>> # xdoctest: +SKIP("undefined variables") 2025-01-24T01:51:52.9366765Z >>> import torch 2025-01-24T01:51:52.9367053Z >>> import torch.distributed as dist 2025-01-24T01:51:52.9367570Z >>> import torch.distributed.algorithms.model_averaging.averagers as averagers 2025-01-24T01:51:52.9368082Z >>> import torch.nn as nn 2025-01-24T01:51:52.9368598Z >>> from torch.distributed.optim import PostLocalSGDOptimizer 2025-01-24T01:51:52.9369182Z >>> from torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook import ( 2025-01-24T01:51:52.9369699Z >>> PostLocalSGDState, 2025-01-24T01:51:52.9370008Z >>> post_localSGD_hook, 2025-01-24T01:51:52.9370296Z >>> ) 2025-01-24T01:51:52.9370387Z >>> 2025-01-24T01:51:52.9370560Z >>> model = nn.parallel.DistributedDataParallel( 2025-01-24T01:51:52.9370708Z >>> module, device_ids=[rank], output_device=rank 2025-01-24T01:51:52.9370803Z >>> ) 2025-01-24T01:51:52.9370907Z >>> 2025-01-24T01:51:52.9371055Z >>> # Register a post-localSGD communication hook. 2025-01-24T01:51:52.9371372Z >>> state = PostLocalSGDState(process_group=None, subgroup=None, start_localSGD_iter=100) 2025-01-24T01:51:52.9371593Z >>> model.register_comm_hook(state, post_localSGD_hook) 2025-01-24T01:51:52.9371682Z >>> 2025-01-24T01:51:52.9371903Z >>> # Create a post-localSGD optimizer that wraps a local optimizer. 2025-01-24T01:51:52.9372195Z >>> # Note that ``warmup_steps`` used in ``PostLocalSGDOptimizer`` must be the same as 2025-01-24T01:51:52.9372378Z >>> # ``start_localSGD_iter`` used in ``PostLocalSGDState``. 2025-01-24T01:51:52.9372592Z >>> local_optim = torch.optim.SGD(params=model.parameters(), lr=0.01) 2025-01-24T01:51:52.9372726Z >>> opt = PostLocalSGDOptimizer( 2025-01-24T01:51:52.9372832Z >>> optim=local_optim, 2025-01-24T01:51:52.9373083Z >>> averager=averagers.PeriodicModelAverager(period=4, warmup_steps=100) 2025-01-24T01:51:52.9373182Z >>> ) 2025-01-24T01:51:52.9373270Z >>> 2025-01-24T01:51:52.9373511Z >>> # In the first 100 steps, DDP runs global gradient averaging at every step. 2025-01-24T01:51:52.9373817Z >>> # After 100 steps, DDP runs gradient averaging within each subgroup (intra-node by default), 2025-01-24T01:51:52.9374216Z >>> # and post-localSGD optimizer runs global model averaging every 4 steps after applying the local optimizer. 2025-01-24T01:51:52.9374328Z >>> for step in range(0, 200): 2025-01-24T01:51:52.9374427Z >>> opt.zero_grad() 2025-01-24T01:51:52.9374558Z >>> loss = loss_fn(output, labels) 2025-01-24T01:51:52.9374656Z >>> loss.backward() 2025-01-24T01:51:52.9374762Z >>> opt.step() 2025-01-24T01:51:52.9374767Z 2025-01-24T01:51:52.9375024Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:51:52.9375028Z 2025-01-24T01:51:52.9473943Z msg = Cannot scrape callname=ZeroRedundancyOptimizer in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/optim/zero_redundancy_optimizer.py line=283. 2025-01-24T01:51:52.9474229Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:51:52.9474236Z 2025-01-24T01:51:52.9474676Z Wrap an arbitrary :class:`optim.Optimizer ` and shards its states across ranks in the group. 2025-01-24T01:51:52.9474686Z 2025-01-24T01:51:52.9474821Z The sharing is done as described by ZeRO_. 2025-01-24T01:51:52.9474830Z 2025-01-24T01:51:52.9474991Z The local optimizer instance in each rank is only 2025-01-24T01:51:52.9475237Z responsible for updating approximately ``1 / world_size`` parameters and 2025-01-24T01:51:52.9475453Z hence only needs to keep ``1 / world_size`` optimizer states. After 2025-01-24T01:51:52.9475702Z parameters are updated locally, each rank will broadcast its parameters to 2025-01-24T01:51:52.9475891Z all other peers to keep all model replicas in the same state. 2025-01-24T01:51:52.9476102Z ``ZeroRedundancyOptimizer`` can be used in conjunction with 2025-01-24T01:51:52.9476365Z :class:`torch.nn.parallel.DistributedDataParallel` to reduce per-rank peak 2025-01-24T01:51:52.9476481Z memory consumption. 2025-01-24T01:51:52.9476486Z 2025-01-24T01:51:52.9476753Z ``ZeroRedundancyOptimizer`` uses a sorted-greedy algorithm to pack a number 2025-01-24T01:51:52.9477003Z of parameters at each rank. Each parameter belongs to a single rank and is 2025-01-24T01:51:52.9477440Z not divided among ranks. The partition is arbitrary and might not match the 2025-01-24T01:51:52.9477575Z the parameter registration or usage order. 2025-01-24T01:51:52.9477579Z 2025-01-24T01:51:52.9477689Z Arguments: 2025-01-24T01:51:52.9477886Z params (``Iterable``): an ``Iterable`` of :class:`torch.Tensor` s 2025-01-24T01:51:52.9478094Z or :class:`dict` s giving all parameters, which will be sharded 2025-01-24T01:51:52.9478192Z across ranks. 2025-01-24T01:51:52.9478197Z 2025-01-24T01:51:52.9478307Z Keyword Args: 2025-01-24T01:51:52.9478533Z optimizer_class (:class:`torch.nn.Optimizer`): the class of the local 2025-01-24T01:51:52.9478629Z optimizer. 2025-01-24T01:51:52.9478857Z process_group (``ProcessGroup``, optional): ``torch.distributed`` 2025-01-24T01:51:52.9479126Z ``ProcessGroup`` (default: ``dist.group.WORLD`` initialized by 2025-01-24T01:51:52.9479485Z :meth:`torch.distributed.init_process_group`). 2025-01-24T01:51:52.9479786Z parameters_as_bucket_view (bool, optional): if ``True``, parameters are 2025-01-24T01:51:52.9480014Z packed into buckets to speed up communication, and ``param.data`` 2025-01-24T01:51:52.9480220Z fields point to bucket views at different offsets; if ``False``, 2025-01-24T01:51:52.9480431Z each individual parameter is communicated separately, and each 2025-01-24T01:51:52.9480601Z ``params.data`` stays intact (default: ``False``). 2025-01-24T01:51:52.9480797Z overlap_with_ddp (bool, optional): if ``True``, :meth:`step` is 2025-01-24T01:51:52.9481016Z overlapped with :class:`DistributedDataParallel` 's gradient 2025-01-24T01:51:52.9481232Z synchronization; this requires (1) either a functional optimizer 2025-01-24T01:51:52.9481432Z for the ``optimizer_class`` argument or one with a functional 2025-01-24T01:51:52.9481620Z equivalent and (2) registering a DDP communication hook 2025-01-24T01:51:52.9481863Z constructed from one of the functions in ``ddp_zero_hook.py``; 2025-01-24T01:51:52.9482037Z parameters are packed into buckets matching those in 2025-01-24T01:51:52.9482197Z :class:`DistributedDataParallel`, meaning that the 2025-01-24T01:51:52.9482365Z ``parameters_as_bucket_view`` argument is ignored. 2025-01-24T01:51:52.9482561Z If ``False``, :meth:`step` runs disjointly after the backward pass 2025-01-24T01:51:52.9482673Z (per normal). 2025-01-24T01:51:52.9482780Z (default: ``False``) 2025-01-24T01:51:52.9483011Z **defaults: any trailing arguments, which are forwarded to the local 2025-01-24T01:51:52.9483108Z optimizer. 2025-01-24T01:51:52.9483112Z 2025-01-24T01:51:52.9483215Z Example:: 2025-01-24T01:51:52.9483220Z 2025-01-24T01:51:52.9483338Z >>> # xdoctest: +SKIP 2025-01-24T01:51:52.9483445Z >>> import torch.nn as nn 2025-01-24T01:51:52.9483664Z >>> from torch.distributed.optim import ZeroRedundancyOptimizer 2025-01-24T01:51:52.9483876Z >>> from torch.nn.parallel import DistributedDataParallel as DDP 2025-01-24T01:51:52.9484119Z >>> model = nn.Sequential(*[nn.Linear(2000, 2000).to(rank) for _ in range(20)]) 2025-01-24T01:51:52.9484239Z >>> ddp = DDP(model, device_ids=[rank]) 2025-01-24T01:51:52.9484359Z >>> opt = ZeroRedundancyOptimizer( 2025-01-24T01:51:52.9484479Z >>> ddp.parameters(), 2025-01-24T01:51:52.9484604Z >>> optimizer_class=torch.optim.Adam, 2025-01-24T01:51:52.9484707Z >>> lr=0.01 2025-01-24T01:51:52.9484796Z >>> ) 2025-01-24T01:51:52.9484907Z >>> ddp(inputs).sum().backward() 2025-01-24T01:51:52.9485012Z >>> opt.step() 2025-01-24T01:51:52.9485017Z 2025-01-24T01:51:52.9485111Z .. warning:: 2025-01-24T01:51:52.9485336Z Currently, ``ZeroRedundancyOptimizer`` requires that all of the 2025-01-24T01:51:52.9485485Z passed-in parameters are the same dense type. 2025-01-24T01:51:52.9485489Z 2025-01-24T01:51:52.9485594Z .. warning:: 2025-01-24T01:51:52.9485859Z If you pass ``overlap_with_ddp=True``, be wary of the following: Given 2025-01-24T01:51:52.9486102Z the way that overlapping :class:`DistributedDataParallel` with 2025-01-24T01:51:52.9486355Z :class:`ZeroRedundancyOptimizer` is currently implemented, the first 2025-01-24T01:51:52.9486574Z two or three training iterations do not perform parameter updates in 2025-01-24T01:51:52.9486782Z the optimizer step, depending on if ``static_graph=False`` or 2025-01-24T01:51:52.9486975Z ``static_graph=True``, respectively. This is because it needs 2025-01-24T01:51:52.9487181Z information about the gradient bucketing strategy used by 2025-01-24T01:51:52.9487407Z :class:`DistributedDataParallel`, which is not finalized until the 2025-01-24T01:51:52.9487611Z second forward pass if ``static_graph=False`` or until the third 2025-01-24T01:51:52.9487884Z forward pass if ``static_graph=True``. To adjust for this, one option 2025-01-24T01:51:52.9487996Z is to prepend dummy inputs. 2025-01-24T01:51:52.9488030Z 2025-01-24T01:51:52.9488310Z .. warning:: ZeroRedundancyOptimizer is experimental and subject to change. 2025-01-24T01:51:52.9488314Z 2025-01-24T01:51:52.9488448Z .. _ZeRO: https://arxiv.org/abs/1910.02054 2025-01-24T01:51:52.9488452Z 2025-01-24T01:51:52.9488456Z 2025-01-24T01:51:52.9488725Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:51:52.9488730Z 2025-01-24T01:51:52.9691820Z msg = Cannot scrape callname=_CustomReducer in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/pipelining/microbatch.py line=28. 2025-01-24T01:51:52.9692325Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:51:52.9692336Z 2025-01-24T01:51:52.9692762Z Custom reducer class that can be used to specify a custom operation that 2025-01-24T01:51:52.9693080Z reduces losses of multiple microbatches into one value. 2025-01-24T01:51:52.9693088Z 2025-01-24T01:51:52.9693255Z Example: 2025-01-24T01:51:52.9693424Z >>> # xdoctest: +SKIP 2025-01-24T01:51:52.9693638Z >>> sum_reducer = _CustomReducer( 2025-01-24T01:51:52.9693816Z >>> torch.tensor(0.0), 2025-01-24T01:51:52.9693986Z >>> lambda a, b: a + b 2025-01-24T01:51:52.9694136Z >>> ) 2025-01-24T01:51:52.9694144Z 2025-01-24T01:51:52.9694619Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:51:52.9694627Z 2025-01-24T01:51:53.0189348Z msg = Cannot scrape callname=async_execution in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/rpc/functions.py line=6. 2025-01-24T01:51:53.0189643Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:51:53.0189652Z 2025-01-24T01:51:53.0189914Z A decorator for a function indicating that the return value of the function 2025-01-24T01:51:53.0190146Z is guaranteed to be a :class:`~torch.futures.Future` object and this 2025-01-24T01:51:53.0190404Z function can run asynchronously on the RPC callee. More specifically, the 2025-01-24T01:51:53.0190668Z callee extracts the :class:`~torch.futures.Future` returned by the wrapped 2025-01-24T01:51:53.0190921Z function and installs subsequent processing steps as a callback to that 2025-01-24T01:51:53.0191154Z :class:`~torch.futures.Future`. The installed callback will read the value 2025-01-24T01:51:53.0191359Z from the :class:`~torch.futures.Future` when completed and send the 2025-01-24T01:51:53.0191555Z value back as the RPC response. That also means the returned 2025-01-24T01:51:53.0191787Z :class:`~torch.futures.Future` only exists on the callee side and is never 2025-01-24T01:51:53.0192033Z sent through RPC. This decorator is useful when the wrapped function's 2025-01-24T01:51:53.0192242Z (``fn``) execution needs to pause and resume due to, e.g., containing 2025-01-24T01:51:53.0192489Z :meth:`~torch.distributed.rpc.rpc_async` or waiting for other signals. 2025-01-24T01:51:53.0192494Z 2025-01-24T01:51:53.0192722Z .. note:: To enable asynchronous execution, applications must pass the 2025-01-24T01:51:53.0193189Z function object returned by this decorator to RPC APIs. If RPC detected 2025-01-24T01:51:53.0193431Z attributes installed by this decorator, it knows that this function 2025-01-24T01:51:53.0193619Z returns a ``Future`` object and will handle that accordingly. 2025-01-24T01:51:53.0193854Z However, this does not mean this decorator has to be outmost one when 2025-01-24T01:51:53.0194082Z defining a function. For example, when combined with ``@staticmethod`` 2025-01-24T01:51:53.0194400Z or ``@classmethod``, ``@rpc.functions.async_execution`` needs to be the 2025-01-24T01:51:53.0194623Z inner decorator to allow the target function be recognized as a static 2025-01-24T01:51:53.0194859Z or class function. This target function can still execute asynchronously 2025-01-24T01:51:53.0195162Z because, when accessed, the static or class method preserves attributes 2025-01-24T01:51:53.0195318Z installed by ``@rpc.functions.async_execution``. 2025-01-24T01:51:53.0195375Z 2025-01-24T01:51:53.0195379Z 2025-01-24T01:51:53.0195529Z Example:: 2025-01-24T01:51:53.0195747Z The returned :class:`~torch.futures.Future` object can come from 2025-01-24T01:51:53.0195883Z :meth:`~torch.distributed.rpc.rpc_async`, 2025-01-24T01:51:53.0196121Z :meth:`~torch.futures.Future.then`, or :class:`~torch.futures.Future` 2025-01-24T01:51:53.0196300Z constructor. The example below shows directly using the 2025-01-24T01:51:53.0196434Z :class:`~torch.futures.Future` returned by 2025-01-24T01:51:53.0196573Z :meth:`~torch.futures.Future.then`. 2025-01-24T01:51:53.0196578Z 2025-01-24T01:51:53.0196701Z >>> from torch.distributed import rpc 2025-01-24T01:51:53.0196815Z >>> 2025-01-24T01:51:53.0196932Z >>> # omitting setup and shutdown RPC 2025-01-24T01:51:53.0197036Z >>> 2025-01-24T01:51:53.0197136Z >>> # On all workers 2025-01-24T01:51:53.0197253Z >>> @rpc.functions.async_execution 2025-01-24T01:51:53.0197387Z >>> def async_add_chained(to, x, y, z): 2025-01-24T01:51:53.0197586Z >>> # This function runs on "worker1" and returns immediately when 2025-01-24T01:51:53.0197791Z >>> # the callback is installed through the `then(cb)` API. In the 2025-01-24T01:51:53.0197978Z >>> # mean time, the `rpc_async` to "worker2" can run concurrently. 2025-01-24T01:51:53.0198159Z >>> # When the return value of that `rpc_async` arrives at 2025-01-24T01:51:53.0198348Z >>> # "worker1", "worker1" will run the lambda function accordingly 2025-01-24T01:51:53.0198544Z >>> # and set the value for the previously returned `Future`, which 2025-01-24T01:51:53.0198743Z >>> # will then trigger RPC to send the result back to "worker0". 2025-01-24T01:51:53.0198914Z >>> return rpc.rpc_async(to, torch.add, args=(x, y)).then( 2025-01-24T01:51:53.0199044Z >>> lambda fut: fut.wait() + z 2025-01-24T01:51:53.0199138Z >>> ) 2025-01-24T01:51:53.0199242Z >>> 2025-01-24T01:51:53.0199338Z >>> # On worker0 2025-01-24T01:51:53.0199445Z >>> # xdoctest: +SKIP 2025-01-24T01:51:53.0199562Z >>> ret = rpc.rpc_sync( 2025-01-24T01:51:53.0199656Z >>> "worker1", 2025-01-24T01:51:53.0199772Z >>> async_add_chained, 2025-01-24T01:51:53.0199897Z >>> args=("worker2", torch.ones(2), 1, 1) 2025-01-24T01:51:53.0199986Z >>> ) 2025-01-24T01:51:53.0200124Z >>> print(ret) # prints tensor([3., 3.]) 2025-01-24T01:51:53.0200129Z 2025-01-24T01:51:53.0200359Z When combined with TorchScript decorators, this decorator must be the 2025-01-24T01:51:53.0200469Z outmost one. 2025-01-24T01:51:53.0200473Z 2025-01-24T01:51:53.0200582Z >>> from torch import Tensor 2025-01-24T01:51:53.0200727Z >>> from torch.futures import Future 2025-01-24T01:51:53.0200849Z >>> from torch.distributed import rpc 2025-01-24T01:51:53.0200939Z >>> 2025-01-24T01:51:53.0201074Z >>> # omitting setup and shutdown RPC 2025-01-24T01:51:53.0201163Z >>> 2025-01-24T01:51:53.0201274Z >>> # On all workers 2025-01-24T01:51:53.0201456Z >>> @torch.jit.script 2025-01-24T01:51:53.0201608Z >>> def script_add(x: Tensor, y: Tensor) -> Tensor: 2025-01-24T01:51:53.0201719Z >>> return x + y 2025-01-24T01:51:53.0201806Z >>> 2025-01-24T01:51:53.0201937Z >>> @rpc.functions.async_execution 2025-01-24T01:51:53.0202038Z >>> @torch.jit.script 2025-01-24T01:51:53.0202228Z >>> def async_add(to: str, x: Tensor, y: Tensor) -> Future[Tensor]: 2025-01-24T01:51:53.0202388Z >>> return rpc.rpc_async(to, script_add, (x, y)) 2025-01-24T01:51:53.0202476Z >>> 2025-01-24T01:51:53.0202581Z >>> # On worker0 2025-01-24T01:51:53.0202683Z >>> ret = rpc.rpc_sync( 2025-01-24T01:51:53.0202798Z >>> "worker1", 2025-01-24T01:51:53.0202889Z >>> async_add, 2025-01-24T01:51:53.0203038Z >>> args=("worker2", torch.ones(2), 1) 2025-01-24T01:51:53.0203138Z >>> ) 2025-01-24T01:51:53.0203256Z >>> print(ret) # prints tensor([2., 2.]) 2025-01-24T01:51:53.0203289Z 2025-01-24T01:51:53.0203526Z When combined with static or class method, this decorator must be the 2025-01-24T01:51:53.0203616Z inner one. 2025-01-24T01:51:53.0203621Z 2025-01-24T01:51:53.0203752Z >>> from torch.distributed import rpc 2025-01-24T01:51:53.0203837Z >>> 2025-01-24T01:51:53.0203951Z >>> # omitting setup and shutdown RPC 2025-01-24T01:51:53.0204048Z >>> 2025-01-24T01:51:53.0204148Z >>> # On all workers 2025-01-24T01:51:53.0204273Z >>> class AsyncExecutionClass: 2025-01-24T01:51:53.0204357Z >>> 2025-01-24T01:51:53.0204459Z >>> @staticmethod 2025-01-24T01:51:53.0204590Z >>> @rpc.functions.async_execution 2025-01-24T01:51:53.0204708Z >>> def static_async_add(to, x, y, z): 2025-01-24T01:51:53.0204892Z >>> return rpc.rpc_async(to, torch.add, args=(x, y)).then( 2025-01-24T01:51:53.0205008Z >>> lambda fut: fut.wait() + z 2025-01-24T01:51:53.0205103Z >>> ) 2025-01-24T01:51:53.0205212Z >>> 2025-01-24T01:51:53.0205316Z >>> @classmethod 2025-01-24T01:51:53.0205458Z >>> @rpc.functions.async_execution 2025-01-24T01:51:53.0205590Z >>> def class_async_add(cls, to, x, y, z): 2025-01-24T01:51:53.0205738Z >>> ret_fut = torch.futures.Future() 2025-01-24T01:51:53.0205898Z >>> rpc.rpc_async(to, torch.add, args=(x, y)).then( 2025-01-24T01:51:53.0206060Z >>> lambda fut: ret_fut.set_result(fut.wait() + z) 2025-01-24T01:51:53.0206169Z >>> ) 2025-01-24T01:51:53.0206278Z >>> return ret_fut 2025-01-24T01:51:53.0206387Z >>> 2025-01-24T01:51:53.0206515Z >>> @rpc.functions.async_execution 2025-01-24T01:51:53.0206649Z >>> def bound_async_add(self, to, x, y, z): 2025-01-24T01:51:53.0206842Z >>> return rpc.rpc_async(to, torch.add, args=(x, y)).then( 2025-01-24T01:51:53.0206964Z >>> lambda fut: fut.wait() + z 2025-01-24T01:51:53.0207121Z >>> ) 2025-01-24T01:51:53.0207218Z >>> 2025-01-24T01:51:53.0207331Z >>> # On worker0 2025-01-24T01:51:53.0207439Z >>> ret = rpc.rpc_sync( 2025-01-24T01:51:53.0207539Z >>> "worker1", 2025-01-24T01:51:53.0207690Z >>> AsyncExecutionClass.static_async_add, 2025-01-24T01:51:53.0207817Z >>> args=("worker2", torch.ones(2), 1, 2) 2025-01-24T01:51:53.0207925Z >>> ) 2025-01-24T01:51:53.0208053Z >>> print(ret) # prints tensor([4., 4.]) 2025-01-24T01:51:53.0208147Z >>> 2025-01-24T01:51:53.0208258Z >>> ret = rpc.rpc_sync( 2025-01-24T01:51:53.0208356Z >>> "worker1", 2025-01-24T01:51:53.0208499Z >>> AsyncExecutionClass.class_async_add, 2025-01-24T01:51:53.0208617Z >>> args=("worker2", torch.ones(2), 1, 2) 2025-01-24T01:51:53.0208705Z >>> ) 2025-01-24T01:51:53.0208835Z >>> print(ret) # prints tensor([4., 4.]) 2025-01-24T01:51:53.0208841Z 2025-01-24T01:51:53.0209009Z This decorator also works with RRef helpers, i.e., . 2025-01-24T01:51:53.0209163Z :meth:`torch.distributed.rpc.RRef.rpc_sync`, 2025-01-24T01:51:53.0209381Z :meth:`torch.distributed.rpc.RRef.rpc_async`, and 2025-01-24T01:51:53.0209536Z :meth:`torch.distributed.rpc.RRef.remote`. 2025-01-24T01:51:53.0209540Z 2025-01-24T01:51:53.0209661Z >>> from torch.distributed import rpc 2025-01-24T01:51:53.0209747Z >>> 2025-01-24T01:51:53.0209903Z >>> # reuse the AsyncExecutionClass class above 2025-01-24T01:51:53.0210057Z >>> rref = rpc.remote("worker1", AsyncExecutionClass) 2025-01-24T01:51:53.0210284Z >>> ret = rref.rpc_sync().static_async_add("worker2", torch.ones(2), 1, 2) 2025-01-24T01:51:53.0210400Z >>> print(ret) # prints tensor([4., 4.]) 2025-01-24T01:51:53.0210500Z >>> 2025-01-24T01:51:53.0210655Z >>> rref = rpc.remote("worker1", AsyncExecutionClass) 2025-01-24T01:51:53.0210922Z >>> ret = rref.rpc_async().static_async_add("worker2", torch.ones(2), 1, 2).wait() 2025-01-24T01:51:53.0211053Z >>> print(ret) # prints tensor([4., 4.]) 2025-01-24T01:51:53.0211166Z >>> 2025-01-24T01:51:53.0211333Z >>> rref = rpc.remote("worker1", AsyncExecutionClass) 2025-01-24T01:51:53.0211567Z >>> ret = rref.remote().static_async_add("worker2", torch.ones(2), 1, 2).to_here() 2025-01-24T01:51:53.0211697Z >>> print(ret) # prints tensor([4., 4.]) 2025-01-24T01:51:53.0211702Z 2025-01-24T01:51:53.0211962Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:51:53.0211967Z 2025-01-24T01:51:53.0239356Z msg = Cannot scrape callname=TensorPipeRpcBackendOptions.set_device_map in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/rpc/options.py line=108. 2025-01-24T01:51:53.0239625Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:51:53.0239631Z 2025-01-24T01:51:53.0239846Z Set device mapping between each RPC caller and callee pair. This 2025-01-24T01:51:53.0240054Z function can be called multiple times to incrementally add 2025-01-24T01:51:53.0240174Z device placement configurations. 2025-01-24T01:51:53.0240182Z 2025-01-24T01:51:53.0240285Z Args: 2025-01-24T01:51:53.0240401Z to (str): Callee name. 2025-01-24T01:51:53.0240612Z device_map (Dict of int, str, or torch.device): Device placement 2025-01-24T01:51:53.0240797Z mappings from this worker to the callee. This map must be 2025-01-24T01:51:53.0240903Z invertible. 2025-01-24T01:51:53.0240907Z 2025-01-24T01:51:53.0240996Z Example: 2025-01-24T01:51:53.0241110Z >>> # xdoctest: +SKIP("distributed") 2025-01-24T01:51:53.0241214Z >>> # both workers 2025-01-24T01:51:53.0241307Z >>> def add(x, y): 2025-01-24T01:51:53.0241467Z >>> print(x) # tensor([1., 1.], device='cuda:1') 2025-01-24T01:51:53.0241573Z >>> return x + y, (x + y).to(2) 2025-01-24T01:51:53.0241672Z >>> 2025-01-24T01:51:53.0241763Z >>> # on worker 0 2025-01-24T01:51:53.0241902Z >>> options = TensorPipeRpcBackendOptions( 2025-01-24T01:51:53.0242018Z >>> num_worker_threads=8, 2025-01-24T01:51:53.0242133Z >>> device_maps={"worker1": {0: 1}} 2025-01-24T01:51:53.0242282Z >>> # maps worker0's cuda:0 to worker1's cuda:1 2025-01-24T01:51:53.0242368Z >>> ) 2025-01-24T01:51:53.0242497Z >>> options.set_device_map("worker1", {1: 2}) 2025-01-24T01:51:53.0242636Z >>> # maps worker0's cuda:1 to worker1's cuda:2 2025-01-24T01:51:53.0242724Z >>> 2025-01-24T01:51:53.0242831Z >>> rpc.init_rpc( 2025-01-24T01:51:53.0242920Z >>> "worker0", 2025-01-24T01:51:53.0243009Z >>> rank=0, 2025-01-24T01:51:53.0243114Z >>> world_size=2, 2025-01-24T01:51:53.0243243Z >>> backend=rpc.BackendType.TENSORPIPE, 2025-01-24T01:51:53.0243373Z >>> rpc_backend_options=options 2025-01-24T01:51:53.0243458Z >>> ) 2025-01-24T01:51:53.0243555Z >>> 2025-01-24T01:51:53.0243651Z >>> x = torch.ones(2) 2025-01-24T01:51:53.0243813Z >>> rets = rpc.rpc_sync("worker1", add, args=(x.to(0), 1)) 2025-01-24T01:51:53.0244015Z >>> # The first argument will be moved to cuda:1 on worker1. When 2025-01-24T01:51:53.0244303Z >>> # sending the return value back, it will follow the invert of 2025-01-24T01:51:53.0244497Z >>> # the device map, and hence will be moved back to cuda:0 and 2025-01-24T01:51:53.0244595Z >>> # cuda:1 on worker0 2025-01-24T01:51:53.0244742Z >>> print(rets[0]) # tensor([2., 2.], device='cuda:0') 2025-01-24T01:51:53.0244986Z >>> print(rets[1]) # tensor([2., 2.], device='cuda:1') 2025-01-24T01:51:53.0244991Z 2025-01-24T01:51:53.0245250Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:51:53.0245255Z 2025-01-24T01:51:53.1447459Z msg = Cannot scrape callname=local_map in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/experimental/_func_map.py line=33. 2025-01-24T01:51:53.1447930Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:51:53.1447937Z 2025-01-24T01:51:53.1448214Z :meth:`local_map` is an experimental API that allows users to pass :class:`DTensor` s 2025-01-24T01:51:53.1448569Z to a function that is written to be applied on ``torch.Tensor`` s. It is done by extracting 2025-01-24T01:51:53.1448855Z the local components of :class:`DTensor`, call the function, and wrap the outputs to 2025-01-24T01:51:53.1449018Z :class:`DTensor` according to the ``out_placements``. 2025-01-24T01:51:53.1449023Z 2025-01-24T01:51:53.1449125Z Args: 2025-01-24T01:51:53.1449338Z func (Callable): the function to be applied on each local shard of 2025-01-24T01:51:53.1449462Z :class:`DTensor` s. 2025-01-24T01:51:53.1449695Z out_placements (Union[`PlacementType`, Tuple[`PlacementType`, ...]]): 2025-01-24T01:51:53.1449953Z the desired placements of the :class:`DTensor` s in ``func``'s flattened output. 2025-01-24T01:51:53.1450212Z If the flattened ``output`` is a single value, the ``out_placements`` should be 2025-01-24T01:51:53.1450456Z of type `PlacementType`. Otherwise if the flattened ``output`` has multiple 2025-01-24T01:51:53.1450726Z values, the ``out_placements`` should be a tuple of `PlacementType` values 1:1 2025-01-24T01:51:53.1450849Z mapping to the flattened ``output``. 2025-01-24T01:51:53.1451070Z Besides, for :class:`Tensor` output, we use `PlacementType` as its 2025-01-24T01:51:53.1451344Z placements (a `Tuple[Placement]` value). For non-Tensor output, the `PlacementType` 2025-01-24T01:51:53.1451445Z should be `None`. 2025-01-24T01:51:53.1451695Z Note that the only exception is when no :class:`DTensor` argument is passed 2025-01-24T01:51:53.1452007Z in. In this case, even if `out_placements` is not `None`, the result function 2025-01-24T01:51:53.1452281Z should ignore the desired placements because the function is not running with 2025-01-24T01:51:53.1452386Z :class:`DTensor` s. 2025-01-24T01:51:53.1452584Z in_placements (Tuple[`PlacementType`, ...], optional): 2025-01-24T01:51:53.1452866Z the required placements of the :class:`DTensor` s in the flattened inputs of ``func``. 2025-01-24T01:51:53.1453128Z If ``in_placements`` is specified, :meth:`local_map` would examine whether the 2025-01-24T01:51:53.1453360Z placements of each :class:`DTensor` argument is the same as the required 2025-01-24T01:51:53.1453545Z placements or not. If the placements are not the same and 2025-01-24T01:51:53.1453805Z ``redistribute_inputs`` is ``False``, an exception will be raised. Otherwise if 2025-01-24T01:51:53.1454050Z ``redistribute_inputs`` is ``True``, the argument will be first redistributed to 2025-01-24T01:51:53.1454329Z the required sharding placements before passing its local tensor to ``func``. 2025-01-24T01:51:53.1454558Z The only exception is when required placements are not ``None`` and the 2025-01-24T01:51:53.1454822Z argument is a :class:`torch.Tensor`. In this case, the placements examination 2025-01-24T01:51:53.1455042Z will be skipped and the argument will be directly passed to ``func``. 2025-01-24T01:51:53.1455375Z If ``in_placements`` is ``None``, no placements examination will be performed. 2025-01-24T01:51:53.1455487Z Default: None 2025-01-24T01:51:53.1455628Z device_mesh (:class:`DeviceMesh`, optional): 2025-01-24T01:51:53.1455869Z the device mesh that all the :class:`DTensor` s are placed on. If not 2025-01-24T01:51:53.1456106Z specified, this will be inferred from the input :class:`DTensor` s' device 2025-01-24T01:51:53.1456387Z mesh. `local_map` requires every :class:`DTensor` s to be placed on the same 2025-01-24T01:51:53.1456500Z device mesh. Default: None. 2025-01-24T01:51:53.1456679Z redistribute_inputs (bool, optional): 2025-01-24T01:51:53.1456970Z the bool value indicating whether to reshard the input :class:`DTensor` s when 2025-01-24T01:51:53.1457222Z their placements are different from the required input placements. If this 2025-01-24T01:51:53.1457498Z value is ``False`` and some :class:`DTensor` input has a different placement, 2025-01-24T01:51:53.1457644Z an exception will be raised. Default: False. 2025-01-24T01:51:53.1457649Z 2025-01-24T01:51:53.1457756Z Returns: 2025-01-24T01:51:53.1458014Z A ``Callable`` that applies ``func`` to each local shard of the input :class:`DTensor` 2025-01-24T01:51:53.1458277Z and returns a :class:`DTensor` constructed from the return value of ``func``. 2025-01-24T01:51:53.1458281Z 2025-01-24T01:51:53.1458372Z Raises: 2025-01-24T01:51:53.1458641Z AssertionError: If the input :class:`DTensor` is not placed on the same device 2025-01-24T01:51:53.1458881Z mesh, or if they are placed on a different device mesh than the ``device_mesh`` 2025-01-24T01:51:53.1458984Z argument passed in. 2025-01-24T01:51:53.1458988Z 2025-01-24T01:51:53.1459248Z AssertionError: For any non-DTensor output, we require its corresponding 2025-01-24T01:51:53.1459509Z output placement in ``out_placements`` be None. An AssertionError will be raised 2025-01-24T01:51:53.1459636Z if this is not the case. 2025-01-24T01:51:53.1459641Z 2025-01-24T01:51:53.1459910Z ValueError: If ``redistribute_inputs=False`` but the input :class:`DTensor` needs 2025-01-24T01:51:53.1460081Z a redistribution according to ``in_placements``. 2025-01-24T01:51:53.1460085Z 2025-01-24T01:51:53.1460179Z Example: 2025-01-24T01:51:53.1460305Z >>> # xdoctest: +SKIP("distributed") 2025-01-24T01:51:53.1460458Z >>> def mm_allreduce_forward(device_mesh, W, X): 2025-01-24T01:51:53.1460585Z >>> partial_sum_tensor = torch.mm(W, X) 2025-01-24T01:51:53.1460840Z >>> reduced_tensor = funcol.all_reduce(partial_sum_tensor, "sum", device_mesh) 2025-01-24T01:51:53.1460948Z >>> return reduced_tensor 2025-01-24T01:51:53.1461051Z >>> 2025-01-24T01:51:53.1461181Z >>> W = torch.randn(12, 8, requires_grad=False) 2025-01-24T01:51:53.1461311Z >>> X = torch.randn(8, 16, requires_grad=False) 2025-01-24T01:51:53.1461427Z >>> Y = torch.mm(W, X) 2025-01-24T01:51:53.1461618Z >>> row_wise = [Shard(0)] # row-wise sharding placements on 1-d mesh 2025-01-24T01:51:53.1461819Z >>> col_wise = [Shard(1)] # col-wise sharding placements on 1-d mesh 2025-01-24T01:51:53.1461907Z >>> 2025-01-24T01:51:53.1462180Z >>> # local_mm_allreduce_forward is the function wrapped with DTensor/Tensor convertion 2025-01-24T01:51:53.1462324Z >>> local_mm_allreduce_forward = local_map( 2025-01-24T01:51:53.1462427Z >>> mm_allreduce_forward, 2025-01-24T01:51:53.1462555Z >>> out_placements=[Replicate()], 2025-01-24T01:51:53.1462677Z >>> in_placements=[col_wise, row_wise], 2025-01-24T01:51:53.1462794Z >>> device_mesh=device_mesh, 2025-01-24T01:51:53.1462881Z >>> ) 2025-01-24T01:51:53.1462965Z >>> 2025-01-24T01:51:53.1463238Z >>> W_dt = distribute_tensor(W, device_mesh, (col_wise)) # col-wisely sharded W tensor 2025-01-24T01:51:53.1463493Z >>> X_dt = distribute_tensor(X, device_mesh, (row_wise)) # row-wisely sharded X tensor 2025-01-24T01:51:53.1463917Z >>> Y_dt = local_mm_allreduce_forward(device_mesh, W_dt, X_dt) # apply local_mm_allreduce_forward to DTensors 2025-01-24T01:51:53.1463922Z 2025-01-24T01:51:53.1464134Z .. note:: This API is currently experimental and subject to change 2025-01-24T01:51:53.1464139Z 2025-01-24T01:51:53.1464512Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:51:53.1464520Z 2025-01-24T01:51:53.1466690Z msg = Cannot scrape callname=register_sharding in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/experimental/_register_sharding.py line=26. 2025-01-24T01:51:53.1466980Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:51:53.1466985Z 2025-01-24T01:51:53.1467343Z :meth:`register_sharding` is an experimental API that allows users to register sharding 2025-01-24T01:51:53.1467600Z strategies for an operator when the tensor inputs and outputs are DTensor. 2025-01-24T01:51:53.1467913Z It can be useful when: (1) there doesn't exist a default sharding strategy for ``op``, 2025-01-24T01:51:53.1468173Z e.g. when ``op`` is a custom operator that is not supported by :class:`DTensor`; (2) 2025-01-24T01:51:53.1468450Z when users would like to overwrite default sharding strategies of existing operators. 2025-01-24T01:51:53.1468455Z 2025-01-24T01:51:53.1468543Z Args: 2025-01-24T01:51:53.1468688Z op (Union[OpOverload, List[OpOverload]]): 2025-01-24T01:51:53.1468885Z An op or a list of ops to register the customized sharding function. 2025-01-24T01:51:53.1468889Z 2025-01-24T01:51:53.1468993Z Returns: 2025-01-24T01:51:53.1469264Z A function decorator which can be used to wrap a function that defines the sharding 2025-01-24T01:51:53.1469559Z strategy for the operator specified in ``op``. The defined sharding strategy will be 2025-01-24T01:51:53.1469842Z registered to DTensor and will override the default sharding strategy if DTensor has 2025-01-24T01:51:53.1470152Z already implemented the operator. The customized sharding function takes the same inputs 2025-01-24T01:51:53.1470407Z as the original op (except that if an arg is a :class:`torch.Tensor`, it will be 2025-01-24T01:51:53.1470681Z replaced by a tensor-like object that DTensor uses internally). The function should 2025-01-24T01:51:53.1470969Z return a sequence of 2-tuples, each specifying acceptable output placements and its 2025-01-24T01:51:53.1471089Z corresponding intput placements. 2025-01-24T01:51:53.1471093Z 2025-01-24T01:51:53.1471196Z Example: 2025-01-24T01:51:53.1471312Z >>> # xdoctest: +SKIP("distributed") 2025-01-24T01:51:53.1471446Z >>> @register_sharding(aten._softmax.default) 2025-01-24T01:51:53.1471620Z >>> def custom_softmax_sharding(x, dim, half_to_float): 2025-01-24T01:51:53.1471765Z >>> softmax_dim = dim if dim >= 0 else dim + x.ndim 2025-01-24T01:51:53.1471984Z >>> acceptable_shardings = [] 2025-01-24T01:51:53.1472077Z >>> 2025-01-24T01:51:53.1472276Z >>> all_replicate = ([Replicate()], [Replicate(), None, None]) 2025-01-24T01:51:53.1472436Z >>> acceptable_shardings.append(all_replicate) 2025-01-24T01:51:53.1472525Z >>> 2025-01-24T01:51:53.1472667Z >>> for sharding_dim in range(x.ndim): 2025-01-24T01:51:53.1472796Z >>> if sharding_dim != softmax_dim: 2025-01-24T01:51:53.1472912Z >>> all_sharded = ( 2025-01-24T01:51:53.1473025Z >>> [Shard(sharding_dim)], 2025-01-24T01:51:53.1473166Z >>> [Shard(sharding_dim), None, None], 2025-01-24T01:51:53.1473256Z >>> ) 2025-01-24T01:51:53.1473400Z >>> acceptable_shardings.append(all_sharded) 2025-01-24T01:51:53.1473501Z >>> 2025-01-24T01:51:53.1473615Z >>> return acceptable_shardings 2025-01-24T01:51:53.1473619Z 2025-01-24T01:51:53.1473832Z .. note:: This API is currently experimental and subject to change 2025-01-24T01:51:53.1473876Z 2025-01-24T01:51:53.1474162Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:51:53.1474166Z 2025-01-24T01:51:53.1722546Z msg = Cannot scrape callname=PrepareModuleInput in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/parallel/style.py line=404. 2025-01-24T01:51:53.1722839Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:51:53.1722844Z 2025-01-24T01:51:53.1723246Z Configure the nn.Module's inputs to convert the input tensors of the nn.Module to DTensors at runtime according to 2025-01-24T01:51:53.1723569Z ``input_layouts``, and perform layout redistribution according to the ``desired_input_layouts``. 2025-01-24T01:51:53.1723574Z 2025-01-24T01:51:53.1723685Z Keyword Args: 2025-01-24T01:51:53.1724010Z input_layouts (Union[Placement, Tuple[Optional[Placement]]]): 2025-01-24T01:51:53.1724359Z The DTensor layouts of input tensors for the nn.Module, this is used to convert the input tensors to 2025-01-24T01:51:53.1724773Z DTensors. If some inputs are not torch.Tensor or no need to convert to DTensors, ``None`` need to be specified 2025-01-24T01:51:53.1724895Z as a placeholder. default: None. 2025-01-24T01:51:53.1725146Z desired_input_layouts (Union[Placement, Tuple[Optional[Placement]]]): 2025-01-24T01:51:53.1725527Z The desired DTensor layout of input tensors for the nn.Module, this is used to ensure the inputs of the nn.Module 2025-01-24T01:51:53.1725946Z have the desired DTensor layouts. This argument needs to have the same length with ``input_layouts``. default: None. 2025-01-24T01:51:53.1726081Z input_kwarg_layouts (Dict[str, Placement]): 2025-01-24T01:51:53.1726476Z The DTensor layouts of input kwargs for the nn.Module, this is used to convert the input kwarg tensors to DTensors. 2025-01-24T01:51:53.1726571Z default: None 2025-01-24T01:51:53.1726735Z desired_input_kwarg_layouts: (Dict[str, Placement]): 2025-01-24T01:51:53.1727125Z The desired DTensor layout of input kwargs for the nn.Module, this is used to ensure the inputs of the nn.Module 2025-01-24T01:51:53.1727276Z have the desired DTensor layouts. default: None. 2025-01-24T01:51:53.1727460Z use_local_output (bool, optional): 2025-01-24T01:51:53.1727946Z Whether to use local :class:`torch.Tensor` instead of :class:`DTensor` for the module inputs, default: False. 2025-01-24T01:51:53.1728090Z Returns: 2025-01-24T01:51:53.1728502Z A :class:`ParallelStyle` object that prepares the sharding layouts of the nn.Module's inputs. 2025-01-24T01:51:53.1728508Z 2025-01-24T01:51:53.1728648Z Example:: 2025-01-24T01:51:53.1728782Z >>> # xdoctest: +SKIP(failing) 2025-01-24T01:51:53.1729140Z >>> from torch.distributed.tensor.parallel import parallelize_module, PrepareModuleInput 2025-01-24T01:51:53.1729348Z >>> from torch.distributed.device_mesh import init_device_mesh 2025-01-24T01:51:53.1729437Z >>> ... 2025-01-24T01:51:53.1729767Z >>> block = TransformerBlock(...) # block is a nn.Module that contains an "attn" Attention submodule 2025-01-24T01:51:53.1729894Z >>> tp_mesh = init_device_mesh("cuda", (8,)) 2025-01-24T01:51:53.1729995Z >>> 2025-01-24T01:51:53.1730333Z >>> # According to the style specified below, the first input of attn will be annotated to Sharded DTensor 2025-01-24T01:51:53.1730482Z >>> # and then redistributed to Replicated DTensor. 2025-01-24T01:51:53.1730599Z >>> parallelize_module( 2025-01-24T01:51:53.1730734Z >>> block, # this can be a submodule or module 2025-01-24T01:51:53.1730838Z >>> tp_mesh, 2025-01-24T01:51:53.1730942Z >>> parallelize_plan={ 2025-01-24T01:51:53.1731062Z >>> "attn": PrepareModuleInput( 2025-01-24T01:51:53.1731214Z >>> input_layouts=(Shard(0), None, None, ...), 2025-01-24T01:51:53.1731386Z >>> desired_input_layouts=(Replicate(), None, None, ...) 2025-01-24T01:51:53.1731491Z >>> ), 2025-01-24T01:51:53.1731648Z >>> } 2025-01-24T01:51:53.1731790Z >>> ) 2025-01-24T01:51:53.1731795Z 2025-01-24T01:51:53.1732052Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:51:53.1732057Z 2025-01-24T01:51:53.1732753Z msg = Cannot scrape callname=PrepareModuleOutput in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/parallel/style.py line=559. 2025-01-24T01:51:53.1733017Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:51:53.1733022Z 2025-01-24T01:51:53.1733431Z Configure the nn.Module's outputs to convert the output tensors of the nn.Module to DTensors at runtime according to 2025-01-24T01:51:53.1733765Z ``output_layouts``, and perform layout redistribution according to the ``desired_output_layouts``. 2025-01-24T01:51:53.1733804Z 2025-01-24T01:51:53.1733899Z Keyword Args: 2025-01-24T01:51:53.1734084Z output_layouts (Union[Placement, Tuple[Placement]]): 2025-01-24T01:51:53.1734460Z The DTensor layouts of output tensors for the nn.Module, this is used to convert the output tensors to 2025-01-24T01:51:53.1734855Z DTensors if they are :class:`torch.Tensor`. If some outputs are not torch.Tensor or no need to convert to DTensors, 2025-01-24T01:51:53.1735002Z ``None`` need to be specified as a placeholder. 2025-01-24T01:51:53.1735215Z desired_output_layouts (Union[Placement, Tuple[Placement]]): 2025-01-24T01:51:53.1735616Z The desired DTensor layouts of output tensors for the nn.Module, this is used to ensure the outputs of the nn.Module 2025-01-24T01:51:53.1735761Z have the desired DTensor layouts. 2025-01-24T01:51:53.1735888Z use_local_output (bool, optional): 2025-01-24T01:51:53.1736252Z Whether to use local :class:`torch.Tensor` instead of :class:`DTensor` for the module outputs, default: True. 2025-01-24T01:51:53.1736357Z Returns: 2025-01-24T01:51:53.1736654Z A ParallelStyle object that prepares the sharding layouts of the nn.Module's outputs. 2025-01-24T01:51:53.1736662Z 2025-01-24T01:51:53.1736773Z Example:: 2025-01-24T01:51:53.1736883Z >>> # xdoctest: +SKIP(failing) 2025-01-24T01:51:53.1737211Z >>> from torch.distributed.tensor.parallel import parallelize_module, PrepareModuleOutput 2025-01-24T01:51:53.1737407Z >>> from torch.distributed.device_mesh import init_device_mesh 2025-01-24T01:51:53.1737496Z >>> ... 2025-01-24T01:51:53.1737817Z >>> block = TransformerBlock(...) # block is a nn.Module that contains an "attn" Attention submodule 2025-01-24T01:51:53.1737946Z >>> tp_mesh = init_device_mesh("cuda", (8,)) 2025-01-24T01:51:53.1738054Z >>> 2025-01-24T01:51:53.1738456Z >>> # According to the style specified below, the output of the TransformerBlock will be converted to Replicated DTensor 2025-01-24T01:51:53.1738612Z >>> # and then redistributed to Sharded DTensor. 2025-01-24T01:51:53.1738716Z >>> parallelize_module( 2025-01-24T01:51:53.1738853Z >>> block, # this can be a submodule or module 2025-01-24T01:51:53.1738964Z >>> tp_mesh, 2025-01-24T01:51:53.1739102Z >>> parallelize_plan = PrepareModuleOutput( 2025-01-24T01:51:53.1739233Z >>> output_layouts=Replicate(), 2025-01-24T01:51:53.1739354Z >>> desired_output_layouts=Shard(0) 2025-01-24T01:51:53.1739443Z >>> ) 2025-01-24T01:51:53.1739548Z >>> ) 2025-01-24T01:51:53.1739552Z 2025-01-24T01:51:53.1739811Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:51:53.1739815Z 2025-01-24T01:51:53.2344186Z msg = Cannot scrape callname=MixtureSameFamily in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/mixture_same_family.py line=13. 2025-01-24T01:51:53.2344463Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:51:53.2344480Z 2025-01-24T01:51:53.2344728Z The `MixtureSameFamily` distribution implements a (batch of) mixture 2025-01-24T01:51:53.2344982Z distribution where all component are from different parameterizations of 2025-01-24T01:51:53.2345401Z the same distribution type. It is parameterized by a `Categorical` 2025-01-24T01:51:53.2345607Z "selecting distribution" (over `k` component) and a component 2025-01-24T01:51:53.2345840Z distribution, i.e., a `Distribution` with a rightmost batch shape 2025-01-24T01:51:53.2346007Z (equal to `[k]`) which indexes each (batch of) component. 2025-01-24T01:51:53.2346012Z 2025-01-24T01:51:53.2346130Z Examples:: 2025-01-24T01:51:53.2346134Z 2025-01-24T01:51:53.2346334Z >>> # xdoctest: +SKIP("undefined vars") 2025-01-24T01:51:53.2346555Z >>> # Construct Gaussian Mixture Model in 1D consisting of 5 equally 2025-01-24T01:51:53.2346698Z >>> # weighted normal distributions 2025-01-24T01:51:53.2346829Z >>> mix = D.Categorical(torch.ones(5,)) 2025-01-24T01:51:53.2347059Z >>> comp = D.Normal(torch.randn(5,), torch.rand(5,)) 2025-01-24T01:51:53.2347193Z >>> gmm = MixtureSameFamily(mix, comp) 2025-01-24T01:51:53.2347270Z 2025-01-24T01:51:53.2347516Z >>> # Construct Gaussian Mixture Model in 2D consisting of 5 equally 2025-01-24T01:51:53.2347653Z >>> # weighted bivariate normal distributions 2025-01-24T01:51:53.2347776Z >>> mix = D.Categorical(torch.ones(5,)) 2025-01-24T01:51:53.2347910Z >>> comp = D.Independent(D.Normal( 2025-01-24T01:51:53.2348045Z ... torch.randn(5,2), torch.rand(5,2)), 1) 2025-01-24T01:51:53.2348187Z >>> gmm = MixtureSameFamily(mix, comp) 2025-01-24T01:51:53.2348192Z 2025-01-24T01:51:53.2348379Z >>> # Construct a batch of 3 Gaussian Mixture Models in 2D each 2025-01-24T01:51:53.2348603Z >>> # consisting of 5 random weighted bivariate normal distributions 2025-01-24T01:51:53.2348728Z >>> mix = D.Categorical(torch.rand(3,5)) 2025-01-24T01:51:53.2348847Z >>> comp = D.Independent(D.Normal( 2025-01-24T01:51:53.2349086Z ... torch.randn(3,5,2), torch.rand(3,5,2)), 1) 2025-01-24T01:51:53.2349216Z >>> gmm = MixtureSameFamily(mix, comp) 2025-01-24T01:51:53.2349225Z 2025-01-24T01:51:53.2349327Z Args: 2025-01-24T01:51:53.2349541Z mixture_distribution: `torch.distributions.Categorical`-like 2025-01-24T01:51:53.2349752Z instance. Manages the probability of selecting component. 2025-01-24T01:51:53.2349924Z The number of categories must match the rightmost batch 2025-01-24T01:51:53.2350119Z dimension of the `component_distribution`. Must have either 2025-01-24T01:51:53.2350277Z scalar `batch_shape` or `batch_shape` matching 2025-01-24T01:51:53.2350415Z `component_distribution.batch_shape[:-1]` 2025-01-24T01:51:53.2350653Z component_distribution: `torch.distributions.Distribution`-like 2025-01-24T01:51:53.2350834Z instance. Right-most batch dimension indexes component. 2025-01-24T01:51:53.2350838Z 2025-01-24T01:51:53.2351111Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:51:53.2351115Z 2025-01-24T01:51:53.2476625Z msg = Cannot scrape callname=RelaxedBernoulli in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/relaxed_bernoulli.py line=112. 2025-01-24T01:51:53.2476912Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:51:53.2476918Z 2025-01-24T01:51:53.2477104Z Creates a RelaxedBernoulli distribution, parametrized by 2025-01-24T01:51:53.2477315Z :attr:`temperature`, and either :attr:`probs` or :attr:`logits` 2025-01-24T01:51:53.2477534Z (but not both). This is a relaxed version of the `Bernoulli` distribution, 2025-01-24T01:51:53.2477726Z so the values are in (0, 1), and has reparametrizable samples. 2025-01-24T01:51:53.2477730Z 2025-01-24T01:51:53.2477841Z Example:: 2025-01-24T01:51:53.2477846Z 2025-01-24T01:51:53.2477988Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2025-01-24T01:51:53.2478139Z >>> m = RelaxedBernoulli(torch.tensor([2.2]), 2025-01-24T01:51:53.2478267Z ... torch.tensor([0.1, 0.2, 0.3, 0.99])) 2025-01-24T01:51:53.2478379Z >>> m.sample() 2025-01-24T01:51:53.2478632Z tensor([ 0.2951, 0.3442, 0.8918, 0.9021]) 2025-01-24T01:51:53.2478637Z 2025-01-24T01:51:53.2478725Z Args: 2025-01-24T01:51:53.2478886Z temperature (Tensor): relaxation temperature 2025-01-24T01:51:53.2479062Z probs (Number, Tensor): the probability of sampling `1` 2025-01-24T01:51:53.2479410Z logits (Number, Tensor): the log-odds of sampling `1` 2025-01-24T01:51:53.2479416Z 2025-01-24T01:51:53.2479679Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:51:53.2479684Z 2025-01-24T01:51:53.2496640Z msg = Cannot scrape callname=RelaxedOneHotCategorical in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/relaxed_categorical.py line=100. 2025-01-24T01:51:53.2497007Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:51:53.2497013Z 2025-01-24T01:51:53.2497253Z Creates a RelaxedOneHotCategorical distribution parametrized by 2025-01-24T01:51:53.2497509Z :attr:`temperature`, and either :attr:`probs` or :attr:`logits`. 2025-01-24T01:51:53.2497768Z This is a relaxed version of the :class:`OneHotCategorical` distribution, so 2025-01-24T01:51:53.2497936Z its samples are on simplex, and are reparametrizable. 2025-01-24T01:51:53.2497940Z 2025-01-24T01:51:53.2498039Z Example:: 2025-01-24T01:51:53.2498043Z 2025-01-24T01:51:53.2498203Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2025-01-24T01:51:53.2498363Z >>> m = RelaxedOneHotCategorical(torch.tensor([2.2]), 2025-01-24T01:51:53.2498508Z ... torch.tensor([0.1, 0.2, 0.3, 0.4])) 2025-01-24T01:51:53.2498612Z >>> m.sample() 2025-01-24T01:51:53.2498749Z tensor([ 0.1294, 0.2324, 0.3859, 0.2523]) 2025-01-24T01:51:53.2498753Z 2025-01-24T01:51:53.2498841Z Args: 2025-01-24T01:51:53.2498986Z temperature (Tensor): relaxation temperature 2025-01-24T01:51:53.2499122Z probs (Tensor): event probabilities 2025-01-24T01:51:53.2499317Z logits (Tensor): unnormalized log probability for each event 2025-01-24T01:51:53.2499325Z 2025-01-24T01:51:53.2499592Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:51:53.2499597Z 2025-01-24T01:51:53.5974792Z msg = Cannot scrape callname=assoc_in in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/unification_tools.py line=245. 2025-01-24T01:51:53.5976453Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:51:53.5977242Z Return a new dict with new, potentially nested, key value pair 2025-01-24T01:51:53.5977571Z 2025-01-24T01:51:53.5977677Z >>> purchase = { 2025-01-24T01:51:53.5978083Z ... "name": "Alice", 2025-01-24T01:51:53.5978463Z ... "order": {"items": ["Apple", "Orange"], "costs": [0.50, 1.25]}, 2025-01-24T01:51:53.5979332Z ... "credit card": "5555-1234-1234-1234", 2025-01-24T01:51:53.5979853Z ... } 2025-01-24T01:51:53.5980216Z >>> assoc_in(purchase, ["order", "costs"], [0.25, 1.00]) # doctest: +SKIP 2025-01-24T01:51:53.5980681Z {'credit card': '5555-1234-1234-1234', 2025-01-24T01:51:53.5981006Z 'name': 'Alice', 2025-01-24T01:51:53.5981429Z 'order': {'costs': [0.25, 1.00], 'items': ['Apple', 'Orange']}} 2025-01-24T01:51:53.5981979Z 2025-01-24T01:51:53.5982485Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:51:53.5983208Z 2025-01-24T01:51:53.5983935Z msg = Cannot scrape callname=update_in in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/unification_tools.py line=261. 2025-01-24T01:51:53.5984985Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:51:53.5985531Z Update value in a (potentially) nested dictionary 2025-01-24T01:51:53.5985805Z 2025-01-24T01:51:53.5985915Z inputs: 2025-01-24T01:51:53.5986164Z d - dictionary on which to operate 2025-01-24T01:51:53.5986724Z keys - list or tuple giving the location of the value to be changed in d 2025-01-24T01:51:53.5987467Z func - function to operate on that value 2025-01-24T01:51:53.5987717Z 2025-01-24T01:51:53.5987910Z If keys == [k0,..,kX] and d[k0]..[kX] == v, update_in returns a copy of the 2025-01-24T01:51:53.5988464Z original dictionary with v replaced by func(v), but does not mutate the 2025-01-24T01:51:53.5988933Z original dictionary. 2025-01-24T01:51:53.5989107Z 2025-01-24T01:51:53.5989326Z If k0 is not a key in d, update_in creates nested dictionaries to the depth 2025-01-24T01:51:53.5989881Z specified by the keys, with the innermost value set to func(default). 2025-01-24T01:51:53.5990229Z 2025-01-24T01:51:53.5990331Z >>> inc = lambda x: x + 1 2025-01-24T01:51:53.5990635Z >>> update_in({"a": 0}, ["a"], inc) 2025-01-24T01:51:53.5991004Z {'a': 1} 2025-01-24T01:51:53.5991137Z 2025-01-24T01:51:53.5991249Z >>> transaction = { 2025-01-24T01:51:53.5991504Z ... "name": "Alice", 2025-01-24T01:51:53.5991942Z ... "purchase": {"items": ["Apple", "Orange"], "costs": [0.50, 1.25]}, 2025-01-24T01:51:53.5992384Z ... "credit card": "5555-1234-1234-1234", 2025-01-24T01:51:53.5992715Z ... } 2025-01-24T01:51:53.5993071Z >>> update_in(transaction, ["purchase", "costs"], sum) # doctest: +SKIP 2025-01-24T01:51:53.5993515Z {'credit card': '5555-1234-1234-1234', 2025-01-24T01:51:53.5993838Z 'name': 'Alice', 2025-01-24T01:51:53.5994176Z 'purchase': {'costs': 1.75, 'items': ['Apple', 'Orange']}} 2025-01-24T01:51:53.5994457Z 2025-01-24T01:51:53.5994594Z >>> # updating a value when k0 is not in d 2025-01-24T01:51:53.5994962Z >>> update_in({}, [1, 2, 3], str, default="bar") 2025-01-24T01:51:53.5995326Z {1: {2: {3: 'bar'}}} 2025-01-24T01:51:53.5995630Z >>> update_in({1: "foo"}, [2, 3, 4], inc, 0) 2025-01-24T01:51:53.5995957Z {1: 'foo', 2: {3: {4: 1}}} 2025-01-24T01:51:53.5996237Z 2025-01-24T01:51:53.5996622Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:51:53.5997015Z 2025-01-24T01:51:53.5997659Z msg = Cannot scrape callname=get_in in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/unification_tools.py line=320. 2025-01-24T01:51:53.5998686Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:51:53.5999246Z Returns coll[i0][i1]...[iX] where [i0, i1, ..., iX]==keys. 2025-01-24T01:51:53.5999524Z 2025-01-24T01:51:53.5999725Z If coll[i0][i1]...[iX] cannot be found, returns ``default``, unless 2025-01-24T01:51:53.6000231Z ``no_default`` is specified, then it raises KeyError or IndexError. 2025-01-24T01:51:53.6000575Z 2025-01-24T01:51:53.6000791Z ``get_in`` is a generalization of ``operator.getitem`` for nested data 2025-01-24T01:51:53.6001268Z structures such as dictionaries and lists. 2025-01-24T01:51:53.6001513Z 2025-01-24T01:51:53.6001635Z >>> transaction = { 2025-01-24T01:51:53.6001919Z ... "name": "Alice", 2025-01-24T01:51:53.6002295Z ... "purchase": {"items": ["Apple", "Orange"], "costs": [0.50, 1.25]}, 2025-01-24T01:51:53.6002737Z ... "credit card": "5555-1234-1234-1234", 2025-01-24T01:51:53.6003067Z ... } 2025-01-24T01:51:53.6003339Z >>> get_in(["purchase", "items", 0], transaction) 2025-01-24T01:51:53.6003690Z 'Apple' 2025-01-24T01:51:53.6003921Z >>> get_in(["name"], transaction) 2025-01-24T01:51:53.6004233Z 'Alice' 2025-01-24T01:51:53.6004497Z >>> get_in(["purchase", "total"], transaction) 2025-01-24T01:51:53.6004900Z >>> get_in(["purchase", "items", "apple"], transaction) 2025-01-24T01:51:53.6005309Z >>> get_in(["purchase", "items", 10], transaction) 2025-01-24T01:51:53.6005696Z >>> get_in(["purchase", "total"], transaction, 0) 2025-01-24T01:51:53.6006039Z 0 2025-01-24T01:51:53.6006277Z >>> get_in(["y"], {}, no_default=True) 2025-01-24T01:51:53.6006624Z Traceback (most recent call last): 2025-01-24T01:51:53.6006943Z ... 2025-01-24T01:51:53.6007206Z KeyError: 'y' 2025-01-24T01:51:53.6007403Z 2025-01-24T01:51:53.6007495Z See Also: 2025-01-24T01:51:53.6007732Z itertoolz.get 2025-01-24T01:51:53.6008002Z operator.getitem 2025-01-24T01:51:53.6008273Z 2025-01-24T01:51:53.6008640Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:51:53.6009029Z 2025-01-24T01:51:53.6009693Z msg = Cannot scrape callname=groupby in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/unification_tools.py line=373. 2025-01-24T01:51:53.6010731Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:51:53.6011236Z Group a collection by a key function 2025-01-24T01:51:53.6011450Z 2025-01-24T01:51:53.6011662Z >>> names = ["Alice", "Bob", "Charlie", "Dan", "Edith", "Frank"] 2025-01-24T01:51:53.6012080Z >>> groupby(len, names) # doctest: +SKIP 2025-01-24T01:51:53.6012471Z {3: ['Bob', 'Dan'], 5: ['Alice', 'Edith', 'Frank'], 7: ['Charlie']} 2025-01-24T01:51:53.6012827Z 2025-01-24T01:51:53.6012938Z >>> iseven = lambda x: x % 2 == 0 2025-01-24T01:51:53.6013330Z >>> groupby(iseven, [1, 2, 3, 4, 5, 6, 7, 8]) # doctest: +SKIP 2025-01-24T01:51:53.6013740Z {False: [1, 3, 5, 7], True: [2, 4, 6, 8]} 2025-01-24T01:51:53.6013971Z 2025-01-24T01:51:53.6014135Z Non-callable keys imply grouping on a member. 2025-01-24T01:51:53.6014390Z 2025-01-24T01:51:53.6014497Z >>> groupby( 2025-01-24T01:51:53.6014727Z ... "gender", 2025-01-24T01:51:53.6014979Z ... [ 2025-01-24T01:51:53.6015242Z ... {"name": "Alice", "gender": "F"}, 2025-01-24T01:51:53.6015603Z ... {"name": "Bob", "gender": "M"}, 2025-01-24T01:51:53.6015964Z ... {"name": "Charlie", "gender": "M"}, 2025-01-24T01:51:53.6016287Z ... ], 2025-01-24T01:51:53.6016534Z ... ) # doctest:+SKIP 2025-01-24T01:51:53.6016831Z {'F': [{'gender': 'F', 'name': 'Alice'}], 2025-01-24T01:51:53.6017174Z 'M': [{'gender': 'M', 'name': 'Bob'}, 2025-01-24T01:51:53.6017522Z {'gender': 'M', 'name': 'Charlie'}]} 2025-01-24T01:51:53.6017747Z 2025-01-24T01:51:53.6017889Z Not to be confused with ``itertools.groupby`` 2025-01-24T01:51:53.6018150Z 2025-01-24T01:51:53.6018242Z See Also: 2025-01-24T01:51:53.6018475Z countby 2025-01-24T01:51:53.6018705Z 2025-01-24T01:51:53.6019083Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:51:53.6019460Z 2025-01-24T01:51:54.1587551Z msg = Cannot scrape callname=SyncBatchNorm in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/batchnorm.py line=601. 2025-01-24T01:51:54.1588608Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:51:54.1589204Z Applies Batch Normalization over a N-Dimensional input. 2025-01-24T01:51:54.1589499Z 2025-01-24T01:51:54.1589864Z The N-D input is a mini-batch of [N-2]D inputs with additional channel dimension) as described in the paper 2025-01-24T01:51:54.1590629Z `Batch Normalization: Accelerating Deep Network Training by Reducing 2025-01-24T01:51:54.1591386Z Internal Covariate Shift `__ . 2025-01-24T01:51:54.1591948Z 2025-01-24T01:51:54.1592155Z .. math:: 2025-01-24T01:51:54.1592442Z 2025-01-24T01:51:54.1592750Z y = \frac{x - \mathrm{E}[x]}{ \sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta 2025-01-24T01:51:54.1593110Z 2025-01-24T01:51:54.1593348Z The mean and standard-deviation are calculated per-dimension over all 2025-01-24T01:51:54.1593944Z mini-batches of the same process groups. :math:`\gamma` and :math:`\beta` 2025-01-24T01:51:54.1594558Z are learnable parameter vectors of size `C` (where `C` is the input size). 2025-01-24T01:51:54.1595107Z By default, the elements of :math:`\gamma` are sampled from 2025-01-24T01:51:54.1595620Z :math:`\mathcal{U}(0, 1)` and the elements of :math:`\beta` are set to 0. 2025-01-24T01:51:54.1596423Z The standard-deviation is calculated via the biased estimator, equivalent to 2025-01-24T01:51:54.1596942Z `torch.var(input, unbiased=False)`. 2025-01-24T01:51:54.1597166Z 2025-01-24T01:51:54.1597424Z Also by default, during training this layer keeps running estimates of its 2025-01-24T01:51:54.1598032Z computed mean and variance, which are then used for normalization during 2025-01-24T01:51:54.1598633Z evaluation. The running estimates are kept with a default :attr:`momentum` 2025-01-24T01:51:54.1599104Z of 0.1. 2025-01-24T01:51:54.1599246Z 2025-01-24T01:51:54.1599476Z If :attr:`track_running_stats` is set to ``False``, this layer then does not 2025-01-24T01:51:54.1600060Z keep running estimates, and batch statistics are instead used during 2025-01-24T01:51:54.1600526Z evaluation time as well. 2025-01-24T01:51:54.1600765Z 2025-01-24T01:51:54.1600877Z .. note:: 2025-01-24T01:51:54.1601238Z This :attr:`momentum` argument is different from one used in optimizer 2025-01-24T01:51:54.1601886Z classes and the conventional notion of momentum. Mathematically, the 2025-01-24T01:51:54.1602382Z update rule for running statistics here is 2025-01-24T01:51:54.1602912Z :math:`\hat{x}_\text{new} = (1 - \text{momentum}) \times \hat{x} + \text{momentum} \times x_t`, 2025-01-24T01:51:54.1603517Z where :math:`\hat{x}` is the estimated statistic and :math:`x_t` is the 2025-01-24T01:51:54.1603945Z new observed value. 2025-01-24T01:51:54.1604155Z 2025-01-24T01:51:54.1604460Z Because the Batch Normalization is done for each channel in the ``C`` dimension, computing 2025-01-24T01:51:54.1605145Z statistics on ``(N, +)`` slices, it's common terminology to call this Volumetric Batch 2025-01-24T01:51:54.1605705Z Normalization or Spatio-temporal Batch Normalization. 2025-01-24T01:51:54.1606037Z 2025-01-24T01:51:54.1606208Z Currently :class:`SyncBatchNorm` only supports 2025-01-24T01:51:54.1606774Z :class:`~torch.nn.DistributedDataParallel` (DDP) with single GPU per process. Use 2025-01-24T01:51:54.1607399Z :meth:`torch.nn.SyncBatchNorm.convert_sync_batchnorm()` to convert 2025-01-24T01:51:54.1607957Z :attr:`BatchNorm*D` layer to :class:`SyncBatchNorm` before wrapping 2025-01-24T01:51:54.1608394Z Network with DDP. 2025-01-24T01:51:54.1608556Z 2025-01-24T01:51:54.1608664Z Args: 2025-01-24T01:51:54.1608972Z num_features: :math:`C` from an expected input of size 2025-01-24T01:51:54.1609351Z :math:`(N, C, +)` 2025-01-24T01:51:54.1609739Z eps: a value added to the denominator for numerical stability. 2025-01-24T01:51:54.1610165Z Default: ``1e-5`` 2025-01-24T01:51:54.1610562Z momentum: the value used for the running_mean and running_var 2025-01-24T01:51:54.1611101Z computation. Can be set to ``None`` for cumulative moving average 2025-01-24T01:51:54.1611569Z (i.e. simple average). Default: 0.1 2025-01-24T01:51:54.1612009Z affine: a boolean value that when set to ``True``, this module has 2025-01-24T01:51:54.1612505Z learnable affine parameters. Default: ``True`` 2025-01-24T01:51:54.1613005Z track_running_stats: a boolean value that when set to ``True``, this 2025-01-24T01:51:54.1613580Z module tracks the running mean and variance, and when set to ``False``, 2025-01-24T01:51:54.1614179Z this module does not track such statistics, and initializes statistics 2025-01-24T01:51:54.1614743Z buffers :attr:`running_mean` and :attr:`running_var` as ``None``. 2025-01-24T01:51:54.1615290Z When these buffers are ``None``, this module always uses batch statistics. 2025-01-24T01:51:54.1615807Z in both training and eval modes. Default: ``True`` 2025-01-24T01:51:54.1616337Z process_group: synchronization of stats happen within each process group 2025-01-24T01:51:54.1616945Z individually. Default behavior is synchronization across the whole 2025-01-24T01:51:54.1617435Z world 2025-01-24T01:51:54.1617578Z 2025-01-24T01:51:54.1617701Z Shape: 2025-01-24T01:51:54.1617948Z - Input: :math:`(N, C, +)` 2025-01-24T01:51:54.1618314Z - Output: :math:`(N, C, +)` (same shape as input) 2025-01-24T01:51:54.1618585Z 2025-01-24T01:51:54.1618679Z .. note:: 2025-01-24T01:51:54.1619070Z Synchronization of batchnorm statistics occurs only while training, i.e. 2025-01-24T01:51:54.1619651Z synchronization is disabled when ``model.eval()`` is set or if 2025-01-24T01:51:54.1620096Z ``self.training`` is otherwise ``False``. 2025-01-24T01:51:54.1620351Z 2025-01-24T01:51:54.1620453Z Examples:: 2025-01-24T01:51:54.1620603Z 2025-01-24T01:51:54.1620707Z >>> # xdoctest: +SKIP 2025-01-24T01:51:54.1621014Z >>> # With Learnable Parameters 2025-01-24T01:51:54.1621387Z >>> m = nn.SyncBatchNorm(100) 2025-01-24T01:51:54.1621724Z >>> # creating process group (optional) 2025-01-24T01:51:54.1622149Z >>> # ranks is a list of int identifying rank ids. 2025-01-24T01:51:54.1622523Z >>> ranks = list(range(8)) 2025-01-24T01:51:54.1622841Z >>> r1, r2 = ranks[:4], ranks[4:] 2025-01-24T01:51:54.1623219Z >>> # Note: every rank calls into new_group for every 2025-01-24T01:51:54.1623635Z >>> # process group created, even if that rank is not 2025-01-24T01:51:54.1624016Z >>> # part of the group. 2025-01-24T01:51:54.1624472Z >>> process_groups = [torch.distributed.new_group(pids) for pids in [r1, r2]] 2025-01-24T01:51:54.1625055Z >>> process_group = process_groups[0 if dist.get_rank() <= 3 else 1] 2025-01-24T01:51:54.1625507Z >>> # Without Learnable Parameters 2025-01-24T01:51:54.1625953Z >>> m = nn.BatchNorm3d(100, affine=False, process_group=process_group) 2025-01-24T01:51:54.1626508Z >>> input = torch.randn(20, 100, 35, 45, 10) 2025-01-24T01:51:54.1626865Z >>> output = m(input) 2025-01-24T01:51:54.1627069Z 2025-01-24T01:51:54.1627197Z >>> # network is nn.BatchNorm layer 2025-01-24T01:51:54.1627721Z >>> sync_bn_network = nn.SyncBatchNorm.convert_sync_batchnorm(network, process_group) 2025-01-24T01:51:54.1628300Z >>> # only single gpu per process is currently supported 2025-01-24T01:51:54.1628815Z >>> ddp_sync_bn_network = torch.nn.parallel.DistributedDataParallel( 2025-01-24T01:51:54.1629267Z >>> sync_bn_network, 2025-01-24T01:51:54.1629633Z >>> device_ids=[args.local_rank], 2025-01-24T01:51:54.1630026Z >>> output_device=args.local_rank) 2025-01-24T01:51:54.1630372Z 2025-01-24T01:51:54.1630756Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:51:54.1631129Z 2025-01-24T01:51:54.1631788Z msg = Cannot scrape callname=SyncBatchNorm.convert_sync_batchnorm in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/batchnorm.py line=824. 2025-01-24T01:51:54.1632833Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:51:54.1633533Z Converts all :attr:`BatchNorm*D` layers in the model to :class:`torch.nn.SyncBatchNorm` layers. 2025-01-24T01:51:54.1633969Z 2025-01-24T01:51:54.1634062Z Args: 2025-01-24T01:51:54.1634449Z module (nn.Module): module containing one or more :attr:`BatchNorm*D` layers 2025-01-24T01:51:54.1635044Z process_group (optional): process group to scope synchronization, 2025-01-24T01:51:54.1635489Z default is the whole world 2025-01-24T01:51:54.1635722Z 2025-01-24T01:51:54.1635812Z Returns: 2025-01-24T01:51:54.1636222Z The original :attr:`module` with the converted :class:`torch.nn.SyncBatchNorm` 2025-01-24T01:51:54.1636824Z layers. If the original :attr:`module` is a :attr:`BatchNorm*D` layer, 2025-01-24T01:51:54.1637383Z a new :class:`torch.nn.SyncBatchNorm` layer object will be returned 2025-01-24T01:51:54.1638169Z instead. 2025-01-24T01:51:54.1638355Z 2025-01-24T01:51:54.1638454Z Example:: 2025-01-24T01:51:54.1638612Z 2025-01-24T01:51:54.1638735Z >>> # Network with nn.BatchNorm layer 2025-01-24T01:51:54.1639129Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2025-01-24T01:51:54.1639514Z >>> module = torch.nn.Sequential( 2025-01-24T01:51:54.1639878Z >>> torch.nn.Linear(20, 100), 2025-01-24T01:51:54.1640248Z >>> torch.nn.BatchNorm1d(100), 2025-01-24T01:51:54.1640575Z >>> ).cuda() 2025-01-24T01:51:54.1640902Z >>> # creating process group (optional) 2025-01-24T01:51:54.1641388Z >>> # ranks is a list of int identifying rank ids. 2025-01-24T01:51:54.1641763Z >>> ranks = list(range(8)) 2025-01-24T01:51:54.1642131Z >>> r1, r2 = ranks[:4], ranks[4:] 2025-01-24T01:51:54.1642501Z >>> # Note: every rank calls into new_group for every 2025-01-24T01:51:54.1642969Z >>> # process group created, even if that rank is not 2025-01-24T01:51:54.1643362Z >>> # part of the group. 2025-01-24T01:51:54.1643702Z >>> # xdoctest: +SKIP("distributed") 2025-01-24T01:51:54.1644206Z >>> process_groups = [torch.distributed.new_group(pids) for pids in [r1, r2]] 2025-01-24T01:51:54.1644785Z >>> process_group = process_groups[0 if dist.get_rank() <= 3 else 1] 2025-01-24T01:51:54.1645424Z >>> sync_bn_module = torch.nn.SyncBatchNorm.convert_sync_batchnorm(module, process_group) 2025-01-24T01:51:54.1645854Z 2025-01-24T01:51:54.1645943Z 2025-01-24T01:51:54.1646335Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:51:54.1646708Z 2025-01-24T01:51:54.1838031Z msg = Cannot scrape callname=Unflatten in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/flatten.py line=60. 2025-01-24T01:51:54.1838972Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:51:54.1839368Z 2025-01-24T01:51:54.1839699Z Unflattens a tensor dim expanding it to a desired shape. For use with :class:`~nn.Sequential`. 2025-01-24T01:51:54.1840130Z 2025-01-24T01:51:54.1840421Z * :attr:`dim` specifies the dimension of the input tensor to be unflattened, and it can 2025-01-24T01:51:54.1841045Z be either `int` or `str` when `Tensor` or `NamedTensor` is used, respectively. 2025-01-24T01:51:54.1841410Z 2025-01-24T01:51:54.1841732Z * :attr:`unflattened_size` is the new shape of the unflattened dimension of the tensor and it can be 2025-01-24T01:51:54.1842439Z a `tuple` of ints or a `list` of ints or `torch.Size` for `Tensor` input; a `NamedShape` 2025-01-24T01:51:54.1843004Z (tuple of `(name, size)` tuples) for `NamedTensor` input. 2025-01-24T01:51:54.1843291Z 2025-01-24T01:51:54.1843402Z Shape: 2025-01-24T01:51:54.1843761Z - Input: :math:`(*, S_{\text{dim}}, *)`, where :math:`S_{\text{dim}}` is the size at 2025-01-24T01:51:54.1844371Z dimension :attr:`dim` and :math:`*` means any number of dimensions including none. 2025-01-24T01:51:54.1844961Z - Output: :math:`(*, U_1, ..., U_n, *)`, where :math:`U` = :attr:`unflattened_size` and 2025-01-24T01:51:54.1845443Z :math:`\prod_{i=1}^n U_i = S_{\text{dim}}`. 2025-01-24T01:51:54.1845701Z 2025-01-24T01:51:54.1845792Z Args: 2025-01-24T01:51:54.1846076Z dim (Union[int, str]): Dimension to be unflattened 2025-01-24T01:51:54.1846714Z unflattened_size (Union[torch.Size, Tuple, List, NamedShape]): New shape of the unflattened dimension 2025-01-24T01:51:54.1847184Z 2025-01-24T01:51:54.1847280Z Examples: 2025-01-24T01:51:54.1847528Z >>> input = torch.randn(2, 50) 2025-01-24T01:51:54.1847850Z >>> # With tuple of ints 2025-01-24T01:51:54.1848145Z >>> m = nn.Sequential( 2025-01-24T01:51:54.1848430Z >>> nn.Linear(50, 50), 2025-01-24T01:51:54.1848717Z >>> nn.Unflatten(1, (2, 5, 5)) 2025-01-24T01:51:54.1849032Z >>> ) 2025-01-24T01:51:54.1849382Z >>> output = m(input) 2025-01-24T01:51:54.1849708Z >>> output.size() 2025-01-24T01:51:54.1849977Z torch.Size([2, 2, 5, 5]) 2025-01-24T01:51:54.1850252Z >>> # With torch.Size 2025-01-24T01:51:54.1850531Z >>> m = nn.Sequential( 2025-01-24T01:51:54.1850817Z >>> nn.Linear(50, 50), 2025-01-24T01:51:54.1851134Z >>> nn.Unflatten(1, torch.Size([2, 5, 5])) 2025-01-24T01:51:54.1851467Z >>> ) 2025-01-24T01:51:54.1851683Z >>> output = m(input) 2025-01-24T01:51:54.1851968Z >>> output.size() 2025-01-24T01:51:54.1852239Z torch.Size([2, 2, 5, 5]) 2025-01-24T01:51:54.1852552Z >>> # With namedshape (tuple of tuples) 2025-01-24T01:51:54.1852950Z >>> input = torch.randn(2, 50, names=('N', 'features')) 2025-01-24T01:51:54.1853483Z >>> unflatten = nn.Unflatten('features', (('C', 2), ('H', 5), ('W', 5))) 2025-01-24T01:51:54.1853933Z >>> output = unflatten(input) 2025-01-24T01:51:54.1854249Z >>> output.size() 2025-01-24T01:51:54.1854565Z torch.Size([2, 2, 5, 5]) 2025-01-24T01:51:54.1854748Z 2025-01-24T01:51:54.1855023Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:51:54.1855398Z 2025-01-24T01:51:54.2178416Z msg = Cannot scrape callname=TripletMarginWithDistanceLoss in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py line=1698. 2025-01-24T01:51:54.2179604Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:51:54.2180213Z Creates a criterion that measures the triplet loss given input 2025-01-24T01:51:54.2180746Z tensors :math:`a`, :math:`p`, and :math:`n` (representing anchor, 2025-01-24T01:51:54.2181289Z positive, and negative examples, respectively), and a nonnegative, 2025-01-24T01:51:54.2181900Z real-valued function ("distance function") used to compute the relationship 2025-01-24T01:51:54.2182506Z between the anchor and positive example ("positive distance") and the 2025-01-24T01:51:54.2183009Z anchor and negative example ("negative distance"). 2025-01-24T01:51:54.2183298Z 2025-01-24T01:51:54.2183513Z The unreduced loss (i.e., with :attr:`reduction` set to ``'none'``) 2025-01-24T01:51:54.2183952Z can be described as: 2025-01-24T01:51:54.2184140Z 2025-01-24T01:51:54.2184250Z .. math:: 2025-01-24T01:51:54.2184539Z \ell(a, p, n) = L = \{l_1,\dots,l_N\}^\top, \quad 2025-01-24T01:51:54.2184957Z l_i = \max \{d(a_i, p_i) - d(a_i, n_i) + {\rm margin}, 0\} 2025-01-24T01:51:54.2185224Z 2025-01-24T01:51:54.2185475Z where :math:`N` is the batch size; :math:`d` is a nonnegative, real-valued function 2025-01-24T01:51:54.2186144Z quantifying the closeness of two tensors, referred to as the :attr:`distance_function`; 2025-01-24T01:51:54.2186875Z and :math:`margin` is a nonnegative margin representing the minimum difference 2025-01-24T01:51:54.2187498Z between the positive and negative distances that is required for the loss to 2025-01-24T01:51:54.2188144Z be 0. The input tensors have :math:`N` elements each and can be of any shape 2025-01-24T01:51:54.2188631Z that the distance function can handle. 2025-01-24T01:51:54.2188861Z 2025-01-24T01:51:54.2188995Z If :attr:`reduction` is not ``'none'`` 2025-01-24T01:51:54.2189339Z (default ``'mean'``), then: 2025-01-24T01:51:54.2189530Z 2025-01-24T01:51:54.2189629Z .. math:: 2025-01-24T01:51:54.2189869Z \ell(x, y) = 2025-01-24T01:51:54.2190125Z \begin{cases} 2025-01-24T01:51:54.2190498Z \operatorname{mean}(L), & \text{if reduction} = \text{`mean';}\\ 2025-01-24T01:51:54.2191021Z \operatorname{sum}(L), & \text{if reduction} = \text{`sum'.} 2025-01-24T01:51:54.2191418Z \end{cases} 2025-01-24T01:51:54.2191579Z 2025-01-24T01:51:54.2191820Z See also :class:`~torch.nn.TripletMarginLoss`, which computes the triplet 2025-01-24T01:51:54.2192436Z loss for input tensors using the :math:`l_p` distance as the distance function. 2025-01-24T01:51:54.2192811Z 2025-01-24T01:51:54.2193039Z Args: 2025-01-24T01:51:54.2193496Z distance_function (Callable, optional): A nonnegative, real-valued function that 2025-01-24T01:51:54.2194101Z quantifies the closeness of two tensors. If not specified, 2025-01-24T01:51:54.2194581Z `nn.PairwiseDistance` will be used. Default: ``None`` 2025-01-24T01:51:54.2195155Z margin (float, optional): A nonnegative margin representing the minimum difference 2025-01-24T01:51:54.2195836Z between the positive and negative distances required for the loss to be 0. Larger 2025-01-24T01:51:54.2196521Z margins penalize cases where the negative examples are not distant enough from the 2025-01-24T01:51:54.2197107Z anchors, relative to the positives. Default: :math:`1`. 2025-01-24T01:51:54.2197708Z swap (bool, optional): Whether to use the distance swap described in the paper 2025-01-24T01:51:54.2198347Z `Learning shallow convolutional feature descriptors with triplet losses` by 2025-01-24T01:51:54.2199032Z V. Balntas, E. Riba et al. If True, and if the positive example is closer to the 2025-01-24T01:51:54.2199670Z negative example than the anchor is, swaps the positive example and the anchor in 2025-01-24T01:51:54.2200217Z the loss computation. Default: ``False``. 2025-01-24T01:51:54.2200761Z reduction (str, optional): Specifies the (optional) reduction to apply to the output: 2025-01-24T01:51:54.2201359Z ``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: no reduction will be applied, 2025-01-24T01:51:54.2201850Z ``'mean'``: the sum of the output will be divided by the number of 2025-01-24T01:51:54.2202408Z elements in the output, ``'sum'``: the output will be summed. Default: ``'mean'`` 2025-01-24T01:51:54.2202782Z 2025-01-24T01:51:54.2202790Z 2025-01-24T01:51:54.2202883Z Shape: 2025-01-24T01:51:54.2203270Z - Input: :math:`(N, *)` where :math:`*` represents any number of additional dimensions 2025-01-24T01:51:54.2203782Z as supported by the distance function. 2025-01-24T01:51:54.2204292Z - Output: A Tensor of shape :math:`(N)` if :attr:`reduction` is ``'none'``, or a scalar 2025-01-24T01:51:54.2204764Z otherwise. 2025-01-24T01:51:54.2204936Z 2025-01-24T01:51:54.2205038Z Examples:: 2025-01-24T01:51:54.2205194Z 2025-01-24T01:51:54.2205302Z >>> # Initialize embeddings 2025-01-24T01:51:54.2205628Z >>> embedding = nn.Embedding(1000, 128) 2025-01-24T01:51:54.2205995Z >>> anchor_ids = torch.randint(0, 1000, (1,)) 2025-01-24T01:51:54.2206372Z >>> positive_ids = torch.randint(0, 1000, (1,)) 2025-01-24T01:51:54.2206763Z >>> negative_ids = torch.randint(0, 1000, (1,)) 2025-01-24T01:51:54.2207136Z >>> anchor = embedding(anchor_ids) 2025-01-24T01:51:54.2207496Z >>> positive = embedding(positive_ids) 2025-01-24T01:51:54.2207860Z >>> negative = embedding(negative_ids) 2025-01-24T01:51:54.2208179Z >>> 2025-01-24T01:51:54.2208429Z >>> # Built-in Distance Function 2025-01-24T01:51:54.2208756Z >>> triplet_loss = \ 2025-01-24T01:51:54.2209225Z >>> nn.TripletMarginWithDistanceLoss(distance_function=nn.PairwiseDistance()) 2025-01-24T01:51:54.2209801Z >>> output = triplet_loss(anchor, positive, negative) 2025-01-24T01:51:54.2210174Z >>> output.backward() 2025-01-24T01:51:54.2210448Z >>> 2025-01-24T01:51:54.2210691Z >>> # Custom Distance Function 2025-01-24T01:51:54.2211011Z >>> def l_infinity(x1, x2): 2025-01-24T01:51:54.2211375Z >>> return torch.max(torch.abs(x1 - x2), dim=1).values 2025-01-24T01:51:54.2211728Z >>> 2025-01-24T01:51:54.2212046Z >>> # xdoctest: +SKIP("FIXME: Would call backwards a second time") 2025-01-24T01:51:54.2212463Z >>> triplet_loss = ( 2025-01-24T01:51:54.2212928Z >>> nn.TripletMarginWithDistanceLoss(distance_function=l_infinity, margin=1.5)) 2025-01-24T01:51:54.2213497Z >>> output = triplet_loss(anchor, positive, negative) 2025-01-24T01:51:54.2213935Z >>> output.backward() 2025-01-24T01:51:54.2214222Z >>> 2025-01-24T01:51:54.2214470Z >>> # Custom Distance Function (Lambda) 2025-01-24T01:51:54.2214808Z >>> triplet_loss = ( 2025-01-24T01:51:54.2215125Z >>> nn.TripletMarginWithDistanceLoss( 2025-01-24T01:51:54.2215584Z >>> distance_function=lambda x, y: 1.0 - F.cosine_similarity(x, y))) 2025-01-24T01:51:54.2216086Z >>> output = triplet_loss(anchor, positive, negative) 2025-01-24T01:51:54.2216468Z >>> output.backward() 2025-01-24T01:51:54.2216655Z 2025-01-24T01:51:54.2216751Z Reference: 2025-01-24T01:51:54.2217217Z V. Balntas, et al.: Learning shallow convolutional feature descriptors with triplet losses: 2025-01-24T01:51:54.2217886Z https://bmva-archive.org.uk/bmvc/2016/papers/paper119/index.html 2025-01-24T01:51:54.2218335Z 2025-01-24T01:51:54.2218724Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 17)) 2025-01-24T01:51:54.2219142Z 2025-01-24T01:51:54.2753944Z msg = Cannot scrape callname=MaxUnpool2d in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py line=395. 2025-01-24T01:51:54.2754884Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:51:54.2755437Z Computes a partial inverse of :class:`MaxPool2d`. 2025-01-24T01:51:54.2755705Z 2025-01-24T01:51:54.2755989Z :class:`MaxPool2d` is not fully invertible, since the non-maximal values are lost. 2025-01-24T01:51:54.2756373Z 2025-01-24T01:51:54.2756617Z :class:`MaxUnpool2d` takes in as input the output of :class:`MaxPool2d` 2025-01-24T01:51:54.2757206Z including the indices of the maximal values and computes a partial inverse 2025-01-24T01:51:54.2757739Z in which all non-maximal values are set to zero. 2025-01-24T01:51:54.2758014Z 2025-01-24T01:51:54.2758109Z Note: 2025-01-24T01:51:54.2758570Z This operation may behave nondeterministically when the input indices has repeat values. 2025-01-24T01:51:54.2759414Z See https://github.com/pytorch/pytorch/issues/80827 and :doc:`/notes/randomness` for more information. 2025-01-24T01:51:54.2759910Z 2025-01-24T01:51:54.2760169Z .. note:: :class:`MaxPool2d` can map several input sizes to the same output 2025-01-24T01:51:54.2760699Z sizes. Hence, the inversion process can get ambiguous. 2025-01-24T01:51:54.2761185Z To accommodate this, you can provide the needed output size 2025-01-24T01:51:54.2761719Z as an additional argument :attr:`output_size` in the forward call. 2025-01-24T01:51:54.2762182Z See the Inputs and Example below. 2025-01-24T01:51:54.2762416Z 2025-01-24T01:51:54.2762521Z Args: 2025-01-24T01:51:54.2762842Z kernel_size (int or tuple): Size of the max pooling window. 2025-01-24T01:51:54.2763316Z stride (int or tuple): Stride of the max pooling window. 2025-01-24T01:51:54.2763752Z It is set to :attr:`kernel_size` by default. 2025-01-24T01:51:54.2764200Z padding (int or tuple): Padding that was added to the input 2025-01-24T01:51:54.2764516Z 2025-01-24T01:51:54.2764616Z Inputs: 2025-01-24T01:51:54.2764879Z - `input`: the input Tensor to invert 2025-01-24T01:51:54.2765329Z - `indices`: the indices given out by :class:`~torch.nn.MaxPool2d` 2025-01-24T01:51:54.2765811Z - `output_size` (optional): the targeted output size 2025-01-24T01:51:54.2766097Z 2025-01-24T01:51:54.2766188Z Shape: 2025-01-24T01:51:54.2766510Z - Input: :math:`(N, C, H_{in}, W_{in})` or :math:`(C, H_{in}, W_{in})`. 2025-01-24T01:51:54.2767036Z - Output: :math:`(N, C, H_{out}, W_{out})` or :math:`(C, H_{out}, W_{out})`, where 2025-01-24T01:51:54.2767367Z 2025-01-24T01:51:54.2767475Z .. math:: 2025-01-24T01:51:54.2767927Z H_{out} = (H_{in} - 1) \times \text{stride[0]} - 2 \times \text{padding[0]} + \text{kernel\_size[0]} 2025-01-24T01:51:54.2768306Z 2025-01-24T01:51:54.2768412Z .. math:: 2025-01-24T01:51:54.2769002Z W_{out} = (W_{in} - 1) \times \text{stride[1]} - 2 \times \text{padding[1]} + \text{kernel\_size[1]} 2025-01-24T01:51:54.2769393Z 2025-01-24T01:51:54.2769561Z or as given by :attr:`output_size` in the call operator 2025-01-24T01:51:54.2769855Z 2025-01-24T01:51:54.2769952Z Example:: 2025-01-24T01:51:54.2770102Z 2025-01-24T01:51:54.2770268Z >>> pool = nn.MaxPool2d(2, stride=2, return_indices=True) 2025-01-24T01:51:54.2770695Z >>> unpool = nn.MaxUnpool2d(2, stride=2) 2025-01-24T01:51:54.2771077Z >>> input = torch.tensor([[[[ 1., 2., 3., 4.], 2025-01-24T01:51:54.2771431Z [ 5., 6., 7., 8.], 2025-01-24T01:51:54.2771777Z [ 9., 10., 11., 12.], 2025-01-24T01:51:54.2772171Z [13., 14., 15., 16.]]]]) 2025-01-24T01:51:54.2772530Z >>> output, indices = pool(input) 2025-01-24T01:51:54.2772879Z >>> unpool(output, indices) 2025-01-24T01:51:54.2773242Z tensor([[[[ 0., 0., 0., 0.], 2025-01-24T01:51:54.2773575Z [ 0., 6., 0., 8.], 2025-01-24T01:51:54.2773894Z [ 0., 0., 0., 0.], 2025-01-24T01:51:54.2774216Z [ 0., 14., 0., 16.]]]]) 2025-01-24T01:51:54.2774655Z >>> # Now using output_size to resolve an ambiguous size for the inverse 2025-01-24T01:51:54.2775125Z >>> input = torch.tensor([[[[ 1., 2., 3., 4., 5.], 2025-01-24T01:51:54.2775507Z [ 6., 7., 8., 9., 10.], 2025-01-24T01:51:54.2775859Z [11., 12., 13., 14., 15.], 2025-01-24T01:51:54.2776216Z [16., 17., 18., 19., 20.]]]]) 2025-01-24T01:51:54.2776588Z >>> output, indices = pool(input) 2025-01-24T01:51:54.2777010Z >>> # This call will not work without specifying output_size 2025-01-24T01:51:54.2777462Z >>> unpool(output, indices, output_size=input.size()) 2025-01-24T01:51:54.2777862Z tensor([[[[ 0., 0., 0., 0., 0.], 2025-01-24T01:51:54.2778195Z [ 0., 7., 0., 9., 0.], 2025-01-24T01:51:54.2778520Z [ 0., 0., 0., 0., 0.], 2025-01-24T01:51:54.2778849Z [ 0., 17., 0., 19., 0.]]]]) 2025-01-24T01:51:54.2779067Z 2025-01-24T01:51:54.2779071Z 2025-01-24T01:51:54.2779411Z 2025-01-24T01:51:54.2779793Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:51:54.2780184Z 2025-01-24T01:51:54.3029359Z msg = Cannot scrape callname=EmbeddingBag in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/sparse.py line=270. 2025-01-24T01:51:54.3030298Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:51:54.3031024Z Compute sums or means of 'bags' of embeddings, without instantiating the intermediate embeddings. 2025-01-24T01:51:54.3031465Z 2025-01-24T01:51:54.3031821Z For bags of constant length, no :attr:`per_sample_weights`, no indices equal to :attr:`padding_idx`, 2025-01-24T01:51:54.3032384Z and with 2D inputs, this class 2025-01-24T01:51:54.3032589Z 2025-01-24T01:51:54.3032916Z * with ``mode="sum"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.sum(dim=1)``, 2025-01-24T01:51:54.3033654Z * with ``mode="mean"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.mean(dim=1)``, 2025-01-24T01:51:54.3034403Z * with ``mode="max"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.max(dim=1)``. 2025-01-24T01:51:54.3034842Z 2025-01-24T01:51:54.3035204Z However, :class:`~torch.nn.EmbeddingBag` is much more time and memory efficient than using a chain of these 2025-01-24T01:51:54.3035786Z operations. 2025-01-24T01:51:54.3035927Z 2025-01-24T01:51:54.3036210Z EmbeddingBag also supports per-sample weights as an argument to the forward 2025-01-24T01:51:54.3036844Z pass. This scales the output of the Embedding before performing a weighted 2025-01-24T01:51:54.3037637Z reduction as specified by ``mode``. If :attr:`per_sample_weights` is passed, the 2025-01-24T01:51:54.3038287Z only supported ``mode`` is ``"sum"``, which computes a weighted sum according to 2025-01-24T01:51:54.3038765Z :attr:`per_sample_weights`. 2025-01-24T01:51:54.3038977Z 2025-01-24T01:51:54.3039067Z Args: 2025-01-24T01:51:54.3039390Z num_embeddings (int): size of the dictionary of embeddings 2025-01-24T01:51:54.3039873Z embedding_dim (int): the size of each embedding vector 2025-01-24T01:51:54.3040484Z max_norm (float, optional): If given, each embedding vector with norm larger than :attr:`max_norm` 2025-01-24T01:51:54.3041071Z is renormalized to have norm :attr:`max_norm`. 2025-01-24T01:51:54.3041744Z norm_type (float, optional): The p of the p-norm to compute for the :attr:`max_norm` option. Default ``2``. 2025-01-24T01:51:54.3042589Z scale_grad_by_freq (bool, optional): if given, this will scale gradients by the inverse of frequency of 2025-01-24T01:51:54.3043218Z the words in the mini-batch. Default ``False``. 2025-01-24T01:51:54.3043702Z Note: this option is not supported when ``mode="max"``. 2025-01-24T01:51:54.3044271Z mode (str, optional): ``"sum"``, ``"mean"`` or ``"max"``. Specifies the way to reduce the bag. 2025-01-24T01:51:54.3044885Z ``"sum"`` computes the weighted sum, taking :attr:`per_sample_weights` 2025-01-24T01:51:54.3045454Z into consideration. ``"mean"`` computes the average of the values 2025-01-24T01:51:54.3045996Z in the bag, ``"max"`` computes the max value over each bag. 2025-01-24T01:51:54.3046434Z Default: ``"mean"`` 2025-01-24T01:51:54.3047007Z sparse (bool, optional): if ``True``, gradient w.r.t. :attr:`weight` matrix will be a sparse tensor. See 2025-01-24T01:51:54.3047732Z Notes for more details regarding sparse gradients. Note: this option is not 2025-01-24T01:51:54.3048265Z supported when ``mode="max"``. 2025-01-24T01:51:54.3048889Z include_last_offset (bool, optional): if ``True``, :attr:`offsets` has one additional element, where the last element 2025-01-24T01:51:54.3049623Z is equivalent to the size of `indices`. This matches the CSR format. 2025-01-24T01:51:54.3050321Z padding_idx (int, optional): If specified, the entries at :attr:`padding_idx` do not contribute to the 2025-01-24T01:51:54.3051067Z gradient; therefore, the embedding vector at :attr:`padding_idx` is not updated 2025-01-24T01:51:54.3051728Z during training, i.e. it remains as a fixed "pad". For a newly constructed 2025-01-24T01:51:54.3052388Z EmbeddingBag, the embedding vector at :attr:`padding_idx` will default to all 2025-01-24T01:51:54.3053044Z zeros, but can be updated to another value to be used as the padding vector. 2025-01-24T01:51:54.3053656Z Note that the embedding vector at :attr:`padding_idx` is excluded from the 2025-01-24T01:51:54.3054142Z reduction. 2025-01-24T01:51:54.3054375Z 2025-01-24T01:51:54.3054472Z Attributes: 2025-01-24T01:51:54.3054947Z weight (Tensor): the learnable weights of the module of shape `(num_embeddings, embedding_dim)` 2025-01-24T01:51:54.3055539Z initialized from :math:`\mathcal{N}(0, 1)`. 2025-01-24T01:51:54.3055797Z 2025-01-24T01:51:54.3055920Z Examples:: 2025-01-24T01:51:54.3056061Z 2025-01-24T01:51:54.3056236Z >>> # an EmbeddingBag module containing 10 tensors of size 3 2025-01-24T01:51:54.3056699Z >>> embedding_sum = nn.EmbeddingBag(10, 3, mode='sum') 2025-01-24T01:51:54.3057170Z >>> # a batch of 2 samples of 4 indices each 2025-01-24T01:51:54.3057609Z >>> input = torch.tensor([1, 2, 4, 5, 4, 3, 2, 9], dtype=torch.long) 2025-01-24T01:51:54.3058080Z >>> offsets = torch.tensor([0, 4], dtype=torch.long) 2025-01-24T01:51:54.3058488Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2025-01-24T01:51:54.3058873Z >>> embedding_sum(input, offsets) 2025-01-24T01:51:54.3059223Z tensor([[-0.8861, -5.4350, -0.0523], 2025-01-24T01:51:54.3059565Z [ 1.1306, -2.5798, -1.0044]]) 2025-01-24T01:51:54.3059782Z 2025-01-24T01:51:54.3059909Z >>> # Example with padding_idx 2025-01-24T01:51:54.3060342Z >>> embedding_sum = nn.EmbeddingBag(10, 3, mode='sum', padding_idx=2) 2025-01-24T01:51:54.3060884Z >>> input = torch.tensor([2, 2, 2, 2, 4, 3, 2, 9], dtype=torch.long) 2025-01-24T01:51:54.3061351Z >>> offsets = torch.tensor([0, 4], dtype=torch.long) 2025-01-24T01:51:54.3061772Z >>> embedding_sum(input, offsets) 2025-01-24T01:51:54.3062110Z tensor([[ 0.0000, 0.0000, 0.0000], 2025-01-24T01:51:54.3062437Z [-0.7082, 3.2145, -2.6251]]) 2025-01-24T01:51:54.3062651Z 2025-01-24T01:51:54.3062830Z >>> # An EmbeddingBag can be loaded from an Embedding like so 2025-01-24T01:51:54.3063286Z >>> embedding = nn.Embedding(10, 3, padding_idx=2) 2025-01-24T01:51:54.3063721Z >>> embedding_sum = nn.EmbeddingBag.from_pretrained( 2025-01-24T01:51:54.3064107Z embedding.weight, 2025-01-24T01:51:54.3064447Z padding_idx=embedding.padding_idx, 2025-01-24T01:51:54.3064799Z mode='sum') 2025-01-24T01:51:54.3065062Z 2025-01-24T01:51:54.3065447Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:51:54.3065837Z 2025-01-24T01:51:54.3403910Z msg = Cannot scrape callname=DistributedDataParallel.join in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/distributed.py line=1759. 2025-01-24T01:51:54.3404985Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:51:54.3405370Z 2025-01-24T01:51:54.3405628Z Context manager for training with uneven inputs across processes in DDP. 2025-01-24T01:51:54.3405986Z 2025-01-24T01:51:54.3406217Z This context manager will keep track of already-joined DDP processes, 2025-01-24T01:51:54.3406788Z and "shadow" the forward and backward passes by inserting collective 2025-01-24T01:51:54.3407362Z communication operations to match with the ones created by non-joined 2025-01-24T01:51:54.3407963Z DDP processes. This will ensure each collective call has a corresponding 2025-01-24T01:51:54.3408554Z call by already-joined DDP processes, preventing hangs or errors that 2025-01-24T01:51:54.3409111Z would otherwise happen when training with uneven inputs across 2025-01-24T01:51:54.3409711Z processes. Alternatively, if the flag ``throw_on_early_termination`` is 2025-01-24T01:51:54.3410290Z specified to be ``True``, all trainers will throw an error once one rank 2025-01-24T01:51:54.3410837Z runs out of inputs, allowing these errors to be caught and handled 2025-01-24T01:51:54.3411286Z according to application logic. 2025-01-24T01:51:54.3411503Z 2025-01-24T01:51:54.3411728Z Once all DDP processes have joined, the context manager will broadcast 2025-01-24T01:51:54.3412321Z the model corresponding to the last joined process to all processes to 2025-01-24T01:51:54.3412830Z ensure the model is the same across all processes 2025-01-24T01:51:54.3413197Z (which is guaranteed by DDP). 2025-01-24T01:51:54.3413402Z 2025-01-24T01:51:54.3413606Z To use this to enable training with uneven inputs across processes, 2025-01-24T01:51:54.3414162Z simply wrap this context manager around your training loop. No further 2025-01-24T01:51:54.3414685Z modifications to the model or data loading is required. 2025-01-24T01:51:54.3414977Z 2025-01-24T01:51:54.3415093Z .. warning:: 2025-01-24T01:51:54.3415604Z If the model or training loop this context manager is wrapped around 2025-01-24T01:51:54.3416121Z has additional distributed collective operations, such as 2025-01-24T01:51:54.3416623Z ``SyncBatchNorm`` in the model's forward pass, then the flag 2025-01-24T01:51:54.3417143Z ``throw_on_early_termination`` must be enabled. This is because this 2025-01-24T01:51:54.3417696Z context manager is not aware of non-DDP collective communication. 2025-01-24T01:51:54.3418210Z This flag will cause all ranks to throw when any one rank 2025-01-24T01:51:54.3418709Z exhausts inputs, allowing these errors to be caught and recovered 2025-01-24T01:51:54.3419165Z from across all ranks. 2025-01-24T01:51:54.3419359Z 2025-01-24T01:51:54.3419446Z Args: 2025-01-24T01:51:54.3419808Z divide_by_initial_world_size (bool): If ``True``, will divide 2025-01-24T01:51:54.3420328Z gradients by the initial ``world_size`` DDP training was launched 2025-01-24T01:51:54.3420879Z with. If ``False``, will compute the effective world size 2025-01-24T01:51:54.3421352Z (number of ranks that have not depleted their inputs yet) and 2025-01-24T01:51:54.3421817Z divide gradients by that during allreduce. Set 2025-01-24T01:51:54.3422270Z ``divide_by_initial_world_size=True`` to ensure every input 2025-01-24T01:51:54.3422787Z sample including the uneven inputs have equal weight in terms of 2025-01-24T01:51:54.3423301Z how much they contribute to the global gradient. This is 2025-01-24T01:51:54.3423761Z achieved by always dividing the gradient by the initial 2025-01-24T01:51:54.3424250Z ``world_size`` even when we encounter uneven inputs. If you set 2025-01-24T01:51:54.3424739Z this to ``False``, we divide the gradient by the remaining 2025-01-24T01:51:54.3425238Z number of nodes. This ensures parity with training on a smaller 2025-01-24T01:51:54.3425756Z ``world_size`` although it also means the uneven inputs would 2025-01-24T01:51:54.3426377Z contribute more towards the global gradient. Typically, you 2025-01-24T01:51:54.3426880Z would want to set this to ``True`` for cases where the last few 2025-01-24T01:51:54.3427410Z inputs of your training job are uneven. In extreme cases, where 2025-01-24T01:51:54.3427926Z there is a large discrepancy in the number of inputs, setting 2025-01-24T01:51:54.3428386Z this to ``False`` might provide better results. 2025-01-24T01:51:54.3428867Z enable (bool): Whether to enable uneven input detection or not. Pass 2025-01-24T01:51:54.3429366Z in ``enable=False`` to disable in cases where you know that 2025-01-24T01:51:54.3429862Z inputs are even across participating processes. Default is 2025-01-24T01:51:54.3430272Z ``True``. 2025-01-24T01:51:54.3430615Z throw_on_early_termination (bool): Whether to throw an error 2025-01-24T01:51:54.3431108Z or continue training when at least one rank has exhausted 2025-01-24T01:51:54.3431699Z inputs. If ``True``, will throw upon the first rank reaching end 2025-01-24T01:51:54.3432395Z of data. If ``False``, will continue training with a smaller 2025-01-24T01:51:54.3433052Z effective world size until all ranks are joined. Note that if 2025-01-24T01:51:54.3433863Z this flag is specified, then the flag 2025-01-24T01:51:54.3434292Z ``divide_by_initial_world_size`` would be ignored. Default 2025-01-24T01:51:54.3434688Z is ``False``. 2025-01-24T01:51:54.3434845Z 2025-01-24T01:51:54.3434850Z 2025-01-24T01:51:54.3434966Z Example:: 2025-01-24T01:51:54.3435089Z 2025-01-24T01:51:54.3435206Z >>> # xdoctest: +SKIP("Distributed") 2025-01-24T01:51:54.3435533Z >>> import torch 2025-01-24T01:51:54.3435816Z >>> import torch.distributed as dist 2025-01-24T01:51:54.3436150Z >>> import os 2025-01-24T01:51:54.3436437Z >>> import torch.multiprocessing as mp 2025-01-24T01:51:54.3436771Z >>> import torch.nn as nn 2025-01-24T01:51:54.3437183Z >>> # On each spawned worker 2025-01-24T01:51:54.3437493Z >>> def worker(rank): 2025-01-24T01:51:54.3437856Z >>> dist.init_process_group("nccl", rank=rank, world_size=2) 2025-01-24T01:51:54.3438274Z >>> torch.cuda.set_device(rank) 2025-01-24T01:51:54.3438626Z >>> model = nn.Linear(1, 1, bias=False).to(rank) 2025-01-24T01:51:54.3439061Z >>> model = torch.nn.parallel.DistributedDataParallel( 2025-01-24T01:51:54.3439514Z >>> model, device_ids=[rank], output_device=rank 2025-01-24T01:51:54.3439868Z >>> ) 2025-01-24T01:51:54.3440139Z >>> # Rank 1 gets one more input than rank 0. 2025-01-24T01:51:54.3440567Z >>> inputs = [torch.tensor([1]).float() for _ in range(10 + rank)] 2025-01-24T01:51:54.3440986Z >>> with model.join(): 2025-01-24T01:51:54.3441315Z >>> for _ in range(5): 2025-01-24T01:51:54.3441629Z >>> for inp in inputs: 2025-01-24T01:51:54.3441997Z >>> loss = model(inp).sum() 2025-01-24T01:51:54.3442330Z >>> loss.backward() 2025-01-24T01:51:54.3442746Z >>> # Without the join() API, the below synchronization will hang 2025-01-24T01:51:54.3443211Z >>> # blocking for rank 1's allreduce to complete. 2025-01-24T01:51:54.3443611Z >>> torch.cuda.synchronize(device=rank) 2025-01-24T01:51:54.3443853Z 2025-01-24T01:51:54.3444124Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:51:54.3444495Z 2025-01-24T01:51:54.3445263Z msg = Cannot scrape callname=DistributedDataParallel._register_fused_optim in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/distributed.py line=2050. 2025-01-24T01:51:54.3446371Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:51:54.3446753Z 2025-01-24T01:51:54.3447068Z Register an optimizer in DDP to optimize parameter immediately after its gradient reduction. 2025-01-24T01:51:54.3447528Z 2025-01-24T01:51:54.3447742Z Registers an optimizer with DDP such that the optimization for a 2025-01-24T01:51:54.3448294Z parameter will run immediately when that parameter's gradient is 2025-01-24T01:51:54.3448838Z finished with reduction, instead of waiting for all parameters' 2025-01-24T01:51:54.3449395Z gradients to finish reduction. This can result in a training speedup 2025-01-24T01:51:54.3449969Z depending on your workload since the optimizer can run while gradient 2025-01-24T01:51:54.3450540Z reduction for other parameters are still ongoing. In addition, this has 2025-01-24T01:51:54.3451144Z the potential to reduce peak memory consumption during training, as it 2025-01-24T01:51:54.3451709Z only needs to load the per-parameter optimizer states of a single 2025-01-24T01:51:54.3452262Z parameter at a time, instead of loading all per-parameter optimizer 2025-01-24T01:51:54.3452703Z states at once. 2025-01-24T01:51:54.3452847Z 2025-01-24T01:51:54.3452938Z Args: 2025-01-24T01:51:54.3453279Z optim (Type): a ``torch.optim.Optimizer`` class to be registered 2025-01-24T01:51:54.3453716Z as a fused optimizer. 2025-01-24T01:51:54.3454079Z *args (Sequence[Any]): Arguments to forward to `optim`. 2025-01-24T01:51:54.3454590Z optim_params (Optional[Iterable[torch.Tensor]]): Set of parameters 2025-01-24T01:51:54.3455175Z to optimize, similar to `params` argument of traditional `torch.optim` 2025-01-24T01:51:54.3455729Z Optimizers. If this is omitted, all DDP model parameters will be 2025-01-24T01:51:54.3456175Z optimized. 2025-01-24T01:51:54.3456538Z **kwargs: (Dict[str, Any]): Keyword arguments to forward to `optim`. 2025-01-24T01:51:54.3456857Z 2025-01-24T01:51:54.3456976Z .. warning :: 2025-01-24T01:51:54.3457342Z _register_fused_optim should only be called once on a DDP instance, 2025-01-24T01:51:54.3457895Z and registering multiple fused optimizers for the same DDP model 2025-01-24T01:51:54.3458368Z is not currently supported. Please ping 2025-01-24T01:51:54.3458952Z https://github.com/pytorch/pytorch/issues/71595 if this is necessary 2025-01-24T01:51:54.3459421Z for your use case. 2025-01-24T01:51:54.3459583Z 2025-01-24T01:51:54.3459700Z .. warning :: 2025-01-24T01:51:54.3460049Z _register_fused_optim and register_comm_hook currently do not 2025-01-24T01:51:54.3460579Z compose together, meaning that custom DDP communication hooks are 2025-01-24T01:51:54.3461105Z not supported with overlapped optimizers. Please ping 2025-01-24T01:51:54.3461635Z https://github.com/pytorch/pytorch/issues/71595 if this is necessary 2025-01-24T01:51:54.3462095Z for your use case. 2025-01-24T01:51:54.3462258Z 2025-01-24T01:51:54.3462373Z .. warning :: 2025-01-24T01:51:54.3462744Z Gradient accumulation and DDP `no_sync` are currently not supported 2025-01-24T01:51:54.3463267Z with overlapped optimizer. Please ping 2025-01-24T01:51:54.3463754Z https://github.com/pytorch/pytorch/issues/71595 if this is necessary 2025-01-24T01:51:54.3464254Z for your use case. 2025-01-24T01:51:54.3464420Z 2025-01-24T01:51:54.3464543Z Example:: 2025-01-24T01:51:54.3464670Z 2025-01-24T01:51:54.3464827Z >>> # xdoctest: +SKIP("No rendezvous handler") 2025-01-24T01:51:54.3465376Z >>> torch.distributed.init_process_group(backend='nccl', world_size=4, init_method='...') 2025-01-24T01:51:54.3466016Z >>> net = torch.nn.parallel.DistributedDataParallel(model, pg) 2025-01-24T01:51:54.3466554Z >>> lr = 1e-2 2025-01-24T01:51:54.3466820Z >>> betas = (0.9, 0.99) 2025-01-24T01:51:54.3467113Z >>> eps = 1e-6 2025-01-24T01:51:54.3467492Z >>> net._register_fused_optim(torch.optim.Adam, lr, betas=betas, eps=eps) 2025-01-24T01:51:54.3467980Z >>> # Example with subset of parameters 2025-01-24T01:51:54.3468373Z >>> params_to_opt = [list(net.parameters())[0]] 2025-01-24T01:51:54.3468756Z >>> net._register_fused_optim( 2025-01-24T01:51:54.3469213Z ... torch.optim.Adam, lr, optim_params=params_to_opt, betas=betas, eps=eps 2025-01-24T01:51:54.3469661Z ... ) 2025-01-24T01:51:54.3469798Z 2025-01-24T01:51:54.3470066Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:51:54.3470452Z 2025-01-24T01:51:54.3932498Z msg = Cannot scrape callname=convert_conv2d_weight_memory_format in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/memory_format.py line=6. 2025-01-24T01:51:54.3933540Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:51:54.3934152Z Convert ``memory_format`` of ``nn.Conv2d.weight`` to ``memory_format``. 2025-01-24T01:51:54.3934515Z 2025-01-24T01:51:54.3934810Z The conversion recursively applies to nested ``nn.Module``, including ``module``. 2025-01-24T01:51:54.3935502Z Note that it only changes the memory_format, but not the semantics of each dimensions. 2025-01-24T01:51:54.3936169Z This function is used to facilitate the computation to adopt NHWC kernels, which 2025-01-24T01:51:54.3936860Z provides considerable speed up for fp16 data on CUDA devices with compute capability >= 7.0 2025-01-24T01:51:54.3937302Z 2025-01-24T01:51:54.3937404Z .. note:: 2025-01-24T01:51:54.3937785Z Calling ``model.to(memory_format=torch.channels_last)`` is more aggressive 2025-01-24T01:51:54.3938605Z than the utility function ``convert_conv2d_weight_memory_format``. Any 2025-01-24T01:51:54.3939379Z layer with 4d weight will be affected by ``model.to``, which does not 2025-01-24T01:51:54.3940022Z necessarily benefit from conversion to specified ``memory_format``. 2025-01-24T01:51:54.3940662Z One place we are confident in is that NHWC(channels_last) conversion for 2025-01-24T01:51:54.3941299Z convolution in cuDNN, as it is beneficial to run convolution in NHWC, 2025-01-24T01:51:54.3941926Z even in cases where we have to apply permutation to input tensors. 2025-01-24T01:51:54.3942266Z 2025-01-24T01:51:54.3942502Z Hence our strategy here is to convert only the weight of convolution to 2025-01-24T01:51:54.3943174Z channels_last. This ensures that; 2025-01-24T01:51:54.3943705Z 1. Fast convolution kernels will be used, the benefit of which could 2025-01-24T01:51:54.3944343Z outweigh overhead of permutation (if input is not in the same format). 2025-01-24T01:51:54.3945011Z 2. No unnecessary permutations are applied on layers that do not benefit 2025-01-24T01:51:54.3945497Z from memory_format conversion. 2025-01-24T01:51:54.3945782Z 2025-01-24T01:51:54.3946033Z The optimal case is that, layers between convolution layers are channels 2025-01-24T01:51:54.3946804Z last compatible. Input tensor would be permuted to channels last when it 2025-01-24T01:51:54.3947470Z encounters the first convolution layer and stay in that memory format. 2025-01-24T01:51:54.3948183Z Hence following convolutions will not need to permute its input tensor. 2025-01-24T01:51:54.3948561Z 2025-01-24T01:51:54.3948906Z In case where a channels last incompatible layer is between convolution 2025-01-24T01:51:54.3949541Z layers, we need to permute the input tensor back to contiguous format 2025-01-24T01:51:54.3950124Z for that layer. The input tensor will go through the remaining layers in 2025-01-24T01:51:54.3950779Z contiguous format and be permuted to channels last when it encounters 2025-01-24T01:51:54.3951426Z another convolution layer. There's no point in propagating that 2025-01-24T01:51:54.3952039Z permutation to an earlier layer, as most layers are quite agnostic to 2025-01-24T01:51:54.3952520Z ``memory_format``. 2025-01-24T01:51:54.3952757Z 2025-01-24T01:51:54.3952995Z This claim might change when PyTorch supports fusion of permutation, as 2025-01-24T01:51:54.3953652Z there might have been a better spot to fuse the permutation other than 2025-01-24T01:51:54.3954186Z immediately before a convolution. 2025-01-24T01:51:54.3954422Z 2025-01-24T01:51:54.3954530Z Args: 2025-01-24T01:51:54.3954925Z module (nn.Module): ``nn.Conv2d`` & ``nn.ConvTranspose2d`` or container 2025-01-24T01:51:54.3955382Z ``nn.Module`` 2025-01-24T01:51:54.3955822Z memory_format: user specified ``memory_format``, 2025-01-24T01:51:54.3956288Z e.g. ``torch.channels_last`` or ``torch.contiguous_format`` 2025-01-24T01:51:54.3956653Z 2025-01-24T01:51:54.3956770Z Returns: 2025-01-24T01:51:54.3957050Z The original module with updated ``nn.Conv2d`` 2025-01-24T01:51:54.3957391Z 2025-01-24T01:51:54.3957488Z Example: 2025-01-24T01:51:54.3957778Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2025-01-24T01:51:54.3958271Z >>> # xdoctest: +REQUIRES(env:CUBLAS_WORKSPACE_CONFIG) 2025-01-24T01:51:54.3958847Z >>> input = torch.randint(1, 10, (2, 8, 4, 4), dtype=torch.float16, device="cuda") 2025-01-24T01:51:54.3959328Z >>> model = nn.Sequential( 2025-01-24T01:51:54.3959720Z >>> nn.Conv2d(8, 4, 3)).cuda().half() 2025-01-24T01:51:54.3960078Z >>> # This is identical to: 2025-01-24T01:51:54.3960614Z >>> # nn.utils.convert_conv2d_weight_memory_format(model, torch.channels_last) 2025-01-24T01:51:54.3961321Z >>> model = nn.utils.convert_conv2d_weight_memory_format(model, torch.channels_last) 2025-01-24T01:51:54.3961890Z >>> out = model(input) 2025-01-24T01:51:54.3962173Z 2025-01-24T01:51:54.3962617Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:51:54.3963010Z 2025-01-24T01:51:54.3963744Z msg = Cannot scrape callname=convert_conv3d_weight_memory_format in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/memory_format.py line=81. 2025-01-24T01:51:54.3964897Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:51:54.3965514Z Convert ``memory_format`` of ``nn.Conv3d.weight`` to ``memory_format`` 2025-01-24T01:51:54.3966206Z The conversion recursively applies to nested ``nn.Module``, including ``module``. 2025-01-24T01:51:54.3967055Z Note that it only changes the memory_format, but not the semantics of each dimensions. 2025-01-24T01:51:54.3967783Z This function is used to facilitate the computation to adopt NHWC kernels, which 2025-01-24T01:51:54.3968560Z provides considerable speed up for fp16 data on CUDA devices with compute capability >= 7.0 2025-01-24T01:51:54.3969074Z 2025-01-24T01:51:54.3969176Z .. note:: 2025-01-24T01:51:54.3969635Z Calling ``model.to(memory_format=torch.channels_last_3d)`` is more aggressive 2025-01-24T01:51:54.3970246Z than the utility function ``convert_conv3d_weight_memory_format``. Any 2025-01-24T01:51:54.3970879Z layer with 4d weight will be affected by ``model.to``, which does not 2025-01-24T01:51:54.3971486Z necessarily benefit from conversion to specified ``memory_format``. 2025-01-24T01:51:54.3972153Z One place we are confident in is that NDHWC(channels_last_3d) conversion for 2025-01-24T01:51:54.3972792Z convolution in cuDNN, as it is beneficial to run convolution in NDHWC, 2025-01-24T01:51:54.3973353Z even in cases where we have to apply permutation to input tensors. 2025-01-24T01:51:54.3973677Z 2025-01-24T01:51:54.3973957Z Hence our strategy here is to convert only the weight of convolution to 2025-01-24T01:51:54.3974464Z channels_last_3d. This ensures that; 2025-01-24T01:51:54.3974921Z 1. Fast convolution kernels will be used, the benefit of which could 2025-01-24T01:51:54.3975503Z outweigh overhead of permutation (if input is not in the same format). 2025-01-24T01:51:54.3976104Z 2. No unnecessary permutations are applied on layers that do not benefit 2025-01-24T01:51:54.3976586Z from memory_format conversion. 2025-01-24T01:51:54.3976811Z 2025-01-24T01:51:54.3977056Z The optimal case is that, layers between convolution layers are channels 2025-01-24T01:51:54.3977668Z last compatible. Input tensor would be permuted to channels last when it 2025-01-24T01:51:54.3978313Z encounters the first convolution layer and stay in that memory format. 2025-01-24T01:51:54.3978917Z Hence following convolutions will not need to permute its input tensor. 2025-01-24T01:51:54.3979490Z 2025-01-24T01:51:54.3979723Z In case where a channels last incompatible layer is between convolution 2025-01-24T01:51:54.3980300Z layers, we need to permute the input tensor back to contiguous format 2025-01-24T01:51:54.3980876Z for that layer. The input tensor will go through the remaining layers in 2025-01-24T01:51:54.3981466Z contiguous format and be permuted to channels last when it encounters 2025-01-24T01:51:54.3982027Z another convolution layer. There's no point in propagating that 2025-01-24T01:51:54.3982597Z permutation to an earlier layer, as most layers are quite agnostic to 2025-01-24T01:51:54.3983059Z ``memory_format``. 2025-01-24T01:51:54.3983254Z 2025-01-24T01:51:54.3983490Z This claim might change when PyTorch supports fusion of permutation, as 2025-01-24T01:51:54.3984077Z there might have been a better spot to fuse the permutation other than 2025-01-24T01:51:54.3984551Z immediately before a convolution. 2025-01-24T01:51:54.3984854Z 2025-01-24T01:51:54.3984946Z Args: 2025-01-24T01:51:54.3985296Z module (nn.Module): ``nn.Conv3d`` & ``nn.ConvTranspose3d`` or container 2025-01-24T01:51:54.3985744Z ``nn.Module`` 2025-01-24T01:51:54.3986119Z memory_format: user specified ``memory_format``, 2025-01-24T01:51:54.3986676Z e.g. ``torch.channels_last`` or ``torch.contiguous_format`` 2025-01-24T01:51:54.3986978Z 2025-01-24T01:51:54.3987087Z Returns: 2025-01-24T01:51:54.3987365Z The original module with updated ``nn.Conv3d`` 2025-01-24T01:51:54.3987638Z 2025-01-24T01:51:54.3987732Z Example: 2025-01-24T01:51:54.3988018Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2025-01-24T01:51:54.3988569Z >>> # xdoctest: +REQUIRES(env:CUBLAS_WORKSPACE_CONFIG) 2025-01-24T01:51:54.3989089Z >>> input = torch.randint(1, 10, (2, 8, 4, 4, 4), dtype=torch.float16, device="cuda") 2025-01-24T01:51:54.3989547Z >>> model = nn.Sequential( 2025-01-24T01:51:54.3989881Z >>> nn.Conv3d(8, 4, 3)).cuda().half() 2025-01-24T01:51:54.3990233Z >>> # This is identical to: 2025-01-24T01:51:54.3990703Z >>> # nn.utils.convert_conv3d_weight_memory_format(model, torch.channels_last_3d) 2025-01-24T01:51:54.3991362Z >>> model = nn.utils.convert_conv3d_weight_memory_format(model, torch.channels_last_3d) 2025-01-24T01:51:54.3991869Z >>> out = model(input) 2025-01-24T01:51:54.3992139Z 2025-01-24T01:51:54.3992564Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:51:54.3992949Z 2025-01-24T01:51:54.4172805Z msg = Cannot scrape callname=random_structured in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py line=935. 2025-01-24T01:51:54.4173964Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:51:54.4174598Z Prune tensor by removing random channels along the specified dimension. 2025-01-24T01:51:54.4174947Z 2025-01-24T01:51:54.4175200Z Prunes tensor corresponding to parameter called ``name`` in ``module`` 2025-01-24T01:51:54.4175780Z by removing the specified ``amount`` of (currently unpruned) channels 2025-01-24T01:51:54.4176259Z along the specified ``dim`` selected at random. 2025-01-24T01:51:54.4176729Z Modifies module in place (and also return the modified module) 2025-01-24T01:51:54.4177258Z by: 2025-01-24T01:51:54.4177434Z 2025-01-24T01:51:54.4177782Z 1) adding a named buffer called ``name+'_mask'`` corresponding to the 2025-01-24T01:51:54.4178596Z binary mask applied to the parameter ``name`` by the pruning method. 2025-01-24T01:51:54.4179568Z 2) replacing the parameter ``name`` by its pruned version, while the 2025-01-24T01:51:54.4180119Z original (unpruned) parameter is stored in a new parameter named 2025-01-24T01:51:54.4180560Z ``name+'_orig'``. 2025-01-24T01:51:54.4180749Z 2025-01-24T01:51:54.4180838Z Args: 2025-01-24T01:51:54.4181157Z module (nn.Module): module containing the tensor to prune 2025-01-24T01:51:54.4181648Z name (str): parameter name within ``module`` on which pruning 2025-01-24T01:51:54.4182042Z will act. 2025-01-24T01:51:54.4182396Z amount (int or float): quantity of parameters to prune. 2025-01-24T01:51:54.4182869Z If ``float``, should be between 0.0 and 1.0 and represent the 2025-01-24T01:51:54.4183386Z fraction of parameters to prune. If ``int``, it represents the 2025-01-24T01:51:54.4183926Z absolute number of parameters to prune. 2025-01-24T01:51:54.4184594Z dim (int): index of the dim along which we define channels to prune. 2025-01-24T01:51:54.4185119Z 2025-01-24T01:51:54.4185221Z Returns: 2025-01-24T01:51:54.4185598Z module (nn.Module): modified (i.e. pruned) version of the input module 2025-01-24T01:51:54.4185960Z 2025-01-24T01:51:54.4186058Z Examples: 2025-01-24T01:51:54.4186382Z >>> # xdoctest: +SKIP 2025-01-24T01:51:54.4186708Z >>> m = prune.random_structured( 2025-01-24T01:51:54.4187074Z ... nn.Linear(5, 3), 'weight', amount=3, dim=1 2025-01-24T01:51:54.4187434Z ... ) 2025-01-24T01:51:54.4187773Z >>> columns_pruned = int(sum(torch.sum(m.weight, dim=0) == 0)) 2025-01-24T01:51:54.4188199Z >>> print(columns_pruned) 2025-01-24T01:51:54.4188505Z 3 2025-01-24T01:51:54.4188723Z 2025-01-24T01:51:54.4189186Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:51:54.4189664Z 2025-01-24T01:51:54.4190291Z msg = Cannot scrape callname=ln_structured in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py line=976. 2025-01-24T01:51:54.4191423Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:51:54.4192204Z Prune tensor by removing channels with the lowest L\ ``n``-norm along the specified dimension. 2025-01-24T01:51:54.4192698Z 2025-01-24T01:51:54.4192962Z Prunes tensor corresponding to parameter called ``name`` in ``module`` 2025-01-24T01:51:54.4193608Z by removing the specified ``amount`` of (currently unpruned) channels 2025-01-24T01:51:54.4194207Z along the specified ``dim`` with the lowest L\ ``n``-norm. 2025-01-24T01:51:54.4194780Z Modifies module in place (and also return the modified module) 2025-01-24T01:51:54.4195184Z by: 2025-01-24T01:51:54.4195376Z 2025-01-24T01:51:54.4195594Z 1) adding a named buffer called ``name+'_mask'`` corresponding to the 2025-01-24T01:51:54.4196269Z binary mask applied to the parameter ``name`` by the pruning method. 2025-01-24T01:51:54.4196891Z 2) replacing the parameter ``name`` by its pruned version, while the 2025-01-24T01:51:54.4197508Z original (unpruned) parameter is stored in a new parameter named 2025-01-24T01:51:54.4198022Z ``name+'_orig'``. 2025-01-24T01:51:54.4198190Z 2025-01-24T01:51:54.4198280Z Args: 2025-01-24T01:51:54.4198661Z module (nn.Module): module containing the tensor to prune 2025-01-24T01:51:54.4199213Z name (str): parameter name within ``module`` on which pruning 2025-01-24T01:51:54.4199629Z will act. 2025-01-24T01:51:54.4200044Z amount (int or float): quantity of parameters to prune. 2025-01-24T01:51:54.4200513Z If ``float``, should be between 0.0 and 1.0 and represent the 2025-01-24T01:51:54.4201093Z fraction of parameters to prune. If ``int``, it represents the 2025-01-24T01:51:54.4201626Z absolute number of parameters to prune. 2025-01-24T01:51:54.4202092Z n (int, float, inf, -inf, 'fro', 'nuc'): See documentation of valid 2025-01-24T01:51:54.4202652Z entries for argument ``p`` in :func:`torch.norm`. 2025-01-24T01:51:54.4203202Z dim (int): index of the dim along which we define channels to prune. 2025-01-24T01:51:54.4203819Z importance_scores (torch.Tensor): tensor of importance scores (of same 2025-01-24T01:51:54.4204384Z shape as module parameter) used to compute mask for pruning. 2025-01-24T01:51:54.4205003Z The values in this tensor indicate the importance of the corresponding 2025-01-24T01:51:54.4205561Z elements in the parameter being pruned. 2025-01-24T01:51:54.4206103Z If unspecified or None, the module parameter will be used in its place. 2025-01-24T01:51:54.4206462Z 2025-01-24T01:51:54.4206569Z Returns: 2025-01-24T01:51:54.4206985Z module (nn.Module): modified (i.e. pruned) version of the input module 2025-01-24T01:51:54.4207345Z 2025-01-24T01:51:54.4207441Z Examples: 2025-01-24T01:51:54.4207777Z >>> from torch.nn.utils import prune 2025-01-24T01:51:54.4208137Z >>> m = prune.ln_structured( 2025-01-24T01:51:54.4208607Z ... nn.Conv2d(5, 3, 2), 'weight', amount=0.3, dim=1, n=float('-inf') 2025-01-24T01:51:54.4208997Z ... ) 2025-01-24T01:51:54.4209296Z 2025-01-24T01:51:54.4209682Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:51:54.4210095Z 2025-01-24T01:51:54.4210678Z msg = Cannot scrape callname=global_unstructured in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py line=1023. 2025-01-24T01:51:54.4211673Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:51:54.4212054Z 2025-01-24T01:51:54.4212503Z Globally prunes tensors corresponding to all parameters in ``parameters`` by applying the specified ``pruning_method``. 2025-01-24T01:51:54.4213048Z 2025-01-24T01:51:54.4213167Z Modifies modules in place by: 2025-01-24T01:51:54.4213375Z 2025-01-24T01:51:54.4213636Z 1) adding a named buffer called ``name+'_mask'`` corresponding to the 2025-01-24T01:51:54.4214309Z binary mask applied to the parameter ``name`` by the pruning method. 2025-01-24T01:51:54.4214876Z 2) replacing the parameter ``name`` by its pruned version, while the 2025-01-24T01:51:54.4215422Z original (unpruned) parameter is stored in a new parameter named 2025-01-24T01:51:54.4215851Z ``name+'_orig'``. 2025-01-24T01:51:54.4216007Z 2025-01-24T01:51:54.4216095Z Args: 2025-01-24T01:51:54.4216427Z parameters (Iterable of (module, name) tuples): parameters of 2025-01-24T01:51:54.4216953Z the model to prune in a global fashion, i.e. by aggregating all 2025-01-24T01:51:54.4217554Z weights prior to deciding which ones to prune. module must be of 2025-01-24T01:51:54.4218046Z type :class:`nn.Module`, and name must be a string. 2025-01-24T01:51:54.4218587Z pruning_method (function): a valid pruning function from this module, 2025-01-24T01:51:54.4219103Z or a custom one implemented by the user that satisfies the 2025-01-24T01:51:54.4219672Z implementation guidelines and has ``PRUNING_TYPE='unstructured'``. 2025-01-24T01:51:54.4220262Z importance_scores (dict): a dictionary mapping (module, name) tuples to 2025-01-24T01:51:54.4220848Z the corresponding parameter's importance scores tensor. The tensor 2025-01-24T01:51:54.4221420Z should be the same shape as the parameter, and is used for computing 2025-01-24T01:51:54.4221848Z mask for pruning. 2025-01-24T01:51:54.4222246Z If unspecified or None, the parameter will be used in place of its 2025-01-24T01:51:54.4222688Z importance scores. 2025-01-24T01:51:54.4223003Z kwargs: other keyword arguments such as: 2025-01-24T01:51:54.4223449Z amount (int or float): quantity of parameters to prune across the 2025-01-24T01:51:54.4223874Z specified parameters. 2025-01-24T01:51:54.4224260Z If ``float``, should be between 0.0 and 1.0 and represent the 2025-01-24T01:51:54.4224840Z fraction of parameters to prune. If ``int``, it represents the 2025-01-24T01:51:54.4225318Z absolute number of parameters to prune. 2025-01-24T01:51:54.4225563Z 2025-01-24T01:51:54.4225672Z Raises: 2025-01-24T01:51:54.4225963Z TypeError: if ``PRUNING_TYPE != 'unstructured'`` 2025-01-24T01:51:54.4226321Z 2025-01-24T01:51:54.4226414Z Note: 2025-01-24T01:51:54.4226767Z Since global structured pruning doesn't make much sense unless the 2025-01-24T01:51:54.4227317Z norm is normalized by the size of the parameter, we now limit the 2025-01-24T01:51:54.4227803Z scope of global pruning to unstructured methods. 2025-01-24T01:51:54.4228065Z 2025-01-24T01:51:54.4228175Z Examples: 2025-01-24T01:51:54.4228427Z >>> from torch.nn.utils import prune 2025-01-24T01:51:54.4228791Z >>> from collections import OrderedDict 2025-01-24T01:51:54.4229157Z >>> net = nn.Sequential(OrderedDict([ 2025-01-24T01:51:54.4229503Z ... ('first', nn.Linear(10, 4)), 2025-01-24T01:51:54.4229838Z ... ('second', nn.Linear(4, 1)), 2025-01-24T01:51:54.4230144Z ... ])) 2025-01-24T01:51:54.4230399Z >>> parameters_to_prune = ( 2025-01-24T01:51:54.4230715Z ... (net.first, 'weight'), 2025-01-24T01:51:54.4231036Z ... (net.second, 'weight'), 2025-01-24T01:51:54.4231345Z ... ) 2025-01-24T01:51:54.4231584Z >>> prune.global_unstructured( 2025-01-24T01:51:54.4231918Z ... parameters_to_prune, 2025-01-24T01:51:54.4232268Z ... pruning_method=prune.L1Unstructured, 2025-01-24T01:51:54.4232621Z ... amount=10, 2025-01-24T01:51:54.4232885Z ... ) 2025-01-24T01:51:54.4233231Z >>> print(sum(torch.nn.utils.parameters_to_vector(net.buffers()) == 0)) 2025-01-24T01:51:54.4233681Z tensor(10) 2025-01-24T01:51:54.4233834Z 2025-01-24T01:51:54.4233838Z 2025-01-24T01:51:54.4234100Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:51:54.4234488Z 2025-01-24T01:51:54.4235066Z msg = Cannot scrape callname=custom_from_mask in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py line=1142. 2025-01-24T01:51:54.4248636Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:51:54.4249435Z Prune tensor corresponding to parameter called ``name`` in ``module`` by applying the pre-computed mask in ``mask``. 2025-01-24T01:51:54.4249940Z 2025-01-24T01:51:54.4250159Z Modifies module in place (and also return the modified module) by: 2025-01-24T01:51:54.4250489Z 2025-01-24T01:51:54.4250707Z 1) adding a named buffer called ``name+'_mask'`` corresponding to the 2025-01-24T01:51:54.4251275Z binary mask applied to the parameter ``name`` by the pruning method. 2025-01-24T01:51:54.4251824Z 2) replacing the parameter ``name`` by its pruned version, while the 2025-01-24T01:51:54.4252480Z original (unpruned) parameter is stored in a new parameter named 2025-01-24T01:51:54.4252920Z ``name+'_orig'``. 2025-01-24T01:51:54.4253088Z 2025-01-24T01:51:54.4253234Z Args: 2025-01-24T01:51:54.4253561Z module (nn.Module): module containing the tensor to prune 2025-01-24T01:51:54.4254035Z name (str): parameter name within ``module`` on which pruning 2025-01-24T01:51:54.4254425Z will act. 2025-01-24T01:51:54.4254760Z mask (Tensor): binary mask to be applied to the parameter. 2025-01-24T01:51:54.4255047Z 2025-01-24T01:51:54.4255147Z Returns: 2025-01-24T01:51:54.4255529Z module (nn.Module): modified (i.e. pruned) version of the input module 2025-01-24T01:51:54.4255870Z 2025-01-24T01:51:54.4255980Z Examples: 2025-01-24T01:51:54.4256239Z >>> from torch.nn.utils import prune 2025-01-24T01:51:54.4256581Z >>> m = prune.custom_from_mask( 2025-01-24T01:51:54.4256971Z ... nn.Linear(5, 3), name='bias', mask=torch.tensor([0, 1, 0]) 2025-01-24T01:51:54.4257346Z ... ) 2025-01-24T01:51:54.4257581Z >>> print(m.bias_mask) 2025-01-24T01:51:54.4257853Z tensor([0., 1., 0.]) 2025-01-24T01:51:54.4258039Z 2025-01-24T01:51:54.4258124Z 2025-01-24T01:51:54.4258495Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:51:54.4258861Z 2025-01-24T01:51:54.5437596Z msg = Cannot scrape callname=AveragedModel in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/swa_utils.py line=117. 2025-01-24T01:51:54.5438527Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:51:54.5439294Z Implements averaged model for Stochastic Weight Averaging (SWA) and Exponential Moving Average (EMA). 2025-01-24T01:51:54.5439784Z 2025-01-24T01:51:54.5440058Z Stochastic Weight Averaging was proposed in `Averaging Weights Leads to 2025-01-24T01:51:54.5440650Z Wider Optima and Better Generalization`_ by Pavel Izmailov, Dmitrii 2025-01-24T01:51:54.5441235Z Podoprikhin, Timur Garipov, Dmitry Vetrov and Andrew Gordon Wilson 2025-01-24T01:51:54.5441679Z (UAI 2018). 2025-01-24T01:51:54.5441820Z 2025-01-24T01:51:54.5442065Z Exponential Moving Average is a variation of `Polyak averaging`_, 2025-01-24T01:51:54.5442667Z but using exponential weights instead of equal weights across iterations. 2025-01-24T01:51:54.5443030Z 2025-01-24T01:51:54.5443293Z AveragedModel class creates a copy of the provided module :attr:`model` 2025-01-24T01:51:54.5443881Z on the device :attr:`device` and allows to compute running averages of the 2025-01-24T01:51:54.5444362Z parameters of the :attr:`model`. 2025-01-24T01:51:54.5444599Z 2025-01-24T01:51:54.5444691Z Args: 2025-01-24T01:51:54.5444985Z model (torch.nn.Module): model to use with SWA/EMA 2025-01-24T01:51:54.5445509Z device (torch.device, optional): if provided, the averaged model will be 2025-01-24T01:51:54.5445994Z stored on the :attr:`device` 2025-01-24T01:51:54.5446582Z avg_fn (function, optional): the averaging function used to update 2025-01-24T01:51:54.5447141Z parameters; the function must take in the current value of the 2025-01-24T01:51:54.5447974Z :class:`AveragedModel` parameter, the current value of :attr:`model` 2025-01-24T01:51:54.5448541Z parameter, and the number of models already averaged; if None, 2025-01-24T01:51:54.5449042Z an equally weighted average is used (default: None) 2025-01-24T01:51:54.5449549Z multi_avg_fn (function, optional): the averaging function used to update 2025-01-24T01:51:54.5450159Z parameters inplace; the function must take in the current values of the 2025-01-24T01:51:54.5450805Z :class:`AveragedModel` parameters as a list, the current values of :attr:`model` 2025-01-24T01:51:54.5451436Z parameters as a list, and the number of models already averaged; if None, 2025-01-24T01:51:54.5452027Z an equally weighted average is used (default: None) 2025-01-24T01:51:54.5452530Z use_buffers (bool): if ``True``, it will compute running averages for 2025-01-24T01:51:54.5453126Z both the parameters and the buffers of the model. (default: ``False``) 2025-01-24T01:51:54.5453490Z 2025-01-24T01:51:54.5453593Z Example: 2025-01-24T01:51:54.5453873Z >>> # xdoctest: +SKIP("undefined variables") 2025-01-24T01:51:54.5454267Z >>> loader, optimizer, model, loss_fn = ... 2025-01-24T01:51:54.5454698Z >>> swa_model = torch.optim.swa_utils.AveragedModel(model) 2025-01-24T01:51:54.5455215Z >>> scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, 2025-01-24T01:51:54.5455692Z >>> T_max=300) 2025-01-24T01:51:54.5456041Z >>> swa_start = 160 2025-01-24T01:51:54.5456376Z >>> swa_scheduler = SWALR(optimizer, swa_lr=0.05) 2025-01-24T01:51:54.5456752Z >>> for i in range(300): 2025-01-24T01:51:54.5457061Z >>> for input, target in loader: 2025-01-24T01:51:54.5457416Z >>> optimizer.zero_grad() 2025-01-24T01:51:54.5457793Z >>> loss_fn(model(input), target).backward() 2025-01-24T01:51:54.5458174Z >>> optimizer.step() 2025-01-24T01:51:54.5458497Z >>> if i > swa_start: 2025-01-24T01:51:54.5458846Z >>> swa_model.update_parameters(model) 2025-01-24T01:51:54.5459199Z >>> swa_scheduler.step() 2025-01-24T01:51:54.5459559Z >>> else: 2025-01-24T01:51:54.5459842Z >>> scheduler.step() 2025-01-24T01:51:54.5460151Z >>> 2025-01-24T01:51:54.5460450Z >>> # Update bn statistics for the swa_model at the end 2025-01-24T01:51:54.5460902Z >>> torch.optim.swa_utils.update_bn(loader, swa_model) 2025-01-24T01:51:54.5461184Z 2025-01-24T01:51:54.5461486Z You can also use custom averaging functions with the `avg_fn` or `multi_avg_fn` parameters. 2025-01-24T01:51:54.5462124Z If no averaging function is provided, the default is to compute 2025-01-24T01:51:54.5462594Z equally-weighted average of the weights (SWA). 2025-01-24T01:51:54.5462871Z 2025-01-24T01:51:54.5462965Z Example: 2025-01-24T01:51:54.5463240Z >>> # xdoctest: +SKIP("undefined variables") 2025-01-24T01:51:54.5463713Z >>> # Compute exponential moving averages of the weights and buffers 2025-01-24T01:51:54.5464213Z >>> ema_model = torch.optim.swa_utils.AveragedModel(model, 2025-01-24T01:51:54.5464732Z >>> torch.optim.swa_utils.get_ema_multi_avg_fn(0.9), use_buffers=True) 2025-01-24T01:51:54.5465086Z 2025-01-24T01:51:54.5465193Z .. note:: 2025-01-24T01:51:54.5465558Z When using SWA/EMA with models containing Batch Normalization you may 2025-01-24T01:51:54.5466122Z need to update the activation statistics for Batch Normalization. 2025-01-24T01:51:54.5466819Z This can be done either by using the :meth:`torch.optim.swa_utils.update_bn` 2025-01-24T01:51:54.5467406Z or by setting :attr:`use_buffers` to `True`. The first approach updates the 2025-01-24T01:51:54.5468005Z statistics in a post-training step by passing data through the model. The 2025-01-24T01:51:54.5468702Z second does it during the parameter update phase by averaging all buffers. 2025-01-24T01:51:54.5469323Z Empirical evidence has shown that updating the statistics in normalization 2025-01-24T01:51:54.5469933Z layers increases accuracy, but you may wish to empirically test which 2025-01-24T01:51:54.5470452Z approach yields the best results in your problem. 2025-01-24T01:51:54.5470722Z 2025-01-24T01:51:54.5470820Z .. note:: 2025-01-24T01:51:54.5471218Z :attr:`avg_fn` and `multi_avg_fn` are not saved in the :meth:`state_dict` of the model. 2025-01-24T01:51:54.5471606Z 2025-01-24T01:51:54.5471698Z .. note:: 2025-01-24T01:51:54.5472047Z When :meth:`update_parameters` is called for the first time (i.e. 2025-01-24T01:51:54.5472597Z :attr:`n_averaged` is `0`) the parameters of `model` are copied 2025-01-24T01:51:54.5473124Z to the parameters of :class:`AveragedModel`. For every subsequent 2025-01-24T01:51:54.5473683Z call of :meth:`update_parameters` the function `avg_fn` is used 2025-01-24T01:51:54.5474114Z to update the parameters. 2025-01-24T01:51:54.5474333Z 2025-01-24T01:51:54.5474559Z .. _Averaging Weights Leads to Wider Optima and Better Generalization: 2025-01-24T01:51:54.5475046Z https://arxiv.org/abs/1803.05407 2025-01-24T01:51:54.5475527Z .. _There Are Many Consistent Explanations of Unlabeled Data: Why You Should 2025-01-24T01:51:54.5475972Z Average: 2025-01-24T01:51:54.5476245Z https://arxiv.org/abs/1806.05594 2025-01-24T01:51:54.5476689Z .. _SWALP: Stochastic Weight Averaging in Low-Precision Training: 2025-01-24T01:51:54.5477138Z https://arxiv.org/abs/1904.11943 2025-01-24T01:51:54.5477607Z .. _Stochastic Weight Averaging in Parallel: Large-Batch Training That 2025-01-24T01:51:54.5478051Z Generalizes Well: 2025-01-24T01:51:54.5478357Z https://arxiv.org/abs/2001.02312 2025-01-24T01:51:54.5478698Z .. _Polyak averaging: 2025-01-24T01:51:54.5479059Z https://paperswithcode.com/method/polyak-averaging 2025-01-24T01:51:54.5479666Z 2025-01-24T01:51:54.5480039Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:51:54.5480425Z 2025-01-24T01:51:54.5480902Z msg = Cannot scrape callname=SWALR in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/swa_utils.py line=369. 2025-01-24T01:51:54.5481782Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:51:54.5482401Z Anneals the learning rate in each parameter group to a fixed value. 2025-01-24T01:51:54.5482731Z 2025-01-24T01:51:54.5482992Z This learning rate scheduler is meant to be used with Stochastic Weight 2025-01-24T01:51:54.5483584Z Averaging (SWA) method (see `torch.optim.swa_utils.AveragedModel`). 2025-01-24T01:51:54.5483917Z 2025-01-24T01:51:54.5484028Z Args: 2025-01-24T01:51:54.5484329Z optimizer (torch.optim.Optimizer): wrapped optimizer 2025-01-24T01:51:54.5484855Z swa_lrs (float or list): the learning rate value for all param groups 2025-01-24T01:51:54.5485331Z together or separately for each group. 2025-01-24T01:51:54.5485796Z annealing_epochs (int): number of epochs in the annealing phase 2025-01-24T01:51:54.5486230Z (default: 10) 2025-01-24T01:51:54.5486624Z annealing_strategy (str): "cos" or "linear"; specifies the annealing 2025-01-24T01:51:54.5487191Z strategy: "cos" for cosine annealing, "linear" for linear annealing 2025-01-24T01:51:54.5487634Z (default: "cos") 2025-01-24T01:51:54.5488014Z last_epoch (int): the index of the last epoch (default: -1) 2025-01-24T01:51:54.5488309Z 2025-01-24T01:51:54.5488515Z The :class:`SWALR` scheduler can be used together with other 2025-01-24T01:51:54.5489053Z schedulers to switch to a constant learning rate late in the training 2025-01-24T01:51:54.5489496Z as in the example below. 2025-01-24T01:51:54.5489785Z 2025-01-24T01:51:54.5489886Z Example: 2025-01-24T01:51:54.5490205Z >>> # xdoctest: +SKIP("Undefined variables") 2025-01-24T01:51:54.5490581Z >>> loader, optimizer, model = ... 2025-01-24T01:51:54.5490933Z >>> lr_lambda = lambda epoch: 0.9 2025-01-24T01:51:54.5491376Z >>> scheduler = torch.optim.lr_scheduler.MultiplicativeLR(optimizer, 2025-01-24T01:51:54.5491836Z >>> lr_lambda=lr_lambda) 2025-01-24T01:51:54.5492240Z >>> swa_scheduler = torch.optim.swa_utils.SWALR(optimizer, 2025-01-24T01:51:54.5492724Z >>> anneal_strategy="linear", anneal_epochs=20, swa_lr=0.05) 2025-01-24T01:51:54.5493136Z >>> swa_start = 160 2025-01-24T01:51:54.5493417Z >>> for i in range(300): 2025-01-24T01:51:54.5493740Z >>> for input, target in loader: 2025-01-24T01:51:54.5494134Z >>> optimizer.zero_grad() 2025-01-24T01:51:54.5494513Z >>> loss_fn(model(input), target).backward() 2025-01-24T01:51:54.5494928Z >>> optimizer.step() 2025-01-24T01:51:54.5495257Z >>> if i > swa_start: 2025-01-24T01:51:54.5495561Z >>> swa_scheduler.step() 2025-01-24T01:51:54.5495883Z >>> else: 2025-01-24T01:51:54.5496143Z >>> scheduler.step() 2025-01-24T01:51:54.5496348Z 2025-01-24T01:51:54.5496590Z .. _Averaging Weights Leads to Wider Optima and Better Generalization: 2025-01-24T01:51:54.5497060Z https://arxiv.org/abs/1803.05407 2025-01-24T01:51:54.5497367Z 2025-01-24T01:51:54.5497741Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:51:54.5498122Z 2025-01-24T01:51:55.0485473Z msg = Cannot scrape callname=assert_close in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_comparison.py line=1263. 2025-01-24T01:51:55.0486821Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:51:55.0487673Z Asserts that ``actual`` and ``expected`` are close. 2025-01-24T01:51:55.0488113Z 2025-01-24T01:51:55.0488811Z If ``actual`` and ``expected`` are strided, non-quantized, real-valued, and finite, they are considered close if 2025-01-24T01:51:55.0489296Z 2025-01-24T01:51:55.0489422Z .. math:: 2025-01-24T01:51:55.0489555Z 2025-01-24T01:51:55.0489925Z \lvert \text{actual} - \text{expected} \rvert \le \texttt{atol} + \texttt{rtol} \cdot \lvert \text{expected} \rvert 2025-01-24T01:51:55.0490423Z 2025-01-24T01:51:55.0490779Z Non-finite values (``-inf`` and ``inf``) are only considered close if and only if they are equal. ``NaN``'s are 2025-01-24T01:51:55.0491456Z only considered equal to each other if ``equal_nan`` is ``True``. 2025-01-24T01:51:55.0491793Z 2025-01-24T01:51:55.0492004Z In addition, they are only considered close if they have the same 2025-01-24T01:51:55.0492337Z 2025-01-24T01:51:55.0492536Z - :attr:`~torch.Tensor.device` (if ``check_device`` is ``True``), 2025-01-24T01:51:55.0493001Z - ``dtype`` (if ``check_dtype`` is ``True``), 2025-01-24T01:51:55.0493410Z - ``layout`` (if ``check_layout`` is ``True``), and 2025-01-24T01:51:55.0493790Z - stride (if ``check_stride`` is ``True``). 2025-01-24T01:51:55.0494041Z 2025-01-24T01:51:55.0494342Z If either ``actual`` or ``expected`` is a meta tensor, only the attribute checks will be performed. 2025-01-24T01:51:55.0494772Z 2025-01-24T01:51:55.0495135Z If ``actual`` and ``expected`` are sparse (either having COO, CSR, CSC, BSR, or BSC layout), their strided members are 2025-01-24T01:51:55.0496000Z checked individually. Indices, namely ``indices`` for COO, ``crow_indices`` and ``col_indices`` for CSR and BSR, 2025-01-24T01:51:55.0496736Z or ``ccol_indices`` and ``row_indices`` for CSC and BSC layouts, respectively, 2025-01-24T01:51:55.0497487Z are always checked for equality whereas the values are checked for closeness according to the definition above. 2025-01-24T01:51:55.0497991Z 2025-01-24T01:51:55.0498491Z If ``actual`` and ``expected`` are quantized, they are considered close if they have the same 2025-01-24T01:51:55.0499318Z :meth:`~torch.Tensor.qscheme` and the result of :meth:`~torch.Tensor.dequantize` is close according to the 2025-01-24T01:51:55.0499913Z definition above. 2025-01-24T01:51:55.0500096Z 2025-01-24T01:51:55.0500406Z ``actual`` and ``expected`` can be :class:`~torch.Tensor`'s or any tensor-or-scalar-likes from which 2025-01-24T01:51:55.0501220Z :class:`torch.Tensor`'s can be constructed with :func:`torch.as_tensor`. Except for Python scalars the input types 2025-01-24T01:51:55.0502093Z have to be directly related. In addition, ``actual`` and ``expected`` can be :class:`~collections.abc.Sequence`'s 2025-01-24T01:51:55.0503020Z or :class:`~collections.abc.Mapping`'s in which case they are considered close if their structure matches and all 2025-01-24T01:51:55.0503757Z their elements are considered close according to the above definition. 2025-01-24T01:51:55.0504174Z 2025-01-24T01:51:55.0504273Z .. note:: 2025-01-24T01:51:55.0504425Z 2025-01-24T01:51:55.0504758Z Python scalars are an exception to the type relation requirement, because their :func:`type`, i.e. 2025-01-24T01:51:55.0505548Z :class:`int`, :class:`float`, and :class:`complex`, is equivalent to the ``dtype`` of a tensor-like. Thus, 2025-01-24T01:51:55.0506393Z Python scalars of different types can be checked, but require ``check_dtype=False``. 2025-01-24T01:51:55.0506796Z 2025-01-24T01:51:55.0506905Z Args: 2025-01-24T01:51:55.0507154Z actual (Any): Actual input. 2025-01-24T01:51:55.0507474Z expected (Any): Expected input. 2025-01-24T01:51:55.0508071Z allow_subclasses (bool): If ``True`` (default) and except for Python scalars, inputs of directly related types 2025-01-24T01:51:55.0508733Z are allowed. Otherwise type equality is required. 2025-01-24T01:51:55.0509386Z rtol (Optional[float]): Relative tolerance. If specified ``atol`` must also be specified. If omitted, default 2025-01-24T01:51:55.0510145Z values based on the :attr:`~torch.Tensor.dtype` are selected with the below table. 2025-01-24T01:51:55.0510903Z atol (Optional[float]): Absolute tolerance. If specified ``rtol`` must also be specified. If omitted, default 2025-01-24T01:51:55.0511641Z values based on the :attr:`~torch.Tensor.dtype` are selected with the below table. 2025-01-24T01:51:55.0512281Z equal_nan (Union[bool, str]): If ``True``, two ``NaN`` values will be considered equal. 2025-01-24T01:51:55.0512951Z check_device (bool): If ``True`` (default), asserts that corresponding tensors are on the same 2025-01-24T01:51:55.0513613Z :attr:`~torch.Tensor.device`. If this check is disabled, tensors on different 2025-01-24T01:51:55.0514230Z :attr:`~torch.Tensor.device`'s are moved to the CPU before being compared. 2025-01-24T01:51:55.0514948Z check_dtype (bool): If ``True`` (default), asserts that corresponding tensors have the same ``dtype``. If this 2025-01-24T01:51:55.0515773Z check is disabled, tensors with different ``dtype``'s are promoted to a common ``dtype`` (according to 2025-01-24T01:51:55.0516397Z :func:`torch.promote_types`) before being compared. 2025-01-24T01:51:55.0517040Z check_layout (bool): If ``True`` (default), asserts that corresponding tensors have the same ``layout``. If this 2025-01-24T01:51:55.0517865Z check is disabled, tensors with different ``layout``'s are converted to strided tensors before being 2025-01-24T01:51:55.0518427Z compared. 2025-01-24T01:51:55.0518969Z check_stride (bool): If ``True`` and corresponding tensors are strided, asserts that they have the same stride. 2025-01-24T01:51:55.0519831Z msg (Optional[Union[str, Callable[[str], str]]]): Optional error message to use in case a failure occurs during 2025-01-24T01:51:55.0520665Z the comparison. Can also passed as callable in which case it will be called with the generated message and 2025-01-24T01:51:55.0521386Z should return the new message. 2025-01-24T01:51:55.0521629Z 2025-01-24T01:51:55.0521730Z Raises: 2025-01-24T01:51:55.0522121Z ValueError: If no :class:`torch.Tensor` can be constructed from an input. 2025-01-24T01:51:55.0522667Z ValueError: If only ``rtol`` or ``atol`` is specified. 2025-01-24T01:51:55.0523298Z AssertionError: If corresponding inputs are not Python scalars and are not directly related. 2025-01-24T01:51:55.0524117Z AssertionError: If ``allow_subclasses`` is ``False``, but corresponding inputs are not Python scalars and have 2025-01-24T01:51:55.0524725Z different types. 2025-01-24T01:51:55.0525327Z AssertionError: If the inputs are :class:`~collections.abc.Sequence`'s, but their length does not match. 2025-01-24T01:51:55.0526201Z AssertionError: If the inputs are :class:`~collections.abc.Mapping`'s, but their set of keys do not match. 2025-01-24T01:51:55.0527066Z AssertionError: If corresponding tensors do not have the same :attr:`~torch.Tensor.shape`. 2025-01-24T01:51:55.0527826Z AssertionError: If ``check_layout`` is ``True``, but corresponding tensors do not have the same 2025-01-24T01:51:55.0528361Z :attr:`~torch.Tensor.layout`. 2025-01-24T01:51:55.0528821Z AssertionError: If only one of corresponding tensors is quantized. 2025-01-24T01:51:55.0529563Z AssertionError: If corresponding tensors are quantized, but have different :meth:`~torch.Tensor.qscheme`'s. 2025-01-24T01:51:55.0530380Z AssertionError: If ``check_device`` is ``True``, but corresponding tensors are not on the same 2025-01-24T01:51:55.0530928Z :attr:`~torch.Tensor.device`. 2025-01-24T01:51:55.0531501Z AssertionError: If ``check_dtype`` is ``True``, but corresponding tensors do not have the same ``dtype``. 2025-01-24T01:51:55.0532313Z AssertionError: If ``check_stride`` is ``True``, but corresponding strided tensors do not have the same stride. 2025-01-24T01:51:55.0533172Z AssertionError: If the values of corresponding tensors are not close according to the definition above. 2025-01-24T01:51:55.0533672Z 2025-01-24T01:51:55.0534039Z The following table displays the default ``rtol`` and ``atol`` for different ``dtype``'s. In case of mismatching 2025-01-24T01:51:55.0534678Z ``dtype``'s, the maximum of both tolerances is used. 2025-01-24T01:51:55.0534956Z 2025-01-24T01:51:55.0535091Z +---------------------------+------------+----------+ 2025-01-24T01:51:55.0535480Z | ``dtype`` | ``rtol`` | ``atol`` | 2025-01-24T01:51:55.0535846Z +===========================+============+==========+ 2025-01-24T01:51:55.0536215Z | :attr:`~torch.float16` | ``1e-3`` | ``1e-5`` | 2025-01-24T01:51:55.0536612Z +---------------------------+------------+----------+ 2025-01-24T01:51:55.0537004Z | :attr:`~torch.bfloat16` | ``1.6e-2`` | ``1e-5`` | 2025-01-24T01:51:55.0537402Z +---------------------------+------------+----------+ 2025-01-24T01:51:55.0537793Z | :attr:`~torch.float32` | ``1.3e-6`` | ``1e-5`` | 2025-01-24T01:51:55.0538165Z +---------------------------+------------+----------+ 2025-01-24T01:51:55.0538558Z | :attr:`~torch.float64` | ``1e-7`` | ``1e-7`` | 2025-01-24T01:51:55.0538949Z +---------------------------+------------+----------+ 2025-01-24T01:51:55.0539346Z | :attr:`~torch.complex32` | ``1e-3`` | ``1e-5`` | 2025-01-24T01:51:55.0539740Z +---------------------------+------------+----------+ 2025-01-24T01:51:55.0540129Z | :attr:`~torch.complex64` | ``1.3e-6`` | ``1e-5`` | 2025-01-24T01:51:55.0540505Z +---------------------------+------------+----------+ 2025-01-24T01:51:55.0540894Z | :attr:`~torch.complex128` | ``1e-7`` | ``1e-7`` | 2025-01-24T01:51:55.0541281Z +---------------------------+------------+----------+ 2025-01-24T01:51:55.0541666Z | :attr:`~torch.quint8` | ``1.3e-6`` | ``1e-5`` | 2025-01-24T01:51:55.0542120Z +---------------------------+------------+----------+ 2025-01-24T01:51:55.0542496Z | :attr:`~torch.quint2x4` | ``1.3e-6`` | ``1e-5`` | 2025-01-24T01:51:55.0542889Z +---------------------------+------------+----------+ 2025-01-24T01:51:55.0543276Z | :attr:`~torch.quint4x2` | ``1.3e-6`` | ``1e-5`` | 2025-01-24T01:51:55.0543664Z +---------------------------+------------+----------+ 2025-01-24T01:51:55.0544051Z | :attr:`~torch.qint8` | ``1.3e-6`` | ``1e-5`` | 2025-01-24T01:51:55.0544422Z +---------------------------+------------+----------+ 2025-01-24T01:51:55.0544807Z | :attr:`~torch.qint32` | ``1.3e-6`` | ``1e-5`` | 2025-01-24T01:51:55.0545200Z +---------------------------+------------+----------+ 2025-01-24T01:51:55.0545612Z | other | ``0.0`` | ``0.0`` | 2025-01-24T01:51:55.0545991Z +---------------------------+------------+----------+ 2025-01-24T01:51:55.0546340Z 2025-01-24T01:51:55.0546503Z .. note:: 2025-01-24T01:51:55.0546639Z 2025-01-24T01:51:55.0547027Z :func:`~torch.testing.assert_close` is highly configurable with strict default settings. Users are encouraged 2025-01-24T01:51:55.0547901Z to :func:`~functools.partial` it to fit their use case. For example, if an equality check is needed, one might 2025-01-24T01:51:55.0548649Z define an ``assert_equal`` that uses zero tolerances for every ``dtype`` by default: 2025-01-24T01:51:55.0549041Z 2025-01-24T01:51:55.0549145Z >>> import functools 2025-01-24T01:51:55.0549601Z >>> assert_equal = functools.partial(torch.testing.assert_close, rtol=0, atol=0) 2025-01-24T01:51:55.0550102Z >>> assert_equal(1e-9, 1e-10) 2025-01-24T01:51:55.0550428Z Traceback (most recent call last): 2025-01-24T01:51:55.0550765Z ... 2025-01-24T01:51:55.0551032Z AssertionError: Scalars are not equal! 2025-01-24T01:51:55.0551374Z 2025-01-24T01:51:55.0551646Z Expected 1e-10 but got 1e-09. 2025-01-24T01:51:55.0551989Z Absolute difference: 9.000000000000001e-10 2025-01-24T01:51:55.0552352Z Relative difference: 9.0 2025-01-24T01:51:55.0552564Z 2025-01-24T01:51:55.0552657Z Examples: 2025-01-24T01:51:55.0552918Z >>> # tensor to tensor comparison 2025-01-24T01:51:55.0553287Z >>> expected = torch.tensor([1e0, 1e-1, 1e-2]) 2025-01-24T01:51:55.0553666Z >>> actual = torch.acos(torch.cos(expected)) 2025-01-24T01:51:55.0554068Z >>> torch.testing.assert_close(actual, expected) 2025-01-24T01:51:55.0554337Z 2025-01-24T01:51:55.0554451Z >>> # scalar to scalar comparison 2025-01-24T01:51:55.0554779Z >>> import math 2025-01-24T01:51:55.0555060Z >>> expected = math.sqrt(2.0) 2025-01-24T01:51:55.0555390Z >>> actual = 2.0 / math.sqrt(2.0) 2025-01-24T01:51:55.0555752Z >>> torch.testing.assert_close(actual, expected) 2025-01-24T01:51:55.0556020Z 2025-01-24T01:51:55.0556149Z >>> # numpy array to numpy array comparison 2025-01-24T01:51:55.0556522Z >>> import numpy as np 2025-01-24T01:51:55.0556842Z >>> expected = np.array([1e0, 1e-1, 1e-2]) 2025-01-24T01:51:55.0557213Z >>> actual = np.arccos(np.cos(expected)) 2025-01-24T01:51:55.0557592Z >>> torch.testing.assert_close(actual, expected) 2025-01-24T01:51:55.0557862Z 2025-01-24T01:51:55.0557984Z >>> # sequence to sequence comparison 2025-01-24T01:51:55.0558325Z >>> import numpy as np 2025-01-24T01:51:55.0558773Z >>> # The types of the sequences do not have to match. They only have to have the same 2025-01-24T01:51:55.0559291Z >>> # length and their elements have to match. 2025-01-24T01:51:55.0559705Z >>> expected = [torch.tensor([1.0]), 2.0, np.array(3.0)] 2025-01-24T01:51:55.0560083Z >>> actual = tuple(expected) 2025-01-24T01:51:55.0560450Z >>> torch.testing.assert_close(actual, expected) 2025-01-24T01:51:55.0560721Z 2025-01-24T01:51:55.0560843Z >>> # mapping to mapping comparison 2025-01-24T01:51:55.0561293Z >>> from collections import OrderedDict 2025-01-24T01:51:55.0561649Z >>> import numpy as np 2025-01-24T01:51:55.0561942Z >>> foo = torch.tensor(1.0) 2025-01-24T01:51:55.0562254Z >>> bar = 2.0 2025-01-24T01:51:55.0562526Z >>> baz = np.array(3.0) 2025-01-24T01:51:55.0562992Z >>> # The types and a possible ordering of mappings do not have to match. They only 2025-01-24T01:51:55.0563587Z >>> # have to have the same set of keys and their elements have to match. 2025-01-24T01:51:55.0564118Z >>> expected = OrderedDict([("foo", foo), ("bar", bar), ("baz", baz)]) 2025-01-24T01:51:55.0564599Z >>> actual = {"baz": baz, "bar": bar, "foo": foo} 2025-01-24T01:51:55.0565006Z >>> torch.testing.assert_close(actual, expected) 2025-01-24T01:51:55.0565308Z 2025-01-24T01:51:55.0565440Z >>> expected = torch.tensor([1.0, 2.0, 3.0]) 2025-01-24T01:51:55.0565806Z >>> actual = expected.clone() 2025-01-24T01:51:55.0566236Z >>> # By default, directly related instances can be compared 2025-01-24T01:51:55.0566742Z >>> torch.testing.assert_close(torch.nn.Parameter(actual), expected) 2025-01-24T01:51:55.0567290Z >>> # This check can be made more strict with allow_subclasses=False 2025-01-24T01:51:55.0567733Z >>> torch.testing.assert_close( 2025-01-24T01:51:55.0568168Z ... torch.nn.Parameter(actual), expected, allow_subclasses=False 2025-01-24T01:51:55.0568590Z ... ) 2025-01-24T01:51:55.0568838Z Traceback (most recent call last): 2025-01-24T01:51:55.0569166Z ... 2025-01-24T01:51:55.0569509Z TypeError: No comparison pair was able to handle inputs of type 2025-01-24T01:51:55.0570083Z and . 2025-01-24T01:51:55.0570671Z >>> # If the inputs are not directly related, they are never considered close 2025-01-24T01:51:55.0571210Z >>> torch.testing.assert_close(actual.numpy(), expected) 2025-01-24T01:51:55.0571783Z Traceback (most recent call last): 2025-01-24T01:51:55.0572379Z ... 2025-01-24T01:51:55.0572919Z TypeError: No comparison pair was able to handle inputs of type 2025-01-24T01:51:55.0573461Z and . 2025-01-24T01:51:55.0573949Z >>> # Exceptions to these rules are Python scalars. They can be checked regardless of 2025-01-24T01:51:55.0574459Z >>> # their type if check_dtype=False. 2025-01-24T01:51:55.0574885Z >>> torch.testing.assert_close(1.0, 1, check_dtype=False) 2025-01-24T01:51:55.0575191Z 2025-01-24T01:51:55.0575300Z >>> # NaN != NaN by default. 2025-01-24T01:51:55.0575650Z >>> expected = torch.tensor(float("Nan")) 2025-01-24T01:51:55.0576024Z >>> actual = expected.clone() 2025-01-24T01:51:55.0576404Z >>> torch.testing.assert_close(actual, expected) 2025-01-24T01:51:55.0576781Z Traceback (most recent call last): 2025-01-24T01:51:55.0577112Z ... 2025-01-24T01:51:55.0577382Z AssertionError: Scalars are not close! 2025-01-24T01:51:55.0577732Z 2025-01-24T01:51:55.0578007Z Expected nan but got nan. 2025-01-24T01:51:55.0578351Z Absolute difference: nan (up to 1e-05 allowed) 2025-01-24T01:51:55.0578765Z Relative difference: nan (up to 1.3e-06 allowed) 2025-01-24T01:51:55.0579445Z >>> torch.testing.assert_close(actual, expected, equal_nan=True) 2025-01-24T01:51:55.0579773Z 2025-01-24T01:51:55.0579919Z >>> expected = torch.tensor([1.0, 2.0, 3.0]) 2025-01-24T01:51:55.0580288Z >>> actual = torch.tensor([1.0, 4.0, 5.0]) 2025-01-24T01:51:55.0580666Z >>> # The default error message can be overwritten. 2025-01-24T01:51:55.0581228Z >>> torch.testing.assert_close(actual, expected, msg="Argh, the tensors are not close!") 2025-01-24T01:51:55.0581768Z Traceback (most recent call last): 2025-01-24T01:51:55.0582104Z ... 2025-01-24T01:51:55.0582561Z AssertionError: Argh, the tensors are not close! 2025-01-24T01:51:55.0583047Z >>> # If msg is a callable, it can be used to augment the generated message with 2025-01-24T01:51:55.0583507Z >>> # extra information 2025-01-24T01:51:55.0583822Z >>> torch.testing.assert_close( 2025-01-24T01:51:55.0584259Z ... actual, expected, msg=lambda msg: f"Header\n\n{msg}\n\nFooter" 2025-01-24T01:51:55.0584680Z ... ) 2025-01-24T01:51:55.0584942Z Traceback (most recent call last): 2025-01-24T01:51:55.0585264Z ... 2025-01-24T01:51:55.0585509Z AssertionError: Header 2025-01-24T01:51:55.0585812Z 2025-01-24T01:51:55.0586088Z Tensor-likes are not close! 2025-01-24T01:51:55.0586490Z 2025-01-24T01:51:55.0586810Z Mismatched elements: 2 / 3 (66.7%) 2025-01-24T01:51:55.0587277Z Greatest absolute difference: 2.0 at index (1,) (up to 1e-05 allowed) 2025-01-24T01:51:55.0587915Z Greatest relative difference: 1.0 at index (1,) (up to 1.3e-06 allowed) 2025-01-24T01:51:55.0588370Z 2025-01-24T01:51:55.0588623Z Footer 2025-01-24T01:51:55.0588842Z 2025-01-24T01:51:55.0589228Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:51:55.0589617Z 2025-01-24T01:51:56.1872003Z msg = Cannot scrape callname=register_pytree_node in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py line=100. 2025-01-24T01:51:56.1873003Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:51:56.1873552Z Register a container-like type as pytree node. 2025-01-24T01:51:56.1873814Z 2025-01-24T01:51:56.1873919Z Args: 2025-01-24T01:51:56.1874273Z cls (type): A Python type to treat as an internal pytree node. 2025-01-24T01:51:56.1874853Z flatten_fn (callable): A function to be used during flattening, taking an instance of 2025-01-24T01:51:56.1875521Z ``cls`` and returning a pair, with (1) an iterable for the children to be flattened 2025-01-24T01:51:56.1876197Z recursively, and (2) some hashable auxiliary data to be stored in the treespec and to be 2025-01-24T01:51:56.1876738Z passed to the ``unflatten_fn``. 2025-01-24T01:51:56.1877257Z unflatten_fn (callable): A function taking two arguments: the auxiliary data that was 2025-01-24T01:51:56.1877933Z returned by ``flatten_fn`` and stored in the treespec, and the unflattened children. 2025-01-24T01:51:56.1878470Z The function should return an instance of ``cls``. 2025-01-24T01:51:56.1879021Z serialized_type_name (str, optional): A keyword argument used to specify the fully 2025-01-24T01:51:56.1879793Z qualified name used when serializing the tree spec. 2025-01-24T01:51:56.1880407Z to_dumpable_context (callable, optional): An optional keyword argument to custom specify how 2025-01-24T01:51:56.1881144Z to convert the context of the pytree to a custom json dumpable representation. This is 2025-01-24T01:51:56.1881830Z used for json serialization, which is being used in :mod:`torch.export` right now. 2025-01-24T01:51:56.1882525Z from_dumpable_context (callable, optional): An optional keyword argument to custom specify 2025-01-24T01:51:56.1883325Z how to convert the custom json dumpable representation of the context back to the 2025-01-24T01:51:56.1883983Z original context. This is used for json deserialization, which is being used in 2025-01-24T01:51:56.1884491Z :mod:`torch.export` right now. 2025-01-24T01:51:56.1884719Z 2025-01-24T01:51:56.1884847Z Example:: 2025-01-24T01:51:56.1884983Z 2025-01-24T01:51:56.1885101Z >>> # xdoctest: +SKIP 2025-01-24T01:51:56.1885434Z >>> # Registry a Python type with lambda functions 2025-01-24T01:51:56.1885816Z >>> register_pytree_node( 2025-01-24T01:51:56.1886121Z ... set, 2025-01-24T01:51:56.1886700Z ... lambda s: (sorted(s), None, None), 2025-01-24T01:51:56.1887083Z ... lambda children, _: set(children), 2025-01-24T01:51:56.1887415Z ... ) 2025-01-24T01:51:56.1887647Z 2025-01-24T01:51:56.1888031Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:51:56.1888407Z 2025-01-24T01:51:56.2464755Z msg = Cannot scrape callname=SelectiveCheckpointContext in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/checkpoint.py line=1200. 2025-01-24T01:51:56.2465781Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:51:56.2466168Z 2025-01-24T01:51:56.2466454Z Context passed to policy function during selective checkpointing. 2025-01-24T01:51:56.2466786Z 2025-01-24T01:51:56.2467166Z This class is used to pass relevant metadata to the policy function during 2025-01-24T01:51:56.2467788Z selective checkpointing. The metadata includes whether the current invocation 2025-01-24T01:51:56.2468430Z of the policy function is during recomputation or not. 2025-01-24T01:51:56.2468725Z 2025-01-24T01:51:56.2468822Z Example: 2025-01-24T01:51:56.2469074Z >>> # xdoctest: +SKIP(stub) 2025-01-24T01:51:56.2469370Z >>> 2025-01-24T01:51:56.2469613Z >>> def policy_fn(ctx, op, *args, **kwargs): 2025-01-24T01:51:56.2470008Z >>> print(ctx.is_recompute) 2025-01-24T01:51:56.2470449Z >>> 2025-01-24T01:51:56.2470902Z >>> context_fn = functools.partial(create_selective_checkpoint_contexts, policy_fn) 2025-01-24T01:51:56.2471550Z >>> 2025-01-24T01:51:56.2471925Z >>> out = torch.utils.checkpoint.checkpoint( 2025-01-24T01:51:56.2472354Z >>> fn, x, y, 2025-01-24T01:51:56.2472626Z >>> use_reentrant=False, 2025-01-24T01:51:56.2472942Z >>> context_fn=context_fn, 2025-01-24T01:51:56.2473248Z >>> ) 2025-01-24T01:51:56.2473371Z 2025-01-24T01:51:56.2473629Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:51:56.2474021Z 2025-01-24T01:51:56.2474695Z msg = Cannot scrape callname=create_selective_checkpoint_contexts in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/checkpoint.py line=1334. 2025-01-24T01:51:56.2475716Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:51:56.2476115Z 2025-01-24T01:51:56.2476361Z Helper to avoid recomputing certain ops during activation checkpointing. 2025-01-24T01:51:56.2476734Z 2025-01-24T01:51:56.2476954Z Use this with `torch.utils.checkpoint.checkpoint` to control which 2025-01-24T01:51:56.2477467Z operations are recomputed during the backward pass. 2025-01-24T01:51:56.2477743Z 2025-01-24T01:51:56.2477844Z Args: 2025-01-24T01:51:56.2478094Z policy_fn_or_list (Callable or List): 2025-01-24T01:51:56.2478498Z - If a policy function is provided, it should accept a 2025-01-24T01:51:56.2479033Z :class:`SelectiveCheckpointContext`, the :class:`OpOverload`, args and 2025-01-24T01:51:56.2479876Z kwargs to the op, and return a :class:`CheckpointPolicy` enum value 2025-01-24T01:51:56.2480458Z indicating whether the execution of the op should be recomputed or not. 2025-01-24T01:51:56.2481037Z - If a list of operations is provided, it is equivalent to a policy 2025-01-24T01:51:56.2481545Z returning `CheckpointPolicy.MUST_SAVE` for the specified 2025-01-24T01:51:56.2482082Z operations and `CheckpointPolicy.PREFER_RECOMPUTE` for all other 2025-01-24T01:51:56.2482533Z operations. 2025-01-24T01:51:56.2482909Z allow_cache_entry_mutation (bool, optional): By default, an error is 2025-01-24T01:51:56.2483472Z raised if any tensors cached by selective activation checkpoint are 2025-01-24T01:51:56.2484032Z mutated in order to ensure correctness. If set to `True`, this check 2025-01-24T01:51:56.2484462Z is disabled. 2025-01-24T01:51:56.2484723Z Returns: 2025-01-24T01:51:56.2484972Z A tuple of two context managers. 2025-01-24T01:51:56.2485306Z 2025-01-24T01:51:56.2485413Z Example: 2025-01-24T01:51:56.2485709Z >>> # xdoctest: +REQUIRES(LINUX) 2025-01-24T01:51:56.2486022Z >>> import functools 2025-01-24T01:51:56.2486299Z >>> 2025-01-24T01:51:56.2486562Z >>> x = torch.rand(10, 10, requires_grad=True) 2025-01-24T01:51:56.2486948Z >>> y = torch.rand(10, 10, requires_grad=True) 2025-01-24T01:51:56.2487287Z >>> 2025-01-24T01:51:56.2487499Z >>> ops_to_save = [ 2025-01-24T01:51:56.2487788Z >>> torch.ops.aten.mm.default, 2025-01-24T01:51:56.2488103Z >>> ] 2025-01-24T01:51:56.2488324Z >>> 2025-01-24T01:51:56.2488564Z >>> def policy_fn(ctx, op, *args, **kwargs): 2025-01-24T01:51:56.2488918Z >>> if op in ops_to_save: 2025-01-24T01:51:56.2489261Z >>> return CheckpointPolicy.MUST_SAVE 2025-01-24T01:51:56.2489651Z >>> else: 2025-01-24T01:51:56.2489951Z >>> return CheckpointPolicy.PREFER_RECOMPUTE 2025-01-24T01:51:56.2490295Z >>> 2025-01-24T01:51:56.2490755Z >>> context_fn = functools.partial(create_selective_checkpoint_contexts, policy_fn) 2025-01-24T01:51:56.2491249Z >>> 2025-01-24T01:51:56.2491477Z >>> # or equivalently 2025-01-24T01:51:56.2491938Z >>> context_fn = functools.partial(create_selective_checkpoint_contexts, ops_to_save) 2025-01-24T01:51:56.2492417Z >>> 2025-01-24T01:51:56.2492643Z >>> def fn(x, y): 2025-01-24T01:51:56.2493013Z >>> return torch.sigmoid(torch.matmul(torch.matmul(x, y), y)) * y 2025-01-24T01:51:56.2493432Z >>> 2025-01-24T01:51:56.2493704Z >>> out = torch.utils.checkpoint.checkpoint( 2025-01-24T01:51:56.2494044Z >>> fn, x, y, 2025-01-24T01:51:56.2494313Z >>> use_reentrant=False, 2025-01-24T01:51:56.2494624Z >>> context_fn=context_fn, 2025-01-24T01:51:56.2494924Z >>> ) 2025-01-24T01:51:56.2495045Z 2025-01-24T01:51:56.2495319Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:51:56.2495692Z 2025-01-24T01:51:56.2697444Z msg = Cannot scrape callname=CppExtension in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/cpp_extension.py line=965. 2025-01-24T01:51:56.2698384Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:51:56.2698766Z 2025-01-24T01:51:56.2698914Z Create a :class:`setuptools.Extension` for C++. 2025-01-24T01:51:56.2699185Z 2025-01-24T01:51:56.2699429Z Convenience method that creates a :class:`setuptools.Extension` with the 2025-01-24T01:51:56.2700024Z bare minimum (but often sufficient) arguments to build a C++ extension. 2025-01-24T01:51:56.2700378Z 2025-01-24T01:51:56.2700589Z All arguments are forwarded to the :class:`setuptools.Extension` 2025-01-24T01:51:56.2701071Z constructor. Full list arguments can be found at 2025-01-24T01:51:56.2701677Z https://setuptools.pypa.io/en/latest/userguide/ext_modules.html#extension-api-reference 2025-01-24T01:51:56.2702127Z 2025-01-24T01:51:56.2702246Z .. note:: 2025-01-24T01:51:56.2702610Z The PyTorch python API (as provided in libtorch_python) cannot be built 2025-01-24T01:51:56.2703204Z with the flag ``py_limited_api=True``. When this flag is passed, it is 2025-01-24T01:51:56.2704039Z the user's responsibility in their library to not use APIs from 2025-01-24T01:51:56.2704911Z libtorch_python (in particular pytorch/python bindings) and to only use 2025-01-24T01:51:56.2705687Z APIs from libtorch (aten objects, operators and the dispatcher). For 2025-01-24T01:51:56.2706337Z example, to give access to custom ops from python, the library should 2025-01-24T01:51:56.2706825Z register the ops through the dispatcher. 2025-01-24T01:51:56.2707079Z 2025-01-24T01:51:56.2707172Z Example: 2025-01-24T01:51:56.2707407Z >>> # xdoctest: +SKIP 2025-01-24T01:51:56.2707745Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2025-01-24T01:51:56.2708122Z >>> from setuptools import setup 2025-01-24T01:51:56.2708568Z >>> from torch.utils.cpp_extension import BuildExtension, CppExtension 2025-01-24T01:51:56.2709138Z >>> setup( 2025-01-24T01:51:56.2709453Z ... name='extension', 2025-01-24T01:51:56.2709756Z ... ext_modules=[ 2025-01-24T01:51:56.2710026Z ... CppExtension( 2025-01-24T01:51:56.2710332Z ... name='extension', 2025-01-24T01:51:56.2710676Z ... sources=['extension.cpp'], 2025-01-24T01:51:56.2711045Z ... extra_compile_args=['-g'], 2025-01-24T01:51:56.2711444Z ... extra_link_args=['-Wl,--no-as-needed', '-lm']) 2025-01-24T01:51:56.2711807Z ... ], 2025-01-24T01:51:56.2712050Z ... cmdclass={ 2025-01-24T01:51:56.2712336Z ... 'build_ext': BuildExtension 2025-01-24T01:51:56.2712666Z ... }) 2025-01-24T01:51:56.2712803Z 2025-01-24T01:51:56.2713121Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:51:56.2713496Z 2025-01-24T01:51:56.2714350Z msg = Cannot scrape callname=CUDAExtension in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/cpp_extension.py line=1027. 2025-01-24T01:51:56.2715874Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:51:56.2716269Z 2025-01-24T01:51:56.2716453Z Create a :class:`setuptools.Extension` for CUDA/C++. 2025-01-24T01:51:56.2716730Z 2025-01-24T01:51:56.2716976Z Convenience method that creates a :class:`setuptools.Extension` with the 2025-01-24T01:51:56.2717557Z bare minimum (but often sufficient) arguments to build a CUDA/C++ 2025-01-24T01:51:56.2718124Z extension. This includes the CUDA include path, library path and runtime 2025-01-24T01:51:56.2718577Z library. 2025-01-24T01:51:56.2718698Z 2025-01-24T01:51:56.2718922Z All arguments are forwarded to the :class:`setuptools.Extension` 2025-01-24T01:51:56.2719407Z constructor. Full list arguments can be found at 2025-01-24T01:51:56.2719990Z https://setuptools.pypa.io/en/latest/userguide/ext_modules.html#extension-api-reference 2025-01-24T01:51:56.2720446Z 2025-01-24T01:51:56.2720549Z .. note:: 2025-01-24T01:51:56.2720916Z The PyTorch python API (as provided in libtorch_python) cannot be built 2025-01-24T01:51:56.2721480Z with the flag ``py_limited_api=True``. When this flag is passed, it is 2025-01-24T01:51:56.2722016Z the user's responsibility in their library to not use APIs from 2025-01-24T01:51:56.2722582Z libtorch_python (in particular pytorch/python bindings) and to only use 2025-01-24T01:51:56.2723146Z APIs from libtorch (aten objects, operators and the dispatcher). For 2025-01-24T01:51:56.2723998Z example, to give access to custom ops from python, the library should 2025-01-24T01:51:56.2724551Z register the ops through the dispatcher. 2025-01-24T01:51:56.2724975Z 2025-01-24T01:51:56.2725168Z Example: 2025-01-24T01:51:56.2725576Z >>> # xdoctest: +SKIP 2025-01-24T01:51:56.2725907Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2025-01-24T01:51:56.2726293Z >>> from setuptools import setup 2025-01-24T01:51:56.2726748Z >>> from torch.utils.cpp_extension import BuildExtension, CUDAExtension 2025-01-24T01:51:56.2727203Z >>> setup( 2025-01-24T01:51:56.2727462Z ... name='cuda_extension', 2025-01-24T01:51:56.2727761Z ... ext_modules=[ 2025-01-24T01:51:56.2728044Z ... CUDAExtension( 2025-01-24T01:51:56.2728357Z ... name='cuda_extension', 2025-01-24T01:51:56.2728769Z ... sources=['extension.cpp', 'extension_kernel.cu'], 2025-01-24T01:51:56.2729195Z ... extra_compile_args={'cxx': ['-g'], 2025-01-24T01:51:56.2729558Z ... 'nvcc': ['-O2']}, 2025-01-24T01:51:56.2729962Z ... extra_link_args=['-Wl,--no-as-needed', '-lcuda']) 2025-01-24T01:51:56.2730338Z ... ], 2025-01-24T01:51:56.2730583Z ... cmdclass={ 2025-01-24T01:51:56.2730873Z ... 'build_ext': BuildExtension 2025-01-24T01:51:56.2731181Z ... }) 2025-01-24T01:51:56.2731330Z 2025-01-24T01:51:56.2731445Z Compute capabilities: 2025-01-24T01:51:56.2731701Z 2025-01-24T01:51:56.2732038Z By default the extension will be compiled to run on all archs of the cards visible during the 2025-01-24T01:51:56.2732774Z building process of the extension, plus PTX. If down the road a new card is installed the 2025-01-24T01:51:56.2733506Z extension may need to be recompiled. If a visible card has a compute capability (CC) that's 2025-01-24T01:51:56.2734241Z newer than the newest version for which your nvcc can build fully-compiled binaries, PyTorch 2025-01-24T01:51:56.2734956Z will make nvcc fall back to building kernels with the newest version of PTX your nvcc does 2025-01-24T01:51:56.2735500Z support (see below for details on PTX). 2025-01-24T01:51:56.2735738Z 2025-01-24T01:51:56.2736097Z You can override the default behavior using `TORCH_CUDA_ARCH_LIST` to explicitly specify which 2025-01-24T01:51:56.2736657Z CCs you want the extension to support: 2025-01-24T01:51:56.2736878Z 2025-01-24T01:51:56.2737082Z ``TORCH_CUDA_ARCH_LIST="6.1 8.6" python build_my_extension.py`` 2025-01-24T01:51:56.2737688Z ``TORCH_CUDA_ARCH_LIST="5.2 6.0 6.1 7.0 7.5 8.0 8.6+PTX" python build_my_extension.py`` 2025-01-24T01:51:56.2738047Z 2025-01-24T01:51:56.2738386Z The +PTX option causes extension kernel binaries to include PTX instructions for the specified 2025-01-24T01:51:56.2739142Z CC. PTX is an intermediate representation that allows kernels to runtime-compile for any CC >= 2025-01-24T01:51:56.2739890Z the specified CC (for example, 8.6+PTX generates PTX that can runtime-compile for any GPU with 2025-01-24T01:51:56.2740619Z CC >= 8.6). This improves your binary's forward compatibility. However, relying on older PTX to 2025-01-24T01:51:56.2741363Z provide forward compat by runtime-compiling for newer CCs can modestly reduce performance on 2025-01-24T01:51:56.2742095Z those newer CCs. If you know exact CC(s) of the GPUs you want to target, you're always better 2025-01-24T01:51:56.2742818Z off specifying them individually. For example, if you want your extension to run on 8.0 and 8.6, 2025-01-24T01:51:56.2743575Z "8.0+PTX" would work functionally because it includes PTX that can runtime-compile for 8.6, but 2025-01-24T01:51:56.2744115Z "8.0 8.6" would be better. 2025-01-24T01:51:56.2744302Z 2025-01-24T01:51:56.2744604Z Note that while it's possible to include all supported archs, the more archs get included the 2025-01-24T01:51:56.2745323Z slower the building process will be, as it will build a separate kernel image for each arch. 2025-01-24T01:51:56.2745731Z 2025-01-24T01:51:56.2746077Z Note that CUDA-11.5 nvcc will hit internal compiler error while parsing torch/extension.h on Windows. 2025-01-24T01:51:56.2746866Z To workaround the issue, move python binding logic to pure C++ file. 2025-01-24T01:51:56.2747194Z 2025-01-24T01:51:56.2747304Z Example use: 2025-01-24T01:51:56.2747599Z #include 2025-01-24T01:51:56.2748000Z at::Tensor SigmoidAlphaBlendForwardCuda(....) 2025-01-24T01:51:56.2748275Z 2025-01-24T01:51:56.2748371Z Instead of: 2025-01-24T01:51:56.2748632Z #include 2025-01-24T01:51:56.2749005Z torch::Tensor SigmoidAlphaBlendForwardCuda(...) 2025-01-24T01:51:56.2749275Z 2025-01-24T01:51:56.2749558Z Currently open issue for nvcc bug: https://github.com/pytorch/pytorch/issues/69460 2025-01-24T01:51:56.2750472Z Complete workaround code example: https://github.com/facebookresearch/pytorch3d/commit/cb170ac024a949f1f9614ffe6af1c38d972f7d48 2025-01-24T01:51:56.2751109Z 2025-01-24T01:51:56.2751225Z Relocatable device code linking: 2025-01-24T01:51:56.2751442Z 2025-01-24T01:51:56.2751727Z If you want to reference device symbols across compilation units (across object files), 2025-01-24T01:51:56.2752405Z the object files need to be built with `relocatable device code` (-rdc=true or -dc). 2025-01-24T01:51:56.2753159Z An exception to this rule is "dynamic parallelism" (nested kernel launches) which is not used a lot anymore. 2025-01-24T01:51:56.2753978Z `Relocatable device code` is less optimized so it needs to be used only on object files that need it. 2025-01-24T01:51:56.2754837Z Using `-dlto` (Device Link Time Optimization) at the device code compilation step and `dlink` step 2025-01-24T01:51:56.2755479Z helps reduce the protentional perf degradation of `-rdc`. 2025-01-24T01:51:56.2755956Z Note that it needs to be used at both steps to be useful. 2025-01-24T01:51:56.2756234Z 2025-01-24T01:51:56.2756622Z If you have `rdc` objects you need to have an extra `-dlink` (device linking) step before the CPU symbol linking step. 2025-01-24T01:51:56.2757294Z There is also a case where `-dlink` is used without `-rdc`: 2025-01-24T01:51:56.2757865Z when an extension is linked against a static lib containing rdc-compiled objects 2025-01-24T01:51:56.2758599Z like the [NVSHMEM library](https://developer.nvidia.com/nvshmem). 2025-01-24T01:51:56.2758947Z 2025-01-24T01:51:56.2759198Z Note: Ninja is required to build a CUDA Extension with RDC linking. 2025-01-24T01:51:56.2759541Z 2025-01-24T01:51:56.2759634Z Example: 2025-01-24T01:51:56.2759909Z >>> # xdoctest: +SKIP 2025-01-24T01:51:56.2760253Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2025-01-24T01:51:56.2760615Z >>> CUDAExtension( 2025-01-24T01:51:56.2760903Z ... name='cuda_extension', 2025-01-24T01:51:56.2761290Z ... sources=['extension.cpp', 'extension_kernel.cu'], 2025-01-24T01:51:56.2761683Z ... dlink=True, 2025-01-24T01:51:56.2761988Z ... dlink_libraries=["dlink_lib"], 2025-01-24T01:51:56.2762360Z ... extra_compile_args={'cxx': ['-g'], 2025-01-24T01:51:56.2762722Z ... 'nvcc': ['-O2', '-rdc=true']}) 2025-01-24T01:51:56.2762982Z 2025-01-24T01:51:56.2763240Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:51:56.2763627Z 2025-01-24T01:51:56.2764198Z msg = Cannot scrape callname=load in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/cpp_extension.py line=1309. 2025-01-24T01:51:56.2765070Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:51:56.2765469Z 2025-01-24T01:51:56.2765615Z Load a PyTorch C++ extension just-in-time (JIT). 2025-01-24T01:51:56.2765883Z 2025-01-24T01:51:56.2766092Z To load an extension, a Ninja build file is emitted, which is used to 2025-01-24T01:51:56.2766637Z compile the given sources into a dynamic library. This library is 2025-01-24T01:51:56.2767191Z subsequently loaded into the current Python process as a module and 2025-01-24T01:51:56.2767674Z returned from this function, ready for use. 2025-01-24T01:51:56.2767913Z 2025-01-24T01:51:56.2768124Z By default, the directory to which the build file is emitted and the 2025-01-24T01:51:56.2768692Z resulting library compiled to is ``/torch_extensions/``, where 2025-01-24T01:51:56.2769267Z ```` is the temporary folder on the current platform and ```` 2025-01-24T01:51:56.2769814Z the name of the extension. This location can be overridden in two ways. 2025-01-24T01:51:56.2770370Z First, if the ``TORCH_EXTENSIONS_DIR`` environment variable is set, it 2025-01-24T01:51:56.2770931Z replaces ``/torch_extensions`` and all extensions will be compiled 2025-01-24T01:51:56.2771483Z into subfolders of this directory. Second, if the ``build_directory`` 2025-01-24T01:51:56.2772069Z argument to this function is supplied, it overrides the entire path, i.e. 2025-01-24T01:51:56.2772597Z the library will be compiled into that folder directly. 2025-01-24T01:51:56.2772888Z 2025-01-24T01:51:56.2773103Z To compile the sources, the default system compiler (``c++``) is used, 2025-01-24T01:51:56.2773686Z which can be overridden by setting the ``CXX`` environment variable. To pass 2025-01-24T01:51:56.2774285Z additional arguments to the compilation process, ``extra_cflags`` or 2025-01-24T01:51:56.2774855Z ``extra_ldflags`` can be provided. For example, to compile your extension 2025-01-24T01:51:56.2775422Z with optimizations, pass ``extra_cflags=['-O3']``. You can also use 2025-01-24T01:51:56.2775926Z ``extra_cflags`` to pass further include directories. 2025-01-24T01:51:56.2776317Z 2025-01-24T01:51:56.2776563Z CUDA support with mixed compilation is provided. Simply pass CUDA source 2025-01-24T01:51:56.2777124Z files (``.cu`` or ``.cuh``) along with other sources. Such files will be 2025-01-24T01:51:56.2777680Z detected and compiled with nvcc rather than the C++ compiler. This includes 2025-01-24T01:51:56.2778251Z passing the CUDA lib64 directory as a library directory, and linking 2025-01-24T01:51:56.2778742Z ``cudart``. You can pass additional flags to nvcc via 2025-01-24T01:51:56.2779460Z ``extra_cuda_cflags``, just like with ``extra_cflags`` for C++. Various 2025-01-24T01:51:56.2780048Z heuristics for finding the CUDA install directory are used, which usually 2025-01-24T01:51:56.2780705Z work fine. If not, setting the ``CUDA_HOME`` environment variable is the 2025-01-24T01:51:56.2781135Z safest option. 2025-01-24T01:51:56.2781297Z 2025-01-24T01:51:56.2781399Z Args: 2025-01-24T01:51:56.2781792Z name: The name of the extension to build. This MUST be the same as the 2025-01-24T01:51:56.2782247Z name of the pybind11 module! 2025-01-24T01:51:56.2782685Z sources: A list of relative or absolute paths to C++ source files. 2025-01-24T01:51:56.2783250Z extra_cflags: optional list of compiler flags to forward to the build. 2025-01-24T01:51:56.2783814Z extra_cuda_cflags: optional list of compiler flags to forward to nvcc 2025-01-24T01:51:56.2784284Z when building CUDA sources. 2025-01-24T01:51:56.2784724Z extra_ldflags: optional list of linker flags to forward to the build. 2025-01-24T01:51:56.2785295Z extra_include_paths: optional list of include directories to forward 2025-01-24T01:51:56.2785747Z to the build. 2025-01-24T01:51:56.2786083Z build_directory: optional path to use as build workspace. 2025-01-24T01:51:56.2786696Z verbose: If ``True``, turns on verbose logging of load steps. 2025-01-24T01:51:56.2787231Z with_cuda: Determines whether CUDA headers and libraries are added to 2025-01-24T01:51:56.2787755Z the build. If set to ``None`` (default), this value is 2025-01-24T01:51:56.2788276Z automatically determined based on the existence of ``.cu`` or 2025-01-24T01:51:56.2788784Z ``.cuh`` in ``sources``. Set it to `True`` to force CUDA headers 2025-01-24T01:51:56.2789183Z and libraries to be included. 2025-01-24T01:51:56.2789621Z is_python_module: If ``True`` (default), imports the produced shared 2025-01-24T01:51:56.2790144Z library as a Python module. If ``False``, behavior depends on 2025-01-24T01:51:56.2790557Z ``is_standalone``. 2025-01-24T01:51:56.2790951Z is_standalone: If ``False`` (default) loads the constructed extension 2025-01-24T01:51:56.2791472Z into the process as a plain dynamic library. If ``True``, build a 2025-01-24T01:51:56.2791903Z standalone executable. 2025-01-24T01:51:56.2792109Z 2025-01-24T01:51:56.2792199Z Returns: 2025-01-24T01:51:56.2792443Z If ``is_python_module`` is ``True``: 2025-01-24T01:51:56.2792858Z Returns the loaded PyTorch extension as a Python module. 2025-01-24T01:51:56.2793148Z 2025-01-24T01:51:56.2793368Z If ``is_python_module`` is ``False`` and ``is_standalone`` is ``False``: 2025-01-24T01:51:56.2793903Z Returns nothing. (The shared library is loaded into the process as 2025-01-24T01:51:56.2794338Z a side effect.) 2025-01-24T01:51:56.2794513Z 2025-01-24T01:51:56.2794625Z If ``is_standalone`` is ``True``. 2025-01-24T01:51:56.2795047Z Return the path to the executable. (On Windows, TORCH_LIB_PATH is 2025-01-24T01:51:56.2795560Z added to the PATH environment variable as a side effect.) 2025-01-24T01:51:56.2795852Z 2025-01-24T01:51:56.2795956Z Example: 2025-01-24T01:51:56.2796178Z >>> # xdoctest: +SKIP 2025-01-24T01:51:56.2796510Z >>> from torch.utils.cpp_extension import load 2025-01-24T01:51:56.2796869Z >>> module = load( 2025-01-24T01:51:56.2797141Z ... name='extension', 2025-01-24T01:51:56.2797589Z ... sources=['extension.cpp', 'extension_kernel.cu'], 2025-01-24T01:51:56.2797985Z ... extra_cflags=['-O2'], 2025-01-24T01:51:56.2798290Z ... verbose=True) 2025-01-24T01:51:56.2798474Z 2025-01-24T01:51:56.2798730Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:51:56.2799104Z 2025-01-24T01:51:56.2799653Z msg = Cannot scrape callname=load_inline in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/cpp_extension.py line=1602. 2025-01-24T01:51:56.2800566Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:51:56.2800946Z 2025-01-24T01:51:56.2801172Z Load a PyTorch C++ extension just-in-time (JIT) from string sources. 2025-01-24T01:51:56.2801508Z 2025-01-24T01:51:56.2801776Z This function behaves exactly like :func:`load`, but takes its sources as 2025-01-24T01:51:56.2802369Z strings rather than filenames. These strings are stored to files in the 2025-01-24T01:51:56.2802981Z build directory, after which the behavior of :func:`load_inline` is 2025-01-24T01:51:56.2803434Z identical to :func:`load`. 2025-01-24T01:51:56.2803617Z 2025-01-24T01:51:56.2803732Z See `the 2025-01-24T01:51:56.2804199Z tests `_ 2025-01-24T01:51:56.2804766Z for good examples of using this function. 2025-01-24T01:51:56.2805021Z 2025-01-24T01:51:56.2805259Z Sources may omit two required parts of a typical non-inline C++ extension: 2025-01-24T01:51:56.2805878Z the necessary header includes, as well as the (pybind11) binding code. More 2025-01-24T01:51:56.2806501Z precisely, strings passed to ``cpp_sources`` are first concatenated into a 2025-01-24T01:51:56.2807072Z single ``.cpp`` file. This file is then prepended with ``#include 2025-01-24T01:51:56.2807481Z ``. 2025-01-24T01:51:56.2807665Z 2025-01-24T01:51:56.2807898Z Furthermore, if the ``functions`` argument is supplied, bindings will be 2025-01-24T01:51:56.2808505Z automatically generated for each function specified. ``functions`` can 2025-01-24T01:51:56.2809092Z either be a list of function names, or a dictionary mapping from function 2025-01-24T01:51:56.2809674Z names to docstrings. If a list is given, the name of each function is used 2025-01-24T01:51:56.2810129Z as its docstring. 2025-01-24T01:51:56.2810276Z 2025-01-24T01:51:56.2810494Z The sources in ``cuda_sources`` are concatenated into a separate ``.cu`` 2025-01-24T01:51:56.2811019Z file and prepended with ``torch/types.h``, ``cuda.h`` and 2025-01-24T01:51:56.2811533Z ``cuda_runtime.h`` includes. The ``.cpp`` and ``.cu`` files are compiled 2025-01-24T01:51:56.2812092Z separately, but ultimately linked into a single library. Note that no 2025-01-24T01:51:56.2812673Z bindings are generated for functions in ``cuda_sources`` per se. To bind 2025-01-24T01:51:56.2813248Z to a CUDA kernel, you must create a C++ function that calls it, and either 2025-01-24T01:51:56.2813793Z declare or define this C++ function in one of the ``cpp_sources`` (and 2025-01-24T01:51:56.2814247Z include its name in ``functions``). 2025-01-24T01:51:56.2814469Z 2025-01-24T01:51:56.2814654Z See :func:`load` for a description of arguments omitted below. 2025-01-24T01:51:56.2814947Z 2025-01-24T01:51:56.2815046Z Args: 2025-01-24T01:51:56.2815397Z cpp_sources: A string, or list of strings, containing C++ source code. 2025-01-24T01:51:56.2815966Z cuda_sources: A string, or list of strings, containing CUDA source code. 2025-01-24T01:51:56.2816513Z functions: A list of function names for which to generate function 2025-01-24T01:51:56.2817059Z bindings. If a dictionary is given, it should map function names to 2025-01-24T01:51:56.2817590Z docstrings (which are otherwise just the function names). 2025-01-24T01:51:56.2818126Z with_cuda: Determines whether CUDA headers and libraries are added to 2025-01-24T01:51:56.2818644Z the build. If set to ``None`` (default), this value is 2025-01-24T01:51:56.2819193Z automatically determined based on whether ``cuda_sources`` is 2025-01-24T01:51:56.2819693Z provided. Set it to ``True`` to force CUDA headers 2025-01-24T01:51:56.2820087Z and libraries to be included. 2025-01-24T01:51:56.2820528Z with_pytorch_error_handling: Determines whether pytorch error and 2025-01-24T01:51:56.2821071Z warning macros are handled by pytorch instead of pybind. To do 2025-01-24T01:51:56.2821615Z this, each function ``foo`` is called via an intermediary ``_safe_foo`` 2025-01-24T01:51:56.2822150Z function. This redirection might cause issues in obscure cases 2025-01-24T01:51:56.2822665Z of cpp. This flag should be set to ``False`` when this redirect 2025-01-24T01:51:56.2823071Z causes issues. 2025-01-24T01:51:56.2823230Z 2025-01-24T01:51:56.2823387Z Example: 2025-01-24T01:51:56.2823678Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2025-01-24T01:51:56.2824101Z >>> from torch.utils.cpp_extension import load_inline 2025-01-24T01:51:56.2824515Z >>> source = """ 2025-01-24T01:51:56.2824838Z at::Tensor sin_add(at::Tensor x, at::Tensor y) { 2025-01-24T01:51:56.2825203Z return x.sin() + y.sin(); 2025-01-24T01:51:56.2825491Z } 2025-01-24T01:51:56.2825691Z """ 2025-01-24T01:51:56.2825959Z >>> module = load_inline(name='inline_extension', 2025-01-24T01:51:56.2826451Z ... cpp_sources=[source], 2025-01-24T01:51:56.2826820Z ... functions=['sin_add']) 2025-01-24T01:51:56.2827052Z 2025-01-24T01:51:56.2827162Z .. note:: 2025-01-24T01:51:56.2827524Z Since load_inline will just-in-time compile the source code, please ensure 2025-01-24T01:51:56.2828132Z that you have the right toolchains installed in the runtime. For example, 2025-01-24T01:51:56.2828711Z when loading C++, make sure a C++ compiler is available. If you're loading 2025-01-24T01:51:56.2829303Z a CUDA extension, you will need to additionally install the corresponding CUDA 2025-01-24T01:51:56.2829933Z toolkit (nvcc and any other dependencies your code has). Compiling toolchains 2025-01-24T01:51:56.2830551Z are not included when you install torch and must be additionally installed. 2025-01-24T01:51:56.2830908Z 2025-01-24T01:51:56.2831179Z During compiling, by default, the Ninja backend uses #CPUS + 2 workers to build 2025-01-24T01:51:56.2831771Z the extension. This may use up too many resources on some systems. One 2025-01-24T01:51:56.2832343Z can control the number of workers by setting the `MAX_JOBS` environment 2025-01-24T01:51:56.2832807Z variable to a non-negative number. 2025-01-24T01:51:56.2833026Z 2025-01-24T01:51:56.2833294Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:51:56.2833664Z 2025-01-24T01:51:56.2956233Z msg = Cannot scrape callname=ThroughputBenchmark in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/throughput_benchmark.py line=61. 2025-01-24T01:51:56.2957250Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:51:56.2957633Z 2025-01-24T01:51:56.2957944Z This class is a wrapper around a c++ component throughput_benchmark::ThroughputBenchmark. 2025-01-24T01:51:56.2958354Z 2025-01-24T01:51:56.2958667Z This wrapper on the throughput_benchmark::ThroughputBenchmark component is responsible 2025-01-24T01:51:56.2959329Z for executing a PyTorch module (nn.Module or ScriptModule) under an inference 2025-01-24T01:51:56.2959939Z server like load. It can emulate multiple calling threads to a single module 2025-01-24T01:51:56.2960540Z provided. In the future we plan to enhance this component to support inter and 2025-01-24T01:51:56.2961155Z intra-op parallelism as well as multiple models running in a single process. 2025-01-24T01:51:56.2961519Z 2025-01-24T01:51:56.2961794Z Please note that even though nn.Module is supported, it might incur an overhead 2025-01-24T01:51:56.2962400Z from the need to hold GIL every time we execute Python code or pass around 2025-01-24T01:51:56.2963138Z inputs as Python objects. As soon as you have a ScriptModule version of your 2025-01-24T01:51:56.2963757Z model for inference deployment it is better to switch to using it in this 2025-01-24T01:51:56.2964225Z benchmark. 2025-01-24T01:51:56.2964353Z 2025-01-24T01:51:56.2964470Z Example:: 2025-01-24T01:51:56.2964598Z 2025-01-24T01:51:56.2964740Z >>> # xdoctest: +SKIP("undefined vars") 2025-01-24T01:51:56.2965123Z >>> from torch.utils import ThroughputBenchmark 2025-01-24T01:51:56.2965529Z >>> bench = ThroughputBenchmark(my_module) 2025-01-24T01:51:56.2965956Z >>> # Pre-populate benchmark's data set with the inputs 2025-01-24T01:51:56.2966348Z >>> for input in inputs: 2025-01-24T01:51:56.2966764Z ... # Both args and kwargs work, same as any PyTorch Module / ScriptModule 2025-01-24T01:51:56.2967291Z ... bench.add_input(input[0], x2=input[1]) 2025-01-24T01:51:56.2967739Z >>> # Inputs supplied above are randomly used during the execution 2025-01-24T01:51:56.2968215Z >>> stats = bench.benchmark( 2025-01-24T01:51:56.2968525Z ... num_calling_threads=4, 2025-01-24T01:51:56.2968845Z ... num_warmup_iters = 100, 2025-01-24T01:51:56.2969169Z ... num_iters = 1000, 2025-01-24T01:51:56.2969440Z ... ) 2025-01-24T01:51:56.2969757Z >>> print("Avg latency (ms): {}".format(stats.latency_avg_ms)) 2025-01-24T01:51:56.2970243Z >>> print("Number of iterations: {}".format(stats.num_iters)) 2025-01-24T01:51:56.2970538Z 2025-01-24T01:51:56.2970811Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:51:56.2971183Z 2025-01-24T01:51:56.5584456Z msg = Cannot scrape callname=DistributedSampler in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/distributed.py line=18. 2025-01-24T01:51:56.5585779Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:51:56.5586532Z Sampler that restricts data loading to a subset of the dataset. 2025-01-24T01:51:56.5586895Z 2025-01-24T01:51:56.5587045Z It is especially useful in conjunction with 2025-01-24T01:51:56.5587561Z :class:`torch.nn.parallel.DistributedDataParallel`. In such a case, each 2025-01-24T01:51:56.5588210Z process can pass a :class:`~torch.utils.data.DistributedSampler` instance as a 2025-01-24T01:51:56.5588820Z :class:`~torch.utils.data.DataLoader` sampler, and load a subset of the 2025-01-24T01:51:56.5589316Z original dataset that is exclusive to it. 2025-01-24T01:51:56.5589573Z 2025-01-24T01:51:56.5589691Z .. note:: 2025-01-24T01:51:56.5590076Z Dataset is assumed to be of constant size and that any instance of it always 2025-01-24T01:51:56.5590592Z returns the same elements in the same order. 2025-01-24T01:51:56.5590846Z 2025-01-24T01:51:56.5601219Z Args: 2025-01-24T01:51:56.5601624Z dataset: Dataset used for sampling. 2025-01-24T01:51:56.5602115Z num_replicas (int, optional): Number of processes participating in 2025-01-24T01:51:56.5602742Z distributed training. By default, :attr:`world_size` is retrieved from the 2025-01-24T01:51:56.5603230Z current distributed group. 2025-01-24T01:51:56.5603707Z rank (int, optional): Rank of the current process within :attr:`num_replicas`. 2025-01-24T01:51:56.5604290Z By default, :attr:`rank` is retrieved from the current distributed 2025-01-24T01:51:56.5604731Z group. 2025-01-24T01:51:56.5605121Z shuffle (bool, optional): If ``True`` (default), sampler will shuffle the 2025-01-24T01:51:56.5605576Z indices. 2025-01-24T01:51:56.5605923Z seed (int, optional): random seed used to shuffle the sampler if 2025-01-24T01:51:56.5606452Z :attr:`shuffle=True`. This number should be identical across all 2025-01-24T01:51:56.5606951Z processes in the distributed group. Default: ``0``. 2025-01-24T01:51:56.5607473Z drop_last (bool, optional): if ``True``, then the sampler will drop the 2025-01-24T01:51:56.5608248Z tail of the data to make it evenly divisible across the number of 2025-01-24T01:51:56.5608781Z replicas. If ``False``, the sampler will add extra indices to make 2025-01-24T01:51:56.5609311Z the data evenly divisible across the replicas. Default: ``False``. 2025-01-24T01:51:56.5609660Z 2025-01-24T01:51:56.5609767Z .. warning:: 2025-01-24T01:51:56.5610120Z In distributed mode, calling the :meth:`set_epoch` method at 2025-01-24T01:51:56.5610702Z the beginning of each epoch **before** creating the :class:`DataLoader` iterator 2025-01-24T01:51:56.5611355Z is necessary to make shuffling work properly across multiple epochs. Otherwise, 2025-01-24T01:51:56.5611870Z the same ordering will be always used. 2025-01-24T01:51:56.5612124Z 2025-01-24T01:51:56.5612279Z Example:: 2025-01-24T01:51:56.5612434Z 2025-01-24T01:51:56.5612542Z >>> # xdoctest: +SKIP 2025-01-24T01:51:56.5613029Z >>> sampler = DistributedSampler(dataset) if is_distributed else None 2025-01-24T01:51:56.5613569Z >>> loader = DataLoader(dataset, shuffle=(sampler is None), 2025-01-24T01:51:56.5613994Z ... sampler=sampler) 2025-01-24T01:51:56.5614361Z >>> for epoch in range(start_epoch, n_epochs): 2025-01-24T01:51:56.5614733Z ... if is_distributed: 2025-01-24T01:51:56.5615066Z ... sampler.set_epoch(epoch) 2025-01-24T01:51:56.5615410Z ... train(loader) 2025-01-24T01:51:56.5615693Z 2025-01-24T01:51:56.5616066Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:51:56.5616456Z 2025-01-24T01:51:56.7788601Z gathering tests 2025-01-24T01:51:56.7799825Z running 709 test(s) 2025-01-24T01:51:56.7856015Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::typename:0, line 1066 <- wrt source file 2025-01-24T01:51:56.7863730Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::typename:0 2025-01-24T01:51:56.7864904Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::is_tensor:0, line 1102 <- wrt source file 2025-01-24T01:51:56.7868478Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::is_tensor:0 2025-01-24T01:51:56.7870186Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::set_default_device:0, line 1171 <- wrt source file 2025-01-24T01:51:56.7871914Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::set_default_device:0 2025-01-24T01:51:56.7873551Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::set_default_tensor_type:0, line 1220 <- wrt source file 2025-01-24T01:51:56.7875352Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::set_default_tensor_type:0 2025-01-24T01:51:56.7876613Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::set_default_dtype:0, line 1257 <- wrt source file 2025-01-24T01:51:56.7878002Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::set_default_dtype:0 2025-01-24T01:51:56.7879589Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::use_deterministic_algorithms:0, line 1412 <- wrt source file 2025-01-24T01:51:56.7881419Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::use_deterministic_algorithms:0 2025-01-24T01:51:56.7882957Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::compile:0, line 2512 <- wrt source file 2025-01-24T01:51:56.7884148Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::compile:0 2025-01-24T01:51:56.7885594Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::_is_device_backend_autoload_enabled:0, line 2774 <- wrt source file 2025-01-24T01:51:56.7886982Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::_is_device_backend_autoload_enabled:0 2025-01-24T01:51:56.7888321Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_C.cpython-313-x86_64-linux-gnu.so::Generator:0, line 15 <- wrt source file 2025-01-24T01:51:56.7889679Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_C.cpython-313-x86_64-linux-gnu.so::Generator:0 2025-01-24T01:51:56.7891065Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_C.cpython-313-x86_64-linux-gnu.so::_LinAlgError:0, line 5 <- wrt source file 2025-01-24T01:51:56.7892448Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_C.cpython-313-x86_64-linux-gnu.so::_LinAlgError:0 2025-01-24T01:51:56.7893748Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_custom_ops.py::custom_op:0, line 55 <- wrt source file 2025-01-24T01:51:56.7894899Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_custom_ops.py::custom_op:0 2025-01-24T01:51:56.7895993Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_custom_ops.py::impl:0, line 137 <- wrt source file 2025-01-24T01:51:56.7897103Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_custom_ops.py::impl:0 2025-01-24T01:51:56.7898239Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_custom_ops.py::impl_abstract:0, line 206 <- wrt source file 2025-01-24T01:51:56.8509281Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_custom_ops.py::impl_abstract:0 2025-01-24T01:51:56.8510824Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_namedtensor_internals.py::update_names:0, line 118 <- wrt source file 2025-01-24T01:51:56.8512297Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_namedtensor_internals.py::update_names:0 2025-01-24T01:51:56.8513687Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py::Tensor.register_hook:0, line 672 <- wrt source file 2025-01-24T01:51:56.8517862Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py::Tensor.register_hook:0 2025-01-24T01:51:56.8519309Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py::Tensor.register_post_accumulate_grad_hook:0, line 729 <- wrt source file 2025-01-24T01:51:56.8534808Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py::Tensor.register_post_accumulate_grad_hook:0 2025-01-24T01:51:56.8536126Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py::Tensor.refine_names:0, line 1338 <- wrt source file 2025-01-24T01:51:56.8654533Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py::Tensor.refine_names:0 2025-01-24T01:51:56.8657459Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py::Tensor.align_to:0, line 1383 <- wrt source file 2025-01-24T01:51:56.8662381Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py::Tensor.align_to:0 2025-01-24T01:51:56.8663538Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py::Tensor.rename:0, line 1456 <- wrt source file 2025-01-24T01:51:56.8669512Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py::Tensor.rename:0 2025-01-24T01:51:56.8670819Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py::Tensor.to_sparse_coo:0, line 1486 <- wrt source file 2025-01-24T01:51:56.8674993Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py::Tensor.to_sparse_coo:0 2025-01-24T01:51:56.8676189Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor_str.py::set_printoptions:0, line 53 <- wrt source file 2025-01-24T01:51:56.8693901Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor_str.py::set_printoptions:0 2025-01-24T01:51:56.8695133Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::broadcast_tensors:0, line 64 <- wrt source file 2025-01-24T01:51:56.8700177Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::broadcast_tensors:0 2025-01-24T01:51:56.8701406Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::broadcast_shapes:0, line 92 <- wrt source file 2025-01-24T01:51:56.8702902Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::broadcast_shapes:0 2025-01-24T01:51:56.8704045Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::split:0, line 179 <- wrt source file 2025-01-24T01:51:56.8714995Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::split:0 2025-01-24T01:51:56.8716103Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::einsum:0, line 293 <- wrt source file 2025-01-24T01:51:56.8763659Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::einsum:0 2025-01-24T01:51:56.8764928Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::_unique_consecutive_impl:0, line 1020 <- wrt source file 2025-01-24T01:51:56.8774710Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::_unique_consecutive_impl:0 2025-01-24T01:51:56.8775941Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::tensordot:0, line 1295 <- wrt source file 2025-01-24T01:51:56.8785172Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::tensordot:0 2025-01-24T01:51:56.8786429Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::cartesian_prod:0, line 1379 <- wrt source file 2025-01-24T01:51:56.8792410Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::cartesian_prod:0 2025-01-24T01:51:56.8793576Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::block_diag:0, line 1413 <- wrt source file 2025-01-24T01:51:56.8800951Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::block_diag:0 2025-01-24T01:51:56.8802087Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::cdist:0, line 1464 <- wrt source file 2025-01-24T01:51:56.8814164Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::cdist:0 2025-01-24T01:51:56.8815304Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::atleast_1d:0, line 1505 <- wrt source file 2025-01-24T01:51:56.8829980Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::atleast_1d:0 2025-01-24T01:51:56.8831138Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::atleast_2d:0, line 1541 <- wrt source file 2025-01-24T01:51:56.8846131Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::atleast_2d:0 2025-01-24T01:51:56.8847483Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::atleast_3d:0, line 1579 <- wrt source file 2025-01-24T01:51:56.8866389Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::atleast_3d:0 2025-01-24T01:51:56.8867518Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::norm:0, line 1752 <- wrt source file 2025-01-24T01:51:56.8897531Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::norm:0 2025-01-24T01:51:56.8898678Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::unravel_index:0, line 1919 <- wrt source file 2025-01-24T01:51:56.8923207Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::unravel_index:0 2025-01-24T01:51:56.8924707Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::chain_matmul:0, line 2019 <- wrt source file 2025-01-24T01:51:56.8926952Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::chain_matmul:0 2025-01-24T01:51:56.8928499Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::_lu_impl:0, line 2119 <- wrt source file 2025-01-24T01:51:56.8929652Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::_lu_impl:0 2025-01-24T01:51:56.8930707Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/hub.py::list:0, line 468 <- wrt source file 2025-01-24T01:51:56.8932226Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/hub.py::list:0 2025-01-24T01:51:56.8933397Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/hub.py::help:0, line 528 <- wrt source file 2025-01-24T01:51:56.8934434Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/hub.py::help:0 2025-01-24T01:51:56.8935476Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/hub.py::_load_local:0, line 667 <- wrt source file 2025-01-24T01:51:56.8936559Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/hub.py::_load_local:0 2025-01-24T01:51:56.8937661Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::Library.define:0, line 148 <- wrt source file 2025-01-24T01:51:56.8938830Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::Library.define:0 2025-01-24T01:51:56.8940052Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::Library._impl_with_aoti_compile:0, line 248 <- wrt source file 2025-01-24T01:51:56.8949805Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::Library._impl_with_aoti_compile:0 2025-01-24T01:51:56.8951046Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::Library.impl:0, line 303 <- wrt source file 2025-01-24T01:51:56.8954067Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::Library.impl:0 2025-01-24T01:51:56.8955175Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::define:0, line 496 <- wrt source file 2025-01-24T01:51:56.8971120Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::define:0 2025-01-24T01:51:56.8972194Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::impl:0, line 602 <- wrt source file 2025-01-24T01:51:56.8991582Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::impl:0 2025-01-24T01:51:56.8993223Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::register_kernel:0, line 783 <- wrt source file 2025-01-24T01:51:56.8994475Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::register_kernel:0 2025-01-24T01:51:56.8995687Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::register_torch_dispatch:0, line 1104 <- wrt source file 2025-01-24T01:51:56.9086131Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::register_torch_dispatch:0 2025-01-24T01:51:56.9087328Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::register_vmap:0, line 1193 <- wrt source file 2025-01-24T01:51:56.9244087Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::register_vmap:0 2025-01-24T01:51:56.9245308Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/overrides.py::get_ignored_functions:0, line 112 <- wrt source file 2025-01-24T01:51:56.9250004Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/overrides.py::get_ignored_functions:0 2025-01-24T01:51:56.9251237Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/overrides.py::get_testing_overrides:0, line 418 <- wrt source file 2025-01-24T01:51:56.9282337Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/overrides.py::get_testing_overrides:0 2025-01-24T01:51:56.9283574Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/overrides.py::wrap_torch_function:0, line 1573 <- wrt source file 2025-01-24T01:51:56.9286030Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/overrides.py::wrap_torch_function:0 2025-01-24T01:51:56.9287277Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/overrides.py::handle_torch_function:0, line 1708 <- wrt source file 2025-01-24T01:51:56.9289062Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/overrides.py::handle_torch_function:0 2025-01-24T01:51:56.9290334Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/overrides.py::is_tensor_method_or_property:0, line 1956 <- wrt source file 2025-01-24T01:51:56.9314816Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/overrides.py::is_tensor_method_or_property:0 2025-01-24T01:51:56.9316067Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/overrides.py::is_tensor_like:0, line 1975 <- wrt source file 2025-01-24T01:51:56.9321806Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/overrides.py::is_tensor_like:0 2025-01-24T01:51:56.9323704Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/quasirandom.py::SobolEngine:0, line 39 <- wrt source file 2025-01-24T01:51:56.9325626Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/quasirandom.py::SobolEngine:0 2025-01-24T01:51:56.9326947Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/serialization.py::add_safe_globals:0, line 286 <- wrt source file 2025-01-24T01:51:56.9328217Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/serialization.py::add_safe_globals:0 2025-01-24T01:51:56.9329683Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/serialization.py::safe_globals:0, line 311 <- wrt source file 2025-01-24T01:51:56.9331925Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/serialization.py::safe_globals:0 2025-01-24T01:51:56.9333140Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/serialization.py::skip_data:0, line 385 <- wrt source file 2025-01-24T01:51:56.9334910Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/serialization.py::skip_data:0 2025-01-24T01:51:56.9336130Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/serialization.py::register_package:0, line 457 <- wrt source file 2025-01-24T01:51:56.9337395Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/serialization.py::register_package:0 2025-01-24T01:51:56.9338572Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/serialization.py::save:0, line 920 <- wrt source file 2025-01-24T01:51:56.9339720Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/serialization.py::save:0 2025-01-24T01:51:56.9340959Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/torch_version.py::TorchVersion:0, line 19 <- wrt source file 2025-01-24T01:51:56.9342205Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/torch_version.py::TorchVersion:0 2025-01-24T01:51:56.9343432Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_inductor/__init__.py::list_mode_options:0, line 277 <- wrt source file 2025-01-24T01:51:56.9344729Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_inductor/__init__.py::list_mode_options:0 2025-01-24T01:51:56.9346452Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_inductor/__init__.py::list_options:0, line 314 <- wrt source file 2025-01-24T01:51:56.9347712Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_inductor/__init__.py::list_options:0 2025-01-24T01:51:56.9349070Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/__init__.py::compute_required_storage_length:0, line 1794 <- wrt source file 2025-01-24T01:51:56.9351012Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/__init__.py::compute_required_storage_length:0 2025-01-24T01:51:56.9352396Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/accelerator/__init__.py::current_accelerator:0, line 61 <- wrt source file 2025-01-24T01:51:56.9355358Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/accelerator/__init__.py::current_accelerator:0 2025-01-24T01:51:56.9357031Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::allow_in_graph:0, line 114 <- wrt source file 2025-01-24T01:51:56.9358289Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::allow_in_graph:0 2025-01-24T01:51:56.9359555Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::substitute_in_graph:0, line 168 <- wrt source file 2025-01-24T01:51:56.9816056Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::substitute_in_graph:0 2025-01-24T01:51:56.9817311Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::wrap_numpy:0, line 354 <- wrt source file 2025-01-24T01:51:56.9818654Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::wrap_numpy:0 2025-01-24T01:51:56.9819911Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::is_compiling:0, line 386 <- wrt source file 2025-01-24T01:51:56.9821147Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::is_compiling:0 2025-01-24T01:51:56.9822581Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::is_dynamo_compiling:0, line 407 <- wrt source file 2025-01-24T01:51:56.9824101Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::is_dynamo_compiling:0 2025-01-24T01:51:56.9825562Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::is_exporting:0, line 425 <- wrt source file 2025-01-24T01:51:56.9827467Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::is_exporting:0 2025-01-24T01:51:56.9829122Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::save_cache_artifacts:0, line 440 <- wrt source file 2025-01-24T01:51:56.9830974Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::save_cache_artifacts:0 2025-01-24T01:51:56.9832994Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::load_cache_artifacts:0, line 455 <- wrt source file 2025-01-24T01:51:56.9835150Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::load_cache_artifacts:0 2025-01-24T01:51:56.9837338Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/export/__init__.py::save:0, line 414 <- wrt source file 2025-01-24T01:51:56.9838492Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/export/__init__.py::save:0 2025-01-24T01:51:56.9839592Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/export/__init__.py::load:0, line 496 <- wrt source file 2025-01-24T01:51:56.9840737Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/export/__init__.py::load:0 2025-01-24T01:51:56.9841936Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/export/__init__.py::register_dataclass:0, line 594 <- wrt source file 2025-01-24T01:51:56.9843233Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/export/__init__.py::register_dataclass:0 2025-01-24T01:51:56.9844528Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/futures/__init__.py::Future.add_done_callback:0, line 200 <- wrt source file 2025-01-24T01:51:56.9845883Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/futures/__init__.py::Future.add_done_callback:0 2025-01-24T01:51:56.9847187Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/futures/__init__.py::Future.set_exception:0, line 262 <- wrt source file 2025-01-24T01:51:56.9848514Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/futures/__init__.py::Future.set_exception:0 2025-01-24T01:51:56.9849756Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/futures/__init__.py::collect_all:0, line 295 <- wrt source file 2025-01-24T01:51:56.9850964Z * SKIPPED: 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/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nested/__init__.py::as_nested_tensor:0 2025-01-24T01:51:56.9889623Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nested/__init__.py::nested_tensor:0, line 240 <- wrt source file 2025-01-24T01:51:56.9891850Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nested/__init__.py::nested_tensor:0 2025-01-24T01:51:56.9893974Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nested/__init__.py::narrow:0, line 315 <- wrt source file 2025-01-24T01:51:56.9940635Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nested/__init__.py::narrow:0 2025-01-24T01:51:56.9943001Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nested/__init__.py::nested_tensor_from_jagged:0, line 405 <- wrt source file 2025-01-24T01:51:56.9959558Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nested/__init__.py::nested_tensor_from_jagged:0 2025-01-24T01:51:56.9962153Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/sparse/__init__.py::check_sparse_tensor_invariants:0, line 475 <- wrt source file 2025-01-24T01:51:56.9968975Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/sparse/__init__.py::check_sparse_tensor_invariants:0 2025-01-24T01:51:56.9971189Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/sparse/__init__.py::as_sparse_gradcheck:0, line 561 <- wrt source file 2025-01-24T01:51:57.0025478Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/sparse/__init__.py::as_sparse_gradcheck:0 2025-01-24T01:51:57.0027954Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_dynamo/decorators.py::substitute_in_graph:0, line 259 <- wrt source file 2025-01-24T01:51:57.0030486Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_dynamo/decorators.py::substitute_in_graph:0 2025-01-24T01:51:57.0033002Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_dynamo/variables/base.py::VariableTracker.python_type:0, line 286 <- wrt source file 2025-01-24T01:51:57.0035510Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_dynamo/variables/base.py::VariableTracker.python_type:0 2025-01-24T01:51:57.0038060Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_export/utils.py::register_module_as_pytree_input_node:0, line 1187 <- wrt source file 2025-01-24T01:51:57.0040644Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_export/utils.py::register_module_as_pytree_input_node:0 2025-01-24T01:51:57.0042999Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/aot_autograd.py::aot_function:0, line 886 <- wrt source file 2025-01-24T01:51:57.0338361Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/aot_autograd.py::aot_function:0 2025-01-24T01:51:57.0340571Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/apis.py::grad:0, line 323 <- wrt source file 2025-01-24T01:51:57.0342659Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/apis.py::grad:0 2025-01-24T01:51:57.0344973Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/benchmark_utils.py::benchmark_utilization:0, line 184 <- wrt source file 2025-01-24T01:51:57.0347606Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/benchmark_utils.py::benchmark_utilization:0 2025-01-24T01:51:57.0350000Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::vjp:0, line 232 <- wrt source file 2025-01-24T01:51:57.0378413Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::vjp:0 2025-01-24T01:51:57.0380880Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::jacrev:0, line 474 <- wrt source file 2025-01-24T01:51:57.0446300Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::jacrev:0 2025-01-24T01:51:57.0448656Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::jvp:0, line 1023 <- wrt source file 2025-01-24T01:51:57.0909855Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::jvp:0 2025-01-24T01:51:57.0912340Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::jacfwd:0, line 1181 <- wrt source file 2025-01-24T01:51:57.0970773Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::jacfwd:0 2025-01-24T01:51:57.0973140Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::hessian:0, line 1341 <- wrt source file 2025-01-24T01:51:57.0996653Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::hessian:0 2025-01-24T01:51:57.0999053Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::functionalize:0, line 1505 <- wrt source file 2025-01-24T01:51:57.1001582Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::functionalize:0 2025-01-24T01:51:57.1003980Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::linearize:0, line 1704 <- wrt source file 2025-01-24T01:51:57.1156086Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::linearize:0 2025-01-24T01:51:57.1158485Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/functional_call.py::functional_call:0, line 36 <- wrt source file 2025-01-24T01:51:57.1160981Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/functional_call.py::functional_call:0 2025-01-24T01:51:57.1163305Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/fx_minifier.py::minifier:0, line 194 <- wrt source file 2025-01-24T01:51:57.1165597Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/fx_minifier.py::minifier:0 2025-01-24T01:51:57.1168237Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py::CompilerWrapper.post_compile:0, line 116 <- wrt source file 2025-01-24T01:51:57.1171280Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py::CompilerWrapper.post_compile:0 2025-01-24T01:51:57.1174024Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_higher_order_ops/associative_scan.py::associative_scan:0, line 120 <- wrt source file 2025-01-24T01:51:57.1176626Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_higher_order_ops/associative_scan.py::associative_scan:0 2025-01-24T01:51:57.1179454Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_higher_order_ops/associative_scan.py::generic_associative_scan:0, line 259 <- wrt source file 2025-01-24T01:51:57.1182254Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_higher_order_ops/associative_scan.py::generic_associative_scan:0 2025-01-24T01:51:57.1184959Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_higher_order_ops/cond.py::cond:0, line 112 <- wrt source file 2025-01-24T01:51:57.1187193Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_higher_order_ops/cond.py::cond:0 2025-01-24T01:51:57.1189349Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_higher_order_ops/scan.py::scan:0, line 96 <- wrt source file 2025-01-24T01:51:57.1191540Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_higher_order_ops/scan.py::scan:0 2025-01-24T01:51:57.1194060Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_inductor/cpp_builder.py::get_name_and_dir_from_output_file_path:0, line 1337 <- wrt source file 2025-01-24T01:51:57.1196805Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_inductor/cpp_builder.py::get_name_and_dir_from_output_file_path:0 2025-01-24T01:51:57.1199323Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/custom_ops.py::custom_op:0, line 97 <- wrt source file 2025-01-24T01:51:57.1505537Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/custom_ops.py::custom_op:0 2025-01-24T01:51:57.1508059Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/custom_ops.py::CustomOpDef.set_kernel_enabled:0, line 226 <- wrt source file 2025-01-24T01:51:57.1587711Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/custom_ops.py::CustomOpDef.set_kernel_enabled:0 2025-01-24T01:51:57.1590314Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_kernel:0, line 295 <- wrt source file 2025-01-24T01:51:57.1592923Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_kernel:0 2025-01-24T01:51:57.1595460Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_fake:0, line 401 <- wrt source file 2025-01-24T01:51:57.1664956Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_fake:0 2025-01-24T01:51:57.1667554Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_autograd:0, line 528 <- wrt source file 2025-01-24T01:51:57.1823339Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_autograd:0 2025-01-24T01:51:57.1825897Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_vmap:0, line 700 <- wrt source file 2025-01-24T01:51:57.1978833Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_vmap:0 2025-01-24T01:51:57.1980782Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/fake_class_registry.py::register_fake_class:0, line 197 <- wrt source file 2025-01-24T01:51:57.1982228Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/fake_class_registry.py::register_fake_class:0 2025-01-24T01:51:57.1983622Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/fake_impl.py::FakeImplCtx.new_dynamic_size:0, line 161 <- wrt source file 2025-01-24T01:51:57.2049879Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/fake_impl.py::FakeImplCtx.new_dynamic_size:0 2025-01-24T01:51:57.2051525Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/infer_schema.py::infer_schema:0, line 46 <- wrt source file 2025-01-24T01:51:57.2055899Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/infer_schema.py::infer_schema:0 2025-01-24T01:51:57.2057282Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_logging/_internal.py::set_logs:0, line 430 <- wrt source file 2025-01-24T01:51:57.2058502Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_logging/_internal.py::set_logs:0 2025-01-24T01:51:57.2059719Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_equal:0, line 170 <- wrt source file 2025-01-24T01:51:57.2108455Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_equal:0 2025-01-24T01:51:57.2109833Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::print_assert_equal:0, line 301 <- wrt source file 2025-01-24T01:51:57.2111271Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::print_assert_equal:0 2025-01-24T01:51:57.2112575Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_array_less:0, line 992 <- wrt source file 2025-01-24T01:51:57.2157957Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_array_less:0 2025-01-24T01:51:57.2160396Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_string_equal:0, line 1057 <- wrt source file 2025-01-24T01:51:57.2163043Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_string_equal:0 2025-01-24T01:51:57.2165851Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_allclose:0, line 1278 <- wrt source file 2025-01-24T01:51:57.2179709Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_allclose:0 2025-01-24T01:51:57.2182095Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_array_almost_equal_nulp:0, line 1344 <- wrt source file 2025-01-24T01:51:57.2184834Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_array_almost_equal_nulp:0 2025-01-24T01:51:57.2187360Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_array_max_ulp:0, line 1407 <- wrt source file 2025-01-24T01:51:57.2190112Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_array_max_ulp:0 2025-01-24T01:51:57.2192502Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::nulp_diff:0, line 1452 <- wrt source file 2025-01-24T01:51:57.2194765Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::nulp_diff:0 2025-01-24T01:51:57.2196963Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_warns:0, line 1562 <- wrt source file 2025-01-24T01:51:57.2199385Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_warns:0 2025-01-24T01:51:57.2201651Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/context.py::TorchRefsMode:0, line 86 <- wrt source file 2025-01-24T01:51:57.2203947Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/context.py::TorchRefsMode:0 2025-01-24T01:51:57.2206410Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/amp/grad_scaler.py::GradScaler:0, line 64 <- wrt source file 2025-01-24T01:51:57.2208703Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/amp/grad_scaler.py::GradScaler:0 2025-01-24T01:51:57.2210923Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/intrinsic/qat/modules/linear_relu.py::LinearReLU:0, line 23 <- wrt source file 2025-01-24T01:51:57.2213634Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/intrinsic/qat/modules/linear_relu.py::LinearReLU:0 2025-01-24T01:51:57.2216538Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/intrinsic/quantized/dynamic/modules/linear_relu.py::LinearReLU:0, line 22 <- wrt source file 2025-01-24T01:51:57.2219881Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/intrinsic/quantized/dynamic/modules/linear_relu.py::LinearReLU:0 2025-01-24T01:51:57.2223061Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py::LinearReLU:0, line 25 <- wrt source file 2025-01-24T01:51:57.2226317Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py::LinearReLU:0 2025-01-24T01:51:57.2229577Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py::LinearLeakyReLU:0, line 66 <- wrt source file 2025-01-24T01:51:57.2232968Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py::LinearLeakyReLU:0 2025-01-24T01:51:57.2236249Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py::LinearTanh:0, line 140 <- wrt source file 2025-01-24T01:51:57.2239631Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py::LinearTanh:0 2025-01-24T01:51:57.2242723Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantizable/modules/rnn.py::LSTMCell:0, line 30 <- wrt source file 2025-01-24T01:51:57.2245209Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantizable/modules/rnn.py::LSTMCell:0 2025-01-24T01:51:57.2247512Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantizable/modules/rnn.py::LSTM:0, line 410 <- wrt source file 2025-01-24T01:51:57.2273894Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantizable/modules/rnn.py::LSTM:0 2025-01-24T01:51:57.2276817Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/functional.py::conv1d:0, line 210 <- wrt source file 2025-01-24T01:51:57.2279684Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/functional.py::conv1d:0 2025-01-24T01:51:57.2281957Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/functional.py::conv2d:0, line 282 <- wrt source file 2025-01-24T01:51:57.2284291Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/functional.py::conv2d:0 2025-01-24T01:51:57.2286740Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/functional.py::conv3d:0, line 358 <- wrt source file 2025-01-24T01:51:57.2289070Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/functional.py::conv3d:0 2025-01-24T01:51:57.2291432Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/__init__.py::Quantize:0, line 95 <- wrt source file 2025-01-24T01:51:57.2294323Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/__init__.py::Quantize:0 2025-01-24T01:51:57.2296864Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/__init__.py::DeQuantize:0, line 145 <- wrt source file 2025-01-24T01:51:57.2299447Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/__init__.py::DeQuantize:0 2025-01-24T01:51:57.2302031Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::Conv1d:0, line 43 <- wrt source file 2025-01-24T01:51:57.2304795Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::Conv1d:0 2025-01-24T01:51:57.2307472Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::Conv2d:0, line 124 <- wrt source file 2025-01-24T01:51:57.2310274Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::Conv2d:0 2025-01-24T01:51:57.2312904Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::Conv3d:0, line 208 <- wrt source file 2025-01-24T01:51:57.2315583Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::Conv3d:0 2025-01-24T01:51:57.2317976Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::ConvTranspose1d:0, line 294 <- wrt source file 2025-01-24T01:51:57.2320529Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::ConvTranspose1d:0 2025-01-24T01:51:57.2322694Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::ConvTranspose2d:0, line 376 <- wrt source file 2025-01-24T01:51:57.2324273Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::ConvTranspose2d:0 2025-01-24T01:51:57.2325797Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::ConvTranspose3d:0, line 458 <- wrt source file 2025-01-24T01:51:57.2327328Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::ConvTranspose3d:0 2025-01-24T01:51:57.2328800Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/linear.py::Linear:0, line 30 <- wrt source file 2025-01-24T01:51:57.2330270Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/linear.py::Linear:0 2025-01-24T01:51:57.2331672Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::LSTM:0, line 516 <- wrt source file 2025-01-24T01:51:57.2333216Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::LSTM:0 2025-01-24T01:51:57.2334588Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::GRU:0, line 801 <- wrt source file 2025-01-24T01:51:57.2335972Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::GRU:0 2025-01-24T01:51:57.2337354Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::RNNCell:0, line 1203 <- wrt source file 2025-01-24T01:51:57.2338850Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::RNNCell:0 2025-01-24T01:51:57.2340299Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::LSTMCell:0, line 1269 <- wrt source file 2025-01-24T01:51:57.2341758Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::LSTMCell:0 2025-01-24T01:51:57.2343455Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::GRUCell:0, line 1322 <- wrt source file 2025-01-24T01:51:57.2344889Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::GRUCell:0 2025-01-24T01:51:57.2346402Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/activation.py::ReLU6:0, line 36 <- wrt source file 2025-01-24T01:51:57.2348042Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/activation.py::ReLU6:0 2025-01-24T01:51:57.2349567Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/conv.py::Conv2d:0, line 505 <- wrt source file 2025-01-24T01:51:57.2350903Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/conv.py::Conv2d:0 2025-01-24T01:51:57.2352184Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/conv.py::Conv3d:0, line 634 <- wrt source file 2025-01-24T01:51:57.2353507Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/conv.py::Conv3d:0 2025-01-24T01:51:57.2354871Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/conv.py::ConvTranspose1d:0, line 890 <- wrt source file 2025-01-24T01:51:57.2356403Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/conv.py::ConvTranspose1d:0 2025-01-24T01:51:57.2359058Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/conv.py::ConvTranspose2d:0, line 1012 <- wrt source file 2025-01-24T01:51:57.2362201Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/conv.py::ConvTranspose2d:0 2025-01-24T01:51:57.2365239Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/conv.py::ConvTranspose3d:0, line 1138 <- wrt source file 2025-01-24T01:51:57.2367865Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/conv.py::ConvTranspose3d:0 2025-01-24T01:51:57.2370279Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/embedding_ops.py::Embedding:0, line 112 <- wrt source file 2025-01-24T01:51:57.2373217Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/embedding_ops.py::Embedding:0 2025-01-24T01:51:57.2376349Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/embedding_ops.py::EmbeddingBag:0, line 275 <- wrt source file 2025-01-24T01:51:57.2379828Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/embedding_ops.py::EmbeddingBag:0 2025-01-24T01:51:57.2383015Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/functional_modules.py::FloatFunctional:0, line 23 <- wrt source file 2025-01-24T01:51:57.2386556Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/functional_modules.py::FloatFunctional:0 2025-01-24T01:51:57.2390042Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/functional_modules.py::QFunctional:0, line 176 <- wrt source file 2025-01-24T01:51:57.2393325Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/functional_modules.py::QFunctional:0 2025-01-24T01:51:57.2395385Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/linear.py::Linear:0, line 138 <- wrt source file 2025-01-24T01:51:57.2396772Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/linear.py::Linear:0 2025-01-24T01:51:57.2398497Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/pruning/_experimental/activation_sparsifier/activation_sparsifier.py::ActivationSparsifier:0, line 62 <- wrt source file 2025-01-24T01:51:57.2400676Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/pruning/_experimental/activation_sparsifier/activation_sparsifier.py::ActivationSparsifier:0 2025-01-24T01:51:57.2403455Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/pruning/_experimental/data_scheduler/base_data_scheduler.py::BaseDataScheduler.get_schedule_param:0, line 98 <- wrt source file 2025-01-24T01:51:57.2528281Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/pruning/_experimental/data_scheduler/base_data_scheduler.py::BaseDataScheduler.get_schedule_param:0 2025-01-24T01:51:57.2532339Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/pruning/_experimental/data_sparsifier/base_data_sparsifier.py::BaseDataSparsifier:0, line 55 <- wrt source file 2025-01-24T01:51:57.2535966Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/pruning/_experimental/data_sparsifier/base_data_sparsifier.py::BaseDataSparsifier:0 2025-01-24T01:51:57.2538860Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/pruning/scheduler/lambda_scheduler.py::LambdaSL:0, line 22 <- wrt source file 2025-01-24T01:51:57.2541501Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/pruning/scheduler/lambda_scheduler.py::LambdaSL:0 2025-01-24T01:51:57.2544157Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/pruning/sparsifier/base_sparsifier.py::BaseSparsifier:0, line 47 <- wrt source file 2025-01-24T01:51:57.2546991Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/pruning/sparsifier/base_sparsifier.py::BaseSparsifier:0 2025-01-24T01:51:57.2549573Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fuse_modules.py::fuse_modules:0, line 176 <- wrt source file 2025-01-24T01:51:57.2552081Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fuse_modules.py::fuse_modules:0 2025-01-24T01:51:57.2554591Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_conv_bn:0, line 31 <- wrt source file 2025-01-24T01:51:57.2557251Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_conv_bn:0 2025-01-24T01:51:57.2559895Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_conv_bn_relu:0, line 76 <- wrt source file 2025-01-24T01:51:57.2562657Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_conv_bn_relu:0 2025-01-24T01:51:57.2565353Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_linear_bn:0, line 130 <- wrt source file 2025-01-24T01:51:57.2568265Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_linear_bn:0 2025-01-24T01:51:57.2571006Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_convtranspose_bn:0, line 163 <- wrt source file 2025-01-24T01:51:57.2573844Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_convtranspose_bn:0 2025-01-24T01:51:57.2576381Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/observer.py::_with_args:0, line 108 <- wrt source file 2025-01-24T01:51:57.2578796Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/observer.py::_with_args:0 2025-01-24T01:51:57.2581360Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/observer.py::_with_callable_args:0, line 130 <- wrt source file 2025-01-24T01:51:57.2584005Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/observer.py::_with_callable_args:0 2025-01-24T01:51:57.2586459Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_fx.py::fuse_fx:0, line 218 <- wrt source file 2025-01-24T01:51:57.2588837Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_fx.py::fuse_fx:0 2025-01-24T01:51:57.2591197Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_fx.py::prepare_fx:0, line 286 <- wrt source file 2025-01-24T01:51:57.2593647Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_fx.py::prepare_fx:0 2025-01-24T01:51:57.2596092Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_fx.py::prepare_qat_fx:0, line 424 <- wrt source file 2025-01-24T01:51:57.2598609Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_fx.py::prepare_qat_fx:0 2025-01-24T01:51:57.2601027Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_fx.py::convert_fx:0, line 598 <- wrt source file 2025-01-24T01:51:57.2603468Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_fx.py::convert_fx:0 2025-01-24T01:51:57.2605990Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_fx.py::convert_to_reference_fx:0, line 658 <- wrt source file 2025-01-24T01:51:57.2608677Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_fx.py::convert_to_reference_fx:0 2025-01-24T01:51:57.2611430Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_fx.py::_convert_to_reference_decomposed_fx:0, line 710 <- wrt source file 2025-01-24T01:51:57.2614347Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_fx.py::_convert_to_reference_decomposed_fx:0 2025-01-24T01:51:57.2617013Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_pt2e.py::prepare_pt2e:0, line 47 <- wrt source file 2025-01-24T01:51:57.2619550Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_pt2e.py::prepare_pt2e:0 2025-01-24T01:51:57.2622058Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_pt2e.py::prepare_qat_pt2e:0, line 125 <- wrt source file 2025-01-24T01:51:57.2624706Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_pt2e.py::prepare_qat_pt2e:0 2025-01-24T01:51:57.2627329Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_pt2e.py::convert_pt2e:0, line 221 <- wrt source file 2025-01-24T01:51:57.2629862Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_pt2e.py::convert_pt2e:0 2025-01-24T01:51:57.2632312Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/utils.py::get_combined_dict:0, line 145 <- wrt source file 2025-01-24T01:51:57.2634758Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/utils.py::get_combined_dict:0 2025-01-24T01:51:57.2637204Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/utils.py::_get_path_of_module:0, line 517 <- wrt source file 2025-01-24T01:51:57.2639735Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/utils.py::_get_path_of_module:0 2025-01-24T01:51:57.2642235Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/utils.py::_get_signature_locals:0, line 539 <- wrt source file 2025-01-24T01:51:57.2644766Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/utils.py::_get_signature_locals:0 2025-01-24T01:51:57.2647242Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/utils.py::_get_default_kwargs:0, line 553 <- wrt source file 2025-01-24T01:51:57.2649733Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/utils.py::_get_default_kwargs:0 2025-01-24T01:51:57.2652159Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/utils.py::_normalize_kwargs:0, line 575 <- wrt source file 2025-01-24T01:51:57.2654620Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/utils.py::_normalize_kwargs:0 2025-01-24T01:51:57.2657010Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/utils.py::_get_num_pos_args:0, line 702 <- wrt source file 2025-01-24T01:51:57.2659436Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/utils.py::_get_num_pos_args:0 2025-01-24T01:51:57.2662080Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/backend_config/onednn.py::_fuse_linear_bn_leaky_relu:0, line 85 <- wrt source file 2025-01-24T01:51:57.2664996Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/backend_config/onednn.py::_fuse_linear_bn_leaky_relu:0 2025-01-24T01:51:57.2667893Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fx/_model_report/model_report.py::ModelReport:0, line 84 <- wrt source file 2025-01-24T01:51:57.2670723Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fx/_model_report/model_report.py::ModelReport:0 2025-01-24T01:51:57.2673533Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/pt2e/_affine_quantization.py::_get_reduction_params:0, line 102 <- wrt source file 2025-01-24T01:51:57.2676446Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/pt2e/_affine_quantization.py::_get_reduction_params:0 2025-01-24T01:51:57.2679412Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/pt2e/_affine_quantization.py::_register_custom_op:0, line 148 <- wrt source file 2025-01-24T01:51:57.2682305Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/pt2e/_affine_quantization.py::_register_custom_op:0 2025-01-24T01:51:57.2685218Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/pt2e/prepare.py::_get_edge_or_node_to_group_id:0, line 188 <- wrt source file 2025-01-24T01:51:57.2688044Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/pt2e/prepare.py::_get_edge_or_node_to_group_id:0 2025-01-24T01:51:57.2690910Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/pt2e/utils.py::_replace_literals_with_new_placeholders:0, line 429 <- wrt source file 2025-01-24T01:51:57.2693927Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/pt2e/utils.py::_replace_literals_with_new_placeholders:0 2025-01-24T01:51:57.2696527Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/anomaly_mode.py::detect_anomaly:0, line 27 <- wrt source file 2025-01-24T01:51:57.2698948Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/anomaly_mode.py::detect_anomaly:0 2025-01-24T01:51:57.2701204Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/forward_ad.py::make_dual:0, line 83 <- wrt source file 2025-01-24T01:51:57.2703412Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/forward_ad.py::make_dual:0 2025-01-24T01:51:57.2705619Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/forward_ad.py::unpack_dual:0, line 153 <- wrt source file 2025-01-24T01:51:57.2707951Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/forward_ad.py::unpack_dual:0 2025-01-24T01:51:57.2710172Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/forward_ad.py::dual_level:0, line 189 <- wrt source file 2025-01-24T01:51:57.2712436Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/forward_ad.py::dual_level:0 2025-01-24T01:51:57.2714815Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/function.py::FunctionCtx.save_for_backward:0, line 66 <- wrt source file 2025-01-24T01:51:57.2717421Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/function.py::FunctionCtx.save_for_backward:0 2025-01-24T01:51:57.2719980Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/function.py::FunctionCtx.save_for_forward:0, line 109 <- wrt source file 2025-01-24T01:51:57.2722574Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/function.py::FunctionCtx.save_for_forward:0 2025-01-24T01:51:57.2725045Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/function.py::FunctionCtx.mark_dirty:0, line 160 <- wrt source file 2025-01-24T01:51:57.2727537Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/function.py::FunctionCtx.mark_dirty:0 2025-01-24T01:51:57.2730114Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/function.py::FunctionCtx.mark_non_differentiable:0, line 207 <- wrt source file 2025-01-24T01:51:57.2732859Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/function.py::FunctionCtx.mark_non_differentiable:0 2025-01-24T01:51:57.2735530Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/function.py::FunctionCtx.set_materialize_grads:0, line 236 <- wrt source file 2025-01-24T01:51:57.2738226Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/function.py::FunctionCtx.set_materialize_grads:0 2025-01-24T01:51:57.2740675Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/function.py::Function:0, line 479 <- wrt source file 2025-01-24T01:51:57.2742915Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/function.py::Function:0 2025-01-24T01:51:57.2745053Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/functional.py::vjp:0, line 293 <- wrt source file 2025-01-24T01:51:57.2747266Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/functional.py::vjp:0 2025-01-24T01:51:57.2749390Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/functional.py::jvp:0, line 395 <- wrt source file 2025-01-24T01:51:57.2751593Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/functional.py::jvp:0 2025-01-24T01:51:57.2753769Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/functional.py::jacobian:0, line 630 <- wrt source file 2025-01-24T01:51:57.2756059Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/functional.py::jacobian:0 2025-01-24T01:51:57.2758232Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/functional.py::hessian:0, line 884 <- wrt source file 2025-01-24T01:51:57.2760463Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/functional.py::hessian:0 2025-01-24T01:51:57.2762622Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/functional.py::vhp:0, line 1000 <- wrt source file 2025-01-24T01:51:57.2764775Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/functional.py::vhp:0 2025-01-24T01:51:57.2766888Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/functional.py::hvp:0, line 1099 <- wrt source file 2025-01-24T01:51:57.2769072Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/functional.py::hvp:0 2025-01-24T01:51:57.2771156Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/grad_mode.py::no_grad:0, line 50 <- wrt source file 2025-01-24T01:51:57.2773298Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/grad_mode.py::no_grad:0 2025-01-24T01:51:57.2775736Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/grad_mode.py::enable_grad:0, line 108 <- wrt source file 2025-01-24T01:51:57.2778025Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/grad_mode.py::enable_grad:0 2025-01-24T01:51:57.2780400Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/grad_mode.py::set_grad_enabled:0, line 166 <- wrt source file 2025-01-24T01:51:57.2782743Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/grad_mode.py::set_grad_enabled:0 2025-01-24T01:51:57.2785037Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/grad_mode.py::inference_mode:0, line 232 <- wrt source file 2025-01-24T01:51:57.2787917Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/grad_mode.py::inference_mode:0 2025-01-24T01:51:57.2790103Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/graph.py::Node.name:0, line 53 <- wrt source file 2025-01-24T01:51:57.2792228Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/graph.py::Node.name:0 2025-01-24T01:51:57.2794425Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/graph.py::Node.register_hook:0, line 110 <- wrt source file 2025-01-24T01:51:57.2796917Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/graph.py::Node.register_hook:0 2025-01-24T01:51:57.2799285Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/graph.py::Node.register_prehook:0, line 147 <- wrt source file 2025-01-24T01:51:57.2801661Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/graph.py::Node.register_prehook:0 2025-01-24T01:51:57.2803950Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/graph.py::saved_tensors_hooks:0, line 271 <- wrt source file 2025-01-24T01:51:57.2806282Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/graph.py::saved_tensors_hooks:0 2025-01-24T01:51:57.2808534Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/graph.py::save_on_cpu:0, line 336 <- wrt source file 2025-01-24T01:51:57.2810754Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/graph.py::save_on_cpu:0 2025-01-24T01:51:57.2813056Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/graph.py::disable_saved_tensors_hooks:0, line 393 <- wrt source file 2025-01-24T01:51:57.2815552Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/graph.py::disable_saved_tensors_hooks:0 2025-01-24T01:51:57.2817972Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/graph.py::register_multi_grad_hook:0, line 470 <- wrt source file 2025-01-24T01:51:57.2820393Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/graph.py::register_multi_grad_hook:0 2025-01-24T01:51:57.2822831Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/graph.py::allow_mutation_on_saved_tensors:0, line 726 <- wrt source file 2025-01-24T01:51:57.2825408Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/graph.py::allow_mutation_on_saved_tensors:0 2025-01-24T01:51:57.2827814Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/profiler.py::profile:0, line 178 <- wrt source file 2025-01-24T01:51:57.2829996Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/profiler.py::profile:0 2025-01-24T01:51:57.2832233Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/profiler.py::record_function:0, line 715 <- wrt source file 2025-01-24T01:51:57.2834583Z * SKIPPED: 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2025-01-24T01:51:57.2849917Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/jiterator.py::_create_jit_fn:1, line 125 <- wrt source file 2025-01-24T01:51:57.2852184Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/jiterator.py::_create_jit_fn:1 2025-01-24T01:51:57.2854388Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/jiterator.py::_create_jit_fn:2, line 138 <- wrt source file 2025-01-24T01:51:57.2856598Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/jiterator.py::_create_jit_fn:2 2025-01-24T01:51:57.2858893Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/jiterator.py::_create_multi_output_jit_fn:0, line 171 <- wrt source file 2025-01-24T01:51:57.2861361Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/jiterator.py::_create_multi_output_jit_fn:0 2025-01-24T01:51:57.2863642Z * DOCTEST : 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line 457 <- wrt source file 2025-01-24T01:51:57.3015101Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/_shard/sharded_tensor/__init__.py::custom_sharded_op_impl:0 2025-01-24T01:51:57.3017916Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/_shard/sharded_tensor/_ops/_common.py::_sharded_op_common:0, line 18 <- wrt source file 2025-01-24T01:51:57.3020794Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/_shard/sharded_tensor/_ops/_common.py::_sharded_op_common:0 2025-01-24T01:51:57.3023477Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/_tools/memory_tracker.py::MemoryTracker:0, line 55 <- wrt source file 2025-01-24T01:51:57.3026206Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/_tools/memory_tracker.py::MemoryTracker:0 2025-01-24T01:51:57.3028673Z * DOCTEST : 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line 23 <- wrt source file 2025-01-24T01:51:57.3318058Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/cauchy.py::Cauchy:0 2025-01-24T01:51:57.3320200Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/chi2.py::Chi2:0, line 16 <- wrt source file 2025-01-24T01:51:57.3322347Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/chi2.py::Chi2:0 2025-01-24T01:51:57.3324698Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/constraints.py::is_dependent:0, line 164 <- wrt source file 2025-01-24T01:51:57.3327222Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/constraints.py::is_dependent:0 2025-01-24T01:51:57.3329732Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/constraints.py::_DependentProperty:0, line 185 <- wrt source file 2025-01-24T01:51:57.3332358Z * SKIPPED: 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/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/kumaraswamy.py::Kumaraswamy:0 2025-01-24T01:51:57.3394334Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/laplace.py::Laplace:0, line 20 <- wrt source file 2025-01-24T01:51:57.3396666Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/laplace.py::Laplace:0 2025-01-24T01:51:57.3399026Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/lkj_cholesky.py::LKJCholesky:0, line 41 <- wrt source file 2025-01-24T01:51:57.3401473Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/lkj_cholesky.py::LKJCholesky:0 2025-01-24T01:51:57.3403827Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/log_normal.py::LogNormal:0, line 21 <- wrt source file 2025-01-24T01:51:57.3406187Z * SUCCESS: 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2025-01-24T01:51:57.3422210Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/multinomial.py::Multinomial:0 2025-01-24T01:51:57.3424794Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/multivariate_normal.py::MultivariateNormal:0, line 102 <- wrt source file 2025-01-24T01:51:57.3427678Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/multivariate_normal.py::MultivariateNormal:0 2025-01-24T01:51:57.3430121Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/normal.py::Normal:0, line 22 <- wrt source file 2025-01-24T01:51:57.3432350Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/normal.py::Normal:0 2025-01-24T01:51:57.3434805Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/one_hot_categorical.py::OneHotCategorical:0, line 32 <- wrt source file 2025-01-24T01:51:57.3437523Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/one_hot_categorical.py::OneHotCategorical:0 2025-01-24T01:51:57.3439943Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/pareto.py::Pareto:0, line 20 <- wrt source file 2025-01-24T01:51:57.3442208Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/pareto.py::Pareto:0 2025-01-24T01:51:57.3444465Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/poisson.py::Poisson:0, line 24 <- wrt source file 2025-01-24T01:51:57.3446743Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/poisson.py::Poisson:0 2025-01-24T01:51:57.3448989Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/studentT.py::StudentT:0, line 21 <- wrt source file 2025-01-24T01:51:57.3451308Z * SUCCESS: 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2025-01-24T01:51:57.3525310Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/interpreter.py::Interpreter:0, line 48 <- wrt source file 2025-01-24T01:51:57.3527857Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/interpreter.py::Interpreter:0 2025-01-24T01:51:57.3530039Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/interpreter.py::Transformer:0, line 458 <- wrt source file 2025-01-24T01:51:57.3532239Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/interpreter.py::Transformer:0 2025-01-24T01:51:57.3534490Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/subgraph_rewriter.py::replace_pattern:0, line 125 <- wrt source file 2025-01-24T01:51:57.3536882Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/subgraph_rewriter.py::replace_pattern:0 2025-01-24T01:51:57.3539096Z * DOCTEST : 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/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::gumbel_softmax:0 2025-01-24T01:51:57.4566098Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::embedding:0, line 2487 <- wrt source file 2025-01-24T01:51:57.4572459Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::embedding:0 2025-01-24T01:51:57.4574721Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::embedding_bag:0, line 2627 <- wrt source file 2025-01-24T01:51:57.4582458Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::embedding_bag:0 2025-01-24T01:51:57.4584763Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::ctc_loss:0, line 3059 <- wrt source file 2025-01-24T01:51:57.4599704Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::ctc_loss:0 2025-01-24T01:51:57.4601779Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::nll_loss:0, line 3136 <- wrt source file 2025-01-24T01:51:57.4605851Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::nll_loss:0 2025-01-24T01:51:57.4607992Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::cross_entropy:0, line 3466 <- wrt source file 2025-01-24T01:51:57.4614762Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::cross_entropy:0 2025-01-24T01:51:57.4617011Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::binary_cross_entropy:0, line 3538 <- wrt source file 2025-01-24T01:51:57.4621402Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::binary_cross_entropy:0 2025-01-24T01:51:57.4623824Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::binary_cross_entropy_with_logits:0, line 3615 <- wrt source file 2025-01-24T01:51:57.4627994Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::binary_cross_entropy_with_logits:0 2025-01-24T01:51:57.4630251Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::pad:0, line 5178 <- wrt source file 2025-01-24T01:51:57.4638016Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::pad:0 2025-01-24T01:51:57.4640024Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/grad.py::conv1d_input:0, line 32 <- wrt source file 2025-01-24T01:51:57.4645815Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/grad.py::conv1d_input:0 2025-01-24T01:51:57.4647845Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/grad.py::conv1d_weight:0, line 79 <- wrt source file 2025-01-24T01:51:57.4650754Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/grad.py::conv1d_weight:0 2025-01-24T01:51:57.4652763Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/grad.py::conv2d_input:0, line 130 <- wrt source file 2025-01-24T01:51:57.4658809Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/grad.py::conv2d_input:0 2025-01-24T01:51:57.4660923Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/grad.py::conv2d_weight:0, line 177 <- wrt source file 2025-01-24T01:51:57.4663616Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/grad.py::conv2d_weight:0 2025-01-24T01:51:57.4665708Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/grad.py::conv3d_input:0, line 228 <- wrt source file 2025-01-24T01:51:57.4715397Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/grad.py::conv3d_input:0 2025-01-24T01:51:57.4718110Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/grad.py::conv3d_weight:0, line 275 <- wrt source file 2025-01-24T01:51:57.4761567Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/grad.py::conv3d_weight:0 2025-01-24T01:51:57.4764407Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::calculate_gain:0, line 102 <- wrt source file 2025-01-24T01:51:57.4766706Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::calculate_gain:0 2025-01-24T01:51:57.4768700Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::uniform_:0, line 159 <- wrt source file 2025-01-24T01:51:57.4770710Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::uniform_:0 2025-01-24T01:51:57.4772641Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::normal_:0, line 186 <- wrt source file 2025-01-24T01:51:57.4774616Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::normal_:0 2025-01-24T01:51:57.4776594Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::trunc_normal_:0, line 221 <- wrt source file 2025-01-24T01:51:57.4792317Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::trunc_normal_:0 2025-01-24T01:51:57.4794378Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::constant_:0, line 235 <- wrt source file 2025-01-24T01:51:57.4796414Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::constant_:0 2025-01-24T01:51:57.4798348Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::ones_:0, line 252 <- wrt source file 2025-01-24T01:51:57.4800281Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::ones_:0 2025-01-24T01:51:57.4802192Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::zeros_:0, line 265 <- wrt source file 2025-01-24T01:51:57.4804161Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::zeros_:0 2025-01-24T01:51:57.4806056Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::eye_:0, line 281 <- wrt source file 2025-01-24T01:51:57.4807979Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::eye_:0 2025-01-24T01:51:57.4809865Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::dirac_:0, line 303 <- wrt source file 2025-01-24T01:51:57.4811901Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::dirac_:0 2025-01-24T01:51:57.4813899Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::xavier_uniform_:0, line 389 <- wrt source file 2025-01-24T01:51:57.4816025Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::xavier_uniform_:0 2025-01-24T01:51:57.4818102Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::xavier_normal_:0, line 429 <- wrt source file 2025-01-24T01:51:57.4820356Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::xavier_normal_:0 2025-01-24T01:51:57.4822492Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::kaiming_uniform_:0, line 488 <- wrt source file 2025-01-24T01:51:57.4824625Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::kaiming_uniform_:0 2025-01-24T01:51:57.4826791Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::kaiming_normal_:0, line 553 <- wrt source file 2025-01-24T01:51:57.4828903Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::kaiming_normal_:0 2025-01-24T01:51:57.4831027Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::orthogonal_:0, line 592 <- wrt source file 2025-01-24T01:51:57.4833077Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::orthogonal_:0 2025-01-24T01:51:57.4835119Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::sparse_:0, line 645 <- wrt source file 2025-01-24T01:51:57.4837100Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::sparse_:0 2025-01-24T01:51:57.4839193Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/attention/__init__.py::sdpa_kernel:0, line 104 <- wrt source file 2025-01-24T01:51:57.4841496Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/attention/__init__.py::sdpa_kernel:0 2025-01-24T01:51:57.4843732Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/attention/bias.py::CausalBias:0, line 94 <- wrt source file 2025-01-24T01:51:57.4845969Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/attention/bias.py::CausalBias:0 2025-01-24T01:51:57.4848211Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Threshold:0, line 70 <- wrt source file 2025-01-24T01:51:57.4850526Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Threshold:0 2025-01-24T01:51:57.4852730Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::ReLU:0, line 112 <- wrt source file 2025-01-24T01:51:57.4854952Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::ReLU:0 2025-01-24T01:51:57.4857130Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::RReLU:0, line 171 <- wrt source file 2025-01-24T01:51:57.4859374Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::RReLU:0 2025-01-24T01:51:57.4861594Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Hardtanh:0, line 227 <- wrt source file 2025-01-24T01:51:57.4863884Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Hardtanh:0 2025-01-24T01:51:57.4866072Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::ReLU6:0, line 292 <- wrt source file 2025-01-24T01:51:57.4868377Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::ReLU6:0 2025-01-24T01:51:57.4870600Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Sigmoid:0, line 320 <- wrt source file 2025-01-24T01:51:57.4872880Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Sigmoid:0 2025-01-24T01:51:57.4875199Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Hardsigmoid:0, line 352 <- wrt source file 2025-01-24T01:51:57.4877594Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Hardsigmoid:0 2025-01-24T01:51:57.4880060Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Tanh:0, line 385 <- wrt source file 2025-01-24T01:51:57.4882286Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Tanh:0 2025-01-24T01:51:57.4884439Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::SiLU:0, line 418 <- wrt source file 2025-01-24T01:51:57.4886735Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::SiLU:0 2025-01-24T01:51:57.4888919Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Mish:0, line 457 <- wrt source file 2025-01-24T01:51:57.4891304Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Mish:0 2025-01-24T01:51:57.4893538Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Hardswish:0, line 502 <- wrt source file 2025-01-24T01:51:57.4895947Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Hardswish:0 2025-01-24T01:51:57.4898166Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::ELU:0, line 545 <- wrt source file 2025-01-24T01:51:57.4900377Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::ELU:0 2025-01-24T01:51:57.4902529Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::CELU:0, line 587 <- wrt source file 2025-01-24T01:51:57.4904737Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::CELU:0 2025-01-24T01:51:57.4906970Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::SELU:0, line 640 <- wrt source file 2025-01-24T01:51:57.4909179Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::SELU:0 2025-01-24T01:51:57.4911337Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::GLU:0, line 678 <- wrt source file 2025-01-24T01:51:57.4913524Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::GLU:0 2025-01-24T01:51:57.4915700Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::GELU:0, line 720 <- wrt source file 2025-01-24T01:51:57.4917924Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::GELU:0 2025-01-24T01:51:57.4920162Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Hardshrink:0, line 763 <- wrt source file 2025-01-24T01:51:57.4922492Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Hardshrink:0 2025-01-24T01:51:57.4924768Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::LeakyReLU:0, line 812 <- wrt source file 2025-01-24T01:51:57.4927423Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::LeakyReLU:0 2025-01-24T01:51:57.4930065Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::LogSigmoid:0, line 848 <- wrt source file 2025-01-24T01:51:57.4932864Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::LogSigmoid:0 2025-01-24T01:51:57.4935189Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Softplus:0, line 881 <- wrt source file 2025-01-24T01:51:57.4937484Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Softplus:0 2025-01-24T01:51:57.4939742Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Softshrink:0, line 924 <- wrt source file 2025-01-24T01:51:57.4942072Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Softshrink:0 2025-01-24T01:51:57.4944503Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::MultiheadAttention:0, line 1031 <- wrt source file 2025-01-24T01:51:57.4947127Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::MultiheadAttention:0 2025-01-24T01:51:57.4949476Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::PReLU:0, line 1494 <- wrt source file 2025-01-24T01:51:57.4951715Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::PReLU:0 2025-01-24T01:51:57.4953943Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Softsign:0, line 1536 <- wrt source file 2025-01-24T01:51:57.4956248Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Softsign:0 2025-01-24T01:51:57.4958868Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Tanhshrink:0, line 1559 <- wrt source file 2025-01-24T01:51:57.4961591Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Tanhshrink:0 2025-01-24T01:51:57.4964207Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Softmin:0, line 1594 <- wrt source file 2025-01-24T01:51:57.4966834Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Softmin:0 2025-01-24T01:51:57.4969411Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Softmax:0, line 1652 <- wrt source file 2025-01-24T01:51:57.4972035Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Softmax:0 2025-01-24T01:51:57.4974644Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Softmax2d:0, line 1693 <- wrt source file 2025-01-24T01:51:57.4977338Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Softmax2d:0 2025-01-24T01:51:57.4980071Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::LogSoftmax:0, line 1729 <- wrt source file 2025-01-24T01:51:57.4982824Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::LogSoftmax:0 2025-01-24T01:51:57.4985495Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/batchnorm.py::BatchNorm1d:0, line 330 <- wrt source file 2025-01-24T01:51:57.4987976Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/batchnorm.py::BatchNorm1d:0 2025-01-24T01:51:57.4990284Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/batchnorm.py::BatchNorm2d:0, line 441 <- wrt source file 2025-01-24T01:51:57.5242023Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/batchnorm.py::BatchNorm2d:0 2025-01-24T01:51:57.5244782Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/batchnorm.py::BatchNorm3d:0, line 552 <- wrt source file 2025-01-24T01:51:57.7833796Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/batchnorm.py::BatchNorm3d:0 2025-01-24T01:51:57.7990113Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/channelshuffle.py::ChannelShuffle:0, line 21 <- wrt source file 2025-01-24T01:51:57.8010998Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/channelshuffle.py::ChannelShuffle:0 2025-01-24T01:51:57.8013629Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::Sequential:0, line 76 <- wrt source file 2025-01-24T01:51:57.8015953Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::Sequential:0 2025-01-24T01:51:57.8018319Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::ModuleList:0, line 282 <- wrt source file 2025-01-24T01:51:57.8020631Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::ModuleList:0 2025-01-24T01:51:57.8022901Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::ModuleDict:0, line 464 <- wrt source file 2025-01-24T01:51:57.8025197Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::ModuleDict:0 2025-01-24T01:51:57.8027557Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::ParameterList:0, line 596 <- wrt source file 2025-01-24T01:51:57.8029936Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::ParameterList:0 2025-01-24T01:51:57.8032270Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::ParameterDict:0, line 748 <- wrt source file 2025-01-24T01:51:57.8034659Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::ParameterDict:0 2025-01-24T01:51:57.8037005Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/distance.py::PairwiseDistance:0, line 38 <- wrt source file 2025-01-24T01:51:57.8039399Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/distance.py::PairwiseDistance:0 2025-01-24T01:51:57.8041771Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/distance.py::CosineSimilarity:0, line 77 <- wrt source file 2025-01-24T01:51:57.8044173Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/distance.py::CosineSimilarity:0 2025-01-24T01:51:57.8046415Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/dropout.py::Dropout:0, line 60 <- wrt source file 2025-01-24T01:51:57.8048602Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/dropout.py::Dropout:0 2025-01-24T01:51:57.8050765Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/dropout.py::Dropout1d:0, line 105 <- wrt source file 2025-01-24T01:51:57.8052993Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/dropout.py::Dropout1d:0 2025-01-24T01:51:57.8055186Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/dropout.py::Dropout2d:0, line 157 <- wrt source file 2025-01-24T01:51:57.8071235Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/dropout.py::Dropout2d:0 2025-01-24T01:51:57.8074050Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/dropout.py::Dropout3d:0, line 202 <- wrt source file 2025-01-24T01:51:57.8148522Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/dropout.py::Dropout3d:0 2025-01-24T01:51:57.8151116Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/dropout.py::AlphaDropout:0, line 245 <- wrt source file 2025-01-24T01:51:57.8154502Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/dropout.py::AlphaDropout:0 2025-01-24T01:51:57.8157130Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/dropout.py::FeatureAlphaDropout:0, line 294 <- wrt source file 2025-01-24T01:51:57.8235440Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/dropout.py::FeatureAlphaDropout:0 2025-01-24T01:51:57.8238226Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/flatten.py::Flatten:0, line 30 <- wrt source file 2025-01-24T01:51:57.8244210Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/flatten.py::Flatten:0 2025-01-24T01:51:57.8246299Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/fold.py::Fold:0, line 111 <- wrt source file 2025-01-24T01:51:57.8251713Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/fold.py::Fold:0 2025-01-24T01:51:57.8254108Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/fold.py::Unfold:0, line 261 <- wrt source file 2025-01-24T01:51:57.8288118Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/fold.py::Unfold:0 2025-01-24T01:51:57.8291103Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm1d:0, line 187 <- wrt source file 2025-01-24T01:51:57.8303173Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm1d:0 2025-01-24T01:51:57.8305708Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm2d:0, line 303 <- wrt source file 2025-01-24T01:51:57.8495524Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm2d:0 2025-01-24T01:51:57.8498334Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm3d:0, line 419 <- wrt source file 2025-01-24T01:51:58.1108356Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm3d:0 2025-01-24T01:51:58.1265399Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/lazy.py::LazyModuleMixin:0, line 87 <- wrt source file 2025-01-24T01:51:58.1267988Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/lazy.py::LazyModuleMixin:0 2025-01-24T01:51:58.1270199Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/linear.py::Identity:0, line 34 <- wrt source file 2025-01-24T01:51:58.1274422Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/linear.py::Identity:0 2025-01-24T01:51:58.1276737Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/linear.py::Linear:0, line 80 <- wrt source file 2025-01-24T01:51:58.1290814Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/linear.py::Linear:0 2025-01-24T01:51:58.1293606Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/linear.py::Bilinear:0, line 179 <- wrt source file 2025-01-24T01:51:58.1313525Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/linear.py::Bilinear:0 2025-01-24T01:51:58.1316039Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::L1Loss:0, line 115 <- wrt source file 2025-01-24T01:51:58.1323076Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::L1Loss:0 2025-01-24T01:51:58.1325193Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::NLLLoss:0, line 211 <- wrt source file 2025-01-24T01:51:58.1349594Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::NLLLoss:0 2025-01-24T01:51:58.1352156Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::PoissonNLLLoss:0, line 321 <- wrt source file 2025-01-24T01:51:58.1358590Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::PoissonNLLLoss:0 2025-01-24T01:51:58.1361227Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::GaussianNLLLoss:0, line 406 <- wrt source file 2025-01-24T01:51:58.1374521Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::GaussianNLLLoss:0 2025-01-24T01:51:58.1376727Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::KLDivLoss:0, line 519 <- wrt source file 2025-01-24T01:51:58.1385221Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::KLDivLoss:0 2025-01-24T01:51:58.1387830Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::MSELoss:0, line 597 <- wrt source file 2025-01-24T01:51:58.1392783Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::MSELoss:0 2025-01-24T01:51:58.1394940Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::BCELoss:0, line 679 <- wrt source file 2025-01-24T01:51:58.1399715Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::BCELoss:0 2025-01-24T01:51:58.1401971Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::BCEWithLogitsLoss:0, line 750 <- wrt source file 2025-01-24T01:51:58.1412082Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::BCEWithLogitsLoss:0 2025-01-24T01:51:58.1414455Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::MultiLabelMarginLoss:0, line 943 <- wrt source file 2025-01-24T01:51:58.1420512Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::MultiLabelMarginLoss:0 2025-01-24T01:51:58.1422880Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::CrossEntropyLoss:0, line 1263 <- wrt source file 2025-01-24T01:51:58.1429528Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::CrossEntropyLoss:0 2025-01-24T01:51:58.1432264Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::CosineEmbeddingLoss:0, line 1403 <- wrt source file 2025-01-24T01:51:58.1439649Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::CosineEmbeddingLoss:0 2025-01-24T01:51:58.1442206Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::MarginRankingLoss:0, line 1468 <- wrt source file 2025-01-24T01:51:58.1448947Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::MarginRankingLoss:0 2025-01-24T01:51:58.1451287Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::MultiMarginLoss:0, line 1547 <- wrt source file 2025-01-24T01:51:58.1456736Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::MultiMarginLoss:0 2025-01-24T01:51:58.1459056Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::TripletMarginLoss:0, line 1647 <- wrt source file 2025-01-24T01:51:58.1468774Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::TripletMarginLoss:0 2025-01-24T01:51:58.1471121Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::CTCLoss:0, line 1888 <- wrt source file 2025-01-24T01:51:58.1499891Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::CTCLoss:0 2025-01-24T01:51:58.1502198Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.register_buffer:0, line 536 <- wrt source file 2025-01-24T01:51:58.1504664Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.register_buffer:0 2025-01-24T01:51:58.1507079Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.apply:0, line 1018 <- wrt source file 2025-01-24T01:51:58.1514851Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.apply:0 2025-01-24T01:51:58.1517154Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.to:0, line 1272 <- wrt source file 2025-01-24T01:51:58.1522290Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.to:0 2025-01-24T01:51:58.1524920Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.state_dict:0, line 2190 <- wrt source file 2025-01-24T01:51:58.1527287Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.state_dict:0 2025-01-24T01:51:58.1529632Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.parameters:0, line 2632 <- wrt source file 2025-01-24T01:51:58.1532017Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.parameters:0 2025-01-24T01:51:58.1534427Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.named_parameters:0, line 2660 <- wrt source file 2025-01-24T01:51:58.1536946Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.named_parameters:0 2025-01-24T01:51:58.1539306Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.buffers:0, line 2687 <- wrt source file 2025-01-24T01:51:58.1541627Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.buffers:0 2025-01-24T01:51:58.1543965Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.named_buffers:0, line 2714 <- wrt source file 2025-01-24T01:51:58.1546436Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.named_buffers:0 2025-01-24T01:51:58.1548844Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.named_children:0, line 2745 <- wrt source file 2025-01-24T01:51:58.1551518Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.named_children:0 2025-01-24T01:51:58.1553863Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.modules:0, line 2769 <- wrt source file 2025-01-24T01:51:58.1556164Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.modules:0 2025-01-24T01:51:58.1558502Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.named_modules:0, line 2807 <- wrt source file 2025-01-24T01:51:58.1561123Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.named_modules:0 2025-01-24T01:51:58.1563725Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/normalization.py::LocalResponseNorm:0, line 38 <- wrt source file 2025-01-24T01:51:58.1579922Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/normalization.py::LocalResponseNorm:0 2025-01-24T01:51:58.1582928Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/normalization.py::LayerNorm:0, line 151 <- wrt source file 2025-01-24T01:51:58.1589475Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/normalization.py::LayerNorm:0 2025-01-24T01:51:58.1592206Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/normalization.py::GroupNorm:0, line 262 <- wrt source file 2025-01-24T01:51:58.1598268Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/normalization.py::GroupNorm:0 2025-01-24T01:51:58.1600949Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/normalization.py::RMSNorm:0, line 355 <- wrt source file 2025-01-24T01:51:58.1603885Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/normalization.py::RMSNorm:0 2025-01-24T01:51:58.1606176Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::CircularPad1d:0, line 69 <- wrt source file 2025-01-24T01:51:58.1610441Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::CircularPad1d:0 2025-01-24T01:51:58.1612740Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::CircularPad2d:0, line 120 <- wrt source file 2025-01-24T01:51:58.1630567Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::CircularPad2d:0 2025-01-24T01:51:58.1632867Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::CircularPad3d:0, line 184 <- wrt source file 2025-01-24T01:51:58.8301146Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::CircularPad3d:0 2025-01-24T01:51:58.8583598Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ConstantPad1d:0, line 238 <- wrt source file 2025-01-24T01:51:58.8594382Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ConstantPad1d:0 2025-01-24T01:51:58.8596602Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ConstantPad2d:0, line 291 <- wrt source file 2025-01-24T01:51:58.8602532Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ConstantPad2d:0 2025-01-24T01:51:58.8604861Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ConstantPad3d:0, line 347 <- wrt source file 2025-01-24T01:51:58.8628933Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ConstantPad3d:0 2025-01-24T01:51:58.8631133Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ReflectionPad1d:0, line 391 <- wrt source file 2025-01-24T01:51:58.8637133Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ReflectionPad1d:0 2025-01-24T01:51:58.8639355Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ReflectionPad2d:0, line 435 <- wrt source file 2025-01-24T01:51:58.8644905Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ReflectionPad2d:0 2025-01-24T01:51:58.8647473Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ReflectionPad3d:0, line 492 <- wrt source file 2025-01-24T01:51:58.8650441Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ReflectionPad3d:0 2025-01-24T01:51:58.8652701Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ReplicationPad1d:0, line 550 <- wrt source file 2025-01-24T01:51:58.8658341Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ReplicationPad1d:0 2025-01-24T01:51:58.8660742Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ReplicationPad2d:0, line 593 <- wrt source file 2025-01-24T01:51:58.8664340Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ReplicationPad2d:0 2025-01-24T01:51:58.8666881Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ReplicationPad3d:0, line 650 <- wrt source file 2025-01-24T01:51:59.4023799Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ReplicationPad3d:0 2025-01-24T01:51:59.4302108Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ZeroPad1d:0, line 684 <- wrt source file 2025-01-24T01:51:59.4311033Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ZeroPad1d:0 2025-01-24T01:51:59.4313245Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ZeroPad2d:0, line 739 <- wrt source file 2025-01-24T01:51:59.4317410Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ZeroPad2d:0 2025-01-24T01:51:59.4319648Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ZeroPad3d:0, line 798 <- wrt source file 2025-01-24T01:51:59.4341774Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ZeroPad3d:0 2025-01-24T01:51:59.4344095Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pixelshuffle.py::PixelShuffle:0, line 40 <- wrt source file 2025-01-24T01:51:59.4349047Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pixelshuffle.py::PixelShuffle:0 2025-01-24T01:51:59.4351786Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pixelshuffle.py::PixelUnshuffle:0, line 93 <- wrt source file 2025-01-24T01:51:59.4356381Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pixelshuffle.py::PixelUnshuffle:0 2025-01-24T01:51:59.4358978Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::MaxPool1d:0, line 118 <- wrt source file 2025-01-24T01:51:59.4365076Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::MaxPool1d:0 2025-01-24T01:51:59.4367816Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::MaxPool2d:0, line 195 <- wrt source file 2025-01-24T01:51:59.4420024Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::MaxPool2d:0 2025-01-24T01:51:59.4422265Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::MaxPool3d:0, line 278 <- wrt source file 2025-01-24T01:51:59.6787795Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::MaxPool3d:0 2025-01-24T01:51:59.6844900Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::MaxUnpool1d:0, line 352 <- wrt source file 2025-01-24T01:51:59.6856994Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::MaxUnpool1d:0 2025-01-24T01:51:59.6859306Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::MaxUnpool3d:0, line 534 <- wrt source file 2025-01-24T01:51:59.7663925Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::MaxUnpool3d:0 2025-01-24T01:51:59.7666210Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AvgPool1d:0, line 622 <- wrt source file 2025-01-24T01:51:59.7683199Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AvgPool1d:0 2025-01-24T01:51:59.7685458Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AvgPool2d:0, line 714 <- wrt source file 2025-01-24T01:51:59.7724967Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AvgPool2d:0 2025-01-24T01:51:59.7727234Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AvgPool3d:0, line 827 <- wrt source file 2025-01-24T01:51:59.9461733Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AvgPool3d:0 2025-01-24T01:51:59.9520248Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::FractionalMaxPool2d:0, line 917 <- wrt source file 2025-01-24T01:51:59.9570906Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::FractionalMaxPool2d:0 2025-01-24T01:51:59.9573388Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::FractionalMaxPool3d:0, line 1003 <- wrt source file 2025-01-24T01:52:00.0347073Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::FractionalMaxPool3d:0 2025-01-24T01:52:00.0349242Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::LPPool1d:0, line 1117 <- wrt source file 2025-01-24T01:52:00.0357945Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::LPPool1d:0 2025-01-24T01:52:00.0360526Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::LPPool2d:0, line 1168 <- wrt source file 2025-01-24T01:52:00.0413718Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::LPPool2d:0 2025-01-24T01:52:00.0416760Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::LPPool3d:0, line 1227 <- wrt source file 2025-01-24T01:52:00.2726042Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::LPPool3d:0 2025-01-24T01:52:00.2784249Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool1d:0, line 1282 <- wrt source file 2025-01-24T01:52:00.2801132Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool1d:0 2025-01-24T01:52:00.2803590Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool2d:0, line 1316 <- wrt source file 2025-01-24T01:52:00.2810458Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool2d:0 2025-01-24T01:52:00.2813034Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool3d:0, line 1359 <- wrt source file 2025-01-24T01:52:00.2843185Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool3d:0 2025-01-24T01:52:00.2845855Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool1d:0, line 1406 <- wrt source file 2025-01-24T01:52:00.2848620Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool1d:0 2025-01-24T01:52:00.2851116Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool2d:0, line 1437 <- wrt source file 2025-01-24T01:52:00.2857901Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool2d:0 2025-01-24T01:52:00.2860367Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool3d:0, line 1476 <- wrt source file 2025-01-24T01:52:00.2881824Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool3d:0 2025-01-24T01:52:00.2884029Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/rnn.py::RNN:0, line 591 <- wrt source file 2025-01-24T01:52:00.2893648Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/rnn.py::RNN:0 2025-01-24T01:52:00.2895662Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/rnn.py::LSTM:0, line 948 <- wrt source file 2025-01-24T01:52:00.3256125Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/rnn.py::LSTM:0 2025-01-24T01:52:00.3258173Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/rnn.py::GRU:0, line 1285 <- wrt source file 2025-01-24T01:52:00.3273316Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/rnn.py::GRU:0 2025-01-24T01:52:00.3275455Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/rnn.py::RNNCell:0, line 1536 <- wrt source file 2025-01-24T01:52:00.3285258Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/rnn.py::RNNCell:0 2025-01-24T01:52:00.3287363Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/rnn.py::LSTMCell:0, line 1658 <- wrt source file 2025-01-24T01:52:00.3296021Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/rnn.py::LSTMCell:0 2025-01-24T01:52:00.3298129Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/rnn.py::GRUCell:0, line 1772 <- wrt source file 2025-01-24T01:52:00.3309551Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/rnn.py::GRUCell:0 2025-01-24T01:52:00.3311877Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/sparse.py::Embedding:0, line 69 <- wrt source file 2025-01-24T01:52:00.3324074Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/sparse.py::Embedding:0 2025-01-24T01:52:00.3326410Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/sparse.py::Embedding.from_pretrained:0, line 241 <- wrt source file 2025-01-24T01:52:00.3330953Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/sparse.py::Embedding.from_pretrained:0 2025-01-24T01:52:00.3333441Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/sparse.py::EmbeddingBag.from_pretrained:0, line 519 <- wrt source file 2025-01-24T01:52:00.3339466Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/sparse.py::EmbeddingBag.from_pretrained:0 2025-01-24T01:52:00.3342013Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py::Transformer:0, line 91 <- wrt source file 2025-01-24T01:52:00.9207949Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py::Transformer:0 2025-01-24T01:52:00.9280854Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py::Transformer.forward:0, line 259 <- wrt source file 2025-01-24T01:52:00.9283715Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py::Transformer.forward:0 2025-01-24T01:52:00.9286425Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py::TransformerEncoder:0, line 329 <- wrt source file 2025-01-24T01:52:00.9898634Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py::TransformerEncoder:0 2025-01-24T01:52:00.9903297Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py::TransformerDecoder:0, line 547 <- wrt source file 2025-01-24T01:52:01.1138131Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py::TransformerDecoder:0 2025-01-24T01:52:01.1147008Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py::TransformerEncoderLayer:0, line 673 <- wrt source file 2025-01-24T01:52:01.1364346Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py::TransformerEncoderLayer:0 2025-01-24T01:52:01.1367022Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py::TransformerDecoderLayer:0, line 982 <- wrt source file 2025-01-24T01:52:01.1727418Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py::TransformerDecoderLayer:0 2025-01-24T01:52:01.1729923Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/upsampling.py::Upsample:0, line 77 <- wrt source file 2025-01-24T01:52:01.1752421Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/upsampling.py::Upsample:0 2025-01-24T01:52:01.1754731Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/upsampling.py::UpsamplingNearest2d:0, line 223 <- wrt source file 2025-01-24T01:52:01.1765737Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/upsampling.py::UpsamplingNearest2d:0 2025-01-24T01:52:01.1768291Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/upsampling.py::UpsamplingBilinear2d:0, line 273 <- wrt source file 2025-01-24T01:52:01.1774725Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/upsampling.py::UpsamplingBilinear2d:0 2025-01-24T01:52:01.1777350Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/data_parallel.py::DataParallel:0, line 127 <- wrt source file 2025-01-24T01:52:01.1779982Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/data_parallel.py::DataParallel:0 2025-01-24T01:52:01.1782555Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel:0, line 625 <- wrt source file 2025-01-24T01:52:01.1785256Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel:0 2025-01-24T01:52:01.1788232Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.no_sync:0, line 1429 <- wrt source file 2025-01-24T01:52:01.1791197Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.no_sync:0 2025-01-24T01:52:01.1794131Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.register_comm_hook:0, line 1992 <- wrt source file 2025-01-24T01:52:01.1797195Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.register_comm_hook:0 2025-01-24T01:52:01.1800228Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.register_comm_hook:1, line 2002 <- wrt source file 2025-01-24T01:52:01.1803280Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.register_comm_hook:1 2025-01-24T01:52:01.1806392Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel._register_builtin_comm_hook:0, line 2037 <- wrt source file 2025-01-24T01:52:01.1809606Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel._register_builtin_comm_hook:0 2025-01-24T01:52:01.1812416Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/_per_sample_grad.py::call_for_per_sample_grads:0, line 35 <- wrt source file 2025-01-24T01:52:01.1815034Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/_per_sample_grad.py::call_for_per_sample_grads:0 2025-01-24T01:52:01.1817356Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/init.py::skip_init:0, line 33 <- wrt source file 2025-01-24T01:52:01.1819494Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/init.py::skip_init:0 2025-01-24T01:52:01.1821796Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/parametrizations.py::orthogonal:0, line 265 <- wrt source file 2025-01-24T01:52:01.1824244Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/parametrizations.py::orthogonal:0 2025-01-24T01:52:01.1826693Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/parametrizations.py::weight_norm:0, line 360 <- wrt source file 2025-01-24T01:52:01.1829151Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/parametrizations.py::weight_norm:0 2025-01-24T01:52:01.1831581Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/parametrizations.py::spectral_norm:0, line 591 <- wrt source file 2025-01-24T01:52:01.1834087Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/parametrizations.py::spectral_norm:0 2025-01-24T01:52:01.1836755Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/parametrize.py::register_parametrization:0, line 505 <- wrt source file 2025-01-24T01:52:01.1839381Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/parametrize.py::register_parametrization:0 2025-01-24T01:52:01.1841693Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py::identity:0, line 844 <- wrt source file 2025-01-24T01:52:01.1843835Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py::identity:0 2025-01-24T01:52:01.1846092Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py::random_unstructured:0, line 880 <- wrt source file 2025-01-24T01:52:01.1848446Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py::random_unstructured:0 2025-01-24T01:52:01.1850771Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py::l1_unstructured:0, line 923 <- wrt source file 2025-01-24T01:52:01.1853035Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py::l1_unstructured:0 2025-01-24T01:52:01.1855182Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py::remove:0, line 1190 <- wrt source file 2025-01-24T01:52:01.1857304Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py::remove:0 2025-01-24T01:52:01.1859383Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py::is_pruned:0, line 1218 <- wrt source file 2025-01-24T01:52:01.1861779Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py::is_pruned:0 2025-01-24T01:52:01.1863972Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/rnn.py::pad_packed_sequence:0, line 350 <- wrt source file 2025-01-24T01:52:01.1876982Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/rnn.py::pad_packed_sequence:0 2025-01-24T01:52:01.1879326Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/rnn.py::pad_sequence:0, line 428 <- wrt source file 2025-01-24T01:52:01.1883431Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/rnn.py::pad_sequence:0 2025-01-24T01:52:01.1885594Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/rnn.py::unpad_sequence:0, line 486 <- wrt source file 2025-01-24T01:52:01.1896403Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/rnn.py::unpad_sequence:0 2025-01-24T01:52:01.1898605Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/rnn.py::pack_sequence:0, line 542 <- wrt source file 2025-01-24T01:52:01.1904016Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/rnn.py::pack_sequence:0 2025-01-24T01:52:01.1906182Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/rnn.py::unpack_sequence:0, line 570 <- wrt source file 2025-01-24T01:52:01.1920477Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/rnn.py::unpack_sequence:0 2025-01-24T01:52:01.1922767Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/spectral_norm.py::spectral_norm:0, line 313 <- wrt source file 2025-01-24T01:52:01.1927661Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/spectral_norm.py::spectral_norm:0 2025-01-24T01:52:01.1930270Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/spectral_norm.py::remove_spectral_norm:0, line 345 <- wrt source file 2025-01-24T01:52:01.1934959Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/spectral_norm.py::remove_spectral_norm:0 2025-01-24T01:52:01.1937400Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/stateless.py::functional_call:0, line 196 <- wrt source file 2025-01-24T01:52:01.1940472Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/stateless.py::functional_call:0 2025-01-24T01:52:01.1942660Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/weight_norm.py::weight_norm:0, line 133 <- wrt source file 2025-01-24T01:52:01.1948586Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/weight_norm.py::weight_norm:0 2025-01-24T01:52:01.1950981Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/weight_norm.py::remove_weight_norm:0, line 155 <- wrt source file 2025-01-24T01:52:01.1957000Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/weight_norm.py::remove_weight_norm:0 2025-01-24T01:52:01.1959223Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/_expanded_weights/conv_utils.py::unfold3d:0, line 315 <- wrt source file 2025-01-24T01:52:01.1960703Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/_expanded_weights/conv_utils.py::unfold3d:0 2025-01-24T01:52:01.1962286Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/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 2025-01-24T01:52:01.2042855Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/_expanded_weights/expanded_weights_utils.py::sum_over_all_but_batch_and_last_n:0 2025-01-24T01:52:01.2045622Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::LambdaLR:0, line 306 <- wrt source file 2025-01-24T01:52:01.2047898Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::LambdaLR:0 2025-01-24T01:52:01.2050256Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::MultiplicativeLR:0, line 408 <- wrt source file 2025-01-24T01:52:01.2052742Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::MultiplicativeLR:0 2025-01-24T01:52:01.2055009Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::StepLR:0, line 508 <- wrt source file 2025-01-24T01:52:01.2057252Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::StepLR:0 2025-01-24T01:52:01.2059466Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::MultiStepLR:0, line 568 <- wrt source file 2025-01-24T01:52:01.2061755Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::MultiStepLR:0 2025-01-24T01:52:01.2063966Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::ConstantLR:0, line 633 <- wrt source file 2025-01-24T01:52:01.2066136Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::ConstantLR:0 2025-01-24T01:52:01.2068463Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::LinearLR:0, line 711 <- wrt source file 2025-01-24T01:52:01.2070656Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::LinearLR:0 2025-01-24T01:52:01.2072918Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::SequentialLR:0, line 844 <- wrt source file 2025-01-24T01:52:01.2075086Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::SequentialLR:0 2025-01-24T01:52:01.2077277Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::PolynomialLR:0, line 993 <- wrt source file 2025-01-24T01:52:01.2079649Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::PolynomialLR:0 2025-01-24T01:52:01.2082111Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::ChainedScheduler:0, line 1149 <- wrt source file 2025-01-24T01:52:01.2084486Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::ChainedScheduler:0 2025-01-24T01:52:01.2087028Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::ReduceLROnPlateau:0, line 1292 <- wrt source file 2025-01-24T01:52:01.2089487Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::ReduceLROnPlateau:0 2025-01-24T01:52:01.2091729Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::CyclicLR:0, line 1540 <- wrt source file 2025-01-24T01:52:01.2093888Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::CyclicLR:0 2025-01-24T01:52:01.2096343Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::CosineAnnealingWarmRestarts.step:0, line 1810 <- wrt source file 2025-01-24T01:52:01.2099201Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::CosineAnnealingWarmRestarts.step:0 2025-01-24T01:52:01.2101849Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::CosineAnnealingWarmRestarts.step:1, line 1826 <- wrt source file 2025-01-24T01:52:01.2104681Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::CosineAnnealingWarmRestarts.step:1 2025-01-24T01:52:01.2106991Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::OneCycleLR:0, line 1970 <- wrt source file 2025-01-24T01:52:01.2108295Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::OneCycleLR:0 2025-01-24T01:52:01.2109507Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/swa_utils.py::update_bn:0, line 331 <- wrt source file 2025-01-24T01:52:01.2110711Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/swa_utils.py::update_bn:0 2025-01-24T01:52:01.2111900Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/package/glob_group.py::GlobGroup:0, line 22 <- wrt source file 2025-01-24T01:52:01.2113136Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/package/glob_group.py::GlobGroup:0 2025-01-24T01:52:01.2114524Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/profiler/profiler.py::_KinetoProfile.toggle_collection_dynamic:0, line 281 <- wrt source file 2025-01-24T01:52:01.2116316Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/profiler/profiler.py::_KinetoProfile.toggle_collection_dynamic:0 2025-01-24T01:52:01.2117700Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/profiler/profiler.py::profile:0, line 598 <- wrt source file 2025-01-24T01:52:01.2119045Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/profiler/profiler.py::profile:0 2025-01-24T01:52:01.2120403Z * DOCTEST : 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* DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_internal/common_utils.py::random_hermitian_psd_matrix:0, line 4613 <- wrt source file 2025-01-24T01:52:01.2152906Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_internal/common_utils.py::random_hermitian_psd_matrix:0 2025-01-24T01:52:01.2155690Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_internal/common_utils.py::random_hermitian_pd_matrix:0, line 4643 <- wrt source file 2025-01-24T01:52:01.2158518Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_internal/common_utils.py::random_hermitian_pd_matrix:0 2025-01-24T01:52:01.2161189Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_internal/logging_utils.py::logs_to_string:0, line 192 <- wrt source file 2025-01-24T01:52:01.2163779Z * SKIPPED: 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/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::tree_is_leaf:0, line 243 <- wrt source file 2025-01-24T01:52:01.2180858Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::tree_is_leaf:0 2025-01-24T01:52:01.2182955Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::tree_flatten:0, line 286 <- wrt source file 2025-01-24T01:52:01.2185139Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::tree_flatten:0 2025-01-24T01:52:01.2187568Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::tree_unflatten:0, line 328 <- wrt source file 2025-01-24T01:52:01.2190001Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::tree_unflatten:0 2025-01-24T01:52:01.2192176Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::tree_iter:0, line 358 <- wrt source file 2025-01-24T01:52:01.2193945Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::tree_iter:0 2025-01-24T01:52:01.2195146Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::tree_leaves:0, line 393 <- wrt source file 2025-01-24T01:52:01.2196377Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::tree_leaves:0 2025-01-24T01:52:01.2197606Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::tree_structure:0, line 428 <- wrt source file 2025-01-24T01:52:01.2198875Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::tree_structure:0 2025-01-24T01:52:01.2200480Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::tree_map:0, line 465 <- wrt source file 2025-01-24T01:52:01.2201712Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::tree_map:0 2025-01-24T01:52:01.2202935Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::broadcast_prefix:0, line 859 <- wrt source file 2025-01-24T01:52:01.2204222Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::broadcast_prefix:0 2025-01-24T01:52:01.2205405Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_pytree.py::tree_map:0, line 957 <- wrt source file 2025-01-24T01:52:01.2206760Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_pytree.py::tree_map:0 2025-01-24T01:52:01.2208086Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/backend_registration.py::rename_privateuse1_backend:0, line 69 <- wrt source file 2025-01-24T01:52:01.2209588Z * SKIPPED: 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file 2025-01-24T01:52:01.2225245Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/checkpoint.py::checkpoint_sequential:0 2025-01-24T01:52:01.2227378Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/checkpoint.py::set_checkpoint_early_stop:0, line 749 <- wrt source file 2025-01-24T01:52:01.2229378Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/checkpoint.py::set_checkpoint_early_stop:0 2025-01-24T01:52:01.2231168Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/dlpack.py::from_dlpack:0, line 72 <- wrt source file 2025-01-24T01:52:01.2233012Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/dlpack.py::from_dlpack:0 2025-01-24T01:52:01.2234988Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_sympy/functions.py::MinMaxBase._collapse_arguments:0, line 712 <- wrt source file 2025-01-24T01:52:01.2647160Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_sympy/functions.py::MinMaxBase._collapse_arguments:0 2025-01-24T01:52:01.2649707Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/dataset.py::IterableDataset:0, line 88 <- wrt source file 2025-01-24T01:52:01.2653494Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/dataset.py::IterableDataset:0 2025-01-24T01:52:01.2656414Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/dataset.py::StackDataset:0, line 213 <- wrt source file 2025-01-24T01:52:01.2660632Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/dataset.py::StackDataset:0 2025-01-24T01:52:01.2664830Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/dataset.py::random_split:0, line 435 <- wrt source file 2025-01-24T01:52:01.2668988Z * SKIPPED: 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/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/sampler.py::BatchSampler:0 2025-01-24T01:52:01.2688677Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/_utils/collate.py::default_convert:0, line 39 <- wrt source file 2025-01-24T01:52:01.2691326Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/_utils/collate.py::default_convert:0 2025-01-24T01:52:01.2693910Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/_utils/collate.py::collate:0, line 137 <- wrt source file 2025-01-24T01:52:01.2696337Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/_utils/collate.py::collate:0 2025-01-24T01:52:01.2698871Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/_utils/collate.py::default_collate:0, line 364 <- wrt source file 2025-01-24T01:52:01.2701372Z * SKIPPED: 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38 <- wrt source file 2025-01-24T01:52:01.2735093Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/combining.py::ConcaterIterDataPipe:0 2025-01-24T01:52:01.2738110Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/combining.py::ForkerIterDataPipe:0, line 88 <- wrt source file 2025-01-24T01:52:01.2741012Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/combining.py::ForkerIterDataPipe:0 2025-01-24T01:52:01.2743782Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/combining.py::_ChildDataPipe:0, line 307 <- wrt source file 2025-01-24T01:52:01.2746598Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/combining.py::_ChildDataPipe:0 2025-01-24T01:52:01.2749492Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/combining.py::DemultiplexerIterDataPipe:0, line 393 <- wrt source file 2025-01-24T01:52:01.2752550Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/combining.py::DemultiplexerIterDataPipe:0 2025-01-24T01:52:01.2755606Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/combining.py::MultiplexerIterDataPipe:0, line 603 <- wrt source file 2025-01-24T01:52:01.2758886Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/combining.py::MultiplexerIterDataPipe:0 2025-01-24T01:52:01.2761761Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/combining.py::ZipperIterDataPipe:0, line 671 <- wrt source file 2025-01-24T01:52:01.2764661Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/combining.py::ZipperIterDataPipe:0 2025-01-24T01:52:01.2767614Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/filelister.py::FileListerIterDataPipe:0, line 31 <- wrt source file 2025-01-24T01:52:01.2770619Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/filelister.py::FileListerIterDataPipe:0 2025-01-24T01:52:01.2773608Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/fileopener.py::FileOpenerIterDataPipe:0, line 35 <- wrt source file 2025-01-24T01:52:01.2776619Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/fileopener.py::FileOpenerIterDataPipe:0 2025-01-24T01:52:01.2779596Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/grouping.py::BatcherIterDataPipe:0, line 53 <- wrt source file 2025-01-24T01:52:01.2782487Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/grouping.py::BatcherIterDataPipe:0 2025-01-24T01:52:01.2785341Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/grouping.py::UnBatcherIterDataPipe:0, line 113 <- wrt source file 2025-01-24T01:52:01.2788482Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/grouping.py::UnBatcherIterDataPipe:0 2025-01-24T01:52:01.2791380Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/grouping.py::GrouperIterDataPipe:0, line 180 <- wrt source file 2025-01-24T01:52:01.2794284Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/grouping.py::GrouperIterDataPipe:0 2025-01-24T01:52:01.2797109Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/selecting.py::FilterIterDataPipe:0, line 37 <- wrt source file 2025-01-24T01:52:01.2799985Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/selecting.py::FilterIterDataPipe:0 2025-01-24T01:52:01.2802950Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/streamreader.py::StreamReaderIterDataPipe:0, line 25 <- wrt source file 2025-01-24T01:52:01.2806041Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/streamreader.py::StreamReaderIterDataPipe:0 2025-01-24T01:52:01.2809023Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/utils.py::IterableWrapperIterDataPipe:0, line 26 <- wrt source file 2025-01-24T01:52:01.2811991Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/utils.py::IterableWrapperIterDataPipe:0 2025-01-24T01:52:01.2814840Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/map/callable.py::MapperMapDataPipe:0, line 35 <- wrt source file 2025-01-24T01:52:01.2818620Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/map/callable.py::MapperMapDataPipe:0 2025-01-24T01:52:01.2821547Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/map/combinatorics.py::ShufflerIterDataPipe:0, line 34 <- wrt source file 2025-01-24T01:52:01.2824554Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/map/combinatorics.py::ShufflerIterDataPipe:0 2025-01-24T01:52:01.2827541Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/map/combining.py::ConcaterMapDataPipe:0, line 29 <- wrt source file 2025-01-24T01:52:01.2830424Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/map/combining.py::ConcaterMapDataPipe:0 2025-01-24T01:52:01.2833268Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/map/combining.py::ZipperMapDataPipe:0, line 73 <- wrt source file 2025-01-24T01:52:01.2836168Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/map/combining.py::ZipperMapDataPipe:0 2025-01-24T01:52:01.2838938Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/map/grouping.py::BatcherMapDataPipe:0, line 29 <- wrt source file 2025-01-24T01:52:01.2841764Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/map/grouping.py::BatcherMapDataPipe:0 2025-01-24T01:52:01.2844587Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/map/utils.py::SequenceWrapperMapDataPipe:0, line 26 <- wrt source file 2025-01-24T01:52:01.2848853Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/map/utils.py::SequenceWrapperMapDataPipe:0 2025-01-24T01:52:01.2851641Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/utils/common.py::validate_input_col:0, line 37 <- wrt source file 2025-01-24T01:52:01.2854440Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/utils/common.py::validate_input_col:0 2025-01-24T01:52:01.2857149Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/utils/decoder.py::basichandlers:0, line 47 <- wrt source file 2025-01-24T01:52:01.2859867Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/utils/decoder.py::basichandlers:0 2025-01-24T01:52:01.2862473Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/hipify/hipify_python.py::find_closure_group:0, line 440 <- wrt source file 2025-01-24T01:52:01.2865062Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/hipify/hipify_python.py::find_closure_group:0 2025-01-24T01:52:01.2867753Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/hipify/hipify_python.py::replace_extern_shared:0, line 536 <- wrt source file 2025-01-24T01:52:01.2870430Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/hipify/hipify_python.py::replace_extern_shared:0 2025-01-24T01:52:01.2873060Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.__init__:0, line 216 <- wrt source file 2025-01-24T01:52:01.2875717Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.__init__:0 2025-01-24T01:52:01.2878380Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_hparams:0, line 314 <- wrt source file 2025-01-24T01:52:01.2914770Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_hparams:0 2025-01-24T01:52:01.2917544Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_scalar:0, line 362 <- wrt source file 2025-01-24T01:52:01.2920262Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_scalar:0 2025-01-24T01:52:01.2922917Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_scalars:0, line 394 <- wrt source file 2025-01-24T01:52:01.2925604Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_scalars:0 2025-01-24T01:52:01.2928305Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_tensor:0, line 441 <- wrt source file 2025-01-24T01:52:01.2931072Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_tensor:0 2025-01-24T01:52:01.2933729Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_histogram:0, line 480 <- wrt source file 2025-01-24T01:52:01.2936483Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_histogram:0 2025-01-24T01:52:01.2939202Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_histogram_raw:0, line 533 <- wrt source file 2025-01-24T01:52:01.2942008Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_histogram_raw:0 2025-01-24T01:52:01.2944699Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_image:0, line 599 <- wrt source file 2025-01-24T01:52:01.2947457Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_image:0 2025-01-24T01:52:01.2950096Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_images:0, line 648 <- wrt source file 2025-01-24T01:52:01.2952774Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_images:0 2025-01-24T01:52:01.2955377Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_text:0, line 811 <- wrt source file 2025-01-24T01:52:01.2958018Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_text:0 2025-01-24T01:52:01.2960700Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_embedding:0, line 878 <- wrt source file 2025-01-24T01:52:01.2963435Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_embedding:0 2025-01-24T01:52:01.2966124Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_pr_curve:0, line 989 <- wrt source file 2025-01-24T01:52:01.2968846Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_pr_curve:0 2025-01-24T01:52:01.2971764Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_custom_scalars_multilinechart:0, line 1063 <- wrt source file 2025-01-24T01:52:01.2975076Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_custom_scalars_multilinechart:0 2025-01-24T01:52:01.2978120Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_custom_scalars_marginchart:0, line 1084 <- wrt source file 2025-01-24T01:52:01.2981355Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_custom_scalars_marginchart:0 2025-01-24T01:52:01.2984279Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_custom_scalars:0, line 1108 <- wrt source file 2025-01-24T01:52:01.2987271Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_custom_scalars:0 2025-01-24T01:52:01.2990419Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_mesh:0, line 1154 <- wrt source file 2025-01-24T01:52:01.2993153Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_mesh:0 2025-01-24T01:52:01.2994548Z ============ 2025-01-24T01:52:01.2995033Z Finished doctests 2025-01-24T01:52:01.2995456Z 338 / 709 passed 2025-01-24T01:52:01.2995902Z  2025-01-24T01:52:01.2996426Z === Found 106 parse-time warnings === 2025-01-24T01:52:01.2997192Z --- Parse Warning: 1 / 106 --- 2025-01-24T01:52:01.2999180Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=Tensor.dim_order in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py line=1498. 2025-01-24T01:52:01.3001442Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:52:01.3002304Z 2025-01-24T01:52:01.3002713Z dim_order(ambiguity_check=False) -> tuple 2025-01-24T01:52:01.3003304Z 2025-01-24T01:52:01.3003935Z Returns the uniquely determined tuple of int describing the dim order or 2025-01-24T01:52:01.3004793Z physical layout of :attr:`self`. 2025-01-24T01:52:01.3005321Z 2025-01-24T01:52:01.3005851Z The dim order represents how dimensions are laid out in memory, 2025-01-24T01:52:01.3006725Z starting from the outermost to the innermost dimension. 2025-01-24T01:52:01.3007395Z 2025-01-24T01:52:01.3007935Z Note that the dim order may not always be uniquely determined. 2025-01-24T01:52:01.3009207Z If `ambiguity_check` is True, this function raises a RuntimeError when the dim order cannot be uniquely determined; 2025-01-24T01:52:01.3010878Z If `ambiguity_check` is a list of memory formats, this function raises a RuntimeError when tensor can not be interpreted 2025-01-24T01:52:01.3012282Z into exactly one of the given memory formats, or it cannot be uniquely determined. 2025-01-24T01:52:01.3013564Z If `ambiguity_check` is False, it will return one of legal dim order(s) without checking its uniqueness. 2025-01-24T01:52:01.3014587Z Otherwise, it will raise TypeError. 2025-01-24T01:52:01.3015128Z 2025-01-24T01:52:01.3015483Z Args: 2025-01-24T01:52:01.3016274Z ambiguity_check (bool or List[torch.memory_format]): The check method for ambiguity of dim order. 2025-01-24T01:52:01.3017236Z 2025-01-24T01:52:01.3017637Z >>> torch.empty((2, 3, 5, 7)).dim_order() 2025-01-24T01:52:01.3018207Z (0, 1, 2, 3) 2025-01-24T01:52:01.3018739Z >>> torch.empty((2, 3, 5, 7)).transpose(1, 2).dim_order() 2025-01-24T01:52:01.3019385Z (0, 2, 1, 3) 2025-01-24T01:52:01.3020000Z >>> torch.empty((2, 3, 5, 7), memory_format=torch.channels_last).dim_order() 2025-01-24T01:52:01.3020746Z (0, 2, 3, 1) 2025-01-24T01:52:01.3021211Z >>> torch.empty((1, 2, 3, 4)).dim_order() 2025-01-24T01:52:01.3021894Z (0, 1, 2, 3) 2025-01-24T01:52:01.3022360Z >>> try: 2025-01-24T01:52:01.3022896Z ... torch.empty((1, 2, 3, 4)).dim_order(ambiguity_check=True) 2025-01-24T01:52:01.3023623Z ... except RuntimeError as e: 2025-01-24T01:52:01.3024170Z ... print(e) 2025-01-24T01:52:01.3025021Z The tensor does not have unique dim order, or cannot map to exact one of the given memory formats. 2025-01-24T01:52:01.3026015Z >>> torch.empty((1, 2, 3, 4)).dim_order( 2025-01-24T01:52:01.3026894Z ... ambiguity_check=[torch.contiguous_format, torch.channels_last] 2025-01-24T01:52:01.3027726Z ... ) # It can be mapped to contiguous format 2025-01-24T01:52:01.3028331Z (0, 1, 2, 3) 2025-01-24T01:52:01.3028747Z >>> try: 2025-01-24T01:52:01.3029397Z ... torch.empty((1, 2, 3, 4)).dim_order(ambiguity_check="ILLEGAL") 2025-01-24T01:52:01.3030127Z ... except TypeError as e: 2025-01-24T01:52:01.3030706Z ... print(e) 2025-01-24T01:52:01.3031430Z The ambiguity_check argument must be a bool or a list of memory formats. 2025-01-24T01:52:01.3032249Z .. warning:: 2025-01-24T01:52:01.3032846Z The dim_order tensor API is experimental and subject to change. 2025-01-24T01:52:01.3033549Z 2025-01-24T01:52:01.3034216Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:52:01.3035040Z 2025-01-24T01:52:01.3035418Z warnings.warn(msg) 2025-01-24T01:52:01.3035870Z 2025-01-24T01:52:01.3036429Z --- Parse Warning: 2 / 106 --- 2025-01-24T01:52:01.3038403Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=meshgrid in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py line=432. 2025-01-24T01:52:01.3040631Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:52:01.3041781Z Creates grids of coordinates specified by the 1D inputs in `attr`:tensors. 2025-01-24T01:52:01.3042600Z 2025-01-24T01:52:01.3043108Z This is helpful when you want to visualize data over some 2025-01-24T01:52:01.3043911Z range of inputs. See below for a plotting example. 2025-01-24T01:52:01.3044530Z 2025-01-24T01:52:01.3045018Z Given :math:`N` 1D tensors :math:`T_0 \ldots T_{N-1}` as 2025-01-24T01:52:01.3045862Z inputs with corresponding sizes :math:`S_0 \ldots S_{N-1}`, 2025-01-24T01:52:01.3046764Z this creates :math:`N` N-dimensional tensors :math:`G_0 \ldots 2025-01-24T01:52:01.3047596Z G_{N-1}`, each with shape :math:`(S_0, ..., S_{N-1})` where 2025-01-24T01:52:01.3048399Z the output :math:`G_i` is constructed by expanding :math:`T_i` 2025-01-24T01:52:01.3049147Z to the result shape. 2025-01-24T01:52:01.3049641Z 2025-01-24T01:52:01.3050003Z .. note:: 2025-01-24T01:52:01.3050554Z 0D inputs are treated equivalently to 1D inputs of a 2025-01-24T01:52:01.3051222Z single element. 2025-01-24T01:52:01.3051700Z 2025-01-24T01:52:01.3052066Z .. warning:: 2025-01-24T01:52:01.3052679Z `torch.meshgrid(*tensors)` currently has the same behavior 2025-01-24T01:52:01.3053514Z as calling `numpy.meshgrid(*arrays, indexing='ij')`. 2025-01-24T01:52:01.3054159Z 2025-01-24T01:52:01.3054614Z In the future `torch.meshgrid` will transition to 2025-01-24T01:52:01.3055290Z `indexing='xy'` as the default. 2025-01-24T01:52:01.3055855Z 2025-01-24T01:52:01.3056398Z https://github.com/pytorch/pytorch/issues/50276 tracks 2025-01-24T01:52:01.3057257Z this issue with the goal of migrating to NumPy's behavior. 2025-01-24T01:52:01.3057930Z 2025-01-24T01:52:01.3058300Z .. seealso:: 2025-01-24T01:52:01.3058741Z 2025-01-24T01:52:01.3059242Z :func:`torch.cartesian_prod` has the same effect but it 2025-01-24T01:52:01.3060092Z collects the data in a tensor of vectors. 2025-01-24T01:52:01.3060671Z 2025-01-24T01:52:01.3061025Z Args: 2025-01-24T01:52:01.3061741Z tensors (list of Tensor): list of scalars or 1 dimensional tensors. Scalars will be 2025-01-24T01:52:01.3062733Z treated as tensors of size :math:`(1,)` automatically 2025-01-24T01:52:01.3063391Z 2025-01-24T01:52:01.3063887Z indexing: (str, optional): the indexing mode, either "xy" 2025-01-24T01:52:01.3064713Z or "ij", defaults to "ij". See warning for future changes. 2025-01-24T01:52:01.3065382Z 2025-01-24T01:52:01.3065850Z If "xy" is selected, the first dimension corresponds 2025-01-24T01:52:01.3066758Z to the cardinality of the second input and the second 2025-01-24T01:52:01.3067554Z dimension corresponds to the cardinality of the first 2025-01-24T01:52:01.3068290Z input. 2025-01-24T01:52:01.3068737Z 2025-01-24T01:52:01.3069205Z If "ij" is selected, the dimensions are in the same 2025-01-24T01:52:01.3069922Z order as the cardinality of the inputs. 2025-01-24T01:52:01.3070497Z 2025-01-24T01:52:01.3070864Z Returns: 2025-01-24T01:52:01.3071412Z seq (sequence of Tensors): If the input has :math:`N` 2025-01-24T01:52:01.3072198Z tensors of size :math:`S_0 \ldots S_{N-1}``, then the 2025-01-24T01:52:01.3073009Z output will also have :math:`N` tensors, where each tensor 2025-01-24T01:52:01.3073756Z is of shape :math:`(S_0, ..., S_{N-1})`. 2025-01-24T01:52:01.3074326Z 2025-01-24T01:52:01.3074695Z Example:: 2025-01-24T01:52:01.3075117Z 2025-01-24T01:52:01.3075511Z >>> x = torch.tensor([1, 2, 3]) 2025-01-24T01:52:01.3076087Z >>> y = torch.tensor([4, 5, 6]) 2025-01-24T01:52:01.3076639Z 2025-01-24T01:52:01.3077177Z Observe the element-wise pairings across the grid, (1, 4), 2025-01-24T01:52:01.3077962Z (1, 5), ..., (3, 6). This is the same thing as the 2025-01-24T01:52:01.3078583Z cartesian product. 2025-01-24T01:52:01.3079394Z >>> grid_x, grid_y = torch.meshgrid(x, y, indexing='ij') 2025-01-24T01:52:01.3080067Z >>> grid_x 2025-01-24T01:52:01.3080527Z tensor([[1, 1, 1], 2025-01-24T01:52:01.3081027Z [2, 2, 2], 2025-01-24T01:52:01.3081524Z [3, 3, 3]]) 2025-01-24T01:52:01.3082013Z >>> grid_y 2025-01-24T01:52:01.3082478Z tensor([[4, 5, 6], 2025-01-24T01:52:01.3082981Z [4, 5, 6], 2025-01-24T01:52:01.3083483Z [4, 5, 6]]) 2025-01-24T01:52:01.3083973Z 2025-01-24T01:52:01.3084457Z This correspondence can be seen when these grids are 2025-01-24T01:52:01.3085165Z stacked properly. 2025-01-24T01:52:01.3085868Z >>> torch.equal(torch.cat(tuple(torch.dstack([grid_x, grid_y]))), 2025-01-24T01:52:01.3086668Z ... torch.cartesian_prod(x, y)) 2025-01-24T01:52:01.3087259Z True 2025-01-24T01:52:01.3087650Z 2025-01-24T01:52:01.3088159Z `torch.meshgrid` is commonly used to produce a grid for 2025-01-24T01:52:01.3088851Z plotting. 2025-01-24T01:52:01.3089372Z >>> # xdoctest: +REQUIRES(module:matplotlib) 2025-01-24T01:52:01.3090038Z >>> # xdoctest: +REQUIRES(env:DOCTEST_SHOW) 2025-01-24T01:52:01.3090698Z >>> import matplotlib.pyplot as plt 2025-01-24T01:52:01.3091336Z >>> xs = torch.linspace(-5, 5, steps=100) 2025-01-24T01:52:01.3091978Z >>> ys = torch.linspace(-5, 5, steps=100) 2025-01-24T01:52:01.3092645Z >>> x, y = torch.meshgrid(xs, ys, indexing='xy') 2025-01-24T01:52:01.3093488Z >>> z = torch.sin(torch.sqrt(x * x + y * y)) 2025-01-24T01:52:01.3094135Z >>> ax = plt.axes(projection='3d') 2025-01-24T01:52:01.3094798Z >>> ax.plot_surface(x.numpy(), y.numpy(), z.numpy()) 2025-01-24T01:52:01.3095453Z >>> plt.show() 2025-01-24T01:52:01.3095932Z 2025-01-24T01:52:01.3096351Z .. image:: ../_static/img/meshgrid.png 2025-01-24T01:52:01.3096936Z :width: 512 2025-01-24T01:52:01.3097378Z 2025-01-24T01:52:01.3097742Z 2025-01-24T01:52:01.3098415Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:52:01.3099234Z 2025-01-24T01:52:01.3099608Z warnings.warn(msg) 2025-01-24T01:52:01.3100033Z 2025-01-24T01:52:01.3100672Z --- Parse Warning: 3 / 106 --- 2025-01-24T01:52:01.3102660Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=_unique_impl in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py line=821. 2025-01-24T01:52:01.3104966Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:52:01.3106432Z unique(input, sorted=True, return_inverse=False, return_counts=False, dim=None) -> tuple[Tensor, Tensor, Tensor] 2025-01-24T01:52:01.3107500Z 2025-01-24T01:52:01.3107949Z Returns the unique elements of the input tensor. 2025-01-24T01:52:01.3108570Z 2025-01-24T01:52:01.3109300Z .. note:: This function is different from :func:`torch.unique_consecutive` in the sense that 2025-01-24T01:52:01.3110413Z this function also eliminates non-consecutive duplicate values. 2025-01-24T01:52:01.3111149Z 2025-01-24T01:52:01.3111736Z .. note:: Currently in the CUDA implementation and the CPU implementation, 2025-01-24T01:52:01.3112884Z `torch.unique` always sort the tensor at the beginning regardless of the `sort` argument. 2025-01-24T01:52:01.3114151Z Sorting could be slow, so if your input tensor is already sorted, it is recommended to use 2025-01-24T01:52:01.3115199Z :func:`torch.unique_consecutive` which avoids the sorting. 2025-01-24T01:52:01.3115872Z 2025-01-24T01:52:01.3116213Z Args: 2025-01-24T01:52:01.3116627Z input (Tensor): the input tensor 2025-01-24T01:52:01.3117397Z sorted (bool): Whether to sort the unique elements in ascending order 2025-01-24T01:52:01.3118189Z before returning as output. 2025-01-24T01:52:01.3118952Z return_inverse (bool): Whether to also return the indices for where 2025-01-24T01:52:01.3119920Z elements in the original input ended up in the returned unique list. 2025-01-24T01:52:01.3120922Z return_counts (bool): Whether to also return the counts for each unique 2025-01-24T01:52:01.3121693Z element. 2025-01-24T01:52:01.3122343Z dim (int, optional): the dimension to operate upon. If ``None``, the 2025-01-24T01:52:01.3123274Z unique of the flattened input is returned. Otherwise, each of the 2025-01-24T01:52:01.3124202Z tensors indexed by the given dimension is treated as one of the 2025-01-24T01:52:01.3125150Z elements to apply the unique operation upon. See examples for more 2025-01-24T01:52:01.3125918Z details. Default: ``None`` 2025-01-24T01:52:01.3126442Z 2025-01-24T01:52:01.3126793Z Returns: 2025-01-24T01:52:01.3127532Z (Tensor, Tensor (optional), Tensor (optional)): A tensor or a tuple of tensors containing 2025-01-24T01:52:01.3128407Z 2025-01-24T01:52:01.3128922Z - **output** (*Tensor*): the output list of unique scalar elements. 2025-01-24T01:52:01.3129725Z - **inverse_indices** (*Tensor*): (optional) if 2025-01-24T01:52:01.3130492Z :attr:`return_inverse` is True, there will be an additional 2025-01-24T01:52:01.3131480Z returned tensor (same shape as input) representing the indices 2025-01-24T01:52:01.3132396Z for where elements in the original input map to in the output; 2025-01-24T01:52:01.3133282Z otherwise, this function will only return a single tensor. 2025-01-24T01:52:01.3134049Z - **counts** (*Tensor*): (optional) if 2025-01-24T01:52:01.3134770Z :attr:`return_counts` is True, there will be an additional 2025-01-24T01:52:01.3135628Z returned tensor (same shape as output or output.size(dim), 2025-01-24T01:52:01.3136510Z if dim was specified) representing the number of occurrences 2025-01-24T01:52:01.3137249Z for each unique value or tensor. 2025-01-24T01:52:01.3137828Z 2025-01-24T01:52:01.3138177Z Example:: 2025-01-24T01:52:01.3138565Z 2025-01-24T01:52:01.3139145Z >>> output = torch.unique(torch.tensor([1, 3, 2, 3], dtype=torch.long)) 2025-01-24T01:52:01.3139947Z >>> output 2025-01-24T01:52:01.3140361Z tensor([1, 2, 3]) 2025-01-24T01:52:01.3140813Z 2025-01-24T01:52:01.3141227Z >>> output, inverse_indices = torch.unique( 2025-01-24T01:52:01.3142067Z ... torch.tensor([1, 3, 2, 3], dtype=torch.long), sorted=True, return_inverse=True) 2025-01-24T01:52:01.3142873Z >>> output 2025-01-24T01:52:01.3143293Z tensor([1, 2, 3]) 2025-01-24T01:52:01.3143768Z >>> inverse_indices 2025-01-24T01:52:01.3144250Z tensor([0, 2, 1, 2]) 2025-01-24T01:52:01.3144729Z 2025-01-24T01:52:01.3145145Z >>> output, inverse_indices = torch.unique( 2025-01-24T01:52:01.3145978Z ... torch.tensor([[1, 3], [2, 3]], dtype=torch.long), sorted=True, return_inverse=True) 2025-01-24T01:52:01.3146860Z >>> output 2025-01-24T01:52:01.3147284Z tensor([1, 2, 3]) 2025-01-24T01:52:01.3147760Z >>> inverse_indices 2025-01-24T01:52:01.3148245Z tensor([[0, 2], 2025-01-24T01:52:01.3148685Z [1, 2]]) 2025-01-24T01:52:01.3149130Z 2025-01-24T01:52:01.3149492Z >>> a = torch.tensor([ 2025-01-24T01:52:01.3149979Z ... [ 2025-01-24T01:52:01.3150402Z ... [1, 1, 0, 0], 2025-01-24T01:52:01.3150898Z ... [1, 1, 0, 0], 2025-01-24T01:52:01.3151406Z ... [0, 0, 1, 1], 2025-01-24T01:52:01.3151891Z ... ], 2025-01-24T01:52:01.3152291Z ... [ 2025-01-24T01:52:01.3152712Z ... [0, 0, 1, 1], 2025-01-24T01:52:01.3153204Z ... [0, 0, 1, 1], 2025-01-24T01:52:01.3153720Z ... [1, 1, 1, 1], 2025-01-24T01:52:01.3154204Z ... ], 2025-01-24T01:52:01.3154614Z ... [ 2025-01-24T01:52:01.3155013Z ... [1, 1, 0, 0], 2025-01-24T01:52:01.3155514Z ... [1, 1, 0, 0], 2025-01-24T01:52:01.3156017Z ... [0, 0, 1, 1], 2025-01-24T01:52:01.3156523Z ... ], 2025-01-24T01:52:01.3156923Z ... ]) 2025-01-24T01:52:01.3157298Z 2025-01-24T01:52:01.3157872Z >>> # If we call `torch.unique(a, dim=0)`, each of the tensors `a[idx, :, :]` 2025-01-24T01:52:01.3158828Z >>> # will be compared. We can see that `a[0, :, :]` and `a[2, :, :]` match 2025-01-24T01:52:01.3159643Z >>> # each other, so one of them will be removed. 2025-01-24T01:52:01.3160250Z >>> (a[0, :, :] == a[2, :, :]).all() 2025-01-24T01:52:01.3160792Z tensor(True) 2025-01-24T01:52:01.3161267Z >>> a_unique_dim0 = torch.unique(a, dim=0) 2025-01-24T01:52:01.3161851Z >>> a_unique_dim0 2025-01-24T01:52:01.3162337Z tensor([[[0, 0, 1, 1], 2025-01-24T01:52:01.3162836Z [0, 0, 1, 1], 2025-01-24T01:52:01.3163333Z [1, 1, 1, 1]], 2025-01-24T01:52:01.3163888Z [[1, 1, 0, 0], 2025-01-24T01:52:01.3164434Z [1, 1, 0, 0], 2025-01-24T01:52:01.3164922Z [0, 0, 1, 1]]]) 2025-01-24T01:52:01.3165408Z 2025-01-24T01:52:01.3165965Z >>> # Notice which sub-tensors from `a` match with the sub-tensors from 2025-01-24T01:52:01.3166719Z >>> # `a_unique_dim0`: 2025-01-24T01:52:01.3167260Z >>> (a_unique_dim0[0, :, :] == a[1, :, :]).all() 2025-01-24T01:52:01.3167840Z tensor(True) 2025-01-24T01:52:01.3168337Z >>> (a_unique_dim0[1, :, :] == a[0, :, :]).all() 2025-01-24T01:52:01.3168915Z tensor(True) 2025-01-24T01:52:01.3169333Z 2025-01-24T01:52:01.3169893Z >>> # For `torch.unique(a, dim=1)`, each of the tensors `a[:, idx, :]` are 2025-01-24T01:52:01.3170830Z >>> # compared. `a[:, 0, :]` and `a[:, 1, :]` match each other, so one of 2025-01-24T01:52:01.3171550Z >>> # them will be removed. 2025-01-24T01:52:01.3172139Z >>> (a[:, 0, :] == a[:, 1, :]).all() 2025-01-24T01:52:01.3172674Z tensor(True) 2025-01-24T01:52:01.3173136Z >>> torch.unique(a, dim=1) 2025-01-24T01:52:01.3173661Z tensor([[[0, 0, 1, 1], 2025-01-24T01:52:01.3174155Z [1, 1, 0, 0]], 2025-01-24T01:52:01.3174638Z [[1, 1, 1, 1], 2025-01-24T01:52:01.3175101Z [0, 0, 1, 1]], 2025-01-24T01:52:01.3175594Z [[0, 0, 1, 1], 2025-01-24T01:52:01.3176077Z [1, 1, 0, 0]]]) 2025-01-24T01:52:01.3176548Z 2025-01-24T01:52:01.3177101Z >>> # For `torch.unique(a, dim=2)`, the tensors `a[:, :, idx]` are compared. 2025-01-24T01:52:01.3177947Z >>> # `a[:, :, 0]` and `a[:, :, 1]` match each other. Also, `a[:, :, 2]` and 2025-01-24T01:52:01.3178753Z >>> # `a[:, :, 3]` match each other as well. So in this case, two of the 2025-01-24T01:52:01.3179817Z >>> # sub-tensors will be removed. 2025-01-24T01:52:01.3180416Z >>> (a[:, :, 0] == a[:, :, 1]).all() 2025-01-24T01:52:01.3180968Z tensor(True) 2025-01-24T01:52:01.3181419Z >>> (a[:, :, 2] == a[:, :, 3]).all() 2025-01-24T01:52:01.3181970Z tensor(True) 2025-01-24T01:52:01.3182446Z >>> torch.unique(a, dim=2) 2025-01-24T01:52:01.3182987Z tensor([[[0, 1], 2025-01-24T01:52:01.3183464Z [0, 1], 2025-01-24T01:52:01.3183910Z [1, 0]], 2025-01-24T01:52:01.3184373Z [[1, 0], 2025-01-24T01:52:01.3184838Z [1, 0], 2025-01-24T01:52:01.3185306Z [1, 1]], 2025-01-24T01:52:01.3185780Z [[0, 1], 2025-01-24T01:52:01.3186273Z [0, 1], 2025-01-24T01:52:01.3186743Z [1, 0]]]) 2025-01-24T01:52:01.3187206Z 2025-01-24T01:52:01.3187879Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:52:01.3188725Z 2025-01-24T01:52:01.3189093Z warnings.warn(msg) 2025-01-24T01:52:01.3189550Z 2025-01-24T01:52:01.3190135Z --- Parse Warning: 4 / 106 --- 2025-01-24T01:52:01.3192002Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=load in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/hub.py line=560. 2025-01-24T01:52:01.3194130Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:52:01.3194965Z 2025-01-24T01:52:01.3195454Z Load a model from a github repo or a local directory. 2025-01-24T01:52:01.3196091Z 2025-01-24T01:52:01.3196702Z Note: Loading a model is the typical use case, but this can also be used to 2025-01-24T01:52:01.3197722Z for loading other objects such as tokenizers, loss functions, etc. 2025-01-24T01:52:01.3198460Z 2025-01-24T01:52:01.3198952Z If ``source`` is 'github', ``repo_or_dir`` is expected to be 2025-01-24T01:52:01.3199928Z of the form ``repo_owner/repo_name[:ref]`` with an optional 2025-01-24T01:52:01.3200623Z ref (a tag or a branch). 2025-01-24T01:52:01.3201102Z 2025-01-24T01:52:01.3201612Z If ``source`` is 'local', ``repo_or_dir`` is expected to be a 2025-01-24T01:52:01.3202308Z path to a local directory. 2025-01-24T01:52:01.3202813Z 2025-01-24T01:52:01.3203180Z Args: 2025-01-24T01:52:01.3203631Z repo_or_dir (str): If ``source`` is 'github', 2025-01-24T01:52:01.3204591Z this should correspond to a github repo with format ``repo_owner/repo_name[:ref]`` with 2025-01-24T01:52:01.3205871Z an optional ref (tag or branch), for example 'pytorch/vision:0.10'. If ``ref`` is not specified, 2025-01-24T01:52:01.3207169Z the default branch is assumed to be ``main`` if it exists, and otherwise ``master``. 2025-01-24T01:52:01.3208229Z If ``source`` is 'local' then it should be a path to a local directory. 2025-01-24T01:52:01.3209220Z model (str): the name of a callable (entrypoint) defined in the 2025-01-24T01:52:01.3209970Z repo/dir's ``hubconf.py``. 2025-01-24T01:52:01.3210714Z *args (optional): the corresponding args for callable ``model``. 2025-01-24T01:52:01.3211595Z source (str, optional): 'github' or 'local'. Specifies how 2025-01-24T01:52:01.3212442Z ``repo_or_dir`` is to be interpreted. Default is 'github'. 2025-01-24T01:52:01.3213405Z trust_repo (bool, str or None): ``"check"``, ``True``, ``False`` or ``None``. 2025-01-24T01:52:01.3214429Z This parameter was introduced in v1.12 and helps ensuring that users 2025-01-24T01:52:01.3215279Z only run code from repos that they trust. 2025-01-24T01:52:01.3215878Z 2025-01-24T01:52:01.3216408Z - If ``False``, a prompt will ask the user whether the repo should 2025-01-24T01:52:01.3217137Z be trusted. 2025-01-24T01:52:01.3217771Z - If ``True``, the repo will be added to the trusted list and loaded 2025-01-24T01:52:01.3218574Z without requiring explicit confirmation. 2025-01-24T01:52:01.3219326Z - If ``"check"``, the repo will be checked against the list of 2025-01-24T01:52:01.3220209Z trusted repos in the cache. If it is not present in that list, the 2025-01-24T01:52:01.3221164Z behaviour will fall back onto the ``trust_repo=False`` option. 2025-01-24T01:52:01.3222076Z - If ``None``: this will raise a warning, inviting the user to set 2025-01-24T01:52:01.3222956Z ``trust_repo`` to either ``False``, ``True`` or ``"check"``. This 2025-01-24T01:52:01.3223882Z is only present for backward compatibility and will be removed in 2025-01-24T01:52:01.3224634Z v2.0. 2025-01-24T01:52:01.3225026Z 2025-01-24T01:52:01.3225590Z Default is ``None`` and will eventually change to ``"check"`` in v2.0. 2025-01-24T01:52:01.3226648Z force_reload (bool, optional): whether to force a fresh download of 2025-01-24T01:52:01.3227598Z the github repo unconditionally. Does not have any effect if 2025-01-24T01:52:01.3228385Z ``source = 'local'``. Default is ``False``. 2025-01-24T01:52:01.3229164Z verbose (bool, optional): If ``False``, mute messages about hitting 2025-01-24T01:52:01.3230135Z local caches. Note that the message about first download cannot be 2025-01-24T01:52:01.3231041Z muted. Does not have any effect if ``source = 'local'``. 2025-01-24T01:52:01.3231742Z Default is ``True``. 2025-01-24T01:52:01.3232617Z skip_validation (bool, optional): if ``False``, torchhub will check that the branch or commit 2025-01-24T01:52:01.3233895Z specified by the ``github`` argument properly belongs to the repo owner. This will make 2025-01-24T01:52:01.3235111Z requests to the GitHub API; you can specify a non-default GitHub token by setting the 2025-01-24T01:52:01.3236233Z ``GITHUB_TOKEN`` environment variable. Default is ``False``. 2025-01-24T01:52:01.3237167Z **kwargs (optional): the corresponding kwargs for callable ``model``. 2025-01-24T01:52:01.3237914Z 2025-01-24T01:52:01.3238273Z Returns: 2025-01-24T01:52:01.3238824Z The output of the ``model`` callable when called with the given 2025-01-24T01:52:01.3239533Z ``*args`` and ``**kwargs``. 2025-01-24T01:52:01.3240034Z 2025-01-24T01:52:01.3240387Z Example: 2025-01-24T01:52:01.3240843Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_HUB) 2025-01-24T01:52:01.3241451Z >>> # from a github repo 2025-01-24T01:52:01.3241978Z >>> repo = "pytorch/vision" 2025-01-24T01:52:01.3242523Z >>> model = torch.hub.load( 2025-01-24T01:52:01.3243252Z ... repo, "resnet50", weights="ResNet50_Weights.IMAGENET1K_V1" 2025-01-24T01:52:01.3243950Z ... ) 2025-01-24T01:52:01.3244343Z >>> # from a local directory 2025-01-24T01:52:01.3244982Z >>> path = "/some/local/path/pytorch/vision" 2025-01-24T01:52:01.3245597Z >>> # xdoctest: +SKIP 2025-01-24T01:52:01.3246350Z >>> model = torch.hub.load(path, "resnet50", weights="ResNet50_Weights.DEFAULT") 2025-01-24T01:52:01.3247156Z 2025-01-24T01:52:01.3247818Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:52:01.3248627Z 2025-01-24T01:52:01.3249002Z warnings.warn(msg) 2025-01-24T01:52:01.3249447Z 2025-01-24T01:52:01.3250019Z --- Parse Warning: 5 / 106 --- 2025-01-24T01:52:01.3252008Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=download_url_to_file in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/hub.py line=687. 2025-01-24T01:52:01.3254239Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:52:01.3255212Z Download object at the given URL to a local path. 2025-01-24T01:52:01.3255845Z 2025-01-24T01:52:01.3256205Z Args: 2025-01-24T01:52:01.3256653Z url (str): URL of the object to download 2025-01-24T01:52:01.3257492Z dst (str): Full path where object will be saved, e.g. ``/tmp/temporary_file`` 2025-01-24T01:52:01.3258735Z hash_prefix (str, optional): If not None, the SHA256 downloaded file should start with ``hash_prefix``. 2025-01-24T01:52:01.3259726Z Default: None 2025-01-24T01:52:01.3260504Z progress (bool, optional): whether or not to display a progress bar to stderr 2025-01-24T01:52:01.3261350Z Default: True 2025-01-24T01:52:01.3261823Z 2025-01-24T01:52:01.3262157Z Example: 2025-01-24T01:52:01.3262630Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_HUB) 2025-01-24T01:52:01.3263291Z >>> # xdoctest: +REQUIRES(POSIX) 2025-01-24T01:52:01.3263904Z >>> torch.hub.download_url_to_file( 2025-01-24T01:52:01.3264720Z ... "https://s3.amazonaws.com/pytorch/models/resnet18-5c106cde.pth", 2025-01-24T01:52:01.3265516Z ... "/tmp/temporary_file", 2025-01-24T01:52:01.3266070Z ... ) 2025-01-24T01:52:01.3266585Z 2025-01-24T01:52:01.3266946Z 2025-01-24T01:52:01.3267619Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:52:01.3268434Z 2025-01-24T01:52:01.3268806Z warnings.warn(msg) 2025-01-24T01:52:01.3269255Z 2025-01-24T01:52:01.3269788Z --- Parse Warning: 6 / 106 --- 2025-01-24T01:52:01.3271794Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=load_state_dict_from_url in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/hub.py line=812. 2025-01-24T01:52:01.3274072Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:52:01.3275038Z Loads the Torch serialized object at the given URL. 2025-01-24T01:52:01.3275730Z 2025-01-24T01:52:01.3276289Z If downloaded file is a zip file, it will be automatically 2025-01-24T01:52:01.3276995Z decompressed. 2025-01-24T01:52:01.3277407Z 2025-01-24T01:52:01.3277991Z If the object is already present in `model_dir`, it's deserialized and 2025-01-24T01:52:01.3278759Z returned. 2025-01-24T01:52:01.3279523Z The default value of ``model_dir`` is ``/checkpoints`` where 2025-01-24T01:52:01.3280477Z ``hub_dir`` is the directory returned by :func:`~torch.hub.get_dir`. 2025-01-24T01:52:01.3281186Z 2025-01-24T01:52:01.3281540Z Args: 2025-01-24T01:52:01.3281995Z url (str): URL of the object to download 2025-01-24T01:52:01.3282775Z model_dir (str, optional): directory in which to save the object 2025-01-24T01:52:01.3284062Z map_location (optional): a function or a dict specifying how to remap storage locations (see torch.load) 2025-01-24T01:52:01.3285437Z progress (bool, optional): whether or not to display a progress bar to stderr. 2025-01-24T01:52:01.3286285Z Default: True 2025-01-24T01:52:01.3287195Z check_hash(bool, optional): If True, the filename part of the URL should follow the naming convention 2025-01-24T01:52:01.3288396Z ``filename-.ext`` where ```` is the first eight or more 2025-01-24T01:52:01.3289405Z digits of the SHA256 hash of the contents of the file. The hash is used to 2025-01-24T01:52:01.3290389Z ensure unique names and to verify the contents of the file. 2025-01-24T01:52:01.3291117Z Default: False 2025-01-24T01:52:01.3292034Z file_name (str, optional): name for the downloaded file. Filename from ``url`` will be used if not set. 2025-01-24T01:52:01.3293463Z weights_only(bool, optional): If True, only weights will be loaded and no complex pickled objects. 2025-01-24T01:52:01.3294746Z Recommended for untrusted sources. See :func:`~torch.load` for more details. 2025-01-24T01:52:01.3295582Z 2025-01-24T01:52:01.3295942Z Example: 2025-01-24T01:52:01.3296405Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_HUB) 2025-01-24T01:52:01.3297123Z >>> state_dict = torch.hub.load_state_dict_from_url( 2025-01-24T01:52:01.3297982Z ... "https://s3.amazonaws.com/pytorch/models/resnet18-5c106cde.pth" 2025-01-24T01:52:01.3298744Z ... ) 2025-01-24T01:52:01.3299137Z 2025-01-24T01:52:01.3299470Z 2025-01-24T01:52:01.3300139Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:52:01.3300968Z 2025-01-24T01:52:01.3301344Z warnings.warn(msg) 2025-01-24T01:52:01.3301793Z 2025-01-24T01:52:01.3302327Z --- Parse Warning: 7 / 106 --- 2025-01-24T01:52:01.3304328Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=Library.fallback in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py line=373. 2025-01-24T01:52:01.3306636Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-01-24T01:52:01.3307751Z Registers the function implementation as the fallback for the given key. 2025-01-24T01:52:01.3308570Z 2025-01-24T01:52:01.3309160Z This function only works for a library with global namespace ("_"). 2025-01-24T01:52:01.3309907Z 2025-01-24T01:52:01.3310267Z Args: 2025-01-24T01:52:01.3311009Z fn: function used as fallback for the given dispatch key or :func:`~fallthrough_kernel` 2025-01-24T01:52:01.3311932Z to register a fallthrough. 2025-01-24T01:52:01.3312936Z dispatch_key: dispatch key that the input function should be registered for. By default, it uses 2025-01-24T01:52:01.3314045Z the dispatch key that the library was created with. 2025-01-24T01:52:01.3315387Z with_keyset: flag controlling if the current dispatcher call keyset should be passed as the first argument 2025-01-24T01:52:01.3316872Z to :attr:`fn` when calling. This should be used to create the appropriate keyset for redispatch calls. 2025-01-24T01:52:01.3317820Z 2025-01-24T01:52:01.3318192Z Example:: 2025-01-24T01:52:01.3318662Z >>> my_lib = Library("_", "IMPL") 2025-01-24T01:52:01.3319292Z >>> def fallback_kernel(op, *args, **kwargs): 2025-01-24T01:52:01.3319962Z >>> # Handle all autocast ops generically 2025-01-24T01:52:01.3320563Z >>> # ... 2025-01-24T01:52:01.3321120Z >>> my_lib.fallback(fallback_kernel, "Autocast") 2025-01-24T01:52:01.3321745Z 2025-01-24T01:52:01.3323153Z Original Error: IndentationError('expected an indented block after function definition on line 2', ('', 5, 1, 'my_lib.fallback(fallback_kernel, "Autocast")\n', 5, 7)) 2025-01-24T01:52:01.3324707Z 2025-01-24T01:52:01.3325141Z my_lib.fallback(fallback_kernel, "Autocast") 2025-01-24T01:52:01.3325741Z ^ 2025-01-24T01:52:01.3326123Z warnings.warn(msg) 2025-01-24T01:52:01.3326576Z 2025-01-24T01:52:01.3327110Z --- Parse Warning: 8 / 106 --- 2025-01-24T01:52:01.3329085Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=register_fake in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py line=834. 2025-01-24T01:52:01.3331296Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-01-24T01:52:01.3332388Z Register a FakeTensor implementation ("fake impl") for this operator. 2025-01-24T01:52:01.3333176Z 2025-01-24T01:52:01.3333674Z Also sometimes known as a "meta kernel", "abstract impl". 2025-01-24T01:52:01.3334358Z 2025-01-24T01:52:01.3334995Z An "FakeTensor implementation" specifies the behavior of this operator on 2025-01-24T01:52:01.3336072Z Tensors that carry no data ("FakeTensor"). Given some input Tensors with 2025-01-24T01:52:01.3337113Z certain properties (sizes/strides/storage_offset/device), it specifies 2025-01-24T01:52:01.3338015Z what the properties of the output Tensors are. 2025-01-24T01:52:01.3338610Z 2025-01-24T01:52:01.3339227Z The FakeTensor implementation has the same signature as the operator. 2025-01-24T01:52:01.3340237Z It is run for both FakeTensors and meta tensors. To write a FakeTensor 2025-01-24T01:52:01.3341222Z implementation, assume that all Tensor inputs to the operator are 2025-01-24T01:52:01.3342208Z regular CPU/CUDA/Meta tensors, but they do not have storage, and 2025-01-24T01:52:01.3343147Z you are trying to return regular CPU/CUDA/Meta tensor(s) as output. 2025-01-24T01:52:01.3344157Z The FakeTensor implementation must consist of only PyTorch operations 2025-01-24T01:52:01.3345146Z (and may not directly access the storage or data of any input or 2025-01-24T01:52:01.3345894Z intermediate Tensors). 2025-01-24T01:52:01.3346463Z 2025-01-24T01:52:01.3346929Z This API may be used as a decorator (see examples). 2025-01-24T01:52:01.3347558Z 2025-01-24T01:52:01.3348007Z For a detailed guide on custom ops, please see 2025-01-24T01:52:01.3348879Z https://pytorch.org/tutorials/advanced/custom_ops_landing_page.html 2025-01-24T01:52:01.3349664Z 2025-01-24T01:52:01.3350019Z Examples: 2025-01-24T01:52:01.3350444Z >>> import torch 2025-01-24T01:52:01.3350938Z >>> import numpy as np 2025-01-24T01:52:01.3351483Z >>> from torch import Tensor 2025-01-24T01:52:01.3352008Z >>> 2025-01-24T01:52:01.3352589Z >>> # Example 1: an operator without data-dependent output shape 2025-01-24T01:52:01.3353538Z >>> @torch.library.custom_op("mylib::custom_linear", mutates_args=()) 2025-01-24T01:52:01.3354645Z >>> def custom_linear(x: Tensor, weight: Tensor, bias: Tensor) -> Tensor: 2025-01-24T01:52:01.3355590Z >>> raise NotImplementedError("Implementation goes here") 2025-01-24T01:52:01.3356289Z >>> 2025-01-24T01:52:01.3356812Z >>> @torch.library.register_fake("mylib::custom_linear") 2025-01-24T01:52:01.3357512Z >>> def _(x, weight, bias): 2025-01-24T01:52:01.3358078Z >>> assert x.dim() == 2 2025-01-24T01:52:01.3358650Z >>> assert weight.dim() == 2 2025-01-24T01:52:01.3359253Z >>> assert bias.dim() == 1 2025-01-24T01:52:01.3359850Z >>> assert x.shape[1] == weight.shape[1] 2025-01-24T01:52:01.3360520Z >>> assert weight.shape[0] == bias.shape[0] 2025-01-24T01:52:01.3361215Z >>> assert x.device == weight.device 2025-01-24T01:52:01.3361798Z >>> 2025-01-24T01:52:01.3362235Z >>> return (x @ weight.t()) + bias 2025-01-24T01:52:01.3362832Z >>> 2025-01-24T01:52:01.3363370Z >>> with torch._subclasses.fake_tensor.FakeTensorMode(): 2025-01-24T01:52:01.3364068Z >>> x = torch.randn(2, 3) 2025-01-24T01:52:01.3364627Z >>> w = torch.randn(3, 3) 2025-01-24T01:52:01.3365188Z >>> b = torch.randn(3) 2025-01-24T01:52:01.3365772Z >>> y = torch.ops.mylib.custom_linear(x, w, b) 2025-01-24T01:52:01.3366378Z >>> 2025-01-24T01:52:01.3366792Z >>> assert y.shape == (2, 3) 2025-01-24T01:52:01.3367327Z >>> 2025-01-24T01:52:01.3367868Z >>> # Example 2: an operator with data-dependent output shape 2025-01-24T01:52:01.3368768Z >>> @torch.library.custom_op("mylib::custom_nonzero", mutates_args=()) 2025-01-24T01:52:01.3369612Z >>> def custom_nonzero(x: Tensor) -> Tensor: 2025-01-24T01:52:01.3370242Z >>> x_np = x.numpy(force=True) 2025-01-24T01:52:01.3370878Z >>> res = np.stack(np.nonzero(x_np), axis=1) 2025-01-24T01:52:01.3371571Z >>> return torch.tensor(res, device=x.device) 2025-01-24T01:52:01.3372174Z >>> 2025-01-24T01:52:01.3372698Z >>> @torch.library.register_fake("mylib::custom_nonzero") 2025-01-24T01:52:01.3373380Z >>> def _(x): 2025-01-24T01:52:01.3373934Z >>> # Number of nonzero-elements is data-dependent. 2025-01-24T01:52:01.3374678Z >>> # Since we cannot peek at the data in an fake impl, 2025-01-24T01:52:01.3375443Z >>> # we use the ctx object to construct a new symint that 2025-01-24T01:52:01.3376145Z >>> # represents the data-dependent size. 2025-01-24T01:52:01.3376791Z >>> ctx = torch.library.get_ctx() 2025-01-24T01:52:01.3377417Z >>> nnz = ctx.new_dynamic_size() 2025-01-24T01:52:01.3378012Z >>> shape = [nnz, x.dim()] 2025-01-24T01:52:01.3378652Z >>> result = x.new_empty(shape, dtype=torch.int64) 2025-01-24T01:52:01.3379523Z >>> return result 2025-01-24T01:52:01.3380005Z >>> 2025-01-24T01:52:01.3380571Z >>> from torch.fx.experimental.proxy_tensor import make_fx 2025-01-24T01:52:01.3381267Z >>> 2025-01-24T01:52:01.3381707Z >>> x = torch.tensor([0, 1, 2, 3, 4, 0]) 2025-01-24T01:52:01.3382564Z >>> trace = make_fx(torch.ops.mylib.custom_nonzero, tracing_mode="symbolic")(x) 2025-01-24T01:52:01.3383409Z >>> trace.print_readable() 2025-01-24T01:52:01.3383949Z >>> 2025-01-24T01:52:01.3384579Z >>> assert torch.allclose(trace(x), torch.ops.mylib.custom_nonzero(x)) 2025-01-24T01:52:01.3385346Z 2025-01-24T01:52:01.3385694Z 2025-01-24T01:52:01.3386910Z Original Error: IndentationError('expected an indented block after function definition on line 37', ('', 38, 1, '_._ = None\n', 38, 2)) 2025-01-24T01:52:01.3388252Z 2025-01-24T01:52:01.3388614Z _._ = None 2025-01-24T01:52:01.3389108Z ^ 2025-01-24T01:52:01.3389542Z warnings.warn(msg) 2025-01-24T01:52:01.3389976Z 2025-01-24T01:52:01.3390546Z --- Parse Warning: 9 / 106 --- 2025-01-24T01:52:01.3392559Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=register_autograd in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py line=955. 2025-01-24T01:52:01.3394832Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:52:01.3395794Z Register a backward formula for this custom op. 2025-01-24T01:52:01.3396399Z 2025-01-24T01:52:01.3396969Z In order for an operator to work with autograd, you need to register 2025-01-24T01:52:01.3397725Z a backward formula: 2025-01-24T01:52:01.3398454Z 1. You must tell us how to compute gradients during the backward pass 2025-01-24T01:52:01.3399251Z by providing us a "backward" function. 2025-01-24T01:52:01.3400112Z 2. If you need any values from the forward to compute gradients, you can 2025-01-24T01:52:01.3400951Z use `setup_context` to save values for backward. 2025-01-24T01:52:01.3401553Z 2025-01-24T01:52:01.3402149Z ``backward`` runs during the backward pass. It accepts ``(ctx, *grads)``: 2025-01-24T01:52:01.3403111Z - ``grads`` is one or more gradients. The number of gradients matches 2025-01-24T01:52:01.3403902Z the number of outputs of the operator. 2025-01-24T01:52:01.3404690Z The ``ctx`` object is `the same ctx object `_ used by 2025-01-24T01:52:01.3405734Z :class:`torch.autograd.Function`. The semantics of ``backward_fn`` are the 2025-01-24T01:52:01.3406669Z same as :meth:`torch.autograd.Function.backward`. 2025-01-24T01:52:01.3407310Z 2025-01-24T01:52:01.3407895Z ``setup_context(ctx, inputs, output)`` runs during the forward pass. 2025-01-24T01:52:01.3408906Z Please save quantities needed for backward onto the ``ctx`` object via 2025-01-24T01:52:01.3409958Z either :meth:`torch.autograd.function.FunctionCtx.save_for_backward` 2025-01-24T01:52:01.3410952Z or assigning them as attributes of ``ctx``. If your custom op has 2025-01-24T01:52:01.3411920Z kwarg-only arguments, we expect the signature of ``setup_context`` 2025-01-24T01:52:01.3412871Z to be ``setup_context(ctx, inputs, keyword_only_inputs, output)``. 2025-01-24T01:52:01.3413583Z 2025-01-24T01:52:01.3414170Z Both ``setup_context_fn`` and ``backward_fn`` must be traceable. That is, 2025-01-24T01:52:01.3415193Z they may not directly access :meth:`torch.Tensor.data_ptr` and they must 2025-01-24T01:52:01.3416237Z not depend on or mutate global state. If you need a non-traceable backward, 2025-01-24T01:52:01.3417297Z you can make it a separate custom_op that you call inside ``backward_fn``. 2025-01-24T01:52:01.3418078Z 2025-01-24T01:52:01.3418678Z If you need different autograd behavior on different devices, then we 2025-01-24T01:52:01.3419710Z recommend creating two different custom operators, one for each device 2025-01-24T01:52:01.3420767Z that needs different behavior, and switching between them at runtime. 2025-01-24T01:52:01.3421542Z 2025-01-24T01:52:01.3421904Z Examples: 2025-01-24T01:52:01.3422321Z >>> import torch 2025-01-24T01:52:01.3422804Z >>> import numpy as np 2025-01-24T01:52:01.3423363Z >>> from torch import Tensor 2025-01-24T01:52:01.3423913Z >>> 2025-01-24T01:52:01.3424562Z >>> @torch.library.custom_op("mylib::numpy_sin", mutates_args=()) 2025-01-24T01:52:01.3425354Z >>> def numpy_sin(x: Tensor) -> Tensor: 2025-01-24T01:52:01.3425952Z >>> x_np = x.cpu().numpy() 2025-01-24T01:52:01.3426609Z >>> y_np = np.sin(x_np) 2025-01-24T01:52:01.3427256Z >>> return torch.from_numpy(y_np).to(device=x.device) 2025-01-24T01:52:01.3428016Z >>> 2025-01-24T01:52:01.3428524Z >>> def setup_context(ctx, inputs, output) -> Tensor: 2025-01-24T01:52:01.3429159Z >>> x, = inputs 2025-01-24T01:52:01.3429678Z >>> ctx.save_for_backward(x) 2025-01-24T01:52:01.3430227Z >>> 2025-01-24T01:52:01.3430654Z >>> def backward(ctx, grad): 2025-01-24T01:52:01.3431227Z >>> x, = ctx.saved_tensors 2025-01-24T01:52:01.3431774Z >>> return grad * x.cos() 2025-01-24T01:52:01.3432307Z >>> 2025-01-24T01:52:01.3432762Z >>> torch.library.register_autograd( 2025-01-24T01:52:01.3433508Z ... "mylib::numpy_sin", backward, setup_context=setup_context 2025-01-24T01:52:01.3434192Z ... ) 2025-01-24T01:52:01.3434602Z >>> 2025-01-24T01:52:01.3435058Z >>> x = torch.randn(3, requires_grad=True) 2025-01-24T01:52:01.3435666Z >>> y = numpy_sin(x) 2025-01-24T01:52:01.3436360Z >>> (grad_x,) = torch.autograd.grad(y, x, torch.ones_like(y)) 2025-01-24T01:52:01.3437103Z >>> assert torch.allclose(grad_x, x.cos()) 2025-01-24T01:52:01.3437673Z >>> 2025-01-24T01:52:01.3438116Z >>> # Example with a keyword-only arg 2025-01-24T01:52:01.3438893Z >>> @torch.library.custom_op("mylib::numpy_mul", mutates_args=()) 2025-01-24T01:52:01.3439749Z >>> def numpy_mul(x: Tensor, *, val: float) -> Tensor: 2025-01-24T01:52:01.3440414Z >>> x_np = x.cpu().numpy() 2025-01-24T01:52:01.3440974Z >>> y_np = x_np * val 2025-01-24T01:52:01.3441594Z >>> return torch.from_numpy(y_np).to(device=x.device) 2025-01-24T01:52:01.3442243Z >>> 2025-01-24T01:52:01.3442887Z >>> def setup_context(ctx, inputs, keyword_only_inputs, output) -> Tensor: 2025-01-24T01:52:01.3443743Z >>> ctx.val = keyword_only_inputs["val"] 2025-01-24T01:52:01.3444335Z >>> 2025-01-24T01:52:01.3444746Z >>> def backward(ctx, grad): 2025-01-24T01:52:01.3445315Z >>> return grad * ctx.val 2025-01-24T01:52:01.3445852Z >>> 2025-01-24T01:52:01.3446301Z >>> torch.library.register_autograd( 2025-01-24T01:52:01.3447057Z ... "mylib::numpy_mul", backward, setup_context=setup_context 2025-01-24T01:52:01.3447733Z ... ) 2025-01-24T01:52:01.3448128Z >>> 2025-01-24T01:52:01.3448587Z >>> x = torch.randn(3, requires_grad=True) 2025-01-24T01:52:01.3449213Z >>> y = numpy_mul(x, val=3.14) 2025-01-24T01:52:01.3449890Z >>> (grad_x,) = torch.autograd.grad(y, x, torch.ones_like(y)) 2025-01-24T01:52:01.3450704Z >>> assert torch.allclose(grad_x, torch.full_like(x, 3.14)) 2025-01-24T01:52:01.3451381Z 2025-01-24T01:52:01.3451734Z 2025-01-24T01:52:01.3452409Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:52:01.3453247Z 2025-01-24T01:52:01.3453604Z warnings.warn(msg) 2025-01-24T01:52:01.3454060Z 2025-01-24T01:52:01.3454627Z --- Parse Warning: 10 / 106 --- 2025-01-24T01:52:01.3456571Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=opcheck in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py line=1367. 2025-01-24T01:52:01.3458760Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:52:01.3459849Z Given an operator and some sample arguments, tests if the operator is 2025-01-24T01:52:01.3460627Z registered correctly. 2025-01-24T01:52:01.3461111Z 2025-01-24T01:52:01.3461688Z That is, when you use the torch.library/TORCH_LIBRARY APIs to create a 2025-01-24T01:52:01.3462726Z custom op, you specified metadata (e.g. mutability info) about the custom op 2025-01-24T01:52:01.3463799Z and these APIs require that the functions you pass them satisfy certain 2025-01-24T01:52:01.3464920Z properties (e.g. no data pointer access in the fake/meta/abstract kernel) 2025-01-24T01:52:01.3465819Z ``opcheck`` tests these metadata and properties. 2025-01-24T01:52:01.3466554Z 2025-01-24T01:52:01.3466960Z Concretely, we test the following: 2025-01-24T01:52:01.3467518Z 2025-01-24T01:52:01.3468014Z - test_schema: If the schema matches the implementation of 2025-01-24T01:52:01.3468953Z the operator. For example: if the schema specifies a Tensor is mutated, 2025-01-24T01:52:01.3469956Z then we check the implementation mutates the Tensor. If the schema 2025-01-24T01:52:01.3470896Z specifies that we return a new Tensor, then we check that the 2025-01-24T01:52:01.3471933Z implementation returns a new Tensor (instead of an existing one or 2025-01-24T01:52:01.3472744Z a view of an existing one). 2025-01-24T01:52:01.3473515Z - test_autograd_registration: If the operator supports training 2025-01-24T01:52:01.3474455Z (autograd): we check that its autograd formula is registered via 2025-01-24T01:52:01.3475414Z torch.library.register_autograd or a manual registration to one 2025-01-24T01:52:01.3476382Z or more DispatchKey::Autograd keys. Any other DispatchKey-based 2025-01-24T01:52:01.3477218Z registrations may lead to undefined behavior. 2025-01-24T01:52:01.3477984Z - test_faketensor: If the operator has a FakeTensor kernel 2025-01-24T01:52:01.3478829Z (and if it is correct). The FakeTensor kernel is necessary ( 2025-01-24T01:52:01.3479903Z but not sufficient) for the operator to work with PyTorch compilation 2025-01-24T01:52:01.3480909Z APIs (torch.compile/export/FX). We check that a FakeTensor kernel 2025-01-24T01:52:01.3481859Z (also sometimes known as a meta kernel) was registered for the 2025-01-24T01:52:01.3482772Z operator and that it is correct. This test takes the result of 2025-01-24T01:52:01.3483682Z running the operator on real tensors and the result of running 2025-01-24T01:52:01.3484629Z the operator on FakeTensors and checks that they have the same 2025-01-24T01:52:01.3485475Z Tensor metadata (sizes/strides/dtype/device/etc). 2025-01-24T01:52:01.3486325Z - test_aot_dispatch_dynamic: If the operator has correct behavior 2025-01-24T01:52:01.3487230Z with PyTorch compilation APIs (torch.compile/export/FX). 2025-01-24T01:52:01.3488140Z This checks that the outputs (and gradients, if applicable) are the 2025-01-24T01:52:01.3488990Z same under eager-mode PyTorch and torch.compile. 2025-01-24T01:52:01.3489812Z This test is a superset of ``test_faketensor`` and is an e2e test; 2025-01-24T01:52:01.3490677Z other things it tests are that the operator supports 2025-01-24T01:52:01.3491572Z functionalization and that the backward pass (if it exists) also 2025-01-24T01:52:01.3492448Z supports FakeTensor and functionalization. 2025-01-24T01:52:01.3493049Z 2025-01-24T01:52:01.3493611Z For best results, please call ``opcheck`` multiple times with a 2025-01-24T01:52:01.3494509Z representative set of inputs. If your operator supports 2025-01-24T01:52:01.3495472Z autograd, please use ``opcheck`` with inputs with ``requires_grad = True``; 2025-01-24T01:52:01.3496532Z if your operator supports multiple devices (e.g. CPU and CUDA), please 2025-01-24T01:52:01.3497450Z use ``opcheck`` with inputs on all supported devices. 2025-01-24T01:52:01.3498083Z 2025-01-24T01:52:01.3498440Z Args: 2025-01-24T01:52:01.3498980Z op: The operator. Must either be a function decorated with 2025-01-24T01:52:01.3499900Z :func:`torch.library.custom_op` or an OpOverload/OpOverloadPacket 2025-01-24T01:52:01.3500881Z found in torch.ops.* (e.g. torch.ops.aten.sin, torch.ops.mylib.foo) 2025-01-24T01:52:01.3501807Z args: The args to the operator 2025-01-24T01:52:01.3502416Z kwargs: The kwargs to the operator 2025-01-24T01:52:01.3503142Z test_utils: Tests that we should run. Default: all of them. 2025-01-24T01:52:01.3503925Z Example: ("test_schema", "test_faketensor") 2025-01-24T01:52:01.3504713Z raise_exception: If we should raise an exception on the first 2025-01-24T01:52:01.3505580Z error. If False, we will return a dict with information 2025-01-24T01:52:01.3506363Z on if each test passed or not. 2025-01-24T01:52:01.3506934Z 2025-01-24T01:52:01.3507296Z .. warning:: 2025-01-24T01:52:01.3507709Z 2025-01-24T01:52:01.3508281Z opcheck and :func:`torch.autograd.gradcheck` test different things; 2025-01-24T01:52:01.3509321Z opcheck tests if your usage of torch.library APIs is correct while 2025-01-24T01:52:01.3510302Z :func:`torch.autograd.gradcheck` tests if your autograd formula is 2025-01-24T01:52:01.3511371Z mathematically correct. Use both to test custom ops that support 2025-01-24T01:52:01.3512164Z gradient computation. 2025-01-24T01:52:01.3512663Z 2025-01-24T01:52:01.3513006Z Example: 2025-01-24T01:52:01.3513401Z 2025-01-24T01:52:01.3513842Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2025-01-24T01:52:01.3514659Z >>> @torch.library.custom_op("mylib::numpy_mul", mutates_args=()) 2025-01-24T01:52:01.3515482Z >>> def numpy_mul(x: Tensor, y: float) -> Tensor: 2025-01-24T01:52:01.3516123Z >>> x_np = x.numpy(force=True) 2025-01-24T01:52:01.3516697Z >>> z_np = x_np * y 2025-01-24T01:52:01.3517288Z >>> return torch.from_numpy(z_np).to(x.device) 2025-01-24T01:52:01.3517901Z >>> 2025-01-24T01:52:01.3518330Z >>> @numpy_mul.register_fake 2025-01-24T01:52:01.3518869Z >>> def _(x, y): 2025-01-24T01:52:01.3519391Z >>> return torch.empty_like(x) 2025-01-24T01:52:01.3519947Z >>> 2025-01-24T01:52:01.3520411Z >>> def setup_context(ctx, inputs, output): 2025-01-24T01:52:01.3521009Z >>> y, = inputs 2025-01-24T01:52:01.3521473Z >>> ctx.y = y 2025-01-24T01:52:01.3521939Z >>> 2025-01-24T01:52:01.3522360Z >>> def backward(ctx, grad): 2025-01-24T01:52:01.3522945Z >>> return grad * ctx.y, None 2025-01-24T01:52:01.3523504Z >>> 2025-01-24T01:52:01.3524120Z >>> numpy_mul.register_autograd(backward, setup_context=setup_context) 2025-01-24T01:52:01.3524892Z >>> 2025-01-24T01:52:01.3525300Z >>> sample_inputs = [ 2025-01-24T01:52:01.3525826Z >>> (torch.randn(3), 3.14), 2025-01-24T01:52:01.3526433Z >>> (torch.randn(2, 3, device='cuda'), 2.718), 2025-01-24T01:52:01.3527111Z >>> (torch.randn(1, 10, requires_grad=True), 1.234), 2025-01-24T01:52:01.3527935Z >>> (torch.randn(64, 64, device='cuda', requires_grad=True), 90.18), 2025-01-24T01:52:01.3528651Z >>> ] 2025-01-24T01:52:01.3529038Z >>> 2025-01-24T01:52:01.3529466Z >>> for args in sample_inputs: 2025-01-24T01:52:01.3530073Z >>> torch.library.opcheck(numpy_mul, args) 2025-01-24T01:52:01.3530662Z 2025-01-24T01:52:01.3531009Z 2025-01-24T01:52:01.3531674Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:52:01.3532494Z 2025-01-24T01:52:01.3532853Z warnings.warn(msg) 2025-01-24T01:52:01.3533291Z 2025-01-24T01:52:01.3533870Z --- Parse Warning: 11 / 106 --- 2025-01-24T01:52:01.3535824Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=load in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/serialization.py line=1238. 2025-01-24T01:52:01.3538137Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:52:01.3539435Z load(f, map_location=None, pickle_module=pickle, *, weights_only=True, mmap=None, **pickle_load_args) 2025-01-24T01:52:01.3540395Z 2025-01-24T01:52:01.3540917Z Loads an object saved with :func:`torch.save` from a file. 2025-01-24T01:52:01.3541598Z 2025-01-24T01:52:01.3579470Z :func:`torch.load` uses Python's unpickling facilities but treats storages, 2025-01-24T01:52:01.3580685Z which underlie tensors, specially. They are first deserialized on the 2025-01-24T01:52:01.3581723Z CPU and are then moved to the device they were saved from. If this fails 2025-01-24T01:52:01.3582752Z (e.g. because the run time system doesn't have certain devices), an exception 2025-01-24T01:52:01.3583980Z is raised. However, storages can be dynamically remapped to an alternative 2025-01-24T01:52:01.3584912Z set of devices using the :attr:`map_location` argument. 2025-01-24T01:52:01.3585638Z 2025-01-24T01:52:01.3586360Z If :attr:`map_location` is a callable, it will be called once for each serialized 2025-01-24T01:52:01.3587426Z storage with two arguments: storage and location. The storage argument 2025-01-24T01:52:01.3588467Z will be the initial deserialization of the storage, residing on the CPU. 2025-01-24T01:52:01.3589478Z Each serialized storage has a location tag associated with it which 2025-01-24T01:52:01.3590470Z identifies the device it was saved from, and this tag is the second 2025-01-24T01:52:01.3591523Z argument passed to :attr:`map_location`. The builtin location tags are ``'cpu'`` 2025-01-24T01:52:01.3592603Z for CPU tensors and ``'cuda:device_id'`` (e.g. ``'cuda:2'``) for CUDA tensors. 2025-01-24T01:52:01.3593601Z :attr:`map_location` should return either ``None`` or a storage. If 2025-01-24T01:52:01.3594641Z :attr:`map_location` returns a storage, it will be used as the final deserialized 2025-01-24T01:52:01.3595759Z object, already moved to the right device. Otherwise, :func:`torch.load` will 2025-01-24T01:52:01.3596863Z fall back to the default behavior, as if :attr:`map_location` wasn't specified. 2025-01-24T01:52:01.3597669Z 2025-01-24T01:52:01.3598290Z If :attr:`map_location` is a :class:`torch.device` object or a string containing 2025-01-24T01:52:01.3599355Z a device tag, it indicates the location where all tensors should be loaded. 2025-01-24T01:52:01.3600142Z 2025-01-24T01:52:01.3600793Z Otherwise, if :attr:`map_location` is a dict, it will be used to remap location tags 2025-01-24T01:52:01.3601857Z appearing in the file (keys), to ones that specify where to put the 2025-01-24T01:52:01.3602625Z storages (values). 2025-01-24T01:52:01.3603078Z 2025-01-24T01:52:01.3603672Z User extensions can register their own location tags and tagging and 2025-01-24T01:52:01.3604765Z deserialization methods using :func:`torch.serialization.register_package`. 2025-01-24T01:52:01.3605615Z 2025-01-24T01:52:01.3605974Z Args: 2025-01-24T01:52:01.3606779Z f: a file-like object (has to implement :meth:`read`, :meth:`readline`, :meth:`tell`, and :meth:`seek`), 2025-01-24T01:52:01.3607867Z or a string or os.PathLike object containing a file name 2025-01-24T01:52:01.3608952Z map_location: a function, :class:`torch.device`, string or a dict specifying how to remap storage 2025-01-24T01:52:01.3609915Z locations 2025-01-24T01:52:01.3610618Z pickle_module: module used for unpickling metadata and objects (has to 2025-01-24T01:52:01.3611553Z match the :attr:`pickle_module` used to serialize file) 2025-01-24T01:52:01.3612462Z weights_only: Indicates whether unpickler should be restricted to 2025-01-24T01:52:01.3613355Z loading only tensors, primitive types, dictionaries 2025-01-24T01:52:01.3614364Z and any types added via :func:`torch.serialization.add_safe_globals`. 2025-01-24T01:52:01.3615210Z See :ref:`weights-only` for more details. 2025-01-24T01:52:01.3616264Z mmap: Indicates whether the file should be mmaped rather than loading all the storages into memory. 2025-01-24T01:52:01.3617699Z Typically, tensor storages in the file will first be moved from disk to CPU memory, after which they 2025-01-24T01:52:01.3619155Z are moved to the location that they were tagged with when saving, or specified by ``map_location``. This 2025-01-24T01:52:01.3620580Z second step is a no-op if the final location is CPU. When the ``mmap`` flag is set, instead of copying the 2025-01-24T01:52:01.3621844Z tensor storages from disk to CPU memory in the first step, ``f`` is mmaped. 2025-01-24T01:52:01.3622946Z pickle_load_args: (Python 3 only) optional keyword arguments passed over to 2025-01-24T01:52:01.3624039Z :func:`pickle_module.load` and :func:`pickle_module.Unpickler`, e.g., 2025-01-24T01:52:01.3624833Z :attr:`errors=...`. 2025-01-24T01:52:01.3625335Z 2025-01-24T01:52:01.3625714Z .. warning:: 2025-01-24T01:52:01.3626427Z :func:`torch.load()` unless `weights_only` parameter is set to `True`, 2025-01-24T01:52:01.3627385Z uses ``pickle`` module implicitly, which is known to be insecure. 2025-01-24T01:52:01.3628466Z It is possible to construct malicious pickle data which will execute arbitrary code 2025-01-24T01:52:01.3629617Z during unpickling. Never load data that could have come from an untrusted 2025-01-24T01:52:01.3630804Z source in an unsafe mode, or that could have been tampered with. **Only load data you trust**. 2025-01-24T01:52:01.3631710Z 2025-01-24T01:52:01.3632044Z .. note:: 2025-01-24T01:52:01.3632746Z When you call :func:`torch.load()` on a file which contains GPU tensors, those tensors 2025-01-24T01:52:01.3633898Z will be loaded to GPU by default. You can call ``torch.load(.., map_location='cpu')`` 2025-01-24T01:52:01.3635065Z and then :meth:`load_state_dict` to avoid GPU RAM surge when loading a model checkpoint. 2025-01-24T01:52:01.3635923Z 2025-01-24T01:52:01.3636262Z .. note:: 2025-01-24T01:52:01.3636949Z By default, we decode byte strings as ``utf-8``. This is to avoid a common error 2025-01-24T01:52:01.3637999Z case ``UnicodeDecodeError: 'ascii' codec can't decode byte 0x...`` 2025-01-24T01:52:01.3638973Z when loading files saved by Python 2 in Python 3. If this default 2025-01-24T01:52:01.3640024Z is incorrect, you may use an extra :attr:`encoding` keyword argument to specify how 2025-01-24T01:52:01.3641135Z these objects should be loaded, e.g., :attr:`encoding='latin1'` decodes them 2025-01-24T01:52:01.3642203Z to strings using ``latin1`` encoding, and :attr:`encoding='bytes'` keeps them 2025-01-24T01:52:01.3643268Z as byte arrays which can be decoded later with ``byte_array.decode(...)``. 2025-01-24T01:52:01.3644049Z 2025-01-24T01:52:01.3644409Z Example: 2025-01-24T01:52:01.3644876Z >>> # xdoctest: +SKIP("undefined filepaths") 2025-01-24T01:52:01.3645548Z >>> torch.load("tensors.pt", weights_only=True) 2025-01-24T01:52:01.3646207Z # Load all tensors onto the CPU 2025-01-24T01:52:01.3647049Z >>> torch.load("tensors.pt", map_location=torch.device("cpu"), weights_only=True) 2025-01-24T01:52:01.3647974Z # Load all tensors onto the CPU, using a function 2025-01-24T01:52:01.3648604Z >>> torch.load( 2025-01-24T01:52:01.3649338Z ... "tensors.pt", map_location=lambda storage, loc: storage, weights_only=True 2025-01-24T01:52:01.3650148Z ... ) 2025-01-24T01:52:01.3650584Z # Load all tensors onto GPU 1 2025-01-24T01:52:01.3651205Z >>> torch.load( 2025-01-24T01:52:01.3651732Z ... "tensors.pt", 2025-01-24T01:52:01.3652358Z ... map_location=lambda storage, loc: storage.cuda(1), 2025-01-24T01:52:01.3653035Z ... weights_only=True, 2025-01-24T01:52:01.3653603Z ... ) # type: ignore[attr-defined] 2025-01-24T01:52:01.3654211Z # Map tensors from GPU 1 to GPU 0 2025-01-24T01:52:01.3655056Z >>> torch.load("tensors.pt", map_location={"cuda:1": "cuda:0"}, weights_only=True) 2025-01-24T01:52:01.3655918Z # Load tensor from io.BytesIO object 2025-01-24T01:52:01.3656785Z # Loading from a buffer setting weights_only=False, warning this can be unsafe 2025-01-24T01:52:01.3657645Z >>> with open("tensor.pt", "rb") as f: 2025-01-24T01:52:01.3658304Z ... buffer = io.BytesIO(f.read()) 2025-01-24T01:52:01.3658938Z >>> torch.load(buffer, weights_only=False) 2025-01-24T01:52:01.3659685Z # Load a module with 'ascii' encoding for unpickling 2025-01-24T01:52:01.3660610Z # Loading from a module setting weights_only=False, warning this can be unsafe 2025-01-24T01:52:01.3661629Z >>> torch.load("module.pt", encoding="ascii", weights_only=False) 2025-01-24T01:52:01.3662334Z 2025-01-24T01:52:01.3662990Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:52:01.3663808Z 2025-01-24T01:52:01.3664176Z warnings.warn(msg) 2025-01-24T01:52:01.3664619Z 2025-01-24T01:52:01.3665204Z --- Parse Warning: 12 / 106 --- 2025-01-24T01:52:01.3667346Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=is_available in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/accelerator/__init__.py line=37. 2025-01-24T01:52:01.3669656Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-01-24T01:52:01.3670675Z Check if there is an available :ref:`accelerator`. 2025-01-24T01:52:01.3671372Z 2025-01-24T01:52:01.3671718Z Returns: 2025-01-24T01:52:01.3672465Z bool: A boolean indicating if there is an available :ref:`accelerator`. 2025-01-24T01:52:01.3673326Z 2025-01-24T01:52:01.3673687Z Example:: 2025-01-24T01:52:01.3674080Z 2025-01-24T01:52:01.3674744Z >>> assert torch.accelerator.is_available() "No available accelerators detected." 2025-01-24T01:52:01.3675587Z 2025-01-24T01:52:01.3676808Z Original Error: SyntaxError('invalid syntax', ('', 1, 41, 'assert torch.accelerator.is_available() "No available accelerators detected."\n', 1, 78)) 2025-01-24T01:52:01.3678189Z 2025-01-24T01:52:01.3678859Z assert torch.accelerator.is_available() "No available accelerators detected." 2025-01-24T01:52:01.3679866Z ^ 2025-01-24T01:52:01.3680406Z warnings.warn(msg) 2025-01-24T01:52:01.3680852Z 2025-01-24T01:52:01.3681389Z --- Parse Warning: 13 / 106 --- 2025-01-24T01:52:01.3683454Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=synchronize in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/accelerator/__init__.py line=138. 2025-01-24T01:52:01.3685815Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-01-24T01:52:01.3686845Z Wait for all kernels in all streams on the given device to complete. 2025-01-24T01:52:01.3687557Z 2025-01-24T01:52:01.3687903Z Args: 2025-01-24T01:52:01.3688688Z device (:class:`torch.device`, str, int, optional): device for which to synchronize. It must match 2025-01-24T01:52:01.3689884Z the current :ref:`accelerator` device type. If not given, 2025-01-24T01:52:01.3690861Z use :func:`torch.accelerator.current_device_index` by default. 2025-01-24T01:52:01.3691667Z 2025-01-24T01:52:01.3692473Z .. note:: This function is a no-op if the current :ref:`accelerator` is not initialized. 2025-01-24T01:52:01.3693387Z 2025-01-24T01:52:01.3693742Z Example:: 2025-01-24T01:52:01.3694129Z 2025-01-24T01:52:01.3694552Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2025-01-24T01:52:01.3695479Z >>> assert torch.accelerator.is_available() "No available accelerators detected." 2025-01-24T01:52:01.3696435Z >>> start_event = torch.Event(enable_timing=True) 2025-01-24T01:52:01.3697138Z >>> end_event = torch.Event(enable_timing=True) 2025-01-24T01:52:01.3697771Z >>> start_event.record() 2025-01-24T01:52:01.3698606Z >>> tensor = torch.randn(100, device=torch.accelerator.current_accelerator()) 2025-01-24T01:52:01.3699432Z >>> sum = torch.sum(tensor) 2025-01-24T01:52:01.3699978Z >>> end_event.record() 2025-01-24T01:52:01.3700629Z >>> torch.accelerator.synchronize() 2025-01-24T01:52:01.3701352Z >>> elapsed_time_ms = start_event.elapsed_time(end_event) 2025-01-24T01:52:01.3702021Z 2025-01-24T01:52:01.3703229Z Original Error: SyntaxError('invalid syntax', ('', 2, 41, 'assert torch.accelerator.is_available() "No available accelerators detected."\n', 2, 78)) 2025-01-24T01:52:01.3704618Z 2025-01-24T01:52:01.3705286Z assert torch.accelerator.is_available() "No available accelerators detected." 2025-01-24T01:52:01.3706144Z ^ 2025-01-24T01:52:01.3706739Z warnings.warn(msg) 2025-01-24T01:52:01.3707168Z 2025-01-24T01:52:01.3707714Z --- Parse Warning: 14 / 106 --- 2025-01-24T01:52:01.3709673Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=cudart in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/__init__.py line=344. 2025-01-24T01:52:01.3711869Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-01-24T01:52:01.3712749Z Retrieves the CUDA runtime API module. 2025-01-24T01:52:01.3713310Z 2025-01-24T01:52:01.3713633Z 2025-01-24T01:52:01.3714278Z This function initializes the CUDA runtime environment if it is not already 2025-01-24T01:52:01.3715370Z initialized and returns the CUDA runtime API module (_cudart). The CUDA 2025-01-24T01:52:01.3716406Z runtime API module provides access to various CUDA runtime functions. 2025-01-24T01:52:01.3717165Z 2025-01-24T01:52:01.3717490Z Args: 2025-01-24T01:52:01.3717873Z ``None`` 2025-01-24T01:52:01.3718277Z 2025-01-24T01:52:01.3718621Z Returns: 2025-01-24T01:52:01.3719110Z module: The CUDA runtime API module (_cudart). 2025-01-24T01:52:01.3719697Z 2025-01-24T01:52:01.3720038Z Raises: 2025-01-24T01:52:01.3720677Z RuntimeError: If CUDA cannot be re-initialized in a forked subprocess. 2025-01-24T01:52:01.3721968Z AssertionError: If PyTorch is not compiled with CUDA support or if libcudart functions are unavailable. 2025-01-24T01:52:01.3722991Z 2025-01-24T01:52:01.3723406Z Example of CUDA operations with profiling: 2025-01-24T01:52:01.3724008Z >>> import torch 2025-01-24T01:52:01.3724558Z >>> from torch.cuda import cudart, check_error 2025-01-24T01:52:01.3725168Z >>> import os 2025-01-24T01:52:01.3725606Z >>> 2025-01-24T01:52:01.3726028Z >>> os.environ['CUDA_PROFILE'] = '1' 2025-01-24T01:52:01.3726584Z >>> 2025-01-24T01:52:01.3727051Z >>> def perform_cuda_operations_with_streams(): 2025-01-24T01:52:01.3727707Z >>> stream = torch.cuda.Stream() 2025-01-24T01:52:01.3728330Z >>> with torch.cuda.stream(stream): 2025-01-24T01:52:01.3728949Z >>> x = torch.randn(100, 100, device='cuda') 2025-01-24T01:52:01.3729706Z >>> y = torch.randn(100, 100, device='cuda') 2025-01-24T01:52:01.3730323Z >>> z = torch.mul(x, y) 2025-01-24T01:52:01.3730846Z >>> return z 2025-01-24T01:52:01.3731294Z >>> 2025-01-24T01:52:01.3731698Z >>> torch.cuda.synchronize() 2025-01-24T01:52:01.3732303Z >>> print("====== Start nsys profiling ======") 2025-01-24T01:52:01.3732980Z >>> check_error(cudart().cudaProfilerStart()) 2025-01-24T01:52:01.3733660Z >>> with torch.autograd.profiler.emit_nvtx(): 2025-01-24T01:52:01.3734357Z >>> result = perform_cuda_operations_with_streams() 2025-01-24T01:52:01.3735039Z >>> print("CUDA operations completed.") 2025-01-24T01:52:01.3735784Z >>> check_error(torch.cuda.cudart().cudaProfilerStop()) 2025-01-24T01:52:01.3736500Z >>> print("====== End nsys profiling ======") 2025-01-24T01:52:01.3737067Z 2025-01-24T01:52:01.3737647Z To run this example and save the profiling information, execute: 2025-01-24T01:52:01.3738849Z >>> $ nvprof --profile-from-start off --csv --print-summary -o trace_name.prof -f -- python cudart_test.py 2025-01-24T01:52:01.3739827Z 2025-01-24T01:52:01.3740458Z This command profiles the CUDA operations in the provided script and saves 2025-01-24T01:52:01.3741454Z the profiling information to a file named `trace_name.prof`. 2025-01-24T01:52:01.3742425Z The `--profile-from-start off` option ensures that profiling starts only 2025-01-24T01:52:01.3743308Z after the `cudaProfilerStart` call in the script. 2025-01-24T01:52:01.3744154Z The `--csv` and `--print-summary` options format the profiling output as a 2025-01-24T01:52:01.3744976Z CSV file and print a summary, respectively. 2025-01-24T01:52:01.3745847Z The `-o` option specifies the output file name, and the `-f` option forces the 2025-01-24T01:52:01.3746840Z overwrite of the output file if it already exists. 2025-01-24T01:52:01.3747466Z 2025-01-24T01:52:01.3748845Z 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)) 2025-01-24T01:52:01.3750347Z 2025-01-24T01:52:01.3751149Z $ nvprof --profile-from-start off --csv --print-summary -o trace_name.prof -f -- python cudart_test.py 2025-01-24T01:52:01.3752131Z ^ 2025-01-24T01:52:01.3752485Z warnings.warn(msg) 2025-01-24T01:52:01.3752910Z 2025-01-24T01:52:01.3753441Z --- Parse Warning: 15 / 106 --- 2025-01-24T01:52:01.3755428Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=Future.then in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/futures/__init__.py line=105. 2025-01-24T01:52:01.3757691Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:52:01.3758524Z 2025-01-24T01:52:01.3759117Z Append the given callback function to this ``Future``, which will be run 2025-01-24T01:52:01.3760101Z when the ``Future`` is completed. Multiple callbacks can be added to 2025-01-24T01:52:01.3761033Z the same ``Future``, but the order in which they will be executed cannot 2025-01-24T01:52:01.3761936Z be guaranteed (to enforce a certain order consider chaining: 2025-01-24T01:52:01.3762825Z ``fut.then(cb1).then(cb2)``). The callback must take one argument, which 2025-01-24T01:52:01.3763764Z is the reference to this ``Future``. The callback function can use the 2025-01-24T01:52:01.3764709Z :meth:`value` method to get the value. Note that if this ``Future`` is 2025-01-24T01:52:01.3765687Z already completed, the given callback will be run immediately inline. 2025-01-24T01:52:01.3766430Z 2025-01-24T01:52:01.3766950Z If the ``Future``'s value contains tensors that reside on GPUs, the 2025-01-24T01:52:01.3767985Z callback might be invoked while the async kernels that are populating 2025-01-24T01:52:01.3769007Z those tensors haven't yet finished executing on the device. However, the 2025-01-24T01:52:01.3770010Z callback will be invoked with some dedicated streams set as current 2025-01-24T01:52:01.3770942Z (fetched from a global pool) which will be synchronized with those 2025-01-24T01:52:01.3771919Z kernels. Hence any operation performed by the callback on these tensors 2025-01-24T01:52:01.3772909Z will be scheduled on the device after the kernels complete. In other 2025-01-24T01:52:01.3773845Z words, as long as the callback doesn't switch streams, it can safely 2025-01-24T01:52:01.3774818Z manipulate the result without any additional synchronization. This is 2025-01-24T01:52:01.3775758Z similar to the non-blocking behavior of :meth:`wait`. 2025-01-24T01:52:01.3776377Z 2025-01-24T01:52:01.3776951Z Similarly, if the callback returns a value that contains tensors that 2025-01-24T01:52:01.3777921Z reside on a GPU, it can do so even if the kernels that are producing 2025-01-24T01:52:01.3778870Z these tensors are still running on the device, as long as the callback 2025-01-24T01:52:01.3780024Z didn't change streams during its execution. If one wants to change 2025-01-24T01:52:01.3780967Z streams, one must be careful to re-synchronize them with the original 2025-01-24T01:52:01.3781944Z streams, that is, those that were current when the callback was invoked. 2025-01-24T01:52:01.3782689Z 2025-01-24T01:52:01.3783030Z Args: 2025-01-24T01:52:01.3783583Z callback(``Callable``): a ``Callable`` that takes this ``Future`` as 2025-01-24T01:52:01.3784339Z the only argument. 2025-01-24T01:52:01.3784869Z 2025-01-24T01:52:01.3785204Z Returns: 2025-01-24T01:52:01.3785705Z A new ``Future`` object that holds the return value of the 2025-01-24T01:52:01.3786582Z ``callback`` and will be marked as completed when the given 2025-01-24T01:52:01.3787269Z ``callback`` finishes. 2025-01-24T01:52:01.3787733Z 2025-01-24T01:52:01.3788217Z .. note:: Note that if the callback function throws, either 2025-01-24T01:52:01.3789088Z through the original future being completed with an exception and 2025-01-24T01:52:01.3790011Z calling ``fut.wait()``, or through other code in the callback, the 2025-01-24T01:52:01.3790931Z future returned by ``then`` will be marked appropriately with the 2025-01-24T01:52:01.3791849Z encountered error. However, if this callback later completes 2025-01-24T01:52:01.3792788Z additional futures, those futures are not marked as completed with 2025-01-24T01:52:01.3793749Z an error and the user is responsible for handling completion/waiting 2025-01-24T01:52:01.3794515Z on those futures independently. 2025-01-24T01:52:01.3795037Z 2025-01-24T01:52:01.3795365Z Example:: 2025-01-24T01:52:01.3795832Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_FUTURES) 2025-01-24T01:52:01.3796466Z >>> def callback(fut): 2025-01-24T01:52:01.3797024Z ... print(f"RPC return value is {fut.wait()}.") 2025-01-24T01:52:01.3797665Z >>> fut = torch.futures.Future() 2025-01-24T01:52:01.3798327Z >>> # The inserted callback will print the return value when 2025-01-24T01:52:01.3799039Z >>> # receiving the response from "worker1" 2025-01-24T01:52:01.3799648Z >>> cb_fut = fut.then(callback) 2025-01-24T01:52:01.3800208Z >>> chain_cb_fut = cb_fut.then( 2025-01-24T01:52:01.3800841Z ... lambda x : print(f"Chained cb done. {x.wait()}") 2025-01-24T01:52:01.3801444Z ... ) 2025-01-24T01:52:01.3801843Z >>> fut.set_result(5) 2025-01-24T01:52:01.3802341Z RPC return value is 5. 2025-01-24T01:52:01.3802848Z Chained cb done. None 2025-01-24T01:52:01.3803329Z 2025-01-24T01:52:01.3803974Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:52:01.3804905Z 2025-01-24T01:52:01.3805334Z warnings.warn(msg) 2025-01-24T01:52:01.3805777Z 2025-01-24T01:52:01.3806346Z --- Parse Warning: 16 / 106 --- 2025-01-24T01:52:01.3808389Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=Future.set_result in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/futures/__init__.py line=213. 2025-01-24T01:52:01.3810720Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:52:01.3811569Z 2025-01-24T01:52:01.3812134Z Set the result for this ``Future``, which will mark this ``Future`` as 2025-01-24T01:52:01.3813124Z completed and trigger all attached callbacks. Note that a ``Future`` 2025-01-24T01:52:01.3813980Z cannot be marked completed twice. 2025-01-24T01:52:01.3814499Z 2025-01-24T01:52:01.3815098Z If the result contains tensors that reside on GPUs, this method can be 2025-01-24T01:52:01.3816208Z called even if the asynchronous kernels that are populating those 2025-01-24T01:52:01.3817184Z tensors haven't yet completed running on the device, provided that the 2025-01-24T01:52:01.3818209Z streams on which those kernels were enqueued are set as the current ones 2025-01-24T01:52:01.3819205Z when this method is called. Put simply, it's safe to call this method 2025-01-24T01:52:01.3820192Z immediately after launching those kernels, without any additional 2025-01-24T01:52:01.3821206Z synchronization, as long as one doesn't change streams in between. This 2025-01-24T01:52:01.3822232Z method will record events on all the relevant current streams and will 2025-01-24T01:52:01.3823202Z use them to ensure proper scheduling for all the consumers of this 2025-01-24T01:52:01.3823941Z ``Future``. 2025-01-24T01:52:01.3824309Z 2025-01-24T01:52:01.3824652Z Args: 2025-01-24T01:52:01.3825145Z result (object): the result object of this ``Future``. 2025-01-24T01:52:01.3825797Z 2025-01-24T01:52:01.3826157Z Example:: 2025-01-24T01:52:01.3826700Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_FUTURES) 2025-01-24T01:52:01.3827341Z >>> import threading 2025-01-24T01:52:01.3827821Z >>> import time 2025-01-24T01:52:01.3828298Z >>> def slow_set_future(fut, value): 2025-01-24T01:52:01.3828865Z ... time.sleep(0.5) 2025-01-24T01:52:01.3829360Z ... fut.set_result(value) 2025-01-24T01:52:01.3829914Z >>> fut = torch.futures.Future() 2025-01-24T01:52:01.3830486Z >>> t = threading.Thread( 2025-01-24T01:52:01.3831009Z ... target=slow_set_future, 2025-01-24T01:52:01.3831578Z ... args=(fut, torch.ones(2) * 3) 2025-01-24T01:52:01.3832104Z ... ) 2025-01-24T01:52:01.3832485Z >>> t.start() 2025-01-24T01:52:01.3832928Z >>> print(fut.wait()) 2025-01-24T01:52:01.3833404Z tensor([3., 3.]) 2025-01-24T01:52:01.3833838Z >>> t.join() 2025-01-24T01:52:01.3834245Z 2025-01-24T01:52:01.3834903Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:52:01.3835724Z 2025-01-24T01:52:01.3836101Z warnings.warn(msg) 2025-01-24T01:52:01.3836525Z 2025-01-24T01:52:01.3837084Z --- Parse Warning: 17 / 106 --- 2025-01-24T01:52:01.3839107Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=_compile_shader in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/mps/__init__.py line=144. 2025-01-24T01:52:01.3841388Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:52:01.3842489Z Compiles compute shader from source and allows one to invoke kernels 2025-01-24T01:52:01.3843382Z defined there from the comfort of Python runtime 2025-01-24T01:52:01.3843999Z Example:: 2025-01-24T01:52:01.3844390Z 2025-01-24T01:52:01.3844828Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_MPS) 2025-01-24T01:52:01.3845590Z >>> lib = torch.mps._compile_shader( 2025-01-24T01:52:01.3846702Z ... "kernel void full(device float* out, constant float& val, uint idx [[thread_position_in_grid]]) { out[idx] = val; }" 2025-01-24T01:52:01.3847758Z ... ) 2025-01-24T01:52:01.3848203Z >>> x = torch.zeros(16, device="mps") 2025-01-24T01:52:01.3848780Z >>> lib.full(x, 3.14) 2025-01-24T01:52:01.3849271Z 2025-01-24T01:52:01.3849936Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:52:01.3850744Z 2025-01-24T01:52:01.3851119Z warnings.warn(msg) 2025-01-24T01:52:01.3851555Z 2025-01-24T01:52:01.3852081Z --- Parse Warning: 18 / 106 --- 2025-01-24T01:52:01.3854153Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=masked_select in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nested/__init__.py line=469. 2025-01-24T01:52:01.3856480Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:52:01.3857317Z 2025-01-24T01:52:01.3858252Z Constructs a nested tensor given a strided tensor input and a strided mask, the resulting jagged layout nested tensor 2025-01-24T01:52:01.3859900Z will have values retain values where the mask is equal to True. The dimensionality of the mask is preserved and is 2025-01-24T01:52:01.3861498Z represented with the offsets, this is unlike :func:`masked_select` where the output is collapsed to a 1D tensor. 2025-01-24T01:52:01.3862549Z 2025-01-24T01:52:01.3862894Z Args: 2025-01-24T01:52:01.3863769Z tensor (:class:`torch.Tensor`): a strided tensor from which the jagged layout nested tensor is constructed from. 2025-01-24T01:52:01.3865158Z mask (:class:`torch.Tensor`): a strided mask tensor which is applied to the tensor input 2025-01-24T01:52:01.3866019Z 2025-01-24T01:52:01.3866462Z Example:: 2025-01-24T01:52:01.3866847Z 2025-01-24T01:52:01.3867214Z >>> tensor = torch.randn(3, 3) 2025-01-24T01:52:01.3867752Z >>> mask = torch.tensor( 2025-01-24T01:52:01.3868398Z ... [[False, False, True], [True, False, True], [False, False, True]] 2025-01-24T01:52:01.3869112Z ... ) 2025-01-24T01:52:01.3869560Z >>> nt = torch.nested.masked_select(tensor, mask) 2025-01-24T01:52:01.3870154Z >>> nt.shape 2025-01-24T01:52:01.3870560Z torch.Size([3, j4]) 2025-01-24T01:52:01.3871044Z >>> # Length of each item in the batch: 2025-01-24T01:52:01.3871626Z >>> nt.offsets().diff() 2025-01-24T01:52:01.3872090Z tensor([1, 2, 1]) 2025-01-24T01:52:01.3872485Z 2025-01-24T01:52:01.3872862Z >>> tensor = torch.randn(6, 5) 2025-01-24T01:52:01.3873405Z >>> mask = torch.tensor([False]) 2025-01-24T01:52:01.3874003Z >>> nt = torch.nested.masked_select(tensor, mask) 2025-01-24T01:52:01.3874585Z >>> nt.shape 2025-01-24T01:52:01.3875001Z torch.Size([6, j5]) 2025-01-24T01:52:01.3875489Z >>> # Length of each item in the batch: 2025-01-24T01:52:01.3876053Z >>> nt.offsets().diff() 2025-01-24T01:52:01.3876524Z tensor([0, 0, 0, 0, 0, 0]) 2025-01-24T01:52:01.3876975Z 2025-01-24T01:52:01.3877631Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:52:01.3878451Z 2025-01-24T01:52:01.3878813Z warnings.warn(msg) 2025-01-24T01:52:01.3879417Z 2025-01-24T01:52:01.3879965Z --- Parse Warning: 19 / 106 --- 2025-01-24T01:52:01.3881899Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=sum in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/sparse/__init__.py line=202. 2025-01-24T01:52:01.3884108Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:52:01.3885090Z Return the sum of each row of the given sparse tensor. 2025-01-24T01:52:01.3885834Z 2025-01-24T01:52:01.3886476Z Returns the sum of each row of the sparse tensor :attr:`input` in the given 2025-01-24T01:52:01.3887427Z dimensions :attr:`dim`. If :attr:`dim` is a list of dimensions, 2025-01-24T01:52:01.3888363Z reduce over all of them. When sum over all ``sparse_dim``, this method 2025-01-24T01:52:01.3889222Z returns a dense tensor instead of a sparse tensor. 2025-01-24T01:52:01.3889838Z 2025-01-24T01:52:01.3890481Z All summed :attr:`dim` are squeezed (see :func:`torch.squeeze`), resulting an output 2025-01-24T01:52:01.3891484Z tensor having :attr:`dim` fewer dimensions than :attr:`input`. 2025-01-24T01:52:01.3892186Z 2025-01-24T01:52:01.3892774Z During backward, only gradients at ``nnz`` locations of :attr:`input` 2025-01-24T01:52:01.3893858Z will propagate back. Note that the gradients of :attr:`input` is coalesced. 2025-01-24T01:52:01.3894659Z 2025-01-24T01:52:01.3895063Z Args: 2025-01-24T01:52:01.3895494Z input (Tensor): the input sparse tensor 2025-01-24T01:52:01.3896405Z dim (int or tuple of ints): a dimension or a list of dimensions to reduce. Default: reduce 2025-01-24T01:52:01.3897276Z over all dims. 2025-01-24T01:52:01.3898042Z dtype (:class:`torch.dtype`, optional): the desired data type of returned Tensor. 2025-01-24T01:52:01.3898922Z Default: dtype of :attr:`input`. 2025-01-24T01:52:01.3899459Z 2025-01-24T01:52:01.3899815Z Example:: 2025-01-24T01:52:01.3900197Z 2025-01-24T01:52:01.3900535Z >>> nnz = 3 2025-01-24T01:52:01.3900972Z >>> dims = [5, 5, 2, 3] 2025-01-24T01:52:01.3901574Z >>> I = torch.cat([torch.randint(0, dims[0], size=(nnz,)), 2025-01-24T01:52:01.3902389Z torch.randint(0, dims[1], size=(nnz,))], 0).reshape(2, nnz) 2025-01-24T01:52:01.3903150Z >>> V = torch.randn(nnz, dims[2], dims[3]) 2025-01-24T01:52:01.3903760Z >>> size = torch.Size(dims) 2025-01-24T01:52:01.3904378Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2025-01-24T01:52:01.3905041Z >>> S = torch.sparse_coo_tensor(I, V, size) 2025-01-24T01:52:01.3905626Z >>> S 2025-01-24T01:52:01.3906061Z tensor(indices=tensor([[2, 0, 3], 2025-01-24T01:52:01.3906708Z [2, 4, 1]]), 2025-01-24T01:52:01.3907307Z values=tensor([[[-0.6438, -1.6467, 1.4004], 2025-01-24T01:52:01.3907915Z [ 0.3411, 0.0918, -0.2312]], 2025-01-24T01:52:01.3908469Z 2025-01-24T01:52:01.3908852Z [[ 0.5348, 0.0634, -2.0494], 2025-01-24T01:52:01.3909442Z [-0.7125, -1.0646, 2.1844]], 2025-01-24T01:52:01.3909999Z 2025-01-24T01:52:01.3910360Z [[ 0.1276, 0.1874, -0.6334], 2025-01-24T01:52:01.3910947Z [-1.9682, -0.5340, 0.7483]]]), 2025-01-24T01:52:01.3911611Z size=(5, 5, 2, 3), nnz=3, layout=torch.sparse_coo) 2025-01-24T01:52:01.3912218Z 2025-01-24T01:52:01.3912742Z # when sum over only part of sparse_dims, return a sparse tensor 2025-01-24T01:52:01.3913364Z >>> torch.sparse.sum(S, [1, 3]) 2025-01-24T01:52:01.3913713Z tensor(indices=tensor([[0, 2, 3]]), 2025-01-24T01:52:01.3914078Z values=tensor([[-1.4512, 0.4073], 2025-01-24T01:52:01.3914433Z [-0.8901, 0.2017], 2025-01-24T01:52:01.3914781Z [-0.3183, -1.7539]]), 2025-01-24T01:52:01.3915156Z size=(5, 2), nnz=3, layout=torch.sparse_coo) 2025-01-24T01:52:01.3915491Z 2025-01-24T01:52:01.3915780Z # when sum over all sparse dim, return a dense tensor 2025-01-24T01:52:01.3916179Z # with summed dims squeezed 2025-01-24T01:52:01.3916519Z >>> torch.sparse.sum(S, [0, 1, 3]) 2025-01-24T01:52:01.3916954Z tensor([-2.6596, -1.1450]) 2025-01-24T01:52:01.3917253Z 2025-01-24T01:52:01.3917646Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:52:01.3918122Z 2025-01-24T01:52:01.3918353Z warnings.warn(msg) 2025-01-24T01:52:01.3918625Z 2025-01-24T01:52:01.3918975Z --- Parse Warning: 20 / 106 --- 2025-01-24T01:52:01.3920070Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=vmap in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/apis.py line=39. 2025-01-24T01:52:01.3921297Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:52:01.3921779Z 2025-01-24T01:52:01.3922161Z vmap is the vectorizing map; ``vmap(func)`` returns a new function that 2025-01-24T01:52:01.3922717Z maps ``func`` over some dimension of the inputs. Semantically, vmap 2025-01-24T01:52:01.3923293Z pushes the map into PyTorch operations called by ``func``, effectively 2025-01-24T01:52:01.3923761Z vectorizing those operations. 2025-01-24T01:52:01.3924069Z 2025-01-24T01:52:01.3924416Z vmap is useful for handling batch dimensions: one can write a function 2025-01-24T01:52:01.3924974Z ``func`` that runs on examples and then lift it to a function that can 2025-01-24T01:52:01.3925518Z take batches of examples with ``vmap(func)``. vmap can also be used to 2025-01-24T01:52:01.3926044Z compute batched gradients when composed with autograd. 2025-01-24T01:52:01.3926433Z 2025-01-24T01:52:01.3926662Z .. note:: 2025-01-24T01:52:01.3927001Z :func:`torch.vmap` is aliased to :func:`torch.func.vmap` for 2025-01-24T01:52:01.3927442Z convenience. Use whichever one you'd like. 2025-01-24T01:52:01.3927795Z 2025-01-24T01:52:01.3928015Z Args: 2025-01-24T01:52:01.3928374Z func (function): A Python function that takes one or more arguments. 2025-01-24T01:52:01.3928846Z Must return one or more Tensors. 2025-01-24T01:52:01.3929282Z in_dims (int or nested structure): Specifies which dimension of the 2025-01-24T01:52:01.3929797Z inputs should be mapped over. ``in_dims`` should have a 2025-01-24T01:52:01.3930304Z structure like the inputs. If the ``in_dim`` for a particular 2025-01-24T01:52:01.3930829Z input is None, then that indicates there is no map dimension. 2025-01-24T01:52:01.3931258Z Default: 0. 2025-01-24T01:52:01.3931637Z out_dims (int or Tuple[int]): Specifies where the mapped dimension 2025-01-24T01:52:01.3932157Z should appear in the outputs. If ``out_dims`` is a Tuple, then 2025-01-24T01:52:01.3932645Z it should have one element per output. Default: 0. 2025-01-24T01:52:01.3933123Z randomness (str): Specifies whether the randomness in this 2025-01-24T01:52:01.3933651Z vmap should be the same or different across batches. If 'different', 2025-01-24T01:52:01.3934202Z the randomness for each batch will be different. If 'same', the 2025-01-24T01:52:01.3934745Z randomness will be the same across batches. If 'error', any calls to 2025-01-24T01:52:01.3935304Z random functions will error. Default: 'error'. WARNING: this flag 2025-01-24T01:52:01.3935857Z only applies to random PyTorch operations and does not apply to 2025-01-24T01:52:01.3936340Z Python's random module or numpy randomness. 2025-01-24T01:52:01.3936836Z chunk_size (None or int): If None (default), apply a single vmap over inputs. 2025-01-24T01:52:01.3937422Z If not None, then compute the vmap :attr:`chunk_size` samples at a time. 2025-01-24T01:52:01.3938027Z Note that :attr:`chunk_size=1` is equivalent to computing the vmap with a for-loop. 2025-01-24T01:52:01.3938698Z If you run into memory issues computing the vmap, please try a non-None chunk_size. 2025-01-24T01:52:01.3939225Z 2025-01-24T01:52:01.3939475Z Returns: 2025-01-24T01:52:01.3939815Z Returns a new "batched" function. It takes the same inputs as 2025-01-24T01:52:01.3940311Z ``func``, except each input has an extra dimension at the index 2025-01-24T01:52:01.3940828Z specified by ``in_dims``. It takes returns the same outputs as 2025-01-24T01:52:01.3941346Z ``func``, except each output has an extra dimension at the index 2025-01-24T01:52:01.3941773Z specified by ``out_dims``. 2025-01-24T01:52:01.3942076Z 2025-01-24T01:52:01.3942295Z .. warning: 2025-01-24T01:52:01.3942638Z :func:`vmap` works best with functional-style code. Please do not 2025-01-24T01:52:01.3943162Z perform any side-effects in ``func``, with the exception of 2025-01-24T01:52:01.3943748Z in-place PyTorch operations. Examples of side-effects include mutating 2025-01-24T01:52:01.3944351Z Python data structures and assigning values to variables not captured 2025-01-24T01:52:01.3944844Z in ``func``. 2025-01-24T01:52:01.3944938Z 2025-01-24T01:52:01.3945179Z One example of using :func:`vmap` is to compute batched dot products. PyTorch 2025-01-24T01:52:01.3945416Z doesn't provide a batched ``torch.dot`` API; instead of unsuccessfully 2025-01-24T01:52:01.3945647Z rummaging through docs, use :func:`vmap` to construct a new function. 2025-01-24T01:52:01.3945744Z 2025-01-24T01:52:01.3945897Z >>> torch.dot # [D], [D] -> [] 2025-01-24T01:52:01.3946117Z >>> batched_dot = torch.func.vmap(torch.dot) # [N, D], [N, D] -> [N] 2025-01-24T01:52:01.3946338Z >>> x, y = torch.randn(2, 5), torch.randn(2, 5) 2025-01-24T01:52:01.3946454Z >>> batched_dot(x, y) 2025-01-24T01:52:01.3946558Z 2025-01-24T01:52:01.3946798Z :func:`vmap` can be helpful in hiding batch dimensions, leading to a simpler 2025-01-24T01:52:01.3946927Z model authoring experience. 2025-01-24T01:52:01.3947024Z 2025-01-24T01:52:01.3947151Z >>> batch_size, feature_size = 3, 5 2025-01-24T01:52:01.3947351Z >>> weights = torch.randn(feature_size, requires_grad=True) 2025-01-24T01:52:01.3947445Z >>> 2025-01-24T01:52:01.3947569Z >>> def model(feature_vec): 2025-01-24T01:52:01.3947712Z >>> # Very simple linear model with activation 2025-01-24T01:52:01.3947860Z >>> return feature_vec.dot(weights).relu() 2025-01-24T01:52:01.3947950Z >>> 2025-01-24T01:52:01.3948110Z >>> examples = torch.randn(batch_size, feature_size) 2025-01-24T01:52:01.3948252Z >>> result = torch.vmap(model)(examples) 2025-01-24T01:52:01.3948343Z 2025-01-24T01:52:01.3948611Z :func:`vmap` can also help vectorize computations that were previously difficult 2025-01-24T01:52:01.3948855Z or impossible to batch. One example is higher-order gradient computation. 2025-01-24T01:52:01.3949094Z The PyTorch autograd engine computes vjps (vector-Jacobian products). 2025-01-24T01:52:01.3949343Z Computing a full Jacobian matrix for some function f: R^N -> R^N usually 2025-01-24T01:52:01.3949600Z requires N calls to ``autograd.grad``, one per Jacobian row. Using :func:`vmap`, 2025-01-24T01:52:01.3949854Z we can vectorize the whole computation, computing the Jacobian in a single 2025-01-24T01:52:01.3949966Z call to ``autograd.grad``. 2025-01-24T01:52:01.3950064Z 2025-01-24T01:52:01.3950154Z >>> # Setup 2025-01-24T01:52:01.3950246Z >>> N = 5 2025-01-24T01:52:01.3950364Z >>> f = lambda x: x ** 2 2025-01-24T01:52:01.3950493Z >>> x = torch.randn(N, requires_grad=True) 2025-01-24T01:52:01.3950601Z >>> y = f(x) 2025-01-24T01:52:01.3950702Z >>> I_N = torch.eye(N) 2025-01-24T01:52:01.3950791Z >>> 2025-01-24T01:52:01.3950908Z >>> # Sequential approach 2025-01-24T01:52:01.3951130Z >>> jacobian_rows = [torch.autograd.grad(y, x, v, retain_graph=True)[0] 2025-01-24T01:52:01.3951260Z >>> for v in I_N.unbind()] 2025-01-24T01:52:01.3951469Z >>> jacobian = torch.stack(jacobian_rows) 2025-01-24T01:52:01.3951573Z >>> 2025-01-24T01:52:01.3951699Z >>> # vectorized gradient computation 2025-01-24T01:52:01.3951800Z >>> def get_vjp(v): 2025-01-24T01:52:01.3951943Z >>> return torch.autograd.grad(y, x, v) 2025-01-24T01:52:01.3952067Z >>> jacobian = torch.vmap(get_vjp)(I_N) 2025-01-24T01:52:01.3952168Z 2025-01-24T01:52:01.3952434Z :func:`vmap` can also be nested, producing an output with multiple batched dimensions 2025-01-24T01:52:01.3952521Z 2025-01-24T01:52:01.3952687Z >>> torch.dot # [D], [D] -> [] 2025-01-24T01:52:01.3952963Z >>> batched_dot = torch.vmap(torch.vmap(torch.dot)) # [N1, N0, D], [N1, N0, D] -> [N1, N0] 2025-01-24T01:52:01.3953145Z >>> x, y = torch.randn(2, 3, 5), torch.randn(2, 3, 5) 2025-01-24T01:52:01.3953281Z >>> batched_dot(x, y) # tensor of size [2, 3] 2025-01-24T01:52:01.3953411Z 2025-01-24T01:52:01.3953658Z If the inputs are not batched along the first dimension, ``in_dims`` specifies 2025-01-24T01:52:01.3953816Z the dimension that each inputs are batched along as 2025-01-24T01:52:01.3953919Z 2025-01-24T01:52:01.3954064Z >>> torch.dot # [N], [N] -> [] 2025-01-24T01:52:01.3954304Z >>> batched_dot = torch.vmap(torch.dot, in_dims=1) # [N, D], [N, D] -> [D] 2025-01-24T01:52:01.3954434Z >>> x, y = torch.randn(2, 5), torch.randn(2, 5) 2025-01-24T01:52:01.3954689Z >>> batched_dot(x, y) # output is [5] instead of [2] if batched along the 0th dimension 2025-01-24T01:52:01.3954775Z 2025-01-24T01:52:01.3955037Z If there are multiple inputs each of which is batched along different dimensions, 2025-01-24T01:52:01.3955260Z ``in_dims`` must be a tuple with the batch dimension for each input as 2025-01-24T01:52:01.3955349Z 2025-01-24T01:52:01.3955513Z >>> torch.dot # [D], [D] -> [] 2025-01-24T01:52:01.3955754Z >>> batched_dot = torch.vmap(torch.dot, in_dims=(0, None)) # [N, D], [D] -> [N] 2025-01-24T01:52:01.3955896Z >>> x, y = torch.randn(2, 5), torch.randn(5) 2025-01-24T01:52:01.3956145Z >>> batched_dot(x, y) # second arg doesn't have a batch dim because in_dim[1] was None 2025-01-24T01:52:01.3956234Z 2025-01-24T01:52:01.3956493Z If the input is a Python struct, ``in_dims`` must be a tuple containing a struct 2025-01-24T01:52:01.3956611Z matching the shape of the input: 2025-01-24T01:52:01.3956710Z 2025-01-24T01:52:01.3956859Z >>> f = lambda dict: torch.dot(dict['x'], dict['y']) 2025-01-24T01:52:01.3956987Z >>> x, y = torch.randn(2, 5), torch.randn(5) 2025-01-24T01:52:01.3957107Z >>> input = {'x': x, 'y': y} 2025-01-24T01:52:01.3957292Z >>> batched_dot = torch.vmap(f, in_dims=({'x': 0, 'y': None},)) 2025-01-24T01:52:01.3957414Z >>> batched_dot(input) 2025-01-24T01:52:01.3957509Z 2025-01-24T01:52:01.3957805Z By default, the output is batched along the first dimension. However, it can be batched 2025-01-24T01:52:01.3957938Z along any dimension by using ``out_dims`` 2025-01-24T01:52:01.3958026Z 2025-01-24T01:52:01.3958141Z >>> f = lambda x: x ** 2 2025-01-24T01:52:01.3958245Z >>> x = torch.randn(2, 5) 2025-01-24T01:52:01.3958385Z >>> batched_pow = torch.vmap(f, out_dims=1) 2025-01-24T01:52:01.3958494Z >>> batched_pow(x) # [5, 2] 2025-01-24T01:52:01.3958580Z 2025-01-24T01:52:01.3958890Z For any function that uses kwargs, the returned function will not batch the kwargs but will 2025-01-24T01:52:01.3958987Z accept kwargs 2025-01-24T01:52:01.3959090Z 2025-01-24T01:52:01.3959201Z >>> x = torch.randn([2, 5]) 2025-01-24T01:52:01.3959319Z >>> def fn(x, scale=4.): 2025-01-24T01:52:01.3959424Z >>> return x * scale 2025-01-24T01:52:01.3959515Z >>> 2025-01-24T01:52:01.3959675Z >>> batched_pow = torch.vmap(fn) 2025-01-24T01:52:01.3959848Z >>> assert torch.allclose(batched_pow(x), x * 4) 2025-01-24T01:52:01.3960098Z >>> batched_pow(x, scale=x) # scale is not batched, output has shape [2, 2, 5] 2025-01-24T01:52:01.3960191Z 2025-01-24T01:52:01.3960292Z .. note:: 2025-01-24T01:52:01.3960529Z vmap does not provide general autobatching or handle variable-length 2025-01-24T01:52:01.3960638Z sequences out of the box. 2025-01-24T01:52:01.3960740Z 2025-01-24T01:52:01.3961001Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:52:01.3961103Z 2025-01-24T01:52:01.3961214Z warnings.warn(msg) 2025-01-24T01:52:01.3961309Z 2025-01-24T01:52:01.3961562Z --- Parse Warning: 21 / 106 --- 2025-01-24T01:52:01.3962454Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=triton_op in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/triton.py line=21. 2025-01-24T01:52:01.3962771Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:52:01.3963033Z Create a custom operator whose implementation is backed by 1+ triton kernels. 2025-01-24T01:52:01.3963140Z 2025-01-24T01:52:01.3963356Z This is a more structured way of using triton kernels with PyTorch. 2025-01-24T01:52:01.3963619Z Prefer using triton kernels with no ``torch.library`` custom operator wrappers 2025-01-24T01:52:01.3963891Z (like :func:`torch.library.custom_op`, :func:`torch.library.triton_op`) because 2025-01-24T01:52:01.3963996Z that is simpler; 2025-01-24T01:52:01.3964272Z only use :func:`torch.library.custom_op`/:func:`torch.library.triton_op` if you 2025-01-24T01:52:01.3964511Z want to create an operator that behaves like PyTorch built-in operators. 2025-01-24T01:52:01.3964745Z For example, you may use a ``torch.library`` wrapper API to define the 2025-01-24T01:52:01.3964976Z behavior of the triton kernel when passed a tensor subclass or under 2025-01-24T01:52:01.3965087Z a TorchDispatchMode. 2025-01-24T01:52:01.3965189Z 2025-01-24T01:52:01.3965447Z Use :func:`torch.library.triton_op` instead of :func:`torch.library.custom_op` 2025-01-24T01:52:01.3965573Z when the implementation 2025-01-24T01:52:01.3965802Z consists of 1+ triton kernels. :func:`torch.library.custom_op` treats 2025-01-24T01:52:01.3965998Z custom operators as opaque (:func:`torch.compile` and 2025-01-24T01:52:01.3966238Z :func:`torch.export.export` will never trace into them), but ``triton_op`` 2025-01-24T01:52:01.3966470Z makes the implementation visible to these subsystems, allowing them 2025-01-24T01:52:01.3966611Z to optimize the triton kernel(s). 2025-01-24T01:52:01.3966704Z 2025-01-24T01:52:01.3966915Z Note that ``fn`` must only consist of calls to PyTorch-understood 2025-01-24T01:52:01.3967157Z operators and triton kernels. Any triton kernels called inside ``fn`` 2025-01-24T01:52:01.3967362Z must be wrapped in a call to :func:`torch._library.wrap_triton``. 2025-01-24T01:52:01.3967462Z 2025-01-24T01:52:01.3967556Z Args: 2025-01-24T01:52:01.3967800Z name (str): A name for the custom op that looks like "{namespace}::{name}", 2025-01-24T01:52:01.3968019Z e.g. "mylib::my_linear". The name is used as the op's stable identifier 2025-01-24T01:52:01.3968206Z in PyTorch subsystems (e.g. torch.export, FX graphs). 2025-01-24T01:52:01.3968450Z To avoid name collisions, please use your project name as the namespace; 2025-01-24T01:52:01.3968675Z e.g. all custom ops in pytorch/fbgemm use "fbgemm" as the namespace. 2025-01-24T01:52:01.3968957Z mutates_args (Iterable[str] or "unknown"): The names of args that the function mutates. 2025-01-24T01:52:01.3969233Z This MUST be accurate, otherwise, the behavior is undefined. If "unknown", 2025-01-24T01:52:01.3969545Z it pessimistically assumes that all inputs to the operator are being mutated. 2025-01-24T01:52:01.3969738Z schema (None | str): A schema string for the operator. If None 2025-01-24T01:52:01.3969964Z (recommended) we'll infer a schema for the operator from its type 2025-01-24T01:52:01.3970177Z annotations. We recommend letting us infer a schema unless you 2025-01-24T01:52:01.3970316Z have a specific reason not to. 2025-01-24T01:52:01.3970476Z Example: "(Tensor x, int y) -> (Tensor, Tensor)". 2025-01-24T01:52:01.3970566Z 2025-01-24T01:52:01.3970676Z Example:: 2025-01-24T01:52:01.3970766Z 2025-01-24T01:52:01.3970955Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2025-01-24T01:52:01.3971061Z >>> import torch 2025-01-24T01:52:01.3971225Z >>> from torch.library import triton_op, wrap_triton 2025-01-24T01:52:01.3971361Z >>> 2025-01-24T01:52:01.3971464Z >>> import triton 2025-01-24T01:52:01.3971605Z >>> from triton import language as tl 2025-01-24T01:52:01.3971698Z >>> 2025-01-24T01:52:01.3971809Z >>> @triton.jit 2025-01-24T01:52:01.3971914Z >>> def add_kernel( 2025-01-24T01:52:01.3972014Z >>> in_ptr0, 2025-01-24T01:52:01.3972123Z >>> in_ptr1, 2025-01-24T01:52:01.3972222Z >>> out_ptr, 2025-01-24T01:52:01.3972335Z >>> n_elements, 2025-01-24T01:52:01.3972459Z >>> BLOCK_SIZE: "tl.constexpr", 2025-01-24T01:52:01.3972550Z >>> ): 2025-01-24T01:52:01.3972685Z >>> pid = tl.program_id(axis=0) 2025-01-24T01:52:01.3972815Z >>> block_start = pid * BLOCK_SIZE 2025-01-24T01:52:01.3972986Z >>> offsets = block_start + tl.arange(0, BLOCK_SIZE) 2025-01-24T01:52:01.3973106Z >>> mask = offsets < n_elements 2025-01-24T01:52:01.3973260Z >>> x = tl.load(in_ptr0 + offsets, mask=mask) 2025-01-24T01:52:01.3973398Z >>> y = tl.load(in_ptr1 + offsets, mask=mask) 2025-01-24T01:52:01.3973506Z >>> output = x + y 2025-01-24T01:52:01.3973672Z >>> tl.store(out_ptr + offsets, output, mask=mask) 2025-01-24T01:52:01.3973764Z >>> 2025-01-24T01:52:01.3973917Z >>> @triton_op("mylib::add", mutates_args={}) 2025-01-24T01:52:01.3974105Z >>> def add(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor: 2025-01-24T01:52:01.3974231Z >>> output = torch.empty_like(x) 2025-01-24T01:52:01.3974369Z >>> n_elements = output.numel() 2025-01-24T01:52:01.3974464Z >>> 2025-01-24T01:52:01.3974584Z >>> def grid(meta): 2025-01-24T01:52:01.3974763Z >>> return (triton.cdiv(n_elements, meta["BLOCK_SIZE"]),) 2025-01-24T01:52:01.3974869Z >>> 2025-01-24T01:52:01.3975061Z >>> # NB: we need to wrap the triton kernel in a call to wrap_triton 2025-01-24T01:52:01.3975258Z >>> wrap_triton(add_kernel)[grid](x, y, output, n_elements, 16) 2025-01-24T01:52:01.3975374Z >>> return output 2025-01-24T01:52:01.3975468Z >>> 2025-01-24T01:52:01.3975586Z >>> @torch.compile 2025-01-24T01:52:01.3975688Z >>> def f(x, y): 2025-01-24T01:52:01.3975794Z >>> return add(x, y) 2025-01-24T01:52:01.3975905Z >>> 2025-01-24T01:52:01.3976029Z >>> x = torch.randn(3, device="cuda") 2025-01-24T01:52:01.3976164Z >>> y = torch.randn(3, device="cuda") 2025-01-24T01:52:01.3976255Z >>> 2025-01-24T01:52:01.3976368Z >>> z = f(x, y) 2025-01-24T01:52:01.3976496Z >>> assert torch.allclose(z, x + y) 2025-01-24T01:52:01.3976586Z 2025-01-24T01:52:01.3976689Z 2025-01-24T01:52:01.3976978Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:52:01.3977107Z 2025-01-24T01:52:01.3977213Z warnings.warn(msg) 2025-01-24T01:52:01.3977304Z 2025-01-24T01:52:01.3977528Z --- Parse Warning: 22 / 106 --- 2025-01-24T01:52:01.3978398Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=wrap_triton in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/triton.py line=202. 2025-01-24T01:52:01.3978686Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:52:01.3978884Z Allows capture of a triton kernel into a graph via make_fx or 2025-01-24T01:52:01.3979012Z non-strict ``torch.export``. 2025-01-24T01:52:01.3979105Z 2025-01-24T01:52:01.3979532Z These technologies perform Dispatcher-based tracing (via 2025-01-24T01:52:01.3979755Z ``__torch_dispatch__``) and cannot see calls to raw triton kernels. 2025-01-24T01:52:01.3980001Z The ``wrap_triton`` API wraps a triton kernel into a callable that 2025-01-24T01:52:01.3980143Z can actually be traced into a graph. 2025-01-24T01:52:01.3980233Z 2025-01-24T01:52:01.3980465Z Please use this API together with :func:`torch.library.triton_op`. 2025-01-24T01:52:01.3980554Z 2025-01-24T01:52:01.3980651Z Examples: 2025-01-24T01:52:01.3980753Z 2025-01-24T01:52:01.3980860Z >>> # xdoctest: +SKIP 2025-01-24T01:52:01.3980978Z >>> import torch 2025-01-24T01:52:01.3981079Z >>> import triton 2025-01-24T01:52:01.3981210Z >>> from triton import language as tl 2025-01-24T01:52:01.3981477Z >>> from torch.fx.experimental.proxy_tensor import make_fx 2025-01-24T01:52:01.3981616Z >>> from torch.library import wrap_triton 2025-01-24T01:52:01.3981723Z >>> 2025-01-24T01:52:01.3981824Z >>> @triton.jit 2025-01-24T01:52:01.3981929Z >>> def add_kernel( 2025-01-24T01:52:01.3982046Z >>> in_ptr0, 2025-01-24T01:52:01.3982147Z >>> in_ptr1, 2025-01-24T01:52:01.3982256Z >>> out_ptr, 2025-01-24T01:52:01.3982355Z >>> n_elements, 2025-01-24T01:52:01.3982495Z >>> BLOCK_SIZE: "tl.constexpr", 2025-01-24T01:52:01.3982587Z >>> ): 2025-01-24T01:52:01.3982709Z >>> pid = tl.program_id(axis=0) 2025-01-24T01:52:01.3982844Z >>> block_start = pid * BLOCK_SIZE 2025-01-24T01:52:01.3983000Z >>> offsets = block_start + tl.arange(0, BLOCK_SIZE) 2025-01-24T01:52:01.3983131Z >>> mask = offsets < n_elements 2025-01-24T01:52:01.3983268Z >>> x = tl.load(in_ptr0 + offsets, mask=mask) 2025-01-24T01:52:01.3983408Z >>> y = tl.load(in_ptr1 + offsets, mask=mask) 2025-01-24T01:52:01.3983524Z >>> output = x + y 2025-01-24T01:52:01.3983679Z >>> tl.store(out_ptr + offsets, output, mask=mask) 2025-01-24T01:52:01.3983791Z >>> 2025-01-24T01:52:01.3983892Z >>> def add(x, y): 2025-01-24T01:52:01.3984030Z >>> output = torch.empty_like(x) 2025-01-24T01:52:01.3984151Z >>> n_elements = output.numel() 2025-01-24T01:52:01.3984242Z >>> 2025-01-24T01:52:01.3984362Z >>> def grid_fn(meta): 2025-01-24T01:52:01.3984542Z >>> return (triton.cdiv(n_elements, meta["BLOCK_SIZE"]),) 2025-01-24T01:52:01.3984647Z >>> 2025-01-24T01:52:01.3984851Z >>> wrap_triton(add_kernel)[grid_fn](x, y, output, n_elements, 16) 2025-01-24T01:52:01.3984967Z >>> return output 2025-01-24T01:52:01.3985058Z >>> 2025-01-24T01:52:01.3985183Z >>> x = torch.randn(3, device="cuda") 2025-01-24T01:52:01.3985323Z >>> y = torch.randn(3, device="cuda") 2025-01-24T01:52:01.3985435Z >>> gm = make_fx(add)(x, y) 2025-01-24T01:52:01.3985555Z >>> print(gm.code) 2025-01-24T01:52:01.3985759Z >>> # def forward(self, x_1, y_1): 2025-01-24T01:52:01.3986004Z >>> # empty_like = torch.ops.aten.empty_like.default(x_1, pin_memory = False) 2025-01-24T01:52:01.3986353Z >>> # triton_kernel_wrapper_mutation_proxy = triton_kernel_wrapper_mutation( 2025-01-24T01:52:01.3986500Z >>> # kernel_idx = 0, constant_args_idx = 0, 2025-01-24T01:52:01.3986637Z >>> # grid = [(1, 1, 1)], kwargs = { 2025-01-24T01:52:01.3986801Z >>> # 'in_ptr0': x_1, 'in_ptr1': y_1, 'out_ptr': empty_like, 2025-01-24T01:52:01.3986949Z >>> # 'n_elements': 3, 'BLOCK_SIZE': 16 2025-01-24T01:52:01.3987048Z >>> # }) 2025-01-24T01:52:01.3987191Z >>> # return empty_like 2025-01-24T01:52:01.3987296Z 2025-01-24T01:52:01.3987390Z 2025-01-24T01:52:01.3987664Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:52:01.3987784Z 2025-01-24T01:52:01.3987889Z warnings.warn(msg) 2025-01-24T01:52:01.3987997Z 2025-01-24T01:52:01.3988227Z --- Parse Warning: 23 / 106 --- 2025-01-24T01:52:01.3989174Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=assert_almost_equal in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py line=326. 2025-01-24T01:52:01.3989449Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:52:01.3989552Z 2025-01-24T01:52:01.3989770Z Raises an AssertionError if two items are not equal up to desired 2025-01-24T01:52:01.3989871Z precision. 2025-01-24T01:52:01.3989979Z 2025-01-24T01:52:01.3990167Z .. note:: It is recommended to use one of `assert_allclose`, 2025-01-24T01:52:01.3990366Z `assert_array_almost_equal_nulp` or `assert_array_max_ulp` 2025-01-24T01:52:01.3990570Z instead of this function for more consistent floating point 2025-01-24T01:52:01.3990692Z comparisons. 2025-01-24T01:52:01.3990783Z 2025-01-24T01:52:01.3991010Z The test verifies that the elements of `actual` and `desired` satisfy. 2025-01-24T01:52:01.3991118Z 2025-01-24T01:52:01.3991285Z ``abs(desired-actual) < float64(1.5 * 10**(-decimal))`` 2025-01-24T01:52:01.3991388Z 2025-01-24T01:52:01.3991625Z That is a looser test than originally documented, but agrees with what the 2025-01-24T01:52:01.3991883Z actual implementation in `assert_array_almost_equal` did up to rounding 2025-01-24T01:52:01.3992127Z vagaries. An exception is raised at conflicting values. For ndarrays this 2025-01-24T01:52:01.3992256Z delegates to assert_array_almost_equal 2025-01-24T01:52:01.3992365Z 2025-01-24T01:52:01.3992465Z Parameters 2025-01-24T01:52:01.3992576Z ---------- 2025-01-24T01:52:01.3992682Z actual : array_like 2025-01-24T01:52:01.3992796Z The object to check. 2025-01-24T01:52:01.3992916Z desired : array_like 2025-01-24T01:52:01.3993026Z The expected object. 2025-01-24T01:52:01.3993152Z decimal : int, optional 2025-01-24T01:52:01.3993280Z Desired precision, default is 7. 2025-01-24T01:52:01.3993387Z err_msg : str, optional 2025-01-24T01:52:01.3993565Z The error message to be printed in case of failure. 2025-01-24T01:52:01.3993675Z verbose : bool, optional 2025-01-24T01:52:01.3993903Z If True, the conflicting values are appended to the error message. 2025-01-24T01:52:01.3993994Z 2025-01-24T01:52:01.3994107Z Raises 2025-01-24T01:52:01.3994204Z ------ 2025-01-24T01:52:01.3994304Z AssertionError 2025-01-24T01:52:01.3994520Z If actual and desired are not equal up to specified precision. 2025-01-24T01:52:01.3994617Z 2025-01-24T01:52:01.3994727Z See Also 2025-01-24T01:52:01.3994824Z -------- 2025-01-24T01:52:01.3995073Z assert_allclose: Compare two array_like objects for equality with desired 2025-01-24T01:52:01.3995288Z relative and/or absolute precision. 2025-01-24T01:52:01.3995513Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2025-01-24T01:52:01.3995617Z 2025-01-24T01:52:01.3995712Z Examples 2025-01-24T01:52:01.3995809Z -------- 2025-01-24T01:52:01.3995990Z >>> from torch._numpy.testing import assert_almost_equal 2025-01-24T01:52:01.3996130Z >>> assert_almost_equal(2.3333333333333, 2.33333334) 2025-01-24T01:52:01.3996321Z >>> assert_almost_equal(2.3333333333333, 2.33333334, decimal=10) 2025-01-24T01:52:01.3996443Z Traceback (most recent call last): 2025-01-24T01:52:01.3996550Z ... 2025-01-24T01:52:01.3996649Z AssertionError: 2025-01-24T01:52:01.3996803Z Arrays are not almost equal to 10 decimals 2025-01-24T01:52:01.3996914Z ACTUAL: 2.3333333333333 2025-01-24T01:52:01.3997015Z DESIRED: 2.33333334 2025-01-24T01:52:01.3997144Z 2025-01-24T01:52:01.3997304Z >>> assert_almost_equal(np.array([1.0,2.3333333333333]), 2025-01-24T01:52:01.3997439Z ... np.array([1.0,2.33333334]), decimal=9) 2025-01-24T01:52:01.3997569Z Traceback (most recent call last): 2025-01-24T01:52:01.3997660Z ... 2025-01-24T01:52:01.3997773Z AssertionError: 2025-01-24T01:52:01.3997902Z Arrays are not almost equal to 9 decimals 2025-01-24T01:52:01.3998000Z 2025-01-24T01:52:01.3998127Z Mismatched elements: 1 / 2 (50%) 2025-01-24T01:52:01.3998268Z Max absolute difference: 6.666699636781459e-09 2025-01-24T01:52:01.3998418Z Max relative difference: 2.8571569790287484e-09 2025-01-24T01:52:01.3998564Z x: torch.ndarray([1.0000, 2.3333], dtype=float64) 2025-01-24T01:52:01.3998718Z y: torch.ndarray([1.0000, 2.3333], dtype=float64) 2025-01-24T01:52:01.3998811Z 2025-01-24T01:52:01.3998899Z 2025-01-24T01:52:01.3999170Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:52:01.3999266Z 2025-01-24T01:52:01.3999390Z warnings.warn(msg) 2025-01-24T01:52:01.3999480Z 2025-01-24T01:52:01.3999691Z --- Parse Warning: 24 / 106 --- 2025-01-24T01:52:01.4000638Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=assert_approx_equal in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py line=451. 2025-01-24T01:52:01.4000913Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:52:01.4001019Z 2025-01-24T01:52:01.4001253Z Raises an AssertionError if two items are not equal up to significant 2025-01-24T01:52:01.4001364Z digits. 2025-01-24T01:52:01.4001456Z 2025-01-24T01:52:01.4001643Z .. note:: It is recommended to use one of `assert_allclose`, 2025-01-24T01:52:01.4001845Z `assert_array_almost_equal_nulp` or `assert_array_max_ulp` 2025-01-24T01:52:01.4002043Z instead of this function for more consistent floating point 2025-01-24T01:52:01.4002161Z comparisons. 2025-01-24T01:52:01.4002252Z 2025-01-24T01:52:01.4002458Z Given two numbers, check that they are approximately equal. 2025-01-24T01:52:01.4002683Z Approximately equal is defined as the number of significant digits 2025-01-24T01:52:01.4002781Z that agree. 2025-01-24T01:52:01.4002885Z 2025-01-24T01:52:01.4002983Z Parameters 2025-01-24T01:52:01.4003095Z ---------- 2025-01-24T01:52:01.4003194Z actual : scalar 2025-01-24T01:52:01.4003302Z The object to check. 2025-01-24T01:52:01.4003415Z desired : scalar 2025-01-24T01:52:01.4003525Z The expected object. 2025-01-24T01:52:01.4003653Z significant : int, optional 2025-01-24T01:52:01.4003779Z Desired precision, default is 7. 2025-01-24T01:52:01.4003888Z err_msg : str, optional 2025-01-24T01:52:01.4004066Z The error message to be printed in case of failure. 2025-01-24T01:52:01.4004209Z verbose : bool, optional 2025-01-24T01:52:01.4004464Z If True, the conflicting values are appended to the error message. 2025-01-24T01:52:01.4004554Z 2025-01-24T01:52:01.4004664Z Raises 2025-01-24T01:52:01.4004760Z ------ 2025-01-24T01:52:01.4004865Z AssertionError 2025-01-24T01:52:01.4005080Z If actual and desired are not equal up to specified precision. 2025-01-24T01:52:01.4005173Z 2025-01-24T01:52:01.4005280Z See Also 2025-01-24T01:52:01.4005375Z -------- 2025-01-24T01:52:01.4005624Z assert_allclose: Compare two array_like objects for equality with desired 2025-01-24T01:52:01.4005776Z relative and/or absolute precision. 2025-01-24T01:52:01.4005994Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2025-01-24T01:52:01.4006099Z 2025-01-24T01:52:01.4006219Z Examples 2025-01-24T01:52:01.4006314Z -------- 2025-01-24T01:52:01.4006605Z >>> np.testing.assert_approx_equal(0.12345677777777e-20, 0.1234567e-20) # doctest: +SKIP 2025-01-24T01:52:01.4006896Z >>> np.testing.assert_approx_equal(0.12345670e-20, 0.12345671e-20, # doctest: +SKIP 2025-01-24T01:52:01.4007034Z ... significant=8) 2025-01-24T01:52:01.4007290Z >>> np.testing.assert_approx_equal(0.12345670e-20, 0.12345672e-20, # doctest: +SKIP 2025-01-24T01:52:01.4007429Z ... significant=8) 2025-01-24T01:52:01.4007551Z Traceback (most recent call last): 2025-01-24T01:52:01.4007644Z ... 2025-01-24T01:52:01.4007758Z AssertionError: 2025-01-24T01:52:01.4007898Z Items are not equal to 8 significant digits: 2025-01-24T01:52:01.4008012Z ACTUAL: 1.234567e-21 2025-01-24T01:52:01.4008115Z DESIRED: 1.2345672e-21 2025-01-24T01:52:01.4008220Z 2025-01-24T01:52:01.4008392Z the evaluated condition that raises the exception is 2025-01-24T01:52:01.4008481Z 2025-01-24T01:52:01.4008670Z >>> abs(0.12345670e-20/1e-21 - 0.12345672e-20/1e-21) >= 10**-(8-1) 2025-01-24T01:52:01.4008767Z True 2025-01-24T01:52:01.4008870Z 2025-01-24T01:52:01.4008961Z 2025-01-24T01:52:01.4009223Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:52:01.4009328Z 2025-01-24T01:52:01.4009435Z warnings.warn(msg) 2025-01-24T01:52:01.4009537Z 2025-01-24T01:52:01.4009740Z --- Parse Warning: 25 / 106 --- 2025-01-24T01:52:01.4010668Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=assert_array_equal in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py line=730. 2025-01-24T01:52:01.4010950Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:52:01.4011040Z 2025-01-24T01:52:01.4011270Z Raises an AssertionError if two array_like objects are not equal. 2025-01-24T01:52:01.4011361Z 2025-01-24T01:52:01.4011590Z Given two array_like objects, check that the shape is equal and all 2025-01-24T01:52:01.4011822Z elements of these objects are equal (but see the Notes for the special 2025-01-24T01:52:01.4012031Z handling of a scalar). An exception is raised at shape mismatch or 2025-01-24T01:52:01.4012275Z conflicting values. In contrast to the standard usage in numpy, NaNs 2025-01-24T01:52:01.4012500Z are compared like numbers, no assertion is raised if both objects have 2025-01-24T01:52:01.4012625Z NaNs in the same positions. 2025-01-24T01:52:01.4012715Z 2025-01-24T01:52:01.4012960Z The usual caution for verifying equality with floating point numbers is 2025-01-24T01:52:01.4013054Z advised. 2025-01-24T01:52:01.4013141Z 2025-01-24T01:52:01.4013253Z Parameters 2025-01-24T01:52:01.4013349Z ---------- 2025-01-24T01:52:01.4013460Z x : array_like 2025-01-24T01:52:01.4013573Z The actual object to check. 2025-01-24T01:52:01.4013669Z y : array_like 2025-01-24T01:52:01.4013805Z The desired, expected object. 2025-01-24T01:52:01.4013971Z err_msg : str, optional 2025-01-24T01:52:01.4014151Z The error message to be printed in case of failure. 2025-01-24T01:52:01.4014260Z verbose : bool, optional 2025-01-24T01:52:01.4014483Z If True, the conflicting values are appended to the error message. 2025-01-24T01:52:01.4014589Z strict : bool, optional 2025-01-24T01:52:01.4014798Z If True, raise an AssertionError when either the shape or the data 2025-01-24T01:52:01.4014997Z type of the array_like objects does not match. The special 2025-01-24T01:52:01.4015206Z handling for scalars mentioned in the Notes section is disabled. 2025-01-24T01:52:01.4015307Z 2025-01-24T01:52:01.4015401Z Raises 2025-01-24T01:52:01.4015496Z ------ 2025-01-24T01:52:01.4015634Z AssertionError 2025-01-24T01:52:01.4015780Z If actual and desired objects are not equal. 2025-01-24T01:52:01.4015883Z 2025-01-24T01:52:01.4016018Z See Also 2025-01-24T01:52:01.4016112Z -------- 2025-01-24T01:52:01.4016373Z assert_allclose: Compare two array_like objects for equality with desired 2025-01-24T01:52:01.4016509Z relative and/or absolute precision. 2025-01-24T01:52:01.4016738Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2025-01-24T01:52:01.4016828Z 2025-01-24T01:52:01.4016927Z Notes 2025-01-24T01:52:01.4017021Z ----- 2025-01-24T01:52:01.4017214Z When one of `x` and `y` is a scalar and the other is array_like, the 2025-01-24T01:52:01.4017457Z function checks that each element of the array_like object is equal to 2025-01-24T01:52:01.4017690Z the scalar. This behaviour can be disabled with the `strict` parameter. 2025-01-24T01:52:01.4017791Z 2025-01-24T01:52:01.4017892Z Examples 2025-01-24T01:52:01.4017992Z -------- 2025-01-24T01:52:01.4018148Z The first assert does not raise an exception: 2025-01-24T01:52:01.4018241Z 2025-01-24T01:52:01.4018419Z >>> np.testing.assert_array_equal([1.0,2.33333,np.nan], 2025-01-24T01:52:01.4018561Z ... [np.exp(0),2.33333, np.nan]) 2025-01-24T01:52:01.4018670Z 2025-01-24T01:52:01.4018911Z Use `assert_allclose` or one of the nulp (number of floating point values) 2025-01-24T01:52:01.4019034Z functions for these cases instead: 2025-01-24T01:52:01.4019141Z 2025-01-24T01:52:01.4019297Z >>> np.testing.assert_allclose([1.0,np.pi,np.nan], 2025-01-24T01:52:01.4019451Z ... [1, np.sqrt(np.pi)**2, np.nan], 2025-01-24T01:52:01.4019577Z ... rtol=1e-10, atol=0) 2025-01-24T01:52:01.4019669Z 2025-01-24T01:52:01.4019900Z As mentioned in the Notes section, `assert_array_equal` has special 2025-01-24T01:52:01.4020137Z handling for scalars. Here the test checks that each value in `x` is 3: 2025-01-24T01:52:01.4020244Z 2025-01-24T01:52:01.4020364Z >>> x = np.full((2, 5), fill_value=3) 2025-01-24T01:52:01.4020511Z >>> np.testing.assert_array_equal(x, 3) 2025-01-24T01:52:01.4020606Z 2025-01-24T01:52:01.4020832Z Use `strict` to raise an AssertionError when comparing a scalar with an 2025-01-24T01:52:01.4020941Z array: 2025-01-24T01:52:01.4021034Z 2025-01-24T01:52:01.4021207Z >>> np.testing.assert_array_equal(x, 3, strict=True) 2025-01-24T01:52:01.4021332Z Traceback (most recent call last): 2025-01-24T01:52:01.4021428Z ... 2025-01-24T01:52:01.4021548Z AssertionError: 2025-01-24T01:52:01.4021655Z Arrays are not equal 2025-01-24T01:52:01.4021767Z 2025-01-24T01:52:01.4021874Z (shapes (2, 5), () mismatch) 2025-01-24T01:52:01.4021989Z x: torch.ndarray([[3, 3, 3, 3, 3], 2025-01-24T01:52:01.4022105Z [3, 3, 3, 3, 3]]) 2025-01-24T01:52:01.4022212Z y: torch.ndarray(3) 2025-01-24T01:52:01.4022321Z 2025-01-24T01:52:01.4022545Z The `strict` parameter also ensures that the array data types match: 2025-01-24T01:52:01.4022649Z 2025-01-24T01:52:01.4022783Z >>> x = np.array([2, 2, 2]) 2025-01-24T01:52:01.4022944Z >>> y = np.array([2., 2., 2.], dtype=np.float32) 2025-01-24T01:52:01.4023116Z >>> np.testing.assert_array_equal(x, y, strict=True) 2025-01-24T01:52:01.4023237Z Traceback (most recent call last): 2025-01-24T01:52:01.4023341Z ... 2025-01-24T01:52:01.4023443Z AssertionError: 2025-01-24T01:52:01.4023548Z Arrays are not equal 2025-01-24T01:52:01.4023656Z 2025-01-24T01:52:01.4023809Z (dtypes dtype("int64"), dtype("float32") mismatch) 2025-01-24T01:52:01.4023931Z x: torch.ndarray([2, 2, 2]) 2025-01-24T01:52:01.4024040Z y: torch.ndarray([2., 2., 2.]) 2025-01-24T01:52:01.4024128Z 2025-01-24T01:52:01.4024402Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:52:01.4024518Z 2025-01-24T01:52:01.4024634Z warnings.warn(msg) 2025-01-24T01:52:01.4024725Z 2025-01-24T01:52:01.4024930Z --- Parse Warning: 26 / 106 --- 2025-01-24T01:52:01.4025932Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=assert_array_almost_equal in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py line=836. 2025-01-24T01:52:01.4026205Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:52:01.4026398Z 2025-01-24T01:52:01.4026629Z Raises an AssertionError if two objects are not equal up to desired 2025-01-24T01:52:01.4026742Z precision. 2025-01-24T01:52:01.4026832Z 2025-01-24T01:52:01.4027036Z .. note:: It is recommended to use one of `assert_allclose`, 2025-01-24T01:52:01.4027221Z `assert_array_almost_equal_nulp` or `assert_array_max_ulp` 2025-01-24T01:52:01.4027416Z instead of this function for more consistent floating point 2025-01-24T01:52:01.4027536Z comparisons. 2025-01-24T01:52:01.4027628Z 2025-01-24T01:52:01.4027892Z The test verifies identical shapes and that the elements of ``actual`` and 2025-01-24T01:52:01.4028000Z ``desired`` satisfy. 2025-01-24T01:52:01.4028090Z 2025-01-24T01:52:01.4028250Z ``abs(desired-actual) < 1.5 * 10**(-decimal)`` 2025-01-24T01:52:01.4028342Z 2025-01-24T01:52:01.4028588Z That is a looser test than originally documented, but agrees with what the 2025-01-24T01:52:01.4028840Z actual implementation did up to rounding vagaries. An exception is raised 2025-01-24T01:52:01.4029096Z at shape mismatch or conflicting values. In contrast to the standard usage 2025-01-24T01:52:01.4029322Z in numpy, NaNs are compared like numbers, no assertion is raised if both 2025-01-24T01:52:01.4029456Z objects have NaNs in the same positions. 2025-01-24T01:52:01.4029561Z 2025-01-24T01:52:01.4029662Z Parameters 2025-01-24T01:52:01.4029771Z ---------- 2025-01-24T01:52:01.4029867Z x : array_like 2025-01-24T01:52:01.4029979Z The actual object to check. 2025-01-24T01:52:01.4030095Z y : array_like 2025-01-24T01:52:01.4030214Z The desired, expected object. 2025-01-24T01:52:01.4030332Z decimal : int, optional 2025-01-24T01:52:01.4030457Z Desired precision, default is 6. 2025-01-24T01:52:01.4030563Z err_msg : str, optional 2025-01-24T01:52:01.4030741Z The error message to be printed in case of failure. 2025-01-24T01:52:01.4030849Z verbose : bool, optional 2025-01-24T01:52:01.4031075Z If True, the conflicting values are appended to the error message. 2025-01-24T01:52:01.4031167Z 2025-01-24T01:52:01.4031274Z Raises 2025-01-24T01:52:01.4031366Z ------ 2025-01-24T01:52:01.4031466Z AssertionError 2025-01-24T01:52:01.4031683Z If actual and desired are not equal up to specified precision. 2025-01-24T01:52:01.4031774Z 2025-01-24T01:52:01.4031884Z See Also 2025-01-24T01:52:01.4031980Z -------- 2025-01-24T01:52:01.4032225Z assert_allclose: Compare two array_like objects for equality with desired 2025-01-24T01:52:01.4032440Z relative and/or absolute precision. 2025-01-24T01:52:01.4032657Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2025-01-24T01:52:01.4032761Z 2025-01-24T01:52:01.4032857Z Examples 2025-01-24T01:52:01.4032964Z -------- 2025-01-24T01:52:01.4033103Z the first assert does not raise an exception 2025-01-24T01:52:01.4033192Z 2025-01-24T01:52:01.4033386Z >>> np.testing.assert_array_almost_equal([1.0,2.333,np.nan], 2025-01-24T01:52:01.4033510Z ... [1.0,2.333,np.nan]) 2025-01-24T01:52:01.4033614Z 2025-01-24T01:52:01.4033801Z >>> np.testing.assert_array_almost_equal([1.0,2.33333,np.nan], 2025-01-24T01:52:01.4033937Z ... [1.0,2.33339,np.nan], decimal=5) 2025-01-24T01:52:01.4034094Z Traceback (most recent call last): 2025-01-24T01:52:01.4034189Z ... 2025-01-24T01:52:01.4034304Z AssertionError: 2025-01-24T01:52:01.4034461Z Arrays are not almost equal to 5 decimals 2025-01-24T01:52:01.4034562Z 2025-01-24T01:52:01.4034691Z Mismatched elements: 1 / 3 (33.3%) 2025-01-24T01:52:01.4034832Z Max absolute difference: 5.999999999994898e-05 2025-01-24T01:52:01.4034983Z Max relative difference: 2.5713661239633743e-05 2025-01-24T01:52:01.4035157Z x: torch.ndarray([1.0000, 2.3333, nan], dtype=float64) 2025-01-24T01:52:01.4035335Z y: torch.ndarray([1.0000, 2.3334, nan], dtype=float64) 2025-01-24T01:52:01.4035426Z 2025-01-24T01:52:01.4035612Z >>> np.testing.assert_array_almost_equal([1.0,2.33333,np.nan], 2025-01-24T01:52:01.4035751Z ... [1.0,2.33333, 5], decimal=5) 2025-01-24T01:52:01.4035872Z Traceback (most recent call last): 2025-01-24T01:52:01.4035979Z ... 2025-01-24T01:52:01.4036080Z AssertionError: 2025-01-24T01:52:01.4036210Z Arrays are not almost equal to 5 decimals 2025-01-24T01:52:01.4036320Z 2025-01-24T01:52:01.4036439Z x and y nan location mismatch: 2025-01-24T01:52:01.4036621Z x: torch.ndarray([1.0000, 2.3333, nan], dtype=float64) 2025-01-24T01:52:01.4036788Z y: torch.ndarray([1.0000, 2.3333, 5.0000], dtype=float64) 2025-01-24T01:52:01.4036892Z 2025-01-24T01:52:01.4036984Z 2025-01-24T01:52:01.4037245Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:52:01.4037349Z 2025-01-24T01:52:01.4037456Z warnings.warn(msg) 2025-01-24T01:52:01.4037560Z 2025-01-24T01:52:01.4037784Z --- Parse Warning: 27 / 106 --- 2025-01-24T01:52:01.4038748Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=clear_and_catch_warnings in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py line=1786. 2025-01-24T01:52:01.4039034Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:52:01.4039265Z Context manager that resets warning registry for catching warnings 2025-01-24T01:52:01.4039369Z 2025-01-24T01:52:01.4039621Z Warnings can be slippery, because, whenever a warning is triggered, Python 2025-01-24T01:52:01.4039872Z adds a ``__warningregistry__`` member to the *calling* module. This makes 2025-01-24T01:52:01.4040117Z it impossible to retrigger the warning in this module, whatever you put in 2025-01-24T01:52:01.4040379Z the warnings filters. This context manager accepts a sequence of `modules` 2025-01-24T01:52:01.4040529Z as a keyword argument to its constructor and: 2025-01-24T01:52:01.4040621Z 2025-01-24T01:52:01.4040869Z * stores and removes any ``__warningregistry__`` entries in given `modules` 2025-01-24T01:52:01.4040963Z on entry; 2025-01-24T01:52:01.4041172Z * resets ``__warningregistry__`` to its previous state on exit. 2025-01-24T01:52:01.4041263Z 2025-01-24T01:52:01.4041525Z This makes it possible to trigger any warning afresh inside the context 2025-01-24T01:52:01.4041758Z manager without disturbing the state of warnings outside. 2025-01-24T01:52:01.4041853Z 2025-01-24T01:52:01.4042108Z For compatibility with Python 3.0, please consider all arguments to be 2025-01-24T01:52:01.4042209Z keyword-only. 2025-01-24T01:52:01.4042317Z 2025-01-24T01:52:01.4042418Z Parameters 2025-01-24T01:52:01.4042518Z ---------- 2025-01-24T01:52:01.4042641Z record : bool, optional 2025-01-24T01:52:01.4042842Z Specifies whether warnings should be captured by a custom 2025-01-24T01:52:01.4043099Z implementation of ``warnings.showwarning()`` and be appended to a list 2025-01-24T01:52:01.4043341Z returned by the context manager. Otherwise None is returned by the 2025-01-24T01:52:01.4043591Z context manager. The objects appended to the list are arguments whose 2025-01-24T01:52:01.4043797Z attributes mirror the arguments to ``showwarning()``. 2025-01-24T01:52:01.4043918Z modules : sequence, optional 2025-01-24T01:52:01.4044162Z Sequence of modules for which to reset warnings registry on entry and 2025-01-24T01:52:01.4044361Z restore on exit. To work correctly, all 'ignore' filters should 2025-01-24T01:52:01.4044496Z filter by one of these modules. 2025-01-24T01:52:01.4044588Z 2025-01-24T01:52:01.4044683Z Examples 2025-01-24T01:52:01.4054390Z -------- 2025-01-24T01:52:01.4054550Z >>> import warnings 2025-01-24T01:52:01.4054778Z >>> with np.testing.clear_and_catch_warnings( # doctest: +SKIP 2025-01-24T01:52:01.4054914Z ... modules=[np.core.fromnumeric]): 2025-01-24T01:52:01.4055047Z ... warnings.simplefilter('always') 2025-01-24T01:52:01.4055302Z ... warnings.filterwarnings('ignore', module='np.core.fromnumeric') 2025-01-24T01:52:01.4055482Z ... # do something that raises a warning but ignore those in 2025-01-24T01:52:01.4055623Z ... # np.core.fromnumeric 2025-01-24T01:52:01.4055716Z 2025-01-24T01:52:01.4055991Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:52:01.4056084Z 2025-01-24T01:52:01.4056188Z warnings.warn(msg) 2025-01-24T01:52:01.4056290Z 2025-01-24T01:52:01.4056549Z --- Parse Warning: 28 / 106 --- 2025-01-24T01:52:01.4057478Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=Conv1d in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/conv.py line=354. 2025-01-24T01:52:01.4057763Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:52:01.4057985Z Applies a 1D convolution over a quantized input signal composed of 2025-01-24T01:52:01.4058118Z several quantized input planes. 2025-01-24T01:52:01.4058208Z 2025-01-24T01:52:01.4058445Z For details on input arguments, parameters, and implementation see 2025-01-24T01:52:01.4058557Z :class:`~torch.nn.Conv1d`. 2025-01-24T01:52:01.4058646Z 2025-01-24T01:52:01.4058764Z .. note:: 2025-01-24T01:52:01.4058963Z Only `zeros` is supported for the :attr:`padding_mode` argument. 2025-01-24T01:52:01.4059064Z 2025-01-24T01:52:01.4059156Z .. note:: 2025-01-24T01:52:01.4059351Z Only `torch.quint8` is supported for the input data type. 2025-01-24T01:52:01.4059441Z 2025-01-24T01:52:01.4059531Z 2025-01-24T01:52:01.4059640Z Attributes: 2025-01-24T01:52:01.4059854Z weight (Tensor): packed tensor derived from the learnable weight 2025-01-24T01:52:01.4059978Z parameter. 2025-01-24T01:52:01.4060136Z scale (Tensor): scalar for the output scale 2025-01-24T01:52:01.4060308Z zero_point (Tensor): scalar for the output zero point 2025-01-24T01:52:01.4060508Z 2025-01-24T01:52:01.4060695Z See :class:`~torch.nn.Conv1d` for other attributes. 2025-01-24T01:52:01.4060801Z 2025-01-24T01:52:01.4060898Z Examples:: 2025-01-24T01:52:01.4060985Z 2025-01-24T01:52:01.4061156Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_QENGINE) 2025-01-24T01:52:01.4061297Z >>> m = nn.quantized.Conv1d(16, 33, 3, stride=2) 2025-01-24T01:52:01.4061429Z >>> input = torch.randn(20, 16, 100) 2025-01-24T01:52:01.4061546Z >>> # quantize input to quint8 2025-01-24T01:52:01.4061664Z >>> # xdoctest: +SKIP 2025-01-24T01:52:01.4061886Z >>> q_input = torch.quantize_per_tensor(input, scale=1.0, zero_point=0, 2025-01-24T01:52:01.4062022Z ... dtype=torch.quint8) 2025-01-24T01:52:01.4062258Z >>> output = m(q_input) 2025-01-24T01:52:01.4062347Z 2025-01-24T01:52:01.4062455Z 2025-01-24T01:52:01.4062750Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:52:01.4062854Z 2025-01-24T01:52:01.4062961Z warnings.warn(msg) 2025-01-24T01:52:01.4063047Z 2025-01-24T01:52:01.4063264Z --- Parse Warning: 29 / 106 --- 2025-01-24T01:52:01.4064164Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=LSTM in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/rnn.py line=11. 2025-01-24T01:52:01.4064447Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:52:01.4064587Z A quantized long short-term memory (LSTM). 2025-01-24T01:52:01.4064675Z 2025-01-24T01:52:01.4064974Z For the description and the argument types, please, refer to :class:`~torch.nn.LSTM` 2025-01-24T01:52:01.4065064Z 2025-01-24T01:52:01.4065170Z Attributes: 2025-01-24T01:52:01.4065302Z layers : instances of the `_LSTMLayer` 2025-01-24T01:52:01.4065406Z 2025-01-24T01:52:01.4065508Z .. note:: 2025-01-24T01:52:01.4065728Z To access the weights and biases, you need to access them per layer. 2025-01-24T01:52:01.4065924Z See examples in :class:`~torch.ao.nn.quantizable.LSTM` 2025-01-24T01:52:01.4066013Z 2025-01-24T01:52:01.4066124Z Examples:: 2025-01-24T01:52:01.4066351Z >>> # xdoctest: +SKIP 2025-01-24T01:52:01.4066474Z >>> custom_module_config = { 2025-01-24T01:52:01.4066636Z ... 'float_to_observed_custom_module_class': { 2025-01-24T01:52:01.4066772Z ... nn.LSTM: nn.quantizable.LSTM, 2025-01-24T01:52:01.4066879Z ... }, 2025-01-24T01:52:01.4067038Z ... 'observed_to_quantized_custom_module_class': { 2025-01-24T01:52:01.4067201Z ... nn.quantizable.LSTM: nn.quantized.LSTM, 2025-01-24T01:52:01.4067298Z ... } 2025-01-24T01:52:01.4067390Z ... } 2025-01-24T01:52:01.4067636Z >>> tq.prepare(model, prepare_custom_module_class=custom_module_config) 2025-01-24T01:52:01.4067857Z >>> tq.convert(model, convert_custom_module_class=custom_module_config) 2025-01-24T01:52:01.4067959Z 2025-01-24T01:52:01.4068218Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:52:01.4068319Z 2025-01-24T01:52:01.4068425Z warnings.warn(msg) 2025-01-24T01:52:01.4068512Z 2025-01-24T01:52:01.4068728Z --- Parse Warning: 30 / 106 --- 2025-01-24T01:52:01.4069799Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=BaseSparsifier.squash_mask in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/pruning/sparsifier/base_sparsifier.py line=227. 2025-01-24T01:52:01.4070079Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:52:01.4070263Z Squashes the sparse masks into the appropriate tensors. 2025-01-24T01:52:01.4070428Z 2025-01-24T01:52:01.4070643Z If either the `params_to_keep` or `params_to_keep_per_layer` is set, 2025-01-24T01:52:01.4070834Z the module will have a `sparse_params` dict attached to it. 2025-01-24T01:52:01.4070938Z 2025-01-24T01:52:01.4071033Z Args: 2025-01-24T01:52:01.4071233Z params_to_keep: List of keys to save in the module or a dict 2025-01-24T01:52:01.4071400Z representing the modules and keys that will have 2025-01-24T01:52:01.4071534Z sparsity parameters saved 2025-01-24T01:52:01.4071768Z params_to_keep_per_layer: Dict to specify the params that should be 2025-01-24T01:52:01.4071956Z saved for specific layers. The keys in the dict 2025-01-24T01:52:01.4072136Z should be the module fqn, while the values should 2025-01-24T01:52:01.4072334Z be a list of strings with the names of the variables 2025-01-24T01:52:01.4072475Z to save in the `sparse_params` 2025-01-24T01:52:01.4072563Z 2025-01-24T01:52:01.4072662Z Examples: 2025-01-24T01:52:01.4072810Z >>> # xdoctest: +SKIP("locals are undefined") 2025-01-24T01:52:01.4072932Z >>> # Don't save any sparse params 2025-01-24T01:52:01.4073063Z >>> sparsifier.squash_mask() 2025-01-24T01:52:01.4073211Z >>> hasattr(model.submodule1, 'sparse_params') 2025-01-24T01:52:01.4073315Z False 2025-01-24T01:52:01.4073407Z 2025-01-24T01:52:01.4073539Z >>> # Keep sparse params per layer 2025-01-24T01:52:01.4073663Z >>> sparsifier.squash_mask( 2025-01-24T01:52:01.4073789Z ... params_to_keep_per_layer={ 2025-01-24T01:52:01.4073937Z ... 'submodule1.linear1': ('foo', 'bar'), 2025-01-24T01:52:01.4074075Z ... 'submodule2.linear42': ('baz',) 2025-01-24T01:52:01.4074170Z ... }) 2025-01-24T01:52:01.4074342Z >>> print(model.submodule1.linear1.sparse_params) 2025-01-24T01:52:01.4074445Z {'foo': 42, 'bar': 24} 2025-01-24T01:52:01.4074623Z >>> print(model.submodule2.linear42.sparse_params) 2025-01-24T01:52:01.4074724Z {'baz': 0.1} 2025-01-24T01:52:01.4074823Z 2025-01-24T01:52:01.4074954Z >>> # Keep sparse params for all layers 2025-01-24T01:52:01.4075135Z >>> sparsifier.squash_mask(params_to_keep=('foo', 'bar')) 2025-01-24T01:52:01.4075306Z >>> print(model.submodule1.linear1.sparse_params) 2025-01-24T01:52:01.4075411Z {'foo': 42, 'bar': 24} 2025-01-24T01:52:01.4075589Z >>> print(model.submodule2.linear42.sparse_params) 2025-01-24T01:52:01.4075692Z {'foo': 42, 'bar': 24} 2025-01-24T01:52:01.4075794Z 2025-01-24T01:52:01.4075996Z >>> # Keep some sparse params for all layers, and specific ones for 2025-01-24T01:52:01.4076102Z >>> # some other layers 2025-01-24T01:52:01.4076232Z >>> sparsifier.squash_mask( 2025-01-24T01:52:01.4076360Z ... params_to_keep=('foo', 'bar'), 2025-01-24T01:52:01.4076491Z ... params_to_keep_per_layer={ 2025-01-24T01:52:01.4076630Z ... 'submodule2.linear42': ('baz',) 2025-01-24T01:52:01.4076729Z ... }) 2025-01-24T01:52:01.4076899Z >>> print(model.submodule1.linear1.sparse_params) 2025-01-24T01:52:01.4077003Z {'foo': 42, 'bar': 24} 2025-01-24T01:52:01.4077179Z >>> print(model.submodule2.linear42.sparse_params) 2025-01-24T01:52:01.4077297Z {'foo': 42, 'bar': 24, 'baz': 0.1} 2025-01-24T01:52:01.4077401Z 2025-01-24T01:52:01.4077666Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:52:01.4077808Z 2025-01-24T01:52:01.4077926Z warnings.warn(msg) 2025-01-24T01:52:01.4078013Z 2025-01-24T01:52:01.4078223Z --- Parse Warning: 31 / 106 --- 2025-01-24T01:52:01.4079430Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=DTypeConfig in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/backend_config/backend_config.py line=181. 2025-01-24T01:52:01.4079720Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:52:01.4079811Z 2025-01-24T01:52:01.4080072Z Config object that specifies the supported data types passed as arguments to 2025-01-24T01:52:01.4080399Z quantize ops in the reference model spec, for input and output activations, 2025-01-24T01:52:01.4080509Z weights, and biases. 2025-01-24T01:52:01.4080619Z 2025-01-24T01:52:01.4080826Z For example, consider the following reference model: 2025-01-24T01:52:01.4080925Z 2025-01-24T01:52:01.4081102Z quant1 - [dequant1 - fp32_linear - quant2] - dequant2 2025-01-24T01:52:01.4081193Z 2025-01-24T01:52:01.4081429Z The pattern in the square brackets refers to the reference pattern of 2025-01-24T01:52:01.4081673Z statically quantized linear. Setting the input dtype as `torch.quint8` 2025-01-24T01:52:01.4081924Z in the DTypeConfig means we pass in `torch.quint8` as the dtype argument 2025-01-24T01:52:01.4082154Z to the first quantize op (quant1). Similarly, setting the output dtype as 2025-01-24T01:52:01.4082382Z `torch.quint8` means we pass in `torch.quint8` as the dtype argument to 2025-01-24T01:52:01.4082510Z the second quantize op (quant2). 2025-01-24T01:52:01.4082599Z 2025-01-24T01:52:01.4082836Z Note that the dtype here does not refer to the interface dtypes of the 2025-01-24T01:52:01.4083047Z op. For example, the "input dtype" here is not the dtype of the input 2025-01-24T01:52:01.4083290Z tensor passed to the quantized linear op. Though it can still be the 2025-01-24T01:52:01.4083498Z same as the interface dtype, this is not always the case, e.g. the 2025-01-24T01:52:01.4083725Z interface dtype is fp32 in dynamic quantization but the "input dtype" 2025-01-24T01:52:01.4083960Z specified in the DTypeConfig would still be quint8. The semantics of 2025-01-24T01:52:01.4084173Z dtypes here are the same as the semantics of the dtypes specified in 2025-01-24T01:52:01.4084283Z the observers. 2025-01-24T01:52:01.4084419Z 2025-01-24T01:52:01.4084643Z These dtypes are matched against the ones specified in the user's 2025-01-24T01:52:01.4084868Z QConfig. If there is a match, and the QConfig satisfies the constraints 2025-01-24T01:52:01.4085096Z specified in the DTypeConfig (if any), then we will quantize the given 2025-01-24T01:52:01.4085339Z pattern using this DTypeConfig. Otherwise, the QConfig is ignored and 2025-01-24T01:52:01.4085465Z the pattern will not be quantized. 2025-01-24T01:52:01.4085568Z 2025-01-24T01:52:01.4085673Z Example usage:: 2025-01-24T01:52:01.4085777Z 2025-01-24T01:52:01.4085891Z >>> # xdoctest: +SKIP(failing) 2025-01-24T01:52:01.4086008Z >>> dtype_config1 = DTypeConfig( 2025-01-24T01:52:01.4086138Z ... input_dtype=torch.quint8, 2025-01-24T01:52:01.4086257Z ... output_dtype=torch.quint8, 2025-01-24T01:52:01.4086386Z ... weight_dtype=torch.qint8, 2025-01-24T01:52:01.4086498Z ... bias_dtype=torch.float) 2025-01-24T01:52:01.4086595Z 2025-01-24T01:52:01.4086712Z >>> dtype_config2 = DTypeConfig( 2025-01-24T01:52:01.4086850Z ... input_dtype=DTypeWithConstraints( 2025-01-24T01:52:01.4086970Z ... dtype=torch.quint8, 2025-01-24T01:52:01.4087087Z ... quant_min_lower_bound=0, 2025-01-24T01:52:01.4087219Z ... quant_max_upper_bound=255, 2025-01-24T01:52:01.4087351Z ... ), 2025-01-24T01:52:01.4087520Z ... output_dtype=DTypeWithConstraints( 2025-01-24T01:52:01.4087638Z ... dtype=torch.quint8, 2025-01-24T01:52:01.4087753Z ... quant_min_lower_bound=0, 2025-01-24T01:52:01.4087884Z ... quant_max_upper_bound=255, 2025-01-24T01:52:01.4087978Z ... ), 2025-01-24T01:52:01.4088120Z ... weight_dtype=DTypeWithConstraints( 2025-01-24T01:52:01.4088226Z ... dtype=torch.qint8, 2025-01-24T01:52:01.4088348Z ... quant_min_lower_bound=-128, 2025-01-24T01:52:01.4088477Z ... quant_max_upper_bound=127, 2025-01-24T01:52:01.4088569Z ... ), 2025-01-24T01:52:01.4088694Z ... bias_dtype=torch.float) 2025-01-24T01:52:01.4088785Z 2025-01-24T01:52:01.4088931Z >>> dtype_config1.input_dtype 2025-01-24T01:52:01.4089044Z torch.quint8 2025-01-24T01:52:01.4089134Z 2025-01-24T01:52:01.4089284Z >>> dtype_config2.input_dtype 2025-01-24T01:52:01.4089386Z torch.quint8 2025-01-24T01:52:01.4089477Z 2025-01-24T01:52:01.4089631Z >>> dtype_config2.input_dtype_with_constraints 2025-01-24T01:52:01.4090174Z DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=0, quant_max_upper_bound=255, scale_min_lower_bound=None, scale_max_upper_bound=None) 2025-01-24T01:52:01.4090275Z 2025-01-24T01:52:01.4090542Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:52:01.4090638Z 2025-01-24T01:52:01.4090742Z warnings.warn(msg) 2025-01-24T01:52:01.4090831Z 2025-01-24T01:52:01.4091053Z --- Parse Warning: 32 / 106 --- 2025-01-24T01:52:01.4092314Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ModelReportVisualizer.generate_filtered_tables in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fx/_model_report/model_report_visualizer.py line=301. 2025-01-24T01:52:01.4092596Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:52:01.4092691Z 2025-01-24T01:52:01.4092980Z Takes in optional filter values and generates two tables with desired information. 2025-01-24T01:52:01.4093073Z 2025-01-24T01:52:01.4093286Z The generated tables are presented in both a list-of-lists format 2025-01-24T01:52:01.4093388Z 2025-01-24T01:52:01.4093603Z The reason for the two tables are that they handle different things: 2025-01-24T01:52:01.4093786Z 1.) the first table handles all tensor level information 2025-01-24T01:52:01.4094010Z 2.) the second table handles and displays all channel based information 2025-01-24T01:52:01.4094110Z 2025-01-24T01:52:01.4094443Z The reasoning for this is that having all the info in one table can make it ambiguous which collected 2025-01-24T01:52:01.4094780Z statistics are global, and which are actually per-channel, so it's better to split it up into two 2025-01-24T01:52:01.4095148Z tables. This also makes the information much easier to digest given the plethora of statistics collected 2025-01-24T01:52:01.4095241Z 2025-01-24T01:52:01.4095355Z Tensor table columns: 2025-01-24T01:52:01.4095551Z idx layer_fqn feature_1 feature_2 feature_3 .... feature_n 2025-01-24T01:52:01.4095723Z ---- --------- --------- --------- --------- --------- 2025-01-24T01:52:01.4095815Z 2025-01-24T01:52:01.4095930Z Per-Channel table columns: 2025-01-24T01:52:01.4096166Z idx layer_fqn channel feature_1 feature_2 feature_3 .... feature_n 2025-01-24T01:52:01.4096338Z ---- --------- ------- --------- --------- --------- --------- 2025-01-24T01:52:01.4096438Z 2025-01-24T01:52:01.4096530Z Args: 2025-01-24T01:52:01.4096800Z feature_filter (str, optional): Filters the features presented to only those that 2025-01-24T01:52:01.4096929Z contain this filter substring 2025-01-24T01:52:01.4097172Z Default = "", results in all the features being printed 2025-01-24T01:52:01.4097450Z module_fqn_filter (str, optional): Only includes modules that contains this string 2025-01-24T01:52:01.4097706Z Default = "", results in all the modules in the reports to be visible in the table 2025-01-24T01:52:01.4097807Z 2025-01-24T01:52:01.4097928Z Returns a dictionary with two keys: 2025-01-24T01:52:01.4098111Z (Dict[str, Tuple[List, List]]) A dict containing two keys: 2025-01-24T01:52:01.4098255Z "tensor_level_info", "channel_level_info" 2025-01-24T01:52:01.4098375Z Each key maps to a tuple with: 2025-01-24T01:52:01.4098510Z A list of the headers of each table 2025-01-24T01:52:01.4098725Z A list of lists containing the table information row by row 2025-01-24T01:52:01.4098920Z The 0th index row will contain the headers of the columns 2025-01-24T01:52:01.4099079Z The rest of the rows will contain data 2025-01-24T01:52:01.4099169Z 2025-01-24T01:52:01.4099280Z Example Use: 2025-01-24T01:52:01.4099415Z >>> # xdoctest: +SKIP("undefined variables") 2025-01-24T01:52:01.4099583Z >>> mod_report_visualizer.generate_filtered_tables( 2025-01-24T01:52:01.4099713Z ... feature_filter = "per_channel_min", 2025-01-24T01:52:01.4099840Z ... module_fqn_filter = "block1" 2025-01-24T01:52:01.4100119Z ... ) # generates table with per_channel_min info for all modules in block 1 of the model 2025-01-24T01:52:01.4100206Z 2025-01-24T01:52:01.4100482Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:52:01.4100572Z 2025-01-24T01:52:01.4100692Z warnings.warn(msg) 2025-01-24T01:52:01.4100782Z 2025-01-24T01:52:01.4100978Z --- Parse Warning: 33 / 106 --- 2025-01-24T01:52:01.4102271Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ModelReportVisualizer.generate_table_visualization in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fx/_model_report/model_report_visualizer.py line=400. 2025-01-24T01:52:01.4102542Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:52:01.4102645Z 2025-01-24T01:52:01.4102920Z Takes in optional filter values and prints out formatted tables of the information. 2025-01-24T01:52:01.4103022Z 2025-01-24T01:52:01.4103370Z The reason for the two tables printed out instead of one large one are that they handle different things: 2025-01-24T01:52:01.4103551Z 1.) the first table handles all tensor level information 2025-01-24T01:52:01.4103777Z 2.) the second table handles and displays all channel based information 2025-01-24T01:52:01.4103866Z 2025-01-24T01:52:01.4104208Z The reasoning for this is that having all the info in one table can make it ambiguous which collected 2025-01-24T01:52:01.4104551Z statistics are global, and which are actually per-channel, so it's better to split it up into two 2025-01-24T01:52:01.4104920Z tables. This also makes the information much easier to digest given the plethora of statistics collected 2025-01-24T01:52:01.4105011Z 2025-01-24T01:52:01.4105128Z Tensor table columns: 2025-01-24T01:52:01.4105324Z idx layer_fqn feature_1 feature_2 feature_3 .... feature_n 2025-01-24T01:52:01.4105480Z ---- --------- --------- --------- --------- --------- 2025-01-24T01:52:01.4105581Z 2025-01-24T01:52:01.4105697Z Per-Channel table columns: 2025-01-24T01:52:01.4105797Z 2025-01-24T01:52:01.4106020Z idx layer_fqn channel feature_1 feature_2 feature_3 .... feature_n 2025-01-24T01:52:01.4106190Z ---- --------- ------- --------- --------- --------- --------- 2025-01-24T01:52:01.4106383Z 2025-01-24T01:52:01.4106484Z Args: 2025-01-24T01:52:01.4106829Z feature_filter (str, optional): Filters the features presented to only those that 2025-01-24T01:52:01.4106950Z contain this filter substring 2025-01-24T01:52:01.4107128Z Default = "", results in all the features being printed 2025-01-24T01:52:01.4107394Z module_fqn_filter (str, optional): Only includes modules that contains this string 2025-01-24T01:52:01.4107648Z Default = "", results in all the modules in the reports to be visible in the table 2025-01-24T01:52:01.4107750Z 2025-01-24T01:52:01.4107849Z Example Use: 2025-01-24T01:52:01.4107996Z >>> # xdoctest: +SKIP("undefined variables") 2025-01-24T01:52:01.4108169Z >>> mod_report_visualizer.generate_table_visualization( 2025-01-24T01:52:01.4108330Z ... feature_filter = "per_channel_min", 2025-01-24T01:52:01.4108462Z ... module_fqn_filter = "block1" 2025-01-24T01:52:01.4108557Z ... ) 2025-01-24T01:52:01.4108794Z >>> # prints out neatly formatted table with per_channel_min info 2025-01-24T01:52:01.4108929Z >>> # for all modules in block 1 of the model 2025-01-24T01:52:01.4109026Z 2025-01-24T01:52:01.4109285Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:52:01.4109376Z 2025-01-24T01:52:01.4109490Z warnings.warn(msg) 2025-01-24T01:52:01.4109580Z 2025-01-24T01:52:01.4109799Z --- Parse Warning: 34 / 106 --- 2025-01-24T01:52:01.4111058Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ModelReportVisualizer.generate_plot_visualization in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fx/_model_report/model_report_visualizer.py line=566. 2025-01-24T01:52:01.4111342Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:52:01.4111430Z 2025-01-24T01:52:01.4111678Z Takes in a feature and optional module_filter and plots of the desired data. 2025-01-24T01:52:01.4111779Z 2025-01-24T01:52:01.4112058Z For per channel features, it averages the value across the channels and plots a point 2025-01-24T01:52:01.4112332Z per module. The reason for this is that for models with hundreds of channels, it can 2025-01-24T01:52:01.4112614Z be hard to differentiate one channel line from another, and so the point of generating 2025-01-24T01:52:01.4112900Z a single average point per module is to give a sense of general trends that encourage 2025-01-24T01:52:01.4113004Z further deep dives. 2025-01-24T01:52:01.4113094Z 2025-01-24T01:52:01.4113194Z Note: 2025-01-24T01:52:01.4113464Z Only features in the report that have tensor value data are plottable by this class 2025-01-24T01:52:01.4113650Z When the tensor information is plotted, it will plot: 2025-01-24T01:52:01.4113791Z idx as the x val, feature value as the y_val 2025-01-24T01:52:01.4113981Z When the channel information is plotted, it will plot: 2025-01-24T01:52:01.4114252Z the first idx of each module as the x val, feature value as the y_val [for each channel] 2025-01-24T01:52:01.4114488Z The reason for this is that we want to be able to compare values across the 2025-01-24T01:52:01.4114741Z channels for same layer, and it will be hard if values are staggered by idx 2025-01-24T01:52:01.4114912Z This means each module is represented by only 1 x value 2025-01-24T01:52:01.4115017Z Args: 2025-01-24T01:52:01.4115248Z feature_filter (str): Filters the features presented to only those that 2025-01-24T01:52:01.4115379Z contain this filter substring 2025-01-24T01:52:01.4115644Z module_fqn_filter (str, optional): Only includes modules that contains this string 2025-01-24T01:52:01.4115895Z Default = "", results in all the modules in the reports to be visible in the table 2025-01-24T01:52:01.4115999Z 2025-01-24T01:52:01.4116125Z Example Use: 2025-01-24T01:52:01.4116298Z >>> # xdoctest: +SKIP("undefined variables") 2025-01-24T01:52:01.4116469Z >>> mod_report_visualizer.generate_plot_visualization( 2025-01-24T01:52:01.4116600Z ... feature_filter = "per_channel_min", 2025-01-24T01:52:01.4116732Z ... module_fqn_filter = "block1" 2025-01-24T01:52:01.4116825Z ... ) 2025-01-24T01:52:01.4117026Z >>> # outputs line plot of per_channel_min information for all 2025-01-24T01:52:01.4117216Z >>> # modules in block1 of model each channel gets it's own line, 2025-01-24T01:52:01.4117408Z >>> # and it's plotted across the in-order modules on the x-axis 2025-01-24T01:52:01.4117502Z 2025-01-24T01:52:01.4117787Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:52:01.4117889Z 2025-01-24T01:52:01.4117993Z warnings.warn(msg) 2025-01-24T01:52:01.4118095Z 2025-01-24T01:52:01.4118316Z --- Parse Warning: 35 / 106 --- 2025-01-24T01:52:01.4119619Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ModelReportVisualizer.generate_histogram_visualization in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fx/_model_report/model_report_visualizer.py line=646. 2025-01-24T01:52:01.4119891Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:52:01.4119980Z 2025-01-24T01:52:01.4120274Z Takes in a feature and optional module_filter and plots the histogram of desired data. 2025-01-24T01:52:01.4120365Z 2025-01-24T01:52:01.4120472Z Note: 2025-01-24T01:52:01.4120739Z Only features in the report that have tensor value data can be viewed as a histogram 2025-01-24T01:52:01.4121015Z If you want to plot a histogram from all the channel values of a specific feature for 2025-01-24T01:52:01.4121269Z a specific model, make sure to specify both the model and the feature properly 2025-01-24T01:52:01.4121523Z in the filters and you should be able to see a distribution of the channel data 2025-01-24T01:52:01.4121618Z 2025-01-24T01:52:01.4121706Z Args: 2025-01-24T01:52:01.4121982Z feature_filter (str, optional): Filters the features presented to only those that 2025-01-24T01:52:01.4122098Z contain this filter substring 2025-01-24T01:52:01.4122268Z Default = "", results in all the features being printed 2025-01-24T01:52:01.4122544Z module_fqn_filter (str, optional): Only includes modules that contains this string 2025-01-24T01:52:01.4122788Z Default = "", results in all the modules in the reports to be visible in the table 2025-01-24T01:52:01.4123030Z num_bins (int, optional): The number of bins to create the histogram with 2025-01-24T01:52:01.4123221Z Default = 10, the values will be split into 10 equal sized bins 2025-01-24T01:52:01.4123323Z 2025-01-24T01:52:01.4123422Z Example Use: 2025-01-24T01:52:01.4123528Z >>> # xdoctest: +SKIP 2025-01-24T01:52:01.4123841Z >>> mod_report_visualizer.generategenerate_histogram_visualization_plot_visualization( 2025-01-24T01:52:01.4123967Z ... feature_filter = "per_channel_min", 2025-01-24T01:52:01.4124092Z ... module_fqn_filter = "block1" 2025-01-24T01:52:01.4124185Z ... ) 2025-01-24T01:52:01.4124469Z # outputs histogram of per_channel_min information for all modules in block1 of model 2025-01-24T01:52:01.4124731Z information is gathered across all channels for all modules in block 1 for the 2025-01-24T01:52:01.4124953Z per_channel_min and is displayed in a histogram of equally sized bins 2025-01-24T01:52:01.4125050Z 2025-01-24T01:52:01.4125310Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:52:01.4125404Z 2025-01-24T01:52:01.4125507Z warnings.warn(msg) 2025-01-24T01:52:01.4125629Z 2025-01-24T01:52:01.4125851Z --- Parse Warning: 36 / 106 --- 2025-01-24T01:52:01.4126807Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=DeviceMesh.__getitem__ in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/device_mesh.py line=662. 2025-01-24T01:52:01.4127073Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-01-24T01:52:01.4127164Z 2025-01-24T01:52:01.4127446Z Slice the current DeviceMesh based on the mesh_dim_names given to create a submesh. 2025-01-24T01:52:01.4127718Z The submesh created consists of the dimensions and the communicators indicated by 2025-01-24T01:52:01.4127819Z ``mesh_dim_names`` 2025-01-24T01:52:01.4127908Z 2025-01-24T01:52:01.4128020Z Args: 2025-01-24T01:52:01.4128269Z mesh_dim_names (Union[str, Tuple[str]]): the name or the tuple of names of the 2025-01-24T01:52:01.4128491Z mesh dimension of the DeviceMesh to create the submesh for. 2025-01-24T01:52:01.4128591Z Returns: 2025-01-24T01:52:01.4128702Z A :class:`DeviceMesh` object 2025-01-24T01:52:01.4128792Z 2025-01-24T01:52:01.4129095Z The following program runs on each process/rank in an SPMD manner in a world size of 8. 2025-01-24T01:52:01.4129203Z In the first example: 2025-01-24T01:52:01.4129466Z Calling mesh_2d["tp"] on rank 0, 1, 2, 3 returns a 1D submesh of DeviceMesh:([0, 1, 2, 3]). 2025-01-24T01:52:01.4129717Z Calling mesh_2d["tp"] on rank 4, 5, 6, 7 returns a 1D submesh of DeviceMesh:([4, 5, 6, 7]). 2025-01-24T01:52:01.4129952Z Calling mesh_2d["dp"] on rank 0, 4 returns a 1D submesh of DeviceMesh:([0, 4]). 2025-01-24T01:52:01.4130181Z Calling mesh_2d["dp"] on rank 1, 5 returns a 1D submesh of DeviceMesh:([1, 5]). 2025-01-24T01:52:01.4130404Z Calling mesh_2d["dp"] on rank 2, 6 returns a 1D submesh of DeviceMesh:([2, 6]). 2025-01-24T01:52:01.4130641Z Calling mesh_2d["dp"] on rank 3, 7 returns a 1D submesh of DeviceMesh:([3, 7]). 2025-01-24T01:52:01.4130730Z 2025-01-24T01:52:01.4130841Z In the second example: 2025-01-24T01:52:01.4131112Z Calling mesh_3d["dp", "cp"] on rank 0, 1, 4, 5 returns a 2D submesh of DeviceMesh:([[0, 1], [4, 5]]). 2025-01-24T01:52:01.4131384Z Calling mesh_3d["dp", "cp"] on rank 2, 3, 6, 7 returns a 2D submesh of DeviceMesh:([[2, 3], [6, 7]]). 2025-01-24T01:52:01.4131644Z Calling mesh_3d["cp", "dp"] on rank 0, 1, 4, 5 returns a 2D submesh of DeviceMesh:([[0, 4], [1, 5]]). 2025-01-24T01:52:01.4131914Z Calling mesh_3d["cp", "dp"] on rank 2, 3, 6, 7 returns a 2D submesh of DeviceMesh:([[2, 6], [3, 7]]). 2025-01-24T01:52:01.4131996Z 2025-01-24T01:52:01.4132094Z Example:: 2025-01-24T01:52:01.4132213Z >>> # xdoctest: +SKIP("no rank") 2025-01-24T01:52:01.4132391Z >>> from torch.distributed.device_mesh import DeviceMesh 2025-01-24T01:52:01.4132488Z >>> 2025-01-24T01:52:01.4132700Z >>> # Initialize a 2D device mesh as (2, 4) to represent the topology 2025-01-24T01:52:01.4132846Z >>> # of cross-host(dim 0), and within-host (dim 1). 2025-01-24T01:52:01.4133105Z >>> mesh_2d = init_device_mesh(device_type="cuda", (2,4), mesh_dim_names=("dp", "tp")) 2025-01-24T01:52:01.4133213Z >>> tp_mesh = mesh_2d["tp"] 2025-01-24T01:52:01.4133324Z >>> dp_mesh = mesh_2d["dp"] 2025-01-24T01:52:01.4133414Z >>> 2025-01-24T01:52:01.4133524Z >>> # Initialize a 3D mesh. 2025-01-24T01:52:01.4133802Z >>> mesh_3d = init_device_mesh(device_type="cuda", (2,2,2), mesh_dim_names=("dp", "pp", "cp")) 2025-01-24T01:52:01.4134114Z >>> # The order of the mesh_dim_names provided deteremines the order of dimensions in the submesh. 2025-01-24T01:52:01.4134237Z >>> dp_cp_mesh = mesh_3d["dp", "cp"] 2025-01-24T01:52:01.4134355Z >>> cp_dp_mesh = mesh_3d["cp", "dp"] 2025-01-24T01:52:01.4134450Z 2025-01-24T01:52:01.4135190Z 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)) 2025-01-24T01:52:01.4135286Z 2025-01-24T01:52:01.4135535Z mesh_2d = init_device_mesh(device_type="cuda", (2,4), mesh_dim_names=("dp", "tp")) 2025-01-24T01:52:01.4135659Z ^ 2025-01-24T01:52:01.4135771Z warnings.warn(msg) 2025-01-24T01:52:01.4135859Z 2025-01-24T01:52:01.4136058Z --- Parse Warning: 37 / 106 --- 2025-01-24T01:52:01.4137039Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=gather_object in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py line=3062. 2025-01-24T01:52:01.4137317Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:52:01.4137433Z 2025-01-24T01:52:01.4137663Z Gathers picklable objects from the whole group in a single process. 2025-01-24T01:52:01.4137762Z 2025-01-24T01:52:01.4138002Z Similar to :func:`gather`, but Python objects can be passed in. Note that the 2025-01-24T01:52:01.4138165Z object must be picklable in order to be gathered. 2025-01-24T01:52:01.4138253Z 2025-01-24T01:52:01.4138353Z Args: 2025-01-24T01:52:01.4138485Z obj (Any): Input object. Must be picklable. 2025-01-24T01:52:01.4138700Z object_gather_list (list[Any]): Output list. On the ``dst`` rank, it 2025-01-24T01:52:01.4138895Z should be correctly sized as the size of the group for this 2025-01-24T01:52:01.4139114Z collective and will contain the output. Must be ``None`` on non-dst 2025-01-24T01:52:01.4139239Z ranks. (default is ``None``) 2025-01-24T01:52:01.4139565Z dst (int, optional): Destination rank on global process group (regardless of ``group`` argument). 2025-01-24T01:52:01.4139778Z (If both ``dst`` and ``group_dst`` are None, default is global rank 0) 2025-01-24T01:52:01.4140014Z group: (ProcessGroup, optional): The process group to work on. If None, 2025-01-24T01:52:01.4140207Z the default process group will be used. Default is ``None``. 2025-01-24T01:52:01.4140563Z group_dst (int, optional): Destination rank on ``group``. Invalid to specify both ``dst`` and ``group_dst`` 2025-01-24T01:52:01.4140649Z 2025-01-24T01:52:01.4140756Z Returns: 2025-01-24T01:52:01.4140941Z None. On the ``dst`` rank, ``object_gather_list`` will contain the 2025-01-24T01:52:01.4141060Z output of the collective. 2025-01-24T01:52:01.4141146Z 2025-01-24T01:52:01.4141365Z .. note:: Note that this API differs slightly from the gather collective 2025-01-24T01:52:01.4141594Z since it does not provide an async_op handle and thus will be a blocking 2025-01-24T01:52:01.4141685Z call. 2025-01-24T01:52:01.4141783Z 2025-01-24T01:52:01.4142018Z .. note:: For NCCL-based processed groups, internal tensor representations 2025-01-24T01:52:01.4142235Z of objects must be moved to the GPU device before communication takes 2025-01-24T01:52:01.4142395Z place. In this case, the device used is given by 2025-01-24T01:52:01.4142618Z ``torch.cuda.current_device()`` and it is the user's responsiblity to 2025-01-24T01:52:01.4142839Z ensure that this is set so that each rank has an individual GPU, via 2025-01-24T01:52:01.4142950Z ``torch.cuda.set_device()``. 2025-01-24T01:52:01.4143046Z 2025-01-24T01:52:01.4143136Z .. warning:: 2025-01-24T01:52:01.4143337Z :func:`gather_object` uses ``pickle`` module implicitly, which is 2025-01-24T01:52:01.4143571Z known to be insecure. It is possible to construct malicious pickle data 2025-01-24T01:52:01.4143792Z which will execute arbitrary code during unpickling. Only call this 2025-01-24T01:52:01.4143949Z function with data you trust. 2025-01-24T01:52:01.4144059Z 2025-01-24T01:52:01.4144168Z .. warning:: 2025-01-24T01:52:01.4144383Z Calling :func:`gather_object` with GPU tensors is not well supported 2025-01-24T01:52:01.4144608Z and inefficient as it incurs GPU -> CPU transfer since tensors would be 2025-01-24T01:52:01.4144789Z pickled. Please consider using :func:`gather` instead. 2025-01-24T01:52:01.4144876Z 2025-01-24T01:52:01.4144981Z Example:: 2025-01-24T01:52:01.4145119Z >>> # xdoctest: +SKIP("need process group init") 2025-01-24T01:52:01.4145302Z >>> # Note: Process group initialization omitted on each rank. 2025-01-24T01:52:01.4145432Z >>> import torch.distributed as dist 2025-01-24T01:52:01.4145542Z >>> # Assumes world_size of 3. 2025-01-24T01:52:01.4145758Z >>> gather_objects = ["foo", 12, {1: 2}] # any picklable object 2025-01-24T01:52:01.4145884Z >>> output = [None for _ in gather_objects] 2025-01-24T01:52:01.4146060Z >>> dist.gather_object( 2025-01-24T01:52:01.4146184Z ... gather_objects[dist.get_rank()], 2025-01-24T01:52:01.4146422Z ... output if dist.get_rank() == 0 else None, 2025-01-24T01:52:01.4146531Z ... dst=0 2025-01-24T01:52:01.4146617Z ... ) 2025-01-24T01:52:01.4146720Z >>> # On rank 0 2025-01-24T01:52:01.4146808Z >>> output 2025-01-24T01:52:01.4146902Z ['foo', 12, {1: 2}] 2025-01-24T01:52:01.4147000Z 2025-01-24T01:52:01.4147260Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:52:01.4147353Z 2025-01-24T01:52:01.4147451Z warnings.warn(msg) 2025-01-24T01:52:01.4147548Z 2025-01-24T01:52:01.4147756Z --- Parse Warning: 38 / 106 --- 2025-01-24T01:52:01.4148613Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=__doc__ in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/launch.py line=2. 2025-01-24T01:52:01.4148891Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:52:01.4148976Z 2025-01-24T01:52:01.4149112Z Module ``torch.distributed.launch``. 2025-01-24T01:52:01.4149197Z 2025-01-24T01:52:01.4149450Z ``torch.distributed.launch`` is a module that spawns up multiple distributed 2025-01-24T01:52:01.4149619Z training processes on each of the training nodes. 2025-01-24T01:52:01.4149704Z 2025-01-24T01:52:01.4149809Z .. warning:: 2025-01-24T01:52:01.4149891Z 2025-01-24T01:52:01.4150157Z This module is going to be deprecated in favor of :ref:`torchrun `. 2025-01-24T01:52:01.4150242Z 2025-01-24T01:52:01.4150486Z The utility can be used for single-node distributed training, in which one or 2025-01-24T01:52:01.4150730Z more processes per node will be spawned. The utility can be used for either 2025-01-24T01:52:01.4150956Z CPU training or GPU training. If the utility is used for GPU training, 2025-01-24T01:52:01.4151220Z each distributed process will be operating on a single GPU. This can achieve 2025-01-24T01:52:01.4151457Z well-improved single-node training performance. It can also be used in 2025-01-24T01:52:01.4151740Z multi-node distributed training, by spawning up multiple processes on each node 2025-01-24T01:52:01.4151977Z for well-improved multi-node distributed training performance as well. 2025-01-24T01:52:01.4152214Z This will especially be beneficial for systems with multiple Infiniband 2025-01-24T01:52:01.4152488Z interfaces that have direct-GPU support, since all of them can be utilized for 2025-01-24T01:52:01.4152613Z aggregated communication bandwidth. 2025-01-24T01:52:01.4152715Z 2025-01-24T01:52:01.4152960Z In both cases of single-node distributed training or multi-node distributed 2025-01-24T01:52:01.4153217Z training, this utility will launch the given number of processes per node 2025-01-24T01:52:01.4153516Z (``--nproc-per-node``). If used for GPU training, this number needs to be less 2025-01-24T01:52:01.4153745Z or equal to the number of GPUs on the current system (``nproc_per_node``), 2025-01-24T01:52:01.4153970Z and each process will be operating on a single GPU from *GPU 0 to 2025-01-24T01:52:01.4154083Z GPU (nproc_per_node - 1)*. 2025-01-24T01:52:01.4154187Z 2025-01-24T01:52:01.4154296Z **How to use this module:** 2025-01-24T01:52:01.4154401Z 2025-01-24T01:52:01.4154564Z 1. Single-Node multi-process distributed training 2025-01-24T01:52:01.4154655Z 2025-01-24T01:52:01.4154770Z :: 2025-01-24T01:52:01.4154864Z 2025-01-24T01:52:01.4155125Z python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE 2025-01-24T01:52:01.4155351Z YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3 and all other 2025-01-24T01:52:01.4155487Z arguments of your training script) 2025-01-24T01:52:01.4155620Z 2025-01-24T01:52:01.4155846Z 2. Multi-Node multi-process distributed training: (e.g. two nodes) 2025-01-24T01:52:01.4155954Z 2025-01-24T01:52:01.4156047Z 2025-01-24T01:52:01.4156207Z Node 1: *(IP: 192.168.1.1, and has a free port: 1234)* 2025-01-24T01:52:01.4156312Z 2025-01-24T01:52:01.4156407Z :: 2025-01-24T01:52:01.4156516Z 2025-01-24T01:52:01.4156762Z python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE 2025-01-24T01:52:01.4156944Z --nnodes=2 --node-rank=0 --master-addr="192.168.1.1" 2025-01-24T01:52:01.4157166Z --master-port=1234 YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3 2025-01-24T01:52:01.4157329Z and all other arguments of your training script) 2025-01-24T01:52:01.4157438Z 2025-01-24T01:52:01.4157535Z Node 2: 2025-01-24T01:52:01.4157642Z 2025-01-24T01:52:01.4157738Z :: 2025-01-24T01:52:01.4157829Z 2025-01-24T01:52:01.4158087Z python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE 2025-01-24T01:52:01.4158259Z --nnodes=2 --node-rank=1 --master-addr="192.168.1.1" 2025-01-24T01:52:01.4158488Z --master-port=1234 YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3 2025-01-24T01:52:01.4158649Z and all other arguments of your training script) 2025-01-24T01:52:01.4158760Z 2025-01-24T01:52:01.4158934Z 3. To look up what optional arguments this module offers: 2025-01-24T01:52:01.4159027Z 2025-01-24T01:52:01.4159133Z :: 2025-01-24T01:52:01.4159226Z 2025-01-24T01:52:01.4159387Z python -m torch.distributed.launch --help 2025-01-24T01:52:01.4159477Z 2025-01-24T01:52:01.4159565Z 2025-01-24T01:52:01.4159690Z **Important Notices:** 2025-01-24T01:52:01.4159782Z 2025-01-24T01:52:01.4159993Z 1. This utility and multi-process distributed (single-node or 2025-01-24T01:52:01.4160251Z multi-node) GPU training currently only achieves the best performance using 2025-01-24T01:52:01.4160515Z the NCCL distributed backend. Thus NCCL backend is the recommended backend to 2025-01-24T01:52:01.4160634Z use for GPU training. 2025-01-24T01:52:01.4160729Z 2025-01-24T01:52:01.4160962Z 2. In your training program, you must parse the command-line argument: 2025-01-24T01:52:01.4161204Z ``--local-rank=LOCAL_PROCESS_RANK``, which will be provided by this module. 2025-01-24T01:52:01.4161452Z If your training program uses GPUs, you should ensure that your code only 2025-01-24T01:52:01.4161653Z runs on the GPU device of LOCAL_PROCESS_RANK. This can be done by: 2025-01-24T01:52:01.4161744Z 2025-01-24T01:52:01.4161871Z Parsing the local_rank argument 2025-01-24T01:52:01.4161962Z 2025-01-24T01:52:01.4162067Z :: 2025-01-24T01:52:01.4162158Z 2025-01-24T01:52:01.4162265Z >>> # xdoctest: +SKIP 2025-01-24T01:52:01.4162379Z >>> import argparse 2025-01-24T01:52:01.4162516Z >>> parser = argparse.ArgumentParser() 2025-01-24T01:52:01.4162791Z >>> parser.add_argument("--local-rank", "--local_rank", type=int) 2025-01-24T01:52:01.4162908Z >>> args = parser.parse_args() 2025-01-24T01:52:01.4163009Z 2025-01-24T01:52:01.4163141Z Set your device to local rank using either 2025-01-24T01:52:01.4163232Z 2025-01-24T01:52:01.4163337Z :: 2025-01-24T01:52:01.4163427Z 2025-01-24T01:52:01.4163648Z >>> torch.cuda.set_device(args.local_rank) # before your code runs 2025-01-24T01:52:01.4163739Z 2025-01-24T01:52:01.4163832Z or 2025-01-24T01:52:01.4163937Z 2025-01-24T01:52:01.4164030Z :: 2025-01-24T01:52:01.4164135Z 2025-01-24T01:52:01.4164276Z >>> with torch.cuda.device(args.local_rank): 2025-01-24T01:52:01.4164383Z >>> # your code to run 2025-01-24T01:52:01.4164491Z >>> ... 2025-01-24T01:52:01.4164608Z 2025-01-24T01:52:01.4164739Z .. versionchanged:: 2.0.0 2025-01-24T01:52:01.4164831Z 2025-01-24T01:52:01.4165133Z The launcher will passes the ``--local-rank=`` argument to your script. 2025-01-24T01:52:01.4165395Z From PyTorch 2.0.0 onwards, the dashed ``--local-rank`` is preferred over the 2025-01-24T01:52:01.4165547Z previously used underscored ``--local_rank``. 2025-01-24T01:52:01.4165649Z 2025-01-24T01:52:01.4165896Z For backward compatibility, it may be necessary for users to handle both 2025-01-24T01:52:01.4166185Z cases in their argument parsing code. This means including both ``"--local-rank"`` 2025-01-24T01:52:01.4166411Z and ``"--local_rank"`` in the argument parser. If only ``"--local_rank"`` is 2025-01-24T01:52:01.4166671Z provided, the launcher will trigger an error: "error: unrecognized arguments: 2025-01-24T01:52:01.4166927Z --local-rank=". For training code that only supports PyTorch 2.0.0+, 2025-01-24T01:52:01.4167087Z including ``"--local-rank"`` should be sufficient. 2025-01-24T01:52:01.4167195Z 2025-01-24T01:52:01.4167444Z 3. In your training program, you are supposed to call the following function 2025-01-24T01:52:01.4167711Z at the beginning to start the distributed backend. It is strongly recommended 2025-01-24T01:52:01.4167940Z that ``init_method=env://``. Other init methods (e.g. ``tcp://``) may work, 2025-01-24T01:52:01.4168141Z but ``env://`` is the one that is officially supported by this module. 2025-01-24T01:52:01.4168248Z 2025-01-24T01:52:01.4168346Z :: 2025-01-24T01:52:01.4168451Z 2025-01-24T01:52:01.4168663Z >>> torch.distributed.init_process_group(backend='YOUR BACKEND', 2025-01-24T01:52:01.4168819Z >>> init_method='env://') 2025-01-24T01:52:01.4168911Z 2025-01-24T01:52:01.4169160Z 4. In your training program, you can either use regular distributed functions 2025-01-24T01:52:01.4169421Z or use :func:`torch.nn.parallel.DistributedDataParallel` module. If your 2025-01-24T01:52:01.4169640Z training program uses GPUs for training and you would like to use 2025-01-24T01:52:01.4169857Z :func:`torch.nn.parallel.DistributedDataParallel` module, 2025-01-24T01:52:01.4169972Z here is how to configure it. 2025-01-24T01:52:01.4170063Z 2025-01-24T01:52:01.4170167Z :: 2025-01-24T01:52:01.4170254Z 2025-01-24T01:52:01.4170468Z >>> model = torch.nn.parallel.DistributedDataParallel(model, 2025-01-24T01:52:01.4170614Z >>> device_ids=[args.local_rank], 2025-01-24T01:52:01.4170775Z >>> output_device=args.local_rank) 2025-01-24T01:52:01.4170867Z 2025-01-24T01:52:01.4171114Z Please ensure that ``device_ids`` argument is set to be the only GPU device id 2025-01-24T01:52:01.4171370Z that your code will be operating on. This is generally the local rank of the 2025-01-24T01:52:01.4171616Z process. In other words, the ``device_ids`` needs to be ``[args.local_rank]``, 2025-01-24T01:52:01.4171914Z and ``output_device`` needs to be ``args.local_rank`` in order to use this 2025-01-24T01:52:01.4172010Z utility 2025-01-24T01:52:01.4172116Z 2025-01-24T01:52:01.4172370Z 5. Another way to pass ``local_rank`` to the subprocesses via environment variable 2025-01-24T01:52:01.4172595Z ``LOCAL_RANK``. This behavior is enabled when you launch the script with 2025-01-24T01:52:01.4172833Z ``--use-env=True``. You must adjust the subprocess example above to replace 2025-01-24T01:52:01.4173036Z ``args.local_rank`` with ``os.environ['LOCAL_RANK']``; the launcher 2025-01-24T01:52:01.4173228Z will not pass ``--local-rank`` when you specify this flag. 2025-01-24T01:52:01.4173318Z 2025-01-24T01:52:01.4173428Z .. warning:: 2025-01-24T01:52:01.4173521Z 2025-01-24T01:52:01.4173755Z ``local_rank`` is NOT globally unique: it is only unique per process 2025-01-24T01:52:01.4173964Z on a machine. Thus, don't use it to decide if you should, e.g., 2025-01-24T01:52:01.4174121Z write to a networked filesystem. See 2025-01-24T01:52:01.4174365Z https://github.com/pytorch/pytorch/issues/12042 for an example of 2025-01-24T01:52:01.4174538Z how things can go wrong if you don't do this correctly. 2025-01-24T01:52:01.4174630Z 2025-01-24T01:52:01.4174732Z 2025-01-24T01:52:01.4174822Z 2025-01-24T01:52:01.4174927Z 2025-01-24T01:52:01.4175184Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:52:01.4175274Z 2025-01-24T01:52:01.4175394Z warnings.warn(msg) 2025-01-24T01:52:01.4175484Z 2025-01-24T01:52:01.4175715Z --- Parse Warning: 39 / 106 --- 2025-01-24T01:52:01.4176760Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=init_from_local_shards in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/_shard/sharded_tensor/__init__.py line=361. 2025-01-24T01:52:01.4177052Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:52:01.4177144Z 2025-01-24T01:52:01.4177390Z Creates an :class:`ShardedTensor` from local shards and the global metadata. 2025-01-24T01:52:01.4177559Z Needs to be called on all ranks in an SPMD fashion. 2025-01-24T01:52:01.4177647Z 2025-01-24T01:52:01.4177755Z Args: 2025-01-24T01:52:01.4178038Z local_shards (List[:class `torch.distributed._shard.sharded_tensor.Shard`]): A list 2025-01-24T01:52:01.4178229Z of shards that represent the local shards on this rank. 2025-01-24T01:52:01.4178463Z global_size (int...): a list, tuple, or `torch.Size` of integers defining the 2025-01-24T01:52:01.4178594Z shape of the overall sharded tensor. 2025-01-24T01:52:01.4178698Z 2025-01-24T01:52:01.4178802Z Keyword args: 2025-01-24T01:52:01.4179086Z process_group (ProcessGroup, optional): The process group to work on. If None, 2025-01-24T01:52:01.4179432Z the default process group will be used. 2025-01-24T01:52:01.4179635Z init_rrefs (bool, optional): Whether or not to initialize 2025-01-24T01:52:01.4179852Z :class:`torch.distributed.rpc.RRef`s pointing to remote shards. 2025-01-24T01:52:01.4180054Z Need to initialize the RPC Framework if specified as ``True``. 2025-01-24T01:52:01.4180175Z Default: ``False``. 2025-01-24T01:52:01.4180267Z 2025-01-24T01:52:01.4180379Z Returns: 2025-01-24T01:52:01.4180544Z A :class:`ShardedTensor` object handle on this rank 2025-01-24T01:52:01.4180638Z 2025-01-24T01:52:01.4180743Z 2025-01-24T01:52:01.4180841Z Examples: 2025-01-24T01:52:01.4181119Z Suppose we want construct a sharded tensor on two ranks, global size = (10, 5), 2025-01-24T01:52:01.4181315Z each shard have a (5, 5) local tensor, we can do it like below: 2025-01-24T01:52:01.4181418Z 2025-01-24T01:52:01.4181514Z on rank 0: 2025-01-24T01:52:01.4181645Z >>> # xdoctest: +SKIP("not distributed") 2025-01-24T01:52:01.4181894Z >>> local_shard_metadata = ShardMetadata( 2025-01-24T01:52:01.4182007Z >>> shard_offsets=[0, 0], 2025-01-24T01:52:01.4182134Z >>> shard_lengths=[5, 5], 2025-01-24T01:52:01.4182252Z >>> placement="rank:0/cuda:0" 2025-01-24T01:52:01.4182343Z >>> ) 2025-01-24T01:52:01.4182560Z >>> local_shards = [Shard(torch.randn(5, 5), local_shard_metadata)] 2025-01-24T01:52:01.4182762Z >>> sharded_tensor = init_from_local_shards(local_shards, [10, 5]) 2025-01-24T01:52:01.4182868Z 2025-01-24T01:52:01.4182963Z on rank 1: 2025-01-24T01:52:01.4183105Z >>> # xdoctest: +SKIP("not distributed") 2025-01-24T01:52:01.4183239Z >>> local_shard_metadata = ShardMetadata( 2025-01-24T01:52:01.4183386Z >>> shard_offsets=[5, 0], 2025-01-24T01:52:01.4183511Z >>> shard_lengths=[5, 5], 2025-01-24T01:52:01.4183626Z >>> placement="rank:1/cuda:1" 2025-01-24T01:52:01.4183769Z >>> ) 2025-01-24T01:52:01.4183974Z >>> local_shards = [Shard(torch.randn(5, 5), local_shard_metadata)] 2025-01-24T01:52:01.4184170Z >>> sharded_tensor = init_from_local_shards(local_shards, [10, 5]) 2025-01-24T01:52:01.4184269Z 2025-01-24T01:52:01.4184529Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:52:01.4184645Z 2025-01-24T01:52:01.4184751Z warnings.warn(msg) 2025-01-24T01:52:01.4184836Z 2025-01-24T01:52:01.4185057Z --- Parse Warning: 40 / 106 --- 2025-01-24T01:52:01.4186149Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ShardedTensor._init_from_local_tensor in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/_shard/sharded_tensor/api.py line=797. 2025-01-24T01:52:01.4186516Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:52:01.4186614Z 2025-01-24T01:52:01.4186891Z Initialize a ShardedTensor given only one local tensor, global sharded tensor 2025-01-24T01:52:01.4187014Z size and sharding spec on each rank. 2025-01-24T01:52:01.4187110Z 2025-01-24T01:52:01.4187202Z Args: 2025-01-24T01:52:01.4187435Z local_tensor (Tensor): Single tensor of local shard stored in each rank. 2025-01-24T01:52:01.4187718Z sharding_spec (:class:`torch.distributed._shard.sharding_spec.ShardingSpec`): 2025-01-24T01:52:01.4187898Z The specification describing how to shard the Tensor. 2025-01-24T01:52:01.4188086Z global_size (Sequence[int]): Size of the sharded tensor. 2025-01-24T01:52:01.4188346Z process_group (ProcessGroup, optional): The process group to aggregate on. 2025-01-24T01:52:01.4188453Z Default: None 2025-01-24T01:52:01.4188639Z init_rrefs (bool, optional): Whether or not to initialize 2025-01-24T01:52:01.4188860Z :class:`torch.distributed.rpc.RRef`s pointing to remote shards. 2025-01-24T01:52:01.4189074Z Need to initialize the RPC Framework if specified as ``True``. 2025-01-24T01:52:01.4189181Z Default: ``False``. 2025-01-24T01:52:01.4189279Z 2025-01-24T01:52:01.4189375Z Returns: 2025-01-24T01:52:01.4189626Z A :class:`ShardedTensor` sharded based on the given sharding_spec with local 2025-01-24T01:52:01.4189765Z tensor stored in the current rank. 2025-01-24T01:52:01.4189857Z 2025-01-24T01:52:01.4189957Z Examples: 2025-01-24T01:52:01.4190066Z >>> # xdoctest: +SKIP 2025-01-24T01:52:01.4190216Z >>> # All tensors below are of torch.int64 type. 2025-01-24T01:52:01.4190344Z >>> # We have 2 process groups, 2 ranks. 2025-01-24T01:52:01.4190527Z >>> tensor = torch.arange(2, dtype=torch.int64) + 1 + 2 * rank 2025-01-24T01:52:01.4190747Z >>> local_tensor = torch.unsqueeze(torch.cat([tensor, tensor + 2])) 2025-01-24T01:52:01.4190849Z >>> local_tensor 2025-01-24T01:52:01.4191028Z tensor([[1, 2, 3, 4]]) # Rank 0 2025-01-24T01:52:01.4191137Z tensor([[3, 4, 5, 6]]) # Rank 1 2025-01-24T01:52:01.4191242Z >>> sharding_dim = 0 2025-01-24T01:52:01.4191383Z >>> sharding_spec = ChunkShardingSpec( 2025-01-24T01:52:01.4191484Z dim=sharding_dim, 2025-01-24T01:52:01.4191599Z placements=[ 2025-01-24T01:52:01.4191705Z "rank:0/cuda:0", 2025-01-24T01:52:01.4191818Z "rank:1/cuda:1", 2025-01-24T01:52:01.4191913Z ], 2025-01-24T01:52:01.4192001Z ) 2025-01-24T01:52:01.4192281Z >>> st = ShardedTensor._init_from_local_tensor(local_tensor, sharding_spec, [2, 4]) 2025-01-24T01:52:01.4192368Z >>> st 2025-01-24T01:52:01.4192481Z ShardedTensor( 2025-01-24T01:52:01.4192620Z ShardedTensorMetadata( 2025-01-24T01:52:01.4192724Z shards_metadata=[ 2025-01-24T01:52:01.4193010Z ShardMetadata(shard_offsets=[0, 0], shard_sizes=[1, 4], placement=rank:0/cuda:0), 2025-01-24T01:52:01.4193303Z ShardMetadata(shard_offsets=[1, 0], shard_sizes=[1, 4], placement=rank:1/cuda:1), 2025-01-24T01:52:01.4193404Z ], 2025-01-24T01:52:01.4193510Z size=torch.Size([2, 4]) 2025-01-24T01:52:01.4193611Z ) 2025-01-24T01:52:01.4193715Z >>> st.local_tensor() 2025-01-24T01:52:01.4193820Z tensor([1, 2, 3, 4]) # Rank 0 2025-01-24T01:52:01.4193937Z tensor([3, 4, 5, 6]) # Rank 1 2025-01-24T01:52:01.4194025Z 2025-01-24T01:52:01.4194312Z Warning: This API is experimental and subject to change. It lacks of a fully across 2025-01-24T01:52:01.4194557Z rank validations, and we only validate the local shard on the current rank. 2025-01-24T01:52:01.4194775Z We fully rely on the user to ensure local tensor is sharded based on the 2025-01-24T01:52:01.4194889Z sharding spec. 2025-01-24T01:52:01.4194977Z 2025-01-24T01:52:01.4195256Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:52:01.4195340Z 2025-01-24T01:52:01.4195453Z warnings.warn(msg) 2025-01-24T01:52:01.4195544Z 2025-01-24T01:52:01.4195754Z --- Parse Warning: 41 / 106 --- 2025-01-24T01:52:01.4196814Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ShardedTensor.reshard in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/_shard/sharded_tensor/api.py line=1036. 2025-01-24T01:52:01.4197079Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:52:01.4197180Z 2025-01-24T01:52:01.4197438Z Reshard a sharded tensor given the ``resharding_spec``. For now, we only support 2025-01-24T01:52:01.4197548Z single local shard. 2025-01-24T01:52:01.4197637Z 2025-01-24T01:52:01.4197861Z If ``resharding_spec`` is same as the original one, this becomes a no-op. 2025-01-24T01:52:01.4198125Z If only ``resharding_spec`` shares the same sharding dim with the original one, 2025-01-24T01:52:01.4198236Z we swap local shards directly. 2025-01-24T01:52:01.4198513Z For more generic cases, we merge different shards across different ranks and split 2025-01-24T01:52:01.4198770Z the local shards based on the ``resharding_spec`` via `all_to_all` collective API. 2025-01-24T01:52:01.4198864Z 2025-01-24T01:52:01.4198956Z Args: 2025-01-24T01:52:01.4199249Z resharding_spec (:class:`torch.distributed._shard.sharding_spec.ShardingSpec`): The 2025-01-24T01:52:01.4199429Z specification describing how the tensor is sharded. 2025-01-24T01:52:01.4199521Z 2025-01-24T01:52:01.4199623Z Returns: 2025-01-24T01:52:01.4199836Z A :class:`ShardedTensor` object whose local shards are resharded. 2025-01-24T01:52:01.4199924Z 2025-01-24T01:52:01.4200031Z Examples: 2025-01-24T01:52:01.4200136Z >>> # xdoctest: +SKIP 2025-01-24T01:52:01.4200331Z >>> # We have 2 process groups, 2 ranks. 2025-01-24T01:52:01.4200514Z >>> tensor = torch.arange(4, dtype=torch.int64) + 1 + 2 * rank 2025-01-24T01:52:01.4200645Z >>> tensor = torch.stack([tensor, tensor]) 2025-01-24T01:52:01.4200750Z >>> tensor 2025-01-24T01:52:01.4200875Z tensor([[1, 2, 3, 4], [1, 2, 3, 4]]) # Rank 0 2025-01-24T01:52:01.4201009Z tensor([[3, 4, 5, 6], [3, 4, 5, 6]]) # Rank 1 2025-01-24T01:52:01.4201134Z tensor([[5, 6, 7, 8], [5, 6, 7, 8]]) # Rank 2 2025-01-24T01:52:01.4201272Z tensor([[7, 8, 9, 10], [7, 8, 9, 10]]) # Rank 3 2025-01-24T01:52:01.4201378Z >>> sharding_dim = 0 2025-01-24T01:52:01.4201493Z >>> spec = ChunkShardingSpec( 2025-01-24T01:52:01.4201609Z dim=sharding_dim, 2025-01-24T01:52:01.4201753Z placements=[ 2025-01-24T01:52:01.4201863Z "rank:0/cuda:0", 2025-01-24T01:52:01.4201963Z "rank:1/cuda:1", 2025-01-24T01:52:01.4202091Z "rank:2/cuda:2", 2025-01-24T01:52:01.4202201Z "rank:3/cuda:3", 2025-01-24T01:52:01.4202288Z ], 2025-01-24T01:52:01.4202391Z ) 2025-01-24T01:52:01.4202503Z >>> current_offsets = [0] * 2 2025-01-24T01:52:01.4202632Z >>> current_offsets[0] = rank * 2 2025-01-24T01:52:01.4202753Z >>> shard_metadata = ShardMetadata( 2025-01-24T01:52:01.4202908Z shard_offsets=copy.deepcopy(current_offsets), 2025-01-24T01:52:01.4203038Z shard_sizes=tensor.size(), 2025-01-24T01:52:01.4203167Z placement=spec.placements[rank], 2025-01-24T01:52:01.4203270Z ) 2025-01-24T01:52:01.4203372Z >>> local_shards = [ 2025-01-24T01:52:01.4203463Z Shard( 2025-01-24T01:52:01.4203578Z tensor=tensor, 2025-01-24T01:52:01.4203691Z metadata=shard_metadata, 2025-01-24T01:52:01.4203796Z ) 2025-01-24T01:52:01.4203886Z ] 2025-01-24T01:52:01.4204132Z >>> st = ShardedTensor._init_from_local_shards(local_shards, tensor.size()) 2025-01-24T01:52:01.4204238Z >>> sharding_dim = 1 2025-01-24T01:52:01.4204366Z >>> resharding_spec = ChunkShardingSpec( 2025-01-24T01:52:01.4204481Z dim=sharding_dim, 2025-01-24T01:52:01.4204579Z placements=[ 2025-01-24T01:52:01.4204692Z "rank:0/cuda:0", 2025-01-24T01:52:01.4204793Z "rank:1/cuda:1", 2025-01-24T01:52:01.4204889Z "rank:2/cuda:2", 2025-01-24T01:52:01.4205001Z "rank:3/cuda:3", 2025-01-24T01:52:01.4205088Z ], 2025-01-24T01:52:01.4205186Z ) 2025-01-24T01:52:01.4205304Z >>> st.reshard(resharding_spec) 2025-01-24T01:52:01.4205426Z >>> tensor = st.local_shards()[0].tensor 2025-01-24T01:52:01.4205530Z >>> tensor 2025-01-24T01:52:01.4205672Z tensor([[1], [1], [3], [3], [5], [5], [7], [7]]) # Rank 0 2025-01-24T01:52:01.4205832Z tensor([[2], [2], [4], [4], [6], [6], [8], [8]]) # Rank 1 2025-01-24T01:52:01.4205972Z tensor([[3], [3], [5], [5], [7], [7], [9], [9]]) # Rank 2 2025-01-24T01:52:01.4206126Z tensor([[4], [4], [6], [6], [8], [8], [10], [10]]) # Rank 3 2025-01-24T01:52:01.4206214Z 2025-01-24T01:52:01.4206469Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:52:01.4206571Z 2025-01-24T01:52:01.4206669Z warnings.warn(msg) 2025-01-24T01:52:01.4206768Z 2025-01-24T01:52:01.4206966Z --- Parse Warning: 42 / 106 --- 2025-01-24T01:52:01.4207965Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ShardingPlan in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/_shard/sharding_plan/api.py line=12. 2025-01-24T01:52:01.4208237Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:52:01.4208353Z 2025-01-24T01:52:01.4208684Z Representation of a sharding plan, describes how to shard a module 2025-01-24T01:52:01.4208959Z across hosts. `plan` is used to shard module parameters according to the spec provided, 2025-01-24T01:52:01.4209265Z `output_plan` and `return_local_tensor` are optional, they are used to specify the output 2025-01-24T01:52:01.4209520Z layout of a module with a spec, and when to convert back to data parallel fashion. 2025-01-24T01:52:01.4209619Z 2025-01-24T01:52:01.4209709Z Args: 2025-01-24T01:52:01.4209981Z plan (Dict[str, Union[:class:`torch.distributed._shard.sharding_spec.ShardingSpec`, 2025-01-24T01:52:01.4210169Z :class:`torch.distributed._shard.sharder.Sharder`]): 2025-01-24T01:52:01.4210466Z a dict describes how to shard a module, there're currently two ways to shard a module: 2025-01-24T01:52:01.4210739Z 1. directly shard a module parameter by a `ShardingSpec`, keyed by the name of 2025-01-24T01:52:01.4210900Z a parameter to a `ShardingSpec`. 2025-01-24T01:52:01.4211172Z 2. shard a submodule by applying a `Sharder` on it, keyed by the name of a module 2025-01-24T01:52:01.4211284Z to a `Sharder` object. 2025-01-24T01:52:01.4211617Z output_plan (Dict[str, :class:`torch.distributed._shard.sharding_spec.ShardingSpec`), optional): 2025-01-24T01:52:01.4211890Z a dict specifies the layout of a module's output which produces a ShardedTensor, 2025-01-24T01:52:01.4212129Z keyed by the name of module to ShardingSpec("" in key means the root module). 2025-01-24T01:52:01.4212240Z Default: `None` 2025-01-24T01:52:01.4212505Z return_local_tensor (List[str], optional): a list of string, each element enables 2025-01-24T01:52:01.4212755Z a module's sharded output to be returned as a Tensor from its local shards to 2025-01-24T01:52:01.4213015Z ensure further processing in a data parallel fashion. ("" in list means the 2025-01-24T01:52:01.4213117Z root module). 2025-01-24T01:52:01.4213226Z Default: None 2025-01-24T01:52:01.4213318Z Example: 2025-01-24T01:52:01.4213614Z Suppose we want to shard a module with two linear layers and then run it with DDP, we also 2025-01-24T01:52:01.4213904Z want to convert the output of the second linear layer back to DDP, we can do it as follows: 2025-01-24T01:52:01.4214001Z 2025-01-24T01:52:01.4214181Z >>> # xdoctest: +REQUIRES(module:torch._C._distributed_c10d) 2025-01-24T01:52:01.4214296Z >>> class MyModule(nn.Module): 2025-01-24T01:52:01.4214427Z >>> def __init__(self) -> None: 2025-01-24T01:52:01.4214534Z >>> super().__init__() 2025-01-24T01:52:01.4214653Z >>> self.fc1 = nn.Linear() 2025-01-24T01:52:01.4214765Z >>> self.gelu = nn.GELU() 2025-01-24T01:52:01.4214876Z >>> self.fc2 = nn.Linear() 2025-01-24T01:52:01.4215011Z >>> self.relu = nn.Linear() 2025-01-24T01:52:01.4215103Z >>> 2025-01-24T01:52:01.4215229Z >>> def forward(self, input): 2025-01-24T01:52:01.4215411Z >>> return self.relu(self.fc2(self.gelu(self.fc1(input)))) 2025-01-24T01:52:01.4215508Z 2025-01-24T01:52:01.4215598Z 2025-01-24T01:52:01.4215733Z >>> # xdoctest: +SKIP("Undefined spec1, spec2) 2025-01-24T01:52:01.4215861Z >>> sharding_plan = ShardingPlan( 2025-01-24T01:52:01.4215955Z >>> plan={ 2025-01-24T01:52:01.4216073Z >>> "fc1.weight": spec1, 2025-01-24T01:52:01.4216183Z >>> "fc2.weight": spec2 2025-01-24T01:52:01.4216273Z >>> }, 2025-01-24T01:52:01.4216391Z >>> output_plan={ 2025-01-24T01:52:01.4216498Z >>> "fc2": output_spec 2025-01-24T01:52:01.4216597Z >>> }, 2025-01-24T01:52:01.4216714Z >>> return_local_tensor=["fc2"] 2025-01-24T01:52:01.4216814Z >>> ) 2025-01-24T01:52:01.4216937Z 2025-01-24T01:52:01.4217221Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:52:01.4217319Z 2025-01-24T01:52:01.4217424Z warnings.warn(msg) 2025-01-24T01:52:01.4217524Z 2025-01-24T01:52:01.4217721Z --- Parse Warning: 43 / 106 --- 2025-01-24T01:52:01.4218834Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=post_localSGD_hook in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/algorithms/ddp_comm_hooks/post_localSGD_hook.py line=72. 2025-01-24T01:52:01.4219115Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:52:01.4219205Z 2025-01-24T01:52:01.4219356Z Run post-localSGD algorithm. 2025-01-24T01:52:01.4219449Z 2025-01-24T01:52:01.4219700Z This DDP communication hook is used for running post-localSGD algorithm, 2025-01-24T01:52:01.4219897Z by combining with a model averaging component (e.g., 2025-01-24T01:52:01.4220228Z :class:`~torch.distributed.algorithms.model_averaging.averagers.PeriodicModelAverager`) 2025-01-24T01:52:01.4220361Z that runs after the optimizer step. 2025-01-24T01:52:01.4220455Z 2025-01-24T01:52:01.4220553Z Args: 2025-01-24T01:52:01.4220787Z state (PostLocalSGDState): State information to run post-localSGD. 2025-01-24T01:52:01.4221077Z Users mainly need to tune ``start_localSGD_iter`` to determine when to start local SGD. 2025-01-24T01:52:01.4221503Z bucket (dist.GradBucket): Bucket that stores a 1D flattened gradient tensor that batches multiple per-variable tensors. 2025-01-24T01:52:01.4221758Z Note that since DDP comm hook only supports single process single device mode, 2025-01-24T01:52:01.4221930Z only exactly one tensor is stored in this bucket. 2025-01-24T01:52:01.4222019Z 2025-01-24T01:52:01.4222121Z Returns: 2025-01-24T01:52:01.4222373Z Future handler of the communication, which updates the gradients in place. 2025-01-24T01:52:01.4222459Z 2025-01-24T01:52:01.4222575Z Example:: 2025-01-24T01:52:01.4222678Z >>> # xdoctest: +SKIP 2025-01-24T01:52:01.4222947Z >>> state = PostLocalSGDState(process_group=process_group, subgroup=subgroup, 2025-01-24T01:52:01.4223075Z start_localSGD_iter=10) 2025-01-24T01:52:01.4223261Z >>> ddp_model.register_comm_hook(state, post_localSGD_hook) 2025-01-24T01:52:01.4223603Z >>> # Also need to establish a model averaging module and run model averaging after ``optimizer.step()``. 2025-01-24T01:52:01.4223958Z >>> # Please refer to the examples in ``torch.distributed.algorithms.model_averaging.averagers`` module. 2025-01-24T01:52:01.4224050Z 2025-01-24T01:52:01.4224312Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:52:01.4224409Z 2025-01-24T01:52:01.4224517Z warnings.warn(msg) 2025-01-24T01:52:01.4224615Z 2025-01-24T01:52:01.4224811Z --- Parse Warning: 44 / 106 --- 2025-01-24T01:52:01.4225880Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=powerSGD_hook in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/algorithms/ddp_comm_hooks/powerSGD_hook.py line=342. 2025-01-24T01:52:01.4226160Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:52:01.4226343Z 2025-01-24T01:52:01.4226477Z Implement PowerSGD algorithm. 2025-01-24T01:52:01.4226570Z 2025-01-24T01:52:01.4226814Z This DDP communication hook implements PowerSGD gradient compression 2025-01-24T01:52:01.4227062Z algorithm described in the `paper `_. 2025-01-24T01:52:01.4227315Z Once gradient tensors are aggregated across all workers, this hook applies 2025-01-24T01:52:01.4227435Z compression as follows: 2025-01-24T01:52:01.4227561Z 2025-01-24T01:52:01.4228042Z 1. Views the input flattened 1D gradient tensor as a list of per-parameter tensors, and divides all the tensors into two groups: 2025-01-24T01:52:01.4228134Z 2025-01-24T01:52:01.4228569Z 1.1 The tensors that should be compressed before allreduce, because the compression can give enough saving in bandwidth. 2025-01-24T01:52:01.4228659Z 2025-01-24T01:52:01.4229073Z 1.2 Rest of the tensors will be directly allreduced without compression, including all the vector tensors (for biases). 2025-01-24T01:52:01.4229168Z 2025-01-24T01:52:01.4229286Z 2. Handles uncompressed tensors: 2025-01-24T01:52:01.4229386Z 2025-01-24T01:52:01.4229935Z 2.1. Allocate contiguous memory for those uncompressed tensors, and allreduces all the uncompressed tensors as a batch, without compression; 2025-01-24T01:52:01.4230025Z 2025-01-24T01:52:01.4230376Z 2.2. Copies the individual uncompressed tensors from the contiguous memory back to the input tensor. 2025-01-24T01:52:01.4230496Z 2025-01-24T01:52:01.4230748Z 3. Handles the tensors that should be compressed by PowerSGD compression: 2025-01-24T01:52:01.4230838Z 2025-01-24T01:52:01.4231090Z 3.1. For each tensor M, creates two low-rank tensors P and Q for decomposing M, 2025-01-24T01:52:01.4231404Z such that M = PQ^T, where Q is initialized from a standard normal distribution and orthogonalized; 2025-01-24T01:52:01.4231495Z 2025-01-24T01:52:01.4231659Z 3.2. Computes each P in Ps, which is equal to MQ; 2025-01-24T01:52:01.4231746Z 2025-01-24T01:52:01.4231870Z 3.3. Allreduces Ps as a batch; 2025-01-24T01:52:01.4231958Z 2025-01-24T01:52:01.4232090Z 3.4. Orthogonalizes each P in Ps; 2025-01-24T01:52:01.4232179Z 2025-01-24T01:52:01.4232384Z 3.5. Computes each Q in Qs, which is approximately equal to M^TP; 2025-01-24T01:52:01.4232483Z 2025-01-24T01:52:01.4232594Z 3.6. Allreduces Qs as a batch; 2025-01-24T01:52:01.4232700Z 2025-01-24T01:52:01.4233006Z 3.7. Computes each M among all the compressed tensors, which is approximately equal to PQ^T. 2025-01-24T01:52:01.4233096Z 2025-01-24T01:52:01.4233522Z Note that this communication hook enforces vanilla allreduce for the first ``state.start_powerSGD_iter`` iterations. 2025-01-24T01:52:01.4233811Z This not only gives the user more control over the tradeoff between speedup and accuracy, 2025-01-24T01:52:01.4234261Z but also helps abstract away some complexity of the internal optimization of DDP for future communication hook developers. 2025-01-24T01:52:01.4234348Z 2025-01-24T01:52:01.4234448Z Args: 2025-01-24T01:52:01.4234887Z state (PowerSGDState): State information to configure the compression rate and support error feedback, warm start, etc. 2025-01-24T01:52:01.4235252Z To tune the compression configs, mainly need to tune ``matrix_approximation_rank``, ``start_powerSGD_iter`` 2025-01-24T01:52:01.4235383Z and ``min_compression_rate``. 2025-01-24T01:52:01.4235807Z bucket (dist.GradBucket): Bucket that stores a 1D flattened gradient tensor that batches multiple per-variable tensors. 2025-01-24T01:52:01.4236068Z Note that since DDP comm hook only supports single process single device mode, 2025-01-24T01:52:01.4236225Z only exactly one tensor is stored in this bucket. 2025-01-24T01:52:01.4236325Z 2025-01-24T01:52:01.4236414Z Returns: 2025-01-24T01:52:01.4236667Z Future handler of the communication, which updates the gradients in place. 2025-01-24T01:52:01.4236763Z 2025-01-24T01:52:01.4236864Z Example:: 2025-01-24T01:52:01.4236979Z >>> # xdoctest: +SKIP 2025-01-24T01:52:01.4237257Z >>> state = PowerSGDState(process_group=process_group, matrix_approximation_rank=1, 2025-01-24T01:52:01.4237433Z start_powerSGD_iter=10, min_compression_rate=0.5) 2025-01-24T01:52:01.4237669Z >>> ddp_model.register_comm_hook(state, powerSGD_hook) 2025-01-24T01:52:01.4237755Z 2025-01-24T01:52:01.4238025Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:52:01.4238111Z 2025-01-24T01:52:01.4238227Z warnings.warn(msg) 2025-01-24T01:52:01.4238310Z 2025-01-24T01:52:01.4238518Z --- Parse Warning: 45 / 106 --- 2025-01-24T01:52:01.4239641Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=PeriodicModelAverager in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/algorithms/model_averaging/averagers.py line=38. 2025-01-24T01:52:01.4239911Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:52:01.4240042Z 2025-01-24T01:52:01.4240237Z Averages parameters periodically after the warm-up stage. 2025-01-24T01:52:01.4240340Z 2025-01-24T01:52:01.4240627Z This can be used for running `post-local SGD `_, 2025-01-24T01:52:01.4240827Z by running :class:`~torch.nn.DistributedDataParallel` (DDP) 2025-01-24T01:52:01.4241082Z using the subgroups created by :meth:`~torch.distributed.new_subgroups`. 2025-01-24T01:52:01.4241169Z 2025-01-24T01:52:01.4241276Z Args: 2025-01-24T01:52:01.4241446Z period (int): The number of steps per model averaging. 2025-01-24T01:52:01.4241737Z Usually the period should be greater than ``1`` to reduce the communication cost. 2025-01-24T01:52:01.4241875Z Otherwise, only DDP needs to be used. 2025-01-24T01:52:01.4242089Z warmup_steps (int): The number of warm-up steps. During this stage, 2025-01-24T01:52:01.4242224Z model averaging is skipped. 2025-01-24T01:52:01.4242428Z process_group: The process group to be used for all-reduce. 2025-01-24T01:52:01.4242592Z If ``None``, the default process group, which 2025-01-24T01:52:01.4242791Z is created by :func:`torch.distributed.init_process_group`, 2025-01-24T01:52:01.4242932Z will be used. (default: ``None``) 2025-01-24T01:52:01.4243018Z 2025-01-24T01:52:01.4243116Z Example:: 2025-01-24T01:52:01.4243215Z 2025-01-24T01:52:01.4243344Z >>> # xdoctest: +SKIP("undefined variables") 2025-01-24T01:52:01.4243458Z >>> import torch 2025-01-24T01:52:01.4243582Z >>> import torch.distributed as dist 2025-01-24T01:52:01.4243903Z >>> import torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook as post_localSGD 2025-01-24T01:52:01.4244188Z >>> import torch.distributed.algorithms.model_averaging.averagers as averagers 2025-01-24T01:52:01.4244293Z >>> import torch.nn as nn 2025-01-24T01:52:01.4244403Z >>> 2025-01-24T01:52:01.4244585Z >>> dist.init_process_group("nccl", rank=rank, world_size=16) 2025-01-24T01:52:01.4244719Z >>> torch.cuda.set_device(rank) 2025-01-24T01:52:01.4244857Z >>> module = nn.Linear(1, 1, bias=False).cuda() 2025-01-24T01:52:01.4245018Z >>> model = nn.parallel.DistributedDataParallel( 2025-01-24T01:52:01.4245178Z >>> module, device_ids=[rank], output_device=rank 2025-01-24T01:52:01.4245268Z >>> ) 2025-01-24T01:52:01.4245435Z >>> # Register a post-localSGD communication hook. 2025-01-24T01:52:01.4245739Z >>> state = PostLocalSGDState(process_group=None, subgroup=None, start_localSGD_iter=100) 2025-01-24T01:52:01.4245916Z >>> model.register_comm_hook(state, post_localSGD_hook) 2025-01-24T01:52:01.4246007Z >>> 2025-01-24T01:52:01.4246280Z >>> # In the first 100 steps, run global gradient averaging like normal DDP at every step. 2025-01-24T01:52:01.4246460Z >>> # After 100 steps, run model averaging every 4 steps. 2025-01-24T01:52:01.4246779Z >>> # Note that ``warmup_steps`` must be the same as ``start_localSGD_iter`` used in ``PostLocalSGDState``. 2025-01-24T01:52:01.4247105Z >>> averager = averagers.PeriodicModelAverager(period=4, warmup_steps=100) 2025-01-24T01:52:01.4247215Z >>> for step in range(0, 200): 2025-01-24T01:52:01.4247336Z >>> optimizer.zero_grad() 2025-01-24T01:52:01.4247459Z >>> loss = loss_fn(output, labels) 2025-01-24T01:52:01.4247563Z >>> loss.backward() 2025-01-24T01:52:01.4247680Z >>> optimizer.step() 2025-01-24T01:52:01.4247877Z >>> # Will average model parameters globally every 4 steps. Thus, 2025-01-24T01:52:01.4248094Z >>> # inter-node communication only occurs every 4 iterations after 2025-01-24T01:52:01.4248223Z >>> # the initial ``warmup_steps`` period. 2025-01-24T01:52:01.4248428Z >>> averager.average_parameters(model.parameters()) 2025-01-24T01:52:01.4248519Z 2025-01-24T01:52:01.4248778Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:52:01.4248912Z 2025-01-24T01:52:01.4249017Z warnings.warn(msg) 2025-01-24T01:52:01.4249116Z 2025-01-24T01:52:01.4249315Z --- Parse Warning: 46 / 106 --- 2025-01-24T01:52:01.4250542Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=HierarchicalModelAverager in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/algorithms/model_averaging/hierarchical_model_averager.py line=19. 2025-01-24T01:52:01.4250826Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:52:01.4250917Z 2025-01-24T01:52:01.4251276Z Runs hierarchical model averaging (`hierarchical SGD `_). 2025-01-24T01:52:01.4251367Z 2025-01-24T01:52:01.4251697Z Process groups of different sizes are organized in a hierarchy, and they average parameters 2025-01-24T01:52:01.4251912Z by using different periods concurrently after the warm-up stage. 2025-01-24T01:52:01.4252341Z This is an extension of :class:`~torch.distributed.algorithms.model_averaging.averagers.PeriodicModelAverager` 2025-01-24T01:52:01.4252687Z that supports `post-local SGD `_, which essentially only supports 2025-01-24T01:52:01.4253003Z a two-level hierarchy: the intra-machine level and the global level, where the intra-machine 2025-01-24T01:52:01.4253376Z level is usually embedded in :meth:`~torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook`. 2025-01-24T01:52:01.4253684Z Similarly, the process groups within this class do not have such an intra-machine process 2025-01-24T01:52:01.4253979Z subgroup, which should be embedded by the post-local SGD communication hook instead. 2025-01-24T01:52:01.4254071Z 2025-01-24T01:52:01.4254179Z Args: 2025-01-24T01:52:01.4254445Z period_group_size_dict: An ordered dict mapping keys of model averaging period to 2025-01-24T01:52:01.4254657Z process group size, used for initializing process groups of 2025-01-24T01:52:01.4254905Z different sizes in a hierarchy to average parameters concurrently. 2025-01-24T01:52:01.4255127Z Particularly, at each iteration, there will be at most a single 2025-01-24T01:52:01.4255376Z process group that runs averaging -- the period of such group should 2025-01-24T01:52:01.4255594Z have the largest period which the current step can be divided by. 2025-01-24T01:52:01.4255788Z For example, if the dict has three keys: 2, 4, and 8, 2025-01-24T01:52:01.4256003Z then this means totally three process groups will be created to 2025-01-24T01:52:01.4256233Z average parameters every 2, 4, and 8 iterations, respectively. 2025-01-24T01:52:01.4256430Z At the 4th iteration, only the second process group will run 2025-01-24T01:52:01.4256674Z averaging, because the first process group should be a 2025-01-24T01:52:01.4256916Z subset of the second process group, and no need to execute the first 2025-01-24T01:52:01.4257045Z process group redundantly. 2025-01-24T01:52:01.4257260Z On the other hand, the third process group can only be triggered 2025-01-24T01:52:01.4257487Z every 8 iterations, so it will not be triggered at the 4th iteration. 2025-01-24T01:52:01.4257815Z warmup_steps (int): The number of warm-up steps. During this stage, model averaging is skipped. 2025-01-24T01:52:01.4258295Z process_group (ProcessGroup, optional): The overall process group containing all the processes that runs model averaging. 2025-01-24T01:52:01.4258488Z If ``None``, the default process group, which is created 2025-01-24T01:52:01.4258730Z by :func:`torch.distributed.init_process_group`, will be used. 2025-01-24T01:52:01.4258857Z (default: ``None``) 2025-01-24T01:52:01.4258955Z 2025-01-24T01:52:01.4259053Z Example:: 2025-01-24T01:52:01.4259185Z >>> # xdoctest: +SKIP('undefined rank') 2025-01-24T01:52:01.4259313Z >>> from collections import OrderedDict 2025-01-24T01:52:01.4259408Z >>> import torch 2025-01-24T01:52:01.4259544Z >>> import torch.distributed as dist 2025-01-24T01:52:01.4259825Z >>> from torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook import ( 2025-01-24T01:52:01.4259947Z >>> PostLocalSGDState, 2025-01-24T01:52:01.4260058Z >>> post_localSGD_hook, 2025-01-24T01:52:01.4260161Z >>> ) 2025-01-24T01:52:01.4260544Z >>> import torch.distributed.algorithms.model_averaging.hierarchical_model_averager as hierarchicalSGD 2025-01-24T01:52:01.4260659Z >>> import torch.nn as nn 2025-01-24T01:52:01.4260757Z >>> 2025-01-24T01:52:01.4260940Z >>> dist.init_process_group("nccl", rank=rank, world_size=16) 2025-01-24T01:52:01.4261067Z >>> torch.cuda.set_device(rank) 2025-01-24T01:52:01.4261211Z >>> module = nn.Linear(1, 1, bias=False).to(rank) 2025-01-24T01:52:01.4261371Z >>> model = nn.parallel.DistributedDataParallel( 2025-01-24T01:52:01.4261529Z >>> module, device_ids=[rank], output_device=rank 2025-01-24T01:52:01.4261619Z >>> ) 2025-01-24T01:52:01.4261784Z >>> # Register a post-localSGD communication hook. 2025-01-24T01:52:01.4262067Z >>> # Assume that each machine has 4 GPUs, then each intra-machine subgroup has a size of 4. 2025-01-24T01:52:01.4262205Z >>> subgroup, _ = dist.new_subgroups() 2025-01-24T01:52:01.4262527Z >>> state = PostLocalSGDState(process_group=None, subgroup=subgroup, start_localSGD_iter=100) 2025-01-24T01:52:01.4262702Z >>> model.register_comm_hook(state, post_localSGD_hook) 2025-01-24T01:52:01.4262798Z >>> 2025-01-24T01:52:01.4263089Z >>> # Average parameters among each group of 8 processes every 4 iterations, and among all 2025-01-24T01:52:01.4263229Z >>> # the 16 processes every 16 iterations. 2025-01-24T01:52:01.4263421Z >>> averager = hierarchicalSGD.HierarchicalModelAverager( 2025-01-24T01:52:01.4263669Z >>> period_group_size_dict=OrderedDict([(4, 8), (16, 16)]), warmup_steps=100) 2025-01-24T01:52:01.4263985Z >>> # Note that ``warmup_steps`` must be the same as ``start_localSGD_iter`` used in ``PostLocalSGDState``. 2025-01-24T01:52:01.4264269Z >>> # In the first 100 steps, run global gradient averaging like normal DDP at every step. 2025-01-24T01:52:01.4264432Z >>> # After 100 steps, run model averaging at two levels. 2025-01-24T01:52:01.4264545Z >>> for step in range(0, 200): 2025-01-24T01:52:01.4264699Z >>> optimizer.zero_grad() 2025-01-24T01:52:01.4264845Z >>> loss = loss_fn(output, labels) 2025-01-24T01:52:01.4264963Z >>> loss.backward() 2025-01-24T01:52:01.4265071Z >>> optimizer.step() 2025-01-24T01:52:01.4265232Z >>> # Average parameters after ``optimizer.step()``. 2025-01-24T01:52:01.4265531Z >>> # Thus, the inter-node communication only occurs periodically after ``warmup_steps``. 2025-01-24T01:52:01.4265696Z >>> averager.average_parameters(model.parameters()) 2025-01-24T01:52:01.4265796Z 2025-01-24T01:52:01.4265892Z .. warning :: 2025-01-24T01:52:01.4266166Z The last group size in the dict must be the size of the provided ``process_group``, 2025-01-24T01:52:01.4266535Z which indicates model averaging at the highest level of the hierarchy. 2025-01-24T01:52:01.4266853Z If ``process_group`` is not provided, then the last group size should be equal to the world size. 2025-01-24T01:52:01.4266982Z 2025-01-24T01:52:01.4267085Z .. warning :: 2025-01-24T01:52:01.4267339Z `HierarchicalModelAverager` is experimental and subject to change. 2025-01-24T01:52:01.4267430Z 2025-01-24T01:52:01.4267700Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:52:01.4267788Z 2025-01-24T01:52:01.4267891Z warnings.warn(msg) 2025-01-24T01:52:01.4267991Z 2025-01-24T01:52:01.4268215Z --- Parse Warning: 47 / 106 --- 2025-01-24T01:52:01.4269308Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=BroadcastingTorchSaveReader in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/checkpoint/format_utils.py line=40. 2025-01-24T01:52:01.4269583Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:52:01.4269686Z 2025-01-24T01:52:01.4269986Z StorageReader for reading a Torch Save file. This reader will read the entire checkpoint 2025-01-24T01:52:01.4270247Z on the coordinator rank, and then broadcast and shard each tensor to all ranks. 2025-01-24T01:52:01.4270353Z 2025-01-24T01:52:01.4270525Z . N.B. Intended to be used with DynamicMetaLoadPlanner 2025-01-24T01:52:01.4270630Z 2025-01-24T01:52:01.4270728Z .. warning:: 2025-01-24T01:52:01.4270908Z Current implementation only supports loading Tensors. 2025-01-24T01:52:01.4271009Z 2025-01-24T01:52:01.4271130Z >>> # xdoctest: +SKIP("undefined vars") 2025-01-24T01:52:01.4271245Z >>> sd = {"mode": model} 2025-01-24T01:52:01.4271340Z >>> dcp.load( 2025-01-24T01:52:01.4271440Z >>> sd, 2025-01-24T01:52:01.4271600Z >>> storage_reader=BroadcastingTorchSaveReader(), 2025-01-24T01:52:01.4271730Z >>> planner=DynamicMetaLoadPlanner(), 2025-01-24T01:52:01.4271861Z >>> checkpoint_id="path_to_model.pt" 2025-01-24T01:52:01.4271951Z >>> ) 2025-01-24T01:52:01.4272048Z 2025-01-24T01:52:01.4272307Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:52:01.4272393Z 2025-01-24T01:52:01.4272509Z warnings.warn(msg) 2025-01-24T01:52:01.4272597Z 2025-01-24T01:52:01.4272802Z --- Parse Warning: 48 / 106 --- 2025-01-24T01:52:01.4273845Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=DynamicMetaLoadPlanner in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/checkpoint/format_utils.py line=151. 2025-01-24T01:52:01.4274126Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:52:01.4274217Z 2025-01-24T01:52:01.4274595Z Extension of DefaultLoadPlanner, which creates a new Metadata object based on the passed in state dict, 2025-01-24T01:52:01.4274938Z avoiding the need to read metadata from disk. This is useful when reading formats which don't have a 2025-01-24T01:52:01.4275110Z metadata file, like Torch Save files. 2025-01-24T01:52:01.4275232Z 2025-01-24T01:52:01.4275422Z . N.B. Intended to be used with BroadcastingTorchSaveReader 2025-01-24T01:52:01.4275523Z 2025-01-24T01:52:01.4275619Z .. warning:: 2025-01-24T01:52:01.4275797Z Current implementation only supports loading Tensors. 2025-01-24T01:52:01.4275895Z 2025-01-24T01:52:01.4276016Z >>> # xdoctest: +SKIP("undefined vars") 2025-01-24T01:52:01.4276131Z >>> sd = {"mode": model} 2025-01-24T01:52:01.4276226Z >>> dcp.load( 2025-01-24T01:52:01.4276320Z >>> sd, 2025-01-24T01:52:01.4276493Z >>> storage_reader=BroadcastingTorchSaveReader(), 2025-01-24T01:52:01.4276623Z >>> planner=DynamicMetaLoadPlanner(), 2025-01-24T01:52:01.4276757Z >>> checkpoint_id="path_to_model.pt" 2025-01-24T01:52:01.4276878Z >>> ) 2025-01-24T01:52:01.4276969Z 2025-01-24T01:52:01.4277240Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:52:01.4277358Z 2025-01-24T01:52:01.4277473Z warnings.warn(msg) 2025-01-24T01:52:01.4277566Z 2025-01-24T01:52:01.4277759Z --- Parse Warning: 49 / 106 --- 2025-01-24T01:52:01.4278817Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=load_sharded_optimizer_state_dict in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/checkpoint/optimizer.py line=221. 2025-01-24T01:52:01.4279091Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:52:01.4279341Z 2025-01-24T01:52:01.4279560Z Load a state_dict in conjunction with FSDP sharded optimizer state. 2025-01-24T01:52:01.4279668Z 2025-01-24T01:52:01.4279842Z This is the current recommended way to checkpoint FSDP. 2025-01-24T01:52:01.4279960Z >>> # xdoctest: +SKIP 2025-01-24T01:52:01.4280120Z >>> import torch.distributed.checkpoint as dist_cp 2025-01-24T01:52:01.4280217Z >>> # Save 2025-01-24T01:52:01.4280336Z >>> model: torch.nn.Model 2025-01-24T01:52:01.4280463Z >>> optim_params = model.parameters() 2025-01-24T01:52:01.4280631Z >>> optim = torch.optim.SGD(optim_params, lr=0.01) 2025-01-24T01:52:01.4280728Z >>> # Save 2025-01-24T01:52:01.4280951Z >>> with FSDP.state_dict_type(model, StateDictType.SHARDED_STATE_DICT): 2025-01-24T01:52:01.4281066Z >>> state_dict = { 2025-01-24T01:52:01.4281229Z >>> "optimizer": FSDP.optim_state_dict(model, optim), 2025-01-24T01:52:01.4281361Z >>> "model": model.state_dict() 2025-01-24T01:52:01.4281454Z >>> } 2025-01-24T01:52:01.4281577Z >>> dist_cp.save_state_dict( 2025-01-24T01:52:01.4281687Z >>> state_dict=optim_state, 2025-01-24T01:52:01.4281875Z >>> storage_writer=dist_cp.FileSystemWriter("checkpoint"), 2025-01-24T01:52:01.4282026Z >>> planner=dist_cp.DefaultSavePlanner(), 2025-01-24T01:52:01.4282119Z >>> ) 2025-01-24T01:52:01.4282228Z >>> 2025-01-24T01:52:01.4282319Z >>> # Load 2025-01-24T01:52:01.4282556Z >>> with FSDP.state_dict_type(model_tp, StateDictType.SHARDED_STATE_DICT): 2025-01-24T01:52:01.4282708Z >>> model_state_dict = model_tp.state_dict() 2025-01-24T01:52:01.4282811Z >>> checkpoint = { 2025-01-24T01:52:01.4282939Z >>> "model": model_state_dict 2025-01-24T01:52:01.4283030Z >>> } 2025-01-24T01:52:01.4283141Z >>> dist_cp.load_state_dict( 2025-01-24T01:52:01.4283265Z >>> state_dict=checkpoint, 2025-01-24T01:52:01.4283465Z >>> storage_reader=dist_cp.FileSystemReader(checkpoint_file), 2025-01-24T01:52:01.4283618Z >>> planner=dist_cp.DefaultLoadPlanner(), 2025-01-24T01:52:01.4283707Z >>> ) 2025-01-24T01:52:01.4283880Z >>> model.load_state_dict(checkpoint["model_state"]) 2025-01-24T01:52:01.4283972Z >>> 2025-01-24T01:52:01.4284155Z >>> optim_state = dist_cp.load_sharded_optimizer_state_dict( 2025-01-24T01:52:01.4284334Z >>> model_state_dict, 2025-01-24T01:52:01.4284488Z >>> optimizer_key="optimizer", 2025-01-24T01:52:01.4284690Z >>> storage_reader=dist_cp.FileSystemReader("checkpoint"), 2025-01-24T01:52:01.4284784Z >>> ) 2025-01-24T01:52:01.4284878Z >>> 2025-01-24T01:52:01.4285046Z >>> flattened_osd = FSDP.optim_state_dict_to_load( 2025-01-24T01:52:01.4285183Z >>> model, optim, optim_state["optimizer"] 2025-01-24T01:52:01.4285291Z >>> ) 2025-01-24T01:52:01.4285384Z >>> 2025-01-24T01:52:01.4285536Z >>> optim.load_state_dict(flattened_osd) 2025-01-24T01:52:01.4285628Z 2025-01-24T01:52:01.4285891Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:52:01.4285994Z 2025-01-24T01:52:01.4286137Z warnings.warn(msg) 2025-01-24T01:52:01.4286240Z 2025-01-24T01:52:01.4286441Z --- Parse Warning: 50 / 106 --- 2025-01-24T01:52:01.4287446Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=SavePlanner in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/checkpoint/planner.py line=110. 2025-01-24T01:52:01.4287736Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:52:01.4287826Z 2025-01-24T01:52:01.4288139Z Abstract class defining the protocol used by save_state_dict to plan the save process. 2025-01-24T01:52:01.4288234Z 2025-01-24T01:52:01.4288553Z SavePlanners are stateful objects that can be used to customize the whole save process. 2025-01-24T01:52:01.4288645Z 2025-01-24T01:52:01.4288934Z SavePlanner acts as an access proxy to the state_dict, so any transformation done to it 2025-01-24T01:52:01.4289077Z will be visible to the whole process. 2025-01-24T01:52:01.4289164Z 2025-01-24T01:52:01.4289461Z A planner subclass can expect the following sequence of calls during save_state_dict: 2025-01-24T01:52:01.4289554Z 2025-01-24T01:52:01.4289704Z 1) set_up_planner - called on all ranks. 2025-01-24T01:52:01.4289840Z Signals the start of a checkpoint save. 2025-01-24T01:52:01.4289928Z 2025-01-24T01:52:01.4290073Z 2) create_local_plan - called on all ranks. 2025-01-24T01:52:01.4290363Z Process the state_dict and produces a `SavePlan` that will be sent for global planning. 2025-01-24T01:52:01.4290467Z 2025-01-24T01:52:01.4290656Z 3) create_global_plan - called on the coordinator rank only. 2025-01-24T01:52:01.4290857Z Takes the SavePlan from all ranks and make any global decision. 2025-01-24T01:52:01.4290960Z 2025-01-24T01:52:01.4291074Z 4) finish_plan - called on all ranks. 2025-01-24T01:52:01.4291310Z This gives each rank a chance to adjust to global planning decisions. 2025-01-24T01:52:01.4291402Z 2025-01-24T01:52:01.4291575Z 5) resolve_data - called multiple times on each rank 2025-01-24T01:52:01.4291790Z Lookups a value on the `state_dict` for the storage layer to write. 2025-01-24T01:52:01.4291882Z 2025-01-24T01:52:01.4292206Z Users are recommended to extend DefaultSavePlanner instead of this interface directly as 2025-01-24T01:52:01.4292398Z most changes can be expressed by changes in a single method. 2025-01-24T01:52:01.4292497Z 2025-01-24T01:52:01.4292627Z There are 3 usual patterns of extension: 2025-01-24T01:52:01.4292712Z 2025-01-24T01:52:01.4292982Z Rewriting state_dict. This is the simplest way to extend the save process as it 2025-01-24T01:52:01.4293929Z doesn't requite understanding the intrincacies of how SavePlan works: 2025-01-24T01:52:01.4294031Z 2025-01-24T01:52:01.4294151Z >>> # xdoctest: +SKIP("undefined vars") 2025-01-24T01:52:01.4294309Z >>> class RenamePlanner(DefaultSavePlanner): 2025-01-24T01:52:01.4294416Z >>> def set_up_planner( 2025-01-24T01:52:01.4294509Z >>> self, 2025-01-24T01:52:01.4294643Z >>> state_dict: STATE_DICT_TYPE, 2025-01-24T01:52:01.4294833Z >>> storage_meta: Optional[StorageMeta], 2025-01-24T01:52:01.4294957Z >>> is_coordinator: bool, 2025-01-24T01:52:01.4295053Z >>> ) -> None: 2025-01-24T01:52:01.4295190Z >>> # prefix all keys with `foo_`` 2025-01-24T01:52:01.4295492Z >>> super().set_up_planner({"foo_" + k: v for k, v in state_dict.items()}, storage_meta, is_coordinator) 2025-01-24T01:52:01.4295581Z 2025-01-24T01:52:01.4295932Z Modifying local plan and lookup in tandem. This is useful when fine control of how data is persisted 2025-01-24T01:52:01.4296019Z 2025-01-24T01:52:01.4296161Z >>> # xdoctest: +SKIP("undefined vars") 2025-01-24T01:52:01.4296298Z >>> class FP16Planner(DefaultSavePlanner): 2025-01-24T01:52:01.4296437Z >>> def create_local_plan(self): 2025-01-24T01:52:01.4296578Z >>> plan = super().create_local_plan() 2025-01-24T01:52:01.4296683Z >>> for p in plan: 2025-01-24T01:52:01.4296848Z >>> if p.tensor_data is not None: 2025-01-24T01:52:01.4297016Z >>> p.tensor_data.properties.dtype = torch.float16 2025-01-24T01:52:01.4297128Z >>> return plan 2025-01-24T01:52:01.4297219Z >>> 2025-01-24T01:52:01.4297345Z >>> def resolve_data(self, write_item): 2025-01-24T01:52:01.4297490Z >>> item = super().resolve_data(write_item) 2025-01-24T01:52:01.4297768Z >>> return item if write_item.type == WriteItemType.BYTE_IO else item.to(torch.float16) 2025-01-24T01:52:01.4297869Z 2025-01-24T01:52:01.4298220Z Using the global planning step to make central decisions that can't be made individually by each rank 2025-01-24T01:52:01.4298318Z 2025-01-24T01:52:01.4298440Z >>> # xdoctest: +SKIP("undefined vars") 2025-01-24T01:52:01.4298561Z >>> from itertools import zip_longest 2025-01-24T01:52:01.4298693Z >>> from dataclasses import replace 2025-01-24T01:52:01.4298867Z >>> class DDPLoadBalancingPlanner(DefaultSavePlanner): 2025-01-24T01:52:01.4299166Z >>> # This uses the default local plan behavior of having all non-sharded writes in rank 0 2025-01-24T01:52:01.4299308Z >>> # This sample doesn't handle ShardedTensors 2025-01-24T01:52:01.4299443Z >>> def create_global_plan(self, all_plans): 2025-01-24T01:52:01.4299615Z >>> iters = [iter(all_plans[0].items)] * len(all_plans) 2025-01-24T01:52:01.4299719Z >>> items_per_rank = [ 2025-01-24T01:52:01.4299874Z >>> [item for item in items if item is not None] 2025-01-24T01:52:01.4300041Z >>> for items in zip(*zip_longest(*iters), strict=True) 2025-01-24T01:52:01.4300144Z >>> ] 2025-01-24T01:52:01.4300247Z >>> all_plans = [ 2025-01-24T01:52:01.4300368Z >>> replace(plan, items=items) 2025-01-24T01:52:01.4300576Z >>> for plan, items in zip(all_plans, items_per_rank, strict=True) 2025-01-24T01:52:01.4300671Z >>> ] 2025-01-24T01:52:01.4300834Z >>> return super().create_global_plan(all_plans) 2025-01-24T01:52:01.4300923Z 2025-01-24T01:52:01.4301204Z Finally, some planners need to save additional metadata in the checkpoint, this is 2025-01-24T01:52:01.4301482Z accomplished by having each rank contribute their data items in the local plan and 2025-01-24T01:52:01.4301602Z the global planner aggregate them: 2025-01-24T01:52:01.4301699Z 2025-01-24T01:52:01.4301823Z >>> # xdoctest: +SKIP("undefined vars") 2025-01-24T01:52:01.4301996Z >>> class SaveExtraDataPlanner(DefaultSavePlanner): 2025-01-24T01:52:01.4302135Z >>> def create_local_plan(self) -> SavePlan: 2025-01-24T01:52:01.4302262Z >>> plan = super().create_local_plan() 2025-01-24T01:52:01.4302444Z >>> return replace(plan, planner_data="per-rank-data") 2025-01-24T01:52:01.4302533Z >>> 2025-01-24T01:52:01.4302850Z >>> def create_global_plan(self, all_plans: List[SavePlan]) -> Tuple[List[SavePlan], Metadata]: 2025-01-24T01:52:01.4303121Z >>> global_plan, metadata = super().create_global_plan(all_plans) 2025-01-24T01:52:01.4303294Z >>> merged_data = [p.planner_data for p in global_plan] 2025-01-24T01:52:01.4303472Z >>> metadata = replace(metadata, planner_data=merged_data) 2025-01-24T01:52:01.4303590Z >>> return global_plan, metadata 2025-01-24T01:52:01.4303691Z 2025-01-24T01:52:01.4303951Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:52:01.4304052Z 2025-01-24T01:52:01.4304157Z warnings.warn(msg) 2025-01-24T01:52:01.4304244Z 2025-01-24T01:52:01.4304470Z --- Parse Warning: 51 / 106 --- 2025-01-24T01:52:01.4305460Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=LoadPlanner in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/checkpoint/planner.py line=272. 2025-01-24T01:52:01.4305786Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:52:01.4305873Z 2025-01-24T01:52:01.4306177Z Abstract class defining the protocol used by load_state_dict to plan the load process. 2025-01-24T01:52:01.4306359Z 2025-01-24T01:52:01.4306672Z LoadPlanner are stateful objects that can be used to customize the whole load process. 2025-01-24T01:52:01.4306761Z 2025-01-24T01:52:01.4307049Z LoadPlanner acts as an access proxy to the state_dict, so any transformation done to it 2025-01-24T01:52:01.4307179Z will be visible to the whole process. 2025-01-24T01:52:01.4307270Z 2025-01-24T01:52:01.4307567Z A planner subclass can expect the following sequence of calls during load_state_dict: 2025-01-24T01:52:01.4307659Z 2025-01-24T01:52:01.4307792Z 1) set_up_planner - called on all ranks. 2025-01-24T01:52:01.4307944Z Signals the start of loading a checkpoint. 2025-01-24T01:52:01.4308034Z 2025-01-24T01:52:01.4308178Z 2) create_local_plan - called on all ranks. 2025-01-24T01:52:01.4308471Z Process the state_dict and produces a `LoadPlan` that will be sent for global planning. 2025-01-24T01:52:01.4308568Z 2025-01-24T01:52:01.4308758Z 3) create_global_plan - called on the coordinator rank only. 2025-01-24T01:52:01.4308964Z Takes the LoadPlan from all ranks and make any global decision. 2025-01-24T01:52:01.4309064Z 2025-01-24T01:52:01.4309219Z 4) load_bytes - called multiple times on each rank 2025-01-24T01:52:01.4309404Z This is called once per non-tensor value in state_dict. 2025-01-24T01:52:01.4309494Z 2025-01-24T01:52:01.4309721Z 5) resolve_tensor and commit_tensor - called multiple times on each rank 2025-01-24T01:52:01.4309924Z They are called in pair for each Tensor value in state_dict. 2025-01-24T01:52:01.4310014Z 2025-01-24T01:52:01.4310334Z Users are recommended to extend DefaultLoadPlanner instead of this interface directly as 2025-01-24T01:52:01.4310531Z most changes can be expressed by changes in a single method. 2025-01-24T01:52:01.4310630Z 2025-01-24T01:52:01.4310766Z There are two usual patterns of extension: 2025-01-24T01:52:01.4310852Z 2025-01-24T01:52:01.4311120Z Rewriting state_dict. This is the simplest way to extend the load process as it 2025-01-24T01:52:01.4311383Z doesn't requite understanding the intrincacies of how LoadPlan works. We need 2025-01-24T01:52:01.4311626Z to keep a reference to the original state_dict as load happens in place so 2025-01-24T01:52:01.4311759Z we need to be able to perform it in place 2025-01-24T01:52:01.4311858Z 2025-01-24T01:52:01.4311983Z >>> # xdoctest: +SKIP("undefined vars") 2025-01-24T01:52:01.4312124Z >>> class RenamePlanner(DefaultLoadPlanner): 2025-01-24T01:52:01.4312246Z >>> def set_up_planner( 2025-01-24T01:52:01.4312344Z >>> self, 2025-01-24T01:52:01.4312479Z >>> state_dict: STATE_DICT_TYPE, 2025-01-24T01:52:01.4312623Z >>> metadata: Metadata, 2025-01-24T01:52:01.4312759Z >>> is_coordinator: bool, 2025-01-24T01:52:01.4312869Z >>> ) -> None: 2025-01-24T01:52:01.4313004Z >>> self.original_state_dict = state_dict 2025-01-24T01:52:01.4313198Z >>> state_dict = {"foo_" + k: v for k, v in state_dict.items()} 2025-01-24T01:52:01.4313290Z >>> 2025-01-24T01:52:01.4313412Z >>> if self.flatten_sharded_tensors: 2025-01-24T01:52:01.4313583Z >>> state_dict = _flatten_sharded_tensors(state_dict) 2025-01-24T01:52:01.4313674Z >>> 2025-01-24T01:52:01.4313802Z >>> if self.flatten_state_dict: 2025-01-24T01:52:01.4313993Z >>> state_dict, self.mappings = flatten_state_dict(state_dict) 2025-01-24T01:52:01.4314102Z >>> 2025-01-24T01:52:01.4314249Z >>> self.state_dict = state_dict 2025-01-24T01:52:01.4314367Z >>> self.metadata = metadata 2025-01-24T01:52:01.4314543Z >>> self.is_coordinator = is_coordinator 2025-01-24T01:52:01.4314641Z >>> 2025-01-24T01:52:01.4314789Z >>> def load_bytes(self, read_item, value): 2025-01-24T01:52:01.4314908Z >>> # Remove the "foo_" prefix 2025-01-24T01:52:01.4315237Z >>> self.original_state_dict[read_item.dest_index.fqn[4:]] = torch.load(value, weights_only=False) 2025-01-24T01:52:01.4315344Z 2025-01-24T01:52:01.4315437Z 2025-01-24T01:52:01.4315719Z Modifying resolve_tensor and commit_tensor to handle load time transformation. 2025-01-24T01:52:01.4315812Z 2025-01-24T01:52:01.4315949Z >>> # xdoctest: +SKIP("undefined vars") 2025-01-24T01:52:01.4316115Z >>> class MetaModelMaterialize(DefaultSavePlanner): 2025-01-24T01:52:01.4316246Z >>> def resolve_tensor(self, read_item): 2025-01-24T01:52:01.4316405Z >>> tensor = super().resolve_tensor(read_item) 2025-01-24T01:52:01.4316562Z >>> return torch.empty_like(tensor, device="cpu") 2025-01-24T01:52:01.4316671Z >>> 2025-01-24T01:52:01.4316813Z >>> def commit_tensor(self, read_item, tensor): 2025-01-24T01:52:01.4316994Z >>> self.state_dict[read_item.dest_index.fqn] = tensor 2025-01-24T01:52:01.4317088Z 2025-01-24T01:52:01.4317350Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:52:01.4317460Z 2025-01-24T01:52:01.4317569Z warnings.warn(msg) 2025-01-24T01:52:01.4317673Z 2025-01-24T01:52:01.4317892Z --- Parse Warning: 52 / 106 --- 2025-01-24T01:52:01.4318868Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=load in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/checkpoint/state_dict_loader.py line=61. 2025-01-24T01:52:01.4319160Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:52:01.4319253Z 2025-01-24T01:52:01.4319420Z Load a distributed ``state_dict`` in SPMD style. 2025-01-24T01:52:01.4319517Z 2025-01-24T01:52:01.4319726Z Each rank will try to read the least amount of data necessary 2025-01-24T01:52:01.4319971Z to fullfill the requested `state_dict`. When loading :class:`ShardedTensor` 2025-01-24T01:52:01.4320226Z or :class:`DTensor` instances, each rank only reads data for their local shards. 2025-01-24T01:52:01.4320332Z 2025-01-24T01:52:01.4320602Z For each ``Stateful`` object (having both a ``state_dict`` and a ``load_state_dict``), 2025-01-24T01:52:01.4320878Z load will first call ``state_dict`` before attempting deserialization, followed by 2025-01-24T01:52:01.4321051Z ``load_state_dict`` once the deserialization is complete. 2025-01-24T01:52:01.4321335Z For each non-``Stateful`` object, load will deserailize the object, and then replace 2025-01-24T01:52:01.4321497Z it in the ``state_dict`` with the deserialized object. 2025-01-24T01:52:01.4321587Z 2025-01-24T01:52:01.4321704Z .. warning:: 2025-01-24T01:52:01.4321943Z All tensors in ``state_dict`` must be allocated on their 2025-01-24T01:52:01.4322130Z destination device *prior to* calling this function. 2025-01-24T01:52:01.4322220Z 2025-01-24T01:52:01.4322454Z All non-tensor data is loaded using `torch.load()` and modified in place 2025-01-24T01:52:01.4322563Z on state_dict. 2025-01-24T01:52:01.4322653Z 2025-01-24T01:52:01.4322763Z .. warning:: 2025-01-24T01:52:01.4322973Z Users must call `load_state_dict` on the root module to ensure load 2025-01-24T01:52:01.4323173Z pos-processing and non-tensor data properly propagates. 2025-01-24T01:52:01.4323263Z 2025-01-24T01:52:01.4323357Z .. note: 2025-01-24T01:52:01.4323601Z If no process group is initialized, this function will assume the intent 2025-01-24T01:52:01.4323858Z is to load a checkpoint into the local process. This can be useful in the 2025-01-24T01:52:01.4324124Z case of local inference, and when using regular Tensors (as opposed to DTensor 2025-01-24T01:52:01.4324260Z or ShardedTensor) 2025-01-24T01:52:01.4324362Z 2025-01-24T01:52:01.4324456Z .. note: 2025-01-24T01:52:01.4324605Z Rank 0 is assumed to be the coordinator rank. 2025-01-24T01:52:01.4324709Z 2025-01-24T01:52:01.4324802Z Args: 2025-01-24T01:52:01.4324977Z state_dict (Dict[str, Any]): The state_dict to save. 2025-01-24T01:52:01.4325131Z checkpoint_id (Union[str, os.PathLike, None]): 2025-01-24T01:52:01.4325355Z The ID of this checkpoint instance. The meaning of the checkpoint_id 2025-01-24T01:52:01.4325575Z depends on the storage. It can be a path to a folder or to a file. 2025-01-24T01:52:01.4325747Z It can also be a key if the storage is a key-value store. 2025-01-24T01:52:01.4325865Z (Default: ``None``) 2025-01-24T01:52:01.4326005Z storage_reader (Optional[StorageReader]): 2025-01-24T01:52:01.4326231Z Instance of StorageWriter used to perform reads. If this is not 2025-01-24T01:52:01.4326447Z specified, DCP will automatically infer the reader based on the 2025-01-24T01:52:01.4326654Z checkpoint_id. If checkpoint_id is also None, an exception will 2025-01-24T01:52:01.4326786Z be raised. (Default: ``None``) 2025-01-24T01:52:01.4326910Z planner (Optional[LoadPlanner]): 2025-01-24T01:52:01.4327130Z Instance of LoadPlanner. If this is not specificed, the default 2025-01-24T01:52:01.4327269Z planner will be used. (Default: ``None``) 2025-01-24T01:52:01.4327416Z process_group (Optional[ProcessGroup]): 2025-01-24T01:52:01.4327604Z ProcessGroup to be used for cross-rank synchronization. 2025-01-24T01:52:01.4327710Z (Default: ``None``) 2025-01-24T01:52:01.4327811Z 2025-01-24T01:52:01.4327906Z Returns: 2025-01-24T01:52:01.4328011Z None. 2025-01-24T01:52:01.4328103Z 2025-01-24T01:52:01.4328200Z Examples 2025-01-24T01:52:01.4328319Z >>> # xdoctest: +SKIP 2025-01-24T01:52:01.4328428Z >>> my_model = MyModule() 2025-01-24T01:52:01.4328586Z >>> optimizer = Adagrad(my_model.parameters()) 2025-01-24T01:52:01.4328726Z >>> model_state_dict = my_model.state_dict() 2025-01-24T01:52:01.4329037Z >>> fs_storage_reader = torch.distributed.checkpoint.FileSystemReader("/checkpoint/1") 2025-01-24T01:52:01.4329141Z 2025-01-24T01:52:01.4329304Z >>> torch.distributed.checkpoint.load_state_dict( 2025-01-24T01:52:01.4329435Z >>> state_dict=model_state_dict, 2025-01-24T01:52:01.4329562Z >>> storage_reader=fs_storage_reader, 2025-01-24T01:52:01.4329666Z >>> ) 2025-01-24T01:52:01.4329754Z 2025-01-24T01:52:01.4329962Z >>> # module.load_state_dict() function might have customized steps 2025-01-24T01:52:01.4330112Z >>> # to flush the state_dict, must call it to 2025-01-24T01:52:01.4330228Z >>> # ensure correct behavior. 2025-01-24T01:52:01.4330382Z >>> my_model.load_state_dict(model_state_dict) 2025-01-24T01:52:01.4330523Z 2025-01-24T01:52:01.4330619Z .. note:: 2025-01-24T01:52:01.4330853Z load_state_dict uses collectives to coordinate reads across ranks. 2025-01-24T01:52:01.4331075Z For NCCL-based process groups, internal tensor representations of 2025-01-24T01:52:01.4331322Z objects must be moved to the GPU device before communication takes place. 2025-01-24T01:52:01.4331550Z In this case, the device used is given by ``torch.cuda.current_device()`` 2025-01-24T01:52:01.4331801Z and it is the user's responsibility to ensure that this is set so that each 2025-01-24T01:52:01.4331994Z rank has an individual GPU, via ``torch.cuda.set_device()``. 2025-01-24T01:52:01.4332087Z 2025-01-24T01:52:01.4332387Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:52:01.4332480Z 2025-01-24T01:52:01.4332601Z warnings.warn(msg) 2025-01-24T01:52:01.4332718Z 2025-01-24T01:52:01.4332931Z --- Parse Warning: 53 / 106 --- 2025-01-24T01:52:01.4333908Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=save in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/checkpoint/state_dict_saver.py line=67. 2025-01-24T01:52:01.4334185Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:52:01.4334293Z 2025-01-24T01:52:01.4334423Z Save a distributed model in SPMD style. 2025-01-24T01:52:01.4334528Z 2025-01-24T01:52:01.4334730Z This function is different from ``torch.save()`` as it handles 2025-01-24T01:52:01.4335014Z ``ShardedTensor`` , and ``DTensor`` by having each rank only save their local shards. 2025-01-24T01:52:01.4335105Z 2025-01-24T01:52:01.4335374Z For each ``Stateful`` object (having both a ``state_dict`` and a ``load_state_dict``), 2025-01-24T01:52:01.4335548Z save will call ``state_dict`` before serialization. 2025-01-24T01:52:01.4335641Z 2025-01-24T01:52:01.4335752Z .. warning:: 2025-01-24T01:52:01.4335996Z There is no guarantees of Backwards Compatibility across PyTorch versions 2025-01-24T01:52:01.4336107Z for saved state_dicts. 2025-01-24T01:52:01.4336211Z 2025-01-24T01:52:01.4336309Z .. warning:: 2025-01-24T01:52:01.4336537Z If using the `process_group` argument, make sure that only its ranks 2025-01-24T01:52:01.4336750Z call `save_state_dict` and that all data in state_dict belong to it. 2025-01-24T01:52:01.4336854Z 2025-01-24T01:52:01.4336948Z .. note:: 2025-01-24T01:52:01.4337210Z When saving checkpoint for FSDP's `ShardingStrategy.HYBRID_SHARD`, only one of 2025-01-24T01:52:01.4337492Z the shard_group should be calling `save_state_dict` and the corresponding process 2025-01-24T01:52:01.4337610Z group needs to be passed in. 2025-01-24T01:52:01.4337713Z 2025-01-24T01:52:01.4337809Z .. note:: 2025-01-24T01:52:01.4338086Z If no process group is available, this function assumes the intention is to save the 2025-01-24T01:52:01.4338216Z state_dict in the local process. 2025-01-24T01:52:01.4338306Z 2025-01-24T01:52:01.4338407Z .. note: 2025-01-24T01:52:01.4338556Z Rank 0 is assumed to be the coordinator rank. 2025-01-24T01:52:01.4338659Z 2025-01-24T01:52:01.4338746Z 2025-01-24T01:52:01.4338836Z Args: 2025-01-24T01:52:01.4339006Z state_dict (Dict[str, Any]): The state_dict to save. 2025-01-24T01:52:01.4339160Z checkpoint_id (Union[str, os.PathLike, None]): 2025-01-24T01:52:01.4339390Z The ID of this checkpoint instance. The meaning of the checkpoint_id 2025-01-24T01:52:01.4339606Z depends on the storage. It can be a path to a folder or to a file. 2025-01-24T01:52:01.4339783Z It can also be a key if the storage is a key-value store. 2025-01-24T01:52:01.4339902Z (Default: ``None``) 2025-01-24T01:52:01.4340102Z storage_writer (Optional[StorageWriter]): 2025-01-24T01:52:01.4340335Z Instance of StorageWriter used to perform writes. If this is not 2025-01-24T01:52:01.4340546Z specified, DCP will automatically infer the writer based on the 2025-01-24T01:52:01.4340768Z checkpoint_id. If checkpoint_id is also None, an exception will 2025-01-24T01:52:01.4340887Z be raised. (Default: ``None``) 2025-01-24T01:52:01.4341014Z planner (Optional[SavePlanner]): 2025-01-24T01:52:01.4341236Z Instance of SavePlanner. If this is not specificed, the default 2025-01-24T01:52:01.4341372Z planner will be used. (Default: ``None``) 2025-01-24T01:52:01.4341527Z process_group (Optional[ProcessGroup]): 2025-01-24T01:52:01.4341757Z ProcessGroup to be used for cross-rank synchronization. 2025-01-24T01:52:01.4341874Z (Default: ``None``) 2025-01-24T01:52:01.4341965Z 2025-01-24T01:52:01.4342086Z Returns: 2025-01-24T01:52:01.4342267Z Metadata: Metadata object for the saved checkpoint. 2025-01-24T01:52:01.4342357Z 2025-01-24T01:52:01.4342462Z Example: 2025-01-24T01:52:01.4342570Z >>> # xdoctest: +SKIP 2025-01-24T01:52:01.4347408Z >>> my_model = MyModule() 2025-01-24T01:52:01.4347552Z 2025-01-24T01:52:01.4347686Z >>> state_dict = {"model": my_model} 2025-01-24T01:52:01.4347785Z 2025-01-24T01:52:01.4348100Z >>> fs_storage_writer = torch.distributed.checkpoint.FileSystemWriter("/checkpoint/1") 2025-01-24T01:52:01.4348241Z >>> torch.distributed.checkpoint.save( 2025-01-24T01:52:01.4348361Z >>> state_dict=state_dict, 2025-01-24T01:52:01.4348491Z >>> storage_writer=fs_storage_writer, 2025-01-24T01:52:01.4348592Z >>> ) 2025-01-24T01:52:01.4348692Z 2025-01-24T01:52:01.4348787Z .. note:: 2025-01-24T01:52:01.4349031Z save_state_dict uses collectives to coordinate writes across ranks. 2025-01-24T01:52:01.4349265Z For NCCL-based process groups, internal tensor representations of 2025-01-24T01:52:01.4349511Z objects must be moved to the GPU device before communication takes place. 2025-01-24T01:52:01.4349742Z In this case, the device used is given by ``torch.cuda.current_device()`` 2025-01-24T01:52:01.4349956Z and it is the user's responsibility to ensure that this is set so that 2025-01-24T01:52:01.4350171Z each rank has an individual GPU, via ``torch.cuda.set_device()``. 2025-01-24T01:52:01.4350258Z 2025-01-24T01:52:01.4350526Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:52:01.4350616Z 2025-01-24T01:52:01.4350732Z warnings.warn(msg) 2025-01-24T01:52:01.4350827Z 2025-01-24T01:52:01.4351082Z --- Parse Warning: 54 / 106 --- 2025-01-24T01:52:01.4352093Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=async_save in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/checkpoint/state_dict_saver.py line=170. 2025-01-24T01:52:01.4352368Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:52:01.4352650Z Asynchronous version of ``save``. This code first de-stages the state_dict on to the 2025-01-24T01:52:01.4352942Z staging storage (defaults to CPU memory), and then calls the `save` in a separate thread. 2025-01-24T01:52:01.4353043Z 2025-01-24T01:52:01.4353143Z .. warning:: 2025-01-24T01:52:01.4353312Z This feature is experimental and subject to change. 2025-01-24T01:52:01.4353411Z 2025-01-24T01:52:01.4353507Z Args: 2025-01-24T01:52:01.4353677Z state_dict (Dict[str, Any]): The state_dict to save. 2025-01-24T01:52:01.4353837Z checkpoint_id (Union[str, os.PathLike, None]): 2025-01-24T01:52:01.4354056Z The ID of this checkpoint instance. The meaning of the checkpoint_id 2025-01-24T01:52:01.4354405Z depends on the storage. It can be a path to a folder or to a file. 2025-01-24T01:52:01.4354586Z It can also be a key if the storage is a key-value store. 2025-01-24T01:52:01.4354702Z (Default: ``None``) 2025-01-24T01:52:01.4354849Z storage_writer (Optional[StorageWriter]): 2025-01-24T01:52:01.4355080Z Instance of StorageWriter used to perform 'stage' and 'save'. If 2025-01-24T01:52:01.4355327Z this is not specified, DCP will automatically infer the writer based on the 2025-01-24T01:52:01.4355552Z checkpoint_id. If checkpoint_id is also None, an exception will 2025-01-24T01:52:01.4355674Z be raised. (Default: ``None``) 2025-01-24T01:52:01.4355805Z planner (Optional[SavePlanner]): 2025-01-24T01:52:01.4356056Z Instance of SavePlanner. If this is not specificed, the default 2025-01-24T01:52:01.4356222Z planner will be used. (Default: ``None``) 2025-01-24T01:52:01.4356377Z process_group (Optional[ProcessGroup]): 2025-01-24T01:52:01.4356569Z ProcessGroup to be used for cross-rank synchronization. 2025-01-24T01:52:01.4356679Z (Default: ``None``) 2025-01-24T01:52:01.4356776Z 2025-01-24T01:52:01.4356875Z Returns: 2025-01-24T01:52:01.4357105Z Future: A future holding the resultant Metadata object from `save`. 2025-01-24T01:52:01.4357195Z 2025-01-24T01:52:01.4357305Z Example: 2025-01-24T01:52:01.4357412Z >>> # xdoctest: +SKIP 2025-01-24T01:52:01.4357523Z >>> my_model = MyModule() 2025-01-24T01:52:01.4357626Z 2025-01-24T01:52:01.4357750Z >>> state_dict = {"model": my_model} 2025-01-24T01:52:01.4357849Z 2025-01-24T01:52:01.4358160Z >>> fs_storage_writer = torch.distributed.checkpoint.FileSystemWriter("/checkpoint/1") 2025-01-24T01:52:01.4358378Z >>> checkpoint_future = torch.distributed.checkpoint.async_save( 2025-01-24T01:52:01.4358511Z >>> state_dict=state_dict, 2025-01-24T01:52:01.4358644Z >>> storage_writer=fs_storage_writer, 2025-01-24T01:52:01.4358747Z >>> ) 2025-01-24T01:52:01.4358841Z >>> 2025-01-24T01:52:01.4358960Z >>> # ... do some work ... 2025-01-24T01:52:01.4359052Z >>> 2025-01-24T01:52:01.4359176Z >>> checkpoint_future.result() 2025-01-24T01:52:01.4359273Z 2025-01-24T01:52:01.4359365Z 2025-01-24T01:52:01.4359636Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:52:01.4359727Z 2025-01-24T01:52:01.4359831Z warnings.warn(msg) 2025-01-24T01:52:01.4359930Z 2025-01-24T01:52:01.4360140Z --- Parse Warning: 55 / 106 --- 2025-01-24T01:52:01.4361209Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=construct_and_record_rdzv_event in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/elastic/events/__init__.py line=91. 2025-01-24T01:52:01.4361484Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:52:01.4361579Z 2025-01-24T01:52:01.4361794Z Initialize rendezvous event object and record its operations. 2025-01-24T01:52:01.4361882Z 2025-01-24T01:52:01.4361982Z Args: 2025-01-24T01:52:01.4362122Z run_id (str): The run id of the rendezvous. 2025-01-24T01:52:01.4362289Z message (str): The message describing the event. 2025-01-24T01:52:01.4362555Z node_state (NodeState): The state of the node (INIT, RUNNING, SUCCEEDED, FAILED). 2025-01-24T01:52:01.4362746Z name (str): Event name. (E.g. Current action being performed). 2025-01-24T01:52:01.4362885Z hostname (str): Hostname of the node. 2025-01-24T01:52:01.4363034Z pid (Optional[int]): The process id of the node. 2025-01-24T01:52:01.4363394Z master_endpoint (str): The master endpoint for the rendezvous store, if known. 2025-01-24T01:52:01.4363699Z local_id (Optional[int]): The local_id of the node, if defined in dynamic_rendezvous.py 2025-01-24T01:52:01.4363874Z rank (Optional[int]): The rank of the node, if known. 2025-01-24T01:52:01.4363970Z Returns: 2025-01-24T01:52:01.4364064Z None 2025-01-24T01:52:01.4364168Z Example: 2025-01-24T01:52:01.4364302Z >>> # See DynamicRendezvousHandler class 2025-01-24T01:52:01.4364407Z >>> def _record( 2025-01-24T01:52:01.4364506Z ... self, 2025-01-24T01:52:01.4364606Z ... message: str, 2025-01-24T01:52:01.4364767Z ... node_state: NodeState = NodeState.RUNNING, 2025-01-24T01:52:01.4364886Z ... rank: Optional[int] = None, 2025-01-24T01:52:01.4365018Z ... ) -> None: 2025-01-24T01:52:01.4365145Z ... construct_and_record_rdzv_event( 2025-01-24T01:52:01.4365324Z ... name=f"{self.__class__.__name__}.{get_method_name()}", 2025-01-24T01:52:01.4365485Z ... run_id=self._settings.run_id, 2025-01-24T01:52:01.4365587Z ... message=message, 2025-01-24T01:52:01.4365709Z ... node_state=node_state, 2025-01-24T01:52:01.4365836Z ... hostname=self._this_node.addr, 2025-01-24T01:52:01.4365960Z ... pid=self._this_node.pid, 2025-01-24T01:52:01.4366092Z ... local_id=self._this_node.local_id, 2025-01-24T01:52:01.4366201Z ... rank=rank, 2025-01-24T01:52:01.4366293Z ... ) 2025-01-24T01:52:01.4366378Z 2025-01-24T01:52:01.4366648Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:52:01.4366742Z 2025-01-24T01:52:01.4366854Z warnings.warn(msg) 2025-01-24T01:52:01.4366944Z 2025-01-24T01:52:01.4367143Z --- Parse Warning: 56 / 106 --- 2025-01-24T01:52:01.4368082Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=MixedPrecision in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/fsdp/api.py line=114. 2025-01-24T01:52:01.4368354Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:52:01.4368454Z 2025-01-24T01:52:01.4368629Z This configures FSDP-native mixed precision training. 2025-01-24T01:52:01.4368729Z 2025-01-24T01:52:01.4368827Z Attributes: 2025-01-24T01:52:01.4369067Z param_dtype (Optional[torch.dtype]): This specifies the dtype for model 2025-01-24T01:52:01.4369287Z parameters during forward and backward and thus the dtype for 2025-01-24T01:52:01.4369517Z forward and backward computation. Outside forward and backward, the 2025-01-24T01:52:01.4369729Z *sharded* parameters are kept in full precision (e.g. for the 2025-01-24T01:52:01.4369949Z optimizer step), and for model checkpointing, the parameters are 2025-01-24T01:52:01.4370130Z always saved in full precision. (Default: ``None``) 2025-01-24T01:52:01.4370351Z reduce_dtype (Optional[torch.dtype]): This specifies the dtype for 2025-01-24T01:52:01.4370573Z gradient reduction (i.e. reduce-scatter or all-reduce). If this is 2025-01-24T01:52:01.4370769Z ``None`` but ``param_dtype`` is not ``None``, then this takes on 2025-01-24T01:52:01.4370977Z the ``param_dtype`` value, still running gradient reduction in low 2025-01-24T01:52:01.4371203Z precision. This is permitted to differ from ``param_dtype``, e.g. 2025-01-24T01:52:01.4371410Z to force gradient reduction to run in full precision. (Default: 2025-01-24T01:52:01.4371516Z ``None``) 2025-01-24T01:52:01.4371737Z buffer_dtype (Optional[torch.dtype]): This specifies the dtype for 2025-01-24T01:52:01.4371944Z buffers. FSDP does not shard buffers. Rather, FSDP casts them to 2025-01-24T01:52:01.4372160Z ``buffer_dtype`` in the first forward pass and keeps them in that 2025-01-24T01:52:01.4372436Z dtype thereafter. For model checkpointing, the buffers are saved 2025-01-24T01:52:01.4372634Z in full precision except for ``LOCAL_STATE_DICT``. (Default: 2025-01-24T01:52:01.4372728Z ``None``) 2025-01-24T01:52:01.4372938Z keep_low_precision_grads (bool): If ``False``, then FSDP upcasts 2025-01-24T01:52:01.4373161Z gradients to full precision after the backward pass in preparation 2025-01-24T01:52:01.4373373Z for the optimizer step. If ``True``, then FSDP keeps the gradients 2025-01-24T01:52:01.4373594Z in the dtype used for gradient reduction, which can save memory if 2025-01-24T01:52:01.4373810Z using a custom optimizer that supports running in low precision. 2025-01-24T01:52:01.4373950Z (Default: ``False``) 2025-01-24T01:52:01.4374169Z cast_forward_inputs (bool): If ``True``, then this FSDP module casts 2025-01-24T01:52:01.4374414Z its forward args and kwargs to ``param_dtype``. This is to ensure 2025-01-24T01:52:01.4374635Z that parameter and input dtypes match for forward computation, as 2025-01-24T01:52:01.4374845Z required by many ops. This may need to be set to ``True`` when only 2025-01-24T01:52:01.4375077Z applying mixed precision to some but not all FSDP modules, in which 2025-01-24T01:52:01.4375294Z case a mixed-precision FSDP submodule needs to recast its inputs. 2025-01-24T01:52:01.4375410Z (Default: ``False``) 2025-01-24T01:52:01.4375638Z cast_root_forward_inputs (bool): If ``True``, then the root FSDP module 2025-01-24T01:52:01.4375853Z casts its forward args and kwargs to ``param_dtype``, overriding 2025-01-24T01:52:01.4376051Z the value of ``cast_forward_inputs``. For non-root FSDP modules, 2025-01-24T01:52:01.4376199Z this does not do anything. (Default: ``True``) 2025-01-24T01:52:01.4376441Z _module_classes_to_ignore: (Sequence[Type[nn.Module]]): This specifies 2025-01-24T01:52:01.4376633Z module classes to ignore for mixed precision when using an 2025-01-24T01:52:01.4376831Z ``auto_wrap_policy``: Modules of these classes will have FSDP 2025-01-24T01:52:01.4377048Z applied to them separately with mixed precision disabled (meaning 2025-01-24T01:52:01.4377272Z that the final FSDP construction would deviate from the specified 2025-01-24T01:52:01.4377467Z policy). If ``auto_wrap_policy`` is not specified, then this does 2025-01-24T01:52:01.4377681Z not do anything. This API is experimental and subject to change. 2025-01-24T01:52:01.4377796Z (Default: ``(_BatchNorm,)``) 2025-01-24T01:52:01.4377884Z 2025-01-24T01:52:01.4378071Z .. note:: This API is experimental and subject to change. 2025-01-24T01:52:01.4378163Z 2025-01-24T01:52:01.4378401Z .. note:: Only floating point tensors are cast to their specified dtypes. 2025-01-24T01:52:01.4378495Z 2025-01-24T01:52:01.4378683Z .. note:: In ``summon_full_params``, parameters are forced to full 2025-01-24T01:52:01.4378812Z precision, but buffers are not. 2025-01-24T01:52:01.4378898Z 2025-01-24T01:52:01.4379114Z .. note:: Layer norm and batch norm accumulate in ``float32`` even when 2025-01-24T01:52:01.4379544Z their inputs are in a low precision like ``float16`` or ``bfloat16``. 2025-01-24T01:52:01.4379793Z Disabling FSDP's mixed precision for those norm modules only means that 2025-01-24T01:52:01.4380015Z the affine parameters are kept in ``float32``. However, this incurs 2025-01-24T01:52:01.4380268Z separate all-gathers and reduce-scatters for those norm modules, which 2025-01-24T01:52:01.4380495Z may be inefficient, so if the workload permits, the user should prefer 2025-01-24T01:52:01.4380662Z to still apply mixed precision to those modules. 2025-01-24T01:52:01.4380752Z 2025-01-24T01:52:01.4381082Z .. note:: By default, if the user passes a model with any ``_BatchNorm`` 2025-01-24T01:52:01.4381313Z modules and specifies an ``auto_wrap_policy``, then the batch norm 2025-01-24T01:52:01.4381552Z modules will have FSDP applied to them separately with mixed precision 2025-01-24T01:52:01.4381746Z disabled. See the ``_module_classes_to_ignore`` argument. 2025-01-24T01:52:01.4381839Z 2025-01-24T01:52:01.4382068Z .. note:: ``MixedPrecision`` has ``cast_root_forward_inputs=True`` and 2025-01-24T01:52:01.4382286Z ``cast_forward_inputs=False`` by default. For the root FSDP instance, 2025-01-24T01:52:01.4382467Z its ``cast_root_forward_inputs`` takes precedence over its 2025-01-24T01:52:01.4382665Z ``cast_forward_inputs``. For non-root FSDP instances, their 2025-01-24T01:52:01.4382920Z ``cast_root_forward_inputs`` values are ignored. The default setting is 2025-01-24T01:52:01.4383194Z sufficient for the typical case where each FSDP instance has the same 2025-01-24T01:52:01.4383432Z ``MixedPrecision`` configuration and only needs to cast inputs to the 2025-01-24T01:52:01.4383633Z ``param_dtype`` at the beginning of the model's forward pass. 2025-01-24T01:52:01.4383724Z 2025-01-24T01:52:01.4383937Z .. note:: For nested FSDP instances with different ``MixedPrecision`` 2025-01-24T01:52:01.4384193Z configurations, we recommend setting individual ``cast_forward_inputs`` 2025-01-24T01:52:01.4384404Z values to configure casting inputs or not before each instance's 2025-01-24T01:52:01.4384618Z forward. In such a case, since the casts happen before each FSDP 2025-01-24T01:52:01.4384843Z instance's forward, a parent FSDP instance should have its non-FSDP 2025-01-24T01:52:01.4385099Z submodules run before its FSDP submodules to avoid the activation dtype 2025-01-24T01:52:01.4385316Z being changed due to a different ``MixedPrecision`` configuration. 2025-01-24T01:52:01.4385410Z 2025-01-24T01:52:01.4385521Z Example:: 2025-01-24T01:52:01.4385609Z 2025-01-24T01:52:01.4385758Z >>> # xdoctest: +SKIP("undefined variables") 2025-01-24T01:52:01.4385928Z >>> model = nn.Sequential(nn.Linear(3, 3), nn.Linear(3, 3)) 2025-01-24T01:52:01.4386044Z >>> model[1] = FSDP( 2025-01-24T01:52:01.4386142Z >>> model[1], 2025-01-24T01:52:01.4386544Z >>> mixed_precision=MixedPrecision(param_dtype=torch.float16, cast_forward_inputs=True), 2025-01-24T01:52:01.4386651Z >>> ) 2025-01-24T01:52:01.4386748Z >>> model = FSDP( 2025-01-24T01:52:01.4386854Z >>> model, 2025-01-24T01:52:01.4387166Z >>> mixed_precision=MixedPrecision(param_dtype=torch.bfloat16, cast_forward_inputs=True), 2025-01-24T01:52:01.4387264Z >>> ) 2025-01-24T01:52:01.4387362Z 2025-01-24T01:52:01.4387579Z The above shows a working example. On the other hand, if ``model[1]`` 2025-01-24T01:52:01.4387801Z were replaced with ``model[0]``, meaning that the submodule using 2025-01-24T01:52:01.4388029Z different ``MixedPrecision`` ran its forward first, then ``model[1]`` 2025-01-24T01:52:01.4388269Z would incorrectly see ``float16`` activations instead of ``bfloat16`` 2025-01-24T01:52:01.4388359Z ones. 2025-01-24T01:52:01.4388447Z 2025-01-24T01:52:01.4388546Z 2025-01-24T01:52:01.4388801Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:52:01.4388904Z 2025-01-24T01:52:01.4389008Z warnings.warn(msg) 2025-01-24T01:52:01.4389099Z 2025-01-24T01:52:01.4389340Z --- Parse Warning: 57 / 106 --- 2025-01-24T01:52:01.4390529Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=FullyShardedDataParallel.set_state_dict_type in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py line=639. 2025-01-24T01:52:01.4390872Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:52:01.4391127Z Set the ``state_dict_type`` of all the descendant FSDP modules of the target module. 2025-01-24T01:52:01.4391233Z 2025-01-24T01:52:01.4391503Z Also takes (optional) configuration for the model's and optimizer's state dict. 2025-01-24T01:52:01.4391727Z The target module does not have to be a FSDP module. If the target 2025-01-24T01:52:01.4391943Z module is a FSDP module, its ``state_dict_type`` will also be changed. 2025-01-24T01:52:01.4392034Z 2025-01-24T01:52:01.4392245Z .. note:: This API should be called for only the top-level (root) 2025-01-24T01:52:01.4392337Z module. 2025-01-24T01:52:01.4392465Z 2025-01-24T01:52:01.4392677Z .. note:: This API enables users to transparently use the conventional 2025-01-24T01:52:01.4392901Z ``state_dict`` API to take model checkpoints in cases where the 2025-01-24T01:52:01.4393128Z root FSDP module is wrapped by another ``nn.Module``. For example, 2025-01-24T01:52:01.4393348Z the following will ensure ``state_dict`` is called on all non-FSDP 2025-01-24T01:52:01.4393602Z instances, while dispatching into `sharded_state_dict` implementation 2025-01-24T01:52:01.4393702Z for FSDP: 2025-01-24T01:52:01.4393808Z 2025-01-24T01:52:01.4393905Z Example:: 2025-01-24T01:52:01.4393994Z 2025-01-24T01:52:01.4394146Z >>> # xdoctest: +SKIP("undefined variables") 2025-01-24T01:52:01.4394261Z >>> model = DDP(FSDP(...)) 2025-01-24T01:52:01.4394396Z >>> FSDP.set_state_dict_type( 2025-01-24T01:52:01.4394493Z >>> model, 2025-01-24T01:52:01.4394641Z >>> StateDictType.SHARDED_STATE_DICT, 2025-01-24T01:52:01.4394861Z >>> state_dict_config = ShardedStateDictConfig(offload_to_cpu=True), 2025-01-24T01:52:01.4395093Z >>> optim_state_dict_config = OptimStateDictConfig(offload_to_cpu=True), 2025-01-24T01:52:01.4395198Z >>> ) 2025-01-24T01:52:01.4395328Z >>> param_state_dict = model.state_dict() 2025-01-24T01:52:01.4395523Z >>> optim_state_dict = FSDP.optim_state_dict(model, optim) 2025-01-24T01:52:01.4395610Z 2025-01-24T01:52:01.4395703Z Args: 2025-01-24T01:52:01.4395847Z module (torch.nn.Module): Root module. 2025-01-24T01:52:01.4396082Z state_dict_type (StateDictType): the desired ``state_dict_type`` to set. 2025-01-24T01:52:01.4396334Z state_dict_config (Optional[StateDictConfig]): the configuration for the 2025-01-24T01:52:01.4396457Z target ``state_dict_type``. 2025-01-24T01:52:01.4396726Z optim_state_dict_config (Optional[OptimStateDictConfig]): the configuration 2025-01-24T01:52:01.4396856Z for the optimizer state dict. 2025-01-24T01:52:01.4396945Z 2025-01-24T01:52:01.4397053Z Returns: 2025-01-24T01:52:01.4397276Z A StateDictSettings that include the previous state_dict type and 2025-01-24T01:52:01.4397412Z configuration for the module. 2025-01-24T01:52:01.4397502Z 2025-01-24T01:52:01.4397774Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:52:01.4397860Z 2025-01-24T01:52:01.4397962Z warnings.warn(msg) 2025-01-24T01:52:01.4398060Z 2025-01-24T01:52:01.4398259Z --- Parse Warning: 58 / 106 --- 2025-01-24T01:52:01.4399445Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=FullyShardedDataParallel.state_dict_type in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py line=795. 2025-01-24T01:52:01.4399775Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:52:01.4400044Z Set the ``state_dict_type`` of all the descendant FSDP modules of the target module. 2025-01-24T01:52:01.4400131Z 2025-01-24T01:52:01.4400454Z This context manager has the same functions as :meth:`set_state_dict_type`. Read the document of 2025-01-24T01:52:01.4400605Z :meth:`set_state_dict_type` for the detail. 2025-01-24T01:52:01.4400691Z 2025-01-24T01:52:01.4400802Z Example:: 2025-01-24T01:52:01.4400889Z 2025-01-24T01:52:01.4401038Z >>> # xdoctest: +SKIP("undefined variables") 2025-01-24T01:52:01.4401150Z >>> model = DDP(FSDP(...)) 2025-01-24T01:52:01.4401268Z >>> with FSDP.state_dict_type( 2025-01-24T01:52:01.4401402Z >>> model, 2025-01-24T01:52:01.4401538Z >>> StateDictType.SHARDED_STATE_DICT, 2025-01-24T01:52:01.4401667Z >>> ): 2025-01-24T01:52:01.4401798Z >>> checkpoint = model.state_dict() 2025-01-24T01:52:01.4401887Z 2025-01-24T01:52:01.4401985Z Args: 2025-01-24T01:52:01.4402117Z module (torch.nn.Module): Root module. 2025-01-24T01:52:01.4402368Z state_dict_type (StateDictType): the desired ``state_dict_type`` to set. 2025-01-24T01:52:01.4402602Z state_dict_config (Optional[StateDictConfig]): the model ``state_dict`` 2025-01-24T01:52:01.4402779Z configuration for the target ``state_dict_type``. 2025-01-24T01:52:01.4403021Z optim_state_dict_config (Optional[OptimStateDictConfig]): the optimizer 2025-01-24T01:52:01.4403222Z ``state_dict`` configuration for the target ``state_dict_type``. 2025-01-24T01:52:01.4403323Z 2025-01-24T01:52:01.4403582Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:52:01.4403685Z 2025-01-24T01:52:01.4403792Z warnings.warn(msg) 2025-01-24T01:52:01.4403882Z 2025-01-24T01:52:01.4404084Z --- Parse Warning: 59 / 106 --- 2025-01-24T01:52:01.4405263Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=FullyShardedDataParallel.optim_state_dict in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py line=1808. 2025-01-24T01:52:01.4405545Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:52:01.4405631Z 2025-01-24T01:52:01.4405890Z Transform the state-dict of an optimizer corresponding to a sharded model. 2025-01-24T01:52:01.4405977Z 2025-01-24T01:52:01.4406190Z The given state-dict can be transformed to one of three types: 2025-01-24T01:52:01.4406496Z 1) full optimizer state_dict, 2) sharded optimizer state_dict, 3) local optimizer state_dict. 2025-01-24T01:52:01.4406590Z 2025-01-24T01:52:01.4406840Z For full optimizer state_dict, all states are unflattened and not sharded. 2025-01-24T01:52:01.4407061Z Rank0 only and CPU only can be specified via :meth:`state_dict_type` to 2025-01-24T01:52:01.4407167Z avoid OOM. 2025-01-24T01:52:01.4407257Z 2025-01-24T01:52:01.4407503Z For sharded optimizer state_dict, all states are unflattened but sharded. 2025-01-24T01:52:01.4407723Z CPU only can be specified via :meth:`state_dict_type` to further save 2025-01-24T01:52:01.4407815Z memory. 2025-01-24T01:52:01.4407915Z 2025-01-24T01:52:01.4408135Z For local state_dict, no transformation will be performed. But a state 2025-01-24T01:52:01.4408393Z will be converted from nn.Tensor to ShardedTensor to represent its sharding 2025-01-24T01:52:01.4408511Z nature (this is not supported yet). 2025-01-24T01:52:01.4408602Z 2025-01-24T01:52:01.4408710Z Example:: 2025-01-24T01:52:01.4408794Z 2025-01-24T01:52:01.4408943Z >>> # xdoctest: +SKIP("undefined variables") 2025-01-24T01:52:01.4409245Z >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP 2025-01-24T01:52:01.4409410Z >>> from torch.distributed.fsdp import StateDictType 2025-01-24T01:52:01.4409601Z >>> from torch.distributed.fsdp import FullStateDictConfig 2025-01-24T01:52:01.4409812Z >>> from torch.distributed.fsdp import FullOptimStateDictConfig 2025-01-24T01:52:01.4409925Z >>> # Save a checkpoint 2025-01-24T01:52:01.4410028Z >>> model, optim = ... 2025-01-24T01:52:01.4410156Z >>> FSDP.set_state_dict_type( 2025-01-24T01:52:01.4410249Z >>> model, 2025-01-24T01:52:01.4410377Z >>> StateDictType.FULL_STATE_DICT, 2025-01-24T01:52:01.4410526Z >>> FullStateDictConfig(rank0_only=False), 2025-01-24T01:52:01.4410706Z >>> FullOptimStateDictConfig(rank0_only=False), 2025-01-24T01:52:01.4410807Z >>> ) 2025-01-24T01:52:01.4410927Z >>> state_dict = model.state_dict() 2025-01-24T01:52:01.4411135Z >>> optim_state_dict = FSDP.optim_state_dict(model, optim) 2025-01-24T01:52:01.4411300Z >>> save_a_checkpoint(state_dict, optim_state_dict) 2025-01-24T01:52:01.4411405Z >>> # Load a checkpoint 2025-01-24T01:52:01.4411518Z >>> model, optim = ... 2025-01-24T01:52:01.4411679Z >>> state_dict, optim_state_dict = load_a_checkpoint() 2025-01-24T01:52:01.4411807Z >>> FSDP.set_state_dict_type( 2025-01-24T01:52:01.4411903Z >>> model, 2025-01-24T01:52:01.4412030Z >>> StateDictType.FULL_STATE_DICT, 2025-01-24T01:52:01.4412172Z >>> FullStateDictConfig(rank0_only=False), 2025-01-24T01:52:01.4412319Z >>> FullOptimStateDictConfig(rank0_only=False), 2025-01-24T01:52:01.4412418Z >>> ) 2025-01-24T01:52:01.4412542Z >>> model.load_state_dict(state_dict) 2025-01-24T01:52:01.4412708Z >>> optim_state_dict = FSDP.optim_state_dict_to_load( 2025-01-24T01:52:01.4412828Z >>> model, optim, optim_state_dict 2025-01-24T01:52:01.4412924Z >>> ) 2025-01-24T01:52:01.4413068Z >>> optim.load_state_dict(optim_state_dict) 2025-01-24T01:52:01.4413153Z 2025-01-24T01:52:01.4413251Z Args: 2025-01-24T01:52:01.4413450Z model (torch.nn.Module): Root module (which may or may not be a 2025-01-24T01:52:01.4413657Z :class:`FullyShardedDataParallel` instance) whose parameters 2025-01-24T01:52:01.4413801Z were passed into the optimizer ``optim``. 2025-01-24T01:52:01.4413986Z optim (torch.optim.Optimizer): Optimizer for ``model`` 's 2025-01-24T01:52:01.4414088Z parameters. 2025-01-24T01:52:01.4414305Z optim_state_dict (Dict[str, Any]): the target optimizer state_dict to 2025-01-24T01:52:01.4414533Z transform. If the value is None, optim.state_dict() will be used. ( 2025-01-24T01:52:01.4414634Z Default: ``None``) 2025-01-24T01:52:01.4414878Z group (dist.ProcessGroup): Model's process group across which parameters 2025-01-24T01:52:01.4415082Z are sharded or ``None`` if using the default process group. ( 2025-01-24T01:52:01.4415184Z Default: ``None``) 2025-01-24T01:52:01.4415283Z 2025-01-24T01:52:01.4415373Z Returns: 2025-01-24T01:52:01.4415572Z Dict[str, Any]: A :class:`dict` containing the optimizer state for 2025-01-24T01:52:01.4415753Z ``model``. The sharding of the optimizer state is based on 2025-01-24T01:52:01.4415855Z ``state_dict_type``. 2025-01-24T01:52:01.4415950Z 2025-01-24T01:52:01.4416207Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:52:01.4416303Z 2025-01-24T01:52:01.4416401Z warnings.warn(msg) 2025-01-24T01:52:01.4416489Z 2025-01-24T01:52:01.4416696Z --- Parse Warning: 60 / 106 --- 2025-01-24T01:52:01.4417943Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=FullyShardedDataParallel.optim_state_dict_to_load in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py line=1906. 2025-01-24T01:52:01.4418244Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:52:01.4418329Z 2025-01-24T01:52:01.4418694Z Convert an optimizer state-dict so that it can be loaded into the optimizer associated with the FSDP model. 2025-01-24T01:52:01.4418780Z 2025-01-24T01:52:01.4418951Z Given a ``optim_state_dict`` that is transformed through 2025-01-24T01:52:01.4419174Z :meth:`optim_state_dict`, it gets converted to the flattened optimizer 2025-01-24T01:52:01.4419387Z state_dict that can be loaded to ``optim`` which is the optimizer for 2025-01-24T01:52:01.4419611Z ``model``. ``model`` must be sharded by FullyShardedDataParallel. 2025-01-24T01:52:01.4419698Z 2025-01-24T01:52:01.4419840Z >>> # xdoctest: +SKIP("undefined variables") 2025-01-24T01:52:01.4420107Z >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP 2025-01-24T01:52:01.4420271Z >>> from torch.distributed.fsdp import StateDictType 2025-01-24T01:52:01.4420457Z >>> from torch.distributed.fsdp import FullStateDictConfig 2025-01-24T01:52:01.4420658Z >>> from torch.distributed.fsdp import FullOptimStateDictConfig 2025-01-24T01:52:01.4420770Z >>> # Save a checkpoint 2025-01-24T01:52:01.4420871Z >>> model, optim = ... 2025-01-24T01:52:01.4420986Z >>> FSDP.set_state_dict_type( 2025-01-24T01:52:01.4421085Z >>> model, 2025-01-24T01:52:01.4421209Z >>> StateDictType.FULL_STATE_DICT, 2025-01-24T01:52:01.4421354Z >>> FullStateDictConfig(rank0_only=False), 2025-01-24T01:52:01.4421503Z >>> FullOptimStateDictConfig(rank0_only=False), 2025-01-24T01:52:01.4421603Z >>> ) 2025-01-24T01:52:01.4421718Z >>> state_dict = model.state_dict() 2025-01-24T01:52:01.4421841Z >>> original_osd = optim.state_dict() 2025-01-24T01:52:01.4421988Z >>> optim_state_dict = FSDP.optim_state_dict( 2025-01-24T01:52:01.4422076Z >>> model, 2025-01-24T01:52:01.4422179Z >>> optim, 2025-01-24T01:52:01.4422299Z >>> optim_state_dict=original_osd 2025-01-24T01:52:01.4422387Z >>> ) 2025-01-24T01:52:01.4422544Z >>> save_a_checkpoint(state_dict, optim_state_dict) 2025-01-24T01:52:01.4422641Z >>> # Load a checkpoint 2025-01-24T01:52:01.4422752Z >>> model, optim = ... 2025-01-24T01:52:01.4422907Z >>> state_dict, optim_state_dict = load_a_checkpoint() 2025-01-24T01:52:01.4423029Z >>> FSDP.set_state_dict_type( 2025-01-24T01:52:01.4423117Z >>> model, 2025-01-24T01:52:01.4423238Z >>> StateDictType.FULL_STATE_DICT, 2025-01-24T01:52:01.4423381Z >>> FullStateDictConfig(rank0_only=False), 2025-01-24T01:52:01.4423527Z >>> FullOptimStateDictConfig(rank0_only=False), 2025-01-24T01:52:01.4423622Z >>> ) 2025-01-24T01:52:01.4423742Z >>> model.load_state_dict(state_dict) 2025-01-24T01:52:01.4423894Z >>> optim_state_dict = FSDP.optim_state_dict_to_load( 2025-01-24T01:52:01.4424019Z >>> model, optim, optim_state_dict 2025-01-24T01:52:01.4424105Z >>> ) 2025-01-24T01:52:01.4424241Z >>> optim.load_state_dict(optim_state_dict) 2025-01-24T01:52:01.4424326Z 2025-01-24T01:52:01.4424428Z Args: 2025-01-24T01:52:01.4424625Z model (torch.nn.Module): Root module (which may or may not be a 2025-01-24T01:52:01.4424831Z :class:`FullyShardedDataParallel` instance) whose parameters 2025-01-24T01:52:01.4424976Z were passed into the optimizer ``optim``. 2025-01-24T01:52:01.4425163Z optim (torch.optim.Optimizer): Optimizer for ``model`` 's 2025-01-24T01:52:01.4425269Z parameters. 2025-01-24T01:52:01.4425481Z optim_state_dict (Dict[str, Any]): The optimizer states to be loaded. 2025-01-24T01:52:01.4425728Z is_named_optimizer (bool): Is this optimizer a NamedOptimizer or 2025-01-24T01:52:01.4425963Z KeyedOptimizer. Only set to True if ``optim`` is TorchRec's 2025-01-24T01:52:01.4426143Z KeyedOptimizer or torch.distributed's NamedOptimizer. 2025-01-24T01:52:01.4426439Z load_directly (bool): If this is set to True, this API will also 2025-01-24T01:52:01.4426648Z call optim.load_state_dict(result) before returning the result. 2025-01-24T01:52:01.4426882Z Otherwise, users are responsible to call ``optim.load_state_dict()`` 2025-01-24T01:52:01.4426983Z (Default: ``False``) 2025-01-24T01:52:01.4427227Z group (dist.ProcessGroup): Model's process group across which parameters 2025-01-24T01:52:01.4427424Z are sharded or ``None`` if using the default process group. ( 2025-01-24T01:52:01.4427562Z Default: ``None``) 2025-01-24T01:52:01.4427656Z 2025-01-24T01:52:01.4427913Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:52:01.4428038Z 2025-01-24T01:52:01.4428143Z warnings.warn(msg) 2025-01-24T01:52:01.4428229Z 2025-01-24T01:52:01.4428452Z --- Parse Warning: 61 / 106 --- 2025-01-24T01:52:01.4429456Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=_RemoteModule.__init__ in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/nn/api/remote_module.py line=125. 2025-01-24T01:52:01.4429738Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:52:01.4429825Z 2025-01-24T01:52:01.4430067Z RemoteModule instance can only be created after RPC initialization. 2025-01-24T01:52:01.4430155Z 2025-01-24T01:52:01.4430358Z It creates a user-specified module on a specified remote node. 2025-01-24T01:52:01.4430607Z It behaves like a regular ``nn.Module`` except that the ``forward`` method is 2025-01-24T01:52:01.4430724Z executed on the remote node. 2025-01-24T01:52:01.4430977Z It takes care of autograd recording to ensure the backward pass propagates 2025-01-24T01:52:01.4431141Z gradients back to the corresponding remote module. 2025-01-24T01:52:01.4431514Z It can be shared across processors using `RPC framework `__, 2025-01-24T01:52:01.4431719Z without incurring any overheads of copying the actual module, 2025-01-24T01:52:01.4431926Z which is equivalent to an :class:`~torch.distributed.rpc.RRef` 2025-01-24T01:52:01.4432049Z pointing to the remote module. 2025-01-24T01:52:01.4432135Z 2025-01-24T01:52:01.4432353Z The arguments of ``forward_async`` and ``forward`` are the same as 2025-01-24T01:52:01.4432557Z the ``forward`` method of the module returned by the ``module_cls``. 2025-01-24T01:52:01.4432652Z 2025-01-24T01:52:01.4432965Z Apart from ``forward_async`` and ``forward``, no other methods are supported from nn.Module for now. 2025-01-24T01:52:01.4433060Z 2025-01-24T01:52:01.4433321Z Particularly, to create a hybrid model, typically the local modules should be 2025-01-24T01:52:01.4433699Z created outside of remote modules, rather than as submodules of any remote module (by calling ``add_module``). 2025-01-24T01:52:01.4433804Z Hybrid Example: 2025-01-24T01:52:01.4433924Z >>> class HybridModel(nn.Module): 2025-01-24T01:52:01.4434041Z >>> def __init__(self) -> None: 2025-01-24T01:52:01.4434161Z >>> nn.Module.__init__(self) 2025-01-24T01:52:01.4434307Z >>> self.remote_embedding = RemoteModule(...) 2025-01-24T01:52:01.4434444Z >>> self.local_linear = nn.Linear(...) 2025-01-24T01:52:01.4434528Z 2025-01-24T01:52:01.4434745Z For example, if ``module_cls`` returns an instance of ``nn.Linear``, 2025-01-24T01:52:01.4434992Z that has ``forward`` method signature, ``def forward(input: Tensor) -> Tensor:``, 2025-01-24T01:52:01.4435690Z the generated ``RemoteModule`` will have 2 methods in signature of 2025-01-24T01:52:01.4435839Z ``def forward(input: Tensor) -> Tensor:`` and 2025-01-24T01:52:01.4436005Z ``def forward_async(input: Tensor) -> Future[Tensor]:``. 2025-01-24T01:52:01.4436102Z 2025-01-24T01:52:01.4436196Z .. note:: 2025-01-24T01:52:01.4436355Z If the remote module is placed on a cuda device, 2025-01-24T01:52:01.4436593Z any input CPU tensors will be automatically moved to the same cuda device, 2025-01-24T01:52:01.4436998Z and GPU tensors are returned over the wire according to the device map of the remote worker on TensorPipe RPC backend. 2025-01-24T01:52:01.4437100Z 2025-01-24T01:52:01.4437188Z Args: 2025-01-24T01:52:01.4437531Z remote_device (str): Device on the destination worker where we'd like to place this module. 2025-01-24T01:52:01.4437824Z The device can be a local device or a remote device specified by one of the following remote 2025-01-24T01:52:01.4437961Z formats: 2025-01-24T01:52:01.4438049Z 2025-01-24T01:52:01.4438193Z 1. "rank:/" (ex: "rank:0/cuda:0"). 2025-01-24T01:52:01.4438356Z 2. "/" (ex: "trainer0/cuda:0"). 2025-01-24T01:52:01.4438442Z 2025-01-24T01:52:01.4438701Z In addition, the device field can be optional and the default value is "cpu". 2025-01-24T01:52:01.4438820Z module_cls (nn.Module): For example, 2025-01-24T01:52:01.4438931Z >>> class MyModule(nn.Module): 2025-01-24T01:52:01.4439050Z >>> def forward(input): 2025-01-24T01:52:01.4439153Z >>> return input + 1 2025-01-24T01:52:01.4439253Z >>> 2025-01-24T01:52:01.4439355Z >>> module_cls = MyModule 2025-01-24T01:52:01.4439567Z args (Sequence, optional): args to be passed to ``module_cls``. 2025-01-24T01:52:01.4439760Z kwargs (Dict, optional): kwargs to be passed to ``module_cls``. 2025-01-24T01:52:01.4440045Z _module_interface_cls (type, optional): The TorchScript interface type for the module 2025-01-24T01:52:01.4440296Z to be created. The type object should be decorated by @torch.jit.interface. 2025-01-24T01:52:01.4440516Z If not provided, the generated RemoteModule is not torchscript-able. 2025-01-24T01:52:01.4440770Z Warning, this is an experimental API and susceptible to frequent changes. 2025-01-24T01:52:01.4440861Z 2025-01-24T01:52:01.4440961Z Returns: 2025-01-24T01:52:01.4441206Z A remote module instance which wraps the :class:`~nn.Module` created by the 2025-01-24T01:52:01.4441437Z user-provided ``module_cls``, it has a blocking ``forward`` method and an 2025-01-24T01:52:01.4441724Z asynchronous ``forward_async`` method that returns a future of the ``forward`` call 2025-01-24T01:52:01.4441870Z on the user-provided module on the remote side. 2025-01-24T01:52:01.4441968Z 2025-01-24T01:52:01.4442063Z Example:: 2025-01-24T01:52:01.4442220Z Run the following code in two different processes: 2025-01-24T01:52:01.4442318Z 2025-01-24T01:52:01.4442437Z >>> # xdoctest: +SKIP("distributed") 2025-01-24T01:52:01.4442543Z >>> # On worker 0: 2025-01-24T01:52:01.4442637Z >>> import torch 2025-01-24T01:52:01.4442780Z >>> import torch.distributed.rpc as rpc 2025-01-24T01:52:01.4442897Z >>> from torch import nn, Tensor 2025-01-24T01:52:01.4443125Z >>> from torch.distributed.nn.api.remote_module import RemoteModule 2025-01-24T01:52:01.4443232Z >>> 2025-01-24T01:52:01.4443379Z >>> rpc.init_rpc("worker0", rank=0, world_size=2) 2025-01-24T01:52:01.4443522Z >>> remote_linear_module = RemoteModule( 2025-01-24T01:52:01.4443659Z >>> "worker1/cpu", nn.Linear, args=(20, 30), 2025-01-24T01:52:01.4443751Z >>> ) 2025-01-24T01:52:01.4443879Z >>> input = torch.randn(128, 20) 2025-01-24T01:52:01.4444044Z >>> ret_fut = remote_linear_module.forward_async(input) 2025-01-24T01:52:01.4444217Z >>> ret = ret_fut.wait() 2025-01-24T01:52:01.4444324Z >>> rpc.shutdown() 2025-01-24T01:52:01.4444427Z 2025-01-24T01:52:01.4444530Z >>> # On worker 1: 2025-01-24T01:52:01.4444631Z >>> import torch 2025-01-24T01:52:01.4444781Z >>> import torch.distributed.rpc as rpc 2025-01-24T01:52:01.4444876Z >>> 2025-01-24T01:52:01.4445036Z >>> rpc.init_rpc("worker1", rank=1, world_size=2) 2025-01-24T01:52:01.4445141Z >>> rpc.shutdown() 2025-01-24T01:52:01.4445233Z 2025-01-24T01:52:01.4445511Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:52:01.4445607Z 2025-01-24T01:52:01.4445729Z warnings.warn(msg) 2025-01-24T01:52:01.4445822Z 2025-01-24T01:52:01.4446064Z --- Parse Warning: 62 / 106 --- 2025-01-24T01:52:01.4447153Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=_RemoteModule.init_from_module_rref in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/nn/api/remote_module.py line=502. 2025-01-24T01:52:01.4447466Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:52:01.4447573Z 2025-01-24T01:52:01.4447907Z Besides the constructor, a RemoteModule instance can also be initialized given a module RRef. 2025-01-24T01:52:01.4448013Z 2025-01-24T01:52:01.4448349Z This alternate initialization method can be particularly useful if we want to create multiple 2025-01-24T01:52:01.4448691Z RemoteModule instances that share the same underlying module and reduce memory consumption. 2025-01-24T01:52:01.4448784Z 2025-01-24T01:52:01.4449074Z Moreover, this also provides a workaround for passing script RemoteModule over RPC, 2025-01-24T01:52:01.4449276Z which is not supported. The recommended way is as follows: 2025-01-24T01:52:01.4449372Z 2025-01-24T01:52:01.4449519Z 1. the sender creates a RemoteModule; 2025-01-24T01:52:01.4449671Z 2. the sender sends its ``module_rref`` over RPC; 2025-01-24T01:52:01.4450034Z 3. the receiver calls this method to initialize another RemoteModule using the same ``module_rref``. 2025-01-24T01:52:01.4450129Z 2025-01-24T01:52:01.4450227Z Example:: 2025-01-24T01:52:01.4450404Z Run the following code in two different processes: 2025-01-24T01:52:01.4450498Z 2025-01-24T01:52:01.4450635Z >>> # xdoctest: +SKIP("distributed") 2025-01-24T01:52:01.4450735Z >>> # On worker 0: 2025-01-24T01:52:01.4450839Z >>> import torch 2025-01-24T01:52:01.4450987Z >>> import torch.distributed.rpc as rpc 2025-01-24T01:52:01.4451103Z >>> from torch import nn, Tensor 2025-01-24T01:52:01.4451346Z >>> from torch.distributed.nn.api.remote_module import RemoteModule 2025-01-24T01:52:01.4451439Z >>> 2025-01-24T01:52:01.4451588Z >>> rpc.init_rpc("worker0", rank=0, world_size=2) 2025-01-24T01:52:01.4451723Z >>> remote_module = RemoteModule( 2025-01-24T01:52:01.4451857Z >>> "worker1/cpu", nn.Linear, args=(20, 30), 2025-01-24T01:52:01.4451964Z >>> ) 2025-01-24T01:52:01.4452056Z >>> 2025-01-24T01:52:01.4452188Z >>> remote_module1 = rpc.rpc_sync( 2025-01-24T01:52:01.4452293Z >>> "worker1/cpu", 2025-01-24T01:52:01.4452425Z >>> RemoteModule.init_from_module_rref, 2025-01-24T01:52:01.4452601Z >>> ("worker1/cpu", remote_module1.get_module_rref()), 2025-01-24T01:52:01.4452695Z >>> ) 2025-01-24T01:52:01.4452807Z >>> rpc.shutdown() 2025-01-24T01:52:01.4452896Z 2025-01-24T01:52:01.4452995Z >>> # On worker 1: 2025-01-24T01:52:01.4453105Z >>> import torch 2025-01-24T01:52:01.4453240Z >>> import torch.distributed.rpc as rpc 2025-01-24T01:52:01.4453348Z >>> 2025-01-24T01:52:01.4453494Z >>> rpc.init_rpc("worker1", rank=1, world_size=2) 2025-01-24T01:52:01.4453652Z >>> rpc.shutdown() 2025-01-24T01:52:01.4453754Z 2025-01-24T01:52:01.4453850Z Args: 2025-01-24T01:52:01.4454167Z remote_device (str): Device on the destination worker where we'd like to place this module. 2025-01-24T01:52:01.4454467Z The device can be a local device or a remote device specified by one of the following remote 2025-01-24T01:52:01.4454576Z formats: 2025-01-24T01:52:01.4454664Z 2025-01-24T01:52:01.4454815Z 1. "rank:/" (ex: "rank:0/cuda:0"). 2025-01-24T01:52:01.4454985Z 2. "/" (ex: "trainer0/cuda:0"). 2025-01-24T01:52:01.4455076Z 2025-01-24T01:52:01.4455345Z In addition, the device field can be optional and the default value is "cpu". 2025-01-24T01:52:01.4455629Z module_rref (RRef[nn.Module]): The module reference shared by both the caller and 2025-01-24T01:52:01.4455759Z the created remote module. 2025-01-24T01:52:01.4456074Z _module_interface_cls (type, optional): The TorchScript interface type for the module 2025-01-24T01:52:01.4456318Z to be created. The type object should be decorated by @torch.jit.interface. 2025-01-24T01:52:01.4456558Z If not provided, the generated RemoteModule is not torchscript-able. 2025-01-24T01:52:01.4456801Z Warning, this is an experimental API and susceptible to frequent changes. 2025-01-24T01:52:01.4456904Z 2025-01-24T01:52:01.4456999Z Returns: 2025-01-24T01:52:01.4457264Z A remote module instance which wraps the :class:`~nn.Module` created by the 2025-01-24T01:52:01.4457508Z user-provided ``module_rref``, it has a blocking ``forward`` method and an 2025-01-24T01:52:01.4457790Z asynchronous ``forward_async`` method that returns a future of the ``forward`` call 2025-01-24T01:52:01.4457952Z on the user-provided module on the remote side. 2025-01-24T01:52:01.4458044Z 2025-01-24T01:52:01.4458320Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:52:01.4458408Z 2025-01-24T01:52:01.4458514Z warnings.warn(msg) 2025-01-24T01:52:01.4458616Z 2025-01-24T01:52:01.4458822Z --- Parse Warning: 63 / 106 --- 2025-01-24T01:52:01.4459807Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=RemoteModule in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/nn/api/remote_module.py line=594. 2025-01-24T01:52:01.4460078Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:52:01.4460177Z 2025-01-24T01:52:01.4460417Z A RemoteModule instance can only be created after RPC initialization. 2025-01-24T01:52:01.4460506Z 2025-01-24T01:52:01.4460730Z It creates a user-specified module on a specified remote node. 2025-01-24T01:52:01.4460974Z It behaves like a regular ``nn.Module`` except that the ``forward`` method is 2025-01-24T01:52:01.4461110Z executed on the remote node. 2025-01-24T01:52:01.4461358Z It takes care of autograd recording to ensure the backward pass propagates 2025-01-24T01:52:01.4461538Z gradients back to the corresponding remote module. 2025-01-24T01:52:01.4461630Z 2025-01-24T01:52:01.4461858Z It generates two methods ``forward_async`` and ``forward`` based on the 2025-01-24T01:52:01.4462094Z signature of the ``forward`` method of ``module_cls``. ``forward_async`` 2025-01-24T01:52:01.4462355Z runs asynchronously and returns a Future. The arguments of ``forward_async`` 2025-01-24T01:52:01.4462572Z and ``forward`` are the same as the ``forward`` method of the module 2025-01-24T01:52:01.4462690Z returned by the ``module_cls``. 2025-01-24T01:52:01.4462797Z 2025-01-24T01:52:01.4463005Z For example, if ``module_cls`` returns an instance of ``nn.Linear``, 2025-01-24T01:52:01.4463293Z that has ``forward`` method signature: ``def forward(input: Tensor) -> Tensor:``, 2025-01-24T01:52:01.4463567Z the generated ``RemoteModule`` will have 2 methods with the signatures: 2025-01-24T01:52:01.4463658Z 2025-01-24T01:52:01.4463802Z | ``def forward(input: Tensor) -> Tensor:`` 2025-01-24T01:52:01.4463976Z | ``def forward_async(input: Tensor) -> Future[Tensor]:`` 2025-01-24T01:52:01.4464077Z 2025-01-24T01:52:01.4464167Z Args: 2025-01-24T01:52:01.4464473Z remote_device (str): Device on the destination worker where we'd like to place this module. 2025-01-24T01:52:01.4464835Z The format should be "/", where the device field can be parsed as torch.device type. 2025-01-24T01:52:01.4464982Z E.g., "trainer0/cpu", "trainer0", "ps0/cuda:0". 2025-01-24T01:52:01.4465273Z In addition, the device field can be optional and the default value is "cpu". 2025-01-24T01:52:01.4465529Z module_cls (nn.Module): Class for the module to be created remotely. For example, 2025-01-24T01:52:01.4465664Z 2025-01-24T01:52:01.4465786Z >>> class MyModule(nn.Module): 2025-01-24T01:52:01.4465899Z >>> def forward(input): 2025-01-24T01:52:01.4466022Z >>> return input + 1 2025-01-24T01:52:01.4466114Z >>> 2025-01-24T01:52:01.4466326Z >>> module_cls = MyModule 2025-01-24T01:52:01.4466422Z 2025-01-24T01:52:01.4466629Z args (Sequence, optional): args to be passed to ``module_cls``. 2025-01-24T01:52:01.4466845Z kwargs (Dict, optional): kwargs to be passed to ``module_cls``. 2025-01-24T01:52:01.4466936Z 2025-01-24T01:52:01.4467045Z Returns: 2025-01-24T01:52:01.4467298Z A remote module instance which wraps the :class:`~nn.Module` created by the 2025-01-24T01:52:01.4467553Z user-provided ``module_cls``, it has a blocking ``forward`` method and an 2025-01-24T01:52:01.4467837Z asynchronous ``forward_async`` method that returns a future of the ``forward`` call 2025-01-24T01:52:01.4467996Z on the user-provided module on the remote side. 2025-01-24T01:52:01.4468100Z 2025-01-24T01:52:01.4468202Z Example:: 2025-01-24T01:52:01.4468374Z Run the following code in two different processes: 2025-01-24T01:52:01.4468461Z 2025-01-24T01:52:01.4468584Z >>> # xdoctest: +SKIP("distributed") 2025-01-24T01:52:01.4468699Z >>> # On worker 0: 2025-01-24T01:52:01.4468799Z >>> import torch 2025-01-24T01:52:01.4468944Z >>> import torch.distributed.rpc as rpc 2025-01-24T01:52:01.4469061Z >>> from torch import nn, Tensor 2025-01-24T01:52:01.4469305Z >>> from torch.distributed.nn.api.remote_module import RemoteModule 2025-01-24T01:52:01.4469397Z >>> 2025-01-24T01:52:01.4469546Z >>> rpc.init_rpc("worker0", rank=0, world_size=2) 2025-01-24T01:52:01.4469688Z >>> remote_linear_module = RemoteModule( 2025-01-24T01:52:01.4469824Z >>> "worker1/cpu", nn.Linear, args=(20, 30), 2025-01-24T01:52:01.4469937Z >>> ) 2025-01-24T01:52:01.4470052Z >>> input = torch.randn(128, 20) 2025-01-24T01:52:01.4470214Z >>> ret_fut = remote_linear_module.forward_async(input) 2025-01-24T01:52:01.4470334Z >>> ret = ret_fut.wait() 2025-01-24T01:52:01.4470437Z >>> rpc.shutdown() 2025-01-24T01:52:01.4470542Z 2025-01-24T01:52:01.4470641Z >>> # On worker 1: 2025-01-24T01:52:01.4470740Z >>> import torch 2025-01-24T01:52:01.4470884Z >>> import torch.distributed.rpc as rpc 2025-01-24T01:52:01.4470977Z >>> 2025-01-24T01:52:01.4471135Z >>> rpc.init_rpc("worker1", rank=1, world_size=2) 2025-01-24T01:52:01.4471238Z >>> rpc.shutdown() 2025-01-24T01:52:01.4471343Z 2025-01-24T01:52:01.4471543Z Furthermore, a more practical example that is combined with 2025-01-24T01:52:01.4472027Z `DistributedDataParallel `__ (DDP) 2025-01-24T01:52:01.4472443Z can be found in this `tutorial `__. 2025-01-24T01:52:01.4472535Z 2025-01-24T01:52:01.4472808Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:52:01.4472897Z 2025-01-24T01:52:01.4473016Z warnings.warn(msg) 2025-01-24T01:52:01.4473108Z 2025-01-24T01:52:01.4473331Z --- Parse Warning: 64 / 106 --- 2025-01-24T01:52:01.4474343Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=DistributedOptimizer in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/optim/optimizer.py line=130. 2025-01-24T01:52:01.4474644Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:52:01.4474751Z 2025-01-24T01:52:01.4475001Z DistributedOptimizer takes remote references to parameters scattered 2025-01-24T01:52:01.4475290Z across workers and applies the given optimizer locally for each parameter. 2025-01-24T01:52:01.4475382Z 2025-01-24T01:52:01.4475624Z This class uses :meth:`~torch.distributed.autograd.get_gradients` in order 2025-01-24T01:52:01.4475799Z to retrieve the gradients for specific parameters. 2025-01-24T01:52:01.4475891Z 2025-01-24T01:52:01.4476008Z Concurrent calls to 2025-01-24T01:52:01.4476224Z :meth:`~torch.distributed.optim.DistributedOptimizer.step`, 2025-01-24T01:52:01.4476375Z either from the same or different clients, will 2025-01-24T01:52:01.4476621Z be serialized on each worker -- as each worker's optimizer can only work 2025-01-24T01:52:01.4476836Z on one set of gradients at a time. However, there is no guarantee that 2025-01-24T01:52:01.4477104Z the full forward-backward-optimizer sequence will execute for one client 2025-01-24T01:52:01.4477325Z at a time. This means that the gradients being applied may not correspond 2025-01-24T01:52:01.4477568Z to the latest forward pass executed on a given worker. Also, there is no 2025-01-24T01:52:01.4477691Z guaranteed ordering across workers. 2025-01-24T01:52:01.4477780Z 2025-01-24T01:52:01.4478056Z `DistributedOptimizer` creates the local optimizer with TorchScript enabled 2025-01-24T01:52:01.4478294Z by default, so that optimizer updates are not blocked by the Python Global 2025-01-24T01:52:01.4478556Z Interpreter Lock (GIL) in the case of multithreaded training (e.g. Distributed 2025-01-24T01:52:01.4478803Z Model Parallel). This feature is currently enabled for most optimizers. You 2025-01-24T01:52:01.4479069Z can also follow `the recipe`__ in PyTorch tutorials to enable TorchScript support 2025-01-24T01:52:01.4479327Z for your own custom optimizers. 2025-01-24T01:52:01.4479419Z 2025-01-24T01:52:01.4479519Z Args: 2025-01-24T01:52:01.4479726Z optimizer_class (optim.Optimizer): the class of optimizer to 2025-01-24T01:52:01.4479855Z instantiate on each worker. 2025-01-24T01:52:01.4480075Z params_rref (list[RRef]): list of RRefs to local or remote parameters 2025-01-24T01:52:01.4480185Z to optimize. 2025-01-24T01:52:01.4480407Z args: arguments to pass to the optimizer constructor on each worker. 2025-01-24T01:52:01.4480636Z kwargs: arguments to pass to the optimizer constructor on each worker. 2025-01-24T01:52:01.4480731Z 2025-01-24T01:52:01.4480832Z Example:: 2025-01-24T01:52:01.4480960Z >>> # xdoctest: +SKIP("distributed") 2025-01-24T01:52:01.4481129Z >>> import torch.distributed.autograd as dist_autograd 2025-01-24T01:52:01.4481262Z >>> import torch.distributed.rpc as rpc 2025-01-24T01:52:01.4481378Z >>> from torch import optim 2025-01-24T01:52:01.4481577Z >>> from torch.distributed.optim import DistributedOptimizer 2025-01-24T01:52:01.4481674Z >>> 2025-01-24T01:52:01.4481817Z >>> with dist_autograd.context() as context_id: 2025-01-24T01:52:01.4481986Z >>> # Forward pass. 2025-01-24T01:52:01.4482230Z >>> rref1 = rpc.remote("worker1", torch.add, args=(torch.ones(2), 3)) 2025-01-24T01:52:01.4482432Z >>> rref2 = rpc.remote("worker1", torch.add, args=(torch.ones(2), 1)) 2025-01-24T01:52:01.4482572Z >>> loss = rref1.to_here() + rref2.to_here() 2025-01-24T01:52:01.4482662Z >>> 2025-01-24T01:52:01.4482772Z >>> # Backward pass. 2025-01-24T01:52:01.4482925Z >>> dist_autograd.backward(context_id, [loss.sum()]) 2025-01-24T01:52:01.4483009Z >>> 2025-01-24T01:52:01.4483117Z >>> # Optimizer. 2025-01-24T01:52:01.4483244Z >>> dist_optim = DistributedOptimizer( 2025-01-24T01:52:01.4483351Z >>> optim.SGD, 2025-01-24T01:52:01.4483450Z >>> [rref1, rref2], 2025-01-24T01:52:01.4483600Z >>> lr=0.05, 2025-01-24T01:52:01.4483690Z >>> ) 2025-01-24T01:52:01.4483808Z >>> dist_optim.step(context_id) 2025-01-24T01:52:01.4483944Z 2025-01-24T01:52:01.4484114Z __ https://github.com/pytorch/tutorials/pull/1465 2025-01-24T01:52:01.4484209Z 2025-01-24T01:52:01.4484469Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:52:01.4484558Z 2025-01-24T01:52:01.4484673Z warnings.warn(msg) 2025-01-24T01:52:01.4484757Z 2025-01-24T01:52:01.4484972Z --- Parse Warning: 65 / 106 --- 2025-01-24T01:52:01.4486038Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=PostLocalSGDOptimizer in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/optim/post_localSGD_optimizer.py line=9. 2025-01-24T01:52:01.4486323Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:52:01.4486414Z 2025-01-24T01:52:01.4486805Z Wraps an arbitrary :class:`torch.optim.Optimizer` and runs `post-local SGD `_, 2025-01-24T01:52:01.4486981Z This optimizer runs local optimizer at every step. 2025-01-24T01:52:01.4487313Z After the warm-up stage, it averages parameters periodically afer the local optimizer is applied. 2025-01-24T01:52:01.4487414Z 2025-01-24T01:52:01.4487507Z Args: 2025-01-24T01:52:01.4487630Z optim: The local optimizer. 2025-01-24T01:52:01.4487855Z averager: A model averager instance to run post-localSGD algorithm. 2025-01-24T01:52:01.4487940Z 2025-01-24T01:52:01.4488049Z Example:: 2025-01-24T01:52:01.4488136Z 2025-01-24T01:52:01.4488280Z >>> # xdoctest: +SKIP("undefined variables") 2025-01-24T01:52:01.4488381Z >>> import torch 2025-01-24T01:52:01.4488504Z >>> import torch.distributed as dist 2025-01-24T01:52:01.4488792Z >>> import torch.distributed.algorithms.model_averaging.averagers as averagers 2025-01-24T01:52:01.4488896Z >>> import torch.nn as nn 2025-01-24T01:52:01.4489104Z >>> from torch.distributed.optim import PostLocalSGDOptimizer 2025-01-24T01:52:01.4489385Z >>> from torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook import ( 2025-01-24T01:52:01.4489507Z >>> PostLocalSGDState, 2025-01-24T01:52:01.4489612Z >>> post_localSGD_hook, 2025-01-24T01:52:01.4489696Z >>> ) 2025-01-24T01:52:01.4489796Z >>> 2025-01-24T01:52:01.4489953Z >>> model = nn.parallel.DistributedDataParallel( 2025-01-24T01:52:01.4490107Z >>> module, device_ids=[rank], output_device=rank 2025-01-24T01:52:01.4490192Z >>> ) 2025-01-24T01:52:01.4490279Z >>> 2025-01-24T01:52:01.4490442Z >>> # Register a post-localSGD communication hook. 2025-01-24T01:52:01.4490739Z >>> state = PostLocalSGDState(process_group=None, subgroup=None, start_localSGD_iter=100) 2025-01-24T01:52:01.4490921Z >>> model.register_comm_hook(state, post_localSGD_hook) 2025-01-24T01:52:01.4491006Z >>> 2025-01-24T01:52:01.4491257Z >>> # Create a post-localSGD optimizer that wraps a local optimizer. 2025-01-24T01:52:01.4491537Z >>> # Note that ``warmup_steps`` used in ``PostLocalSGDOptimizer`` must be the same as 2025-01-24T01:52:01.4491707Z >>> # ``start_localSGD_iter`` used in ``PostLocalSGDState``. 2025-01-24T01:52:01.4491930Z >>> local_optim = torch.optim.SGD(params=model.parameters(), lr=0.01) 2025-01-24T01:52:01.4492050Z >>> opt = PostLocalSGDOptimizer( 2025-01-24T01:52:01.4492164Z >>> optim=local_optim, 2025-01-24T01:52:01.4492418Z >>> averager=averagers.PeriodicModelAverager(period=4, warmup_steps=100) 2025-01-24T01:52:01.4492517Z >>> ) 2025-01-24T01:52:01.4492606Z >>> 2025-01-24T01:52:01.4492830Z >>> # In the first 100 steps, DDP runs global gradient averaging at every step. 2025-01-24T01:52:01.4493173Z >>> # After 100 steps, DDP runs gradient averaging within each subgroup (intra-node by default), 2025-01-24T01:52:01.4493581Z >>> # and post-localSGD optimizer runs global model averaging every 4 steps after applying the local optimizer. 2025-01-24T01:52:01.4493709Z >>> for step in range(0, 200): 2025-01-24T01:52:01.4493808Z >>> opt.zero_grad() 2025-01-24T01:52:01.4493933Z >>> loss = loss_fn(output, labels) 2025-01-24T01:52:01.4494037Z >>> loss.backward() 2025-01-24T01:52:01.4494133Z >>> opt.step() 2025-01-24T01:52:01.4494231Z 2025-01-24T01:52:01.4494486Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:52:01.4494584Z 2025-01-24T01:52:01.4494687Z warnings.warn(msg) 2025-01-24T01:52:01.4494774Z 2025-01-24T01:52:01.4494985Z --- Parse Warning: 66 / 106 --- 2025-01-24T01:52:01.4496079Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ZeroRedundancyOptimizer in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/optim/zero_redundancy_optimizer.py line=283. 2025-01-24T01:52:01.4496363Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:52:01.4496451Z 2025-01-24T01:52:01.4496865Z Wrap an arbitrary :class:`optim.Optimizer ` and shards its states across ranks in the group. 2025-01-24T01:52:01.4496953Z 2025-01-24T01:52:01.4497083Z The sharing is done as described by ZeRO_. 2025-01-24T01:52:01.4497181Z 2025-01-24T01:52:01.4497331Z The local optimizer instance in each rank is only 2025-01-24T01:52:01.4497587Z responsible for updating approximately ``1 / world_size`` parameters and 2025-01-24T01:52:01.4497794Z hence only needs to keep ``1 / world_size`` optimizer states. After 2025-01-24T01:52:01.4498054Z parameters are updated locally, each rank will broadcast its parameters to 2025-01-24T01:52:01.4498245Z all other peers to keep all model replicas in the same state. 2025-01-24T01:52:01.4498444Z ``ZeroRedundancyOptimizer`` can be used in conjunction with 2025-01-24T01:52:01.4498721Z :class:`torch.nn.parallel.DistributedDataParallel` to reduce per-rank peak 2025-01-24T01:52:01.4498822Z memory consumption. 2025-01-24T01:52:01.4498918Z 2025-01-24T01:52:01.4499178Z ``ZeroRedundancyOptimizer`` uses a sorted-greedy algorithm to pack a number 2025-01-24T01:52:01.4499425Z of parameters at each rank. Each parameter belongs to a single rank and is 2025-01-24T01:52:01.4499673Z not divided among ranks. The partition is arbitrary and might not match the 2025-01-24T01:52:01.4499807Z the parameter registration or usage order. 2025-01-24T01:52:01.4499909Z 2025-01-24T01:52:01.4500002Z Arguments: 2025-01-24T01:52:01.4500214Z params (``Iterable``): an ``Iterable`` of :class:`torch.Tensor` s 2025-01-24T01:52:01.4500411Z or :class:`dict` s giving all parameters, which will be sharded 2025-01-24T01:52:01.4500507Z across ranks. 2025-01-24T01:52:01.4500607Z 2025-01-24T01:52:01.4500728Z Keyword Args: 2025-01-24T01:52:01.4500991Z optimizer_class (:class:`torch.nn.Optimizer`): the class of the local 2025-01-24T01:52:01.4501088Z optimizer. 2025-01-24T01:52:01.4501310Z process_group (``ProcessGroup``, optional): ``torch.distributed`` 2025-01-24T01:52:01.4501513Z ``ProcessGroup`` (default: ``dist.group.WORLD`` initialized by 2025-01-24T01:52:01.4501665Z :meth:`torch.distributed.init_process_group`). 2025-01-24T01:52:01.4501913Z parameters_as_bucket_view (bool, optional): if ``True``, parameters are 2025-01-24T01:52:01.4502130Z packed into buckets to speed up communication, and ``param.data`` 2025-01-24T01:52:01.4502349Z fields point to bucket views at different offsets; if ``False``, 2025-01-24T01:52:01.4502582Z each individual parameter is communicated separately, and each 2025-01-24T01:52:01.4502748Z ``params.data`` stays intact (default: ``False``). 2025-01-24T01:52:01.4503049Z overlap_with_ddp (bool, optional): if ``True``, :meth:`step` is 2025-01-24T01:52:01.4503250Z overlapped with :class:`DistributedDataParallel` 's gradient 2025-01-24T01:52:01.4503480Z synchronization; this requires (1) either a functional optimizer 2025-01-24T01:52:01.4503663Z for the ``optimizer_class`` argument or one with a functional 2025-01-24T01:52:01.4503851Z equivalent and (2) registering a DDP communication hook 2025-01-24T01:52:01.4504063Z constructed from one of the functions in ``ddp_zero_hook.py``; 2025-01-24T01:52:01.4504242Z parameters are packed into buckets matching those in 2025-01-24T01:52:01.4504406Z :class:`DistributedDataParallel`, meaning that the 2025-01-24T01:52:01.4504564Z ``parameters_as_bucket_view`` argument is ignored. 2025-01-24T01:52:01.4504765Z If ``False``, :meth:`step` runs disjointly after the backward pass 2025-01-24T01:52:01.4504871Z (per normal). 2025-01-24T01:52:01.4504989Z (default: ``False``) 2025-01-24T01:52:01.4505204Z **defaults: any trailing arguments, which are forwarded to the local 2025-01-24T01:52:01.4505306Z optimizer. 2025-01-24T01:52:01.4505395Z 2025-01-24T01:52:01.4505493Z Example:: 2025-01-24T01:52:01.4505589Z 2025-01-24T01:52:01.4505691Z >>> # xdoctest: +SKIP 2025-01-24T01:52:01.4505802Z >>> import torch.nn as nn 2025-01-24T01:52:01.4506015Z >>> from torch.distributed.optim import ZeroRedundancyOptimizer 2025-01-24T01:52:01.4506293Z >>> from torch.nn.parallel import DistributedDataParallel as DDP 2025-01-24T01:52:01.4506543Z >>> model = nn.Sequential(*[nn.Linear(2000, 2000).to(rank) for _ in range(20)]) 2025-01-24T01:52:01.4506672Z >>> ddp = DDP(model, device_ids=[rank]) 2025-01-24T01:52:01.4506807Z >>> opt = ZeroRedundancyOptimizer( 2025-01-24T01:52:01.4506916Z >>> ddp.parameters(), 2025-01-24T01:52:01.4507054Z >>> optimizer_class=torch.optim.Adam, 2025-01-24T01:52:01.4507152Z >>> lr=0.01 2025-01-24T01:52:01.4507240Z >>> ) 2025-01-24T01:52:01.4507368Z >>> ddp(inputs).sum().backward() 2025-01-24T01:52:01.4507467Z >>> opt.step() 2025-01-24T01:52:01.4507567Z 2025-01-24T01:52:01.4507667Z .. warning:: 2025-01-24T01:52:01.4507882Z Currently, ``ZeroRedundancyOptimizer`` requires that all of the 2025-01-24T01:52:01.4508043Z passed-in parameters are the same dense type. 2025-01-24T01:52:01.4508133Z 2025-01-24T01:52:01.4508233Z .. warning:: 2025-01-24T01:52:01.4508449Z If you pass ``overlap_with_ddp=True``, be wary of the following: Given 2025-01-24T01:52:01.4508661Z the way that overlapping :class:`DistributedDataParallel` with 2025-01-24T01:52:01.4508899Z :class:`ZeroRedundancyOptimizer` is currently implemented, the first 2025-01-24T01:52:01.4509121Z two or three training iterations do not perform parameter updates in 2025-01-24T01:52:01.4509388Z the optimizer step, depending on if ``static_graph=False`` or 2025-01-24T01:52:01.4509582Z ``static_graph=True``, respectively. This is because it needs 2025-01-24T01:52:01.4509782Z information about the gradient bucketing strategy used by 2025-01-24T01:52:01.4510010Z :class:`DistributedDataParallel`, which is not finalized until the 2025-01-24T01:52:01.4510225Z second forward pass if ``static_graph=False`` or until the third 2025-01-24T01:52:01.4510443Z forward pass if ``static_graph=True``. To adjust for this, one option 2025-01-24T01:52:01.4510560Z is to prepend dummy inputs. 2025-01-24T01:52:01.4510662Z 2025-01-24T01:52:01.4510924Z .. warning:: ZeroRedundancyOptimizer is experimental and subject to change. 2025-01-24T01:52:01.4511027Z 2025-01-24T01:52:01.4511188Z .. _ZeRO: https://arxiv.org/abs/1910.02054 2025-01-24T01:52:01.4511275Z 2025-01-24T01:52:01.4511393Z 2025-01-24T01:52:01.4511657Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:52:01.4511752Z 2025-01-24T01:52:01.4511856Z warnings.warn(msg) 2025-01-24T01:52:01.4511953Z 2025-01-24T01:52:01.4512173Z --- Parse Warning: 67 / 106 --- 2025-01-24T01:52:01.4513160Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=_CustomReducer in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/pipelining/microbatch.py line=28. 2025-01-24T01:52:01.4513438Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:52:01.4513527Z 2025-01-24T01:52:01.4513772Z Custom reducer class that can be used to specify a custom operation that 2025-01-24T01:52:01.4513957Z reduces losses of multiple microbatches into one value. 2025-01-24T01:52:01.4514054Z 2025-01-24T01:52:01.4514146Z Example: 2025-01-24T01:52:01.4514251Z >>> # xdoctest: +SKIP 2025-01-24T01:52:01.4514417Z >>> sum_reducer = _CustomReducer( 2025-01-24T01:52:01.4514524Z >>> torch.tensor(0.0), 2025-01-24T01:52:01.4514635Z >>> lambda a, b: a + b 2025-01-24T01:52:01.4514727Z >>> ) 2025-01-24T01:52:01.4514814Z 2025-01-24T01:52:01.4515083Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:52:01.4515170Z 2025-01-24T01:52:01.4515284Z warnings.warn(msg) 2025-01-24T01:52:01.4515370Z 2025-01-24T01:52:01.4515553Z --- Parse Warning: 68 / 106 --- 2025-01-24T01:52:01.4516494Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=async_execution in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/rpc/functions.py line=6. 2025-01-24T01:52:01.4516763Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:52:01.4516861Z 2025-01-24T01:52:01.4517111Z A decorator for a function indicating that the return value of the function 2025-01-24T01:52:01.4517334Z is guaranteed to be a :class:`~torch.futures.Future` object and this 2025-01-24T01:52:01.4517585Z function can run asynchronously on the RPC callee. More specifically, the 2025-01-24T01:52:01.4517834Z callee extracts the :class:`~torch.futures.Future` returned by the wrapped 2025-01-24T01:52:01.4518076Z function and installs subsequent processing steps as a callback to that 2025-01-24T01:52:01.4518317Z :class:`~torch.futures.Future`. The installed callback will read the value 2025-01-24T01:52:01.4518529Z from the :class:`~torch.futures.Future` when completed and send the 2025-01-24T01:52:01.4518716Z value back as the RPC response. That also means the returned 2025-01-24T01:52:01.4518956Z :class:`~torch.futures.Future` only exists on the callee side and is never 2025-01-24T01:52:01.4519183Z sent through RPC. This decorator is useful when the wrapped function's 2025-01-24T01:52:01.4519469Z (``fn``) execution needs to pause and resume due to, e.g., containing 2025-01-24T01:52:01.4519697Z :meth:`~torch.distributed.rpc.rpc_async` or waiting for other signals. 2025-01-24T01:52:01.4519778Z 2025-01-24T01:52:01.4519995Z .. note:: To enable asynchronous execution, applications must pass the 2025-01-24T01:52:01.4520227Z function object returned by this decorator to RPC APIs. If RPC detected 2025-01-24T01:52:01.4520459Z attributes installed by this decorator, it knows that this function 2025-01-24T01:52:01.4520646Z returns a ``Future`` object and will handle that accordingly. 2025-01-24T01:52:01.4520878Z However, this does not mean this decorator has to be outmost one when 2025-01-24T01:52:01.4521134Z defining a function. For example, when combined with ``@staticmethod`` 2025-01-24T01:52:01.4521348Z or ``@classmethod``, ``@rpc.functions.async_execution`` needs to be the 2025-01-24T01:52:01.4521614Z inner decorator to allow the target function be recognized as a static 2025-01-24T01:52:01.4521849Z or class function. This target function can still execute asynchronously 2025-01-24T01:52:01.4522097Z because, when accessed, the static or class method preserves attributes 2025-01-24T01:52:01.4522254Z installed by ``@rpc.functions.async_execution``. 2025-01-24T01:52:01.4522352Z 2025-01-24T01:52:01.4522435Z 2025-01-24T01:52:01.4522530Z Example:: 2025-01-24T01:52:01.4522746Z The returned :class:`~torch.futures.Future` object can come from 2025-01-24T01:52:01.4522885Z :meth:`~torch.distributed.rpc.rpc_async`, 2025-01-24T01:52:01.4523126Z :meth:`~torch.futures.Future.then`, or :class:`~torch.futures.Future` 2025-01-24T01:52:01.4523309Z constructor. The example below shows directly using the 2025-01-24T01:52:01.4523459Z :class:`~torch.futures.Future` returned by 2025-01-24T01:52:01.4523586Z :meth:`~torch.futures.Future.then`. 2025-01-24T01:52:01.4523679Z 2025-01-24T01:52:01.4523818Z >>> from torch.distributed import rpc 2025-01-24T01:52:01.4523909Z >>> 2025-01-24T01:52:01.4524047Z >>> # omitting setup and shutdown RPC 2025-01-24T01:52:01.4524135Z >>> 2025-01-24T01:52:01.4524236Z >>> # On all workers 2025-01-24T01:52:01.4524368Z >>> @rpc.functions.async_execution 2025-01-24T01:52:01.4524491Z >>> def async_add_chained(to, x, y, z): 2025-01-24T01:52:01.4524703Z >>> # This function runs on "worker1" and returns immediately when 2025-01-24T01:52:01.4524897Z >>> # the callback is installed through the `then(cb)` API. In the 2025-01-24T01:52:01.4525099Z >>> # mean time, the `rpc_async` to "worker2" can run concurrently. 2025-01-24T01:52:01.4525270Z >>> # When the return value of that `rpc_async` arrives at 2025-01-24T01:52:01.4525461Z >>> # "worker1", "worker1" will run the lambda function accordingly 2025-01-24T01:52:01.4525671Z >>> # and set the value for the previously returned `Future`, which 2025-01-24T01:52:01.4525862Z >>> # will then trigger RPC to send the result back to "worker0". 2025-01-24T01:52:01.4526049Z >>> return rpc.rpc_async(to, torch.add, args=(x, y)).then( 2025-01-24T01:52:01.4526167Z >>> lambda fut: fut.wait() + z 2025-01-24T01:52:01.4526272Z >>> ) 2025-01-24T01:52:01.4526361Z >>> 2025-01-24T01:52:01.4526457Z >>> # On worker0 2025-01-24T01:52:01.4526572Z >>> # xdoctest: +SKIP 2025-01-24T01:52:01.4526676Z >>> ret = rpc.rpc_sync( 2025-01-24T01:52:01.4526785Z >>> "worker1", 2025-01-24T01:52:01.4526887Z >>> async_add_chained, 2025-01-24T01:52:01.4527015Z >>> args=("worker2", torch.ones(2), 1, 1) 2025-01-24T01:52:01.4527117Z >>> ) 2025-01-24T01:52:01.4527244Z >>> print(ret) # prints tensor([3., 3.]) 2025-01-24T01:52:01.4527343Z 2025-01-24T01:52:01.4527576Z When combined with TorchScript decorators, this decorator must be the 2025-01-24T01:52:01.4527724Z outmost one. 2025-01-24T01:52:01.4527820Z 2025-01-24T01:52:01.4527932Z >>> from torch import Tensor 2025-01-24T01:52:01.4528068Z >>> from torch.futures import Future 2025-01-24T01:52:01.4528195Z >>> from torch.distributed import rpc 2025-01-24T01:52:01.4528292Z >>> 2025-01-24T01:52:01.4528411Z >>> # omitting setup and shutdown RPC 2025-01-24T01:52:01.4528498Z >>> 2025-01-24T01:52:01.4528602Z >>> # On all workers 2025-01-24T01:52:01.4528702Z >>> @torch.jit.script 2025-01-24T01:52:01.4528863Z >>> def script_add(x: Tensor, y: Tensor) -> Tensor: 2025-01-24T01:52:01.4528957Z >>> return x + y 2025-01-24T01:52:01.4529047Z >>> 2025-01-24T01:52:01.4529202Z >>> @rpc.functions.async_execution 2025-01-24T01:52:01.4529306Z >>> @torch.jit.script 2025-01-24T01:52:01.4529510Z >>> def async_add(to: str, x: Tensor, y: Tensor) -> Future[Tensor]: 2025-01-24T01:52:01.4529686Z >>> return rpc.rpc_async(to, script_add, (x, y)) 2025-01-24T01:52:01.4529775Z >>> 2025-01-24T01:52:01.4529879Z >>> # On worker0 2025-01-24T01:52:01.4529985Z >>> ret = rpc.rpc_sync( 2025-01-24T01:52:01.4530087Z >>> "worker1", 2025-01-24T01:52:01.4530181Z >>> async_add, 2025-01-24T01:52:01.4530313Z >>> args=("worker2", torch.ones(2), 1) 2025-01-24T01:52:01.4530401Z >>> ) 2025-01-24T01:52:01.4530528Z >>> print(ret) # prints tensor([2., 2.]) 2025-01-24T01:52:01.4530624Z 2025-01-24T01:52:01.4530851Z When combined with static or class method, this decorator must be the 2025-01-24T01:52:01.4530949Z inner one. 2025-01-24T01:52:01.4531036Z 2025-01-24T01:52:01.4531162Z >>> from torch.distributed import rpc 2025-01-24T01:52:01.4531260Z >>> 2025-01-24T01:52:01.4531382Z >>> # omitting setup and shutdown RPC 2025-01-24T01:52:01.4531484Z >>> 2025-01-24T01:52:01.4531588Z >>> # On all workers 2025-01-24T01:52:01.4531705Z >>> class AsyncExecutionClass: 2025-01-24T01:52:01.4531797Z >>> 2025-01-24T01:52:01.4531898Z >>> @staticmethod 2025-01-24T01:52:01.4532031Z >>> @rpc.functions.async_execution 2025-01-24T01:52:01.4532150Z >>> def static_async_add(to, x, y, z): 2025-01-24T01:52:01.4532336Z >>> return rpc.rpc_async(to, torch.add, args=(x, y)).then( 2025-01-24T01:52:01.4532453Z >>> lambda fut: fut.wait() + z 2025-01-24T01:52:01.4532546Z >>> ) 2025-01-24T01:52:01.4532641Z >>> 2025-01-24T01:52:01.4532741Z >>> @classmethod 2025-01-24T01:52:01.4532881Z >>> @rpc.functions.async_execution 2025-01-24T01:52:01.4533012Z >>> def class_async_add(cls, to, x, y, z): 2025-01-24T01:52:01.4533142Z >>> ret_fut = torch.futures.Future() 2025-01-24T01:52:01.4533304Z >>> rpc.rpc_async(to, torch.add, args=(x, y)).then( 2025-01-24T01:52:01.4533465Z >>> lambda fut: ret_fut.set_result(fut.wait() + z) 2025-01-24T01:52:01.4533563Z >>> ) 2025-01-24T01:52:01.4533666Z >>> return ret_fut 2025-01-24T01:52:01.4533765Z >>> 2025-01-24T01:52:01.4533889Z >>> @rpc.functions.async_execution 2025-01-24T01:52:01.4534020Z >>> def bound_async_add(self, to, x, y, z): 2025-01-24T01:52:01.4534199Z >>> return rpc.rpc_async(to, torch.add, args=(x, y)).then( 2025-01-24T01:52:01.4534318Z >>> lambda fut: fut.wait() + z 2025-01-24T01:52:01.4534424Z >>> ) 2025-01-24T01:52:01.4534514Z >>> 2025-01-24T01:52:01.4534609Z >>> # On worker0 2025-01-24T01:52:01.4534723Z >>> ret = rpc.rpc_sync( 2025-01-24T01:52:01.4534823Z >>> "worker1", 2025-01-24T01:52:01.4534971Z >>> AsyncExecutionClass.static_async_add, 2025-01-24T01:52:01.4535125Z >>> args=("worker2", torch.ones(2), 1, 2) 2025-01-24T01:52:01.4535249Z >>> ) 2025-01-24T01:52:01.4535376Z >>> print(ret) # prints tensor([4., 4.]) 2025-01-24T01:52:01.4535466Z >>> 2025-01-24T01:52:01.4535576Z >>> ret = rpc.rpc_sync( 2025-01-24T01:52:01.4535673Z >>> "worker1", 2025-01-24T01:52:01.4535819Z >>> AsyncExecutionClass.class_async_add, 2025-01-24T01:52:01.4535945Z >>> args=("worker2", torch.ones(2), 1, 2) 2025-01-24T01:52:01.4536033Z >>> ) 2025-01-24T01:52:01.4536160Z >>> print(ret) # prints tensor([4., 4.]) 2025-01-24T01:52:01.4536247Z 2025-01-24T01:52:01.4536420Z This decorator also works with RRef helpers, i.e., . 2025-01-24T01:52:01.4536569Z :meth:`torch.distributed.rpc.RRef.rpc_sync`, 2025-01-24T01:52:01.4536773Z :meth:`torch.distributed.rpc.RRef.rpc_async`, and 2025-01-24T01:52:01.4536919Z :meth:`torch.distributed.rpc.RRef.remote`. 2025-01-24T01:52:01.4537036Z 2025-01-24T01:52:01.4537173Z >>> from torch.distributed import rpc 2025-01-24T01:52:01.4537267Z >>> 2025-01-24T01:52:01.4537420Z >>> # reuse the AsyncExecutionClass class above 2025-01-24T01:52:01.4537579Z >>> rref = rpc.remote("worker1", AsyncExecutionClass) 2025-01-24T01:52:01.4537796Z >>> ret = rref.rpc_sync().static_async_add("worker2", torch.ones(2), 1, 2) 2025-01-24T01:52:01.4537934Z >>> print(ret) # prints tensor([4., 4.]) 2025-01-24T01:52:01.4538029Z >>> 2025-01-24T01:52:01.4538196Z >>> rref = rpc.remote("worker1", AsyncExecutionClass) 2025-01-24T01:52:01.4538438Z >>> ret = rref.rpc_async().static_async_add("worker2", torch.ones(2), 1, 2).wait() 2025-01-24T01:52:01.4538574Z >>> print(ret) # prints tensor([4., 4.]) 2025-01-24T01:52:01.4538668Z >>> 2025-01-24T01:52:01.4538830Z >>> rref = rpc.remote("worker1", AsyncExecutionClass) 2025-01-24T01:52:01.4539079Z >>> ret = rref.remote().static_async_add("worker2", torch.ones(2), 1, 2).to_here() 2025-01-24T01:52:01.4539211Z >>> print(ret) # prints tensor([4., 4.]) 2025-01-24T01:52:01.4539311Z 2025-01-24T01:52:01.4539574Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:52:01.4539665Z 2025-01-24T01:52:01.4539782Z warnings.warn(msg) 2025-01-24T01:52:01.4539872Z 2025-01-24T01:52:01.4540097Z --- Parse Warning: 69 / 106 --- 2025-01-24T01:52:01.4541178Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=TensorPipeRpcBackendOptions.set_device_map in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/rpc/options.py line=108. 2025-01-24T01:52:01.4541463Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:52:01.4541555Z 2025-01-24T01:52:01.4541766Z Set device mapping between each RPC caller and callee pair. This 2025-01-24T01:52:01.4541973Z function can be called multiple times to incrementally add 2025-01-24T01:52:01.4542096Z device placement configurations. 2025-01-24T01:52:01.4542193Z 2025-01-24T01:52:01.4542288Z Args: 2025-01-24T01:52:01.4542407Z to (str): Callee name. 2025-01-24T01:52:01.4542616Z device_map (Dict of int, str, or torch.device): Device placement 2025-01-24T01:52:01.4542808Z mappings from this worker to the callee. This map must be 2025-01-24T01:52:01.4542915Z invertible. 2025-01-24T01:52:01.4543007Z 2025-01-24T01:52:01.4543112Z Example: 2025-01-24T01:52:01.4543239Z >>> # xdoctest: +SKIP("distributed") 2025-01-24T01:52:01.4543339Z >>> # both workers 2025-01-24T01:52:01.4543452Z >>> def add(x, y): 2025-01-24T01:52:01.4543598Z >>> print(x) # tensor([1., 1.], device='cuda:1') 2025-01-24T01:52:01.4543722Z >>> return x + y, (x + y).to(2) 2025-01-24T01:52:01.4543814Z >>> 2025-01-24T01:52:01.4543949Z >>> # on worker 0 2025-01-24T01:52:01.4544115Z >>> options = TensorPipeRpcBackendOptions( 2025-01-24T01:52:01.4544229Z >>> num_worker_threads=8, 2025-01-24T01:52:01.4544353Z >>> device_maps={"worker1": {0: 1}} 2025-01-24T01:52:01.4544496Z >>> # maps worker0's cuda:0 to worker1's cuda:1 2025-01-24T01:52:01.4544598Z >>> ) 2025-01-24T01:52:01.4544734Z >>> options.set_device_map("worker1", {1: 2}) 2025-01-24T01:52:01.4544870Z >>> # maps worker0's cuda:1 to worker1's cuda:2 2025-01-24T01:52:01.4544968Z >>> 2025-01-24T01:52:01.4545070Z >>> rpc.init_rpc( 2025-01-24T01:52:01.4545176Z >>> "worker0", 2025-01-24T01:52:01.4545268Z >>> rank=0, 2025-01-24T01:52:01.4545369Z >>> world_size=2, 2025-01-24T01:52:01.4545541Z >>> backend=rpc.BackendType.TENSORPIPE, 2025-01-24T01:52:01.4545663Z >>> rpc_backend_options=options 2025-01-24T01:52:01.4545790Z >>> ) 2025-01-24T01:52:01.4545881Z >>> 2025-01-24T01:52:01.4545996Z >>> x = torch.ones(2) 2025-01-24T01:52:01.4546168Z >>> rets = rpc.rpc_sync("worker1", add, args=(x.to(0), 1)) 2025-01-24T01:52:01.4546459Z >>> # The first argument will be moved to cuda:1 on worker1. When 2025-01-24T01:52:01.4546667Z >>> # sending the return value back, it will follow the invert of 2025-01-24T01:52:01.4546851Z >>> # the device map, and hence will be moved back to cuda:0 and 2025-01-24T01:52:01.4546966Z >>> # cuda:1 on worker0 2025-01-24T01:52:01.4547119Z >>> print(rets[0]) # tensor([2., 2.], device='cuda:0') 2025-01-24T01:52:01.4547287Z >>> print(rets[1]) # tensor([2., 2.], device='cuda:1') 2025-01-24T01:52:01.4547380Z 2025-01-24T01:52:01.4547645Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:52:01.4547745Z 2025-01-24T01:52:01.4547849Z warnings.warn(msg) 2025-01-24T01:52:01.4547952Z 2025-01-24T01:52:01.4548168Z --- Parse Warning: 70 / 106 --- 2025-01-24T01:52:01.4549163Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=local_map in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/experimental/_func_map.py line=33. 2025-01-24T01:52:01.4549441Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:52:01.4549530Z 2025-01-24T01:52:01.4549807Z :meth:`local_map` is an experimental API that allows users to pass :class:`DTensor` s 2025-01-24T01:52:01.4550092Z to a function that is written to be applied on ``torch.Tensor`` s. It is done by extracting 2025-01-24T01:52:01.4550373Z the local components of :class:`DTensor`, call the function, and wrap the outputs to 2025-01-24T01:52:01.4550542Z :class:`DTensor` according to the ``out_placements``. 2025-01-24T01:52:01.4550647Z 2025-01-24T01:52:01.4550743Z Args: 2025-01-24T01:52:01.4550962Z func (Callable): the function to be applied on each local shard of 2025-01-24T01:52:01.4551073Z :class:`DTensor` s. 2025-01-24T01:52:01.4551310Z out_placements (Union[`PlacementType`, Tuple[`PlacementType`, ...]]): 2025-01-24T01:52:01.4551580Z the desired placements of the :class:`DTensor` s in ``func``'s flattened output. 2025-01-24T01:52:01.4551832Z If the flattened ``output`` is a single value, the ``out_placements`` should be 2025-01-24T01:52:01.4552083Z of type `PlacementType`. Otherwise if the flattened ``output`` has multiple 2025-01-24T01:52:01.4552348Z values, the ``out_placements`` should be a tuple of `PlacementType` values 1:1 2025-01-24T01:52:01.4552482Z mapping to the flattened ``output``. 2025-01-24T01:52:01.4552706Z Besides, for :class:`Tensor` output, we use `PlacementType` as its 2025-01-24T01:52:01.4552988Z placements (a `Tuple[Placement]` value). For non-Tensor output, the `PlacementType` 2025-01-24T01:52:01.4553176Z should be `None`. 2025-01-24T01:52:01.4553418Z Note that the only exception is when no :class:`DTensor` argument is passed 2025-01-24T01:52:01.4553658Z in. In this case, even if `out_placements` is not `None`, the result function 2025-01-24T01:52:01.4553918Z should ignore the desired placements because the function is not running with 2025-01-24T01:52:01.4554023Z :class:`DTensor` s. 2025-01-24T01:52:01.4554209Z in_placements (Tuple[`PlacementType`, ...], optional): 2025-01-24T01:52:01.4554497Z the required placements of the :class:`DTensor` s in the flattened inputs of ``func``. 2025-01-24T01:52:01.4554752Z If ``in_placements`` is specified, :meth:`local_map` would examine whether the 2025-01-24T01:52:01.4555014Z placements of each :class:`DTensor` argument is the same as the required 2025-01-24T01:52:01.4555214Z placements or not. If the placements are not the same and 2025-01-24T01:52:01.4555494Z ``redistribute_inputs`` is ``False``, an exception will be raised. Otherwise if 2025-01-24T01:52:01.4555742Z ``redistribute_inputs`` is ``True``, the argument will be first redistributed to 2025-01-24T01:52:01.4556020Z the required sharding placements before passing its local tensor to ``func``. 2025-01-24T01:52:01.4556251Z The only exception is when required placements are not ``None`` and the 2025-01-24T01:52:01.4556507Z argument is a :class:`torch.Tensor`. In this case, the placements examination 2025-01-24T01:52:01.4556731Z will be skipped and the argument will be directly passed to ``func``. 2025-01-24T01:52:01.4556976Z If ``in_placements`` is ``None``, no placements examination will be performed. 2025-01-24T01:52:01.4557076Z Default: None 2025-01-24T01:52:01.4557218Z device_mesh (:class:`DeviceMesh`, optional): 2025-01-24T01:52:01.4557448Z the device mesh that all the :class:`DTensor` s are placed on. If not 2025-01-24T01:52:01.4557688Z specified, this will be inferred from the input :class:`DTensor` s' device 2025-01-24T01:52:01.4557941Z mesh. `local_map` requires every :class:`DTensor` s to be placed on the same 2025-01-24T01:52:01.4558058Z device mesh. Default: None. 2025-01-24T01:52:01.4558199Z redistribute_inputs (bool, optional): 2025-01-24T01:52:01.4558456Z the bool value indicating whether to reshard the input :class:`DTensor` s when 2025-01-24T01:52:01.4558712Z their placements are different from the required input placements. If this 2025-01-24T01:52:01.4558956Z value is ``False`` and some :class:`DTensor` input has a different placement, 2025-01-24T01:52:01.4559100Z an exception will be raised. Default: False. 2025-01-24T01:52:01.4559201Z 2025-01-24T01:52:01.4559290Z Returns: 2025-01-24T01:52:01.4559570Z A ``Callable`` that applies ``func`` to each local shard of the input :class:`DTensor` 2025-01-24T01:52:01.4559814Z and returns a :class:`DTensor` constructed from the return value of ``func``. 2025-01-24T01:52:01.4559904Z 2025-01-24T01:52:01.4560009Z Raises: 2025-01-24T01:52:01.4560266Z AssertionError: If the input :class:`DTensor` is not placed on the same device 2025-01-24T01:52:01.4560520Z mesh, or if they are placed on a different device mesh than the ``device_mesh`` 2025-01-24T01:52:01.4560624Z argument passed in. 2025-01-24T01:52:01.4560724Z 2025-01-24T01:52:01.4560970Z AssertionError: For any non-DTensor output, we require its corresponding 2025-01-24T01:52:01.4561237Z output placement in ``out_placements`` be None. An AssertionError will be raised 2025-01-24T01:52:01.4561358Z if this is not the case. 2025-01-24T01:52:01.4561447Z 2025-01-24T01:52:01.4561730Z ValueError: If ``redistribute_inputs=False`` but the input :class:`DTensor` needs 2025-01-24T01:52:01.4561944Z a redistribution according to ``in_placements``. 2025-01-24T01:52:01.4562044Z 2025-01-24T01:52:01.4562138Z Example: 2025-01-24T01:52:01.4562259Z >>> # xdoctest: +SKIP("distributed") 2025-01-24T01:52:01.4562416Z >>> def mm_allreduce_forward(device_mesh, W, X): 2025-01-24T01:52:01.4562542Z >>> partial_sum_tensor = torch.mm(W, X) 2025-01-24T01:52:01.4562801Z >>> reduced_tensor = funcol.all_reduce(partial_sum_tensor, "sum", device_mesh) 2025-01-24T01:52:01.4562913Z >>> return reduced_tensor 2025-01-24T01:52:01.4563002Z >>> 2025-01-24T01:52:01.4563153Z >>> W = torch.randn(12, 8, requires_grad=False) 2025-01-24T01:52:01.4563284Z >>> X = torch.randn(8, 16, requires_grad=False) 2025-01-24T01:52:01.4563425Z >>> Y = torch.mm(W, X) 2025-01-24T01:52:01.4563624Z >>> row_wise = [Shard(0)] # row-wise sharding placements on 1-d mesh 2025-01-24T01:52:01.4563856Z >>> col_wise = [Shard(1)] # col-wise sharding placements on 1-d mesh 2025-01-24T01:52:01.4563946Z >>> 2025-01-24T01:52:01.4564224Z >>> # local_mm_allreduce_forward is the function wrapped with DTensor/Tensor convertion 2025-01-24T01:52:01.4564370Z >>> local_mm_allreduce_forward = local_map( 2025-01-24T01:52:01.4564480Z >>> mm_allreduce_forward, 2025-01-24T01:52:01.4564618Z >>> out_placements=[Replicate()], 2025-01-24T01:52:01.4564743Z >>> in_placements=[col_wise, row_wise], 2025-01-24T01:52:01.4564854Z >>> device_mesh=device_mesh, 2025-01-24T01:52:01.4564958Z >>> ) 2025-01-24T01:52:01.4565046Z >>> 2025-01-24T01:52:01.4565322Z >>> W_dt = distribute_tensor(W, device_mesh, (col_wise)) # col-wisely sharded W tensor 2025-01-24T01:52:01.4565579Z >>> X_dt = distribute_tensor(X, device_mesh, (row_wise)) # row-wisely sharded X tensor 2025-01-24T01:52:01.4565927Z >>> Y_dt = local_mm_allreduce_forward(device_mesh, W_dt, X_dt) # apply local_mm_allreduce_forward to DTensors 2025-01-24T01:52:01.4566016Z 2025-01-24T01:52:01.4566223Z .. note:: This API is currently experimental and subject to change 2025-01-24T01:52:01.4566323Z 2025-01-24T01:52:01.4566581Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:52:01.4566678Z 2025-01-24T01:52:01.4566783Z warnings.warn(msg) 2025-01-24T01:52:01.4566884Z 2025-01-24T01:52:01.4567088Z --- Parse Warning: 71 / 106 --- 2025-01-24T01:52:01.4568171Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=register_sharding in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/experimental/_register_sharding.py line=26. 2025-01-24T01:52:01.4568460Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:52:01.4568550Z 2025-01-24T01:52:01.4568854Z :meth:`register_sharding` is an experimental API that allows users to register sharding 2025-01-24T01:52:01.4569100Z strategies for an operator when the tensor inputs and outputs are DTensor. 2025-01-24T01:52:01.4569371Z It can be useful when: (1) there doesn't exist a default sharding strategy for ``op``, 2025-01-24T01:52:01.4569617Z e.g. when ``op`` is a custom operator that is not supported by :class:`DTensor`; (2) 2025-01-24T01:52:01.4569899Z when users would like to overwrite default sharding strategies of existing operators. 2025-01-24T01:52:01.4570004Z 2025-01-24T01:52:01.4570094Z Args: 2025-01-24T01:52:01.4570248Z op (Union[OpOverload, List[OpOverload]]): 2025-01-24T01:52:01.4570449Z An op or a list of ops to register the customized sharding function. 2025-01-24T01:52:01.4570553Z 2025-01-24T01:52:01.4570644Z Returns: 2025-01-24T01:52:01.4570919Z A function decorator which can be used to wrap a function that defines the sharding 2025-01-24T01:52:01.4571272Z strategy for the operator specified in ``op``. The defined sharding strategy will be 2025-01-24T01:52:01.4571556Z registered to DTensor and will override the default sharding strategy if DTensor has 2025-01-24T01:52:01.4571882Z already implemented the operator. The customized sharding function takes the same inputs 2025-01-24T01:52:01.4572128Z as the original op (except that if an arg is a :class:`torch.Tensor`, it will be 2025-01-24T01:52:01.4572422Z replaced by a tensor-like object that DTensor uses internally). The function should 2025-01-24T01:52:01.4572703Z return a sequence of 2-tuples, each specifying acceptable output placements and its 2025-01-24T01:52:01.4572829Z corresponding intput placements. 2025-01-24T01:52:01.4572931Z 2025-01-24T01:52:01.4573048Z Example: 2025-01-24T01:52:01.4573191Z >>> # xdoctest: +SKIP("distributed") 2025-01-24T01:52:01.4573332Z >>> @register_sharding(aten._softmax.default) 2025-01-24T01:52:01.4573534Z >>> def custom_softmax_sharding(x, dim, half_to_float): 2025-01-24T01:52:01.4573686Z >>> softmax_dim = dim if dim >= 0 else dim + x.ndim 2025-01-24T01:52:01.4573802Z >>> acceptable_shardings = [] 2025-01-24T01:52:01.4573909Z >>> 2025-01-24T01:52:01.4574090Z >>> all_replicate = ([Replicate()], [Replicate(), None, None]) 2025-01-24T01:52:01.4574251Z >>> acceptable_shardings.append(all_replicate) 2025-01-24T01:52:01.4574345Z >>> 2025-01-24T01:52:01.4574470Z >>> for sharding_dim in range(x.ndim): 2025-01-24T01:52:01.4574609Z >>> if sharding_dim != softmax_dim: 2025-01-24T01:52:01.4574712Z >>> all_sharded = ( 2025-01-24T01:52:01.4574842Z >>> [Shard(sharding_dim)], 2025-01-24T01:52:01.4574974Z >>> [Shard(sharding_dim), None, None], 2025-01-24T01:52:01.4575082Z >>> ) 2025-01-24T01:52:01.4575233Z >>> acceptable_shardings.append(all_sharded) 2025-01-24T01:52:01.4575327Z >>> 2025-01-24T01:52:01.4575457Z >>> return acceptable_shardings 2025-01-24T01:52:01.4575542Z 2025-01-24T01:52:01.4575759Z .. note:: This API is currently experimental and subject to change 2025-01-24T01:52:01.4575845Z 2025-01-24T01:52:01.4576105Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:52:01.4576203Z 2025-01-24T01:52:01.4576302Z warnings.warn(msg) 2025-01-24T01:52:01.4576405Z 2025-01-24T01:52:01.4576602Z --- Parse Warning: 72 / 106 --- 2025-01-24T01:52:01.4577637Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=PrepareModuleInput in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/parallel/style.py line=404. 2025-01-24T01:52:01.4577908Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:52:01.4578001Z 2025-01-24T01:52:01.4578405Z Configure the nn.Module's inputs to convert the input tensors of the nn.Module to DTensors at runtime according to 2025-01-24T01:52:01.4578733Z ``input_layouts``, and perform layout redistribution according to the ``desired_input_layouts``. 2025-01-24T01:52:01.4578835Z 2025-01-24T01:52:01.4578930Z Keyword Args: 2025-01-24T01:52:01.4579316Z input_layouts (Union[Placement, Tuple[Optional[Placement]]]): 2025-01-24T01:52:01.4579653Z The DTensor layouts of input tensors for the nn.Module, this is used to convert the input tensors to 2025-01-24T01:52:01.4580013Z DTensors. If some inputs are not torch.Tensor or no need to convert to DTensors, ``None`` need to be specified 2025-01-24T01:52:01.4580159Z as a placeholder. default: None. 2025-01-24T01:52:01.4580394Z desired_input_layouts (Union[Placement, Tuple[Optional[Placement]]]): 2025-01-24T01:52:01.4580852Z The desired DTensor layout of input tensors for the nn.Module, this is used to ensure the inputs of the nn.Module 2025-01-24T01:52:01.4581281Z have the desired DTensor layouts. This argument needs to have the same length with ``input_layouts``. default: None. 2025-01-24T01:52:01.4581436Z input_kwarg_layouts (Dict[str, Placement]): 2025-01-24T01:52:01.4581814Z The DTensor layouts of input kwargs for the nn.Module, this is used to convert the input kwarg tensors to DTensors. 2025-01-24T01:52:01.4581928Z default: None 2025-01-24T01:52:01.4582100Z desired_input_kwarg_layouts: (Dict[str, Placement]): 2025-01-24T01:52:01.4582476Z The desired DTensor layout of input kwargs for the nn.Module, this is used to ensure the inputs of the nn.Module 2025-01-24T01:52:01.4582680Z have the desired DTensor layouts. default: None. 2025-01-24T01:52:01.4582801Z use_local_output (bool, optional): 2025-01-24T01:52:01.4583225Z Whether to use local :class:`torch.Tensor` instead of :class:`DTensor` for the module inputs, default: False. 2025-01-24T01:52:01.4583324Z Returns: 2025-01-24T01:52:01.4583659Z A :class:`ParallelStyle` object that prepares the sharding layouts of the nn.Module's inputs. 2025-01-24T01:52:01.4583751Z 2025-01-24T01:52:01.4583851Z Example:: 2025-01-24T01:52:01.4583976Z >>> # xdoctest: +SKIP(failing) 2025-01-24T01:52:01.4584298Z >>> from torch.distributed.tensor.parallel import parallelize_module, PrepareModuleInput 2025-01-24T01:52:01.4584509Z >>> from torch.distributed.device_mesh import init_device_mesh 2025-01-24T01:52:01.4584604Z >>> ... 2025-01-24T01:52:01.4584925Z >>> block = TransformerBlock(...) # block is a nn.Module that contains an "attn" Attention submodule 2025-01-24T01:52:01.4585063Z >>> tp_mesh = init_device_mesh("cuda", (8,)) 2025-01-24T01:52:01.4585151Z >>> 2025-01-24T01:52:01.4585512Z >>> # According to the style specified below, the first input of attn will be annotated to Sharded DTensor 2025-01-24T01:52:01.4585672Z >>> # and then redistributed to Replicated DTensor. 2025-01-24T01:52:01.4585791Z >>> parallelize_module( 2025-01-24T01:52:01.4585934Z >>> block, # this can be a submodule or module 2025-01-24T01:52:01.4586031Z >>> tp_mesh, 2025-01-24T01:52:01.4586155Z >>> parallelize_plan={ 2025-01-24T01:52:01.4586362Z >>> "attn": PrepareModuleInput( 2025-01-24T01:52:01.4586519Z >>> input_layouts=(Shard(0), None, None, ...), 2025-01-24T01:52:01.4586692Z >>> desired_input_layouts=(Replicate(), None, None, ...) 2025-01-24T01:52:01.4586797Z >>> ), 2025-01-24T01:52:01.4586891Z >>> } 2025-01-24T01:52:01.4586982Z >>> ) 2025-01-24T01:52:01.4587088Z 2025-01-24T01:52:01.4587348Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:52:01.4587450Z 2025-01-24T01:52:01.4587562Z warnings.warn(msg) 2025-01-24T01:52:01.4587652Z 2025-01-24T01:52:01.4587884Z --- Parse Warning: 73 / 106 --- 2025-01-24T01:52:01.4588909Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=PrepareModuleOutput in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/parallel/style.py line=559. 2025-01-24T01:52:01.4589192Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:52:01.4589282Z 2025-01-24T01:52:01.4589692Z Configure the nn.Module's outputs to convert the output tensors of the nn.Module to DTensors at runtime according to 2025-01-24T01:52:01.4590036Z ``output_layouts``, and perform layout redistribution according to the ``desired_output_layouts``. 2025-01-24T01:52:01.4590139Z 2025-01-24T01:52:01.4590240Z Keyword Args: 2025-01-24T01:52:01.4590413Z output_layouts (Union[Placement, Tuple[Placement]]): 2025-01-24T01:52:01.4590832Z The DTensor layouts of output tensors for the nn.Module, this is used to convert the output tensors to 2025-01-24T01:52:01.4591218Z DTensors if they are :class:`torch.Tensor`. If some outputs are not torch.Tensor or no need to convert to DTensors, 2025-01-24T01:52:01.4591377Z ``None`` need to be specified as a placeholder. 2025-01-24T01:52:01.4591583Z desired_output_layouts (Union[Placement, Tuple[Placement]]): 2025-01-24T01:52:01.4591987Z The desired DTensor layouts of output tensors for the nn.Module, this is used to ensure the outputs of the nn.Module 2025-01-24T01:52:01.4592112Z have the desired DTensor layouts. 2025-01-24T01:52:01.4592234Z use_local_output (bool, optional): 2025-01-24T01:52:01.4592634Z Whether to use local :class:`torch.Tensor` instead of :class:`DTensor` for the module outputs, default: True. 2025-01-24T01:52:01.4592728Z Returns: 2025-01-24T01:52:01.4593067Z A ParallelStyle object that prepares the sharding layouts of the nn.Module's outputs. 2025-01-24T01:52:01.4593156Z 2025-01-24T01:52:01.4593263Z Example:: 2025-01-24T01:52:01.4593374Z >>> # xdoctest: +SKIP(failing) 2025-01-24T01:52:01.4593695Z >>> from torch.distributed.tensor.parallel import parallelize_module, PrepareModuleOutput 2025-01-24T01:52:01.4593907Z >>> from torch.distributed.device_mesh import init_device_mesh 2025-01-24T01:52:01.4594000Z >>> ... 2025-01-24T01:52:01.4594320Z >>> block = TransformerBlock(...) # block is a nn.Module that contains an "attn" Attention submodule 2025-01-24T01:52:01.4594456Z >>> tp_mesh = init_device_mesh("cuda", (8,)) 2025-01-24T01:52:01.4594546Z >>> 2025-01-24T01:52:01.4594965Z >>> # According to the style specified below, the output of the TransformerBlock will be converted to Replicated DTensor 2025-01-24T01:52:01.4595110Z >>> # and then redistributed to Sharded DTensor. 2025-01-24T01:52:01.4595232Z >>> parallelize_module( 2025-01-24T01:52:01.4595372Z >>> block, # this can be a submodule or module 2025-01-24T01:52:01.4595475Z >>> tp_mesh, 2025-01-24T01:52:01.4595625Z >>> parallelize_plan = PrepareModuleOutput( 2025-01-24T01:52:01.4595743Z >>> output_layouts=Replicate(), 2025-01-24T01:52:01.4595880Z >>> desired_output_layouts=Shard(0) 2025-01-24T01:52:01.4595972Z >>> ) 2025-01-24T01:52:01.4596072Z >>> ) 2025-01-24T01:52:01.4596163Z 2025-01-24T01:52:01.4596421Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:52:01.4596524Z 2025-01-24T01:52:01.4596624Z warnings.warn(msg) 2025-01-24T01:52:01.4596722Z 2025-01-24T01:52:01.4596918Z --- Parse Warning: 74 / 106 --- 2025-01-24T01:52:01.4597936Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=MixtureSameFamily in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/mixture_same_family.py line=13. 2025-01-24T01:52:01.4598210Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:52:01.4598298Z 2025-01-24T01:52:01.4598543Z The `MixtureSameFamily` distribution implements a (batch of) mixture 2025-01-24T01:52:01.4598794Z distribution where all component are from different parameterizations of 2025-01-24T01:52:01.4599027Z the same distribution type. It is parameterized by a `Categorical` 2025-01-24T01:52:01.4599232Z "selecting distribution" (over `k` component) and a component 2025-01-24T01:52:01.4599453Z distribution, i.e., a `Distribution` with a rightmost batch shape 2025-01-24T01:52:01.4599627Z (equal to `[k]`) which indexes each (batch of) component. 2025-01-24T01:52:01.4599715Z 2025-01-24T01:52:01.4599824Z Examples:: 2025-01-24T01:52:01.4599912Z 2025-01-24T01:52:01.4600048Z >>> # xdoctest: +SKIP("undefined vars") 2025-01-24T01:52:01.4600314Z >>> # Construct Gaussian Mixture Model in 1D consisting of 5 equally 2025-01-24T01:52:01.4600447Z >>> # weighted normal distributions 2025-01-24T01:52:01.4600574Z >>> mix = D.Categorical(torch.ones(5,)) 2025-01-24T01:52:01.4600722Z >>> comp = D.Normal(torch.randn(5,), torch.rand(5,)) 2025-01-24T01:52:01.4600860Z >>> gmm = MixtureSameFamily(mix, comp) 2025-01-24T01:52:01.4600953Z 2025-01-24T01:52:01.4601169Z >>> # Construct Gaussian Mixture Model in 2D consisting of 5 equally 2025-01-24T01:52:01.4601308Z >>> # weighted bivariate normal distributions 2025-01-24T01:52:01.4601430Z >>> mix = D.Categorical(torch.ones(5,)) 2025-01-24T01:52:01.4601562Z >>> comp = D.Independent(D.Normal( 2025-01-24T01:52:01.4601722Z ... torch.randn(5,2), torch.rand(5,2)), 1) 2025-01-24T01:52:01.4601867Z >>> gmm = MixtureSameFamily(mix, comp) 2025-01-24T01:52:01.4601984Z 2025-01-24T01:52:01.4602185Z >>> # Construct a batch of 3 Gaussian Mixture Models in 2D each 2025-01-24T01:52:01.4602397Z >>> # consisting of 5 random weighted bivariate normal distributions 2025-01-24T01:52:01.4602520Z >>> mix = D.Categorical(torch.rand(3,5)) 2025-01-24T01:52:01.4602651Z >>> comp = D.Independent(D.Normal( 2025-01-24T01:52:01.4602791Z ... torch.randn(3,5,2), torch.rand(3,5,2)), 1) 2025-01-24T01:52:01.4602928Z >>> gmm = MixtureSameFamily(mix, comp) 2025-01-24T01:52:01.4603019Z 2025-01-24T01:52:01.4603106Z Args: 2025-01-24T01:52:01.4603334Z mixture_distribution: `torch.distributions.Categorical`-like 2025-01-24T01:52:01.4603531Z instance. Manages the probability of selecting component. 2025-01-24T01:52:01.4603722Z The number of categories must match the rightmost batch 2025-01-24T01:52:01.4603921Z dimension of the `component_distribution`. Must have either 2025-01-24T01:52:01.4604087Z scalar `batch_shape` or `batch_shape` matching 2025-01-24T01:52:01.4604232Z `component_distribution.batch_shape[:-1]` 2025-01-24T01:52:01.4604453Z component_distribution: `torch.distributions.Distribution`-like 2025-01-24T01:52:01.4604645Z instance. Right-most batch dimension indexes component. 2025-01-24T01:52:01.4604729Z 2025-01-24T01:52:01.4604996Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:52:01.4605095Z 2025-01-24T01:52:01.4605211Z warnings.warn(msg) 2025-01-24T01:52:01.4605304Z 2025-01-24T01:52:01.4605500Z --- Parse Warning: 75 / 106 --- 2025-01-24T01:52:01.4606509Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=RelaxedBernoulli in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/relaxed_bernoulli.py line=112. 2025-01-24T01:52:01.4606781Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:52:01.4606887Z 2025-01-24T01:52:01.4607081Z Creates a RelaxedBernoulli distribution, parametrized by 2025-01-24T01:52:01.4607292Z :attr:`temperature`, and either :attr:`probs` or :attr:`logits` 2025-01-24T01:52:01.4607521Z (but not both). This is a relaxed version of the `Bernoulli` distribution, 2025-01-24T01:52:01.4607712Z so the values are in (0, 1), and has reparametrizable samples. 2025-01-24T01:52:01.4607819Z 2025-01-24T01:52:01.4607919Z Example:: 2025-01-24T01:52:01.4608024Z 2025-01-24T01:52:01.4608174Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2025-01-24T01:52:01.4608327Z >>> m = RelaxedBernoulli(torch.tensor([2.2]), 2025-01-24T01:52:01.4608465Z ... torch.tensor([0.1, 0.2, 0.3, 0.99])) 2025-01-24T01:52:01.4608565Z >>> m.sample() 2025-01-24T01:52:01.4608705Z tensor([ 0.2951, 0.3442, 0.8918, 0.9021]) 2025-01-24T01:52:01.4608802Z 2025-01-24T01:52:01.4608937Z Args: 2025-01-24T01:52:01.4609114Z temperature (Tensor): relaxation temperature 2025-01-24T01:52:01.4609294Z probs (Number, Tensor): the probability of sampling `1` 2025-01-24T01:52:01.4609477Z logits (Number, Tensor): the log-odds of sampling `1` 2025-01-24T01:52:01.4609571Z 2025-01-24T01:52:01.4609847Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:52:01.4609941Z 2025-01-24T01:52:01.4610046Z warnings.warn(msg) 2025-01-24T01:52:01.4610153Z 2025-01-24T01:52:01.4610347Z --- Parse Warning: 76 / 106 --- 2025-01-24T01:52:01.4611433Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=RelaxedOneHotCategorical in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/relaxed_categorical.py line=100. 2025-01-24T01:52:01.4611708Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:52:01.4611840Z 2025-01-24T01:52:01.4612066Z Creates a RelaxedOneHotCategorical distribution parametrized by 2025-01-24T01:52:01.4612279Z :attr:`temperature`, and either :attr:`probs` or :attr:`logits`. 2025-01-24T01:52:01.4612524Z This is a relaxed version of the :class:`OneHotCategorical` distribution, so 2025-01-24T01:52:01.4612695Z its samples are on simplex, and are reparametrizable. 2025-01-24T01:52:01.4612797Z 2025-01-24T01:52:01.4612892Z Example:: 2025-01-24T01:52:01.4612993Z 2025-01-24T01:52:01.4613142Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2025-01-24T01:52:01.4613305Z >>> m = RelaxedOneHotCategorical(torch.tensor([2.2]), 2025-01-24T01:52:01.4613457Z ... torch.tensor([0.1, 0.2, 0.3, 0.4])) 2025-01-24T01:52:01.4613555Z >>> m.sample() 2025-01-24T01:52:01.4613691Z tensor([ 0.1294, 0.2324, 0.3859, 0.2523]) 2025-01-24T01:52:01.4613784Z 2025-01-24T01:52:01.4613886Z Args: 2025-01-24T01:52:01.4614038Z temperature (Tensor): relaxation temperature 2025-01-24T01:52:01.4614166Z probs (Tensor): event probabilities 2025-01-24T01:52:01.4614371Z logits (Tensor): unnormalized log probability for each event 2025-01-24T01:52:01.4614458Z 2025-01-24T01:52:01.4614728Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:52:01.4614815Z 2025-01-24T01:52:01.4614920Z warnings.warn(msg) 2025-01-24T01:52:01.4615021Z 2025-01-24T01:52:01.4615209Z --- Parse Warning: 77 / 106 --- 2025-01-24T01:52:01.4616244Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=assoc_in in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/unification_tools.py line=245. 2025-01-24T01:52:01.4616516Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:52:01.4616732Z Return a new dict with new, potentially nested, key value pair 2025-01-24T01:52:01.4616828Z 2025-01-24T01:52:01.4616931Z >>> purchase = { 2025-01-24T01:52:01.4617052Z ... "name": "Alice", 2025-01-24T01:52:01.4617237Z ... "order": {"items": ["Apple", "Orange"], "costs": [0.50, 1.25]}, 2025-01-24T01:52:01.4617377Z ... "credit card": "5555-1234-1234-1234", 2025-01-24T01:52:01.4617470Z ... } 2025-01-24T01:52:01.4617701Z >>> assoc_in(purchase, ["order", "costs"], [0.25, 1.00]) # doctest: +SKIP 2025-01-24T01:52:01.4617820Z {'credit card': '5555-1234-1234-1234', 2025-01-24T01:52:01.4617924Z 'name': 'Alice', 2025-01-24T01:52:01.4618109Z 'order': {'costs': [0.25, 1.00], 'items': ['Apple', 'Orange']}} 2025-01-24T01:52:01.4618206Z 2025-01-24T01:52:01.4618481Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:52:01.4618571Z 2025-01-24T01:52:01.4618704Z warnings.warn(msg) 2025-01-24T01:52:01.4618836Z 2025-01-24T01:52:01.4619029Z --- Parse Warning: 78 / 106 --- 2025-01-24T01:52:01.4620064Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=update_in in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/unification_tools.py line=261. 2025-01-24T01:52:01.4620336Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:52:01.4620508Z Update value in a (potentially) nested dictionary 2025-01-24T01:52:01.4620597Z 2025-01-24T01:52:01.4620693Z inputs: 2025-01-24T01:52:01.4620832Z d - dictionary on which to operate 2025-01-24T01:52:01.4621106Z keys - list or tuple giving the location of the value to be changed in d 2025-01-24T01:52:01.4621256Z func - function to operate on that value 2025-01-24T01:52:01.4621346Z 2025-01-24T01:52:01.4621588Z If keys == [k0,..,kX] and d[k0]..[kX] == v, update_in returns a copy of the 2025-01-24T01:52:01.4621834Z original dictionary with v replaced by func(v), but does not mutate the 2025-01-24T01:52:01.4621945Z original dictionary. 2025-01-24T01:52:01.4622055Z 2025-01-24T01:52:01.4622274Z If k0 is not a key in d, update_in creates nested dictionaries to the depth 2025-01-24T01:52:01.4622516Z specified by the keys, with the innermost value set to func(default). 2025-01-24T01:52:01.4622606Z 2025-01-24T01:52:01.4622711Z >>> inc = lambda x: x + 1 2025-01-24T01:52:01.4622839Z >>> update_in({"a": 0}, ["a"], inc) 2025-01-24T01:52:01.4622934Z {'a': 1} 2025-01-24T01:52:01.4623036Z 2025-01-24T01:52:01.4623143Z >>> transaction = { 2025-01-24T01:52:01.4623261Z ... "name": "Alice", 2025-01-24T01:52:01.4623452Z ... "purchase": {"items": ["Apple", "Orange"], "costs": [0.50, 1.25]}, 2025-01-24T01:52:01.4623578Z ... "credit card": "5555-1234-1234-1234", 2025-01-24T01:52:01.4623687Z ... } 2025-01-24T01:52:01.4623910Z >>> update_in(transaction, ["purchase", "costs"], sum) # doctest: +SKIP 2025-01-24T01:52:01.4624044Z {'credit card': '5555-1234-1234-1234', 2025-01-24T01:52:01.4624141Z 'name': 'Alice', 2025-01-24T01:52:01.4624316Z 'purchase': {'costs': 1.75, 'items': ['Apple', 'Orange']}} 2025-01-24T01:52:01.4624419Z 2025-01-24T01:52:01.4624548Z >>> # updating a value when k0 is not in d 2025-01-24T01:52:01.4624697Z >>> update_in({}, [1, 2, 3], str, default="bar") 2025-01-24T01:52:01.4624793Z {1: {2: {3: 'bar'}}} 2025-01-24T01:52:01.4624928Z >>> update_in({1: "foo"}, [2, 3, 4], inc, 0) 2025-01-24T01:52:01.4625029Z {1: 'foo', 2: {3: {4: 1}}} 2025-01-24T01:52:01.4625122Z 2025-01-24T01:52:01.4625392Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:52:01.4625484Z 2025-01-24T01:52:01.4625604Z warnings.warn(msg) 2025-01-24T01:52:01.4625693Z 2025-01-24T01:52:01.4625889Z --- Parse Warning: 79 / 106 --- 2025-01-24T01:52:01.4627007Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=get_in in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/unification_tools.py line=320. 2025-01-24T01:52:01.4627279Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:52:01.4627467Z Returns coll[i0][i1]...[iX] where [i0, i1, ..., iX]==keys. 2025-01-24T01:52:01.4627559Z 2025-01-24T01:52:01.4627757Z If coll[i0][i1]...[iX] cannot be found, returns ``default``, unless 2025-01-24T01:52:01.4627974Z ``no_default`` is specified, then it raises KeyError or IndexError. 2025-01-24T01:52:01.4628067Z 2025-01-24T01:52:01.4628291Z ``get_in`` is a generalization of ``operator.getitem`` for nested data 2025-01-24T01:52:01.4628499Z structures such as dictionaries and lists. 2025-01-24T01:52:01.4628599Z 2025-01-24T01:52:01.4628701Z >>> transaction = { 2025-01-24T01:52:01.4628816Z ... "name": "Alice", 2025-01-24T01:52:01.4629010Z ... "purchase": {"items": ["Apple", "Orange"], "costs": [0.50, 1.25]}, 2025-01-24T01:52:01.4629136Z ... "credit card": "5555-1234-1234-1234", 2025-01-24T01:52:01.4629236Z ... } 2025-01-24T01:52:01.4629381Z >>> get_in(["purchase", "items", 0], transaction) 2025-01-24T01:52:01.4629491Z 'Apple' 2025-01-24T01:52:01.4629606Z >>> get_in(["name"], transaction) 2025-01-24T01:52:01.4629700Z 'Alice' 2025-01-24T01:52:01.4629851Z >>> get_in(["purchase", "total"], transaction) 2025-01-24T01:52:01.4630031Z >>> get_in(["purchase", "items", "apple"], transaction) 2025-01-24T01:52:01.4630188Z >>> get_in(["purchase", "items", 10], transaction) 2025-01-24T01:52:01.4630356Z >>> get_in(["purchase", "total"], transaction, 0) 2025-01-24T01:52:01.4630466Z 0 2025-01-24T01:52:01.4630585Z >>> get_in(["y"], {}, no_default=True) 2025-01-24T01:52:01.4630708Z Traceback (most recent call last): 2025-01-24T01:52:01.4630809Z ... 2025-01-24T01:52:01.4630911Z KeyError: 'y' 2025-01-24T01:52:01.4631014Z 2025-01-24T01:52:01.4631107Z See Also: 2025-01-24T01:52:01.4631211Z itertoolz.get 2025-01-24T01:52:01.4631325Z operator.getitem 2025-01-24T01:52:01.4631416Z 2025-01-24T01:52:01.4631689Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:52:01.4631780Z 2025-01-24T01:52:01.4631884Z warnings.warn(msg) 2025-01-24T01:52:01.4631986Z 2025-01-24T01:52:01.4632192Z --- Parse Warning: 80 / 106 --- 2025-01-24T01:52:01.4633232Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=groupby in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/unification_tools.py line=373. 2025-01-24T01:52:01.4633512Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:52:01.4633647Z Group a collection by a key function 2025-01-24T01:52:01.4633738Z 2025-01-24T01:52:01.4633913Z >>> names = ["Alice", "Bob", "Charlie", "Dan", "Edith", "Frank"] 2025-01-24T01:52:01.4634054Z >>> groupby(len, names) # doctest: +SKIP 2025-01-24T01:52:01.4634221Z {3: ['Bob', 'Dan'], 5: ['Alice', 'Edith', 'Frank'], 7: ['Charlie']} 2025-01-24T01:52:01.4634327Z 2025-01-24T01:52:01.4634441Z >>> iseven = lambda x: x % 2 == 0 2025-01-24T01:52:01.4634632Z >>> groupby(iseven, [1, 2, 3, 4, 5, 6, 7, 8]) # doctest: +SKIP 2025-01-24T01:52:01.4634753Z {False: [1, 3, 5, 7], True: [2, 4, 6, 8]} 2025-01-24T01:52:01.4634842Z 2025-01-24T01:52:01.4635004Z Non-callable keys imply grouping on a member. 2025-01-24T01:52:01.4635099Z 2025-01-24T01:52:01.4635208Z >>> groupby( 2025-01-24T01:52:01.4641642Z ... "gender", 2025-01-24T01:52:01.4641789Z ... [ 2025-01-24T01:52:01.4641943Z ... {"name": "Alice", "gender": "F"}, 2025-01-24T01:52:01.4642071Z ... {"name": "Bob", "gender": "M"}, 2025-01-24T01:52:01.4642209Z ... {"name": "Charlie", "gender": "M"}, 2025-01-24T01:52:01.4642305Z ... ], 2025-01-24T01:52:01.4642407Z ... ) # doctest:+SKIP 2025-01-24T01:52:01.4642541Z {'F': [{'gender': 'F', 'name': 'Alice'}], 2025-01-24T01:52:01.4642659Z 'M': [{'gender': 'M', 'name': 'Bob'}, 2025-01-24T01:52:01.4642791Z {'gender': 'M', 'name': 'Charlie'}]} 2025-01-24T01:52:01.4642873Z 2025-01-24T01:52:01.4643039Z Not to be confused with ``itertools.groupby`` 2025-01-24T01:52:01.4643137Z 2025-01-24T01:52:01.4643230Z See Also: 2025-01-24T01:52:01.4643337Z countby 2025-01-24T01:52:01.4643506Z 2025-01-24T01:52:01.4643804Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:52:01.4643903Z 2025-01-24T01:52:01.4644011Z warnings.warn(msg) 2025-01-24T01:52:01.4644105Z 2025-01-24T01:52:01.4644356Z --- Parse Warning: 81 / 106 --- 2025-01-24T01:52:01.4645278Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=SyncBatchNorm in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/batchnorm.py line=601. 2025-01-24T01:52:01.4645558Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:52:01.4645745Z Applies Batch Normalization over a N-Dimensional input. 2025-01-24T01:52:01.4645840Z 2025-01-24T01:52:01.4646223Z The N-D input is a mini-batch of [N-2]D inputs with additional channel dimension) as described in the paper 2025-01-24T01:52:01.4646495Z `Batch Normalization: Accelerating Deep Network Training by Reducing 2025-01-24T01:52:01.4646720Z Internal Covariate Shift `__ . 2025-01-24T01:52:01.4646818Z 2025-01-24T01:52:01.4646921Z .. math:: 2025-01-24T01:52:01.4647009Z 2025-01-24T01:52:01.4647246Z y = \frac{x - \mathrm{E}[x]}{ \sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta 2025-01-24T01:52:01.4647334Z 2025-01-24T01:52:01.4647577Z The mean and standard-deviation are calculated per-dimension over all 2025-01-24T01:52:01.4647815Z mini-batches of the same process groups. :math:`\gamma` and :math:`\beta` 2025-01-24T01:52:01.4648061Z are learnable parameter vectors of size `C` (where `C` is the input size). 2025-01-24T01:52:01.4648258Z By default, the elements of :math:`\gamma` are sampled from 2025-01-24T01:52:01.4648466Z :math:`\mathcal{U}(0, 1)` and the elements of :math:`\beta` are set to 0. 2025-01-24T01:52:01.4648749Z The standard-deviation is calculated via the biased estimator, equivalent to 2025-01-24T01:52:01.4648876Z `torch.var(input, unbiased=False)`. 2025-01-24T01:52:01.4648973Z 2025-01-24T01:52:01.4649220Z Also by default, during training this layer keeps running estimates of its 2025-01-24T01:52:01.4649458Z computed mean and variance, which are then used for normalization during 2025-01-24T01:52:01.4649723Z evaluation. The running estimates are kept with a default :attr:`momentum` 2025-01-24T01:52:01.4649821Z of 0.1. 2025-01-24T01:52:01.4649922Z 2025-01-24T01:52:01.4650155Z If :attr:`track_running_stats` is set to ``False``, this layer then does not 2025-01-24T01:52:01.4650393Z keep running estimates, and batch statistics are instead used during 2025-01-24T01:52:01.4650509Z evaluation time as well. 2025-01-24T01:52:01.4650595Z 2025-01-24T01:52:01.4650701Z .. note:: 2025-01-24T01:52:01.4650933Z This :attr:`momentum` argument is different from one used in optimizer 2025-01-24T01:52:01.4651178Z classes and the conventional notion of momentum. Mathematically, the 2025-01-24T01:52:01.4651324Z update rule for running statistics here is 2025-01-24T01:52:01.4651601Z :math:`\hat{x}_\text{new} = (1 - \text{momentum}) \times \hat{x} + \text{momentum} \times x_t`, 2025-01-24T01:52:01.4651812Z where :math:`\hat{x}` is the estimated statistic and :math:`x_t` is the 2025-01-24T01:52:01.4651921Z new observed value. 2025-01-24T01:52:01.4652019Z 2025-01-24T01:52:01.4652324Z Because the Batch Normalization is done for each channel in the ``C`` dimension, computing 2025-01-24T01:52:01.4652591Z statistics on ``(N, +)`` slices, it's common terminology to call this Volumetric Batch 2025-01-24T01:52:01.4652780Z Normalization or Spatio-temporal Batch Normalization. 2025-01-24T01:52:01.4652880Z 2025-01-24T01:52:01.4653037Z Currently :class:`SyncBatchNorm` only supports 2025-01-24T01:52:01.4653391Z :class:`~torch.nn.DistributedDataParallel` (DDP) with single GPU per process. Use 2025-01-24T01:52:01.4653620Z :meth:`torch.nn.SyncBatchNorm.convert_sync_batchnorm()` to convert 2025-01-24T01:52:01.4653832Z :attr:`BatchNorm*D` layer to :class:`SyncBatchNorm` before wrapping 2025-01-24T01:52:01.4653941Z Network with DDP. 2025-01-24T01:52:01.4654033Z 2025-01-24T01:52:01.4654126Z Args: 2025-01-24T01:52:01.4654310Z num_features: :math:`C` from an expected input of size 2025-01-24T01:52:01.4654414Z :math:`(N, C, +)` 2025-01-24T01:52:01.4654616Z eps: a value added to the denominator for numerical stability. 2025-01-24T01:52:01.4654719Z Default: ``1e-5`` 2025-01-24T01:52:01.4654949Z momentum: the value used for the running_mean and running_var 2025-01-24T01:52:01.4655167Z computation. Can be set to ``None`` for cumulative moving average 2025-01-24T01:52:01.4655326Z (i.e. simple average). Default: 0.1 2025-01-24T01:52:01.4655543Z affine: a boolean value that when set to ``True``, this module has 2025-01-24T01:52:01.4655704Z learnable affine parameters. Default: ``True`` 2025-01-24T01:52:01.4655933Z track_running_stats: a boolean value that when set to ``True``, this 2025-01-24T01:52:01.4656168Z module tracks the running mean and variance, and when set to ``False``, 2025-01-24T01:52:01.4656404Z this module does not track such statistics, and initializes statistics 2025-01-24T01:52:01.4656611Z buffers :attr:`running_mean` and :attr:`running_var` as ``None``. 2025-01-24T01:52:01.4656848Z When these buffers are ``None``, this module always uses batch statistics. 2025-01-24T01:52:01.4657013Z in both training and eval modes. Default: ``True`` 2025-01-24T01:52:01.4657269Z process_group: synchronization of stats happen within each process group 2025-01-24T01:52:01.4657514Z individually. Default behavior is synchronization across the whole 2025-01-24T01:52:01.4657609Z world 2025-01-24T01:52:01.4657705Z 2025-01-24T01:52:01.4657797Z Shape: 2025-01-24T01:52:01.4657912Z - Input: :math:`(N, C, +)` 2025-01-24T01:52:01.4658075Z - Output: :math:`(N, C, +)` (same shape as input) 2025-01-24T01:52:01.4658168Z 2025-01-24T01:52:01.4658269Z .. note:: 2025-01-24T01:52:01.4658522Z Synchronization of batchnorm statistics occurs only while training, i.e. 2025-01-24T01:52:01.4658732Z synchronization is disabled when ``model.eval()`` is set or if 2025-01-24T01:52:01.4658882Z ``self.training`` is otherwise ``False``. 2025-01-24T01:52:01.4658972Z 2025-01-24T01:52:01.4659077Z Examples:: 2025-01-24T01:52:01.4659168Z 2025-01-24T01:52:01.4659279Z >>> # xdoctest: +SKIP 2025-01-24T01:52:01.4659402Z >>> # With Learnable Parameters 2025-01-24T01:52:01.4659521Z >>> m = nn.SyncBatchNorm(100) 2025-01-24T01:52:01.4659659Z >>> # creating process group (optional) 2025-01-24T01:52:01.4659810Z >>> # ranks is a list of int identifying rank ids. 2025-01-24T01:52:01.4659927Z >>> ranks = list(range(8)) 2025-01-24T01:52:01.4660043Z >>> r1, r2 = ranks[:4], ranks[4:] 2025-01-24T01:52:01.4660194Z >>> # Note: every rank calls into new_group for every 2025-01-24T01:52:01.4660360Z >>> # process group created, even if that rank is not 2025-01-24T01:52:01.4660464Z >>> # part of the group. 2025-01-24T01:52:01.4660723Z >>> process_groups = [torch.distributed.new_group(pids) for pids in [r1, r2]] 2025-01-24T01:52:01.4660932Z >>> process_group = process_groups[0 if dist.get_rank() <= 3 else 1] 2025-01-24T01:52:01.4661062Z >>> # Without Learnable Parameters 2025-01-24T01:52:01.4661345Z >>> m = nn.BatchNorm3d(100, affine=False, process_group=process_group) 2025-01-24T01:52:01.4661475Z >>> input = torch.randn(20, 100, 35, 45, 10) 2025-01-24T01:52:01.4661585Z >>> output = m(input) 2025-01-24T01:52:01.4661674Z 2025-01-24T01:52:01.4661808Z >>> # network is nn.BatchNorm layer 2025-01-24T01:52:01.4662088Z >>> sync_bn_network = nn.SyncBatchNorm.convert_sync_batchnorm(network, process_group) 2025-01-24T01:52:01.4662270Z >>> # only single gpu per process is currently supported 2025-01-24T01:52:01.4662491Z >>> ddp_sync_bn_network = torch.nn.parallel.DistributedDataParallel( 2025-01-24T01:52:01.4662613Z >>> sync_bn_network, 2025-01-24T01:52:01.4662783Z >>> device_ids=[args.local_rank], 2025-01-24T01:52:01.4662919Z >>> output_device=args.local_rank) 2025-01-24T01:52:01.4663044Z 2025-01-24T01:52:01.4663310Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:52:01.4663410Z 2025-01-24T01:52:01.4663519Z warnings.warn(msg) 2025-01-24T01:52:01.4663607Z 2025-01-24T01:52:01.4663832Z --- Parse Warning: 82 / 106 --- 2025-01-24T01:52:01.4664856Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=SyncBatchNorm.convert_sync_batchnorm in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/batchnorm.py line=824. 2025-01-24T01:52:01.4665137Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:52:01.4665454Z Converts all :attr:`BatchNorm*D` layers in the model to :class:`torch.nn.SyncBatchNorm` layers. 2025-01-24T01:52:01.4665554Z 2025-01-24T01:52:01.4665649Z Args: 2025-01-24T01:52:01.4665902Z module (nn.Module): module containing one or more :attr:`BatchNorm*D` layers 2025-01-24T01:52:01.4666137Z process_group (optional): process group to scope synchronization, 2025-01-24T01:52:01.4666380Z default is the whole world 2025-01-24T01:52:01.4666486Z 2025-01-24T01:52:01.4666581Z Returns: 2025-01-24T01:52:01.4666849Z The original :attr:`module` with the converted :class:`torch.nn.SyncBatchNorm` 2025-01-24T01:52:01.4667072Z layers. If the original :attr:`module` is a :attr:`BatchNorm*D` layer, 2025-01-24T01:52:01.4667284Z a new :class:`torch.nn.SyncBatchNorm` layer object will be returned 2025-01-24T01:52:01.4667385Z instead. 2025-01-24T01:52:01.4667476Z 2025-01-24T01:52:01.4667583Z Example:: 2025-01-24T01:52:01.4667677Z 2025-01-24T01:52:01.4667814Z >>> # Network with nn.BatchNorm layer 2025-01-24T01:52:01.4667966Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2025-01-24T01:52:01.4668093Z >>> module = torch.nn.Sequential( 2025-01-24T01:52:01.4668232Z >>> torch.nn.Linear(20, 100), 2025-01-24T01:52:01.4668363Z >>> torch.nn.BatchNorm1d(100), 2025-01-24T01:52:01.4668471Z >>> ).cuda() 2025-01-24T01:52:01.4668604Z >>> # creating process group (optional) 2025-01-24T01:52:01.4668755Z >>> # ranks is a list of int identifying rank ids. 2025-01-24T01:52:01.4668875Z >>> ranks = list(range(8)) 2025-01-24T01:52:01.4668999Z >>> r1, r2 = ranks[:4], ranks[4:] 2025-01-24T01:52:01.4669164Z >>> # Note: every rank calls into new_group for every 2025-01-24T01:52:01.4669325Z >>> # process group created, even if that rank is not 2025-01-24T01:52:01.4669444Z >>> # part of the group. 2025-01-24T01:52:01.4669575Z >>> # xdoctest: +SKIP("distributed") 2025-01-24T01:52:01.4669833Z >>> process_groups = [torch.distributed.new_group(pids) for pids in [r1, r2]] 2025-01-24T01:52:01.4670116Z >>> process_group = process_groups[0 if dist.get_rank() <= 3 else 1] 2025-01-24T01:52:01.4670419Z >>> sync_bn_module = torch.nn.SyncBatchNorm.convert_sync_batchnorm(module, process_group) 2025-01-24T01:52:01.4670517Z 2025-01-24T01:52:01.4670612Z 2025-01-24T01:52:01.4670879Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:52:01.4670971Z 2025-01-24T01:52:01.4671077Z warnings.warn(msg) 2025-01-24T01:52:01.4671176Z 2025-01-24T01:52:01.4671388Z --- Parse Warning: 83 / 106 --- 2025-01-24T01:52:01.4672304Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=Unflatten in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/flatten.py line=60. 2025-01-24T01:52:01.4672578Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:52:01.4672694Z 2025-01-24T01:52:01.4673025Z Unflattens a tensor dim expanding it to a desired shape. For use with :class:`~nn.Sequential`. 2025-01-24T01:52:01.4673114Z 2025-01-24T01:52:01.4673399Z * :attr:`dim` specifies the dimension of the input tensor to be unflattened, and it can 2025-01-24T01:52:01.4673631Z be either `int` or `str` when `Tensor` or `NamedTensor` is used, respectively. 2025-01-24T01:52:01.4673730Z 2025-01-24T01:52:01.4674048Z * :attr:`unflattened_size` is the new shape of the unflattened dimension of the tensor and it can be 2025-01-24T01:52:01.4674310Z a `tuple` of ints or a `list` of ints or `torch.Size` for `Tensor` input; a `NamedShape` 2025-01-24T01:52:01.4674483Z (tuple of `(name, size)` tuples) for `NamedTensor` input. 2025-01-24T01:52:01.4674581Z 2025-01-24T01:52:01.4674672Z Shape: 2025-01-24T01:52:01.4674893Z - Input: :math:`(*, S_{\text{dim}}, *)`, where :math:`S_{\text{dim}}` is the size at 2025-01-24T01:52:01.4675163Z dimension :attr:`dim` and :math:`*` means any number of dimensions including none. 2025-01-24T01:52:01.4675385Z - Output: :math:`(*, U_1, ..., U_n, *)`, where :math:`U` = :attr:`unflattened_size` and 2025-01-24T01:52:01.4675529Z :math:`\prod_{i=1}^n U_i = S_{\text{dim}}`. 2025-01-24T01:52:01.4675619Z 2025-01-24T01:52:01.4675706Z Args: 2025-01-24T01:52:01.4675875Z dim (Union[int, str]): Dimension to be unflattened 2025-01-24T01:52:01.4676222Z unflattened_size (Union[torch.Size, Tuple, List, NamedShape]): New shape of the unflattened dimension 2025-01-24T01:52:01.4676321Z 2025-01-24T01:52:01.4676417Z Examples: 2025-01-24T01:52:01.4676538Z >>> input = torch.randn(2, 50) 2025-01-24T01:52:01.4676640Z >>> # With tuple of ints 2025-01-24T01:52:01.4676744Z >>> m = nn.Sequential( 2025-01-24T01:52:01.4676858Z >>> nn.Linear(50, 50), 2025-01-24T01:52:01.4676969Z >>> nn.Unflatten(1, (2, 5, 5)) 2025-01-24T01:52:01.4677072Z >>> ) 2025-01-24T01:52:01.4677176Z >>> output = m(input) 2025-01-24T01:52:01.4677273Z >>> output.size() 2025-01-24T01:52:01.4677387Z torch.Size([2, 2, 5, 5]) 2025-01-24T01:52:01.4677487Z >>> # With torch.Size 2025-01-24T01:52:01.4677599Z >>> m = nn.Sequential( 2025-01-24T01:52:01.4677702Z >>> nn.Linear(50, 50), 2025-01-24T01:52:01.4677841Z >>> nn.Unflatten(1, torch.Size([2, 5, 5])) 2025-01-24T01:52:01.4677932Z >>> ) 2025-01-24T01:52:01.4678030Z >>> output = m(input) 2025-01-24T01:52:01.4678138Z >>> output.size() 2025-01-24T01:52:01.4678239Z torch.Size([2, 2, 5, 5]) 2025-01-24T01:52:01.4678380Z >>> # With namedshape (tuple of tuples) 2025-01-24T01:52:01.4678536Z >>> input = torch.randn(2, 50, names=('N', 'features')) 2025-01-24T01:52:01.4678746Z >>> unflatten = nn.Unflatten('features', (('C', 2), ('H', 5), ('W', 5))) 2025-01-24T01:52:01.4678899Z >>> output = unflatten(input) 2025-01-24T01:52:01.4679022Z >>> output.size() 2025-01-24T01:52:01.4679304Z torch.Size([2, 2, 5, 5]) 2025-01-24T01:52:01.4679393Z 2025-01-24T01:52:01.4679663Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:52:01.4679755Z 2025-01-24T01:52:01.4679856Z warnings.warn(msg) 2025-01-24T01:52:01.4679958Z 2025-01-24T01:52:01.4680159Z --- Parse Warning: 84 / 106 --- 2025-01-24T01:52:01.4681162Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=TripletMarginWithDistanceLoss in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py line=1698. 2025-01-24T01:52:01.4681495Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:52:01.4681710Z Creates a criterion that measures the triplet loss given input 2025-01-24T01:52:01.4681952Z tensors :math:`a`, :math:`p`, and :math:`n` (representing anchor, 2025-01-24T01:52:01.4682176Z positive, and negative examples, respectively), and a nonnegative, 2025-01-24T01:52:01.4682446Z real-valued function ("distance function") used to compute the relationship 2025-01-24T01:52:01.4682674Z between the anchor and positive example ("positive distance") and the 2025-01-24T01:52:01.4682842Z anchor and negative example ("negative distance"). 2025-01-24T01:52:01.4682931Z 2025-01-24T01:52:01.4683150Z The unreduced loss (i.e., with :attr:`reduction` set to ``'none'``) 2025-01-24T01:52:01.4683257Z can be described as: 2025-01-24T01:52:01.4683344Z 2025-01-24T01:52:01.4683452Z .. math:: 2025-01-24T01:52:01.4683591Z \ell(a, p, n) = L = \{l_1,\dots,l_N\}^\top, \quad 2025-01-24T01:52:01.4683766Z l_i = \max \{d(a_i, p_i) - d(a_i, n_i) + {\rm margin}, 0\} 2025-01-24T01:52:01.4683856Z 2025-01-24T01:52:01.4684111Z where :math:`N` is the batch size; :math:`d` is a nonnegative, real-valued function 2025-01-24T01:52:01.4684424Z quantifying the closeness of two tensors, referred to as the :attr:`distance_function`; 2025-01-24T01:52:01.4684665Z and :math:`margin` is a nonnegative margin representing the minimum difference 2025-01-24T01:52:01.4684926Z between the positive and negative distances that is required for the loss to 2025-01-24T01:52:01.4685155Z be 0. The input tensors have :math:`N` elements each and can be of any shape 2025-01-24T01:52:01.4685292Z that the distance function can handle. 2025-01-24T01:52:01.4685379Z 2025-01-24T01:52:01.4685501Z If :attr:`reduction` is not ``'none'`` 2025-01-24T01:52:01.4685621Z (default ``'mean'``), then: 2025-01-24T01:52:01.4685709Z 2025-01-24T01:52:01.4685817Z .. math:: 2025-01-24T01:52:01.4685914Z \ell(x, y) = 2025-01-24T01:52:01.4686008Z \begin{cases} 2025-01-24T01:52:01.4686230Z \operatorname{mean}(L), & \text{if reduction} = \text{`mean';}\\ 2025-01-24T01:52:01.4686425Z \operatorname{sum}(L), & \text{if reduction} = \text{`sum'.} 2025-01-24T01:52:01.4686533Z \end{cases} 2025-01-24T01:52:01.4686615Z 2025-01-24T01:52:01.4686861Z See also :class:`~torch.nn.TripletMarginLoss`, which computes the triplet 2025-01-24T01:52:01.4687117Z loss for input tensors using the :math:`l_p` distance as the distance function. 2025-01-24T01:52:01.4687205Z 2025-01-24T01:52:01.4687309Z Args: 2025-01-24T01:52:01.4687585Z distance_function (Callable, optional): A nonnegative, real-valued function that 2025-01-24T01:52:01.4687792Z quantifies the closeness of two tensors. If not specified, 2025-01-24T01:52:01.4687967Z `nn.PairwiseDistance` will be used. Default: ``None`` 2025-01-24T01:52:01.4688248Z margin (float, optional): A nonnegative margin representing the minimum difference 2025-01-24T01:52:01.4688599Z between the positive and negative distances required for the loss to be 0. Larger 2025-01-24T01:52:01.4688886Z margins penalize cases where the negative examples are not distant enough from the 2025-01-24T01:52:01.4689077Z anchors, relative to the positives. Default: :math:`1`. 2025-01-24T01:52:01.4689328Z swap (bool, optional): Whether to use the distance swap described in the paper 2025-01-24T01:52:01.4689606Z `Learning shallow convolutional feature descriptors with triplet losses` by 2025-01-24T01:52:01.4689843Z V. Balntas, E. Riba et al. If True, and if the positive example is closer to the 2025-01-24T01:52:01.4690152Z negative example than the anchor is, swaps the positive example and the anchor in 2025-01-24T01:52:01.4690295Z the loss computation. Default: ``False``. 2025-01-24T01:52:01.4690587Z reduction (str, optional): Specifies the (optional) reduction to apply to the output: 2025-01-24T01:52:01.4690823Z ``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: no reduction will be applied, 2025-01-24T01:52:01.4691010Z ``'mean'``: the sum of the output will be divided by the number of 2025-01-24T01:52:01.4691263Z elements in the output, ``'sum'``: the output will be summed. Default: ``'mean'`` 2025-01-24T01:52:01.4691354Z 2025-01-24T01:52:01.4691452Z 2025-01-24T01:52:01.4691546Z Shape: 2025-01-24T01:52:01.4691792Z - Input: :math:`(N, *)` where :math:`*` represents any number of additional dimensions 2025-01-24T01:52:01.4691937Z as supported by the distance function. 2025-01-24T01:52:01.4692193Z - Output: A Tensor of shape :math:`(N)` if :attr:`reduction` is ``'none'``, or a scalar 2025-01-24T01:52:01.4692306Z otherwise. 2025-01-24T01:52:01.4692395Z 2025-01-24T01:52:01.4692504Z Examples:: 2025-01-24T01:52:01.4692598Z 2025-01-24T01:52:01.4692706Z >>> # Initialize embeddings 2025-01-24T01:52:01.4692843Z >>> embedding = nn.Embedding(1000, 128) 2025-01-24T01:52:01.4692976Z >>> anchor_ids = torch.randint(0, 1000, (1,)) 2025-01-24T01:52:01.4693123Z >>> positive_ids = torch.randint(0, 1000, (1,)) 2025-01-24T01:52:01.4693260Z >>> negative_ids = torch.randint(0, 1000, (1,)) 2025-01-24T01:52:01.4693383Z >>> anchor = embedding(anchor_ids) 2025-01-24T01:52:01.4693507Z >>> positive = embedding(positive_ids) 2025-01-24T01:52:01.4693627Z >>> negative = embedding(negative_ids) 2025-01-24T01:52:01.4693724Z >>> 2025-01-24T01:52:01.4693838Z >>> # Built-in Distance Function 2025-01-24T01:52:01.4693950Z >>> triplet_loss = \ 2025-01-24T01:52:01.4694241Z >>> nn.TripletMarginWithDistanceLoss(distance_function=nn.PairwiseDistance()) 2025-01-24T01:52:01.4694398Z >>> output = triplet_loss(anchor, positive, negative) 2025-01-24T01:52:01.4694517Z >>> output.backward() 2025-01-24T01:52:01.4694607Z >>> 2025-01-24T01:52:01.4694728Z >>> # Custom Distance Function 2025-01-24T01:52:01.4694836Z >>> def l_infinity(x1, x2): 2025-01-24T01:52:01.4695003Z >>> return torch.max(torch.abs(x1 - x2), dim=1).values 2025-01-24T01:52:01.4695093Z >>> 2025-01-24T01:52:01.4695283Z >>> # xdoctest: +SKIP("FIXME: Would call backwards a second time") 2025-01-24T01:52:01.4695404Z >>> triplet_loss = ( 2025-01-24T01:52:01.4695686Z >>> nn.TripletMarginWithDistanceLoss(distance_function=l_infinity, margin=1.5)) 2025-01-24T01:52:01.4695853Z >>> output = triplet_loss(anchor, positive, negative) 2025-01-24T01:52:01.4695956Z >>> output.backward() 2025-01-24T01:52:01.4696042Z >>> 2025-01-24T01:52:01.4696178Z >>> # Custom Distance Function (Lambda) 2025-01-24T01:52:01.4696277Z >>> triplet_loss = ( 2025-01-24T01:52:01.4696429Z >>> nn.TripletMarginWithDistanceLoss( 2025-01-24T01:52:01.4696704Z >>> distance_function=lambda x, y: 1.0 - F.cosine_similarity(x, y))) 2025-01-24T01:52:01.4696873Z >>> output = triplet_loss(anchor, positive, negative) 2025-01-24T01:52:01.4696978Z >>> output.backward() 2025-01-24T01:52:01.4697061Z 2025-01-24T01:52:01.4697169Z Reference: 2025-01-24T01:52:01.4697476Z V. Balntas, et al.: Learning shallow convolutional feature descriptors with triplet losses: 2025-01-24T01:52:01.4697711Z https://bmva-archive.org.uk/bmvc/2016/papers/paper119/index.html 2025-01-24T01:52:01.4697796Z 2025-01-24T01:52:01.4698064Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 17)) 2025-01-24T01:52:01.4698165Z 2025-01-24T01:52:01.4698265Z warnings.warn(msg) 2025-01-24T01:52:01.4698394Z 2025-01-24T01:52:01.4698598Z --- Parse Warning: 85 / 106 --- 2025-01-24T01:52:01.4699509Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=MaxUnpool2d in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py line=395. 2025-01-24T01:52:01.4699808Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:52:01.4699966Z Computes a partial inverse of :class:`MaxPool2d`. 2025-01-24T01:52:01.4700064Z 2025-01-24T01:52:01.4700326Z :class:`MaxPool2d` is not fully invertible, since the non-maximal values are lost. 2025-01-24T01:52:01.4700426Z 2025-01-24T01:52:01.4700656Z :class:`MaxUnpool2d` takes in as input the output of :class:`MaxPool2d` 2025-01-24T01:52:01.4700915Z including the indices of the maximal values and computes a partial inverse 2025-01-24T01:52:01.4701072Z in which all non-maximal values are set to zero. 2025-01-24T01:52:01.4701158Z 2025-01-24T01:52:01.4701263Z Note: 2025-01-24T01:52:01.4701583Z This operation may behave nondeterministically when the input indices has repeat values. 2025-01-24T01:52:01.4701978Z See https://github.com/pytorch/pytorch/issues/80827 and :doc:`/notes/randomness` for more information. 2025-01-24T01:52:01.4702068Z 2025-01-24T01:52:01.4702313Z .. note:: :class:`MaxPool2d` can map several input sizes to the same output 2025-01-24T01:52:01.4702492Z sizes. Hence, the inversion process can get ambiguous. 2025-01-24T01:52:01.4702683Z To accommodate this, you can provide the needed output size 2025-01-24T01:52:01.4702911Z as an additional argument :attr:`output_size` in the forward call. 2025-01-24T01:52:01.4703039Z See the Inputs and Example below. 2025-01-24T01:52:01.4703145Z 2025-01-24T01:52:01.4703238Z Args: 2025-01-24T01:52:01.4703440Z kernel_size (int or tuple): Size of the max pooling window. 2025-01-24T01:52:01.4703617Z stride (int or tuple): Stride of the max pooling window. 2025-01-24T01:52:01.4703764Z It is set to :attr:`kernel_size` by default. 2025-01-24T01:52:01.4703967Z padding (int or tuple): Padding that was added to the input 2025-01-24T01:52:01.4704057Z 2025-01-24T01:52:01.4704159Z Inputs: 2025-01-24T01:52:01.4704288Z - `input`: the input Tensor to invert 2025-01-24T01:52:01.4704494Z - `indices`: the indices given out by :class:`~torch.nn.MaxPool2d` 2025-01-24T01:52:01.4704669Z - `output_size` (optional): the targeted output size 2025-01-24T01:52:01.4704752Z 2025-01-24T01:52:01.4704853Z Shape: 2025-01-24T01:52:01.4705049Z - Input: :math:`(N, C, H_{in}, W_{in})` or :math:`(C, H_{in}, W_{in})`. 2025-01-24T01:52:01.4705272Z - Output: :math:`(N, C, H_{out}, W_{out})` or :math:`(C, H_{out}, W_{out})`, where 2025-01-24T01:52:01.4705357Z 2025-01-24T01:52:01.4705451Z .. math:: 2025-01-24T01:52:01.4705759Z H_{out} = (H_{in} - 1) \times \text{stride[0]} - 2 \times \text{padding[0]} + \text{kernel\_size[0]} 2025-01-24T01:52:01.4705873Z 2025-01-24T01:52:01.4705977Z .. math:: 2025-01-24T01:52:01.4706321Z W_{out} = (W_{in} - 1) \times \text{stride[1]} - 2 \times \text{padding[1]} + \text{kernel\_size[1]} 2025-01-24T01:52:01.4706418Z 2025-01-24T01:52:01.4706590Z or as given by :attr:`output_size` in the call operator 2025-01-24T01:52:01.4706677Z 2025-01-24T01:52:01.4706783Z Example:: 2025-01-24T01:52:01.4706867Z 2025-01-24T01:52:01.4707040Z >>> pool = nn.MaxPool2d(2, stride=2, return_indices=True) 2025-01-24T01:52:01.4707175Z >>> unpool = nn.MaxUnpool2d(2, stride=2) 2025-01-24T01:52:01.4707311Z >>> input = torch.tensor([[[[ 1., 2., 3., 4.], 2025-01-24T01:52:01.4707463Z [ 5., 6., 7., 8.], 2025-01-24T01:52:01.4707577Z [ 9., 10., 11., 12.], 2025-01-24T01:52:01.4707731Z [13., 14., 15., 16.]]]]) 2025-01-24T01:52:01.4707849Z >>> output, indices = pool(input) 2025-01-24T01:52:01.4707962Z >>> unpool(output, indices) 2025-01-24T01:52:01.4708075Z tensor([[[[ 0., 0., 0., 0.], 2025-01-24T01:52:01.4708178Z [ 0., 6., 0., 8.], 2025-01-24T01:52:01.4708288Z [ 0., 0., 0., 0.], 2025-01-24T01:52:01.4708395Z [ 0., 14., 0., 16.]]]]) 2025-01-24T01:52:01.4708621Z >>> # Now using output_size to resolve an ambiguous size for the inverse 2025-01-24T01:52:01.4708766Z >>> input = torch.tensor([[[[ 1., 2., 3., 4., 5.], 2025-01-24T01:52:01.4708882Z [ 6., 7., 8., 9., 10.], 2025-01-24T01:52:01.4709004Z [11., 12., 13., 14., 15.], 2025-01-24T01:52:01.4709119Z [16., 17., 18., 19., 20.]]]]) 2025-01-24T01:52:01.4709251Z >>> output, indices = pool(input) 2025-01-24T01:52:01.4709428Z >>> # This call will not work without specifying output_size 2025-01-24T01:52:01.4709597Z >>> unpool(output, indices, output_size=input.size()) 2025-01-24T01:52:01.4709705Z tensor([[[[ 0., 0., 0., 0., 0.], 2025-01-24T01:52:01.4709807Z [ 0., 7., 0., 9., 0.], 2025-01-24T01:52:01.4709914Z [ 0., 0., 0., 0., 0.], 2025-01-24T01:52:01.4710018Z [ 0., 17., 0., 19., 0.]]]]) 2025-01-24T01:52:01.4710113Z 2025-01-24T01:52:01.4710199Z 2025-01-24T01:52:01.4710285Z 2025-01-24T01:52:01.4710550Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:52:01.4710639Z 2025-01-24T01:52:01.4710752Z warnings.warn(msg) 2025-01-24T01:52:01.4710835Z 2025-01-24T01:52:01.4711059Z --- Parse Warning: 86 / 106 --- 2025-01-24T01:52:01.4711960Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=EmbeddingBag in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/sparse.py line=270. 2025-01-24T01:52:01.4712227Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:52:01.4712554Z Compute sums or means of 'bags' of embeddings, without instantiating the intermediate embeddings. 2025-01-24T01:52:01.4712644Z 2025-01-24T01:52:01.4712973Z For bags of constant length, no :attr:`per_sample_weights`, no indices equal to :attr:`padding_idx`, 2025-01-24T01:52:01.4713084Z and with 2D inputs, this class 2025-01-24T01:52:01.4713182Z 2025-01-24T01:52:01.4713487Z * with ``mode="sum"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.sum(dim=1)``, 2025-01-24T01:52:01.4713800Z * with ``mode="mean"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.mean(dim=1)``, 2025-01-24T01:52:01.4714165Z * with ``mode="max"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.max(dim=1)``. 2025-01-24T01:52:01.4714253Z 2025-01-24T01:52:01.4714624Z However, :class:`~torch.nn.EmbeddingBag` is much more time and memory efficient than using a chain of these 2025-01-24T01:52:01.4714720Z operations. 2025-01-24T01:52:01.4714814Z 2025-01-24T01:52:01.4715077Z EmbeddingBag also supports per-sample weights as an argument to the forward 2025-01-24T01:52:01.4715320Z pass. This scales the output of the Embedding before performing a weighted 2025-01-24T01:52:01.4715580Z reduction as specified by ``mode``. If :attr:`per_sample_weights` is passed, the 2025-01-24T01:52:01.4715843Z only supported ``mode`` is ``"sum"``, which computes a weighted sum according to 2025-01-24T01:52:01.4715960Z :attr:`per_sample_weights`. 2025-01-24T01:52:01.4716070Z 2025-01-24T01:52:01.4716167Z Args: 2025-01-24T01:52:01.4716356Z num_embeddings (int): size of the dictionary of embeddings 2025-01-24T01:52:01.4716531Z embedding_dim (int): the size of each embedding vector 2025-01-24T01:52:01.4716853Z max_norm (float, optional): If given, each embedding vector with norm larger than :attr:`max_norm` 2025-01-24T01:52:01.4717011Z is renormalized to have norm :attr:`max_norm`. 2025-01-24T01:52:01.4717361Z norm_type (float, optional): The p of the p-norm to compute for the :attr:`max_norm` option. Default ``2``. 2025-01-24T01:52:01.4717688Z scale_grad_by_freq (bool, optional): if given, this will scale gradients by the inverse of frequency of 2025-01-24T01:52:01.4717864Z the words in the mini-batch. Default ``False``. 2025-01-24T01:52:01.4718048Z Note: this option is not supported when ``mode="max"``. 2025-01-24T01:52:01.4718312Z mode (str, optional): ``"sum"``, ``"mean"`` or ``"max"``. Specifies the way to reduce the bag. 2025-01-24T01:52:01.4718536Z ``"sum"`` computes the weighted sum, taking :attr:`per_sample_weights` 2025-01-24T01:52:01.4718759Z into consideration. ``"mean"`` computes the average of the values 2025-01-24T01:52:01.4718946Z in the bag, ``"max"`` computes the max value over each bag. 2025-01-24T01:52:01.4719063Z Default: ``"mean"`` 2025-01-24T01:52:01.4719394Z sparse (bool, optional): if ``True``, gradient w.r.t. :attr:`weight` matrix will be a sparse tensor. See 2025-01-24T01:52:01.4719653Z Notes for more details regarding sparse gradients. Note: this option is not 2025-01-24T01:52:01.4719790Z supported when ``mode="max"``. 2025-01-24T01:52:01.4720168Z include_last_offset (bool, optional): if ``True``, :attr:`offsets` has one additional element, where the last element 2025-01-24T01:52:01.4720399Z is equivalent to the size of `indices`. This matches the CSR format. 2025-01-24T01:52:01.4720731Z padding_idx (int, optional): If specified, the entries at :attr:`padding_idx` do not contribute to the 2025-01-24T01:52:01.4721007Z gradient; therefore, the embedding vector at :attr:`padding_idx` is not updated 2025-01-24T01:52:01.4721257Z during training, i.e. it remains as a fixed "pad". For a newly constructed 2025-01-24T01:52:01.4721531Z EmbeddingBag, the embedding vector at :attr:`padding_idx` will default to all 2025-01-24T01:52:01.4721786Z zeros, but can be updated to another value to be used as the padding vector. 2025-01-24T01:52:01.4722088Z Note that the embedding vector at :attr:`padding_idx` is excluded from the 2025-01-24T01:52:01.4722207Z reduction. 2025-01-24T01:52:01.4722293Z 2025-01-24T01:52:01.4722398Z Attributes: 2025-01-24T01:52:01.4722717Z weight (Tensor): the learnable weights of the module of shape `(num_embeddings, embedding_dim)` 2025-01-24T01:52:01.4722862Z initialized from :math:`\mathcal{N}(0, 1)`. 2025-01-24T01:52:01.4722960Z 2025-01-24T01:52:01.4723055Z Examples:: 2025-01-24T01:52:01.4723147Z 2025-01-24T01:52:01.4723325Z >>> # an EmbeddingBag module containing 10 tensors of size 3 2025-01-24T01:52:01.4723491Z >>> embedding_sum = nn.EmbeddingBag(10, 3, mode='sum') 2025-01-24T01:52:01.4723737Z >>> # a batch of 2 samples of 4 indices each 2025-01-24T01:52:01.4723931Z >>> input = torch.tensor([1, 2, 4, 5, 4, 3, 2, 9], dtype=torch.long) 2025-01-24T01:52:01.4724121Z >>> offsets = torch.tensor([0, 4], dtype=torch.long) 2025-01-24T01:52:01.4724271Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2025-01-24T01:52:01.4724403Z >>> embedding_sum(input, offsets) 2025-01-24T01:52:01.4724518Z tensor([[-0.8861, -5.4350, -0.0523], 2025-01-24T01:52:01.4724630Z [ 1.1306, -2.5798, -1.0044]]) 2025-01-24T01:52:01.4724727Z 2025-01-24T01:52:01.4724844Z >>> # Example with padding_idx 2025-01-24T01:52:01.4725065Z >>> embedding_sum = nn.EmbeddingBag(10, 3, mode='sum', padding_idx=2) 2025-01-24T01:52:01.4725256Z >>> input = torch.tensor([2, 2, 2, 2, 4, 3, 2, 9], dtype=torch.long) 2025-01-24T01:52:01.4725422Z >>> offsets = torch.tensor([0, 4], dtype=torch.long) 2025-01-24T01:52:01.4725542Z >>> embedding_sum(input, offsets) 2025-01-24T01:52:01.4725647Z tensor([[ 0.0000, 0.0000, 0.0000], 2025-01-24T01:52:01.4725772Z [-0.7082, 3.2145, -2.6251]]) 2025-01-24T01:52:01.4725856Z 2025-01-24T01:52:01.4726039Z >>> # An EmbeddingBag can be loaded from an Embedding like so 2025-01-24T01:52:01.4726189Z >>> embedding = nn.Embedding(10, 3, padding_idx=2) 2025-01-24T01:52:01.4726360Z >>> embedding_sum = nn.EmbeddingBag.from_pretrained( 2025-01-24T01:52:01.4726466Z embedding.weight, 2025-01-24T01:52:01.4726596Z padding_idx=embedding.padding_idx, 2025-01-24T01:52:01.4726706Z mode='sum') 2025-01-24T01:52:01.4726794Z 2025-01-24T01:52:01.4727061Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:52:01.4727145Z 2025-01-24T01:52:01.4727244Z warnings.warn(msg) 2025-01-24T01:52:01.4727344Z 2025-01-24T01:52:01.4727542Z --- Parse Warning: 87 / 106 --- 2025-01-24T01:52:01.4728572Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=DistributedDataParallel.join in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/distributed.py line=1759. 2025-01-24T01:52:01.4728846Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:52:01.4728940Z 2025-01-24T01:52:01.4729177Z Context manager for training with uneven inputs across processes in DDP. 2025-01-24T01:52:01.4729264Z 2025-01-24T01:52:01.4729496Z This context manager will keep track of already-joined DDP processes, 2025-01-24T01:52:01.4729708Z and "shadow" the forward and backward passes by inserting collective 2025-01-24T01:52:01.4729952Z communication operations to match with the ones created by non-joined 2025-01-24T01:52:01.4730192Z DDP processes. This will ensure each collective call has a corresponding 2025-01-24T01:52:01.4730420Z call by already-joined DDP processes, preventing hangs or errors that 2025-01-24T01:52:01.4730682Z would otherwise happen when training with uneven inputs across 2025-01-24T01:52:01.4730922Z processes. Alternatively, if the flag ``throw_on_early_termination`` is 2025-01-24T01:52:01.4731150Z specified to be ``True``, all trainers will throw an error once one rank 2025-01-24T01:52:01.4731356Z runs out of inputs, allowing these errors to be caught and handled 2025-01-24T01:52:01.4731479Z according to application logic. 2025-01-24T01:52:01.4731567Z 2025-01-24T01:52:01.4731795Z Once all DDP processes have joined, the context manager will broadcast 2025-01-24T01:52:01.4732028Z the model corresponding to the last joined process to all processes to 2025-01-24T01:52:01.4732179Z ensure the model is the same across all processes 2025-01-24T01:52:01.4732294Z (which is guaranteed by DDP). 2025-01-24T01:52:01.4732408Z 2025-01-24T01:52:01.4732620Z To use this to enable training with uneven inputs across processes, 2025-01-24T01:52:01.4732876Z simply wrap this context manager around your training loop. No further 2025-01-24T01:52:01.4733068Z modifications to the model or data loading is required. 2025-01-24T01:52:01.4733155Z 2025-01-24T01:52:01.4733251Z .. warning:: 2025-01-24T01:52:01.4733477Z If the model or training loop this context manager is wrapped around 2025-01-24T01:52:01.4733663Z has additional distributed collective operations, such as 2025-01-24T01:52:01.4733859Z ``SyncBatchNorm`` in the model's forward pass, then the flag 2025-01-24T01:52:01.4734069Z ``throw_on_early_termination`` must be enabled. This is because this 2025-01-24T01:52:01.4734280Z context manager is not aware of non-DDP collective communication. 2025-01-24T01:52:01.4734461Z This flag will cause all ranks to throw when any one rank 2025-01-24T01:52:01.4734675Z exhausts inputs, allowing these errors to be caught and recovered 2025-01-24T01:52:01.4734783Z from across all ranks. 2025-01-24T01:52:01.4734871Z 2025-01-24T01:52:01.4734964Z Args: 2025-01-24T01:52:01.4735153Z divide_by_initial_world_size (bool): If ``True``, will divide 2025-01-24T01:52:01.4735364Z gradients by the initial ``world_size`` DDP training was launched 2025-01-24T01:52:01.4735541Z with. If ``False``, will compute the effective world size 2025-01-24T01:52:01.4735730Z (number of ranks that have not depleted their inputs yet) and 2025-01-24T01:52:01.4735891Z divide gradients by that during allreduce. Set 2025-01-24T01:52:01.4736080Z ``divide_by_initial_world_size=True`` to ensure every input 2025-01-24T01:52:01.4736313Z sample including the uneven inputs have equal weight in terms of 2025-01-24T01:52:01.4736493Z how much they contribute to the global gradient. This is 2025-01-24T01:52:01.4736675Z achieved by always dividing the gradient by the initial 2025-01-24T01:52:01.4736883Z ``world_size`` even when we encounter uneven inputs. If you set 2025-01-24T01:52:01.4737062Z this to ``False``, we divide the gradient by the remaining 2025-01-24T01:52:01.4737276Z number of nodes. This ensures parity with training on a smaller 2025-01-24T01:52:01.4737466Z ``world_size`` although it also means the uneven inputs would 2025-01-24T01:52:01.4737677Z contribute more towards the global gradient. Typically, you 2025-01-24T01:52:01.4737872Z would want to set this to ``True`` for cases where the last few 2025-01-24T01:52:01.4738077Z inputs of your training job are uneven. In extreme cases, where 2025-01-24T01:52:01.4738284Z there is a large discrepancy in the number of inputs, setting 2025-01-24T01:52:01.4738435Z this to ``False`` might provide better results. 2025-01-24T01:52:01.4738674Z enable (bool): Whether to enable uneven input detection or not. Pass 2025-01-24T01:52:01.4738854Z in ``enable=False`` to disable in cases where you know that 2025-01-24T01:52:01.4739122Z inputs are even across participating processes. Default is 2025-01-24T01:52:01.4739222Z ``True``. 2025-01-24T01:52:01.4739413Z throw_on_early_termination (bool): Whether to throw an error 2025-01-24T01:52:01.4739611Z or continue training when at least one rank has exhausted 2025-01-24T01:52:01.4739806Z inputs. If ``True``, will throw upon the first rank reaching end 2025-01-24T01:52:01.4739997Z of data. If ``False``, will continue training with a smaller 2025-01-24T01:52:01.4740202Z effective world size until all ranks are joined. Note that if 2025-01-24T01:52:01.4740348Z this flag is specified, then the flag 2025-01-24T01:52:01.4740558Z ``divide_by_initial_world_size`` would be ignored. Default 2025-01-24T01:52:01.4740661Z is ``False``. 2025-01-24T01:52:01.4740764Z 2025-01-24T01:52:01.4740853Z 2025-01-24T01:52:01.4740998Z Example:: 2025-01-24T01:52:01.4741092Z 2025-01-24T01:52:01.4741221Z >>> # xdoctest: +SKIP("Distributed") 2025-01-24T01:52:01.4741339Z >>> import torch 2025-01-24T01:52:01.4741468Z >>> import torch.distributed as dist 2025-01-24T01:52:01.4741580Z >>> import os 2025-01-24T01:52:01.4741713Z >>> import torch.multiprocessing as mp 2025-01-24T01:52:01.4741838Z >>> import torch.nn as nn 2025-01-24T01:52:01.4741952Z >>> # On each spawned worker 2025-01-24T01:52:01.4742056Z >>> def worker(rank): 2025-01-24T01:52:01.4742254Z >>> dist.init_process_group("nccl", rank=rank, world_size=2) 2025-01-24T01:52:01.4742379Z >>> torch.cuda.set_device(rank) 2025-01-24T01:52:01.4742540Z >>> model = nn.Linear(1, 1, bias=False).to(rank) 2025-01-24T01:52:01.4742727Z >>> model = torch.nn.parallel.DistributedDataParallel( 2025-01-24T01:52:01.4742879Z >>> model, device_ids=[rank], output_device=rank 2025-01-24T01:52:01.4742989Z >>> ) 2025-01-24T01:52:01.4743126Z >>> # Rank 1 gets one more input than rank 0. 2025-01-24T01:52:01.4743337Z >>> inputs = [torch.tensor([1]).float() for _ in range(10 + rank)] 2025-01-24T01:52:01.4743448Z >>> with model.join(): 2025-01-24T01:52:01.4743567Z >>> for _ in range(5): 2025-01-24T01:52:01.4743678Z >>> for inp in inputs: 2025-01-24T01:52:01.4743799Z >>> loss = model(inp).sum() 2025-01-24T01:52:01.4743928Z >>> loss.backward() 2025-01-24T01:52:01.4744123Z >>> # Without the join() API, the below synchronization will hang 2025-01-24T01:52:01.4744284Z >>> # blocking for rank 1's allreduce to complete. 2025-01-24T01:52:01.4744426Z >>> torch.cuda.synchronize(device=rank) 2025-01-24T01:52:01.4744529Z 2025-01-24T01:52:01.4744789Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:52:01.4744882Z 2025-01-24T01:52:01.4745000Z warnings.warn(msg) 2025-01-24T01:52:01.4745090Z 2025-01-24T01:52:01.4745311Z --- Parse Warning: 88 / 106 --- 2025-01-24T01:52:01.4746506Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=DistributedDataParallel._register_fused_optim in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/distributed.py line=2050. 2025-01-24T01:52:01.4746796Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:52:01.4746889Z 2025-01-24T01:52:01.4747206Z Register an optimizer in DDP to optimize parameter immediately after its gradient reduction. 2025-01-24T01:52:01.4747311Z 2025-01-24T01:52:01.4747526Z Registers an optimizer with DDP such that the optimization for a 2025-01-24T01:52:01.4747757Z parameter will run immediately when that parameter's gradient is 2025-01-24T01:52:01.4747971Z finished with reduction, instead of waiting for all parameters' 2025-01-24T01:52:01.4748261Z gradients to finish reduction. This can result in a training speedup 2025-01-24T01:52:01.4748497Z depending on your workload since the optimizer can run while gradient 2025-01-24T01:52:01.4748738Z reduction for other parameters are still ongoing. In addition, this has 2025-01-24T01:52:01.4748983Z the potential to reduce peak memory consumption during training, as it 2025-01-24T01:52:01.4749190Z only needs to load the per-parameter optimizer states of a single 2025-01-24T01:52:01.4749426Z parameter at a time, instead of loading all per-parameter optimizer 2025-01-24T01:52:01.4749528Z states at once. 2025-01-24T01:52:01.4749619Z 2025-01-24T01:52:01.4749728Z Args: 2025-01-24T01:52:01.4749957Z optim (Type): a ``torch.optim.Optimizer`` class to be registered 2025-01-24T01:52:01.4750084Z as a fused optimizer. 2025-01-24T01:52:01.4750257Z *args (Sequence[Any]): Arguments to forward to `optim`. 2025-01-24T01:52:01.4750523Z optim_params (Optional[Iterable[torch.Tensor]]): Set of parameters 2025-01-24T01:52:01.4750759Z to optimize, similar to `params` argument of traditional `torch.optim` 2025-01-24T01:52:01.4750971Z Optimizers. If this is omitted, all DDP model parameters will be 2025-01-24T01:52:01.4751081Z optimized. 2025-01-24T01:52:01.4751291Z **kwargs: (Dict[str, Any]): Keyword arguments to forward to `optim`. 2025-01-24T01:52:01.4751396Z 2025-01-24T01:52:01.4751494Z .. warning :: 2025-01-24T01:52:01.4751729Z _register_fused_optim should only be called once on a DDP instance, 2025-01-24T01:52:01.4751947Z and registering multiple fused optimizers for the same DDP model 2025-01-24T01:52:01.4752086Z is not currently supported. Please ping 2025-01-24T01:52:01.4752340Z https://github.com/pytorch/pytorch/issues/71595 if this is necessary 2025-01-24T01:52:01.4752444Z for your use case. 2025-01-24T01:52:01.4752550Z 2025-01-24T01:52:01.4752650Z .. warning :: 2025-01-24T01:52:01.4752852Z _register_fused_optim and register_comm_hook currently do not 2025-01-24T01:52:01.4753090Z compose together, meaning that custom DDP communication hooks are 2025-01-24T01:52:01.4753267Z not supported with overlapped optimizers. Please ping 2025-01-24T01:52:01.4753517Z https://github.com/pytorch/pytorch/issues/71595 if this is necessary 2025-01-24T01:52:01.4753625Z for your use case. 2025-01-24T01:52:01.4753728Z 2025-01-24T01:52:01.4753824Z .. warning :: 2025-01-24T01:52:01.4754059Z Gradient accumulation and DDP `no_sync` are currently not supported 2025-01-24T01:52:01.4754208Z with overlapped optimizer. Please ping 2025-01-24T01:52:01.4754443Z https://github.com/pytorch/pytorch/issues/71595 if this is necessary 2025-01-24T01:52:01.4754562Z for your use case. 2025-01-24T01:52:01.4754652Z 2025-01-24T01:52:01.4754750Z Example:: 2025-01-24T01:52:01.4754859Z 2025-01-24T01:52:01.4755000Z >>> # xdoctest: +SKIP("No rendezvous handler") 2025-01-24T01:52:01.4755320Z >>> torch.distributed.init_process_group(backend='nccl', world_size=4, init_method='...') 2025-01-24T01:52:01.4755526Z >>> net = torch.nn.parallel.DistributedDataParallel(model, pg) 2025-01-24T01:52:01.4755638Z >>> lr = 1e-2 2025-01-24T01:52:01.4755740Z >>> betas = (0.9, 0.99) 2025-01-24T01:52:01.4755840Z >>> eps = 1e-6 2025-01-24T01:52:01.4756086Z >>> net._register_fused_optim(torch.optim.Adam, lr, betas=betas, eps=eps) 2025-01-24T01:52:01.4756217Z >>> # Example with subset of parameters 2025-01-24T01:52:01.4756376Z >>> params_to_opt = [list(net.parameters())[0]] 2025-01-24T01:52:01.4756491Z >>> net._register_fused_optim( 2025-01-24T01:52:01.4756743Z ... torch.optim.Adam, lr, optim_params=params_to_opt, betas=betas, eps=eps 2025-01-24T01:52:01.4756839Z ... ) 2025-01-24T01:52:01.4756956Z 2025-01-24T01:52:01.4757274Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:52:01.4757365Z 2025-01-24T01:52:01.4757485Z warnings.warn(msg) 2025-01-24T01:52:01.4757573Z 2025-01-24T01:52:01.4757792Z --- Parse Warning: 89 / 106 --- 2025-01-24T01:52:01.4758801Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=convert_conv2d_weight_memory_format in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/memory_format.py line=6. 2025-01-24T01:52:01.4759073Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:52:01.4759316Z Convert ``memory_format`` of ``nn.Conv2d.weight`` to ``memory_format``. 2025-01-24T01:52:01.4759435Z 2025-01-24T01:52:01.4759737Z The conversion recursively applies to nested ``nn.Module``, including ``module``. 2025-01-24T01:52:01.4760051Z Note that it only changes the memory_format, but not the semantics of each dimensions. 2025-01-24T01:52:01.4760318Z This function is used to facilitate the computation to adopt NHWC kernels, which 2025-01-24T01:52:01.4760646Z provides considerable speed up for fp16 data on CUDA devices with compute capability >= 7.0 2025-01-24T01:52:01.4760735Z 2025-01-24T01:52:01.4760848Z .. note:: 2025-01-24T01:52:01.4761091Z Calling ``model.to(memory_format=torch.channels_last)`` is more aggressive 2025-01-24T01:52:01.4761337Z than the utility function ``convert_conv2d_weight_memory_format``. Any 2025-01-24T01:52:01.4761554Z layer with 4d weight will be affected by ``model.to``, which does not 2025-01-24T01:52:01.4761789Z necessarily benefit from conversion to specified ``memory_format``. 2025-01-24T01:52:01.4762035Z One place we are confident in is that NHWC(channels_last) conversion for 2025-01-24T01:52:01.4762268Z convolution in cuDNN, as it is beneficial to run convolution in NHWC, 2025-01-24T01:52:01.4762494Z even in cases where we have to apply permutation to input tensors. 2025-01-24T01:52:01.4762586Z 2025-01-24T01:52:01.4762831Z Hence our strategy here is to convert only the weight of convolution to 2025-01-24T01:52:01.4762956Z channels_last. This ensures that; 2025-01-24T01:52:01.4763177Z 1. Fast convolution kernels will be used, the benefit of which could 2025-01-24T01:52:01.4763433Z outweigh overhead of permutation (if input is not in the same format). 2025-01-24T01:52:01.4763675Z 2. No unnecessary permutations are applied on layers that do not benefit 2025-01-24T01:52:01.4763812Z from memory_format conversion. 2025-01-24T01:52:01.4763904Z 2025-01-24T01:52:01.4764151Z The optimal case is that, layers between convolution layers are channels 2025-01-24T01:52:01.4764394Z last compatible. Input tensor would be permuted to channels last when it 2025-01-24T01:52:01.4764635Z encounters the first convolution layer and stay in that memory format. 2025-01-24T01:52:01.4764899Z Hence following convolutions will not need to permute its input tensor. 2025-01-24T01:52:01.4764992Z 2025-01-24T01:52:01.4765234Z In case where a channels last incompatible layer is between convolution 2025-01-24T01:52:01.4765452Z layers, we need to permute the input tensor back to contiguous format 2025-01-24T01:52:01.4765698Z for that layer. The input tensor will go through the remaining layers in 2025-01-24T01:52:01.4765933Z contiguous format and be permuted to channels last when it encounters 2025-01-24T01:52:01.4766161Z another convolution layer. There's no point in propagating that 2025-01-24T01:52:01.4766390Z permutation to an earlier layer, as most layers are quite agnostic to 2025-01-24T01:52:01.4766497Z ``memory_format``. 2025-01-24T01:52:01.4766628Z 2025-01-24T01:52:01.4766894Z This claim might change when PyTorch supports fusion of permutation, as 2025-01-24T01:52:01.4767140Z there might have been a better spot to fuse the permutation other than 2025-01-24T01:52:01.4767270Z immediately before a convolution. 2025-01-24T01:52:01.4767359Z 2025-01-24T01:52:01.4767468Z Args: 2025-01-24T01:52:01.4767694Z module (nn.Module): ``nn.Conv2d`` & ``nn.ConvTranspose2d`` or container 2025-01-24T01:52:01.4767820Z ``nn.Module`` 2025-01-24T01:52:01.4767979Z memory_format: user specified ``memory_format``, 2025-01-24T01:52:01.4768181Z e.g. ``torch.channels_last`` or ``torch.contiguous_format`` 2025-01-24T01:52:01.4768276Z 2025-01-24T01:52:01.4768397Z Returns: 2025-01-24T01:52:01.4768564Z The original module with updated ``nn.Conv2d`` 2025-01-24T01:52:01.4768654Z 2025-01-24T01:52:01.4768787Z Example: 2025-01-24T01:52:01.4768940Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2025-01-24T01:52:01.4769107Z >>> # xdoctest: +REQUIRES(env:CUBLAS_WORKSPACE_CONFIG) 2025-01-24T01:52:01.4769363Z >>> input = torch.randint(1, 10, (2, 8, 4, 4), dtype=torch.float16, device="cuda") 2025-01-24T01:52:01.4769476Z >>> model = nn.Sequential( 2025-01-24T01:52:01.4769614Z >>> nn.Conv2d(8, 4, 3)).cuda().half() 2025-01-24T01:52:01.4769728Z >>> # This is identical to: 2025-01-24T01:52:01.4769992Z >>> # nn.utils.convert_conv2d_weight_memory_format(model, torch.channels_last) 2025-01-24T01:52:01.4770266Z >>> model = nn.utils.convert_conv2d_weight_memory_format(model, torch.channels_last) 2025-01-24T01:52:01.4770377Z >>> out = model(input) 2025-01-24T01:52:01.4770483Z 2025-01-24T01:52:01.4770744Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:52:01.4770847Z 2025-01-24T01:52:01.4770958Z warnings.warn(msg) 2025-01-24T01:52:01.4771050Z 2025-01-24T01:52:01.4771256Z --- Parse Warning: 90 / 106 --- 2025-01-24T01:52:01.4772252Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=convert_conv3d_weight_memory_format in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/memory_format.py line=81. 2025-01-24T01:52:01.4772532Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:52:01.4772753Z Convert ``memory_format`` of ``nn.Conv3d.weight`` to ``memory_format`` 2025-01-24T01:52:01.4773037Z The conversion recursively applies to nested ``nn.Module``, including ``module``. 2025-01-24T01:52:01.4773314Z Note that it only changes the memory_format, but not the semantics of each dimensions. 2025-01-24T01:52:01.4773581Z This function is used to facilitate the computation to adopt NHWC kernels, which 2025-01-24T01:52:01.4773901Z provides considerable speed up for fp16 data on CUDA devices with compute capability >= 7.0 2025-01-24T01:52:01.4773985Z 2025-01-24T01:52:01.4774091Z .. note:: 2025-01-24T01:52:01.4774343Z Calling ``model.to(memory_format=torch.channels_last_3d)`` is more aggressive 2025-01-24T01:52:01.4774576Z than the utility function ``convert_conv3d_weight_memory_format``. Any 2025-01-24T01:52:01.4774792Z layer with 4d weight will be affected by ``model.to``, which does not 2025-01-24T01:52:01.4775031Z necessarily benefit from conversion to specified ``memory_format``. 2025-01-24T01:52:01.4775272Z One place we are confident in is that NDHWC(channels_last_3d) conversion for 2025-01-24T01:52:01.4775501Z convolution in cuDNN, as it is beneficial to run convolution in NDHWC, 2025-01-24T01:52:01.4775717Z even in cases where we have to apply permutation to input tensors. 2025-01-24T01:52:01.4775840Z 2025-01-24T01:52:01.4776103Z Hence our strategy here is to convert only the weight of convolution to 2025-01-24T01:52:01.4776227Z channels_last_3d. This ensures that; 2025-01-24T01:52:01.4776457Z 1. Fast convolution kernels will be used, the benefit of which could 2025-01-24T01:52:01.4776696Z outweigh overhead of permutation (if input is not in the same format). 2025-01-24T01:52:01.4776935Z 2. No unnecessary permutations are applied on layers that do not benefit 2025-01-24T01:52:01.4777062Z from memory_format conversion. 2025-01-24T01:52:01.4777147Z 2025-01-24T01:52:01.4777383Z The optimal case is that, layers between convolution layers are channels 2025-01-24T01:52:01.4777654Z last compatible. Input tensor would be permuted to channels last when it 2025-01-24T01:52:01.4777896Z encounters the first convolution layer and stay in that memory format. 2025-01-24T01:52:01.4778170Z Hence following convolutions will not need to permute its input tensor. 2025-01-24T01:52:01.4778261Z 2025-01-24T01:52:01.4778491Z In case where a channels last incompatible layer is between convolution 2025-01-24T01:52:01.4778710Z layers, we need to permute the input tensor back to contiguous format 2025-01-24T01:52:01.4778945Z for that layer. The input tensor will go through the remaining layers in 2025-01-24T01:52:01.4779355Z contiguous format and be permuted to channels last when it encounters 2025-01-24T01:52:01.4779582Z another convolution layer. There's no point in propagating that 2025-01-24T01:52:01.4779805Z permutation to an earlier layer, as most layers are quite agnostic to 2025-01-24T01:52:01.4779915Z ``memory_format``. 2025-01-24T01:52:01.4780008Z 2025-01-24T01:52:01.4780245Z This claim might change when PyTorch supports fusion of permutation, as 2025-01-24T01:52:01.4780486Z there might have been a better spot to fuse the permutation other than 2025-01-24T01:52:01.4780614Z immediately before a convolution. 2025-01-24T01:52:01.4780708Z 2025-01-24T01:52:01.4780797Z Args: 2025-01-24T01:52:01.4781016Z module (nn.Module): ``nn.Conv3d`` & ``nn.ConvTranspose3d`` or container 2025-01-24T01:52:01.4781129Z ``nn.Module`` 2025-01-24T01:52:01.4781283Z memory_format: user specified ``memory_format``, 2025-01-24T01:52:01.4781476Z e.g. ``torch.channels_last`` or ``torch.contiguous_format`` 2025-01-24T01:52:01.4781564Z 2025-01-24T01:52:01.4781665Z Returns: 2025-01-24T01:52:01.4781814Z The original module with updated ``nn.Conv3d`` 2025-01-24T01:52:01.4781903Z 2025-01-24T01:52:01.4782007Z Example: 2025-01-24T01:52:01.4782152Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2025-01-24T01:52:01.4782318Z >>> # xdoctest: +REQUIRES(env:CUBLAS_WORKSPACE_CONFIG) 2025-01-24T01:52:01.4782555Z >>> input = torch.randint(1, 10, (2, 8, 4, 4, 4), dtype=torch.float16, device="cuda") 2025-01-24T01:52:01.4782670Z >>> model = nn.Sequential( 2025-01-24T01:52:01.4782794Z >>> nn.Conv3d(8, 4, 3)).cuda().half() 2025-01-24T01:52:01.4782908Z >>> # This is identical to: 2025-01-24T01:52:01.4783173Z >>> # nn.utils.convert_conv3d_weight_memory_format(model, torch.channels_last_3d) 2025-01-24T01:52:01.4783449Z >>> model = nn.utils.convert_conv3d_weight_memory_format(model, torch.channels_last_3d) 2025-01-24T01:52:01.4783560Z >>> out = model(input) 2025-01-24T01:52:01.4783649Z 2025-01-24T01:52:01.4783910Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:52:01.4784012Z 2025-01-24T01:52:01.4784115Z warnings.warn(msg) 2025-01-24T01:52:01.4784210Z 2025-01-24T01:52:01.4784415Z --- Parse Warning: 91 / 106 --- 2025-01-24T01:52:01.4785430Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=random_structured in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py line=935. 2025-01-24T01:52:01.4785702Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:52:01.4785941Z Prune tensor by removing random channels along the specified dimension. 2025-01-24T01:52:01.4786042Z 2025-01-24T01:52:01.4786359Z Prunes tensor corresponding to parameter called ``name`` in ``module`` 2025-01-24T01:52:01.4786594Z by removing the specified ``amount`` of (currently unpruned) channels 2025-01-24T01:52:01.4786741Z along the specified ``dim`` selected at random. 2025-01-24T01:52:01.4786996Z Modifies module in place (and also return the modified module) 2025-01-24T01:52:01.4787087Z by: 2025-01-24T01:52:01.4787210Z 2025-01-24T01:52:01.4787433Z 1) adding a named buffer called ``name+'_mask'`` corresponding to the 2025-01-24T01:52:01.4787653Z binary mask applied to the parameter ``name`` by the pruning method. 2025-01-24T01:52:01.4787874Z 2) replacing the parameter ``name`` by its pruned version, while the 2025-01-24T01:52:01.4788086Z original (unpruned) parameter is stored in a new parameter named 2025-01-24T01:52:01.4788197Z ``name+'_orig'``. 2025-01-24T01:52:01.4788281Z 2025-01-24T01:52:01.4788374Z Args: 2025-01-24T01:52:01.4788562Z module (nn.Module): module containing the tensor to prune 2025-01-24T01:52:01.4788752Z name (str): parameter name within ``module`` on which pruning 2025-01-24T01:52:01.4788859Z will act. 2025-01-24T01:52:01.4789039Z amount (int or float): quantity of parameters to prune. 2025-01-24T01:52:01.4789223Z If ``float``, should be between 0.0 and 1.0 and represent the 2025-01-24T01:52:01.4789448Z fraction of parameters to prune. If ``int``, it represents the 2025-01-24T01:52:01.4789589Z absolute number of parameters to prune. 2025-01-24T01:52:01.4789798Z dim (int): index of the dim along which we define channels to prune. 2025-01-24T01:52:01.4789883Z 2025-01-24T01:52:01.4789986Z Returns: 2025-01-24T01:52:01.4790209Z module (nn.Module): modified (i.e. pruned) version of the input module 2025-01-24T01:52:01.4790300Z 2025-01-24T01:52:01.4790403Z Examples: 2025-01-24T01:52:01.4790508Z >>> # xdoctest: +SKIP 2025-01-24T01:52:01.4790638Z >>> m = prune.random_structured( 2025-01-24T01:52:01.4790776Z ... nn.Linear(5, 3), 'weight', amount=3, dim=1 2025-01-24T01:52:01.4790866Z ... ) 2025-01-24T01:52:01.4791060Z >>> columns_pruned = int(sum(torch.sum(m.weight, dim=0) == 0)) 2025-01-24T01:52:01.4791172Z >>> print(columns_pruned) 2025-01-24T01:52:01.4791273Z 3 2025-01-24T01:52:01.4791363Z 2025-01-24T01:52:01.4791629Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:52:01.4791718Z 2025-01-24T01:52:01.4791823Z warnings.warn(msg) 2025-01-24T01:52:01.4791915Z 2025-01-24T01:52:01.4792122Z --- Parse Warning: 92 / 106 --- 2025-01-24T01:52:01.4793002Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ln_structured in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py line=976. 2025-01-24T01:52:01.4793272Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:52:01.4793594Z Prune tensor by removing channels with the lowest L\ ``n``-norm along the specified dimension. 2025-01-24T01:52:01.4793682Z 2025-01-24T01:52:01.4793923Z Prunes tensor corresponding to parameter called ``name`` in ``module`` 2025-01-24T01:52:01.4794218Z by removing the specified ``amount`` of (currently unpruned) channels 2025-01-24T01:52:01.4794403Z along the specified ``dim`` with the lowest L\ ``n``-norm. 2025-01-24T01:52:01.4794607Z Modifies module in place (and also return the modified module) 2025-01-24T01:52:01.4794696Z by: 2025-01-24T01:52:01.4794784Z 2025-01-24T01:52:01.4795004Z 1) adding a named buffer called ``name+'_mask'`` corresponding to the 2025-01-24T01:52:01.4795230Z binary mask applied to the parameter ``name`` by the pruning method. 2025-01-24T01:52:01.4795449Z 2) replacing the parameter ``name`` by its pruned version, while the 2025-01-24T01:52:01.4795665Z original (unpruned) parameter is stored in a new parameter named 2025-01-24T01:52:01.4795774Z ``name+'_orig'``. 2025-01-24T01:52:01.4795887Z 2025-01-24T01:52:01.4795978Z Args: 2025-01-24T01:52:01.4796167Z module (nn.Module): module containing the tensor to prune 2025-01-24T01:52:01.4796385Z name (str): parameter name within ``module`` on which pruning 2025-01-24T01:52:01.4796487Z will act. 2025-01-24T01:52:01.4796665Z amount (int or float): quantity of parameters to prune. 2025-01-24T01:52:01.4796851Z If ``float``, should be between 0.0 and 1.0 and represent the 2025-01-24T01:52:01.4797057Z fraction of parameters to prune. If ``int``, it represents the 2025-01-24T01:52:01.4797197Z absolute number of parameters to prune. 2025-01-24T01:52:01.4797397Z n (int, float, inf, -inf, 'fro', 'nuc'): See documentation of valid 2025-01-24T01:52:01.4797557Z entries for argument ``p`` in :func:`torch.norm`. 2025-01-24T01:52:01.4797770Z dim (int): index of the dim along which we define channels to prune. 2025-01-24T01:52:01.4798013Z importance_scores (torch.Tensor): tensor of importance scores (of same 2025-01-24T01:52:01.4798223Z shape as module parameter) used to compute mask for pruning. 2025-01-24T01:52:01.4798451Z The values in this tensor indicate the importance of the corresponding 2025-01-24T01:52:01.4798586Z elements in the parameter being pruned. 2025-01-24T01:52:01.4798824Z If unspecified or None, the module parameter will be used in its place. 2025-01-24T01:52:01.4798915Z 2025-01-24T01:52:01.4799014Z Returns: 2025-01-24T01:52:01.4799233Z module (nn.Module): modified (i.e. pruned) version of the input module 2025-01-24T01:52:01.4799328Z 2025-01-24T01:52:01.4799422Z Examples: 2025-01-24T01:52:01.4799551Z >>> from torch.nn.utils import prune 2025-01-24T01:52:01.4799670Z >>> m = prune.ln_structured( 2025-01-24T01:52:01.4799862Z ... nn.Conv2d(5, 3, 2), 'weight', amount=0.3, dim=1, n=float('-inf') 2025-01-24T01:52:01.4799960Z ... ) 2025-01-24T01:52:01.4800050Z 2025-01-24T01:52:01.4800314Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:52:01.4800405Z 2025-01-24T01:52:01.4800508Z warnings.warn(msg) 2025-01-24T01:52:01.4800601Z 2025-01-24T01:52:01.4800795Z --- Parse Warning: 93 / 106 --- 2025-01-24T01:52:01.4801708Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=global_unstructured in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py line=1023. 2025-01-24T01:52:01.4801977Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:52:01.4802069Z 2025-01-24T01:52:01.4802511Z Globally prunes tensors corresponding to all parameters in ``parameters`` by applying the specified ``pruning_method``. 2025-01-24T01:52:01.4802602Z 2025-01-24T01:52:01.4802720Z Modifies modules in place by: 2025-01-24T01:52:01.4802808Z 2025-01-24T01:52:01.4803065Z 1) adding a named buffer called ``name+'_mask'`` corresponding to the 2025-01-24T01:52:01.4803310Z binary mask applied to the parameter ``name`` by the pruning method. 2025-01-24T01:52:01.4803526Z 2) replacing the parameter ``name`` by its pruned version, while the 2025-01-24T01:52:01.4803739Z original (unpruned) parameter is stored in a new parameter named 2025-01-24T01:52:01.4803838Z ``name+'_orig'``. 2025-01-24T01:52:01.4803931Z 2025-01-24T01:52:01.4804023Z Args: 2025-01-24T01:52:01.4804230Z parameters (Iterable of (module, name) tuples): parameters of 2025-01-24T01:52:01.4804434Z the model to prune in a global fashion, i.e. by aggregating all 2025-01-24T01:52:01.4804650Z weights prior to deciding which ones to prune. module must be of 2025-01-24T01:52:01.4804841Z type :class:`nn.Module`, and name must be a string. 2025-01-24T01:52:01.4805076Z pruning_method (function): a valid pruning function from this module, 2025-01-24T01:52:01.4805296Z or a custom one implemented by the user that satisfies the 2025-01-24T01:52:01.4805533Z implementation guidelines and has ``PRUNING_TYPE='unstructured'``. 2025-01-24T01:52:01.4805771Z importance_scores (dict): a dictionary mapping (module, name) tuples to 2025-01-24T01:52:01.4806011Z the corresponding parameter's importance scores tensor. The tensor 2025-01-24T01:52:01.4806227Z should be the same shape as the parameter, and is used for computing 2025-01-24T01:52:01.4806341Z mask for pruning. 2025-01-24T01:52:01.4806556Z If unspecified or None, the parameter will be used in place of its 2025-01-24T01:52:01.4806671Z importance scores. 2025-01-24T01:52:01.4806810Z kwargs: other keyword arguments such as: 2025-01-24T01:52:01.4807015Z amount (int or float): quantity of parameters to prune across the 2025-01-24T01:52:01.4807134Z specified parameters. 2025-01-24T01:52:01.4807319Z If ``float``, should be between 0.0 and 1.0 and represent the 2025-01-24T01:52:01.4807534Z fraction of parameters to prune. If ``int``, it represents the 2025-01-24T01:52:01.4807671Z absolute number of parameters to prune. 2025-01-24T01:52:01.4807770Z 2025-01-24T01:52:01.4807863Z Raises: 2025-01-24T01:52:01.4808024Z TypeError: if ``PRUNING_TYPE != 'unstructured'`` 2025-01-24T01:52:01.4808120Z 2025-01-24T01:52:01.4808211Z Note: 2025-01-24T01:52:01.4808439Z Since global structured pruning doesn't make much sense unless the 2025-01-24T01:52:01.4808648Z norm is normalized by the size of the parameter, we now limit the 2025-01-24T01:52:01.4808805Z scope of global pruning to unstructured methods. 2025-01-24T01:52:01.4808900Z 2025-01-24T01:52:01.4808995Z Examples: 2025-01-24T01:52:01.4809127Z >>> from torch.nn.utils import prune 2025-01-24T01:52:01.4809254Z >>> from collections import OrderedDict 2025-01-24T01:52:01.4809388Z >>> net = nn.Sequential(OrderedDict([ 2025-01-24T01:52:01.4809501Z ... ('first', nn.Linear(10, 4)), 2025-01-24T01:52:01.4809613Z ... ('second', nn.Linear(4, 1)), 2025-01-24T01:52:01.4809710Z ... ])) 2025-01-24T01:52:01.4809822Z >>> parameters_to_prune = ( 2025-01-24T01:52:01.4809936Z ... (net.first, 'weight'), 2025-01-24T01:52:01.4810045Z ... (net.second, 'weight'), 2025-01-24T01:52:01.4810135Z ... ) 2025-01-24T01:52:01.4810255Z >>> prune.global_unstructured( 2025-01-24T01:52:01.4810362Z ... parameters_to_prune, 2025-01-24T01:52:01.4810505Z ... pruning_method=prune.L1Unstructured, 2025-01-24T01:52:01.4810600Z ... amount=10, 2025-01-24T01:52:01.4810694Z ... ) 2025-01-24T01:52:01.4810919Z >>> print(sum(torch.nn.utils.parameters_to_vector(net.buffers()) == 0)) 2025-01-24T01:52:01.4811014Z tensor(10) 2025-01-24T01:52:01.4811109Z 2025-01-24T01:52:01.4811224Z 2025-01-24T01:52:01.4811517Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:52:01.4811607Z 2025-01-24T01:52:01.4811709Z warnings.warn(msg) 2025-01-24T01:52:01.4811802Z 2025-01-24T01:52:01.4811997Z --- Parse Warning: 94 / 106 --- 2025-01-24T01:52:01.4812887Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=custom_from_mask in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py line=1142. 2025-01-24T01:52:01.4813157Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:52:01.4813560Z Prune tensor corresponding to parameter called ``name`` in ``module`` by applying the pre-computed mask in ``mask``. 2025-01-24T01:52:01.4813674Z 2025-01-24T01:52:01.4813892Z Modifies module in place (and also return the modified module) by: 2025-01-24T01:52:01.4814014Z 2025-01-24T01:52:01.4814232Z 1) adding a named buffer called ``name+'_mask'`` corresponding to the 2025-01-24T01:52:01.4814462Z binary mask applied to the parameter ``name`` by the pruning method. 2025-01-24T01:52:01.4814676Z 2) replacing the parameter ``name`` by its pruned version, while the 2025-01-24T01:52:01.4814893Z original (unpruned) parameter is stored in a new parameter named 2025-01-24T01:52:01.4814992Z ``name+'_orig'``. 2025-01-24T01:52:01.4815075Z 2025-01-24T01:52:01.4815173Z Args: 2025-01-24T01:52:01.4815355Z module (nn.Module): module containing the tensor to prune 2025-01-24T01:52:01.4815549Z name (str): parameter name within ``module`` on which pruning 2025-01-24T01:52:01.4815646Z will act. 2025-01-24T01:52:01.4815826Z mask (Tensor): binary mask to be applied to the parameter. 2025-01-24T01:52:01.4815922Z 2025-01-24T01:52:01.4816013Z Returns: 2025-01-24T01:52:01.4816248Z module (nn.Module): modified (i.e. pruned) version of the input module 2025-01-24T01:52:01.4816335Z 2025-01-24T01:52:01.4816435Z Examples: 2025-01-24T01:52:01.4816566Z >>> from torch.nn.utils import prune 2025-01-24T01:52:01.4816677Z >>> m = prune.custom_from_mask( 2025-01-24T01:52:01.4816866Z ... nn.Linear(5, 3), name='bias', mask=torch.tensor([0, 1, 0]) 2025-01-24T01:52:01.4816952Z ... ) 2025-01-24T01:52:01.4817069Z >>> print(m.bias_mask) 2025-01-24T01:52:01.4817171Z tensor([0., 1., 0.]) 2025-01-24T01:52:01.4817255Z 2025-01-24T01:52:01.4817358Z 2025-01-24T01:52:01.4817611Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:52:01.4817706Z 2025-01-24T01:52:01.4817810Z warnings.warn(msg) 2025-01-24T01:52:01.4817897Z 2025-01-24T01:52:01.4818098Z --- Parse Warning: 95 / 106 --- 2025-01-24T01:52:01.4818988Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=AveragedModel in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/swa_utils.py line=117. 2025-01-24T01:52:01.4819267Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:52:01.4819635Z Implements averaged model for Stochastic Weight Averaging (SWA) and Exponential Moving Average (EMA). 2025-01-24T01:52:01.4819732Z 2025-01-24T01:52:01.4819977Z Stochastic Weight Averaging was proposed in `Averaging Weights Leads to 2025-01-24T01:52:01.4820201Z Wider Optima and Better Generalization`_ by Pavel Izmailov, Dmitrii 2025-01-24T01:52:01.4820426Z Podoprikhin, Timur Garipov, Dmitry Vetrov and Andrew Gordon Wilson 2025-01-24T01:52:01.4820518Z (UAI 2018). 2025-01-24T01:52:01.4820614Z 2025-01-24T01:52:01.4820832Z Exponential Moving Average is a variation of `Polyak averaging`_, 2025-01-24T01:52:01.4821118Z but using exponential weights instead of equal weights across iterations. 2025-01-24T01:52:01.4821230Z 2025-01-24T01:52:01.4821473Z AveragedModel class creates a copy of the provided module :attr:`model` 2025-01-24T01:52:01.4821714Z on the device :attr:`device` and allows to compute running averages of the 2025-01-24T01:52:01.4821830Z parameters of the :attr:`model`. 2025-01-24T01:52:01.4821929Z 2025-01-24T01:52:01.4822017Z Args: 2025-01-24T01:52:01.4822182Z model (torch.nn.Module): model to use with SWA/EMA 2025-01-24T01:52:01.4822421Z device (torch.device, optional): if provided, the averaged model will be 2025-01-24T01:52:01.4822535Z stored on the :attr:`device` 2025-01-24T01:52:01.4822784Z avg_fn (function, optional): the averaging function used to update 2025-01-24T01:52:01.4822991Z parameters; the function must take in the current value of the 2025-01-24T01:52:01.4823268Z :class:`AveragedModel` parameter, the current value of :attr:`model` 2025-01-24T01:52:01.4823471Z parameter, and the number of models already averaged; if None, 2025-01-24T01:52:01.4823642Z an equally weighted average is used (default: None) 2025-01-24T01:52:01.4823873Z multi_avg_fn (function, optional): the averaging function used to update 2025-01-24T01:52:01.4824115Z parameters inplace; the function must take in the current values of the 2025-01-24T01:52:01.4824395Z :class:`AveragedModel` parameters as a list, the current values of :attr:`model` 2025-01-24T01:52:01.4824628Z parameters as a list, and the number of models already averaged; if None, 2025-01-24T01:52:01.4824803Z an equally weighted average is used (default: None) 2025-01-24T01:52:01.4825012Z use_buffers (bool): if ``True``, it will compute running averages for 2025-01-24T01:52:01.4825254Z both the parameters and the buffers of the model. (default: ``False``) 2025-01-24T01:52:01.4825346Z 2025-01-24T01:52:01.4825439Z Example: 2025-01-24T01:52:01.4825589Z >>> # xdoctest: +SKIP("undefined variables") 2025-01-24T01:52:01.4825722Z >>> loader, optimizer, model, loss_fn = ... 2025-01-24T01:52:01.4825911Z >>> swa_model = torch.optim.swa_utils.AveragedModel(model) 2025-01-24T01:52:01.4826138Z >>> scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, 2025-01-24T01:52:01.4826356Z >>> T_max=300) 2025-01-24T01:52:01.4826464Z >>> swa_start = 160 2025-01-24T01:52:01.4826611Z >>> swa_scheduler = SWALR(optimizer, swa_lr=0.05) 2025-01-24T01:52:01.4826724Z >>> for i in range(300): 2025-01-24T01:52:01.4826844Z >>> for input, target in loader: 2025-01-24T01:52:01.4826976Z >>> optimizer.zero_grad() 2025-01-24T01:52:01.4827116Z >>> loss_fn(model(input), target).backward() 2025-01-24T01:52:01.4827243Z >>> optimizer.step() 2025-01-24T01:52:01.4827347Z >>> if i > swa_start: 2025-01-24T01:52:01.4827480Z >>> swa_model.update_parameters(model) 2025-01-24T01:52:01.4827604Z >>> swa_scheduler.step() 2025-01-24T01:52:01.4827697Z >>> else: 2025-01-24T01:52:01.4827817Z >>> scheduler.step() 2025-01-24T01:52:01.4827907Z >>> 2025-01-24T01:52:01.4828063Z >>> # Update bn statistics for the swa_model at the end 2025-01-24T01:52:01.4828245Z >>> torch.optim.swa_utils.update_bn(loader, swa_model) 2025-01-24T01:52:01.4828332Z 2025-01-24T01:52:01.4828649Z You can also use custom averaging functions with the `avg_fn` or `multi_avg_fn` parameters. 2025-01-24T01:52:01.4828851Z If no averaging function is provided, the default is to compute 2025-01-24T01:52:01.4829016Z equally-weighted average of the weights (SWA). 2025-01-24T01:52:01.4829160Z 2025-01-24T01:52:01.4829251Z Example: 2025-01-24T01:52:01.4829398Z >>> # xdoctest: +SKIP("undefined variables") 2025-01-24T01:52:01.4829607Z >>> # Compute exponential moving averages of the weights and buffers 2025-01-24T01:52:01.4829790Z >>> ema_model = torch.optim.swa_utils.AveragedModel(model, 2025-01-24T01:52:01.4830007Z >>> torch.optim.swa_utils.get_ema_multi_avg_fn(0.9), use_buffers=True) 2025-01-24T01:52:01.4830104Z 2025-01-24T01:52:01.4830206Z .. note:: 2025-01-24T01:52:01.4830426Z When using SWA/EMA with models containing Batch Normalization you may 2025-01-24T01:52:01.4830647Z need to update the activation statistics for Batch Normalization. 2025-01-24T01:52:01.4830908Z This can be done either by using the :meth:`torch.optim.swa_utils.update_bn` 2025-01-24T01:52:01.4831148Z or by setting :attr:`use_buffers` to `True`. The first approach updates the 2025-01-24T01:52:01.4831425Z statistics in a post-training step by passing data through the model. The 2025-01-24T01:52:01.4831678Z second does it during the parameter update phase by averaging all buffers. 2025-01-24T01:52:01.4831931Z Empirical evidence has shown that updating the statistics in normalization 2025-01-24T01:52:01.4832159Z layers increases accuracy, but you may wish to empirically test which 2025-01-24T01:52:01.4832331Z approach yields the best results in your problem. 2025-01-24T01:52:01.4832415Z 2025-01-24T01:52:01.4832519Z .. note:: 2025-01-24T01:52:01.4832777Z :attr:`avg_fn` and `multi_avg_fn` are not saved in the :meth:`state_dict` of the model. 2025-01-24T01:52:01.4832875Z 2025-01-24T01:52:01.4832966Z .. note:: 2025-01-24T01:52:01.4833171Z When :meth:`update_parameters` is called for the first time (i.e. 2025-01-24T01:52:01.4833372Z :attr:`n_averaged` is `0`) the parameters of `model` are copied 2025-01-24T01:52:01.4833579Z to the parameters of :class:`AveragedModel`. For every subsequent 2025-01-24T01:52:01.4833775Z call of :meth:`update_parameters` the function `avg_fn` is used 2025-01-24T01:52:01.4833891Z to update the parameters. 2025-01-24T01:52:01.4833973Z 2025-01-24T01:52:01.4834214Z .. _Averaging Weights Leads to Wider Optima and Better Generalization: 2025-01-24T01:52:01.4834344Z https://arxiv.org/abs/1803.05407 2025-01-24T01:52:01.4834595Z .. _There Are Many Consistent Explanations of Unlabeled Data: Why You Should 2025-01-24T01:52:01.4834691Z Average: 2025-01-24T01:52:01.4834824Z https://arxiv.org/abs/1806.05594 2025-01-24T01:52:01.4835032Z .. _SWALP: Stochastic Weight Averaging in Low-Precision Training: 2025-01-24T01:52:01.4835150Z https://arxiv.org/abs/1904.11943 2025-01-24T01:52:01.4835392Z .. _Stochastic Weight Averaging in Parallel: Large-Batch Training That 2025-01-24T01:52:01.4835493Z Generalizes Well: 2025-01-24T01:52:01.4835622Z https://arxiv.org/abs/2001.02312 2025-01-24T01:52:01.4835725Z .. _Polyak averaging: 2025-01-24T01:52:01.4835914Z https://paperswithcode.com/method/polyak-averaging 2025-01-24T01:52:01.4836002Z 2025-01-24T01:52:01.4836256Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:52:01.4836352Z 2025-01-24T01:52:01.4836454Z warnings.warn(msg) 2025-01-24T01:52:01.4836555Z 2025-01-24T01:52:01.4836767Z --- Parse Warning: 96 / 106 --- 2025-01-24T01:52:01.4837617Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=SWALR in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/swa_utils.py line=369. 2025-01-24T01:52:01.4837895Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:52:01.4838177Z Anneals the learning rate in each parameter group to a fixed value. 2025-01-24T01:52:01.4838278Z 2025-01-24T01:52:01.4838517Z This learning rate scheduler is meant to be used with Stochastic Weight 2025-01-24T01:52:01.4838749Z Averaging (SWA) method (see `torch.optim.swa_utils.AveragedModel`). 2025-01-24T01:52:01.4838838Z 2025-01-24T01:52:01.4838929Z Args: 2025-01-24T01:52:01.4839126Z optimizer (torch.optim.Optimizer): wrapped optimizer 2025-01-24T01:52:01.4839340Z swa_lrs (float or list): the learning rate value for all param groups 2025-01-24T01:52:01.4839489Z together or separately for each group. 2025-01-24T01:52:01.4839726Z annealing_epochs (int): number of epochs in the annealing phase 2025-01-24T01:52:01.4839842Z (default: 10) 2025-01-24T01:52:01.4840064Z annealing_strategy (str): "cos" or "linear"; specifies the annealing 2025-01-24T01:52:01.4840311Z strategy: "cos" for cosine annealing, "linear" for linear annealing 2025-01-24T01:52:01.4840424Z (default: "cos") 2025-01-24T01:52:01.4840615Z last_epoch (int): the index of the last epoch (default: -1) 2025-01-24T01:52:01.4840716Z 2025-01-24T01:52:01.4840899Z The :class:`SWALR` scheduler can be used together with other 2025-01-24T01:52:01.4841136Z schedulers to switch to a constant learning rate late in the training 2025-01-24T01:52:01.4841242Z as in the example below. 2025-01-24T01:52:01.4841331Z 2025-01-24T01:52:01.4841429Z Example: 2025-01-24T01:52:01.4841567Z >>> # xdoctest: +SKIP("Undefined variables") 2025-01-24T01:52:01.4841703Z >>> loader, optimizer, model = ... 2025-01-24T01:52:01.4841822Z >>> lr_lambda = lambda epoch: 0.9 2025-01-24T01:52:01.4842052Z >>> scheduler = torch.optim.lr_scheduler.MultiplicativeLR(optimizer, 2025-01-24T01:52:01.4842177Z >>> lr_lambda=lr_lambda) 2025-01-24T01:52:01.4842357Z >>> swa_scheduler = torch.optim.swa_utils.SWALR(optimizer, 2025-01-24T01:52:01.4842556Z >>> anneal_strategy="linear", anneal_epochs=20, swa_lr=0.05) 2025-01-24T01:52:01.4842656Z >>> swa_start = 160 2025-01-24T01:52:01.4842774Z >>> for i in range(300): 2025-01-24T01:52:01.4842894Z >>> for input, target in loader: 2025-01-24T01:52:01.4843015Z >>> optimizer.zero_grad() 2025-01-24T01:52:01.4843162Z >>> loss_fn(model(input), target).backward() 2025-01-24T01:52:01.4843271Z >>> optimizer.step() 2025-01-24T01:52:01.4843386Z >>> if i > swa_start: 2025-01-24T01:52:01.4843502Z >>> swa_scheduler.step() 2025-01-24T01:52:01.4843609Z >>> else: 2025-01-24T01:52:01.4843717Z >>> scheduler.step() 2025-01-24T01:52:01.4843809Z 2025-01-24T01:52:01.4844052Z .. _Averaging Weights Leads to Wider Optima and Better Generalization: 2025-01-24T01:52:01.4844175Z https://arxiv.org/abs/1803.05407 2025-01-24T01:52:01.4844276Z 2025-01-24T01:52:01.4844532Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:52:01.4844622Z 2025-01-24T01:52:01.4844735Z warnings.warn(msg) 2025-01-24T01:52:01.4844827Z 2025-01-24T01:52:01.4845031Z --- Parse Warning: 97 / 106 --- 2025-01-24T01:52:01.4845931Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=assert_close in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_comparison.py line=1263. 2025-01-24T01:52:01.4846213Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:52:01.4846365Z Asserts that ``actual`` and ``expected`` are close. 2025-01-24T01:52:01.4846458Z 2025-01-24T01:52:01.4846892Z If ``actual`` and ``expected`` are strided, non-quantized, real-valued, and finite, they are considered close if 2025-01-24T01:52:01.4846984Z 2025-01-24T01:52:01.4847092Z .. math:: 2025-01-24T01:52:01.4847178Z 2025-01-24T01:52:01.4847543Z \lvert \text{actual} - \text{expected} \rvert \le \texttt{atol} + \texttt{rtol} \cdot \lvert \text{expected} \rvert 2025-01-24T01:52:01.4847639Z 2025-01-24T01:52:01.4847988Z Non-finite values (``-inf`` and ``inf``) are only considered close if and only if they are equal. ``NaN``'s are 2025-01-24T01:52:01.4848211Z only considered equal to each other if ``equal_nan`` is ``True``. 2025-01-24T01:52:01.4848294Z 2025-01-24T01:52:01.4848511Z In addition, they are only considered close if they have the same 2025-01-24T01:52:01.4848621Z 2025-01-24T01:52:01.4848824Z - :attr:`~torch.Tensor.device` (if ``check_device`` is ``True``), 2025-01-24T01:52:01.4848997Z - ``dtype`` (if ``check_dtype`` is ``True``), 2025-01-24T01:52:01.4849148Z - ``layout`` (if ``check_layout`` is ``True``), and 2025-01-24T01:52:01.4849288Z - stride (if ``check_stride`` is ``True``). 2025-01-24T01:52:01.4849375Z 2025-01-24T01:52:01.4849691Z If either ``actual`` or ``expected`` is a meta tensor, only the attribute checks will be performed. 2025-01-24T01:52:01.4849778Z 2025-01-24T01:52:01.4850135Z If ``actual`` and ``expected`` are sparse (either having COO, CSR, CSC, BSR, or BSC layout), their strided members are 2025-01-24T01:52:01.4850524Z checked individually. Indices, namely ``indices`` for COO, ``crow_indices`` and ``col_indices`` for CSR and BSR, 2025-01-24T01:52:01.4850754Z or ``ccol_indices`` and ``row_indices`` for CSC and BSC layouts, respectively, 2025-01-24T01:52:01.4851160Z are always checked for equality whereas the values are checked for closeness according to the definition above. 2025-01-24T01:52:01.4851253Z 2025-01-24T01:52:01.4851555Z If ``actual`` and ``expected`` are quantized, they are considered close if they have the same 2025-01-24T01:52:01.4851908Z :meth:`~torch.Tensor.qscheme` and the result of :meth:`~torch.Tensor.dequantize` is close according to the 2025-01-24T01:52:01.4852011Z definition above. 2025-01-24T01:52:01.4852107Z 2025-01-24T01:52:01.4852414Z ``actual`` and ``expected`` can be :class:`~torch.Tensor`'s or any tensor-or-scalar-likes from which 2025-01-24T01:52:01.4852802Z :class:`torch.Tensor`'s can be constructed with :func:`torch.as_tensor`. Except for Python scalars the input types 2025-01-24T01:52:01.4853167Z have to be directly related. In addition, ``actual`` and ``expected`` can be :class:`~collections.abc.Sequence`'s 2025-01-24T01:52:01.4853560Z or :class:`~collections.abc.Mapping`'s in which case they are considered close if their structure matches and all 2025-01-24T01:52:01.4853797Z their elements are considered close according to the above definition. 2025-01-24T01:52:01.4853905Z 2025-01-24T01:52:01.4854002Z .. note:: 2025-01-24T01:52:01.4854085Z 2025-01-24T01:52:01.4854434Z Python scalars are an exception to the type relation requirement, because their :func:`type`, i.e. 2025-01-24T01:52:01.4854759Z :class:`int`, :class:`float`, and :class:`complex`, is equivalent to the ``dtype`` of a tensor-like. Thus, 2025-01-24T01:52:01.4855055Z Python scalars of different types can be checked, but require ``check_dtype=False``. 2025-01-24T01:52:01.4855139Z 2025-01-24T01:52:01.4855240Z Args: 2025-01-24T01:52:01.4855352Z actual (Any): Actual input. 2025-01-24T01:52:01.4855473Z expected (Any): Expected input. 2025-01-24T01:52:01.4855849Z allow_subclasses (bool): If ``True`` (default) and except for Python scalars, inputs of directly related types 2025-01-24T01:52:01.4856011Z are allowed. Otherwise type equality is required. 2025-01-24T01:52:01.4856448Z rtol (Optional[float]): Relative tolerance. If specified ``atol`` must also be specified. If omitted, default 2025-01-24T01:52:01.4856715Z values based on the :attr:`~torch.Tensor.dtype` are selected with the below table. 2025-01-24T01:52:01.4857088Z atol (Optional[float]): Absolute tolerance. If specified ``rtol`` must also be specified. If omitted, default 2025-01-24T01:52:01.4857352Z values based on the :attr:`~torch.Tensor.dtype` are selected with the below table. 2025-01-24T01:52:01.4857598Z equal_nan (Union[bool, str]): If ``True``, two ``NaN`` values will be considered equal. 2025-01-24T01:52:01.4857899Z check_device (bool): If ``True`` (default), asserts that corresponding tensors are on the same 2025-01-24T01:52:01.4858185Z :attr:`~torch.Tensor.device`. If this check is disabled, tensors on different 2025-01-24T01:52:01.4858435Z :attr:`~torch.Tensor.device`'s are moved to the CPU before being compared. 2025-01-24T01:52:01.4858816Z check_dtype (bool): If ``True`` (default), asserts that corresponding tensors have the same ``dtype``. If this 2025-01-24T01:52:01.4859174Z check is disabled, tensors with different ``dtype``'s are promoted to a common ``dtype`` (according to 2025-01-24T01:52:01.4859342Z :func:`torch.promote_types`) before being compared. 2025-01-24T01:52:01.4859708Z check_layout (bool): If ``True`` (default), asserts that corresponding tensors have the same ``layout``. If this 2025-01-24T01:52:01.4860048Z check is disabled, tensors with different ``layout``'s are converted to strided tensors before being 2025-01-24T01:52:01.4860144Z compared. 2025-01-24T01:52:01.4860524Z check_stride (bool): If ``True`` and corresponding tensors are strided, asserts that they have the same stride. 2025-01-24T01:52:01.4860881Z msg (Optional[Union[str, Callable[[str], str]]]): Optional error message to use in case a failure occurs during 2025-01-24T01:52:01.4861259Z the comparison. Can also passed as callable in which case it will be called with the generated message and 2025-01-24T01:52:01.4861381Z should return the new message. 2025-01-24T01:52:01.4861481Z 2025-01-24T01:52:01.4861576Z Raises: 2025-01-24T01:52:01.4861810Z ValueError: If no :class:`torch.Tensor` can be constructed from an input. 2025-01-24T01:52:01.4861993Z ValueError: If only ``rtol`` or ``atol`` is specified. 2025-01-24T01:52:01.4862324Z AssertionError: If corresponding inputs are not Python scalars and are not directly related. 2025-01-24T01:52:01.4862708Z AssertionError: If ``allow_subclasses`` is ``False``, but corresponding inputs are not Python scalars and have 2025-01-24T01:52:01.4862816Z different types. 2025-01-24T01:52:01.4863193Z AssertionError: If the inputs are :class:`~collections.abc.Sequence`'s, but their length does not match. 2025-01-24T01:52:01.4863565Z AssertionError: If the inputs are :class:`~collections.abc.Mapping`'s, but their set of keys do not match. 2025-01-24T01:52:01.4863896Z AssertionError: If corresponding tensors do not have the same :attr:`~torch.Tensor.shape`. 2025-01-24T01:52:01.4864203Z AssertionError: If ``check_layout`` is ``True``, but corresponding tensors do not have the same 2025-01-24T01:52:01.4864327Z :attr:`~torch.Tensor.layout`. 2025-01-24T01:52:01.4864560Z AssertionError: If only one of corresponding tensors is quantized. 2025-01-24T01:52:01.4864950Z AssertionError: If corresponding tensors are quantized, but have different :meth:`~torch.Tensor.qscheme`'s. 2025-01-24T01:52:01.4865258Z AssertionError: If ``check_device`` is ``True``, but corresponding tensors are not on the same 2025-01-24T01:52:01.4865381Z :attr:`~torch.Tensor.device`. 2025-01-24T01:52:01.4865840Z AssertionError: If ``check_dtype`` is ``True``, but corresponding tensors do not have the same ``dtype``. 2025-01-24T01:52:01.4866203Z AssertionError: If ``check_stride`` is ``True``, but corresponding strided tensors do not have the same stride. 2025-01-24T01:52:01.4866669Z AssertionError: If the values of corresponding tensors are not close according to the definition above. 2025-01-24T01:52:01.4866762Z 2025-01-24T01:52:01.4867129Z The following table displays the default ``rtol`` and ``atol`` for different ``dtype``'s. In case of mismatching 2025-01-24T01:52:01.4867296Z ``dtype``'s, the maximum of both tolerances is used. 2025-01-24T01:52:01.4867387Z 2025-01-24T01:52:01.4867527Z +---------------------------+------------+----------+ 2025-01-24T01:52:01.4867695Z | ``dtype`` | ``rtol`` | ``atol`` | 2025-01-24T01:52:01.4867824Z +===========================+============+==========+ 2025-01-24T01:52:01.4867997Z | :attr:`~torch.float16` | ``1e-3`` | ``1e-5`` | 2025-01-24T01:52:01.4868131Z +---------------------------+------------+----------+ 2025-01-24T01:52:01.4868281Z | :attr:`~torch.bfloat16` | ``1.6e-2`` | ``1e-5`` | 2025-01-24T01:52:01.4868415Z +---------------------------+------------+----------+ 2025-01-24T01:52:01.4868561Z | :attr:`~torch.float32` | ``1.3e-6`` | ``1e-5`` | 2025-01-24T01:52:01.4868689Z +---------------------------+------------+----------+ 2025-01-24T01:52:01.4868838Z | :attr:`~torch.float64` | ``1e-7`` | ``1e-7`` | 2025-01-24T01:52:01.4868965Z +---------------------------+------------+----------+ 2025-01-24T01:52:01.4869107Z | :attr:`~torch.complex32` | ``1e-3`` | ``1e-5`` | 2025-01-24T01:52:01.4869247Z +---------------------------+------------+----------+ 2025-01-24T01:52:01.4869390Z | :attr:`~torch.complex64` | ``1.3e-6`` | ``1e-5`` | 2025-01-24T01:52:01.4869534Z +---------------------------+------------+----------+ 2025-01-24T01:52:01.4869677Z | :attr:`~torch.complex128` | ``1e-7`` | ``1e-7`` | 2025-01-24T01:52:01.4869820Z +---------------------------+------------+----------+ 2025-01-24T01:52:01.4869962Z | :attr:`~torch.quint8` | ``1.3e-6`` | ``1e-5`` | 2025-01-24T01:52:01.4870089Z +---------------------------+------------+----------+ 2025-01-24T01:52:01.4870240Z | :attr:`~torch.quint2x4` | ``1.3e-6`` | ``1e-5`` | 2025-01-24T01:52:01.4870368Z +---------------------------+------------+----------+ 2025-01-24T01:52:01.4870519Z | :attr:`~torch.quint4x2` | ``1.3e-6`` | ``1e-5`` | 2025-01-24T01:52:01.4870649Z +---------------------------+------------+----------+ 2025-01-24T01:52:01.4870804Z | :attr:`~torch.qint8` | ``1.3e-6`` | ``1e-5`` | 2025-01-24T01:52:01.4870934Z +---------------------------+------------+----------+ 2025-01-24T01:52:01.4871074Z | :attr:`~torch.qint32` | ``1.3e-6`` | ``1e-5`` | 2025-01-24T01:52:01.4871217Z +---------------------------+------------+----------+ 2025-01-24T01:52:01.4871345Z | other | ``0.0`` | ``0.0`` | 2025-01-24T01:52:01.4871483Z +---------------------------+------------+----------+ 2025-01-24T01:52:01.4871571Z 2025-01-24T01:52:01.4871669Z .. note:: 2025-01-24T01:52:01.4871763Z 2025-01-24T01:52:01.4872149Z :func:`~torch.testing.assert_close` is highly configurable with strict default settings. Users are encouraged 2025-01-24T01:52:01.4872512Z to :func:`~functools.partial` it to fit their use case. For example, if an equality check is needed, one might 2025-01-24T01:52:01.4872780Z define an ``assert_equal`` that uses zero tolerances for every ``dtype`` by default: 2025-01-24T01:52:01.4872875Z 2025-01-24T01:52:01.4872980Z >>> import functools 2025-01-24T01:52:01.4873244Z >>> assert_equal = functools.partial(torch.testing.assert_close, rtol=0, atol=0) 2025-01-24T01:52:01.4873415Z >>> assert_equal(1e-9, 1e-10) 2025-01-24T01:52:01.4873542Z Traceback (most recent call last): 2025-01-24T01:52:01.4873642Z ... 2025-01-24T01:52:01.4873775Z AssertionError: Scalars are not equal! 2025-01-24T01:52:01.4873886Z 2025-01-24T01:52:01.4874005Z Expected 1e-10 but got 1e-09. 2025-01-24T01:52:01.4874139Z Absolute difference: 9.000000000000001e-10 2025-01-24T01:52:01.4874263Z Relative difference: 9.0 2025-01-24T01:52:01.4874351Z 2025-01-24T01:52:01.4874457Z Examples: 2025-01-24T01:52:01.4874580Z >>> # tensor to tensor comparison 2025-01-24T01:52:01.4874722Z >>> expected = torch.tensor([1e0, 1e-1, 1e-2]) 2025-01-24T01:52:01.4874893Z >>> actual = torch.acos(torch.cos(expected)) 2025-01-24T01:52:01.4875046Z >>> torch.testing.assert_close(actual, expected) 2025-01-24T01:52:01.4875174Z 2025-01-24T01:52:01.4875298Z >>> # scalar to scalar comparison 2025-01-24T01:52:01.4875411Z >>> import math 2025-01-24T01:52:01.4875524Z >>> expected = math.sqrt(2.0) 2025-01-24T01:52:01.4875639Z >>> actual = 2.0 / math.sqrt(2.0) 2025-01-24T01:52:01.4875800Z >>> torch.testing.assert_close(actual, expected) 2025-01-24T01:52:01.4875890Z 2025-01-24T01:52:01.4876036Z >>> # numpy array to numpy array comparison 2025-01-24T01:52:01.4876150Z >>> import numpy as np 2025-01-24T01:52:01.4876283Z >>> expected = np.array([1e0, 1e-1, 1e-2]) 2025-01-24T01:52:01.4876421Z >>> actual = np.arccos(np.cos(expected)) 2025-01-24T01:52:01.4876577Z >>> torch.testing.assert_close(actual, expected) 2025-01-24T01:52:01.4876678Z 2025-01-24T01:52:01.4876807Z >>> # sequence to sequence comparison 2025-01-24T01:52:01.4876921Z >>> import numpy as np 2025-01-24T01:52:01.4877186Z >>> # The types of the sequences do not have to match. They only have to have the same 2025-01-24T01:52:01.4877323Z >>> # length and their elements have to match. 2025-01-24T01:52:01.4877492Z >>> expected = [torch.tensor([1.0]), 2.0, np.array(3.0)] 2025-01-24T01:52:01.4877609Z >>> actual = tuple(expected) 2025-01-24T01:52:01.4877771Z >>> torch.testing.assert_close(actual, expected) 2025-01-24T01:52:01.4877858Z 2025-01-24T01:52:01.4877982Z >>> # mapping to mapping comparison 2025-01-24T01:52:01.4878119Z >>> from collections import OrderedDict 2025-01-24T01:52:01.4878225Z >>> import numpy as np 2025-01-24T01:52:01.4878344Z >>> foo = torch.tensor(1.0) 2025-01-24T01:52:01.4878443Z >>> bar = 2.0 2025-01-24T01:52:01.4878561Z >>> baz = np.array(3.0) 2025-01-24T01:52:01.4878821Z >>> # The types and a possible ordering of mappings do not have to match. They only 2025-01-24T01:52:01.4879034Z >>> # have to have the same set of keys and their elements have to match. 2025-01-24T01:52:01.4879406Z >>> expected = OrderedDict([("foo", foo), ("bar", bar), ("baz", baz)]) 2025-01-24T01:52:01.4879550Z >>> actual = {"baz": baz, "bar": bar, "foo": foo} 2025-01-24T01:52:01.4879710Z >>> torch.testing.assert_close(actual, expected) 2025-01-24T01:52:01.4879798Z 2025-01-24T01:52:01.4879938Z >>> expected = torch.tensor([1.0, 2.0, 3.0]) 2025-01-24T01:52:01.4880052Z >>> actual = expected.clone() 2025-01-24T01:52:01.4880226Z >>> # By default, directly related instances can be compared 2025-01-24T01:52:01.4880456Z >>> torch.testing.assert_close(torch.nn.Parameter(actual), expected) 2025-01-24T01:52:01.4880658Z >>> # This check can be made more strict with allow_subclasses=False 2025-01-24T01:52:01.4880784Z >>> torch.testing.assert_close( 2025-01-24T01:52:01.4881084Z ... torch.nn.Parameter(actual), expected, allow_subclasses=False 2025-01-24T01:52:01.4881188Z ... ) 2025-01-24T01:52:01.4881314Z Traceback (most recent call last): 2025-01-24T01:52:01.4881404Z ... 2025-01-24T01:52:01.4881623Z TypeError: No comparison pair was able to handle inputs of type 2025-01-24T01:52:01.4881845Z and . 2025-01-24T01:52:01.4882082Z >>> # If the inputs are not directly related, they are never considered close 2025-01-24T01:52:01.4882262Z >>> torch.testing.assert_close(actual.numpy(), expected) 2025-01-24T01:52:01.4882393Z Traceback (most recent call last): 2025-01-24T01:52:01.4882492Z ... 2025-01-24T01:52:01.4882826Z TypeError: No comparison pair was able to handle inputs of type 2025-01-24T01:52:01.4882948Z and . 2025-01-24T01:52:01.4883258Z >>> # Exceptions to these rules are Python scalars. They can be checked regardless of 2025-01-24T01:52:01.4883390Z >>> # their type if check_dtype=False. 2025-01-24T01:52:01.4883568Z >>> torch.testing.assert_close(1.0, 1, check_dtype=False) 2025-01-24T01:52:01.4883659Z 2025-01-24T01:52:01.4883779Z >>> # NaN != NaN by default. 2025-01-24T01:52:01.4883913Z >>> expected = torch.tensor(float("Nan")) 2025-01-24T01:52:01.4884034Z >>> actual = expected.clone() 2025-01-24T01:52:01.4884187Z >>> torch.testing.assert_close(actual, expected) 2025-01-24T01:52:01.4884322Z Traceback (most recent call last): 2025-01-24T01:52:01.4884412Z ... 2025-01-24T01:52:01.4884544Z AssertionError: Scalars are not close! 2025-01-24T01:52:01.4884653Z 2025-01-24T01:52:01.4884767Z Expected nan but got nan. 2025-01-24T01:52:01.4884929Z Absolute difference: nan (up to 1e-05 allowed) 2025-01-24T01:52:01.4885084Z Relative difference: nan (up to 1.3e-06 allowed) 2025-01-24T01:52:01.4885293Z >>> torch.testing.assert_close(actual, expected, equal_nan=True) 2025-01-24T01:52:01.4885388Z 2025-01-24T01:52:01.4885521Z >>> expected = torch.tensor([1.0, 2.0, 3.0]) 2025-01-24T01:52:01.4885654Z >>> actual = torch.tensor([1.0, 4.0, 5.0]) 2025-01-24T01:52:01.4885808Z >>> # The default error message can be overwritten. 2025-01-24T01:52:01.4886109Z >>> torch.testing.assert_close(actual, expected, msg="Argh, the tensors are not close!") 2025-01-24T01:52:01.4886234Z Traceback (most recent call last): 2025-01-24T01:52:01.4886324Z ... 2025-01-24T01:52:01.4886491Z AssertionError: Argh, the tensors are not close! 2025-01-24T01:52:01.4886720Z >>> # If msg is a callable, it can be used to augment the generated message with 2025-01-24T01:52:01.4886837Z >>> # extra information 2025-01-24T01:52:01.4886961Z >>> torch.testing.assert_close( 2025-01-24T01:52:01.4887178Z ... actual, expected, msg=lambda msg: f"Header\n\n{msg}\n\nFooter" 2025-01-24T01:52:01.4887267Z ... ) 2025-01-24T01:52:01.4887391Z Traceback (most recent call last): 2025-01-24T01:52:01.4887489Z ... 2025-01-24T01:52:01.4887602Z AssertionError: Header 2025-01-24T01:52:01.4887704Z 2025-01-24T01:52:01.4887818Z Tensor-likes are not close! 2025-01-24T01:52:01.4887917Z 2025-01-24T01:52:01.4888043Z Mismatched elements: 2 / 3 (66.7%) 2025-01-24T01:52:01.4888275Z Greatest absolute difference: 2.0 at index (1,) (up to 1e-05 allowed) 2025-01-24T01:52:01.4888521Z Greatest relative difference: 1.0 at index (1,) (up to 1.3e-06 allowed) 2025-01-24T01:52:01.4888617Z 2025-01-24T01:52:01.4888721Z Footer 2025-01-24T01:52:01.4888848Z 2025-01-24T01:52:01.4889142Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:52:01.4889239Z 2025-01-24T01:52:01.4889346Z warnings.warn(msg) 2025-01-24T01:52:01.4889443Z 2025-01-24T01:52:01.4889681Z --- Parse Warning: 98 / 106 --- 2025-01-24T01:52:01.4890607Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=register_pytree_node in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py line=100. 2025-01-24T01:52:01.4890886Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:52:01.4891037Z Register a container-like type as pytree node. 2025-01-24T01:52:01.4891135Z 2025-01-24T01:52:01.4891256Z Args: 2025-01-24T01:52:01.4891463Z cls (type): A Python type to treat as an internal pytree node. 2025-01-24T01:52:01.4891775Z flatten_fn (callable): A function to be used during flattening, taking an instance of 2025-01-24T01:52:01.4892033Z ``cls`` and returning a pair, with (1) an iterable for the children to be flattened 2025-01-24T01:52:01.4892336Z recursively, and (2) some hashable auxiliary data to be stored in the treespec and to be 2025-01-24T01:52:01.4892462Z passed to the ``unflatten_fn``. 2025-01-24T01:52:01.4892755Z unflatten_fn (callable): A function taking two arguments: the auxiliary data that was 2025-01-24T01:52:01.4893026Z returned by ``flatten_fn`` and stored in the treespec, and the unflattened children. 2025-01-24T01:52:01.4893200Z The function should return an instance of ``cls``. 2025-01-24T01:52:01.4893474Z serialized_type_name (str, optional): A keyword argument used to specify the fully 2025-01-24T01:52:01.4893655Z qualified name used when serializing the tree spec. 2025-01-24T01:52:01.4893976Z to_dumpable_context (callable, optional): An optional keyword argument to custom specify how 2025-01-24T01:52:01.4894264Z to convert the context of the pytree to a custom json dumpable representation. This is 2025-01-24T01:52:01.4894547Z used for json serialization, which is being used in :mod:`torch.export` right now. 2025-01-24T01:52:01.4894855Z from_dumpable_context (callable, optional): An optional keyword argument to custom specify 2025-01-24T01:52:01.4895135Z how to convert the custom json dumpable representation of the context back to the 2025-01-24T01:52:01.4895397Z original context. This is used for json deserialization, which is being used in 2025-01-24T01:52:01.4895533Z :mod:`torch.export` right now. 2025-01-24T01:52:01.4895625Z 2025-01-24T01:52:01.4895727Z Example:: 2025-01-24T01:52:01.4895826Z 2025-01-24T01:52:01.4895929Z >>> # xdoctest: +SKIP 2025-01-24T01:52:01.4896105Z >>> # Registry a Python type with lambda functions 2025-01-24T01:52:01.4896211Z >>> register_pytree_node( 2025-01-24T01:52:01.4896315Z ... set, 2025-01-24T01:52:01.4896442Z ... lambda s: (sorted(s), None, None), 2025-01-24T01:52:01.4896569Z ... lambda children, _: set(children), 2025-01-24T01:52:01.4896671Z ... ) 2025-01-24T01:52:01.4896755Z 2025-01-24T01:52:01.4897029Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:52:01.4897115Z 2025-01-24T01:52:01.4897218Z warnings.warn(msg) 2025-01-24T01:52:01.4897312Z 2025-01-24T01:52:01.4897507Z --- Parse Warning: 99 / 106 --- 2025-01-24T01:52:01.4898495Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=SelectiveCheckpointContext in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/checkpoint.py line=1200. 2025-01-24T01:52:01.4898815Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:52:01.4898918Z 2025-01-24T01:52:01.4899134Z Context passed to policy function during selective checkpointing. 2025-01-24T01:52:01.4899221Z 2025-01-24T01:52:01.4899461Z This class is used to pass relevant metadata to the policy function during 2025-01-24T01:52:01.4899730Z selective checkpointing. The metadata includes whether the current invocation 2025-01-24T01:52:01.4899915Z of the policy function is during recomputation or not. 2025-01-24T01:52:01.4900002Z 2025-01-24T01:52:01.4900108Z Example: 2025-01-24T01:52:01.4900214Z >>> # xdoctest: +SKIP(stub) 2025-01-24T01:52:01.4900305Z >>> 2025-01-24T01:52:01.4900450Z >>> def policy_fn(ctx, op, *args, **kwargs): 2025-01-24T01:52:01.4900584Z >>> print(ctx.is_recompute) 2025-01-24T01:52:01.4900687Z >>> 2025-01-24T01:52:01.4900989Z >>> context_fn = functools.partial(create_selective_checkpoint_contexts, policy_fn) 2025-01-24T01:52:01.4901083Z >>> 2025-01-24T01:52:01.4901241Z >>> out = torch.utils.checkpoint.checkpoint( 2025-01-24T01:52:01.4901338Z >>> fn, x, y, 2025-01-24T01:52:01.4901457Z >>> use_reentrant=False, 2025-01-24T01:52:01.4901566Z >>> context_fn=context_fn, 2025-01-24T01:52:01.4901654Z >>> ) 2025-01-24T01:52:01.4901750Z 2025-01-24T01:52:01.4902008Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:52:01.4902110Z 2025-01-24T01:52:01.4902208Z warnings.warn(msg) 2025-01-24T01:52:01.4902311Z 2025-01-24T01:52:01.4902504Z --- Parse Warning: 100 / 106 --- 2025-01-24T01:52:01.4903502Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=create_selective_checkpoint_contexts in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/checkpoint.py line=1334. 2025-01-24T01:52:01.4903785Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:52:01.4903874Z 2025-01-24T01:52:01.4904136Z Helper to avoid recomputing certain ops during activation checkpointing. 2025-01-24T01:52:01.4904225Z 2025-01-24T01:52:01.4904461Z Use this with `torch.utils.checkpoint.checkpoint` to control which 2025-01-24T01:52:01.4904630Z operations are recomputed during the backward pass. 2025-01-24T01:52:01.4904721Z 2025-01-24T01:52:01.4904826Z Args: 2025-01-24T01:52:01.4904956Z policy_fn_or_list (Callable or List): 2025-01-24T01:52:01.4905141Z - If a policy function is provided, it should accept a 2025-01-24T01:52:01.4905393Z :class:`SelectiveCheckpointContext`, the :class:`OpOverload`, args and 2025-01-24T01:52:01.4905608Z kwargs to the op, and return a :class:`CheckpointPolicy` enum value 2025-01-24T01:52:01.4905870Z indicating whether the execution of the op should be recomputed or not. 2025-01-24T01:52:01.4906088Z - If a list of operations is provided, it is equivalent to a policy 2025-01-24T01:52:01.4906386Z returning `CheckpointPolicy.MUST_SAVE` for the specified 2025-01-24T01:52:01.4906618Z operations and `CheckpointPolicy.PREFER_RECOMPUTE` for all other 2025-01-24T01:52:01.4906735Z operations. 2025-01-24T01:52:01.4906954Z allow_cache_entry_mutation (bool, optional): By default, an error is 2025-01-24T01:52:01.4907172Z raised if any tensors cached by selective activation checkpoint are 2025-01-24T01:52:01.4907399Z mutated in order to ensure correctness. If set to `True`, this check 2025-01-24T01:52:01.4907498Z is disabled. 2025-01-24T01:52:01.4907608Z Returns: 2025-01-24T01:52:01.4907729Z A tuple of two context managers. 2025-01-24T01:52:01.4907831Z 2025-01-24T01:52:01.4907925Z Example: 2025-01-24T01:52:01.4908046Z >>> # xdoctest: +REQUIRES(LINUX) 2025-01-24T01:52:01.4908221Z >>> import functools 2025-01-24T01:52:01.4908311Z >>> 2025-01-24T01:52:01.4908454Z >>> x = torch.rand(10, 10, requires_grad=True) 2025-01-24T01:52:01.4908588Z >>> y = torch.rand(10, 10, requires_grad=True) 2025-01-24T01:52:01.4908681Z >>> 2025-01-24T01:52:01.4908788Z >>> ops_to_save = [ 2025-01-24T01:52:01.4908908Z >>> torch.ops.aten.mm.default, 2025-01-24T01:52:01.4909007Z >>> ] 2025-01-24T01:52:01.4909095Z >>> 2025-01-24T01:52:01.4909228Z >>> def policy_fn(ctx, op, *args, **kwargs): 2025-01-24T01:52:01.4909343Z >>> if op in ops_to_save: 2025-01-24T01:52:01.4909480Z >>> return CheckpointPolicy.MUST_SAVE 2025-01-24T01:52:01.4909585Z >>> else: 2025-01-24T01:52:01.4909761Z >>> return CheckpointPolicy.PREFER_RECOMPUTE 2025-01-24T01:52:01.4909865Z >>> 2025-01-24T01:52:01.4910139Z >>> context_fn = functools.partial(create_selective_checkpoint_contexts, policy_fn) 2025-01-24T01:52:01.4910259Z >>> 2025-01-24T01:52:01.4910374Z >>> # or equivalently 2025-01-24T01:52:01.4910654Z >>> context_fn = functools.partial(create_selective_checkpoint_contexts, ops_to_save) 2025-01-24T01:52:01.4910751Z >>> 2025-01-24T01:52:01.4910850Z >>> def fn(x, y): 2025-01-24T01:52:01.4911056Z >>> return torch.sigmoid(torch.matmul(torch.matmul(x, y), y)) * y 2025-01-24T01:52:01.4911157Z >>> 2025-01-24T01:52:01.4911306Z >>> out = torch.utils.checkpoint.checkpoint( 2025-01-24T01:52:01.4911412Z >>> fn, x, y, 2025-01-24T01:52:01.4911521Z >>> use_reentrant=False, 2025-01-24T01:52:01.4911639Z >>> context_fn=context_fn, 2025-01-24T01:52:01.4911726Z >>> ) 2025-01-24T01:52:01.4911817Z 2025-01-24T01:52:01.4912086Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:52:01.4912179Z 2025-01-24T01:52:01.4912294Z warnings.warn(msg) 2025-01-24T01:52:01.4912385Z 2025-01-24T01:52:01.4912597Z --- Parse Warning: 101 / 106 --- 2025-01-24T01:52:01.4913517Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=CppExtension in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/cpp_extension.py line=965. 2025-01-24T01:52:01.4913786Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:52:01.4913885Z 2025-01-24T01:52:01.4914037Z Create a :class:`setuptools.Extension` for C++. 2025-01-24T01:52:01.4914137Z 2025-01-24T01:52:01.4914381Z Convenience method that creates a :class:`setuptools.Extension` with the 2025-01-24T01:52:01.4914616Z bare minimum (but often sufficient) arguments to build a C++ extension. 2025-01-24T01:52:01.4914717Z 2025-01-24T01:52:01.4914934Z All arguments are forwarded to the :class:`setuptools.Extension` 2025-01-24T01:52:01.4915108Z constructor. Full list arguments can be found at 2025-01-24T01:52:01.4915443Z https://setuptools.pypa.io/en/latest/userguide/ext_modules.html#extension-api-reference 2025-01-24T01:52:01.4915547Z 2025-01-24T01:52:01.4915642Z .. note:: 2025-01-24T01:52:01.4915879Z The PyTorch python API (as provided in libtorch_python) cannot be built 2025-01-24T01:52:01.4916101Z with the flag ``py_limited_api=True``. When this flag is passed, it is 2025-01-24T01:52:01.4916304Z the user's responsibility in their library to not use APIs from 2025-01-24T01:52:01.4916549Z libtorch_python (in particular pytorch/python bindings) and to only use 2025-01-24T01:52:01.4916771Z APIs from libtorch (aten objects, operators and the dispatcher). For 2025-01-24T01:52:01.4917007Z example, to give access to custom ops from python, the library should 2025-01-24T01:52:01.4917142Z register the ops through the dispatcher. 2025-01-24T01:52:01.4917235Z 2025-01-24T01:52:01.4917371Z Example: 2025-01-24T01:52:01.4917500Z >>> # xdoctest: +SKIP 2025-01-24T01:52:01.4917667Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2025-01-24T01:52:01.4917785Z >>> from setuptools import setup 2025-01-24T01:52:01.4918013Z >>> from torch.utils.cpp_extension import BuildExtension, CppExtension 2025-01-24T01:52:01.4918117Z >>> setup( 2025-01-24T01:52:01.4918225Z ... name='extension', 2025-01-24T01:52:01.4918335Z ... ext_modules=[ 2025-01-24T01:52:01.4918440Z ... CppExtension( 2025-01-24T01:52:01.4918562Z ... name='extension', 2025-01-24T01:52:01.4918685Z ... sources=['extension.cpp'], 2025-01-24T01:52:01.4918811Z ... extra_compile_args=['-g'], 2025-01-24T01:52:01.4919011Z ... extra_link_args=['-Wl,--no-as-needed', '-lm']) 2025-01-24T01:52:01.4919105Z ... ], 2025-01-24T01:52:01.4919251Z ... cmdclass={ 2025-01-24T01:52:01.4919378Z ... 'build_ext': BuildExtension 2025-01-24T01:52:01.4919471Z ... }) 2025-01-24T01:52:01.4919566Z 2025-01-24T01:52:01.4919830Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:52:01.4919928Z 2025-01-24T01:52:01.4920031Z warnings.warn(msg) 2025-01-24T01:52:01.4920121Z 2025-01-24T01:52:01.4920324Z --- Parse Warning: 102 / 106 --- 2025-01-24T01:52:01.4921240Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=CUDAExtension in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/cpp_extension.py line=1027. 2025-01-24T01:52:01.4921518Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:52:01.4921613Z 2025-01-24T01:52:01.4921789Z Create a :class:`setuptools.Extension` for CUDA/C++. 2025-01-24T01:52:01.4921880Z 2025-01-24T01:52:01.4922135Z Convenience method that creates a :class:`setuptools.Extension` with the 2025-01-24T01:52:01.4922349Z bare minimum (but often sufficient) arguments to build a CUDA/C++ 2025-01-24T01:52:01.4922588Z extension. This includes the CUDA include path, library path and runtime 2025-01-24T01:52:01.4922689Z library. 2025-01-24T01:52:01.4922777Z 2025-01-24T01:52:01.4922999Z All arguments are forwarded to the :class:`setuptools.Extension` 2025-01-24T01:52:01.4923157Z constructor. Full list arguments can be found at 2025-01-24T01:52:01.4923488Z https://setuptools.pypa.io/en/latest/userguide/ext_modules.html#extension-api-reference 2025-01-24T01:52:01.4923586Z 2025-01-24T01:52:01.4923681Z .. note:: 2025-01-24T01:52:01.4923928Z The PyTorch python API (as provided in libtorch_python) cannot be built 2025-01-24T01:52:01.4924144Z with the flag ``py_limited_api=True``. When this flag is passed, it is 2025-01-24T01:52:01.4924358Z the user's responsibility in their library to not use APIs from 2025-01-24T01:52:01.4924599Z libtorch_python (in particular pytorch/python bindings) and to only use 2025-01-24T01:52:01.4924822Z APIs from libtorch (aten objects, operators and the dispatcher). For 2025-01-24T01:52:01.4925051Z example, to give access to custom ops from python, the library should 2025-01-24T01:52:01.4925185Z register the ops through the dispatcher. 2025-01-24T01:52:01.4925284Z 2025-01-24T01:52:01.4925373Z Example: 2025-01-24T01:52:01.4925486Z >>> # xdoctest: +SKIP 2025-01-24T01:52:01.4925640Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2025-01-24T01:52:01.4925759Z >>> from setuptools import setup 2025-01-24T01:52:01.4925993Z >>> from torch.utils.cpp_extension import BuildExtension, CUDAExtension 2025-01-24T01:52:01.4926086Z >>> setup( 2025-01-24T01:52:01.4926206Z ... name='cuda_extension', 2025-01-24T01:52:01.4926305Z ... ext_modules=[ 2025-01-24T01:52:01.4926456Z ... CUDAExtension( 2025-01-24T01:52:01.4926606Z ... name='cuda_extension', 2025-01-24T01:52:01.4926774Z ... sources=['extension.cpp', 'extension_kernel.cu'], 2025-01-24T01:52:01.4926913Z ... extra_compile_args={'cxx': ['-g'], 2025-01-24T01:52:01.4927042Z ... 'nvcc': ['-O2']}, 2025-01-24T01:52:01.4927215Z ... extra_link_args=['-Wl,--no-as-needed', '-lcuda']) 2025-01-24T01:52:01.4927309Z ... ], 2025-01-24T01:52:01.4927406Z ... cmdclass={ 2025-01-24T01:52:01.4927535Z ... 'build_ext': BuildExtension 2025-01-24T01:52:01.4927627Z ... }) 2025-01-24T01:52:01.4927727Z 2025-01-24T01:52:01.4927864Z Compute capabilities: 2025-01-24T01:52:01.4927955Z 2025-01-24T01:52:01.4928274Z By default the extension will be compiled to run on all archs of the cards visible during the 2025-01-24T01:52:01.4928605Z building process of the extension, plus PTX. If down the road a new card is installed the 2025-01-24T01:52:01.4928916Z extension may need to be recompiled. If a visible card has a compute capability (CC) that's 2025-01-24T01:52:01.4929226Z newer than the newest version for which your nvcc can build fully-compiled binaries, PyTorch 2025-01-24T01:52:01.4929530Z will make nvcc fall back to building kernels with the newest version of PTX your nvcc does 2025-01-24T01:52:01.4929660Z support (see below for details on PTX). 2025-01-24T01:52:01.4929752Z 2025-01-24T01:52:01.4930078Z You can override the default behavior using `TORCH_CUDA_ARCH_LIST` to explicitly specify which 2025-01-24T01:52:01.4930206Z CCs you want the extension to support: 2025-01-24T01:52:01.4930301Z 2025-01-24T01:52:01.4930502Z ``TORCH_CUDA_ARCH_LIST="6.1 8.6" python build_my_extension.py`` 2025-01-24T01:52:01.4930750Z ``TORCH_CUDA_ARCH_LIST="5.2 6.0 6.1 7.0 7.5 8.0 8.6+PTX" python build_my_extension.py`` 2025-01-24T01:52:01.4930845Z 2025-01-24T01:52:01.4931174Z The +PTX option causes extension kernel binaries to include PTX instructions for the specified 2025-01-24T01:52:01.4931508Z CC. PTX is an intermediate representation that allows kernels to runtime-compile for any CC >= 2025-01-24T01:52:01.4931820Z the specified CC (for example, 8.6+PTX generates PTX that can runtime-compile for any GPU with 2025-01-24T01:52:01.4932131Z CC >= 8.6). This improves your binary's forward compatibility. However, relying on older PTX to 2025-01-24T01:52:01.4932463Z provide forward compat by runtime-compiling for newer CCs can modestly reduce performance on 2025-01-24T01:52:01.4932747Z those newer CCs. If you know exact CC(s) of the GPUs you want to target, you're always better 2025-01-24T01:52:01.4937041Z off specifying them individually. For example, if you want your extension to run on 8.0 and 8.6, 2025-01-24T01:52:01.4937372Z "8.0+PTX" would work functionally because it includes PTX that can runtime-compile for 8.6, but 2025-01-24T01:52:01.4937486Z "8.0 8.6" would be better. 2025-01-24T01:52:01.4937574Z 2025-01-24T01:52:01.4937894Z Note that while it's possible to include all supported archs, the more archs get included the 2025-01-24T01:52:01.4938194Z slower the building process will be, as it will build a separate kernel image for each arch. 2025-01-24T01:52:01.4938293Z 2025-01-24T01:52:01.4938632Z Note that CUDA-11.5 nvcc will hit internal compiler error while parsing torch/extension.h on Windows. 2025-01-24T01:52:01.4938854Z To workaround the issue, move python binding logic to pure C++ file. 2025-01-24T01:52:01.4938940Z 2025-01-24T01:52:01.4939039Z Example use: 2025-01-24T01:52:01.4939142Z #include 2025-01-24T01:52:01.4939316Z at::Tensor SigmoidAlphaBlendForwardCuda(....) 2025-01-24T01:52:01.4939405Z 2025-01-24T01:52:01.4939502Z Instead of: 2025-01-24T01:52:01.4939616Z #include 2025-01-24T01:52:01.4939889Z torch::Tensor SigmoidAlphaBlendForwardCuda(...) 2025-01-24T01:52:01.4939979Z 2025-01-24T01:52:01.4940265Z Currently open issue for nvcc bug: https://github.com/pytorch/pytorch/issues/69460 2025-01-24T01:52:01.4940790Z Complete workaround code example: https://github.com/facebookresearch/pytorch3d/commit/cb170ac024a949f1f9614ffe6af1c38d972f7d48 2025-01-24T01:52:01.4940879Z 2025-01-24T01:52:01.4940998Z Relocatable device code linking: 2025-01-24T01:52:01.4941085Z 2025-01-24T01:52:01.4941370Z If you want to reference device symbols across compilation units (across object files), 2025-01-24T01:52:01.4941645Z the object files need to be built with `relocatable device code` (-rdc=true or -dc). 2025-01-24T01:52:01.4942039Z An exception to this rule is "dynamic parallelism" (nested kernel launches) which is not used a lot anymore. 2025-01-24T01:52:01.4942382Z `Relocatable device code` is less optimized so it needs to be used only on object files that need it. 2025-01-24T01:52:01.4942746Z Using `-dlto` (Device Link Time Optimization) at the device code compilation step and `dlink` step 2025-01-24T01:52:01.4942944Z helps reduce the protentional perf degradation of `-rdc`. 2025-01-24T01:52:01.4943121Z Note that it needs to be used at both steps to be useful. 2025-01-24T01:52:01.4943213Z 2025-01-24T01:52:01.4943584Z If you have `rdc` objects you need to have an extra `-dlink` (device linking) step before the CPU symbol linking step. 2025-01-24T01:52:01.4943761Z There is also a case where `-dlink` is used without `-rdc`: 2025-01-24T01:52:01.4944032Z when an extension is linked against a static lib containing rdc-compiled objects 2025-01-24T01:52:01.4944252Z like the [NVSHMEM library](https://developer.nvidia.com/nvshmem). 2025-01-24T01:52:01.4944345Z 2025-01-24T01:52:01.4944553Z Note: Ninja is required to build a CUDA Extension with RDC linking. 2025-01-24T01:52:01.4944646Z 2025-01-24T01:52:01.4944740Z Example: 2025-01-24T01:52:01.4944848Z >>> # xdoctest: +SKIP 2025-01-24T01:52:01.4945009Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2025-01-24T01:52:01.4945113Z >>> CUDAExtension( 2025-01-24T01:52:01.4945233Z ... name='cuda_extension', 2025-01-24T01:52:01.4945401Z ... sources=['extension.cpp', 'extension_kernel.cu'], 2025-01-24T01:52:01.4945502Z ... dlink=True, 2025-01-24T01:52:01.4945636Z ... dlink_libraries=["dlink_lib"], 2025-01-24T01:52:01.4945769Z ... extra_compile_args={'cxx': ['-g'], 2025-01-24T01:52:01.4945903Z ... 'nvcc': ['-O2', '-rdc=true']}) 2025-01-24T01:52:01.4945994Z 2025-01-24T01:52:01.4946360Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:52:01.4946453Z 2025-01-24T01:52:01.4946556Z warnings.warn(msg) 2025-01-24T01:52:01.4946652Z 2025-01-24T01:52:01.4946907Z --- Parse Warning: 103 / 106 --- 2025-01-24T01:52:01.4947785Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=load in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/cpp_extension.py line=1309. 2025-01-24T01:52:01.4948058Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:52:01.4948145Z 2025-01-24T01:52:01.4948308Z Load a PyTorch C++ extension just-in-time (JIT). 2025-01-24T01:52:01.4948400Z 2025-01-24T01:52:01.4948624Z To load an extension, a Ninja build file is emitted, which is used to 2025-01-24T01:52:01.4948838Z compile the given sources into a dynamic library. This library is 2025-01-24T01:52:01.4949075Z subsequently loaded into the current Python process as a module and 2025-01-24T01:52:01.4949213Z returned from this function, ready for use. 2025-01-24T01:52:01.4949298Z 2025-01-24T01:52:01.4949560Z By default, the directory to which the build file is emitted and the 2025-01-24T01:52:01.4949831Z resulting library compiled to is ``/torch_extensions/``, where 2025-01-24T01:52:01.4950048Z ```` is the temporary folder on the current platform and ```` 2025-01-24T01:52:01.4950268Z the name of the extension. This location can be overridden in two ways. 2025-01-24T01:52:01.4950490Z First, if the ``TORCH_EXTENSIONS_DIR`` environment variable is set, it 2025-01-24T01:52:01.4950716Z replaces ``/torch_extensions`` and all extensions will be compiled 2025-01-24T01:52:01.4950939Z into subfolders of this directory. Second, if the ``build_directory`` 2025-01-24T01:52:01.4951185Z argument to this function is supplied, it overrides the entire path, i.e. 2025-01-24T01:52:01.4951384Z the library will be compiled into that folder directly. 2025-01-24T01:52:01.4951478Z 2025-01-24T01:52:01.4951691Z To compile the sources, the default system compiler (``c++``) is used, 2025-01-24T01:52:01.4951976Z which can be overridden by setting the ``CXX`` environment variable. To pass 2025-01-24T01:52:01.4952207Z additional arguments to the compilation process, ``extra_cflags`` or 2025-01-24T01:52:01.4952435Z ``extra_ldflags`` can be provided. For example, to compile your extension 2025-01-24T01:52:01.4952663Z with optimizations, pass ``extra_cflags=['-O3']``. You can also use 2025-01-24T01:52:01.4952825Z ``extra_cflags`` to pass further include directories. 2025-01-24T01:52:01.4952918Z 2025-01-24T01:52:01.4953159Z CUDA support with mixed compilation is provided. Simply pass CUDA source 2025-01-24T01:52:01.4953354Z files (``.cu`` or ``.cuh``) along with other sources. Such files will be 2025-01-24T01:52:01.4953605Z detected and compiled with nvcc rather than the C++ compiler. This includes 2025-01-24T01:52:01.4953819Z passing the CUDA lib64 directory as a library directory, and linking 2025-01-24T01:52:01.4953989Z ``cudart``. You can pass additional flags to nvcc via 2025-01-24T01:52:01.4954208Z ``extra_cuda_cflags``, just like with ``extra_cflags`` for C++. Various 2025-01-24T01:52:01.4954458Z heuristics for finding the CUDA install directory are used, which usually 2025-01-24T01:52:01.4954677Z work fine. If not, setting the ``CUDA_HOME`` environment variable is the 2025-01-24T01:52:01.4954780Z safest option. 2025-01-24T01:52:01.4954868Z 2025-01-24T01:52:01.4954957Z Args: 2025-01-24T01:52:01.4955180Z name: The name of the extension to build. This MUST be the same as the 2025-01-24T01:52:01.4955296Z name of the pybind11 module! 2025-01-24T01:52:01.4955508Z sources: A list of relative or absolute paths to C++ source files. 2025-01-24T01:52:01.4955741Z extra_cflags: optional list of compiler flags to forward to the build. 2025-01-24T01:52:01.4955966Z extra_cuda_cflags: optional list of compiler flags to forward to nvcc 2025-01-24T01:52:01.4956087Z when building CUDA sources. 2025-01-24T01:52:01.4956312Z extra_ldflags: optional list of linker flags to forward to the build. 2025-01-24T01:52:01.4956540Z extra_include_paths: optional list of include directories to forward 2025-01-24T01:52:01.4956638Z to the build. 2025-01-24T01:52:01.4956827Z build_directory: optional path to use as build workspace. 2025-01-24T01:52:01.4957008Z verbose: If ``True``, turns on verbose logging of load steps. 2025-01-24T01:52:01.4957238Z with_cuda: Determines whether CUDA headers and libraries are added to 2025-01-24T01:52:01.4957408Z the build. If set to ``None`` (default), this value is 2025-01-24T01:52:01.4957615Z automatically determined based on the existence of ``.cu`` or 2025-01-24T01:52:01.4957800Z ``.cuh`` in ``sources``. Set it to `True`` to force CUDA headers 2025-01-24T01:52:01.4957920Z and libraries to be included. 2025-01-24T01:52:01.4958134Z is_python_module: If ``True`` (default), imports the produced shared 2025-01-24T01:52:01.4958382Z library as a Python module. If ``False``, behavior depends on 2025-01-24T01:52:01.4958487Z ``is_standalone``. 2025-01-24T01:52:01.4958705Z is_standalone: If ``False`` (default) loads the constructed extension 2025-01-24T01:52:01.4958907Z into the process as a plain dynamic library. If ``True``, build a 2025-01-24T01:52:01.4959024Z standalone executable. 2025-01-24T01:52:01.4959113Z 2025-01-24T01:52:01.4959210Z Returns: 2025-01-24T01:52:01.4959329Z If ``is_python_module`` is ``True``: 2025-01-24T01:52:01.4959516Z Returns the loaded PyTorch extension as a Python module. 2025-01-24T01:52:01.4959612Z 2025-01-24T01:52:01.4959843Z If ``is_python_module`` is ``False`` and ``is_standalone`` is ``False``: 2025-01-24T01:52:01.4960066Z Returns nothing. (The shared library is loaded into the process as 2025-01-24T01:52:01.4960208Z a side effect.) 2025-01-24T01:52:01.4960309Z 2025-01-24T01:52:01.4960428Z If ``is_standalone`` is ``True``. 2025-01-24T01:52:01.4960633Z Return the path to the executable. (On Windows, TORCH_LIB_PATH is 2025-01-24T01:52:01.4960822Z added to the PATH environment variable as a side effect.) 2025-01-24T01:52:01.4960912Z 2025-01-24T01:52:01.4961010Z Example: 2025-01-24T01:52:01.4961113Z >>> # xdoctest: +SKIP 2025-01-24T01:52:01.4961261Z >>> from torch.utils.cpp_extension import load 2025-01-24T01:52:01.4961372Z >>> module = load( 2025-01-24T01:52:01.4961478Z ... name='extension', 2025-01-24T01:52:01.4961649Z ... sources=['extension.cpp', 'extension_kernel.cu'], 2025-01-24T01:52:01.4961761Z ... extra_cflags=['-O2'], 2025-01-24T01:52:01.4961863Z ... verbose=True) 2025-01-24T01:52:01.4961955Z 2025-01-24T01:52:01.4962216Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:52:01.4962316Z 2025-01-24T01:52:01.4962422Z warnings.warn(msg) 2025-01-24T01:52:01.4962515Z 2025-01-24T01:52:01.4962724Z --- Parse Warning: 104 / 106 --- 2025-01-24T01:52:01.4963619Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=load_inline in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/cpp_extension.py line=1602. 2025-01-24T01:52:01.4963901Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:52:01.4963989Z 2025-01-24T01:52:01.4964211Z Load a PyTorch C++ extension just-in-time (JIT) from string sources. 2025-01-24T01:52:01.4964300Z 2025-01-24T01:52:01.4964546Z This function behaves exactly like :func:`load`, but takes its sources as 2025-01-24T01:52:01.4964786Z strings rather than filenames. These strings are stored to files in the 2025-01-24T01:52:01.4965003Z build directory, after which the behavior of :func:`load_inline` is 2025-01-24T01:52:01.4965127Z identical to :func:`load`. 2025-01-24T01:52:01.4965216Z 2025-01-24T01:52:01.4965313Z See `the 2025-01-24T01:52:01.4965646Z tests `_ 2025-01-24T01:52:01.4965777Z for good examples of using this function. 2025-01-24T01:52:01.4965877Z 2025-01-24T01:52:01.4966118Z Sources may omit two required parts of a typical non-inline C++ extension: 2025-01-24T01:52:01.4966373Z the necessary header includes, as well as the (pybind11) binding code. More 2025-01-24T01:52:01.4966620Z precisely, strings passed to ``cpp_sources`` are first concatenated into a 2025-01-24T01:52:01.4966821Z single ``.cpp`` file. This file is then prepended with ``#include 2025-01-24T01:52:01.4966929Z ``. 2025-01-24T01:52:01.4967016Z 2025-01-24T01:52:01.4967254Z Furthermore, if the ``functions`` argument is supplied, bindings will be 2025-01-24T01:52:01.4967554Z automatically generated for each function specified. ``functions`` can 2025-01-24T01:52:01.4967793Z either be a list of function names, or a dictionary mapping from function 2025-01-24T01:52:01.4968021Z names to docstrings. If a list is given, the name of each function is used 2025-01-24T01:52:01.4968127Z as its docstring. 2025-01-24T01:52:01.4968215Z 2025-01-24T01:52:01.4968436Z The sources in ``cuda_sources`` are concatenated into a separate ``.cu`` 2025-01-24T01:52:01.4968624Z file and prepended with ``torch/types.h``, ``cuda.h`` and 2025-01-24T01:52:01.4968840Z ``cuda_runtime.h`` includes. The ``.cpp`` and ``.cu`` files are compiled 2025-01-24T01:52:01.4969074Z separately, but ultimately linked into a single library. Note that no 2025-01-24T01:52:01.4969337Z bindings are generated for functions in ``cuda_sources`` per se. To bind 2025-01-24T01:52:01.4969566Z to a CUDA kernel, you must create a C++ function that calls it, and either 2025-01-24T01:52:01.4969818Z declare or define this C++ function in one of the ``cpp_sources`` (and 2025-01-24T01:52:01.4969941Z include its name in ``functions``). 2025-01-24T01:52:01.4970029Z 2025-01-24T01:52:01.4970217Z See :func:`load` for a description of arguments omitted below. 2025-01-24T01:52:01.4970314Z 2025-01-24T01:52:01.4970403Z Args: 2025-01-24T01:52:01.4970631Z cpp_sources: A string, or list of strings, containing C++ source code. 2025-01-24T01:52:01.4970860Z cuda_sources: A string, or list of strings, containing CUDA source code. 2025-01-24T01:52:01.4971078Z functions: A list of function names for which to generate function 2025-01-24T01:52:01.4971297Z bindings. If a dictionary is given, it should map function names to 2025-01-24T01:52:01.4971483Z docstrings (which are otherwise just the function names). 2025-01-24T01:52:01.4971726Z with_cuda: Determines whether CUDA headers and libraries are added to 2025-01-24T01:52:01.4971898Z the build. If set to ``None`` (default), this value is 2025-01-24T01:52:01.4972121Z automatically determined based on whether ``cuda_sources`` is 2025-01-24T01:52:01.4972280Z provided. Set it to ``True`` to force CUDA headers 2025-01-24T01:52:01.4972402Z and libraries to be included. 2025-01-24T01:52:01.4972615Z with_pytorch_error_handling: Determines whether pytorch error and 2025-01-24T01:52:01.4972819Z warning macros are handled by pytorch instead of pybind. To do 2025-01-24T01:52:01.4973052Z this, each function ``foo`` is called via an intermediary ``_safe_foo`` 2025-01-24T01:52:01.4973263Z function. This redirection might cause issues in obscure cases 2025-01-24T01:52:01.4973458Z of cpp. This flag should be set to ``False`` when this redirect 2025-01-24T01:52:01.4973559Z causes issues. 2025-01-24T01:52:01.4973651Z 2025-01-24T01:52:01.4973745Z Example: 2025-01-24T01:52:01.4973905Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2025-01-24T01:52:01.4974076Z >>> from torch.utils.cpp_extension import load_inline 2025-01-24T01:52:01.4974176Z >>> source = """ 2025-01-24T01:52:01.4974333Z at::Tensor sin_add(at::Tensor x, at::Tensor y) { 2025-01-24T01:52:01.4974437Z return x.sin() + y.sin(); 2025-01-24T01:52:01.4974523Z } 2025-01-24T01:52:01.4974620Z """ 2025-01-24T01:52:01.4974767Z >>> module = load_inline(name='inline_extension', 2025-01-24T01:52:01.4974900Z ... cpp_sources=[source], 2025-01-24T01:52:01.4975024Z ... functions=['sin_add']) 2025-01-24T01:52:01.4975117Z 2025-01-24T01:52:01.4975209Z .. note:: 2025-01-24T01:52:01.4975454Z Since load_inline will just-in-time compile the source code, please ensure 2025-01-24T01:52:01.4975695Z that you have the right toolchains installed in the runtime. For example, 2025-01-24T01:52:01.4975972Z when loading C++, make sure a C++ compiler is available. If you're loading 2025-01-24T01:52:01.4976226Z a CUDA extension, you will need to additionally install the corresponding CUDA 2025-01-24T01:52:01.4976481Z toolkit (nvcc and any other dependencies your code has). Compiling toolchains 2025-01-24T01:52:01.4976733Z are not included when you install torch and must be additionally installed. 2025-01-24T01:52:01.4976821Z 2025-01-24T01:52:01.4977077Z During compiling, by default, the Ninja backend uses #CPUS + 2 workers to build 2025-01-24T01:52:01.4977310Z the extension. This may use up too many resources on some systems. One 2025-01-24T01:52:01.4977535Z can control the number of workers by setting the `MAX_JOBS` environment 2025-01-24T01:52:01.4977693Z variable to a non-negative number. 2025-01-24T01:52:01.4977781Z 2025-01-24T01:52:01.4978044Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:52:01.4978158Z 2025-01-24T01:52:01.4978258Z warnings.warn(msg) 2025-01-24T01:52:01.4978346Z 2025-01-24T01:52:01.4978553Z --- Parse Warning: 105 / 106 --- 2025-01-24T01:52:01.4979763Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ThroughputBenchmark in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/throughput_benchmark.py line=61. 2025-01-24T01:52:01.4980038Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:52:01.4980135Z 2025-01-24T01:52:01.4980439Z This class is a wrapper around a c++ component throughput_benchmark::ThroughputBenchmark. 2025-01-24T01:52:01.4980528Z 2025-01-24T01:52:01.4980836Z This wrapper on the throughput_benchmark::ThroughputBenchmark component is responsible 2025-01-24T01:52:01.4981094Z for executing a PyTorch module (nn.Module or ScriptModule) under an inference 2025-01-24T01:52:01.4981344Z server like load. It can emulate multiple calling threads to a single module 2025-01-24T01:52:01.4981590Z provided. In the future we plan to enhance this component to support inter and 2025-01-24T01:52:01.4981849Z intra-op parallelism as well as multiple models running in a single process. 2025-01-24T01:52:01.4981936Z 2025-01-24T01:52:01.4982196Z Please note that even though nn.Module is supported, it might incur an overhead 2025-01-24T01:52:01.4982423Z from the need to hold GIL every time we execute Python code or pass around 2025-01-24T01:52:01.4982668Z inputs as Python objects. As soon as you have a ScriptModule version of your 2025-01-24T01:52:01.4982914Z model for inference deployment it is better to switch to using it in this 2025-01-24T01:52:01.4983004Z benchmark. 2025-01-24T01:52:01.4983100Z 2025-01-24T01:52:01.4983192Z Example:: 2025-01-24T01:52:01.4983278Z 2025-01-24T01:52:01.4983411Z >>> # xdoctest: +SKIP("undefined vars") 2025-01-24T01:52:01.4983562Z >>> from torch.utils import ThroughputBenchmark 2025-01-24T01:52:01.4983703Z >>> bench = ThroughputBenchmark(my_module) 2025-01-24T01:52:01.4983865Z >>> # Pre-populate benchmark's data set with the inputs 2025-01-24T01:52:01.4983970Z >>> for input in inputs: 2025-01-24T01:52:01.4984197Z ... # Both args and kwargs work, same as any PyTorch Module / ScriptModule 2025-01-24T01:52:01.4984331Z ... bench.add_input(input[0], x2=input[1]) 2025-01-24T01:52:01.4984539Z >>> # Inputs supplied above are randomly used during the execution 2025-01-24T01:52:01.4984648Z >>> stats = bench.benchmark( 2025-01-24T01:52:01.4984765Z ... num_calling_threads=4, 2025-01-24T01:52:01.4984874Z ... num_warmup_iters = 100, 2025-01-24T01:52:01.4984976Z ... num_iters = 1000, 2025-01-24T01:52:01.4985073Z ... ) 2025-01-24T01:52:01.4985257Z >>> print("Avg latency (ms): {}".format(stats.latency_avg_ms)) 2025-01-24T01:52:01.4985552Z >>> print("Number of iterations: {}".format(stats.num_iters)) 2025-01-24T01:52:01.4985641Z 2025-01-24T01:52:01.4985899Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:52:01.4985990Z 2025-01-24T01:52:01.4986093Z warnings.warn(msg) 2025-01-24T01:52:01.4986187Z 2025-01-24T01:52:01.4986496Z --- Parse Warning: 106 / 106 --- 2025-01-24T01:52:01.4987453Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=DistributedSampler in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/distributed.py line=18. 2025-01-24T01:52:01.4987721Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-01-24T01:52:01.4987971Z Sampler that restricts data loading to a subset of the dataset. 2025-01-24T01:52:01.4988066Z 2025-01-24T01:52:01.4988209Z It is especially useful in conjunction with 2025-01-24T01:52:01.4988505Z :class:`torch.nn.parallel.DistributedDataParallel`. In such a case, each 2025-01-24T01:52:01.4988771Z process can pass a :class:`~torch.utils.data.DistributedSampler` instance as a 2025-01-24T01:52:01.4989009Z :class:`~torch.utils.data.DataLoader` sampler, and load a subset of the 2025-01-24T01:52:01.4989147Z original dataset that is exclusive to it. 2025-01-24T01:52:01.4989238Z 2025-01-24T01:52:01.4989350Z .. note:: 2025-01-24T01:52:01.4989594Z Dataset is assumed to be of constant size and that any instance of it always 2025-01-24T01:52:01.4989750Z returns the same elements in the same order. 2025-01-24T01:52:01.4989838Z 2025-01-24T01:52:01.4989933Z Args: 2025-01-24T01:52:01.4990064Z dataset: Dataset used for sampling. 2025-01-24T01:52:01.4990288Z num_replicas (int, optional): Number of processes participating in 2025-01-24T01:52:01.4990552Z distributed training. By default, :attr:`world_size` is retrieved from the 2025-01-24T01:52:01.4990679Z current distributed group. 2025-01-24T01:52:01.4990932Z rank (int, optional): Rank of the current process within :attr:`num_replicas`. 2025-01-24T01:52:01.4991140Z By default, :attr:`rank` is retrieved from the current distributed 2025-01-24T01:52:01.4991245Z group. 2025-01-24T01:52:01.4991475Z shuffle (bool, optional): If ``True`` (default), sampler will shuffle the 2025-01-24T01:52:01.4991573Z indices. 2025-01-24T01:52:01.4991776Z seed (int, optional): random seed used to shuffle the sampler if 2025-01-24T01:52:01.4991993Z :attr:`shuffle=True`. This number should be identical across all 2025-01-24T01:52:01.4992173Z processes in the distributed group. Default: ``0``. 2025-01-24T01:52:01.4992390Z drop_last (bool, optional): if ``True``, then the sampler will drop the 2025-01-24T01:52:01.4992602Z tail of the data to make it evenly divisible across the number of 2025-01-24T01:52:01.4992803Z replicas. If ``False``, the sampler will add extra indices to make 2025-01-24T01:52:01.4993019Z the data evenly divisible across the replicas. Default: ``False``. 2025-01-24T01:52:01.4993113Z 2025-01-24T01:52:01.4993211Z .. warning:: 2025-01-24T01:52:01.4993414Z In distributed mode, calling the :meth:`set_epoch` method at 2025-01-24T01:52:01.4993671Z the beginning of each epoch **before** creating the :class:`DataLoader` iterator 2025-01-24T01:52:01.4993945Z is necessary to make shuffling work properly across multiple epochs. Otherwise, 2025-01-24T01:52:01.4994080Z the same ordering will be always used. 2025-01-24T01:52:01.4994171Z 2025-01-24T01:52:01.4994275Z Example:: 2025-01-24T01:52:01.4994363Z 2025-01-24T01:52:01.4994475Z >>> # xdoctest: +SKIP 2025-01-24T01:52:01.4994754Z >>> sampler = DistributedSampler(dataset) if is_distributed else None 2025-01-24T01:52:01.4994932Z >>> loader = DataLoader(dataset, shuffle=(sampler is None), 2025-01-24T01:52:01.4995063Z ... sampler=sampler) 2025-01-24T01:52:01.4995206Z >>> for epoch in range(start_epoch, n_epochs): 2025-01-24T01:52:01.4995321Z ... if is_distributed: 2025-01-24T01:52:01.4995441Z ... sampler.set_epoch(epoch) 2025-01-24T01:52:01.4995547Z ... train(loader) 2025-01-24T01:52:01.4995633Z 2025-01-24T01:52:01.4995892Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-01-24T01:52:01.4995988Z 2025-01-24T01:52:01.4996092Z warnings.warn(msg) 2025-01-24T01:52:01.4996228Z 2025-01-24T01:52:01.4996344Z  2025-01-24T01:52:01.4996522Z === Found 9 run-time warnings === 2025-01-24T01:52:01.4996746Z --- Runtime Warning: 1 / 9 --- 2025-01-24T01:52:01.4997019Z example = 2025-01-24T01:52:01.4998362Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py:1356: 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:1938.) 2025-01-24T01:52:01.4998484Z return super().refine_names(names) 2025-01-24T01:52:01.4998574Z 2025-01-24T01:52:01.4998759Z --- Runtime Warning: 2 / 9 --- 2025-01-24T01:52:01.4999074Z example = 2025-01-24T01:52:01.4999709Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py:285: UserWarning: Warning only once for all operators, other operators may also be overridden. 2025-01-24T01:52:01.5000032Z Overriding a previously registered kernel for the same operator and the same dispatch key 2025-01-24T01:52:01.5000248Z operator: aten::div.Tensor(Tensor self, Tensor other) -> Tensor 2025-01-24T01:52:01.5000554Z registered at /var/lib/jenkins/workspace/build/aten/src/ATen/RegisterSchema.cpp:6 2025-01-24T01:52:01.5000667Z dispatch key: CPU 2025-01-24T01:52:01.5001100Z previous kernel: registered at /var/lib/jenkins/workspace/aten/src/ATen/LegacyBatchingRegistrations.cpp:1079 2025-01-24T01:52:01.5001663Z new kernel: registered at /dev/null:811 (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/core/dispatch/OperatorEntry.cpp:161.) 2025-01-24T01:52:01.5001828Z impl_fn(self.ns, name.split("::")[-1], dispatch_key) 2025-01-24T01:52:01.5001914Z 2025-01-24T01:52:01.5002108Z --- Runtime Warning: 3 / 9 --- 2025-01-24T01:52:01.5002352Z example = 2025-01-24T01:52:01.5004187Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nested/__init__.py:117: UserWarning: The PyTorch API of nested tensors is in prototype stage and will change in the near future. We recommend specifying layout=torch.jagged when constructing a nested tensor, as this layout receives active development, has better operator coverage, and works with torch.compile. (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/NestedTensorImpl.cpp:182.) 2025-01-24T01:52:01.5004438Z return torch._nested_tensor_from_tensor_list(ts, dtype, None, device, None) 2025-01-24T01:52:01.5004532Z 2025-01-24T01:52:01.5004713Z --- Runtime Warning: 4 / 9 --- 2025-01-24T01:52:01.5004985Z example = 2025-01-24T01:52:01.5006622Z :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.) 2025-01-24T01:52:01.5006752Z 2025-01-24T01:52:01.5006934Z --- Runtime Warning: 5 / 9 --- 2025-01-24T01:52:01.5007257Z example = 2025-01-24T01:52:01.5008781Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/const_fold.py:264: 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 2025-01-24T01:52:01.5008963Z new_node = root_const_gm.graph.get_attr(in_node.target) 2025-01-24T01:52:01.5009081Z 2025-01-24T01:52:01.5009265Z --- Runtime Warning: 6 / 9 --- 2025-01-24T01:52:01.5009571Z example = 2025-01-24T01:52:01.5010646Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py:385: 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) 2025-01-24T01:52:01.5010755Z warnings.warn( 2025-01-24T01:52:01.5010844Z 2025-01-24T01:52:01.5011038Z --- Runtime Warning: 7 / 9 --- 2025-01-24T01:52:01.5011370Z example = 2025-01-24T01:52:01.5012447Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py:385: 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) 2025-01-24T01:52:01.5012550Z warnings.warn( 2025-01-24T01:52:01.5012635Z 2025-01-24T01:52:01.5012823Z --- Runtime Warning: 8 / 9 --- 2025-01-24T01:52:01.5013102Z example = 2025-01-24T01:52:01.5013920Z /opt/conda/envs/py_3.13/lib/python3.13/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`. 2025-01-24T01:52:01.5014043Z WeightNorm.apply(module, name, dim) 2025-01-24T01:52:01.5014139Z 2025-01-24T01:52:01.5014320Z --- Runtime Warning: 9 / 9 --- 2025-01-24T01:52:01.5014632Z example = 2025-01-24T01:52:01.5015446Z /opt/conda/envs/py_3.13/lib/python3.13/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`. 2025-01-24T01:52:01.5015572Z WeightNorm.apply(module, name, dim) 2025-01-24T01:52:01.5015664Z 2025-01-24T01:52:01.5015968Z === 338 passed, 371 skipped, 115 warnings in 12.50 seconds === 2025-01-24T01:52:01.5016175Z Running test_autoload_disable 1/1 ... [2025-01-24 01:52:01.292684] 2025-01-24T01:52:03.8094738Z running install 2025-01-24T01:52:03.8095741Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/setuptools/_distutils/cmd.py:66: SetuptoolsDeprecationWarning: setup.py install is deprecated. 2025-01-24T01:52:03.8096621Z !! 2025-01-24T01:52:03.8096795Z 2025-01-24T01:52:03.8096939Z ******************************************************************************** 2025-01-24T01:52:03.8097359Z Please avoid running ``setup.py`` directly. 2025-01-24T01:52:03.8097838Z Instead, use pypa/build, pypa/installer or other 2025-01-24T01:52:03.8098210Z standards-based tools. 2025-01-24T01:52:03.8098666Z 2025-01-24T01:52:03.8099101Z See https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html for details. 2025-01-24T01:52:03.8099659Z ******************************************************************************** 2025-01-24T01:52:03.8099973Z 2025-01-24T01:52:03.8100060Z !! 2025-01-24T01:52:03.8100303Z self.initialize_options() 2025-01-24T01:52:03.8229075Z running build 2025-01-24T01:52:03.8229402Z running build_py 2025-01-24T01:52:03.8306888Z creating build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension 2025-01-24T01:52:03.8309580Z copying torch_test_cpp_extension/__init__.py -> build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension 2025-01-24T01:52:03.8312353Z running build_ext 2025-01-24T01:52:03.9105794Z building 'torch_test_cpp_extension.cpp' extension 2025-01-24T01:52:03.9106682Z creating build/temp.linux-x86_64-cpython-313 2025-01-24T01:52:03.9113096Z g++ -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/TH -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/THC -Iself_compiler_include_dirs_test -I/opt/conda/envs/py_3.13/include/python3.13 -c extension.cpp -o build/temp.linux-x86_64-cpython-313/extension.o -g -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\"_clang\" -DPYBIND11_STDLIB=\"_libstdcpp\" -DPYBIND11_BUILD_ABI=\"_cxxabi1002\" -DTORCH_EXTENSION_NAME=cpp -D_GLIBCXX_USE_CXX11_ABI=1 -std=c++17 2025-01-24T01:52:04.9455307Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/Exceptions.h:12, 2025-01-24T01:52:04.9456346Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/python.h:11, 2025-01-24T01:52:04.9457255Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:9, 2025-01-24T01:52:04.9457852Z from extension.cpp:1: 2025-01-24T01:52:04.9459158Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/pybind11/pybind11.h: In instantiation of ‘class pybind11::class_’: 2025-01-24T01:52:04.9460036Z extension.cpp:45:53: required from here 2025-01-24T01:52:04.9461534Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/pybind11/pybind11.h:1539:7: warning: ‘pybind11::class_’ declared with greater visibility than its base ‘pybind11::detail::generic_type’ [-Wattributes] 2025-01-24T01:52:04.9464723Z 1539 | class class_ : public detail::generic_type { 2025-01-24T01:52:04.9465112Z | ^~~~~~ 2025-01-24T01:52:04.9466877Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/pybind11/pybind11.h: In instantiation of ‘pybind11::class_< , >::class_(pybind11::handle, const char*, const Extra& ...) [with Extra = {}; type_ = MatrixMultiplier; options = {}]’: 2025-01-24T01:52:04.9468314Z extension.cpp:45:53: required from here 2025-01-24T01:52:04.9471578Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/pybind11/pybind11.h:1599:28: warning: ‘pybind11::class_< , >::class_(pybind11::handle, const char*, const Extra& ...) [with Extra = {}; type_ = MatrixMultiplier; options = {}]::’ declared with greater visibility than the type of its field ‘pybind11::class_< , >::class_(pybind11::handle, const char*, const Extra& ...) [with Extra = {}; type_ = MatrixMultiplier; options = {}]::::’ [-Wattributes] 2025-01-24T01:52:04.9474300Z 1599 | with_internals([&](internals &internals) { 2025-01-24T01:52:04.9474961Z | ^~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-01-24T01:52:04.9475508Z 1600 | auto &instances = record.module_local ? get_local_internals().registered_types_cpp 2025-01-24T01:52:04.9476092Z | ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-01-24T01:52:04.9476567Z 1601 | : internals.registered_types_cpp; 2025-01-24T01:52:04.9477004Z | ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-01-24T01:52:04.9477447Z 1602 | instances[std::type_index(typeid(type_alias))] 2025-01-24T01:52:04.9477934Z | ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-01-24T01:52:04.9478361Z 1603 | = instances[std::type_index(typeid(type))]; 2025-01-24T01:52:04.9478780Z | ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-01-24T01:52:04.9479384Z 1604 | }); 2025-01-24T01:52:04.9479839Z | ~ 2025-01-24T01:52:04.9483049Z g++ -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -pthread -B /opt/conda/envs/py_3.13/compiler_compat -shared -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib build/temp.linux-x86_64-cpython-313/extension.o -L/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension/cpp.cpython-313-x86_64-linux-gnu.so 2025-01-24T01:52:05.3273453Z building 'torch_test_cpp_extension.maia' extension 2025-01-24T01:52:05.3278874Z g++ -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/TH -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/THC -Iself_compiler_include_dirs_test -I/opt/conda/envs/py_3.13/include/python3.13 -c maia_extension.cpp -o build/temp.linux-x86_64-cpython-313/maia_extension.o -g -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\"_clang\" -DPYBIND11_STDLIB=\"_libstdcpp\" -DPYBIND11_BUILD_ABI=\"_cxxabi1002\" -DTORCH_EXTENSION_NAME=maia -D_GLIBCXX_USE_CXX11_ABI=1 -std=c++17 2025-01-24T01:52:06.2709988Z g++ -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -pthread -B /opt/conda/envs/py_3.13/compiler_compat -shared -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib build/temp.linux-x86_64-cpython-313/maia_extension.o -L/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension/maia.cpython-313-x86_64-linux-gnu.so 2025-01-24T01:52:06.6272298Z building 'torch_test_cpp_extension.rng' extension 2025-01-24T01:52:06.6276826Z g++ -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/TH -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/THC -Iself_compiler_include_dirs_test -I/opt/conda/envs/py_3.13/include/python3.13 -c rng_extension.cpp -o build/temp.linux-x86_64-cpython-313/rng_extension.o -g -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\"_clang\" -DPYBIND11_STDLIB=\"_libstdcpp\" -DPYBIND11_BUILD_ABI=\"_cxxabi1002\" -DTORCH_EXTENSION_NAME=rng -D_GLIBCXX_USE_CXX11_ABI=1 -std=c++17 2025-01-24T01:52:07.8427383Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec256/vec256.h:8, 2025-01-24T01:52:07.8429343Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec.h:7, 2025-01-24T01:52:07.8430700Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/native/cpu/Loops.h:37, 2025-01-24T01:52:07.8431848Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/native/cpu/DistributionTemplates.h:9, 2025-01-24T01:52:07.8432555Z from rng_extension.cpp:6: 2025-01-24T01:52:07.8433397Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_base.h:1155: warning: ignoring #pragma unroll [-Wunknown-pragmas] 2025-01-24T01:52:07.8434229Z 1155 | # pragma unroll 2025-01-24T01:52:07.8434493Z | 2025-01-24T01:52:07.8435066Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_base.h:1195, 2025-01-24T01:52:07.8436009Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec256/vec256.h:8, 2025-01-24T01:52:07.8436844Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec.h:7, 2025-01-24T01:52:07.8437671Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/native/cpu/Loops.h:37, 2025-01-24T01:52:07.8438606Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/native/cpu/DistributionTemplates.h:9, 2025-01-24T01:52:07.8439288Z from rng_extension.cpp:6: 2025-01-24T01:52:07.8440093Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_n.h:59: warning: ignoring #pragma unroll [-Wunknown-pragmas] 2025-01-24T01:52:07.8440888Z 59 | #pragma unroll 2025-01-24T01:52:07.8441161Z | 2025-01-24T01:52:07.8441858Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_n.h:72: warning: ignoring #pragma unroll [-Wunknown-pragmas] 2025-01-24T01:52:07.8442645Z 72 | #pragma unroll 2025-01-24T01:52:07.8442904Z | 2025-01-24T01:52:07.8443601Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_n.h:87: warning: ignoring #pragma unroll [-Wunknown-pragmas] 2025-01-24T01:52:07.8444394Z 87 | #pragma unroll 2025-01-24T01:52:07.8444663Z | 2025-01-24T01:52:07.8445225Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_base.h:1196, 2025-01-24T01:52:07.8446148Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec256/vec256.h:8, 2025-01-24T01:52:07.8446991Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec.h:7, 2025-01-24T01:52:07.8447809Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/native/cpu/Loops.h:37, 2025-01-24T01:52:07.8448746Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/native/cpu/DistributionTemplates.h:9, 2025-01-24T01:52:07.8449440Z from rng_extension.cpp:6: 2025-01-24T01:52:07.8450344Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_mask.h:153: warning: ignoring #pragma unroll [-Wunknown-pragmas] 2025-01-24T01:52:07.8451225Z 153 | #pragma unroll 2025-01-24T01:52:07.8451480Z | 2025-01-24T01:52:07.8454665Z g++ -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -pthread -B /opt/conda/envs/py_3.13/compiler_compat -shared -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib build/temp.linux-x86_64-cpython-313/rng_extension.o -L/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension/rng.cpython-313-x86_64-linux-gnu.so 2025-01-24T01:52:08.2357223Z running install_lib 2025-01-24T01:52:08.2445303Z copying build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension/cpp.cpython-313-x86_64-linux-gnu.so -> ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_test_cpp_extension 2025-01-24T01:52:08.2544842Z copying build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension/maia.cpython-313-x86_64-linux-gnu.so -> ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_test_cpp_extension 2025-01-24T01:52:08.2642212Z copying build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension/rng.cpython-313-x86_64-linux-gnu.so -> ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_test_cpp_extension 2025-01-24T01:52:08.2748800Z running install_egg_info 2025-01-24T01:52:08.2929120Z running egg_info 2025-01-24T01:52:08.2998973Z writing torch_test_cpp_extension.egg-info/PKG-INFO 2025-01-24T01:52:08.3002070Z writing dependency_links to torch_test_cpp_extension.egg-info/dependency_links.txt 2025-01-24T01:52:08.3004104Z writing entry points to torch_test_cpp_extension.egg-info/entry_points.txt 2025-01-24T01:52:08.3005974Z writing top-level names to torch_test_cpp_extension.egg-info/top_level.txt 2025-01-24T01:52:08.3080314Z reading manifest file 'torch_test_cpp_extension.egg-info/SOURCES.txt' 2025-01-24T01:52:08.3088908Z writing manifest file 'torch_test_cpp_extension.egg-info/SOURCES.txt' 2025-01-24T01:52:08.3090486Z removing './install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_test_cpp_extension-0.0.0-py3.13.egg-info' (and everything under it) 2025-01-24T01:52:08.3092044Z Copying torch_test_cpp_extension.egg-info to ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_test_cpp_extension-0.0.0-py3.13.egg-info 2025-01-24T01:52:08.3103766Z running install_scripts 2025-01-24T01:52:11.3924828Z 2025-01-24T01:52:11.3925313Z Running tests... 2025-01-24T01:52:11.3925731Z ---------------------------------------------------------------------- 2025-01-24T01:52:11.5199479Z . 2025-01-24T01:52:11.5199848Z ---------------------------------------------------------------------- 2025-01-24T01:52:11.5200318Z Ran 1 test in 0.127s 2025-01-24T01:52:11.5200495Z 2025-01-24T01:52:11.5200581Z OK 2025-01-24T01:52:11.5200693Z 2025-01-24T01:52:11.5200814Z Generating XML reports... 2025-01-24T01:52:12.1144386Z Running test_autoload_enable 1/1 ... [2025-01-24 01:52:12.114067] 2025-01-24T01:52:14.6076958Z running install 2025-01-24T01:52:14.6079113Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/setuptools/_distutils/cmd.py:66: SetuptoolsDeprecationWarning: setup.py install is deprecated. 2025-01-24T01:52:14.6080123Z !! 2025-01-24T01:52:14.6080249Z 2025-01-24T01:52:14.6080399Z ******************************************************************************** 2025-01-24T01:52:14.6080811Z Please avoid running ``setup.py`` directly. 2025-01-24T01:52:14.6081259Z Instead, use pypa/build, pypa/installer or other 2025-01-24T01:52:14.6081660Z standards-based tools. 2025-01-24T01:52:14.6081882Z 2025-01-24T01:52:14.6082409Z See https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html for details. 2025-01-24T01:52:14.6083045Z ******************************************************************************** 2025-01-24T01:52:14.6083401Z 2025-01-24T01:52:14.6083545Z !! 2025-01-24T01:52:14.6083779Z self.initialize_options() 2025-01-24T01:52:14.6210327Z running build 2025-01-24T01:52:14.6210609Z running build_py 2025-01-24T01:52:14.6288355Z creating build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension 2025-01-24T01:52:14.6291021Z copying torch_test_cpp_extension/__init__.py -> build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension 2025-01-24T01:52:14.6294438Z running build_ext 2025-01-24T01:52:14.7078694Z building 'torch_test_cpp_extension.cpp' extension 2025-01-24T01:52:14.7079666Z creating build/temp.linux-x86_64-cpython-313 2025-01-24T01:52:14.7085888Z g++ -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/TH -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/THC -Iself_compiler_include_dirs_test -I/opt/conda/envs/py_3.13/include/python3.13 -c extension.cpp -o build/temp.linux-x86_64-cpython-313/extension.o -g -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\"_clang\" -DPYBIND11_STDLIB=\"_libstdcpp\" -DPYBIND11_BUILD_ABI=\"_cxxabi1002\" -DTORCH_EXTENSION_NAME=cpp -D_GLIBCXX_USE_CXX11_ABI=1 -std=c++17 2025-01-24T01:52:15.6611602Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/Exceptions.h:12, 2025-01-24T01:52:15.6612648Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/python.h:11, 2025-01-24T01:52:15.6613556Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:9, 2025-01-24T01:52:15.6614230Z from extension.cpp:1: 2025-01-24T01:52:15.6615550Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/pybind11/pybind11.h: In instantiation of ‘class pybind11::class_’: 2025-01-24T01:52:15.6616423Z extension.cpp:45:53: required from here 2025-01-24T01:52:15.6617893Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/pybind11/pybind11.h:1539:7: warning: ‘pybind11::class_’ declared with greater visibility than its base ‘pybind11::detail::generic_type’ [-Wattributes] 2025-01-24T01:52:15.6619366Z 1539 | class class_ : public detail::generic_type { 2025-01-24T01:52:15.6619840Z | ^~~~~~ 2025-01-24T01:52:15.6621674Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/pybind11/pybind11.h: In instantiation of ‘pybind11::class_< , >::class_(pybind11::handle, const char*, const Extra& ...) [with Extra = {}; type_ = MatrixMultiplier; options = {}]’: 2025-01-24T01:52:15.6623093Z extension.cpp:45:53: required from here 2025-01-24T01:52:15.6626440Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/pybind11/pybind11.h:1599:28: warning: ‘pybind11::class_< , >::class_(pybind11::handle, const char*, const Extra& ...) [with Extra = {}; type_ = MatrixMultiplier; options = {}]::’ declared with greater visibility than the type of its field ‘pybind11::class_< , >::class_(pybind11::handle, const char*, const Extra& ...) [with Extra = {}; type_ = MatrixMultiplier; options = {}]::::’ [-Wattributes] 2025-01-24T01:52:15.6629148Z 1599 | with_internals([&](internals &internals) { 2025-01-24T01:52:15.6629790Z | ^~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-01-24T01:52:15.6630348Z 1600 | auto &instances = record.module_local ? get_local_internals().registered_types_cpp 2025-01-24T01:52:15.6630951Z | ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-01-24T01:52:15.6631409Z 1601 | : internals.registered_types_cpp; 2025-01-24T01:52:15.6631847Z | ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-01-24T01:52:15.6632289Z 1602 | instances[std::type_index(typeid(type_alias))] 2025-01-24T01:52:15.6632722Z | ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-01-24T01:52:15.6633198Z 1603 | = instances[std::type_index(typeid(type))]; 2025-01-24T01:52:15.6633618Z | ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-01-24T01:52:15.6634013Z 1604 | }); 2025-01-24T01:52:15.6634293Z | ~ 2025-01-24T01:52:15.6637507Z g++ -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -pthread -B /opt/conda/envs/py_3.13/compiler_compat -shared -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib build/temp.linux-x86_64-cpython-313/extension.o -L/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension/cpp.cpython-313-x86_64-linux-gnu.so 2025-01-24T01:52:16.0337056Z building 'torch_test_cpp_extension.maia' extension 2025-01-24T01:52:16.0341542Z g++ -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/TH -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/THC -Iself_compiler_include_dirs_test -I/opt/conda/envs/py_3.13/include/python3.13 -c maia_extension.cpp -o build/temp.linux-x86_64-cpython-313/maia_extension.o -g -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\"_clang\" -DPYBIND11_STDLIB=\"_libstdcpp\" -DPYBIND11_BUILD_ABI=\"_cxxabi1002\" -DTORCH_EXTENSION_NAME=maia -D_GLIBCXX_USE_CXX11_ABI=1 -std=c++17 2025-01-24T01:52:16.9808206Z g++ -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -pthread -B /opt/conda/envs/py_3.13/compiler_compat -shared -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib build/temp.linux-x86_64-cpython-313/maia_extension.o -L/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension/maia.cpython-313-x86_64-linux-gnu.so 2025-01-24T01:52:17.3363673Z building 'torch_test_cpp_extension.rng' extension 2025-01-24T01:52:17.3368358Z g++ -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/TH -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/THC -Iself_compiler_include_dirs_test -I/opt/conda/envs/py_3.13/include/python3.13 -c rng_extension.cpp -o build/temp.linux-x86_64-cpython-313/rng_extension.o -g -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE=\"_clang\" -DPYBIND11_STDLIB=\"_libstdcpp\" -DPYBIND11_BUILD_ABI=\"_cxxabi1002\" -DTORCH_EXTENSION_NAME=rng -D_GLIBCXX_USE_CXX11_ABI=1 -std=c++17 2025-01-24T01:52:18.4755485Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec256/vec256.h:8, 2025-01-24T01:52:18.4756689Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec.h:7, 2025-01-24T01:52:18.4757584Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/native/cpu/Loops.h:37, 2025-01-24T01:52:18.4758666Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/native/cpu/DistributionTemplates.h:9, 2025-01-24T01:52:18.4759418Z from rng_extension.cpp:6: 2025-01-24T01:52:18.4760314Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_base.h:1155: warning: ignoring #pragma unroll [-Wunknown-pragmas] 2025-01-24T01:52:18.4761191Z 1155 | # pragma unroll 2025-01-24T01:52:18.4761492Z | 2025-01-24T01:52:18.4762102Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_base.h:1195, 2025-01-24T01:52:18.4763100Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec256/vec256.h:8, 2025-01-24T01:52:18.4764058Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec.h:7, 2025-01-24T01:52:18.4764935Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/native/cpu/Loops.h:37, 2025-01-24T01:52:18.4765859Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/native/cpu/DistributionTemplates.h:9, 2025-01-24T01:52:18.4766524Z from rng_extension.cpp:6: 2025-01-24T01:52:18.4767345Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_n.h:59: warning: ignoring #pragma unroll [-Wunknown-pragmas] 2025-01-24T01:52:18.4768122Z 59 | #pragma unroll 2025-01-24T01:52:18.4768378Z | 2025-01-24T01:52:18.4769062Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_n.h:72: warning: ignoring #pragma unroll [-Wunknown-pragmas] 2025-01-24T01:52:18.4769836Z 72 | #pragma unroll 2025-01-24T01:52:18.4770078Z | 2025-01-24T01:52:18.4770760Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_n.h:87: warning: ignoring #pragma unroll [-Wunknown-pragmas] 2025-01-24T01:52:18.4771533Z 87 | #pragma unroll 2025-01-24T01:52:18.4771801Z | 2025-01-24T01:52:18.4772350Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_base.h:1196, 2025-01-24T01:52:18.4773259Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec256/vec256.h:8, 2025-01-24T01:52:18.4774063Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec.h:7, 2025-01-24T01:52:18.4774859Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/native/cpu/Loops.h:37, 2025-01-24T01:52:18.4775770Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/native/cpu/DistributionTemplates.h:9, 2025-01-24T01:52:18.4776448Z from rng_extension.cpp:6: 2025-01-24T01:52:18.4777257Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_mask.h:153: warning: ignoring #pragma unroll [-Wunknown-pragmas] 2025-01-24T01:52:18.4778203Z 153 | #pragma unroll 2025-01-24T01:52:18.4778457Z | 2025-01-24T01:52:18.4781789Z g++ -pthread -B /opt/conda/envs/py_3.13/compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -pthread -B /opt/conda/envs/py_3.13/compiler_compat -shared -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib build/temp.linux-x86_64-cpython-313/rng_extension.o -L/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension/rng.cpython-313-x86_64-linux-gnu.so 2025-01-24T01:52:18.8598580Z running install_lib 2025-01-24T01:52:18.8680673Z copying build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension/cpp.cpython-313-x86_64-linux-gnu.so -> ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_test_cpp_extension 2025-01-24T01:52:18.8779974Z copying build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension/maia.cpython-313-x86_64-linux-gnu.so -> ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_test_cpp_extension 2025-01-24T01:52:18.8875055Z copying build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension/rng.cpython-313-x86_64-linux-gnu.so -> ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_test_cpp_extension 2025-01-24T01:52:18.8976732Z running install_egg_info 2025-01-24T01:52:18.9153438Z running egg_info 2025-01-24T01:52:18.9222593Z writing torch_test_cpp_extension.egg-info/PKG-INFO 2025-01-24T01:52:18.9225785Z writing dependency_links to torch_test_cpp_extension.egg-info/dependency_links.txt 2025-01-24T01:52:18.9227833Z writing entry points to torch_test_cpp_extension.egg-info/entry_points.txt 2025-01-24T01:52:18.9229591Z writing top-level names to torch_test_cpp_extension.egg-info/top_level.txt 2025-01-24T01:52:18.9304554Z reading manifest file 'torch_test_cpp_extension.egg-info/SOURCES.txt' 2025-01-24T01:52:18.9312895Z writing manifest file 'torch_test_cpp_extension.egg-info/SOURCES.txt' 2025-01-24T01:52:18.9314379Z removing './install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_test_cpp_extension-0.0.0-py3.13.egg-info' (and everything under it) 2025-01-24T01:52:18.9315869Z Copying torch_test_cpp_extension.egg-info to ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_test_cpp_extension-0.0.0-py3.13.egg-info 2025-01-24T01:52:18.9321668Z running install_scripts 2025-01-24T01:52:22.0195739Z 2025-01-24T01:52:22.0196317Z Running tests... 2025-01-24T01:52:22.0196735Z ---------------------------------------------------------------------- 2025-01-24T01:52:22.1459834Z . 2025-01-24T01:52:22.1460284Z ---------------------------------------------------------------------- 2025-01-24T01:52:22.1460725Z Ran 1 test in 0.126s 2025-01-24T01:52:22.1460908Z 2025-01-24T01:52:22.1461015Z OK 2025-01-24T01:52:22.1461129Z 2025-01-24T01:52:22.1461254Z Generating XML reports... 2025-01-24T01:52:22.8471735Z Running dynamo/test_minifier 1/1 ... [2025-01-24 01:52:22.846729] 2025-01-24T01:52:22.8472322Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T01:52:22.8474352Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_minifier.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-01-24 01:52:22.847158] 2025-01-24T01:52:25.8934525Z 2025-01-24T01:52:25.8935797Z dynamo/test_minifier 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_minifier_1.1_2116172aef403900_.log 2025-01-24T01:52:25.8936492Z 2025-01-24T01:52:25.8937843Z Running dynamo/test_modules 1/1 ... [2025-01-24 01:52:25.893619] 2025-01-24T01:52:25.8938560Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T01:52:25.8942188Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_modules.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'] ... [2025-01-24 01:52:25.893950] 2025-01-24T01:52:29.0876006Z 2025-01-24T01:52:29.0876931Z dynamo/test_modules 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_modules_1.1_107424986300efe6_.log 2025-01-24T01:52:29.0877620Z 2025-01-24T01:52:29.0879518Z Running dynamo/test_backends 1/1 ... [2025-01-24 01:52:29.087756] 2025-01-24T01:52:29.0879970Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T01:52:29.0890474Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_backends.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-01-24 01:52:29.088793] 2025-01-24T01:52:32.1311737Z 2025-01-24T01:52:32.1312678Z dynamo/test_backends 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_backends_1.1_f0308698cc020ae2_.log 2025-01-24T01:52:32.1313358Z 2025-01-24T01:52:32.1315197Z Running dynamo/test_cudagraphs 1/1 ... [2025-01-24 01:52:32.131342] 2025-01-24T01:52:32.1315650Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T01:52:32.1318769Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_cudagraphs.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'] ... [2025-01-24 01:52:32.131669] 2025-01-24T01:52:35.1555438Z 2025-01-24T01:52:35.1556452Z dynamo/test_cudagraphs 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_cudagraphs_1.1_820fc1074b78c8d0_.log 2025-01-24T01:52:35.1557192Z 2025-01-24T01:52:35.1558726Z Running dynamo/test_unspec 1/1 ... [2025-01-24 01:52:35.155708] 2025-01-24T01:52:35.1559270Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T01:52:35.1562406Z Executing ['/opt/conda/envs/py_3.13/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'] ... [2025-01-24 01:52:35.156016] 2025-01-24T01:52:38.2979013Z 2025-01-24T01:52:38.2980184Z dynamo/test_unspec 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_unspec_1.1_7fe8ac23d67c5bb1_.log 2025-01-24T01:52:38.2980877Z 2025-01-24T01:52:38.2982342Z Running dynamo/test_config 1/1 ... [2025-01-24 01:52:38.298076] 2025-01-24T01:52:38.2982832Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T01:52:38.2986207Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_config.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-01-24 01:52:38.298396] 2025-01-24T01:52:41.3362446Z 2025-01-24T01:52:41.3363447Z dynamo/test_config 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_config_1.1_6879eb967a01ea43_.log 2025-01-24T01:52:41.3364120Z 2025-01-24T01:52:41.3365793Z Running dynamo/test_global 1/1 ... [2025-01-24 01:52:41.336415] 2025-01-24T01:52:41.3366252Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T01:52:41.3369639Z Executing ['/opt/conda/envs/py_3.13/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'] ... [2025-01-24 01:52:41.336731] 2025-01-24T01:52:44.4234460Z 2025-01-24T01:52:44.4235504Z dynamo/test_global 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_global_1.1_419f16cb4d43ae51_.log 2025-01-24T01:52:44.4236554Z 2025-01-24T01:52:44.4237864Z Running dynamo/test_subgraphs 1/1 ... [2025-01-24 01:52:44.423604] 2025-01-24T01:52:44.4238336Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T01:52:44.4241439Z Executing ['/opt/conda/envs/py_3.13/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'] ... [2025-01-24 01:52:44.423921] 2025-01-24T01:52:47.4461923Z 2025-01-24T01:52:47.4462911Z dynamo/test_subgraphs 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_subgraphs_1.1_d2ce8b063c70dbd0_.log 2025-01-24T01:52:47.4463622Z 2025-01-24T01:52:47.4465589Z Running dynamo/test_export 1/1 ... [2025-01-24 01:52:47.446355] 2025-01-24T01:52:47.4466047Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T01:52:47.4469109Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_export.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-01-24 01:52:47.446666] 2025-01-24T01:52:50.5101778Z 2025-01-24T01:52:50.5102836Z dynamo/test_export 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_export_1.1_78245d634669b68b_.log 2025-01-24T01:52:50.5103506Z 2025-01-24T01:52:50.5105479Z Running dynamo/test_verify_correctness 1/1 ... [2025-01-24 01:52:50.510359] 2025-01-24T01:52:50.5105960Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T01:52:50.5109597Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_verify_correctness.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-01-24 01:52:50.510723] 2025-01-24T01:52:53.5738254Z 2025-01-24T01:52:53.5739346Z dynamo/test_verify_correctness 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_verify_correctness_1.1_042cb9c358d5412b_.log 2025-01-24T01:52:53.5740119Z 2025-01-24T01:52:53.5741962Z Running dynamo/test_subclasses 1/1 ... [2025-01-24 01:52:53.574019] 2025-01-24T01:52:53.5742439Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T01:52:53.5745537Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_subclasses.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-01-24 01:52:53.574341] 2025-01-24T01:52:56.6252625Z 2025-01-24T01:52:56.6253642Z dynamo/test_subclasses 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_subclasses_1.1_e0b59d05eb27ddaa_.log 2025-01-24T01:52:56.6254366Z 2025-01-24T01:52:56.6256076Z Running dynamo/test_comptime 1/1 ... [2025-01-24 01:52:56.625435] 2025-01-24T01:52:56.6256557Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T01:52:56.6259736Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_comptime.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'] ... [2025-01-24 01:52:56.625750] 2025-01-24T01:52:59.6605250Z 2025-01-24T01:52:59.6606202Z dynamo/test_comptime 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_comptime_1.1_3320d562399eb584_.log 2025-01-24T01:52:59.6606917Z 2025-01-24T01:52:59.6608638Z Running dynamo/test_utils 1/1 ... [2025-01-24 01:52:59.660697] 2025-01-24T01:52:59.6609093Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T01:52:59.6612291Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_utils.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-01-24 01:52:59.661002] 2025-01-24T01:53:02.8363096Z 2025-01-24T01:53:02.8364225Z dynamo/test_utils 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_utils_1.1_c2be66aeafc466db_.log 2025-01-24T01:53:02.8364909Z 2025-01-24T01:53:02.8366834Z Running dynamo/test_debug_utils 1/1 ... [2025-01-24 01:53:02.836488] 2025-01-24T01:53:02.8367301Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T01:53:02.8370752Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_debug_utils.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-01-24 01:53:02.836807] 2025-01-24T01:53:05.9145832Z 2025-01-24T01:53:05.9146885Z dynamo/test_debug_utils 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_debug_utils_1.1_601eb4d6a461c7d2_.log 2025-01-24T01:53:05.9147675Z 2025-01-24T01:53:05.9149167Z Running dynamo/test_compile 1/1 ... [2025-01-24 01:53:05.914741] 2025-01-24T01:53:05.9149669Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T01:53:05.9152865Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_compile.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'] ... [2025-01-24 01:53:05.915054] 2025-01-24T01:53:08.9900574Z 2025-01-24T01:53:08.9901571Z dynamo/test_compile 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_compile_1.1_1c44c40311bc1731_.log 2025-01-24T01:53:08.9902330Z 2025-01-24T01:53:08.9905439Z Running dynamo/test_graph_region_tracker 1/1 ... [2025-01-24 01:53:08.990246] 2025-01-24T01:53:08.9905943Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T01:53:08.9908081Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_graph_region_tracker.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-01-24 01:53:08.990566] 2025-01-24T01:53:11.9917947Z 2025-01-24T01:53:11.9918883Z dynamo/test_graph_region_tracker 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_graph_region_tracker_1.1_262c3d614a1ea9be_.log 2025-01-24T01:53:11.9919659Z 2025-01-24T01:53:11.9921416Z Running dynamo/test_structured_trace 1/1 ... [2025-01-24 01:53:11.991981] 2025-01-24T01:53:11.9921889Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T01:53:11.9925253Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_structured_trace.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-01-24 01:53:11.992301] 2025-01-24T01:53:15.0221479Z 2025-01-24T01:53:15.0222513Z dynamo/test_structured_trace 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_structured_trace_1.1_9ff57f39f83a4ee4_.log 2025-01-24T01:53:15.0223270Z 2025-01-24T01:53:15.0224890Z Running dynamo/test_sources 1/1 ... [2025-01-24 01:53:15.022304] 2025-01-24T01:53:15.0225330Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T01:53:15.0228611Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_sources.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-01-24 01:53:15.022637] 2025-01-24T01:53:18.0466959Z 2025-01-24T01:53:18.0468006Z dynamo/test_sources 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_sources_1.1_2486993180c997ee_.log 2025-01-24T01:53:18.0468741Z 2025-01-24T01:53:18.0470757Z Running dynamo/test_model_output 1/1 ... [2025-01-24 01:53:18.046871] 2025-01-24T01:53:18.0471558Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T01:53:18.0474986Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_model_output.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-01-24 01:53:18.047267] 2025-01-24T01:53:21.0774590Z 2025-01-24T01:53:21.0775643Z dynamo/test_model_output 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_model_output_1.1_6d7a78457cd634a0_.log 2025-01-24T01:53:21.0776422Z 2025-01-24T01:53:21.0776768Z Running dynamo/test_compiler_bisector 1/1 ... [2025-01-24 01:53:21.077494] 2025-01-24T01:53:21.0777243Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T01:53:21.0780939Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_compiler_bisector.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'] ... [2025-01-24 01:53:21.077821] 2025-01-24T01:53:24.1494125Z 2025-01-24T01:53:24.1495315Z dynamo/test_compiler_bisector 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_compiler_bisector_1.1_7453929a7473ff81_.log 2025-01-24T01:53:24.1496093Z 2025-01-24T01:53:24.1497488Z Running dynamo/test_bytecode_utils 1/1 ... [2025-01-24 01:53:24.149577] 2025-01-24T01:53:24.1497968Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T01:53:24.1501167Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_bytecode_utils.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-01-24 01:53:24.149902] 2025-01-24T01:53:27.1624210Z 2025-01-24T01:53:27.1625268Z dynamo/test_bytecode_utils 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_bytecode_utils_1.1_735f302fd1ac8623_.log 2025-01-24T01:53:27.1626015Z 2025-01-24T01:53:27.1628039Z Running dynamo/test_torchrec 1/1 ... [2025-01-24 01:53:27.162607] 2025-01-24T01:53:27.1628473Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T01:53:27.1631809Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_torchrec.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'] ... [2025-01-24 01:53:27.162968] 2025-01-24T01:53:30.1648775Z 2025-01-24T01:53:30.1649860Z dynamo/test_torchrec 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_torchrec_1.1_639f672d3f46eeec_.log 2025-01-24T01:53:30.1650565Z 2025-01-24T01:53:30.1652839Z Running dynamo/test_trace_rules 1/1 ... [2025-01-24 01:53:30.165134] 2025-01-24T01:53:30.1653326Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T01:53:30.1656532Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_trace_rules.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-01-24 01:53:30.165434] 2025-01-24T01:53:33.1936180Z 2025-01-24T01:53:33.1937486Z dynamo/test_trace_rules 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_trace_rules_1.1_149c88068a047e21_.log 2025-01-24T01:53:33.1938206Z 2025-01-24T01:53:33.1940388Z Running dynamo/test_guard_manager 1/1 ... [2025-01-24 01:53:33.193813] 2025-01-24T01:53:33.1940900Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T01:53:33.1943462Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_guard_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'] ... [2025-01-24 01:53:33.194118] 2025-01-24T01:53:36.2130701Z 2025-01-24T01:53:36.2132031Z dynamo/test_guard_manager 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_guard_manager_1.1_37b25ff94edf102a_.log 2025-01-24T01:53:36.2132786Z 2025-01-24T01:53:36.2134280Z Running test_tensorboard 1/1 ... [2025-01-24 01:53:36.213265] 2025-01-24T01:53:36.2134773Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T01:53:36.2137943Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_tensorboard.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'] ... [2025-01-24 01:53:36.213589] 2025-01-24T01:53:39.9328034Z 2025-01-24T01:53:39.9329383Z test_tensorboard 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_tensorboard_1.1_ee731dba4b3ca7b5_.log 2025-01-24T01:53:39.9330677Z Running 0 items in this shard: 2025-01-24T01:53:39.9331029Z 2025-01-24T01:53:39.9331774Z Running test_view_ops 1/1 ... [2025-01-24 01:53:39.932983] 2025-01-24T01:53:39.9332197Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T01:53:39.9335234Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_view_ops.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-01-24 01:53:39.933291] 2025-01-24T01:53:43.1015686Z 2025-01-24T01:53:43.1016650Z test_view_ops 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_view_ops_1.1_d49dfd387e814763_.log 2025-01-24T01:53:43.1017451Z Running 0 items in this shard: 2025-01-24T01:53:43.1017716Z 2025-01-24T01:53:43.1019333Z Running test_mkldnn 1/1 ... [2025-01-24 01:53:43.101774] 2025-01-24T01:53:43.1019759Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T01:53:43.1023231Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_mkldnn.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-01-24 01:53:43.102093] 2025-01-24T01:53:46.4204827Z 2025-01-24T01:53:46.4206006Z test_mkldnn 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_mkldnn_1.1_dbdb82c8bdeb6283_.log 2025-01-24T01:53:46.4206728Z Running 0 items in this shard: 2025-01-24T01:53:46.4206926Z 2025-01-24T01:53:46.4208607Z Running test_functional_autograd_benchmark 1/1 ... [2025-01-24 01:53:46.420674] 2025-01-24T01:53:46.4209263Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T01:53:46.4212510Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_functional_autograd_benchmark.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-01-24 01:53:46.420986] 2025-01-24T01:53:49.5392676Z 2025-01-24T01:53:49.5394242Z test_functional_autograd_benchmark 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_functional_autograd_benchmark_1.1_4b3ba4051dd447d7_.log 2025-01-24T01:53:49.5395241Z Running 0 items in this shard: 2025-01-24T01:53:49.5395484Z 2025-01-24T01:53:49.5396017Z Running test_unary_ufuncs 1/1 ... [2025-01-24 01:53:49.539442] 2025-01-24T01:53:49.5396681Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T01:53:49.5399969Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_unary_ufuncs.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-01-24 01:53:49.539752] 2025-01-24T01:53:57.1135771Z 2025-01-24T01:53:57.1136651Z test_unary_ufuncs 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_unary_ufuncs_1.1_202244ec3741e8f3_.log 2025-01-24T01:53:57.1137430Z Running 0 items in this shard: 2025-01-24T01:53:57.1137841Z 2025-01-24T01:53:57.1139820Z Running test_jiterator 1/1 ... [2025-01-24 01:53:57.113795] 2025-01-24T01:53:57.1140255Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T01:53:57.1143730Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_jiterator.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-01-24 01:53:57.114143] 2025-01-24T01:54:00.2520242Z 2025-01-24T01:54:00.2521138Z test_jiterator 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_jiterator_1.1_ccdc9f0f5af42781_.log 2025-01-24T01:54:00.2521903Z Running 0 items in this shard: 2025-01-24T01:54:00.2522102Z 2025-01-24T01:54:00.2524022Z Running xpu/test_gemm 1/1 ... [2025-01-24 01:54:00.252226] 2025-01-24T01:54:00.2524443Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T01:54:00.2528306Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'xpu/test_gemm.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'] ... [2025-01-24 01:54:00.252590] 2025-01-24T01:54:03.3248052Z 2025-01-24T01:54:03.3248978Z xpu/test_gemm 1/1 was successful, full logs can be found in artifacts with path test/test-reports/xpu.test_gemm_1.1_bec67091f920e33c_.log 2025-01-24T01:54:03.3249705Z Running 0 items in this shard: 2025-01-24T01:54:03.3249902Z 2025-01-24T01:54:03.3251720Z Running test_matmul_cuda 1/1 ... [2025-01-24 01:54:03.325002] 2025-01-24T01:54:03.3252144Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T01:54:03.3255314Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_matmul_cuda.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-01-24 01:54:03.325324] 2025-01-24T01:54:06.4828554Z 2025-01-24T01:54:06.4829469Z test_matmul_cuda 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_matmul_cuda_1.1_c70609fc24125d99_.log 2025-01-24T01:54:06.4830208Z Running 0 items in this shard: 2025-01-24T01:54:06.4830421Z 2025-01-24T01:54:06.4832037Z Running optim/test_lrscheduler 1/1 ... [2025-01-24 01:54:06.483039] 2025-01-24T01:54:06.4832491Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T01:54:06.4835609Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'optim/test_lrscheduler.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'] ... [2025-01-24 01:54:06.483365] 2025-01-24T01:54:09.5431667Z 2025-01-24T01:54:09.5432720Z optim/test_lrscheduler 1/1 was successful, full logs can be found in artifacts with path test/test-reports/optim.test_lrscheduler_1.1_b99c93b345c20230_.log 2025-01-24T01:54:09.5433503Z 2025-01-24T01:54:09.5435134Z Running optim/test_optim 1/1 ... [2025-01-24 01:54:09.543344] 2025-01-24T01:54:09.5435561Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T01:54:09.5438776Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'optim/test_optim.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-01-24 01:54:09.543666] 2025-01-24T01:54:12.5295584Z 2025-01-24T01:54:12.5296499Z optim/test_optim 1/1 was successful, full logs can be found in artifacts with path test/test-reports/optim.test_optim_1.1_d2c1c2192b688661_.log 2025-01-24T01:54:12.5297135Z 2025-01-24T01:54:12.5381649Z Running dynamo/test_minifier 1/1 ... [2025-01-24 01:54:12.537860] 2025-01-24T01:54:12.5382260Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T01:54:12.5383609Z Running dynamo/test_modules 1/1 ... [2025-01-24 01:54:12.538119] 2025-01-24T01:54:12.5384113Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T01:54:12.5385221Z Running dynamo/test_backends 1/1 ... [2025-01-24 01:54:12.538264] 2025-01-24T01:54:12.5385890Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T01:54:12.5387484Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_minifier.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-01-24 01:54:12.538384] 2025-01-24T01:54:12.5390064Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_modules.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'] ... [2025-01-24 01:54:12.538645] 2025-01-24T01:54:12.5393067Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_backends.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-01-24 01:54:12.538789] 2025-01-24T01:54:15.9098337Z 2025-01-24T01:54:15.9099765Z dynamo/test_minifier 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_minifier_1.1_504b3cc3490ed2cb_.log 2025-01-24T01:54:15.9101055Z 2025-01-24T01:54:15.9686472Z 2025-01-24T01:54:15.9687997Z dynamo/test_modules 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_modules_1.1_4067876557a1bdfe_.log 2025-01-24T01:54:15.9688967Z 2025-01-24T01:54:15.9690573Z 2025-01-24T01:54:15.9691525Z dynamo/test_backends 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_backends_1.1_881f9bb5335fb57f_.log 2025-01-24T01:54:15.9692573Z 2025-01-24T01:54:16.0270833Z Failed to upload artifacts: [Errno 2] No such file or directory: '/var/lib/jenkins/workspace/test/test-reports/dynamo.test_modules_1.1_1zb6gozu_toprint.log' 2025-01-24T01:54:16.0272471Z Uploading artifacts took 0.12 seconds 2025-01-24T01:54:17.1382332Z Uploading artifacts took 1.11 seconds 2025-01-24T01:54:19.6221787Z Running dynamo/test_cudagraphs 1/1 ... [2025-01-24 01:54:19.621773] 2025-01-24T01:54:19.6222535Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T01:54:19.6223175Z Running dynamo/test_unspec 1/1 ... [2025-01-24 01:54:19.621847] 2025-01-24T01:54:19.6223897Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T01:54:19.6225200Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_cudagraphs.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'] ... [2025-01-24 01:54:19.622119] 2025-01-24T01:54:19.6227447Z Executing ['/opt/conda/envs/py_3.13/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'] ... [2025-01-24 01:54:19.622213] 2025-01-24T01:54:19.7348560Z Running dynamo/test_config 1/1 ... [2025-01-24 01:54:19.734414] 2025-01-24T01:54:19.7349390Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T01:54:19.7352199Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_config.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-01-24 01:54:19.734920] 2025-01-24T01:54:23.0661456Z 2025-01-24T01:54:23.0662456Z dynamo/test_unspec 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_unspec_1.1_4b328c9e76265156_.log 2025-01-24T01:54:23.0663205Z 2025-01-24T01:54:23.0663938Z 2025-01-24T01:54:23.0664972Z dynamo/test_cudagraphs 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_cudagraphs_1.1_13e998898c5f21d6_.log 2025-01-24T01:54:23.0665660Z 2025-01-24T01:54:23.1509069Z 2025-01-24T01:54:23.1510252Z dynamo/test_config 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_config_1.1_cd5e147bb31760b5_.log 2025-01-24T01:54:23.1510923Z 2025-01-24T01:54:26.5928809Z Running dynamo/test_global 1/1 ... [2025-01-24 01:54:26.592491] 2025-01-24T01:54:26.5929572Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T01:54:26.5932155Z Executing ['/opt/conda/envs/py_3.13/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'] ... [2025-01-24 01:54:26.592884] 2025-01-24T01:54:26.6252291Z Running dynamo/test_subgraphs 1/1 ... [2025-01-24 01:54:26.624825] 2025-01-24T01:54:26.6253760Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T01:54:26.6255149Z Executing ['/opt/conda/envs/py_3.13/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'] ... [2025-01-24 01:54:26.625147] 2025-01-24T01:54:26.6749102Z Running dynamo/test_export 1/1 ... [2025-01-24 01:54:26.674546] 2025-01-24T01:54:26.6749580Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T01:54:26.6752345Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_export.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-01-24 01:54:26.674944] 2025-01-24T01:54:29.9648783Z 2025-01-24T01:54:29.9649962Z dynamo/test_global 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_global_1.1_f29e8b4be586df2a_.log 2025-01-24T01:54:29.9650726Z 2025-01-24T01:54:29.9811071Z 2025-01-24T01:54:29.9812014Z dynamo/test_subgraphs 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_subgraphs_1.1_c3e04b00612aacb7_.log 2025-01-24T01:54:29.9812725Z 2025-01-24T01:54:30.1669209Z 2025-01-24T01:54:30.1670584Z dynamo/test_export 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_export_1.1_b7d10734a095836e_.log 2025-01-24T01:54:30.1671337Z 2025-01-24T01:54:33.4953217Z Running dynamo/test_verify_correctness 1/1 ... [2025-01-24 01:54:33.494864] 2025-01-24T01:54:33.4953943Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T01:54:33.4955932Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_verify_correctness.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-01-24 01:54:33.495226] 2025-01-24T01:54:33.4957534Z Running dynamo/test_subclasses 1/1 ... [2025-01-24 01:54:33.495273] 2025-01-24T01:54:33.4958228Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T01:54:33.4959677Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_subclasses.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-01-24 01:54:33.495619] 2025-01-24T01:54:33.7946175Z Running dynamo/test_comptime 1/1 ... [2025-01-24 01:54:33.794190] 2025-01-24T01:54:33.7946834Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T01:54:33.7948661Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_comptime.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'] ... [2025-01-24 01:54:33.794567] 2025-01-24T01:54:36.9406033Z 2025-01-24T01:54:36.9407575Z dynamo/test_verify_correctness 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_verify_correctness_1.1_16b7e43ddc82eb84_.log 2025-01-24T01:54:36.9409123Z 2025-01-24T01:54:36.9424237Z 2025-01-24T01:54:36.9426535Z dynamo/test_subclasses 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_subclasses_1.1_1ef11218462429ec_.log 2025-01-24T01:54:36.9427835Z 2025-01-24T01:54:37.3372058Z 2025-01-24T01:54:37.3373722Z dynamo/test_comptime 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_comptime_1.1_c1ae4d9267499b84_.log 2025-01-24T01:54:37.3375176Z 2025-01-24T01:54:40.5820260Z Running dynamo/test_utils 1/1 ... [2025-01-24 01:54:40.581596] 2025-01-24T01:54:40.5820856Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T01:54:40.5822649Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_utils.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-01-24 01:54:40.581961] 2025-01-24T01:54:40.5961998Z Running dynamo/test_debug_utils 1/1 ... [2025-01-24 01:54:40.595909] 2025-01-24T01:54:40.5962545Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T01:54:40.5967204Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_debug_utils.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-01-24 01:54:40.596305] 2025-01-24T01:54:40.9612621Z Running dynamo/test_compile 1/1 ... [2025-01-24 01:54:40.960848] 2025-01-24T01:54:40.9613208Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T01:54:40.9615020Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_compile.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'] ... [2025-01-24 01:54:40.961205] 2025-01-24T01:54:44.0839656Z 2025-01-24T01:54:44.0841034Z dynamo/test_debug_utils 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_debug_utils_1.1_14762c4266ddfbeb_.log 2025-01-24T01:54:44.0842061Z 2025-01-24T01:54:44.1007411Z 2025-01-24T01:54:44.1008367Z dynamo/test_utils 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_utils_1.1_c14e61151b67d161_.log 2025-01-24T01:54:44.1009015Z 2025-01-24T01:54:44.4302382Z 2025-01-24T01:54:44.4303676Z dynamo/test_compile 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_compile_1.1_41c922fcd7115ca3_.log 2025-01-24T01:54:44.4304350Z 2025-01-24T01:54:47.5636978Z Running dynamo/test_graph_region_tracker 1/1 ... [2025-01-24 01:54:47.563270] 2025-01-24T01:54:47.5637933Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T01:54:47.5640108Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_graph_region_tracker.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-01-24 01:54:47.563638] 2025-01-24T01:54:47.6275366Z Running dynamo/test_structured_trace 1/1 ... [2025-01-24 01:54:47.627011] 2025-01-24T01:54:47.6276174Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T01:54:47.6278879Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_structured_trace.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-01-24 01:54:47.627419] 2025-01-24T01:54:47.9488344Z Running dynamo/test_sources 1/1 ... [2025-01-24 01:54:47.948427] 2025-01-24T01:54:47.9488855Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T01:54:47.9491277Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_sources.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-01-24 01:54:47.948843] 2025-01-24T01:54:51.0411617Z 2025-01-24T01:54:51.0412747Z dynamo/test_graph_region_tracker 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_graph_region_tracker_1.1_c7f39384b62def6b_.log 2025-01-24T01:54:51.0413608Z 2025-01-24T01:54:51.2486089Z 2025-01-24T01:54:51.2487403Z dynamo/test_structured_trace 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_structured_trace_1.1_67fb832eecf33e78_.log 2025-01-24T01:54:51.2488194Z 2025-01-24T01:54:51.5480338Z 2025-01-24T01:54:51.5481751Z dynamo/test_sources 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_sources_1.1_92c927f8d2c5fafc_.log 2025-01-24T01:54:51.5482748Z 2025-01-24T01:54:54.6781536Z Running dynamo/test_model_output 1/1 ... [2025-01-24 01:54:54.677674] 2025-01-24T01:54:54.6782383Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T01:54:54.6784476Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_model_output.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-01-24 01:54:54.678078] 2025-01-24T01:54:54.9322479Z Running dynamo/test_compiler_bisector 1/1 ... [2025-01-24 01:54:54.931884] 2025-01-24T01:54:54.9323167Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T01:54:54.9324810Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_compiler_bisector.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'] ... [2025-01-24 01:54:54.932219] 2025-01-24T01:54:55.2555971Z Running dynamo/test_bytecode_utils 1/1 ... [2025-01-24 01:54:55.255205] 2025-01-24T01:54:55.2556548Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T01:54:55.2558935Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_bytecode_utils.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-01-24 01:54:55.255605] 2025-01-24T01:54:58.1509614Z 2025-01-24T01:54:58.1510906Z dynamo/test_model_output 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_model_output_1.1_ab39c88a18eebb92_.log 2025-01-24T01:54:58.1511634Z 2025-01-24T01:54:58.3601508Z 2025-01-24T01:54:58.3602834Z dynamo/test_compiler_bisector 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_compiler_bisector_1.1_497832d26182c912_.log 2025-01-24T01:54:58.3603609Z 2025-01-24T01:54:58.6590006Z 2025-01-24T01:54:58.6591600Z dynamo/test_bytecode_utils 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_bytecode_utils_1.1_33cf1fa6e6b97eed_.log 2025-01-24T01:54:58.6592945Z 2025-01-24T01:55:01.7065293Z Running dynamo/test_torchrec 1/1 ... [2025-01-24 01:55:01.706075] 2025-01-24T01:55:01.7066360Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T01:55:01.7068773Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_torchrec.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'] ... [2025-01-24 01:55:01.706446] 2025-01-24T01:55:01.9802223Z Running dynamo/test_trace_rules 1/1 ... [2025-01-24 01:55:01.979770] 2025-01-24T01:55:01.9803087Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T01:55:01.9805233Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_trace_rules.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-01-24 01:55:01.980112] 2025-01-24T01:55:02.1959219Z Running dynamo/test_guard_manager 1/1 ... [2025-01-24 01:55:02.195461] 2025-01-24T01:55:02.1960205Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T01:55:02.1962693Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_guard_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'] ... [2025-01-24 01:55:02.195830] 2025-01-24T01:55:05.0184343Z 2025-01-24T01:55:05.0185460Z dynamo/test_torchrec 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_torchrec_1.1_56bd1ba11d8a6d83_.log 2025-01-24T01:55:05.0186212Z 2025-01-24T01:55:05.5061702Z 2025-01-24T01:55:05.5063192Z dynamo/test_trace_rules 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_trace_rules_1.1_02e7e803bac3229e_.log 2025-01-24T01:55:05.6250051Z 2025-01-24T01:55:05.6250065Z 2025-01-24T01:55:05.6251928Z dynamo/test_guard_manager 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_guard_manager_1.1_7f3401459110e525_.log 2025-01-24T01:55:05.6252717Z 2025-01-24T01:55:08.6465098Z Running test_tensorboard 1/1 ... [2025-01-24 01:55:08.646111] 2025-01-24T01:55:08.6472056Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T01:55:08.6473669Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_tensorboard.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'] ... [2025-01-24 01:55:08.646429] 2025-01-24T01:55:09.1321342Z Running test_view_ops 1/1 ... [2025-01-24 01:55:09.131731] 2025-01-24T01:55:09.1322144Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T01:55:09.1324146Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_view_ops.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-01-24 01:55:09.132080] 2025-01-24T01:55:09.3145649Z Running test_mkldnn 1/1 ... [2025-01-24 01:55:09.314083] 2025-01-24T01:55:09.3146222Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T01:55:09.3147454Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_mkldnn.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-01-24 01:55:09.314423] 2025-01-24T01:55:21.8563778Z 2025-01-24T01:55:21.8565195Z test_tensorboard 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_tensorboard_1.1_20def0c821ee98a0_.log 2025-01-24T01:55:21.8619175Z Running 50 items in this shard: test/test_tensorboard.py::TestTensorBoardPyTorchNumpy::test_pytorch_autograd_np, test/test_tensorboard.py::TestTensorBoardPyTorchNumpy::test_pytorch_histogram, test/test_tensorboard.py::TestTensorBoardPyTorchNumpy::test_pytorch_histogram_raw, test/test_tensorboard.py::TestTensorBoardPyTorchNumpy::test_pytorch_np, test/test_tensorboard.py::TestTensorBoardPyTorchNumpy::test_pytorch_write, test/test_tensorboard.py::TestTensorBoardUtils::test_convert_to_HWC_dtype_remains_same, test/test_tensorboard.py::TestTensorBoardUtils::test_numpy_vid_uint8, test/test_tensorboard.py::TestTensorBoardUtils::test_prepare_video, test/test_tensorboard.py::TestTensorBoardUtils::test_to_HWC, test/test_tensorboard.py::TestTensorBoardWriter::test_writer, test/test_tensorboard.py::TestTensorBoardSummaryWriter::test_pathlib, test/test_tensorboard.py::TestTensorBoardSummaryWriter::test_summary_writer_close, test/test_tensorboard.py::TestTensorBoardSummaryWriter::test_summary_writer_ctx, test/test_tensorboard.py::TestTensorBoardEmbedding::test_embedding, test/test_tensorboard.py::TestTensorBoardEmbedding::test_embedding_64, test/test_tensorboard.py::TestTensorBoardSummary::test_audio, test/test_tensorboard.py::TestTensorBoardSummary::test_custom_scalars, test/test_tensorboard.py::TestTensorBoardSummary::test_empty_input, test/test_tensorboard.py::TestTensorBoardSummary::test_float32_image, test/test_tensorboard.py::TestTensorBoardSummary::test_histogram_auto, test/test_tensorboard.py::TestTensorBoardSummary::test_histogram_doane, test/test_tensorboard.py::TestTensorBoardSummary::test_histogram_fd, test/test_tensorboard.py::TestTensorBoardSummary::test_image_with_3_channel_batched, test/test_tensorboard.py::TestTensorBoardSummary::test_image_with_boxes, test/test_tensorboard.py::TestTensorBoardSummary::test_image_with_one_channel, test/test_tensorboard.py::TestTensorBoardSummary::test_image_with_one_channel_batched, test/test_tensorboard.py::TestTensorBoardSummary::test_image_without_channel, test/test_tensorboard.py::TestTensorBoardSummary::test_list_input, test/test_tensorboard.py::TestTensorBoardSummary::test_mesh, test/test_tensorboard.py::TestTensorBoardSummary::test_scalar_new_style, test/test_tensorboard.py::TestTensorBoardSummary::test_text, test/test_tensorboard.py::TestTensorBoardSummary::test_uint8_image, test/test_tensorboard.py::TestTensorBoardSummary::test_video, test/test_tensorboard.py::TestTensorBoardPytorchGraph::test_mlp_graph, test/test_tensorboard.py::TestTensorBoardPytorchGraph::test_nested_nn_squential, test/test_tensorboard.py::TestTensorBoardPytorchGraph::test_pytorch_graph, test/test_tensorboard.py::TestTensorBoardPytorchGraph::test_pytorch_graph_dict_input, test/test_tensorboard.py::TestTensorBoardPytorchGraph::test_torchvision_smoke, test/test_tensorboard.py::TestTensorBoardPytorchGraph::test_wrong_input_size, test/test_tensorboard.py::TestTensorBoardFigure::test_figure, test/test_tensorboard.py::TestTensorBoardFigure::test_figure_list, test/test_tensorboard.py::TestTensorBoardNumpy::test_pytorch_np_expect_fail, test/test_tensorboard.py::TestTensorBoardNumpy::test_scalar, test/test_tensorboard.py::TestTensorProtoSummary::test_complex_tensor_proto, test/test_tensorboard.py::TestTensorProtoSummary::test_empty_tensor_proto, test/test_tensorboard.py::TestTensorProtoSummary::test_float_tensor_proto, test/test_tensorboard.py::TestTensorProtoSummary::test_half_tensor_proto_bfloat16_proto_type_14, test/test_tensorboard.py::TestTensorProtoSummary::test_half_tensor_proto_float16_proto_type_19, test/test_tensorboard.py::TestTensorProtoSummary::test_int_tensor_proto, test/test_tensorboard.py::TestTensorProtoSummary::test_scalar_tensor_proto 2025-01-24T01:55:21.8636137Z 2025-01-24T01:55:27.0571222Z Running test_functional_autograd_benchmark 1/1 ... [2025-01-24 01:55:27.056638] 2025-01-24T01:55:27.0572184Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T01:55:27.0574843Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_functional_autograd_benchmark.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-01-24 01:55:27.057116] 2025-01-24T01:56:14.5211471Z 2025-01-24T01:56:14.5215194Z test_functional_autograd_benchmark 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_functional_autograd_benchmark_1.1_8757b84f6788b2ca_.log 2025-01-24T01:56:14.5221505Z Running 2 items in this shard: test/test_functional_autograd_benchmark.py::TestFunctionalAutogradBenchmark::test_fast_tasks, test/test_functional_autograd_benchmark.py::TestFunctionalAutogradBenchmark::test_slow_tasks 2025-01-24T01:56:14.5223598Z 2025-01-24T01:56:18.3387725Z Running test_unary_ufuncs 1/1 ... [2025-01-24 01:56:18.338326] 2025-01-24T01:56:18.3388481Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T01:56:18.3392527Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_unary_ufuncs.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-01-24 01:56:18.338737] 2025-01-24T01:56:33.1791067Z 2025-01-24T01:56:33.1793116Z test_view_ops 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_view_ops_1.1_6723bef76106e85c_.log 2025-01-24T01:56:33.1928802Z Running 441 items in this shard: test/test_view_ops.py::TestViewOpsCPU::test_T_view_cpu, test/test_view_ops.py::TestViewOpsCPU::test_advanced_indexing_assignment_cpu, test/test_view_ops.py::TestViewOpsCPU::test_advanced_indexing_nonview_cpu, test/test_view_ops.py::TestViewOpsCPU::test_as_strided_gradients_cpu, test/test_view_ops.py::TestViewOpsCPU::test_as_strided_inplace_view_cpu, test/test_view_ops.py::TestViewOpsCPU::test_as_strided_view_cpu, test/test_view_ops.py::TestViewOpsCPU::test_basic_indexing_ellipses_view_cpu, test/test_view_ops.py::TestViewOpsCPU::test_basic_indexing_newaxis_view_cpu, test/test_view_ops.py::TestViewOpsCPU::test_basic_indexing_slice_view_cpu, test/test_view_ops.py::TestViewOpsCPU::test_chunk_view_cpu, test/test_view_ops.py::TestViewOpsCPU::test_conj_imag_view_cpu_complex128, test/test_view_ops.py::TestViewOpsCPU::test_conj_imag_view_cpu_complex64, test/test_view_ops.py::TestViewOpsCPU::test_conj_self_cpu_bfloat16, test/test_view_ops.py::TestViewOpsCPU::test_conj_self_cpu_float16, test/test_view_ops.py::TestViewOpsCPU::test_conj_self_cpu_float32, test/test_view_ops.py::TestViewOpsCPU::test_conj_self_cpu_float64, test/test_view_ops.py::TestViewOpsCPU::test_conj_self_cpu_int16, test/test_view_ops.py::TestViewOpsCPU::test_conj_self_cpu_int32, test/test_view_ops.py::TestViewOpsCPU::test_conj_self_cpu_int64, test/test_view_ops.py::TestViewOpsCPU::test_conj_self_cpu_int8, test/test_view_ops.py::TestViewOpsCPU::test_conj_self_cpu_uint8, test/test_view_ops.py::TestViewOpsCPU::test_conj_view_with_shared_memory_cpu, test/test_view_ops.py::TestViewOpsCPU::test_contiguous_nonview_cpu, test/test_view_ops.py::TestViewOpsCPU::test_contiguous_self_cpu, test/test_view_ops.py::TestViewOpsCPU::test_diagonal_view_cpu, test/test_view_ops.py::TestViewOpsCPU::test_expand_as_view_cpu, test/test_view_ops.py::TestViewOpsCPU::test_expand_view_cpu, test/test_view_ops.py::TestViewOpsCPU::test_flatten_nonview_cpu, test/test_view_ops.py::TestViewOpsCPU::test_flatten_view_cpu, test/test_view_ops.py::TestViewOpsCPU::test_imag_noncomplex_cpu_bfloat16, test/test_view_ops.py::TestViewOpsCPU::test_imag_noncomplex_cpu_float16, test/test_view_ops.py::TestViewOpsCPU::test_imag_noncomplex_cpu_float32, test/test_view_ops.py::TestViewOpsCPU::test_imag_noncomplex_cpu_float64, test/test_view_ops.py::TestViewOpsCPU::test_imag_noncomplex_cpu_int16, test/test_view_ops.py::TestViewOpsCPU::test_imag_noncomplex_cpu_int32, test/test_view_ops.py::TestViewOpsCPU::test_imag_noncomplex_cpu_int64, test/test_view_ops.py::TestViewOpsCPU::test_imag_noncomplex_cpu_int8, test/test_view_ops.py::TestViewOpsCPU::test_imag_noncomplex_cpu_uint8, test/test_view_ops.py::TestViewOpsCPU::test_movedim_view_cpu, test/test_view_ops.py::TestViewOpsCPU::test_narrow_view_cpu, test/test_view_ops.py::TestViewOpsCPU::test_permute_view_cpu, test/test_view_ops.py::TestViewOpsCPU::test_real_imag_view_cpu_complex128, test/test_view_ops.py::TestViewOpsCPU::test_real_imag_view_cpu_complex64, test/test_view_ops.py::TestViewOpsCPU::test_reshape_as_view_cpu, test/test_view_ops.py::TestViewOpsCPU::test_reshape_nonview_cpu, test/test_view_ops.py::TestViewOpsCPU::test_reshape_view_cpu, test/test_view_ops.py::TestViewOpsCPU::test_select_view_cpu, test/test_view_ops.py::TestViewOpsCPU::test_set_real_imag_cpu_complex128_bfloat16, test/test_view_ops.py::TestViewOpsCPU::test_set_real_imag_cpu_complex128_bool, test/test_view_ops.py::TestViewOpsCPU::test_set_real_imag_cpu_complex128_complex128, test/test_view_ops.py::TestViewOpsCPU::test_set_real_imag_cpu_complex128_complex64, test/test_view_ops.py::TestViewOpsCPU::test_set_real_imag_cpu_complex128_float16, test/test_view_ops.py::TestViewOpsCPU::test_set_real_imag_cpu_complex128_float32, test/test_view_ops.py::TestViewOpsCPU::test_set_real_imag_cpu_complex128_float64, test/test_view_ops.py::TestViewOpsCPU::test_set_real_imag_cpu_complex128_int16, test/test_view_ops.py::TestViewOpsCPU::test_set_real_imag_cpu_complex128_int32, test/test_view_ops.py::TestViewOpsCPU::test_set_real_imag_cpu_complex128_int64, test/test_view_ops.py::TestViewOpsCPU::test_set_real_imag_cpu_complex128_int8, test/test_view_ops.py::TestViewOpsCPU::test_set_real_imag_cpu_complex128_uint8, test/test_view_ops.py::TestViewOpsCPU::test_set_real_imag_cpu_complex64_bfloat16, test/test_view_ops.py::TestViewOpsCPU::test_set_real_imag_cpu_complex64_bool, test/test_view_ops.py::TestViewOpsCPU::test_set_real_imag_cpu_complex64_complex128, test/test_view_ops.py::TestViewOpsCPU::test_set_real_imag_cpu_complex64_complex64, test/test_view_ops.py::TestViewOpsCPU::test_set_real_imag_cpu_complex64_float16, test/test_view_ops.py::TestViewOpsCPU::test_set_real_imag_cpu_complex64_float32, test/test_view_ops.py::TestViewOpsCPU::test_set_real_imag_cpu_complex64_float64, test/test_view_ops.py::TestViewOpsCPU::test_set_real_imag_cpu_complex64_int16, test/test_view_ops.py::TestViewOpsCPU::test_set_real_imag_cpu_complex64_int32, test/test_view_ops.py::TestViewOpsCPU::test_set_real_imag_cpu_complex64_int64, test/test_view_ops.py::TestViewOpsCPU::test_set_real_imag_cpu_complex64_int8, test/test_view_ops.py::TestViewOpsCPU::test_set_real_imag_cpu_complex64_uint8, test/test_view_ops.py::TestViewOpsCPU::test_split_view_cpu, test/test_view_ops.py::TestViewOpsCPU::test_squeeze_inplace_view_cpu, test/test_view_ops.py::TestViewOpsCPU::test_squeeze_view_cpu, test/test_view_ops.py::TestViewOpsCPU::test_t_inplace_view_cpu, test/test_view_ops.py::TestViewOpsCPU::test_t_view_cpu, test/test_view_ops.py::TestViewOpsCPU::test_transpose_inplace_view_cpu, test/test_view_ops.py::TestViewOpsCPU::test_transpose_view_cpu, test/test_view_ops.py::TestViewOpsCPU::test_unbind_cpu, test/test_view_ops.py::TestViewOpsCPU::test_unbind_view_cpu, test/test_view_ops.py::TestViewOpsCPU::test_unfold_view_cpu, test/test_view_ops.py::TestViewOpsCPU::test_unsqueeze_inplace_view_cpu, test/test_view_ops.py::TestViewOpsCPU::test_unsqueeze_view_cpu, test/test_view_ops.py::TestViewOpsCPU::test_view_as_complex_cpu, test/test_view_ops.py::TestViewOpsCPU::test_view_as_real_cpu_complex128, test/test_view_ops.py::TestViewOpsCPU::test_view_as_real_cpu_complex32, test/test_view_ops.py::TestViewOpsCPU::test_view_as_real_cpu_complex64, test/test_view_ops.py::TestViewOpsCPU::test_view_as_view_cpu, test/test_view_ops.py::TestViewOpsCPU::test_view_copy_cpu, test/test_view_ops.py::TestViewOpsCPU::test_view_copy_out_cpu, test/test_view_ops.py::TestViewOpsCPU::test_view_copy_output_contiguous_cpu, test/test_view_ops.py::TestViewOpsCPU::test_view_dtype_new_cpu_bool, test/test_view_ops.py::TestViewOpsCPU::test_view_dtype_new_cpu_complex128, test/test_view_ops.py::TestViewOpsCPU::test_view_dtype_new_cpu_complex64, test/test_view_ops.py::TestViewOpsCPU::test_view_dtype_new_cpu_float16, test/test_view_ops.py::TestViewOpsCPU::test_view_dtype_new_cpu_float32, test/test_view_ops.py::TestViewOpsCPU::test_view_dtype_new_cpu_float64, test/test_view_ops.py::TestViewOpsCPU::test_view_dtype_new_cpu_int16, test/test_view_ops.py::TestViewOpsCPU::test_view_dtype_new_cpu_int32, test/test_view_ops.py::TestViewOpsCPU::test_view_dtype_new_cpu_int64, test/test_view_ops.py::TestViewOpsCPU::test_view_dtype_new_cpu_int8, test/test_view_ops.py::TestViewOpsCPU::test_view_dtype_new_cpu_uint8, test/test_view_ops.py::TestViewOpsCPU::test_view_dtype_upsize_errors_cpu_bfloat16, test/test_view_ops.py::TestViewOpsCPU::test_view_dtype_upsize_errors_cpu_bool, test/test_view_ops.py::TestViewOpsCPU::test_view_dtype_upsize_errors_cpu_complex128, test/test_view_ops.py::TestViewOpsCPU::test_view_dtype_upsize_errors_cpu_complex64, test/test_view_ops.py::TestViewOpsCPU::test_view_dtype_upsize_errors_cpu_float16, test/test_view_ops.py::TestViewOpsCPU::test_view_dtype_upsize_errors_cpu_float32, test/test_view_ops.py::TestViewOpsCPU::test_view_dtype_upsize_errors_cpu_float64, test/test_view_ops.py::TestViewOpsCPU::test_view_dtype_upsize_errors_cpu_int16, test/test_view_ops.py::TestViewOpsCPU::test_view_dtype_upsize_errors_cpu_int32, test/test_view_ops.py::TestViewOpsCPU::test_view_dtype_upsize_errors_cpu_int64, test/test_view_ops.py::TestViewOpsCPU::test_view_dtype_upsize_errors_cpu_int8, test/test_view_ops.py::TestViewOpsCPU::test_view_dtype_upsize_errors_cpu_uint8, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_dsplit_cpu_bfloat16, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_dsplit_cpu_bool, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_dsplit_cpu_complex128, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_dsplit_cpu_complex64, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_dsplit_cpu_float16, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_dsplit_cpu_float32, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_dsplit_cpu_float64, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_dsplit_cpu_int16, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_dsplit_cpu_int32, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_dsplit_cpu_int64, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_dsplit_cpu_int8, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_dsplit_cpu_uint8, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_hsplit_cpu_bfloat16, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_hsplit_cpu_bool, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_hsplit_cpu_complex128, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_hsplit_cpu_complex64, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_hsplit_cpu_float16, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_hsplit_cpu_float32, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_hsplit_cpu_float64, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_hsplit_cpu_int16, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_hsplit_cpu_int32, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_hsplit_cpu_int64, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_hsplit_cpu_int8, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_hsplit_cpu_uint8, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_split_cpu_bfloat16, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_split_cpu_bool, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_split_cpu_complex128, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_split_cpu_complex64, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_split_cpu_float16, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_split_cpu_float32, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_split_cpu_float64, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_split_cpu_int16, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_split_cpu_int32, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_split_cpu_int64, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_split_cpu_int8, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_split_cpu_uint8, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_vsplit_cpu_bfloat16, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_vsplit_cpu_bool, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_vsplit_cpu_complex128, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_vsplit_cpu_complex64, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_vsplit_cpu_float16, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_vsplit_cpu_float32, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_vsplit_cpu_float64, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_vsplit_cpu_int16, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_vsplit_cpu_int32, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_vsplit_cpu_int64, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_vsplit_cpu_int8, test/test_view_ops.py::TestViewOpsCPU::test_view_tensor_vsplit_cpu_uint8, test/test_view_ops.py::TestViewOpsCPU::test_view_view_cpu, test/test_view_ops.py::TestViewOpsLAZY::test_T_view_lazy, test/test_view_ops.py::TestViewOpsLAZY::test_advanced_indexing_assignment_lazy, test/test_view_ops.py::TestViewOpsLAZY::test_advanced_indexing_nonview_lazy, test/test_view_ops.py::TestViewOpsLAZY::test_as_strided_gradients_lazy, test/test_view_ops.py::TestViewOpsLAZY::test_as_strided_inplace_view_lazy, test/test_view_ops.py::TestViewOpsLAZY::test_as_strided_view_lazy, test/test_view_ops.py::TestViewOpsLAZY::test_basic_indexing_ellipses_view_lazy, test/test_view_ops.py::TestViewOpsLAZY::test_basic_indexing_newaxis_view_lazy, test/test_view_ops.py::TestViewOpsLAZY::test_basic_indexing_slice_view_lazy, test/test_view_ops.py::TestViewOpsLAZY::test_chunk_view_lazy, test/test_view_ops.py::TestViewOpsLAZY::test_conj_imag_view_lazy_complex128, test/test_view_ops.py::TestViewOpsLAZY::test_conj_imag_view_lazy_complex64, test/test_view_ops.py::TestViewOpsLAZY::test_conj_self_lazy_bfloat16, test/test_view_ops.py::TestViewOpsLAZY::test_conj_self_lazy_float16, test/test_view_ops.py::TestViewOpsLAZY::test_conj_self_lazy_float32, test/test_view_ops.py::TestViewOpsLAZY::test_conj_self_lazy_float64, test/test_view_ops.py::TestViewOpsLAZY::test_conj_self_lazy_int16, test/test_view_ops.py::TestViewOpsLAZY::test_conj_self_lazy_int32, test/test_view_ops.py::TestViewOpsLAZY::test_conj_self_lazy_int64, test/test_view_ops.py::TestViewOpsLAZY::test_conj_self_lazy_int8, test/test_view_ops.py::TestViewOpsLAZY::test_conj_self_lazy_uint8, test/test_view_ops.py::TestViewOpsLAZY::test_conj_view_with_shared_memory_lazy, test/test_view_ops.py::TestViewOpsLAZY::test_contiguous_nonview_lazy, test/test_view_ops.py::TestViewOpsLAZY::test_contiguous_self_lazy, test/test_view_ops.py::TestViewOpsLAZY::test_diagonal_view_lazy, test/test_view_ops.py::TestViewOpsLAZY::test_expand_as_view_lazy, test/test_view_ops.py::TestViewOpsLAZY::test_expand_view_lazy, test/test_view_ops.py::TestViewOpsLAZY::test_flatten_nonview_lazy, test/test_view_ops.py::TestViewOpsLAZY::test_flatten_view_lazy, test/test_view_ops.py::TestViewOpsLAZY::test_imag_noncomplex_lazy_bfloat16, test/test_view_ops.py::TestViewOpsLAZY::test_imag_noncomplex_lazy_float16, test/test_view_ops.py::TestViewOpsLAZY::test_imag_noncomplex_lazy_float32, test/test_view_ops.py::TestViewOpsLAZY::test_imag_noncomplex_lazy_float64, test/test_view_ops.py::TestViewOpsLAZY::test_imag_noncomplex_lazy_int16, test/test_view_ops.py::TestViewOpsLAZY::test_imag_noncomplex_lazy_int32, test/test_view_ops.py::TestViewOpsLAZY::test_imag_noncomplex_lazy_int64, test/test_view_ops.py::TestViewOpsLAZY::test_imag_noncomplex_lazy_int8, test/test_view_ops.py::TestViewOpsLAZY::test_imag_noncomplex_lazy_uint8, test/test_view_ops.py::TestViewOpsLAZY::test_movedim_view_lazy, test/test_view_ops.py::TestViewOpsLAZY::test_narrow_view_lazy, test/test_view_ops.py::TestViewOpsLAZY::test_permute_view_lazy, test/test_view_ops.py::TestViewOpsLAZY::test_real_imag_view_lazy_complex128, test/test_view_ops.py::TestViewOpsLAZY::test_real_imag_view_lazy_complex64, test/test_view_ops.py::TestViewOpsLAZY::test_reshape_as_view_lazy, test/test_view_ops.py::TestViewOpsLAZY::test_reshape_nonview_lazy, test/test_view_ops.py::TestViewOpsLAZY::test_reshape_view_lazy, test/test_view_ops.py::TestViewOpsLAZY::test_select_view_lazy, test/test_view_ops.py::TestViewOpsLAZY::test_set_real_imag_lazy_complex128_bfloat16, test/test_view_ops.py::TestViewOpsLAZY::test_set_real_imag_lazy_complex128_bool, test/test_view_ops.py::TestViewOpsLAZY::test_set_real_imag_lazy_complex128_complex128, test/test_view_ops.py::TestViewOpsLAZY::test_set_real_imag_lazy_complex128_complex64, test/test_view_ops.py::TestViewOpsLAZY::test_set_real_imag_lazy_complex128_float16, test/test_view_ops.py::TestViewOpsLAZY::test_set_real_imag_lazy_complex128_float32, test/test_view_ops.py::TestViewOpsLAZY::test_set_real_imag_lazy_complex128_float64, test/test_view_ops.py::TestViewOpsLAZY::test_set_real_imag_lazy_complex128_int16, test/test_view_ops.py::TestViewOpsLAZY::test_set_real_imag_lazy_complex128_int32, test/test_view_ops.py::TestViewOpsLAZY::test_set_real_imag_lazy_complex128_int64, test/test_view_ops.py::TestViewOpsLAZY::test_set_real_imag_lazy_complex128_int8, test/test_view_ops.py::TestViewOpsLAZY::test_set_real_imag_lazy_complex128_uint8, test/test_view_ops.py::TestViewOpsLAZY::test_set_real_imag_lazy_complex64_bfloat16, test/test_view_ops.py::TestViewOpsLAZY::test_set_real_imag_lazy_complex64_bool, test/test_view_ops.py::TestViewOpsLAZY::test_set_real_imag_lazy_complex64_complex128, test/test_view_ops.py::TestViewOpsLAZY::test_set_real_imag_lazy_complex64_complex64, test/test_view_ops.py::TestViewOpsLAZY::test_set_real_imag_lazy_complex64_float16, test/test_view_ops.py::TestViewOpsLAZY::test_set_real_imag_lazy_complex64_float32, test/test_view_ops.py::TestViewOpsLAZY::test_set_real_imag_lazy_complex64_float64, test/test_view_ops.py::TestViewOpsLAZY::test_set_real_imag_lazy_complex64_int16, test/test_view_ops.py::TestViewOpsLAZY::test_set_real_imag_lazy_complex64_int32, test/test_view_ops.py::TestViewOpsLAZY::test_set_real_imag_lazy_complex64_int64, test/test_view_ops.py::TestViewOpsLAZY::test_set_real_imag_lazy_complex64_int8, test/test_view_ops.py::TestViewOpsLAZY::test_set_real_imag_lazy_complex64_uint8, test/test_view_ops.py::TestViewOpsLAZY::test_split_view_lazy, test/test_view_ops.py::TestViewOpsLAZY::test_squeeze_inplace_view_lazy, test/test_view_ops.py::TestViewOpsLAZY::test_squeeze_view_lazy, test/test_view_ops.py::TestViewOpsLAZY::test_t_inplace_view_lazy, test/test_view_ops.py::TestViewOpsLAZY::test_t_view_lazy, test/test_view_ops.py::TestViewOpsLAZY::test_transpose_inplace_view_lazy, test/test_view_ops.py::TestViewOpsLAZY::test_transpose_view_lazy, test/test_view_ops.py::TestViewOpsLAZY::test_unbind_lazy, test/test_view_ops.py::TestViewOpsLAZY::test_unbind_view_lazy, test/test_view_ops.py::TestViewOpsLAZY::test_unfold_view_lazy, test/test_view_ops.py::TestViewOpsLAZY::test_unsqueeze_inplace_view_lazy, test/test_view_ops.py::TestViewOpsLAZY::test_unsqueeze_view_lazy, test/test_view_ops.py::TestViewOpsLAZY::test_view_as_complex_lazy, test/test_view_ops.py::TestViewOpsLAZY::test_view_as_real_lazy_complex128, test/test_view_ops.py::TestViewOpsLAZY::test_view_as_real_lazy_complex32, test/test_view_ops.py::TestViewOpsLAZY::test_view_as_real_lazy_complex64, test/test_view_ops.py::TestViewOpsLAZY::test_view_as_view_lazy, test/test_view_ops.py::TestViewOpsLAZY::test_view_copy_lazy, test/test_view_ops.py::TestViewOpsLAZY::test_view_copy_out_lazy, test/test_view_ops.py::TestViewOpsLAZY::test_view_copy_output_contiguous_lazy, test/test_view_ops.py::TestViewOpsLAZY::test_view_dtype_new_lazy_bool, test/test_view_ops.py::TestViewOpsLAZY::test_view_dtype_new_lazy_complex128, test/test_view_ops.py::TestViewOpsLAZY::test_view_dtype_new_lazy_complex64, test/test_view_ops.py::TestViewOpsLAZY::test_view_dtype_new_lazy_float16, test/test_view_ops.py::TestViewOpsLAZY::test_view_dtype_new_lazy_float32, test/test_view_ops.py::TestViewOpsLAZY::test_view_dtype_new_lazy_float64, test/test_view_ops.py::TestViewOpsLAZY::test_view_dtype_new_lazy_int16, test/test_view_ops.py::TestViewOpsLAZY::test_view_dtype_new_lazy_int32, test/test_view_ops.py::TestViewOpsLAZY::test_view_dtype_new_lazy_int64, test/test_view_ops.py::TestViewOpsLAZY::test_view_dtype_new_lazy_int8, test/test_view_ops.py::TestViewOpsLAZY::test_view_dtype_new_lazy_uint8, test/test_view_ops.py::TestViewOpsLAZY::test_view_dtype_upsize_errors_lazy_bfloat16, test/test_view_ops.py::TestViewOpsLAZY::test_view_dtype_upsize_errors_lazy_bool, test/test_view_ops.py::TestViewOpsLAZY::test_view_dtype_upsize_errors_lazy_complex128, test/test_view_ops.py::TestViewOpsLAZY::test_view_dtype_upsize_errors_lazy_complex64, test/test_view_ops.py::TestViewOpsLAZY::test_view_dtype_upsize_errors_lazy_float16, test/test_view_ops.py::TestViewOpsLAZY::test_view_dtype_upsize_errors_lazy_float32, test/test_view_ops.py::TestViewOpsLAZY::test_view_dtype_upsize_errors_lazy_float64, test/test_view_ops.py::TestViewOpsLAZY::test_view_dtype_upsize_errors_lazy_int16, test/test_view_ops.py::TestViewOpsLAZY::test_view_dtype_upsize_errors_lazy_int32, test/test_view_ops.py::TestViewOpsLAZY::test_view_dtype_upsize_errors_lazy_int64, test/test_view_ops.py::TestViewOpsLAZY::test_view_dtype_upsize_errors_lazy_int8, test/test_view_ops.py::TestViewOpsLAZY::test_view_dtype_upsize_errors_lazy_uint8, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_dsplit_lazy_bfloat16, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_dsplit_lazy_bool, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_dsplit_lazy_complex128, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_dsplit_lazy_complex64, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_dsplit_lazy_float16, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_dsplit_lazy_float32, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_dsplit_lazy_float64, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_dsplit_lazy_int16, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_dsplit_lazy_int32, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_dsplit_lazy_int64, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_dsplit_lazy_int8, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_dsplit_lazy_uint8, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_hsplit_lazy_bfloat16, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_hsplit_lazy_bool, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_hsplit_lazy_complex128, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_hsplit_lazy_complex64, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_hsplit_lazy_float16, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_hsplit_lazy_float32, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_hsplit_lazy_float64, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_hsplit_lazy_int16, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_hsplit_lazy_int32, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_hsplit_lazy_int64, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_hsplit_lazy_int8, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_hsplit_lazy_uint8, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_split_lazy_bfloat16, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_split_lazy_bool, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_split_lazy_complex128, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_split_lazy_complex64, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_split_lazy_float16, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_split_lazy_float32, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_split_lazy_float64, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_split_lazy_int16, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_split_lazy_int32, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_split_lazy_int64, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_split_lazy_int8, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_split_lazy_uint8, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_vsplit_lazy_bfloat16, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_vsplit_lazy_bool, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_vsplit_lazy_complex128, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_vsplit_lazy_complex64, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_vsplit_lazy_float16, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_vsplit_lazy_float32, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_vsplit_lazy_float64, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_vsplit_lazy_int16, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_vsplit_lazy_int32, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_vsplit_lazy_int64, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_vsplit_lazy_int8, test/test_view_ops.py::TestViewOpsLAZY::test_view_tensor_vsplit_lazy_uint8, test/test_view_ops.py::TestViewOpsLAZY::test_view_view_lazy, test/test_view_ops.py::TestOldViewOpsCPU::test_T_cpu, test/test_view_ops.py::TestOldViewOpsCPU::test_atleast_cpu_complex128, test/test_view_ops.py::TestOldViewOpsCPU::test_atleast_cpu_complex64, test/test_view_ops.py::TestOldViewOpsCPU::test_atleast_cpu_float16, test/test_view_ops.py::TestOldViewOpsCPU::test_atleast_cpu_float32, test/test_view_ops.py::TestOldViewOpsCPU::test_atleast_cpu_float64, test/test_view_ops.py::TestOldViewOpsCPU::test_atleast_cpu_int16, test/test_view_ops.py::TestOldViewOpsCPU::test_atleast_cpu_int32, test/test_view_ops.py::TestOldViewOpsCPU::test_atleast_cpu_int64, test/test_view_ops.py::TestOldViewOpsCPU::test_atleast_cpu_int8, test/test_view_ops.py::TestOldViewOpsCPU::test_atleast_cpu_uint8, test/test_view_ops.py::TestOldViewOpsCPU::test_atleast_gradient_cpu, test/test_view_ops.py::TestOldViewOpsCPU::test_big_transpose_cpu, test/test_view_ops.py::TestOldViewOpsCPU::test_broadcast_shapes_cpu, test/test_view_ops.py::TestOldViewOpsCPU::test_broadcast_tensors_cpu_float32, test/test_view_ops.py::TestOldViewOpsCPU::test_broadcast_to_cpu_bool, test/test_view_ops.py::TestOldViewOpsCPU::test_broadcast_to_cpu_complex128, test/test_view_ops.py::TestOldViewOpsCPU::test_broadcast_to_cpu_complex64, test/test_view_ops.py::TestOldViewOpsCPU::test_broadcast_to_cpu_float16, test/test_view_ops.py::TestOldViewOpsCPU::test_broadcast_to_cpu_float32, test/test_view_ops.py::TestOldViewOpsCPU::test_broadcast_to_cpu_float64, test/test_view_ops.py::TestOldViewOpsCPU::test_broadcast_to_cpu_int16, test/test_view_ops.py::TestOldViewOpsCPU::test_broadcast_to_cpu_int32, test/test_view_ops.py::TestOldViewOpsCPU::test_broadcast_to_cpu_int64, test/test_view_ops.py::TestOldViewOpsCPU::test_broadcast_to_cpu_int8, test/test_view_ops.py::TestOldViewOpsCPU::test_broadcast_to_cpu_uint8, test/test_view_ops.py::TestOldViewOpsCPU::test_chunk_cpu, test/test_view_ops.py::TestOldViewOpsCPU::test_conj_neg_view_numpy_error_cpu, test/test_view_ops.py::TestOldViewOpsCPU::test_contiguous_cpu, test/test_view_ops.py::TestOldViewOpsCPU::test_crow_col_indices_cpu, test/test_view_ops.py::TestOldViewOpsCPU::test_empty_reshape_cpu, test/test_view_ops.py::TestOldViewOpsCPU::test_expand_cpu, test/test_view_ops.py::TestOldViewOpsCPU::test_flatten_cpu, test/test_view_ops.py::TestOldViewOpsCPU::test_memory_format_resize__cpu, test/test_view_ops.py::TestOldViewOpsCPU::test_memory_format_resize_as_cpu, test/test_view_ops.py::TestOldViewOpsCPU::test_narrow_cpu, test/test_view_ops.py::TestOldViewOpsCPU::test_narrow_tensor_cpu, test/test_view_ops.py::TestOldViewOpsCPU::test_python_types_cpu, test/test_view_ops.py::TestOldViewOpsCPU::test_ravel_cpu, test/test_view_ops.py::TestOldViewOpsCPU::test_reshape_cpu, test/test_view_ops.py::TestOldViewOpsCPU::test_reshape_view_semantics_cpu_bfloat16, test/test_view_ops.py::TestOldViewOpsCPU::test_reshape_view_semantics_cpu_bool, test/test_view_ops.py::TestOldViewOpsCPU::test_reshape_view_semantics_cpu_complex128, test/test_view_ops.py::TestOldViewOpsCPU::test_reshape_view_semantics_cpu_complex64, test/test_view_ops.py::TestOldViewOpsCPU::test_reshape_view_semantics_cpu_float16, test/test_view_ops.py::TestOldViewOpsCPU::test_reshape_view_semantics_cpu_float32, test/test_view_ops.py::TestOldViewOpsCPU::test_reshape_view_semantics_cpu_float64, test/test_view_ops.py::TestOldViewOpsCPU::test_reshape_view_semantics_cpu_int16, test/test_view_ops.py::TestOldViewOpsCPU::test_reshape_view_semantics_cpu_int32, test/test_view_ops.py::TestOldViewOpsCPU::test_reshape_view_semantics_cpu_int64, test/test_view_ops.py::TestOldViewOpsCPU::test_reshape_view_semantics_cpu_int8, test/test_view_ops.py::TestOldViewOpsCPU::test_reshape_view_semantics_cpu_uint8, test/test_view_ops.py::TestOldViewOpsCPU::test_resize_all_dtypes_and_devices_cpu, test/test_view_ops.py::TestOldViewOpsCPU::test_resize_as_all_dtypes_and_devices_cpu, test/test_view_ops.py::TestOldViewOpsCPU::test_resize_as_preserves_strides_cpu, test/test_view_ops.py::TestOldViewOpsCPU::test_resize_overflow_cpu, test/test_view_ops.py::TestOldViewOpsCPU::test_split_cpu, test/test_view_ops.py::TestOldViewOpsCPU::test_t_cpu, test/test_view_ops.py::TestOldViewOpsCPU::test_tensor_split_errors_cpu, test/test_view_ops.py::TestOldViewOpsCPU::test_tensor_split_indices_cpu_bool, test/test_view_ops.py::TestOldViewOpsCPU::test_tensor_split_indices_cpu_complex128, test/test_view_ops.py::TestOldViewOpsCPU::test_tensor_split_indices_cpu_complex64, test/test_view_ops.py::TestOldViewOpsCPU::test_tensor_split_indices_cpu_float16, test/test_view_ops.py::TestOldViewOpsCPU::test_tensor_split_indices_cpu_float32, test/test_view_ops.py::TestOldViewOpsCPU::test_tensor_split_indices_cpu_float64, test/test_view_ops.py::TestOldViewOpsCPU::test_tensor_split_indices_cpu_int16, test/test_view_ops.py::TestOldViewOpsCPU::test_tensor_split_indices_cpu_int32, test/test_view_ops.py::TestOldViewOpsCPU::test_tensor_split_indices_cpu_int64, test/test_view_ops.py::TestOldViewOpsCPU::test_tensor_split_indices_cpu_int8, test/test_view_ops.py::TestOldViewOpsCPU::test_tensor_split_indices_cpu_uint8, test/test_view_ops.py::TestOldViewOpsCPU::test_tensor_split_sections_cpu_bool, test/test_view_ops.py::TestOldViewOpsCPU::test_tensor_split_sections_cpu_complex128, test/test_view_ops.py::TestOldViewOpsCPU::test_tensor_split_sections_cpu_complex64, test/test_view_ops.py::TestOldViewOpsCPU::test_tensor_split_sections_cpu_float16, test/test_view_ops.py::TestOldViewOpsCPU::test_tensor_split_sections_cpu_float32, test/test_view_ops.py::TestOldViewOpsCPU::test_tensor_split_sections_cpu_float64, test/test_view_ops.py::TestOldViewOpsCPU::test_tensor_split_sections_cpu_int16, test/test_view_ops.py::TestOldViewOpsCPU::test_tensor_split_sections_cpu_int32, test/test_view_ops.py::TestOldViewOpsCPU::test_tensor_split_sections_cpu_int64, test/test_view_ops.py::TestOldViewOpsCPU::test_tensor_split_sections_cpu_int8, test/test_view_ops.py::TestOldViewOpsCPU::test_tensor_split_sections_cpu_uint8, test/test_view_ops.py::TestOldViewOpsCPU::test_transpose_invalid_cpu_complex128, test/test_view_ops.py::TestOldViewOpsCPU::test_transpose_invalid_cpu_float32, test/test_view_ops.py::TestOldViewOpsCPU::test_transpose_invalid_cpu_int64, test/test_view_ops.py::TestOldViewOpsCPU::test_transpose_vs_numpy_cpu_complex128, test/test_view_ops.py::TestOldViewOpsCPU::test_transpose_vs_numpy_cpu_float32, test/test_view_ops.py::TestOldViewOpsCPU::test_transpose_vs_numpy_cpu_int64, test/test_view_ops.py::TestOldViewOpsCPU::test_transposes_cpu_bfloat16, test/test_view_ops.py::TestOldViewOpsCPU::test_transposes_cpu_bool, test/test_view_ops.py::TestOldViewOpsCPU::test_transposes_cpu_complex128, test/test_view_ops.py::TestOldViewOpsCPU::test_transposes_cpu_complex64, test/test_view_ops.py::TestOldViewOpsCPU::test_transposes_cpu_float16, test/test_view_ops.py::TestOldViewOpsCPU::test_transposes_cpu_float32, test/test_view_ops.py::TestOldViewOpsCPU::test_transposes_cpu_float64, test/test_view_ops.py::TestOldViewOpsCPU::test_transposes_cpu_int16, test/test_view_ops.py::TestOldViewOpsCPU::test_transposes_cpu_int32, test/test_view_ops.py::TestOldViewOpsCPU::test_transposes_cpu_int64, test/test_view_ops.py::TestOldViewOpsCPU::test_transposes_cpu_int8, test/test_view_ops.py::TestOldViewOpsCPU::test_transposes_cpu_uint8, test/test_view_ops.py::TestOldViewOpsCPU::test_transposes_errors_cpu_bfloat16, test/test_view_ops.py::TestOldViewOpsCPU::test_transposes_errors_cpu_bool, test/test_view_ops.py::TestOldViewOpsCPU::test_transposes_errors_cpu_complex128, test/test_view_ops.py::TestOldViewOpsCPU::test_transposes_errors_cpu_complex64, test/test_view_ops.py::TestOldViewOpsCPU::test_transposes_errors_cpu_float16, test/test_view_ops.py::TestOldViewOpsCPU::test_transposes_errors_cpu_float32, test/test_view_ops.py::TestOldViewOpsCPU::test_transposes_errors_cpu_float64, test/test_view_ops.py::TestOldViewOpsCPU::test_transposes_errors_cpu_int16, test/test_view_ops.py::TestOldViewOpsCPU::test_transposes_errors_cpu_int32, test/test_view_ops.py::TestOldViewOpsCPU::test_transposes_errors_cpu_int64, test/test_view_ops.py::TestOldViewOpsCPU::test_transposes_errors_cpu_int8, test/test_view_ops.py::TestOldViewOpsCPU::test_transposes_errors_cpu_uint8, test/test_view_ops.py::TestOldViewOpsCPU::test_unsqueeze_cpu, test/test_view_ops.py::TestOldViewOpsCPU::test_view_all_dtypes_and_devices_cpu, test/test_view_ops.py::TestOldViewOpsCPU::test_view_cpu, test/test_view_ops.py::TestOldViewOpsCPU::test_view_empty_cpu 2025-01-24T01:56:33.2056305Z 2025-01-24T01:56:37.4413823Z Running test_jiterator 1/1 ... [2025-01-24 01:56:37.440263] 2025-01-24T01:56:37.4414696Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T01:56:37.4416831Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_jiterator.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-01-24 01:56:37.440707] 2025-01-24T01:56:41.0725082Z 2025-01-24T01:56:41.0726719Z test_jiterator 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_jiterator_1.1_0119591c427e919b_.log 2025-01-24T01:56:41.0728008Z Running 0 items in this shard: 2025-01-24T01:56:41.0728286Z 2025-01-24T01:56:45.1592563Z Running xpu/test_gemm 1/1 ... [2025-01-24 01:56:45.158703] 2025-01-24T01:56:45.1593543Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T01:56:45.1600774Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'xpu/test_gemm.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'] ... [2025-01-24 01:56:45.159099] 2025-01-24T01:56:49.9567151Z 2025-01-24T01:56:49.9568430Z xpu/test_gemm 1/1 was successful, full logs can be found in artifacts with path test/test-reports/xpu.test_gemm_1.1_8d9a07e61845f9b6_.log 2025-01-24T01:56:49.9569708Z Running 0 items in this shard: 2025-01-24T01:56:49.9570062Z 2025-01-24T01:56:53.5605851Z Running test_matmul_cuda 1/1 ... [2025-01-24 01:56:53.560073] 2025-01-24T01:56:53.5606683Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T01:56:53.5612514Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_matmul_cuda.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-01-24 01:56:53.560407] 2025-01-24T01:56:58.1733853Z 2025-01-24T01:56:58.1739081Z test_matmul_cuda 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_matmul_cuda_1.1_d792935b30c15225_.log 2025-01-24T01:56:58.1740914Z Running 0 items in this shard: 2025-01-24T01:56:58.1741433Z 2025-01-24T01:57:02.5967969Z Running optim/test_lrscheduler 1/1 ... [2025-01-24 01:57:02.596377] 2025-01-24T01:57:02.5968737Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T01:57:02.5971065Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'optim/test_lrscheduler.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'] ... [2025-01-24 01:57:02.596764] 2025-01-24T01:57:06.2510444Z 2025-01-24T01:57:06.2511655Z optim/test_lrscheduler 1/1 was successful, full logs can be found in artifacts with path test/test-reports/optim.test_lrscheduler_1.1_fa032ff79238af95_.log 2025-01-24T01:57:06.2512471Z 2025-01-24T01:57:10.0335079Z Running optim/test_optim 1/1 ... [2025-01-24 01:57:10.033089] 2025-01-24T01:57:10.0335922Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T01:57:10.0337526Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'optim/test_optim.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-01-24 01:57:10.033455] 2025-01-24T01:57:13.7715365Z 2025-01-24T01:57:13.7716740Z optim/test_optim 1/1 was successful, full logs can be found in artifacts with path test/test-reports/optim.test_optim_1.1_1b1ddf224b36e9b8_.log 2025-01-24T01:57:13.7717984Z 2025-01-24T01:57:14.5332914Z 2025-01-24T01:57:14.5334182Z test_mkldnn 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_mkldnn_1.1_2f41cf638f618ca2_.log 2025-01-24T01:57:14.5375235Z Running 83 items in this shard: test/test_mkldnn.py::TestMkldnnCPU::test_0_dimension_tensor_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_adaptive_avg_pool2d_bf16_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_adaptive_avg_pool2d_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_add_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_autograd_from_mkldnn_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_autograd_to_mkldnn_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_avg_pool2d_bf16_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_avg_pool2d_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_avg_pool2d_stride_none_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_avg_pool3d_bf16_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_avg_pool3d_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_batch_norm_2d_bf16_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_batch_norm_2d_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_batch_norm_3d_bf16_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_batch_norm_3d_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_clone_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_conv1d_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_conv1d_functional_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_conv2d_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_conv2d_legacy_jit_model_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_conv3d_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_conv_deconv_1d_lower_precision_cpu_bfloat16, test/test_mkldnn.py::TestMkldnnCPU::test_conv_deconv_1d_lower_precision_cpu_float16, test/test_mkldnn.py::TestMkldnnCPU::test_conv_deconv_2d_lower_precision_cpu_bfloat16, test/test_mkldnn.py::TestMkldnnCPU::test_conv_deconv_2d_lower_precision_cpu_float16, test/test_mkldnn.py::TestMkldnnCPU::test_conv_deconv_3d_lower_precision_cpu_bfloat16, test/test_mkldnn.py::TestMkldnnCPU::test_conv_deconv_3d_lower_precision_cpu_float16, test/test_mkldnn.py::TestMkldnnCPU::test_conv_nhwc_fp32_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_conv_nhwc_lower_precision_cpu_bfloat16, test/test_mkldnn.py::TestMkldnnCPU::test_conv_nhwc_lower_precision_cpu_float16, test/test_mkldnn.py::TestMkldnnCPU::test_conv_transpose1d_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_conv_transpose2d_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_conv_transpose3d_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_conv_transpose_nhwc_fp32_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_conv_transpose_nhwc_lower_precision_cpu_bfloat16, test/test_mkldnn.py::TestMkldnnCPU::test_conv_transpose_nhwc_lower_precision_cpu_float16, test/test_mkldnn.py::TestMkldnnCPU::test_conversion_byte_char_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_conversion_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_copy_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_detach_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_empty_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_gelu_bf16_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_gelu_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_is_mkldnn_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_is_mkldnn_jit_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_legacy_new_failure_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_linear_backward_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_linear_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_linear_lowp_cpu_bfloat16, test/test_mkldnn.py::TestMkldnnCPU::test_linear_lowp_cpu_float16, test/test_mkldnn.py::TestMkldnnCPU::test_linear_non_contiguous_weight_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_lstm_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_matmul_lower_precision_cpu_bfloat16, test/test_mkldnn.py::TestMkldnnCPU::test_matmul_lower_precision_cpu_float16, test/test_mkldnn.py::TestMkldnnCPU::test_max_pool2d_bf16_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_max_pool2d_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_max_pool2d_stride_none_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_max_pool3d_bf16_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_max_pool3d_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_max_pool_unsupported_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_mkldnn_conv_shapecheck_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_mul_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_prelu_bf16_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_prelu_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_relu__cpu, test/test_mkldnn.py::TestMkldnnCPU::test_relu_bf16_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_relu_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_relu_inplace_bf16_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_repr_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_reshape_backward_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_reshape_blocked_format_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_reshape_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_resnet18_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_resnext50_32x4d_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_set_data_tensorimpl_type_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_sigmoid_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_softmax_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_tanh_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_transpose_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_transpose_invalid_dime_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_unsupported_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_view_cpu, test/test_mkldnn.py::TestMkldnnCPU::test_zero__cpu 2025-01-24T01:57:14.5414222Z 2025-01-24T02:03:41.0275741Z 2025-01-24T02:03:41.0276646Z test_unary_ufuncs 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_unary_ufuncs_1.1_dc6d51b1c6ba025b_.log 2025-01-24T02:03:42.0925349Z Running 24601 items in this shard: test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_abs_angle_complex_to_float_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_abs_angle_complex_to_float_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_abs_big_number_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_abs_signed_zero_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_abs_signed_zero_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_abs_zero_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_abs_zero_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing__refs__conversions_bfloat16_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing__refs__conversions_bfloat16_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing__refs__conversions_bfloat16_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing__refs__conversions_bfloat16_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing__refs__conversions_bfloat16_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing__refs__conversions_bfloat16_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing__refs__conversions_bfloat16_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing__refs__conversions_bfloat16_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing__refs__conversions_bfloat16_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing__refs__conversions_bfloat16_cpu_int32, 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test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_deg2rad_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_deg2rad_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_deg2rad_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_digamma_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_digamma_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_digamma_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_digamma_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_digamma_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_digamma_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_digamma_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_digamma_cpu_int64, 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test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_isreal_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_jiterator_unary_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_jiterator_unary_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_jiterator_unary_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_jiterator_unary_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_jiterator_unary_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_jiterator_unary_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_jiterator_unary_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_jiterator_unary_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_batch_vs_slicing_jiterator_unary_cpu_int32, 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test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_log_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_log_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_log_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_log_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_log_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_log_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_log_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_log_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_logical_not_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_logical_not_cpu_bool, 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test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_elu_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_elu_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_elu_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_hardshrink_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_hardshrink_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_hardshrink_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_hardshrink_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_hardtanh_cpu_bfloat16, 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test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_mish_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_mish_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_mish_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_prelu_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_prelu_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_prelu_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_prelu_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_relu6_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_relu6_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_relu6_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_relu6_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_relu6_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_relu6_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_relu6_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_relu6_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_relu6_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_relu_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_relu_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_relu_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_relu_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_relu_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_relu_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_relu_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_relu_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_relu_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_selu_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_selu_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_selu_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_selu_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_softplus_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_softplus_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_softplus_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_softplus_cpu_float64, 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test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_tanhshrink_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_tanhshrink_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_tanhshrink_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_tanhshrink_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_tanhshrink_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_tanhshrink_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_tanhshrink_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other__refs_nn_functional_threshold_cpu_bfloat16, 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test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_abs_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_abs_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_abs_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_abs_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_abs_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_abs_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_abs_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_abs_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_acos_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_acos_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_acos_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_acos_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_acos_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_acos_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_acos_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_acos_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_acos_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_acos_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_acos_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_acos_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_acosh_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_acosh_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_acosh_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_acosh_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_acosh_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_acosh_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_acosh_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_acosh_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_acosh_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_acosh_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_acosh_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_acosh_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_angle_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_angle_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_angle_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_angle_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_angle_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_angle_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_angle_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_angle_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_angle_cpu_int32, 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test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_asinh_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_asinh_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_asinh_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_asinh_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_asinh_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_asinh_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_asinh_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_atan_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_atan_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_atan_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_atan_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_atan_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_atan_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_atan_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_atan_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_atan_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_atan_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_atan_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_atan_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_atanh_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_atanh_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_atanh_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_atanh_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_atanh_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_atanh_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_atanh_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_atanh_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_atanh_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_atanh_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_atanh_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_atanh_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_bfloat16_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_bfloat16_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_bfloat16_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_bfloat16_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_bfloat16_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_bfloat16_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_bfloat16_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_bfloat16_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_bfloat16_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_bfloat16_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_bfloat16_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_bfloat16_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_bfloat16_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_bitwise_not_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_bitwise_not_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_bitwise_not_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_bitwise_not_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_bitwise_not_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_bitwise_not_cpu_uint8, 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test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_chalf_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_chalf_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_chalf_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_chalf_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_chalf_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_chalf_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_chalf_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_chalf_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_chalf_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_chalf_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_chalf_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_chalf_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_chalf_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_char_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_char_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_char_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_char_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_char_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_char_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_char_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_char_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_char_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_char_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_char_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_char_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_char_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_conj_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_conj_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_conj_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_conj_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_conj_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_conj_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_conj_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_conj_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_conj_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_conj_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_conj_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_conj_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_conj_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_conj_physical_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_conj_physical_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_conj_physical_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_conj_physical_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_conj_physical_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_conj_physical_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_conj_physical_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_conj_physical_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_conj_physical_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_conj_physical_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_conj_physical_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_conj_physical_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_conj_physical_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_cos_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_cos_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_cos_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_cos_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_cos_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_cos_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_cos_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_cos_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_cos_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_cos_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_cos_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_cos_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_cosh_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_cosh_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_cosh_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_cosh_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_cosh_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_cosh_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_cosh_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_cosh_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_cosh_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_cosh_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_cosh_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_cosh_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_deg2rad_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_deg2rad_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_deg2rad_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_deg2rad_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_deg2rad_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_deg2rad_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_deg2rad_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_deg2rad_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_deg2rad_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_deg2rad_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_digamma_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_digamma_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_digamma_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_digamma_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_digamma_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_digamma_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_digamma_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_digamma_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_digamma_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_digamma_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_double_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_double_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_double_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_double_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_double_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_double_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_double_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_double_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_double_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_double_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_double_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_double_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_double_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_erf_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_erf_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_erf_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_erf_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_erf_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_erf_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_erf_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_erf_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_erf_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_erf_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_erfc_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_erfc_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_erfc_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_erfc_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_erfc_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_erfc_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_erfc_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_erfc_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_erfc_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_erfc_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_erfinv_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_erfinv_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_erfinv_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_erfinv_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_erfinv_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_erfinv_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_erfinv_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_erfinv_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_erfinv_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_erfinv_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_exp2_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_exp2_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_exp2_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_exp2_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_exp2_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_exp2_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_exp2_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_exp2_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_exp2_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_exp2_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_exp2_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_exp2_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_exp_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_exp_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_exp_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_exp_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_exp_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_exp_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_exp_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_exp_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_exp_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_exp_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_exp_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_exp_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_expm1_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_expm1_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_expm1_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_expm1_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_expm1_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_expm1_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_expm1_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_expm1_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_expm1_cpu_int32, 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test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_fill_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_fill_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_fill_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_fill_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_fill_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_fill_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_float_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_float_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_float_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_float_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_float_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_float_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_float_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_float_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_float_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_float_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_float_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_float_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_float_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_floor_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_floor_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_floor_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_floor_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_floor_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_floor_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_floor_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_floor_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_floor_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_frac_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_frac_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_frac_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_frac_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_frexp_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_frexp_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_frexp_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_frexp_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_half_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_half_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_half_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_half_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_half_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_half_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_half_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_half_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_half_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_half_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_half_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_half_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_i0_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_i0_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_i0_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_i0_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_i0_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_i0_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_i0_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_i0_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_i0_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_i0_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_imag_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_imag_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_imag_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_int_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_int_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_int_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_int_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_int_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_int_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_int_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_int_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_int_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_int_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_int_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_int_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isfinite_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isfinite_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isfinite_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isfinite_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isfinite_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isfinite_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isfinite_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isfinite_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isfinite_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isfinite_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isfinite_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isfinite_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isfinite_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isinf_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isinf_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isinf_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isinf_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isinf_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isinf_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isinf_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isinf_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isinf_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isinf_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isinf_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isinf_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isinf_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isnan_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isnan_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isnan_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isnan_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isnan_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isnan_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isnan_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isnan_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isnan_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isnan_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isnan_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isnan_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isneginf_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isneginf_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isneginf_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isneginf_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isneginf_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isneginf_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isneginf_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isneginf_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isneginf_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isneginf_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isposinf_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isposinf_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isposinf_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isposinf_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isposinf_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isposinf_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isposinf_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isposinf_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isposinf_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isposinf_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isreal_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isreal_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isreal_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isreal_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isreal_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isreal_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isreal_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isreal_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isreal_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isreal_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isreal_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isreal_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_isreal_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_jiterator_unary_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_jiterator_unary_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_jiterator_unary_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_jiterator_unary_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_jiterator_unary_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_jiterator_unary_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_jiterator_unary_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_jiterator_unary_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_jiterator_unary_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_jiterator_unary_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_jiterator_unary_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_jiterator_unary_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_lgamma_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_lgamma_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_lgamma_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_lgamma_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_lgamma_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_lgamma_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_lgamma_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_lgamma_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_lgamma_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_lgamma_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_log10_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_log10_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_log10_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_log10_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_log10_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_log10_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_log10_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_log10_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_log10_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_log10_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_log10_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_log10_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_log1p_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_log1p_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_log1p_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_log1p_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_log1p_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_log1p_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_log1p_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_log1p_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_log1p_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_log1p_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_log1p_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_log1p_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_log2_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_log2_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_log2_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_log2_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_log2_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_log2_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_log2_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_log2_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_log2_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_log2_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_log2_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_log2_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_log_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_log_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_log_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_log_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_log_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_log_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_log_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_log_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_log_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_log_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_log_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_log_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_logical_not_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_logical_not_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_logical_not_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_logical_not_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_logical_not_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_logical_not_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_logical_not_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_logical_not_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_logical_not_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_logical_not_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_logical_not_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_logical_not_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_logit_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_logit_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_logit_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_logit_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_logit_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_logit_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_logit_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_logit_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_logit_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_logit_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_long_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_long_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_long_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_long_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_long_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_long_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_long_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_long_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_long_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_long_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_long_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_long_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_long_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_mvlgamma_mvlgamma_p_1_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_mvlgamma_mvlgamma_p_1_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_mvlgamma_mvlgamma_p_1_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_mvlgamma_mvlgamma_p_1_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_mvlgamma_mvlgamma_p_1_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_mvlgamma_mvlgamma_p_1_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_mvlgamma_mvlgamma_p_1_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_mvlgamma_mvlgamma_p_1_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_mvlgamma_mvlgamma_p_1_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_mvlgamma_mvlgamma_p_3_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_mvlgamma_mvlgamma_p_3_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_mvlgamma_mvlgamma_p_3_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_mvlgamma_mvlgamma_p_3_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_mvlgamma_mvlgamma_p_3_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_mvlgamma_mvlgamma_p_3_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_mvlgamma_mvlgamma_p_3_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_mvlgamma_mvlgamma_p_3_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_mvlgamma_mvlgamma_p_3_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_mvlgamma_mvlgamma_p_5_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_mvlgamma_mvlgamma_p_5_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_mvlgamma_mvlgamma_p_5_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_mvlgamma_mvlgamma_p_5_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_mvlgamma_mvlgamma_p_5_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_mvlgamma_mvlgamma_p_5_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_mvlgamma_mvlgamma_p_5_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_mvlgamma_mvlgamma_p_5_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_mvlgamma_mvlgamma_p_5_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nan_to_num_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nan_to_num_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nan_to_num_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nan_to_num_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nan_to_num_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nan_to_num_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nan_to_num_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nan_to_num_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nan_to_num_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nan_to_num_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_neg_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_neg_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_neg_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_neg_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_neg_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_neg_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_neg_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_neg_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_neg_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_neg_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_neg_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_neg_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_celu_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_celu_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_celu_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_celu_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_elu_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_elu_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_elu_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_elu_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_hardshrink_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_hardshrink_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_hardshrink_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_hardshrink_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_hardsigmoid_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_hardsigmoid_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_hardsigmoid_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_hardsigmoid_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_hardtanh_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_hardtanh_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_hardtanh_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_hardtanh_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_hardtanh_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_hardtanh_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_hardtanh_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_hardtanh_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_logsigmoid_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_logsigmoid_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_logsigmoid_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_logsigmoid_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_mish_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_mish_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_mish_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_mish_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_prelu_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_prelu_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_prelu_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_prelu_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_relu6_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_relu6_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_relu6_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_relu6_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_relu6_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_relu6_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_relu6_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_relu6_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_relu6_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_relu_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_relu_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_relu_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_relu_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_relu_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_relu_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_relu_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_relu_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_relu_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_rrelu_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_rrelu_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_rrelu_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_selu_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_selu_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_selu_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_selu_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_silu_complex_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_silu_complex_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_silu_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_silu_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_silu_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_silu_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_softplus_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_softplus_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_softplus_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_softplus_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_softshrink_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_softshrink_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_softshrink_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_softshrink_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_softsign_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_softsign_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_softsign_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_softsign_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_softsign_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_softsign_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_softsign_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_softsign_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_softsign_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_softsign_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_softsign_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_tanhshrink_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_tanhshrink_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_tanhshrink_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_tanhshrink_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_tanhshrink_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_tanhshrink_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_tanhshrink_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_tanhshrink_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_tanhshrink_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_tanhshrink_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_tanhshrink_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_threshold_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_threshold_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_threshold_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_threshold_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_threshold_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_threshold_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_threshold_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_threshold_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_nn_functional_threshold_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_polygamma_polygamma_n_0_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_polygamma_polygamma_n_0_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_polygamma_polygamma_n_0_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_polygamma_polygamma_n_0_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_polygamma_polygamma_n_0_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_polygamma_polygamma_n_0_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_polygamma_polygamma_n_0_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_polygamma_polygamma_n_0_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_polygamma_polygamma_n_0_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_polygamma_polygamma_n_0_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_polygamma_polygamma_n_1_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_polygamma_polygamma_n_1_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_polygamma_polygamma_n_1_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_polygamma_polygamma_n_1_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_polygamma_polygamma_n_1_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_polygamma_polygamma_n_1_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_polygamma_polygamma_n_1_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_polygamma_polygamma_n_1_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_polygamma_polygamma_n_1_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_polygamma_polygamma_n_2_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_polygamma_polygamma_n_2_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_polygamma_polygamma_n_2_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_polygamma_polygamma_n_2_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_polygamma_polygamma_n_2_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_polygamma_polygamma_n_2_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_polygamma_polygamma_n_2_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_polygamma_polygamma_n_2_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_polygamma_polygamma_n_2_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_polygamma_polygamma_n_3_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_polygamma_polygamma_n_3_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_polygamma_polygamma_n_3_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_polygamma_polygamma_n_3_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_polygamma_polygamma_n_3_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_polygamma_polygamma_n_3_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_polygamma_polygamma_n_3_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_polygamma_polygamma_n_3_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_polygamma_polygamma_n_3_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_polygamma_polygamma_n_4_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_polygamma_polygamma_n_4_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_polygamma_polygamma_n_4_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_polygamma_polygamma_n_4_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_polygamma_polygamma_n_4_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_polygamma_polygamma_n_4_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_polygamma_polygamma_n_4_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_polygamma_polygamma_n_4_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_polygamma_polygamma_n_4_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_positive_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_positive_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_positive_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_positive_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_positive_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_positive_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_positive_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_positive_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_positive_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_positive_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_positive_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_positive_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_rad2deg_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_rad2deg_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_rad2deg_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_rad2deg_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_rad2deg_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_rad2deg_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_rad2deg_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_rad2deg_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_rad2deg_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_rad2deg_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_real_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_real_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_real_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_real_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_real_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_real_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_real_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_real_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_real_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_real_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_real_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_real_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_real_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_reciprocal_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_reciprocal_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_reciprocal_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_reciprocal_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_reciprocal_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_reciprocal_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_reciprocal_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_reciprocal_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_reciprocal_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_reciprocal_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_reciprocal_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_reciprocal_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_round_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_round_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_round_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_round_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_round_cpu_int16, 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test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_round_decimals_3_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_round_decimals_neg_3_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_round_decimals_neg_3_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_round_decimals_neg_3_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_rsqrt_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_rsqrt_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_rsqrt_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_rsqrt_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_rsqrt_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_every_other_rsqrt_cpu_float32, 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test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_acos_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_acos_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_acos_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_acos_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_acos_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_acos_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_acos_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_acos_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_acos_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_acos_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_acosh_cpu_bfloat16, 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test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_acosh_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_angle_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_angle_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_angle_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_angle_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_angle_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_angle_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_angle_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_angle_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_angle_cpu_int32, 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test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_bfloat16_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_bfloat16_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_bfloat16_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_bfloat16_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_bfloat16_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_bfloat16_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_bfloat16_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_bfloat16_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_bfloat16_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_bfloat16_cpu_int8, 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test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_bool_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_bool_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_bool_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_bool_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_bool_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_bool_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_bool_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_bool_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_bool_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_bool_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_byte_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_byte_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_byte_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_byte_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_byte_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_byte_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_byte_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_byte_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_byte_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_byte_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_byte_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_byte_cpu_uint8, 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test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_ceil_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_cfloat_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_cfloat_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_cfloat_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_cfloat_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_cfloat_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_cfloat_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_cfloat_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_cfloat_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_cfloat_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_cfloat_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_cfloat_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_cfloat_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_cfloat_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_chalf_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_chalf_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_chalf_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_chalf_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_chalf_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_chalf_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_chalf_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_chalf_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_chalf_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_chalf_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_chalf_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_chalf_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_chalf_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_char_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_char_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_char_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_char_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_char_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_char_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_char_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_char_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_char_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_char_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_char_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_char_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_char_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_conj_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_conj_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_conj_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_conj_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_conj_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_conj_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_conj_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_conj_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_conj_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_conj_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_conj_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_conj_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_conj_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_conj_physical_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_conj_physical_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_conj_physical_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_conj_physical_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_conj_physical_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_conj_physical_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_conj_physical_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_conj_physical_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_conj_physical_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_conj_physical_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_conj_physical_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_conj_physical_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_conj_physical_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_cos_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_cos_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_cos_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_cos_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_cos_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_cos_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_cos_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_cos_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_cos_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_cos_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_cos_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_cos_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_cosh_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_cosh_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_cosh_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_cosh_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_cosh_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_cosh_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_cosh_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_cosh_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_cosh_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_cosh_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_cosh_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_cosh_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_deg2rad_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_deg2rad_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_deg2rad_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_deg2rad_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_deg2rad_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_deg2rad_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_deg2rad_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_deg2rad_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_deg2rad_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_deg2rad_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_digamma_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_digamma_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_digamma_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_digamma_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_digamma_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_digamma_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_digamma_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_digamma_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_digamma_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_digamma_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_double_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_double_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_double_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_double_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_double_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_double_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_double_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_double_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_double_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_double_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_double_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_double_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_double_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_erf_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_erf_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_erf_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_erf_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_erf_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_erf_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_erf_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_erf_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_erf_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_erf_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_erfc_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_erfc_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_erfc_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_erfc_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_erfc_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_erfc_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_erfc_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_erfc_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_erfc_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_erfc_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_erfinv_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_erfinv_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_erfinv_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_erfinv_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_erfinv_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_erfinv_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_erfinv_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_erfinv_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_erfinv_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_erfinv_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_exp2_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_exp2_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_exp2_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_exp2_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_exp2_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_exp2_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_exp2_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_exp2_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_exp2_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_exp2_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_exp2_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_exp2_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_exp_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_exp_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_exp_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_exp_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_exp_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_exp_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_exp_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_exp_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_exp_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_exp_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_exp_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_exp_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_expm1_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_expm1_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_expm1_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_expm1_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_expm1_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_expm1_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_expm1_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_expm1_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_expm1_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_expm1_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_expm1_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_expm1_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_fill_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_fill_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_fill_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_fill_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_fill_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_fill_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_fill_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_fill_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_fill_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_fill_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_fill_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_fill_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_fill_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_float_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_float_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_float_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_float_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_float_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_float_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_float_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_float_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_float_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_float_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_float_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_float_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_float_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_floor_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_floor_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_floor_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_floor_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_floor_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_floor_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_floor_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_floor_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_floor_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_frac_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_frac_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_frac_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_frac_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_frexp_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_frexp_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_frexp_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_frexp_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_half_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_half_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_half_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_half_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_half_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_half_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_half_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_half_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_half_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_half_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_half_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_half_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_i0_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_i0_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_i0_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_i0_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_i0_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_i0_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_i0_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_i0_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_i0_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_i0_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_imag_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_imag_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_imag_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_int_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_int_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_int_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_int_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_int_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_int_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_int_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_int_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_int_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_int_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_int_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_int_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isfinite_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isfinite_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isfinite_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isfinite_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isfinite_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isfinite_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isfinite_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isfinite_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isfinite_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isfinite_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isfinite_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isfinite_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isfinite_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isinf_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isinf_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isinf_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isinf_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isinf_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isinf_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isinf_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isinf_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isinf_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isinf_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isinf_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isinf_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isinf_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isnan_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isnan_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isnan_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isnan_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isnan_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isnan_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isnan_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isnan_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isnan_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isnan_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isnan_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isnan_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isneginf_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isneginf_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isneginf_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isneginf_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isneginf_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isneginf_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isneginf_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isneginf_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isneginf_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isneginf_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isposinf_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isposinf_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isposinf_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isposinf_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isposinf_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isposinf_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isposinf_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isposinf_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isposinf_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isposinf_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isreal_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isreal_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isreal_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isreal_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isreal_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isreal_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isreal_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isreal_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isreal_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isreal_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isreal_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isreal_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_isreal_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_jiterator_unary_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_jiterator_unary_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_jiterator_unary_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_jiterator_unary_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_jiterator_unary_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_jiterator_unary_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_jiterator_unary_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_jiterator_unary_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_jiterator_unary_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_jiterator_unary_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_jiterator_unary_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_jiterator_unary_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_lgamma_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_lgamma_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_lgamma_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_lgamma_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_lgamma_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_lgamma_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_lgamma_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_lgamma_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_lgamma_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_lgamma_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_log10_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_log10_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_log10_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_log10_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_log10_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_log10_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_log10_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_log10_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_log10_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_log10_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_log10_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_log10_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_log1p_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_log1p_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_log1p_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_log1p_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_log1p_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_log1p_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_log1p_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_log1p_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_log1p_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_log1p_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_log1p_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_log1p_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_log2_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_log2_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_log2_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_log2_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_log2_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_log2_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_log2_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_log2_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_log2_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_log2_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_log2_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_log2_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_log_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_log_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_log_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_log_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_log_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_log_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_log_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_log_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_log_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_log_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_log_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_log_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_logical_not_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_logical_not_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_logical_not_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_logical_not_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_logical_not_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_logical_not_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_logical_not_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_logical_not_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_logical_not_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_logical_not_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_logical_not_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_logical_not_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_logit_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_logit_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_logit_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_logit_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_logit_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_logit_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_logit_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_logit_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_logit_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_logit_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_long_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_long_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_long_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_long_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_long_cpu_complex64, 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test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_mvlgamma_mvlgamma_p_5_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_mvlgamma_mvlgamma_p_5_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_mvlgamma_mvlgamma_p_5_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_mvlgamma_mvlgamma_p_5_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_mvlgamma_mvlgamma_p_5_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_mvlgamma_mvlgamma_p_5_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_mvlgamma_mvlgamma_p_5_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nan_to_num_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nan_to_num_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nan_to_num_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nan_to_num_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nan_to_num_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nan_to_num_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nan_to_num_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nan_to_num_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nan_to_num_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nan_to_num_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_neg_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_neg_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_neg_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_neg_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_neg_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_neg_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_neg_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_neg_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_neg_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_neg_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_neg_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_neg_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_celu_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_celu_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_celu_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_celu_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_elu_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_elu_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_elu_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_elu_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_hardshrink_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_hardshrink_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_hardshrink_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_hardshrink_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_hardsigmoid_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_hardsigmoid_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_hardsigmoid_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_hardsigmoid_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_hardtanh_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_hardtanh_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_hardtanh_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_hardtanh_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_hardtanh_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_hardtanh_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_hardtanh_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_hardtanh_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_logsigmoid_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_logsigmoid_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_logsigmoid_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_logsigmoid_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_mish_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_mish_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_mish_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_mish_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_prelu_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_prelu_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_prelu_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_prelu_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_relu6_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_relu6_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_relu6_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_relu6_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_relu6_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_relu6_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_relu6_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_relu6_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_relu6_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_relu_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_relu_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_relu_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_relu_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_relu_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_relu_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_relu_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_relu_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_relu_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_rrelu_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_rrelu_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_rrelu_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_selu_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_selu_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_selu_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_selu_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_silu_complex_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_silu_complex_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_silu_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_silu_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_silu_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_silu_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_softplus_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_softplus_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_softplus_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_softplus_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_softshrink_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_softshrink_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_softshrink_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_softshrink_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_softsign_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_softsign_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_softsign_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_softsign_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_softsign_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_softsign_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_softsign_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_softsign_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_softsign_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_softsign_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_softsign_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_tanhshrink_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_tanhshrink_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_tanhshrink_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_tanhshrink_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_tanhshrink_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_tanhshrink_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_tanhshrink_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_tanhshrink_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_tanhshrink_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_tanhshrink_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_tanhshrink_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_threshold_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_threshold_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_threshold_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_threshold_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_threshold_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_threshold_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_threshold_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_threshold_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_nn_functional_threshold_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_polygamma_polygamma_n_0_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_polygamma_polygamma_n_0_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_polygamma_polygamma_n_0_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_polygamma_polygamma_n_0_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_polygamma_polygamma_n_0_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_polygamma_polygamma_n_0_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_polygamma_polygamma_n_0_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_polygamma_polygamma_n_0_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_polygamma_polygamma_n_0_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_polygamma_polygamma_n_0_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_polygamma_polygamma_n_1_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_polygamma_polygamma_n_1_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_polygamma_polygamma_n_1_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_polygamma_polygamma_n_1_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_polygamma_polygamma_n_1_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_polygamma_polygamma_n_1_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_polygamma_polygamma_n_1_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_polygamma_polygamma_n_1_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_polygamma_polygamma_n_1_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_polygamma_polygamma_n_2_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_polygamma_polygamma_n_2_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_polygamma_polygamma_n_2_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_polygamma_polygamma_n_2_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_polygamma_polygamma_n_2_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_polygamma_polygamma_n_2_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_polygamma_polygamma_n_2_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_polygamma_polygamma_n_2_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_polygamma_polygamma_n_2_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_polygamma_polygamma_n_3_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_polygamma_polygamma_n_3_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_polygamma_polygamma_n_3_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_polygamma_polygamma_n_3_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_polygamma_polygamma_n_3_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_polygamma_polygamma_n_3_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_polygamma_polygamma_n_3_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_polygamma_polygamma_n_3_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_polygamma_polygamma_n_3_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_polygamma_polygamma_n_4_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_polygamma_polygamma_n_4_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_polygamma_polygamma_n_4_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_polygamma_polygamma_n_4_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_polygamma_polygamma_n_4_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_polygamma_polygamma_n_4_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_polygamma_polygamma_n_4_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_polygamma_polygamma_n_4_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_polygamma_polygamma_n_4_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_positive_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_positive_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_positive_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_positive_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_positive_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_positive_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_positive_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_positive_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_positive_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_positive_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_positive_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_positive_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_rad2deg_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_rad2deg_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_rad2deg_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_rad2deg_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_rad2deg_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_rad2deg_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_rad2deg_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_rad2deg_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_rad2deg_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_rad2deg_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_real_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_real_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_real_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_real_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_real_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_real_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_real_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_real_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_real_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_real_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_real_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_real_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_real_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_reciprocal_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_reciprocal_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_reciprocal_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_reciprocal_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_reciprocal_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_reciprocal_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_reciprocal_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_reciprocal_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_reciprocal_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_reciprocal_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_reciprocal_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_reciprocal_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_round_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_round_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_round_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_round_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_round_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_round_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_round_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_round_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_round_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_round_decimals_0_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_round_decimals_0_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_round_decimals_0_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_round_decimals_0_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_round_decimals_3_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_round_decimals_3_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_round_decimals_3_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_round_decimals_neg_3_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_round_decimals_neg_3_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_round_decimals_neg_3_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_rsqrt_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_rsqrt_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_rsqrt_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_rsqrt_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_rsqrt_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_rsqrt_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_rsqrt_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_rsqrt_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_rsqrt_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_rsqrt_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_rsqrt_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_rsqrt_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sgn_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sgn_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sgn_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sgn_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sgn_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sgn_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sgn_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sgn_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sgn_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sgn_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sgn_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sgn_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sgn_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_short_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_short_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_short_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_short_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_short_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_short_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_short_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_short_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_short_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_short_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_short_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_short_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sigmoid_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sigmoid_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sigmoid_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sigmoid_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sigmoid_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sigmoid_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sigmoid_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sigmoid_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sigmoid_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sigmoid_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sigmoid_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sigmoid_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sign_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sign_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sign_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sign_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sign_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sign_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sign_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sign_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sign_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sign_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_signbit_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_signbit_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_signbit_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_signbit_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_signbit_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_signbit_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_signbit_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_signbit_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_signbit_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_signbit_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sin_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sin_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sin_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sin_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sin_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sin_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sin_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sin_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sin_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sin_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sin_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sin_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sinc_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sinc_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sinc_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sinc_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sinc_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sinc_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sinc_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sinc_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sinc_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sinc_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sinc_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sinc_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sinh_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sinh_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sinh_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sinh_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sinh_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sinh_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sinh_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sinh_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sinh_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sinh_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sinh_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_sinh_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_airy_ai_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_airy_ai_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_airy_ai_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_airy_ai_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_airy_ai_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_airy_ai_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_airy_ai_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_airy_ai_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_bessel_j0_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_bessel_j0_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_bessel_j0_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_bessel_j0_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_bessel_j0_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_bessel_j0_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_bessel_j0_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_bessel_j0_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_bessel_j1_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_bessel_j1_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_bessel_j1_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_bessel_j1_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_bessel_j1_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_bessel_j1_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_bessel_j1_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_bessel_j1_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_bessel_y0_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_bessel_y0_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_bessel_y0_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_bessel_y0_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_bessel_y0_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_bessel_y0_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_bessel_y0_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_bessel_y0_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_bessel_y1_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_bessel_y1_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_bessel_y1_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_bessel_y1_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_bessel_y1_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_bessel_y1_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_bessel_y1_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_bessel_y1_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_entr_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_entr_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_entr_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_entr_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_entr_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_entr_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_entr_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_entr_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_entr_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_entr_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_erfcx_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_erfcx_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_erfcx_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_special_erfcx_cpu_int16, 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test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_tan_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_tan_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_tanh_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_tanh_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_tanh_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_tanh_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_tanh_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_tanh_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_tanh_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_tanh_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_tanh_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_tanh_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_tanh_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_tanh_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_trunc_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_trunc_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_trunc_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_trunc_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_trunc_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_trunc_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_trunc_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_trunc_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_contig_vs_transposed_trunc_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_digamma_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_digamma_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_digamma_special_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_digamma_special_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_exp_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_exp_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_exp_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_exp_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_exp_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_exp_slow_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_acos_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_acos_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_acos_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_acos_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_acosh_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_acosh_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_acosh_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_acosh_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_asin_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_asin_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_asin_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_asin_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_atanh_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_atanh_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_atanh_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_atanh_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_erfinv_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_erfinv_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_erfinv_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_erfinv_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_log10_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_log10_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_log10_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_log10_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_log1p_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_log1p_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_log1p_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_log1p_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_log2_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_log2_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_log2_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_log2_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_log_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_log_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_log_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_log_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_rsqrt_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_rsqrt_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_rsqrt_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_rsqrt_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_special_logit_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_special_logit_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_special_logit_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_special_logit_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_special_multigammaln_mvlgamma_p_1_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_special_multigammaln_mvlgamma_p_1_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_special_multigammaln_mvlgamma_p_1_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_special_multigammaln_mvlgamma_p_1_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_special_multigammaln_mvlgamma_p_3_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_special_multigammaln_mvlgamma_p_3_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_special_multigammaln_mvlgamma_p_3_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_special_multigammaln_mvlgamma_p_3_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_special_multigammaln_mvlgamma_p_5_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_special_multigammaln_mvlgamma_p_5_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_special_multigammaln_mvlgamma_p_5_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_special_multigammaln_mvlgamma_p_5_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_special_ndtri_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_special_ndtri_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_sqrt_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_sqrt_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_sqrt_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains__refs_sqrt_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_acos_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_acos_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_acos_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_acos_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_acosh_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_acosh_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_acosh_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_acosh_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_asin_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_asin_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_asin_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_asin_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_atanh_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_atanh_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_atanh_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_atanh_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_erfinv_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_erfinv_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_erfinv_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_erfinv_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_log10_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_log10_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_log10_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_log10_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_log1p_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_log1p_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_log1p_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_log1p_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_log2_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_log2_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_log2_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_log2_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_log_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_log_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_log_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_log_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_logit_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_logit_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_logit_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_logit_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_mvlgamma_mvlgamma_p_1_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_mvlgamma_mvlgamma_p_1_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_mvlgamma_mvlgamma_p_1_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_mvlgamma_mvlgamma_p_1_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_mvlgamma_mvlgamma_p_3_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_mvlgamma_mvlgamma_p_3_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_mvlgamma_mvlgamma_p_3_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_mvlgamma_mvlgamma_p_3_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_mvlgamma_mvlgamma_p_5_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_mvlgamma_mvlgamma_p_5_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_mvlgamma_mvlgamma_p_5_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_mvlgamma_mvlgamma_p_5_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_rsqrt_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_rsqrt_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_rsqrt_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_rsqrt_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_special_ndtri_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_special_ndtri_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_sqrt_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_sqrt_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_sqrt_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_float_domains_sqrt_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_frexp_assert_raises_cpu, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_frexp_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_frexp_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_frexp_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_hardshrink_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_hardshrink_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_hardshrink_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_hardshrink_edge_cases_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_hardshrink_edge_cases_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_hardshrink_edge_cases_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_hardsigmoid_backward_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_hardsigmoid_backward_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_hardsigmoid_backward_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_hardsigmoid_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_hardsigmoid_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_hardsigmoid_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_hardswish_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_hardswish_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_hardswish_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_i0_range1_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_i0_range1_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_i0_range1_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_i0_range2_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_i0_range2_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_i0_range2_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_i0_range3_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_i0_special_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_i0_special_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_i0_special_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_igamma_common_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_igamma_common_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_igamma_common_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_igamma_common_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_igamma_edge_cases_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_igamma_edge_cases_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_igamma_edge_cases_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_igamma_edge_cases_cpu_float64, 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test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_bool_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_bool_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_bool_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_bool_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_bool_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_bool_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_bool_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_bool_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_bool_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_bool_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_bool_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_byte_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_byte_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_byte_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_byte_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_byte_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_byte_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_byte_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_byte_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_byte_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_byte_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_byte_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_byte_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_cdouble_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_cdouble_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_cdouble_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_cdouble_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_cdouble_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_cdouble_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_cdouble_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_cdouble_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_cdouble_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_cdouble_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_cdouble_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_cdouble_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_cdouble_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_ceil_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_ceil_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_ceil_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_ceil_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_ceil_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_ceil_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_ceil_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_ceil_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_ceil_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_cfloat_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_cfloat_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_cfloat_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_cfloat_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_cfloat_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_cfloat_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_cfloat_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_cfloat_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_cfloat_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_cfloat_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_cfloat_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_cfloat_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_cfloat_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_chalf_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_chalf_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_chalf_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_chalf_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_chalf_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_chalf_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_chalf_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_chalf_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_chalf_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_chalf_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_chalf_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_chalf_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_chalf_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_char_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_char_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_char_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_char_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_char_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_char_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_char_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_char_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_char_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_char_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_char_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_char_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_char_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_conj_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_conj_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_conj_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_conj_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_conj_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_conj_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_conj_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_conj_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_conj_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_conj_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_conj_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_conj_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_conj_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_conj_physical_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_conj_physical_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_conj_physical_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_conj_physical_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_conj_physical_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_conj_physical_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_conj_physical_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_conj_physical_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_conj_physical_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_conj_physical_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_conj_physical_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_conj_physical_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_conj_physical_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_cos_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_cos_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_cos_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_cos_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_cos_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_cos_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_cos_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_cos_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_cos_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_cos_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_cos_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_cos_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_cosh_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_cosh_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_cosh_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_cosh_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_cosh_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_cosh_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_cosh_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_cosh_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_cosh_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_cosh_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_cosh_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_cosh_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_deg2rad_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_deg2rad_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_deg2rad_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_deg2rad_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_deg2rad_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_deg2rad_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_deg2rad_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_deg2rad_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_deg2rad_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_deg2rad_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_digamma_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_digamma_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_digamma_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_digamma_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_digamma_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_digamma_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_digamma_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_digamma_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_digamma_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_digamma_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_double_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_double_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_double_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_double_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_double_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_double_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_double_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_double_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_double_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_double_cpu_int32, 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test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_erf_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_erf_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_erfc_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_erfc_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_erfc_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_erfc_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_erfc_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_erfc_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_erfc_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_erfc_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_erfc_cpu_int8, 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test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_isreal_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_isreal_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_isreal_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_isreal_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_isreal_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_isreal_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_isreal_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_isreal_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_isreal_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_isreal_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_isreal_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_jiterator_unary_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_jiterator_unary_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_jiterator_unary_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_jiterator_unary_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_jiterator_unary_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_jiterator_unary_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_jiterator_unary_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_jiterator_unary_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_jiterator_unary_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_jiterator_unary_cpu_int64, 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test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_mvlgamma_mvlgamma_p_3_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_mvlgamma_mvlgamma_p_3_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_mvlgamma_mvlgamma_p_3_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_mvlgamma_mvlgamma_p_3_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_mvlgamma_mvlgamma_p_3_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_mvlgamma_mvlgamma_p_3_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_mvlgamma_mvlgamma_p_3_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_mvlgamma_mvlgamma_p_5_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_mvlgamma_mvlgamma_p_5_cpu_float16, 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test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_neg_cpu_complex32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_neg_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_neg_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_neg_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_neg_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_neg_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_neg_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_neg_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_neg_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_neg_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_celu_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_celu_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_celu_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_celu_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_elu_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_elu_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_elu_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_elu_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_hardshrink_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_hardshrink_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_hardshrink_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_hardshrink_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_hardsigmoid_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_hardsigmoid_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_hardsigmoid_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_hardsigmoid_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_hardtanh_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_hardtanh_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_hardtanh_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_hardtanh_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_hardtanh_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_hardtanh_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_hardtanh_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_hardtanh_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_logsigmoid_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_logsigmoid_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_logsigmoid_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_logsigmoid_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_mish_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_mish_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_mish_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_mish_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_prelu_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_prelu_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_prelu_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_prelu_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_relu6_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_relu6_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_relu6_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_relu6_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_relu6_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_relu6_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_relu6_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_relu6_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_relu6_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_relu_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_relu_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_relu_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_relu_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_relu_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_relu_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_relu_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_relu_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_relu_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_rrelu_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_rrelu_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_rrelu_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_selu_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_selu_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_selu_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_selu_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_silu_complex_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_silu_complex_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_silu_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_silu_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_silu_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_silu_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_softplus_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_softplus_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_softplus_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_softplus_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_softshrink_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_softshrink_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_softshrink_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_softshrink_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_softsign_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_softsign_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_softsign_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_softsign_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_softsign_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_softsign_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_softsign_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_softsign_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_softsign_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_softsign_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_softsign_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_tanhshrink_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_tanhshrink_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_tanhshrink_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_tanhshrink_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_tanhshrink_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_tanhshrink_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_tanhshrink_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_tanhshrink_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_tanhshrink_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_tanhshrink_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_tanhshrink_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_threshold_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_threshold_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_threshold_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_threshold_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_threshold_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_threshold_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_threshold_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_threshold_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_nn_functional_threshold_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_polygamma_polygamma_n_0_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_polygamma_polygamma_n_0_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_polygamma_polygamma_n_0_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_polygamma_polygamma_n_0_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_polygamma_polygamma_n_0_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_polygamma_polygamma_n_0_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_polygamma_polygamma_n_0_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_polygamma_polygamma_n_0_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_polygamma_polygamma_n_0_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_polygamma_polygamma_n_0_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_polygamma_polygamma_n_1_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_polygamma_polygamma_n_1_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_polygamma_polygamma_n_1_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_polygamma_polygamma_n_1_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_polygamma_polygamma_n_1_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_polygamma_polygamma_n_1_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_polygamma_polygamma_n_1_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_polygamma_polygamma_n_1_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_polygamma_polygamma_n_1_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_polygamma_polygamma_n_2_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_polygamma_polygamma_n_2_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_polygamma_polygamma_n_2_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_polygamma_polygamma_n_2_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_polygamma_polygamma_n_2_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_polygamma_polygamma_n_2_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_polygamma_polygamma_n_2_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_polygamma_polygamma_n_2_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_polygamma_polygamma_n_2_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_polygamma_polygamma_n_3_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_polygamma_polygamma_n_3_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_polygamma_polygamma_n_3_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_polygamma_polygamma_n_3_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_polygamma_polygamma_n_3_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_polygamma_polygamma_n_3_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_polygamma_polygamma_n_3_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_polygamma_polygamma_n_3_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_polygamma_polygamma_n_3_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_polygamma_polygamma_n_4_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_polygamma_polygamma_n_4_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_polygamma_polygamma_n_4_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_polygamma_polygamma_n_4_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_polygamma_polygamma_n_4_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_polygamma_polygamma_n_4_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_polygamma_polygamma_n_4_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_polygamma_polygamma_n_4_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_polygamma_polygamma_n_4_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_positive_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_positive_cpu_complex128, 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test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_rad2deg_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_rad2deg_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_rad2deg_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_rad2deg_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_rad2deg_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_rad2deg_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_rad2deg_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_rad2deg_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_rad2deg_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_rad2deg_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_real_cpu_bfloat16, 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test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_real_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_reciprocal_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_reciprocal_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_reciprocal_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_reciprocal_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_reciprocal_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_reciprocal_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_reciprocal_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_reciprocal_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_reciprocal_cpu_int32, 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test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_round_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_round_decimals_0_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_round_decimals_0_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_round_decimals_0_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_round_decimals_0_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_round_decimals_3_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_round_decimals_3_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_round_decimals_3_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_round_decimals_neg_3_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_round_decimals_neg_3_cpu_float32, 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test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_sinh_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_sinh_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_sinh_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_sinh_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_sinh_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_sinh_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_sinh_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_special_airy_ai_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_special_airy_ai_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_special_airy_ai_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_special_airy_ai_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_special_airy_ai_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_special_airy_ai_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_special_airy_ai_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_special_airy_ai_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_special_bessel_j0_cpu_bool, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_special_bessel_j0_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_special_bessel_j0_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_special_bessel_j0_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_special_bessel_j0_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_non_contig_expand_special_bessel_j0_cpu_int64, 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test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_tanh_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_tanh_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_tanh_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_tanh_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_tanh_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_trunc_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_trunc_cpu_float16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_trunc_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_trunc_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_trunc_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_trunc_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_trunc_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_trunc_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_reference_numerics_small_trunc_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_silu_complex_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_silu_complex_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_silu_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_silu_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_sinc_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_special_i0_i1_vs_scipy_cpu_bfloat16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_special_i0_i1_vs_scipy_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_special_i0_i1_vs_scipy_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_special_log_ndtr_vs_scipy_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_special_log_ndtr_vs_scipy_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_special_ndtr_vs_scipy_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_special_ndtr_vs_scipy_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_threshold_cpu_complex128, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_threshold_cpu_complex64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_threshold_cpu_float32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_threshold_cpu_float64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_threshold_cpu_int16, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_threshold_cpu_int32, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_threshold_cpu_int64, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_threshold_cpu_int8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_threshold_cpu_uint8, test/test_unary_ufuncs.py::TestUnaryUfuncsCPU::test_unary_out_op_mem_overlap_cpu_float64 2025-01-24T02:03:43.0925005Z 2025-01-24T02:03:43.0925205Z Running test batch 'tests to run' cost 3225.82 seconds 2025-01-24T02:03:43.0925510Z 2025-01-24T02:03:43.0925608Z real 53m51.827s 2025-01-24T02:03:43.0925857Z user 96m58.521s 2025-01-24T02:03:43.0926099Z sys 10m6.114s 2025-01-24T02:03:43.0926344Z + assert_git_not_dirty 2025-01-24T02:03:43.0926637Z + [[ linux-focal-py3.13-clang10 != *rocm* ]] 2025-01-24T02:03:43.0927005Z + [[ linux-focal-py3.13-clang10 != *xla* ]] 2025-01-24T02:03:43.0927357Z ++ git status --porcelain 2025-01-24T02:03:43.0927681Z ++ grep -v '?? third_party' 2025-01-24T02:04:13.0455027Z ++ true 2025-01-24T02:04:13.0455662Z + git_status= 2025-01-24T02:04:13.0456064Z + [[ -n '' ]] 2025-01-24T02:04:13.0456475Z + [[ 1 == 1 ]] 2025-01-24T02:04:13.0456811Z + test_aten 2025-01-24T02:04:13.0457270Z + echo 'Running ATen tests with pytorch lib' 2025-01-24T02:04:13.0457874Z Running ATen tests with pytorch lib 2025-01-24T02:04:13.0458361Z + [[ -n '' ]] 2025-01-24T02:04:13.0458771Z + echo 'Running test with the build folder' 2025-01-24T02:04:13.0459145Z Running test with the build folder 2025-01-24T02:04:13.0459479Z + TEST_BASE_DIR=build/bin 2025-01-24T02:04:13.0459981Z + ln -sf /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib/libc10.so build/bin 2025-01-24T02:04:13.0499710Z + ln -sf '/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib/libcaffe2*' build/bin 2025-01-24T02:04:13.0509605Z + ln -sf '/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib/libmkldnn*' build/bin 2025-01-24T02:04:13.0518795Z + ln -sf '/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib/libnccl*' build/bin 2025-01-24T02:04:13.0529526Z + ln -sf /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib/libtorch.so /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib/libtorch_cpu.so /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib/libtorch_global_deps.so /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib/libtorch_python.so /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib/libtorchbind_test.so build/bin 2025-01-24T02:04:13.0536439Z + ls build/bin 2025-01-24T02:04:13.0611477Z BackoffTest cpu_generator_test 2025-01-24T02:04:13.0612136Z CMakeFiles cpu_profiling_allocator_test 2025-01-24T02:04:13.0612846Z CTestTestfile.cmake cpu_rng_test 2025-01-24T02:04:13.0613469Z CppSignature_test dispatch_key_set_test 2025-01-24T02:04:13.0613963Z Dict_test dlconvertor_test 2025-01-24T02:04:13.0614476Z Dimname_test example_allreduce 2025-01-24T02:04:13.0615305Z FileStoreTest extension_backend_test 2025-01-24T02:04:13.0615843Z HashStoreTest half_test 2025-01-24T02:04:13.0616257Z IListRef_test inline_container_test 2025-01-24T02:04:13.0616941Z KernelFunction_test ivalue_test 2025-01-24T02:04:13.0617353Z List_test kernel_function_legacy_test 2025-01-24T02:04:13.0617736Z Makefile kernel_function_test 2025-01-24T02:04:13.0618118Z MaybeOwned_test kernel_lambda_legacy_test 2025-01-24T02:04:13.0618521Z NamedTensor_test kernel_lambda_test 2025-01-24T02:04:13.0618933Z ProcessGroupGlooTest kernel_stackbased_test 2025-01-24T02:04:13.0619345Z StorageUtils_test lazy_tensor_test 2025-01-24T02:04:13.0619709Z TCPStoreTest legacy_vmap_test 2025-01-24T02:04:13.0620045Z aot_model_compiler_test libc10.so 2025-01-24T02:04:13.0620393Z apply_utils_test 'libcaffe2*' 2025-01-24T02:04:13.0620716Z atest 'libmkldnn*' 2025-01-24T02:04:13.0621113Z backend_fallback_test 'libnccl*' 2025-01-24T02:04:13.0621446Z basic libtorch.so 2025-01-24T02:04:13.0621740Z broadcast_test libtorch_cpu.so 2025-01-24T02:04:13.0622112Z c10_ArrayRef_test libtorch_global_deps.so 2025-01-24T02:04:13.0622502Z c10_Bitset_test libtorch_python.so 2025-01-24T02:04:13.0622933Z c10_CompileTimeFunctionPointer_test libtorchbind_test.so 2025-01-24T02:04:13.0623543Z c10_ConstexprCrc_test make_boxed_from_unboxed_functor_test 2025-01-24T02:04:13.0624064Z c10_DeadlockDetection_test math_kernel_test 2025-01-24T02:04:13.0624472Z c10_DeviceGuard_test memory_format_test 2025-01-24T02:04:13.0624863Z c10_Device_test memory_overlapping_test 2025-01-24T02:04:13.0625276Z c10_DispatchKeySet_test mobile_memory_cleanup 2025-01-24T02:04:13.0625659Z c10_Half_test native_test 2025-01-24T02:04:13.0626014Z c10_InlineDeviceGuard_test op_allowlist_test 2025-01-24T02:04:13.0626554Z c10_InlineStreamGuard_test op_registration_test 2025-01-24T02:04:13.0626976Z c10_LeftRight_test operator_name_test 2025-01-24T02:04:13.0627368Z c10_Metaprogramming_test operators_test 2025-01-24T02:04:13.0627793Z c10_NetworkFlow_test packedtensoraccessor_test 2025-01-24T02:04:13.0628191Z c10_Scalar_test parallel_benchmark 2025-01-24T02:04:13.0628552Z c10_SizesAndStrides_test pow_test 2025-01-24T02:04:13.0628899Z c10_StreamGuard_test protoc 2025-01-24T02:04:13.0629241Z c10_SymInt_test protoc-3.13.0.0 2025-01-24T02:04:13.0629610Z c10_Synchronized_test quantized_test 2025-01-24T02:04:13.0629973Z c10_ThreadLocal_test reduce_ops_test 2025-01-24T02:04:13.0630374Z c10_TypeIndex_test reportMemoryUsage_test 2025-01-24T02:04:13.0630768Z c10_TypeList_test scalar_tensor_test 2025-01-24T02:04:13.0631116Z c10_TypeTraits_test scalar_test 2025-01-24T02:04:13.0631496Z c10_accumulate_test static_runtime_bench 2025-01-24T02:04:13.0631899Z c10_bfloat16_test static_runtime_test 2025-01-24T02:04:13.0632289Z c10_bit_cast_test stride_properties_test 2025-01-24T02:04:13.0632676Z c10_complex_math_test tensor_iterator_test 2025-01-24T02:04:13.0633049Z c10_complex_test test_api 2025-01-24T02:04:13.0633367Z c10_cow_test test_cpp_rpc 2025-01-24T02:04:13.0633696Z c10_error_test test_dist_autograd 2025-01-24T02:04:13.0634089Z c10_exception_test test_edge_op_registration 2025-01-24T02:04:13.0634462Z c10_flags_test test_jit 2025-01-24T02:04:13.0634787Z c10_generic_math_test test_lazy 2025-01-24T02:04:13.0635173Z c10_intrusive_ptr_benchmark test_mobile_nnc 2025-01-24T02:04:13.0635578Z c10_intrusive_ptr_test test_parallel 2025-01-24T02:04:13.0635942Z c10_irange_test test_tensorexpr 2025-01-24T02:04:13.0636292Z c10_lazy_test thread_init_test 2025-01-24T02:04:13.0636634Z c10_logging_test torch_shm_manager 2025-01-24T02:04:13.0637076Z c10_optional_test tutorial_tensorexpr 2025-01-24T02:04:13.0637490Z c10_ordered_preserving_dict_test type_ptr_test 2025-01-24T02:04:13.0637883Z c10_registry_test type_test 2025-01-24T02:04:13.0638257Z c10_small_vector_test undefined_tensor_test 2025-01-24T02:04:13.0638652Z c10_ssize_test vec_test_all_types_AVX2 2025-01-24T02:04:13.0639065Z c10_string_util_test vec_test_all_types_AVX512 2025-01-24T02:04:13.0639507Z c10_string_view_test vec_test_all_types_DEFAULT 2025-01-24T02:04:13.0639933Z c10_tempfile_test verify_api_visibility 2025-01-24T02:04:13.0640306Z c10_typeid_test weakref_test 2025-01-24T02:04:13.0640642Z cmake_install.cmake wrapdim_test 2025-01-24T02:04:13.0641004Z cpu_allocator_test xla_tensor_test 2025-01-24T02:04:13.0641355Z + aten/tools/run_tests.sh build/bin 2025-01-24T02:04:13.0655177Z + set -e 2025-01-24T02:04:13.0657820Z ++ dirname aten/tools/run_tests.sh 2025-01-24T02:04:13.0671734Z + VALGRIND_SUP=/var/lib/jenkins/workspace/aten/tools/valgrind.sup 2025-01-24T02:04:13.0672408Z + export CPP_TESTS_DIR=build/bin 2025-01-24T02:04:13.0672839Z + CPP_TESTS_DIR=build/bin 2025-01-24T02:04:13.0673126Z + VALGRIND=ON 2025-01-24T02:04:13.0675021Z + 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 2025-01-24T02:04:13.1692000Z /var/lib/jenkins/workspace/test/run_test.py:24: DeprecationWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html 2025-01-24T02:04:13.1692920Z import pkg_resources 2025-01-24T02:04:16.7200521Z Downloading https://ossci-metrics.s3.amazonaws.com/disabled-tests-condensed.json to /var/lib/jenkins/workspace/test/.pytorch-disabled-tests.json 2025-01-24T02:04:16.7399289Z Found test times from artifacts 2025-01-24T02:04:16.8163544Z Found test times from artifacts 2025-01-24T02:04:16.8187383Z Running all tests 2025-01-24T02:04:16.8191302Z Running parallel tests on 3 processes 2025-01-24T02:04:16.8193032Z Name: tests to run (est. time: 0.0min) 2025-01-24T02:04:16.8193506Z Serial tests (0): 2025-01-24T02:04:16.8193901Z Parallel tests (19): 2025-01-24T02:04:16.8194245Z cpp/Dict_test 1/1 2025-01-24T02:04:16.8194626Z cpp/Dimname_test 1/1 2025-01-24T02:04:16.8195095Z cpp/NamedTensor_test 1/1 2025-01-24T02:04:16.8195663Z cpp/apply_utils_test 1/1 2025-01-24T02:04:16.8195976Z cpp/atest 1/1 2025-01-24T02:04:16.8196224Z cpp/basic 1/1 2025-01-24T02:04:16.8196495Z cpp/broadcast_test 1/1 2025-01-24T02:04:16.8196807Z cpp/cpu_generator_test 1/1 2025-01-24T02:04:16.8197130Z cpp/dlconvertor_test 1/1 2025-01-24T02:04:16.8197466Z cpp/extension_backend_test 1/1 2025-01-24T02:04:16.8197799Z cpp/lazy_tensor_test 1/1 2025-01-24T02:04:16.8198117Z cpp/legacy_vmap_test 1/1 2025-01-24T02:04:16.8198427Z cpp/native_test 1/1 2025-01-24T02:04:16.8198713Z cpp/operators_test 1/1 2025-01-24T02:04:16.8199018Z cpp/scalar_tensor_test 1/1 2025-01-24T02:04:16.8199315Z cpp/scalar_test 1/1 2025-01-24T02:04:16.8199611Z cpp/tensor_iterator_test 1/1 2025-01-24T02:04:16.8199944Z cpp/undefined_tensor_test 1/1 2025-01-24T02:04:16.8200275Z cpp/wrapdim_test 1/1 2025-01-24T02:04:16.8200584Z Name: excluded (est. time: 0.0min) 2025-01-24T02:04:16.8200894Z Serial tests (0): 2025-01-24T02:04:16.8201168Z Parallel tests (0): 2025-01-24T02:04:16.8260926Z Running cpp/Dict_test 1/1 ... [2025-01-24 02:04:16.825787] 2025-01-24T02:04:16.8261701Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T02:04:16.8268525Z 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-3d561bc149560731.xml', '-x', '--reruns=2'] ... [2025-01-24 02:04:16.826417] 2025-01-24T02:04:19.5978098Z 2025-01-24T02:04:19.5979533Z cpp/Dict_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.Dict_test_1.1_a69537ab60d2a317_.log 2025-01-24T02:04:19.5980176Z 2025-01-24T02:04:19.5980393Z Running cpp/Dimname_test 1/1 ... [2025-01-24 02:04:19.597626] 2025-01-24T02:04:19.5980847Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T02:04:19.5984092Z 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-b3f4776fcd5cb5e0.xml', '-x', '--reruns=2'] ... [2025-01-24 02:04:19.598114] 2025-01-24T02:04:21.3156655Z 2025-01-24T02:04:21.3157660Z cpp/Dimname_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.Dimname_test_1.1_7c77174183a192a4_.log 2025-01-24T02:04:21.3158593Z 2025-01-24T02:04:21.3158926Z Running cpp/NamedTensor_test 1/1 ... [2025-01-24 02:04:21.315498] 2025-01-24T02:04:21.3159478Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T02:04:21.3160858Z 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-b31318c44d23f0bd.xml', '-x', '--reruns=2'] ... [2025-01-24 02:04:21.315807] 2025-01-24T02:04:23.0329075Z 2025-01-24T02:04:23.0330397Z cpp/NamedTensor_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.NamedTensor_test_1.1_050d58ab515546ad_.log 2025-01-24T02:04:23.0331112Z 2025-01-24T02:04:23.0331320Z Running cpp/apply_utils_test 1/1 ... [2025-01-24 02:04:23.032796] 2025-01-24T02:04:23.0331763Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T02:04:23.0333927Z 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-2e7286051418254a.xml', '-x', '--reruns=2'] ... [2025-01-24 02:04:23.033164] 2025-01-24T02:04:24.7006735Z 2025-01-24T02:04:24.7007759Z 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_a576ffc2691e12b4_.log 2025-01-24T02:04:24.7008452Z 2025-01-24T02:04:24.7008654Z Running cpp/atest 1/1 ... [2025-01-24 02:04:24.700542] 2025-01-24T02:04:24.7009068Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T02:04:24.7011993Z 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-12336cd5da614ba0.xml', '-x', '--reruns=2'] ... [2025-01-24 02:04:24.700947] 2025-01-24T02:04:26.3681017Z 2025-01-24T02:04:26.3682169Z cpp/atest 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.atest_1.1_25cd360ad5e2af9c_.log 2025-01-24T02:04:26.3683149Z 2025-01-24T02:04:26.3685773Z Running cpp/basic 1/1 ... [2025-01-24 02:04:26.367965] 2025-01-24T02:04:26.3686411Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T02:04:26.3687862Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/basic', '-m', 'serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-d562097d86140d92.xml', '-x', '--reruns=2'] ... [2025-01-24 02:04:26.368367] 2025-01-24T02:04:28.0857781Z 2025-01-24T02:04:28.0858915Z cpp/basic 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.basic_1.1_9e11980ce9684f8c_.log 2025-01-24T02:04:28.0859523Z 2025-01-24T02:04:28.0859750Z Running cpp/broadcast_test 1/1 ... [2025-01-24 02:04:28.085632] 2025-01-24T02:04:28.0860275Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T02:04:28.0862012Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/broadcast_test', '-m', 'serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-0016ef941498f888.xml', '-x', '--reruns=2'] ... [2025-01-24 02:04:28.085944] 2025-01-24T02:04:29.8035867Z 2025-01-24T02:04:29.8036928Z cpp/broadcast_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.broadcast_test_1.1_857c0ab311409371_.log 2025-01-24T02:04:29.8037615Z 2025-01-24T02:04:29.8037869Z Running cpp/cpu_generator_test 1/1 ... [2025-01-24 02:04:29.803449] 2025-01-24T02:04:29.8038321Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T02:04:29.8040577Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/cpu_generator_test', '-m', 'serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-6525fe1e329f13c6.xml', '-x', '--reruns=2'] ... [2025-01-24 02:04:29.803822] 2025-01-24T02:04:31.5209931Z 2025-01-24T02:04:31.5210902Z cpp/cpu_generator_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.cpu_generator_test_1.1_6383c33391b1ae96_.log 2025-01-24T02:04:31.5212243Z 2025-01-24T02:04:31.5212461Z Running cpp/dlconvertor_test 1/1 ... [2025-01-24 02:04:31.520867] 2025-01-24T02:04:31.5212969Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T02:04:31.5214828Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/dlconvertor_test', '-m', 'serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-1d1ec4e8ac2bacb5.xml', '-x', '--reruns=2'] ... [2025-01-24 02:04:31.521241] 2025-01-24T02:04:33.2386452Z 2025-01-24T02:04:33.2387501Z cpp/dlconvertor_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.dlconvertor_test_1.1_7745b32d362e009f_.log 2025-01-24T02:04:33.2388192Z 2025-01-24T02:04:33.2388442Z Running cpp/extension_backend_test 1/1 ... [2025-01-24 02:04:33.238505] 2025-01-24T02:04:33.2388968Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T02:04:33.2390838Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/extension_backend_test', '-m', 'serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-1a4548bcce93e32e.xml', '-x', '--reruns=2'] ... [2025-01-24 02:04:33.238853] 2025-01-24T02:04:34.9560423Z 2025-01-24T02:04:34.9561772Z cpp/extension_backend_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.extension_backend_test_1.1_5f660829fc884fee_.log 2025-01-24T02:04:34.9562748Z 2025-01-24T02:04:34.9563106Z Running cpp/lazy_tensor_test 1/1 ... [2025-01-24 02:04:34.955737] 2025-01-24T02:04:34.9563548Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T02:04:34.9564756Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/lazy_tensor_test', '-m', 'serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-474650e226ef415a.xml', '-x', '--reruns=2'] ... [2025-01-24 02:04:34.956080] 2025-01-24T02:04:36.6731472Z 2025-01-24T02:04:36.6732621Z cpp/lazy_tensor_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.lazy_tensor_test_1.1_4b45d3f421d66473_.log 2025-01-24T02:04:36.6733311Z 2025-01-24T02:04:36.6733537Z Running cpp/legacy_vmap_test 1/1 ... [2025-01-24 02:04:36.672975] 2025-01-24T02:04:36.6734035Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T02:04:36.6735807Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/legacy_vmap_test', '-m', 'serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-767e35fe8aa51f9d.xml', '-x', '--reruns=2'] ... [2025-01-24 02:04:36.673348] 2025-01-24T02:04:38.3902996Z 2025-01-24T02:04:38.3904486Z cpp/legacy_vmap_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.legacy_vmap_test_1.1_0a52569bd0f2d072_.log 2025-01-24T02:04:38.3905252Z 2025-01-24T02:04:38.3905460Z Running cpp/native_test 1/1 ... [2025-01-24 02:04:38.390213] 2025-01-24T02:04:38.3905919Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T02:04:38.3908963Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/native_test', '-m', 'serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-60adf26fa2ba3460.xml', '-x', '--reruns=2'] ... [2025-01-24 02:04:38.390561] 2025-01-24T02:04:40.0576824Z 2025-01-24T02:04:40.0578129Z cpp/native_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.native_test_1.1_fe29df51dbd53932_.log 2025-01-24T02:04:40.0578799Z 2025-01-24T02:04:40.0579006Z Running cpp/operators_test 1/1 ... [2025-01-24 02:04:40.057536] 2025-01-24T02:04:40.0579669Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T02:04:40.0582178Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/operators_test', '-m', 'serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-81169688ba940f34.xml', '-x', '--reruns=2'] ... [2025-01-24 02:04:40.057910] 2025-01-24T02:04:41.7757698Z 2025-01-24T02:04:41.7758696Z cpp/operators_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.operators_test_1.1_2a2713169e1f12bd_.log 2025-01-24T02:04:41.7759379Z 2025-01-24T02:04:41.7759598Z Running cpp/scalar_tensor_test 1/1 ... [2025-01-24 02:04:41.775625] 2025-01-24T02:04:41.7760045Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T02:04:41.7762888Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/scalar_tensor_test', '-m', 'serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-00953d441628fde8.xml', '-x', '--reruns=2'] ... [2025-01-24 02:04:41.775979] 2025-01-24T02:04:43.5433275Z 2025-01-24T02:04:43.5434682Z cpp/scalar_tensor_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.scalar_tensor_test_1.1_07c03b147c455c87_.log 2025-01-24T02:04:43.5435745Z 2025-01-24T02:04:43.5436106Z Running cpp/scalar_test 1/1 ... [2025-01-24 02:04:43.543160] 2025-01-24T02:04:43.5436736Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T02:04:43.5438807Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/scalar_test', '-m', 'serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-64f642b653e7eab8.xml', '-x', '--reruns=2'] ... [2025-01-24 02:04:43.543557] 2025-01-24T02:04:45.2607835Z 2025-01-24T02:04:45.2608795Z cpp/scalar_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.scalar_test_1.1_6b6087daeba61162_.log 2025-01-24T02:04:45.2609516Z 2025-01-24T02:04:45.2609835Z Running cpp/tensor_iterator_test 1/1 ... [2025-01-24 02:04:45.260614] 2025-01-24T02:04:45.2610501Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T02:04:45.2612228Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/tensor_iterator_test', '-m', 'serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-3d7d626abbde7dc7.xml', '-x', '--reruns=2'] ... [2025-01-24 02:04:45.260932] 2025-01-24T02:04:46.9783485Z 2025-01-24T02:04:46.9784709Z cpp/tensor_iterator_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.tensor_iterator_test_1.1_c80c10ecf6d480cb_.log 2025-01-24T02:04:46.9785430Z 2025-01-24T02:04:46.9785687Z Running cpp/undefined_tensor_test 1/1 ... [2025-01-24 02:04:46.978202] 2025-01-24T02:04:46.9786174Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T02:04:46.9788061Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/undefined_tensor_test', '-m', 'serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-7dfdda056465cfcc.xml', '-x', '--reruns=2'] ... [2025-01-24 02:04:46.978532] 2025-01-24T02:04:48.6957574Z 2025-01-24T02:04:48.6958930Z cpp/undefined_tensor_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.undefined_tensor_test_1.1_3c714fb2c8b85adc_.log 2025-01-24T02:04:48.6959770Z 2025-01-24T02:04:48.6960233Z Running cpp/wrapdim_test 1/1 ... [2025-01-24 02:04:48.695582] 2025-01-24T02:04:48.6960817Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T02:04:48.6962452Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/wrapdim_test', '-m', 'serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-1efcbe30e5dbbea9.xml', '-x', '--reruns=2'] ... [2025-01-24 02:04:48.695931] 2025-01-24T02:04:50.4130119Z 2025-01-24T02:04:50.4131052Z cpp/wrapdim_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.wrapdim_test_1.1_2fdbba1931ad7b80_.log 2025-01-24T02:04:50.4131717Z 2025-01-24T02:04:50.4142824Z Running cpp/Dict_test 1/1 ... [2025-01-24 02:04:50.413997] 2025-01-24T02:04:50.4143454Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T02:04:50.4144176Z Running cpp/Dimname_test 1/1 ... [2025-01-24 02:04:50.414203] 2025-01-24T02:04:50.4145122Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T02:04:50.4145753Z Running cpp/NamedTensor_test 1/1 ... [2025-01-24 02:04:50.414293] 2025-01-24T02:04:50.4146547Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T02:04:50.4150477Z 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-f430f29eabed7022.xml', '-x', '--reruns=2'] ... [2025-01-24 02:04:50.414656] 2025-01-24T02:04:50.4152951Z 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-e874c6f4e45d8968.xml', '-x', '--reruns=2'] ... [2025-01-24 02:04:50.414779] 2025-01-24T02:04:50.4155246Z 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-146360fc8e2cbb58.xml', '-x', '--reruns=2'] ... [2025-01-24 02:04:50.414913] 2025-01-24T02:04:54.4470407Z 2025-01-24T02:04:54.4472621Z cpp/Dimname_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.Dimname_test_1.1_521a32cb4150017e_.log 2025-01-24T02:04:54.4475371Z 2025-01-24T02:04:55.7550630Z 2025-01-24T02:04:55.7552492Z cpp/NamedTensor_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.NamedTensor_test_1.1_9d22701e230e27f6_.log 2025-01-24T02:04:55.7553785Z 2025-01-24T02:04:58.3860557Z Running cpp/apply_utils_test 1/1 ... [2025-01-24 02:04:58.385619] 2025-01-24T02:04:58.3861718Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T02:04:58.3865062Z 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-0b7fc8a314e5a9c1.xml', '-x', '--reruns=2'] ... [2025-01-24 02:04:58.386100] 2025-01-24T02:05:00.0351100Z Running cpp/atest 1/1 ... [2025-01-24 02:05:00.034556] 2025-01-24T02:05:00.0351871Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T02:05:00.0358310Z 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-d708f18c62eb192a.xml', '-x', '--reruns=2'] ... [2025-01-24 02:05:00.035315] 2025-01-24T02:05:02.2804719Z 2025-01-24T02:05:02.2805978Z cpp/Dict_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.Dict_test_1.1_169319e185779e8c_.log 2025-01-24T02:05:02.2806880Z 2025-01-24T02:05:02.3619907Z 2025-01-24T02:05:02.3624071Z 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_7dd39c5cf7cfbe72_.log 2025-01-24T02:05:02.3625321Z 2025-01-24T02:05:05.7196246Z 2025-01-24T02:05:05.7198157Z cpp/atest 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.atest_1.1_4c55cfb0f1b6c0ac_.log 2025-01-24T02:05:05.7199165Z 2025-01-24T02:05:06.0527337Z Running cpp/basic 1/1 ... [2025-01-24 02:05:06.052315] 2025-01-24T02:05:06.0528008Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T02:05:06.0531328Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/basic', '-m', 'not serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-65f42b06aa9865ed.xml', '-x', '--reruns=2'] ... [2025-01-24 02:05:06.052763] 2025-01-24T02:05:06.2263043Z Running cpp/broadcast_test 1/1 ... [2025-01-24 02:05:06.225910] 2025-01-24T02:05:06.2263830Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T02:05:06.2266894Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/broadcast_test', '-m', 'not serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-bc314aafb8c6ca3a.xml', '-x', '--reruns=2'] ... [2025-01-24 02:05:06.226330] 2025-01-24T02:05:09.2463954Z 2025-01-24T02:05:09.2465688Z cpp/broadcast_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.broadcast_test_1.1_6c071ba37f27a3a4_.log 2025-01-24T02:05:09.2467286Z 2025-01-24T02:05:09.4233894Z 2025-01-24T02:05:09.4234703Z cpp/basic 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.basic_1.1_b664ed388566a321_.log 2025-01-24T02:05:09.4235805Z 2025-01-24T02:05:09.8151616Z Running cpp/cpu_generator_test 1/1 ... [2025-01-24 02:05:09.814768] 2025-01-24T02:05:09.8152190Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T02:05:09.8155874Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/cpu_generator_test', '-m', 'not serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-49bf50fe45126828.xml', '-x', '--reruns=2'] ... [2025-01-24 02:05:09.815240] 2025-01-24T02:05:12.8336746Z Running cpp/dlconvertor_test 1/1 ... [2025-01-24 02:05:12.833230] 2025-01-24T02:05:12.8337582Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T02:05:12.8340452Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/dlconvertor_test', '-m', 'not serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-94b3b8061b276c30.xml', '-x', '--reruns=2'] ... [2025-01-24 02:05:12.833674] 2025-01-24T02:05:13.0412030Z Running cpp/extension_backend_test 1/1 ... [2025-01-24 02:05:13.040644] 2025-01-24T02:05:13.0412863Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T02:05:13.0416072Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/extension_backend_test', '-m', 'not serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-4fb4b8b0280ae299.xml', '-x', '--reruns=2'] ... [2025-01-24 02:05:13.041146] 2025-01-24T02:05:15.4000503Z 2025-01-24T02:05:15.4001978Z cpp/cpu_generator_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.cpu_generator_test_1.1_6ff0c8a81218e9a0_.log 2025-01-24T02:05:15.4003245Z 2025-01-24T02:05:15.9124095Z 2025-01-24T02:05:15.9125471Z cpp/dlconvertor_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.dlconvertor_test_1.1_8f2a3a2f0fb1abdd_.log 2025-01-24T02:05:15.9126542Z 2025-01-24T02:05:16.1617286Z 2025-01-24T02:05:16.1618967Z cpp/extension_backend_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.extension_backend_test_1.1_af39f1dc58a87c6f_.log 2025-01-24T02:05:16.1620255Z 2025-01-24T02:05:18.9763838Z Running cpp/lazy_tensor_test 1/1 ... [2025-01-24 02:05:18.975980] 2025-01-24T02:05:18.9764374Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T02:05:18.9768184Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/lazy_tensor_test', '-m', 'not serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-3747ff25f145c39f.xml', '-x', '--reruns=2'] ... [2025-01-24 02:05:18.976391] 2025-01-24T02:05:19.5107277Z Running cpp/legacy_vmap_test 1/1 ... [2025-01-24 02:05:19.510254] 2025-01-24T02:05:19.5108070Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T02:05:19.5111970Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/legacy_vmap_test', '-m', 'not serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-e5176b91c04911b0.xml', '-x', '--reruns=2'] ... [2025-01-24 02:05:19.510737] 2025-01-24T02:05:19.8040236Z Running cpp/native_test 1/1 ... [2025-01-24 02:05:19.803578] 2025-01-24T02:05:19.8041021Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T02:05:19.8044292Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/native_test', '-m', 'not serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-f4b3689b20fda8a4.xml', '-x', '--reruns=2'] ... [2025-01-24 02:05:19.804066] 2025-01-24T02:05:22.1967166Z 2025-01-24T02:05:22.1968558Z cpp/lazy_tensor_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.lazy_tensor_test_1.1_14ccebc02a61e9cb_.log 2025-01-24T02:05:22.1969764Z 2025-01-24T02:05:23.1339616Z 2025-01-24T02:05:23.1343459Z cpp/native_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.native_test_1.1_390f61fb11392ff6_.log 2025-01-24T02:05:23.1344566Z 2025-01-24T02:05:26.0969811Z 2025-01-24T02:05:26.0971602Z cpp/legacy_vmap_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.legacy_vmap_test_1.1_4e084f5d2c016643_.log 2025-01-24T02:05:26.0972783Z 2025-01-24T02:05:26.1760792Z Running cpp/operators_test 1/1 ... [2025-01-24 02:05:26.175655] 2025-01-24T02:05:26.1761534Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T02:05:26.1765088Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/operators_test', '-m', 'not serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-7c9807a941051bd0.xml', '-x', '--reruns=2'] ... [2025-01-24 02:05:26.176118] 2025-01-24T02:05:26.6987664Z Running cpp/scalar_tensor_test 1/1 ... [2025-01-24 02:05:26.698221] 2025-01-24T02:05:26.6988565Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T02:05:26.6991080Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/scalar_tensor_test', '-m', 'not serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-aa3a6dd25eebec3f.xml', '-x', '--reruns=2'] ... [2025-01-24 02:05:26.698677] 2025-01-24T02:05:29.4966686Z 2025-01-24T02:05:29.4968426Z cpp/operators_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.operators_test_1.1_b25e9c324f200d9b_.log 2025-01-24T02:05:29.4969873Z 2025-01-24T02:05:29.7188570Z 2025-01-24T02:05:29.7189920Z cpp/scalar_tensor_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.scalar_tensor_test_1.1_2b756748c812c2a3_.log 2025-01-24T02:05:29.7190965Z 2025-01-24T02:05:30.0539225Z Running cpp/scalar_test 1/1 ... [2025-01-24 02:05:30.053493] 2025-01-24T02:05:30.0540137Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T02:05:30.0542561Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/scalar_test', '-m', 'not serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-58ce250e53d50a8b.xml', '-x', '--reruns=2'] ... [2025-01-24 02:05:30.053900] 2025-01-24T02:05:33.1238176Z 2025-01-24T02:05:33.1239235Z cpp/scalar_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.scalar_test_1.1_27bc310592047774_.log 2025-01-24T02:05:33.1239961Z 2025-01-24T02:05:33.2335822Z Running cpp/tensor_iterator_test 1/1 ... [2025-01-24 02:05:33.233215] 2025-01-24T02:05:33.2336352Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T02:05:33.2339676Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/tensor_iterator_test', '-m', 'not serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-447543985535c122.xml', '-x', '--reruns=2'] ... [2025-01-24 02:05:33.233627] 2025-01-24T02:05:33.4401519Z Running cpp/undefined_tensor_test 1/1 ... [2025-01-24 02:05:33.439789] 2025-01-24T02:05:33.4402011Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T02:05:33.4405199Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/undefined_tensor_test', '-m', 'not serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-55a4f2982a86847c.xml', '-x', '--reruns=2'] ... [2025-01-24 02:05:33.440243] 2025-01-24T02:05:36.3107509Z 2025-01-24T02:05:36.3109475Z cpp/undefined_tensor_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.undefined_tensor_test_1.1_db344b305ba31456_.log 2025-01-24T02:05:36.3113055Z 2025-01-24T02:05:37.1798270Z Running cpp/wrapdim_test 1/1 ... [2025-01-24 02:05:37.179376] 2025-01-24T02:05:37.1799355Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-01-24T02:05:37.1802780Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/wrapdim_test', '-m', 'not serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-61839435c303ccd9.xml', '-x', '--reruns=2'] ... [2025-01-24 02:05:37.179864] 2025-01-24T02:05:40.1091525Z 2025-01-24T02:05:40.1093788Z cpp/wrapdim_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.wrapdim_test_1.1_0b515cfe8abca7f1_.log 2025-01-24T02:05:40.1095394Z 2025-01-24T02:05:45.5702742Z 2025-01-24T02:05:45.5704155Z cpp/tensor_iterator_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.tensor_iterator_test_1.1_4f85736d0295f896_.log 2025-01-24T02:05:45.5705005Z 2025-01-24T02:05:46.3245559Z Running test batch 'tests to run' cost 89.51 seconds 2025-01-24T02:05:46.9099629Z + run_if_exists tensor_interop_test 2025-01-24T02:05:46.9100305Z + local test_name=tensor_interop_test 2025-01-24T02:05:46.9100840Z + [[ -x build/bin/tensor_interop_test ]] 2025-01-24T02:05:46.9101409Z + echo 'Warning: tensor_interop_test does not exist.' 2025-01-24T02:05:46.9102037Z Warning: tensor_interop_test does not exist. 2025-01-24T02:05:46.9102572Z + run_if_exists cudnn_test 2025-01-24T02:05:46.9103101Z + local test_name=cudnn_test 2025-01-24T02:05:46.9103584Z + [[ -x build/bin/cudnn_test ]] 2025-01-24T02:05:46.9104111Z + echo 'Warning: cudnn_test does not exist.' 2025-01-24T02:05:46.9104641Z Warning: cudnn_test does not exist. 2025-01-24T02:05:46.9105162Z + run_if_exists cuda_generator_test 2025-01-24T02:05:46.9105693Z + local test_name=cuda_generator_test 2025-01-24T02:05:46.9106321Z + [[ -x build/bin/cuda_generator_test ]] 2025-01-24T02:05:46.9107004Z + echo 'Warning: cuda_generator_test does not exist.' 2025-01-24T02:05:46.9107543Z Warning: cuda_generator_test does not exist. 2025-01-24T02:05:46.9108161Z + run_if_exists apply_test 2025-01-24T02:05:46.9108711Z + local test_name=apply_test 2025-01-24T02:05:46.9109185Z + [[ -x build/bin/apply_test ]] 2025-01-24T02:05:46.9109501Z + echo 'Warning: apply_test does not exist.' 2025-01-24T02:05:46.9109861Z Warning: apply_test does not exist. 2025-01-24T02:05:46.9110194Z + run_if_exists stream_test 2025-01-24T02:05:46.9110489Z + local test_name=stream_test 2025-01-24T02:05:46.9110797Z + [[ -x build/bin/stream_test ]] 2025-01-24T02:05:46.9111121Z + echo 'Warning: stream_test does not exist.' 2025-01-24T02:05:46.9111486Z Warning: stream_test does not exist. 2025-01-24T02:05:46.9111813Z + run_if_exists cuda_half_test 2025-01-24T02:05:46.9112121Z + local test_name=cuda_half_test 2025-01-24T02:05:46.9112435Z + [[ -x build/bin/cuda_half_test ]] 2025-01-24T02:05:46.9112771Z + echo 'Warning: cuda_half_test does not exist.' 2025-01-24T02:05:46.9113148Z Warning: cuda_half_test does not exist. 2025-01-24T02:05:46.9113492Z + run_if_exists cuda_vectorized_test 2025-01-24T02:05:46.9113829Z + local test_name=cuda_vectorized_test 2025-01-24T02:05:46.9114427Z + [[ -x build/bin/cuda_vectorized_test ]] 2025-01-24T02:05:46.9114810Z + echo 'Warning: cuda_vectorized_test does not exist.' 2025-01-24T02:05:46.9115230Z Warning: cuda_vectorized_test does not exist. 2025-01-24T02:05:46.9115608Z + run_if_exists cuda_distributions_test 2025-01-24T02:05:46.9115968Z + local test_name=cuda_distributions_test 2025-01-24T02:05:46.9116340Z + [[ -x build/bin/cuda_distributions_test ]] 2025-01-24T02:05:46.9116743Z + echo 'Warning: cuda_distributions_test does not exist.' 2025-01-24T02:05:46.9117179Z Warning: cuda_distributions_test does not exist. 2025-01-24T02:05:46.9117568Z + run_if_exists cuda_optional_test 2025-01-24T02:05:46.9117897Z + local test_name=cuda_optional_test 2025-01-24T02:05:46.9118232Z + [[ -x build/bin/cuda_optional_test ]] 2025-01-24T02:05:46.9118600Z + echo 'Warning: cuda_optional_test does not exist.' 2025-01-24T02:05:46.9118997Z Warning: cuda_optional_test does not exist. 2025-01-24T02:05:46.9119432Z + run_if_exists cuda_tensor_interop_test 2025-01-24T02:05:46.9119790Z + local test_name=cuda_tensor_interop_test 2025-01-24T02:05:46.9120158Z + [[ -x build/bin/cuda_tensor_interop_test ]] 2025-01-24T02:05:46.9120571Z + echo 'Warning: cuda_tensor_interop_test does not exist.' 2025-01-24T02:05:46.9120996Z Warning: cuda_tensor_interop_test does not exist. 2025-01-24T02:05:46.9121370Z + run_if_exists cuda_complex_test 2025-01-24T02:05:46.9121693Z + local test_name=cuda_complex_test 2025-01-24T02:05:46.9122093Z + [[ -x build/bin/cuda_complex_test ]] 2025-01-24T02:05:46.9122514Z + echo 'Warning: cuda_complex_test does not exist.' 2025-01-24T02:05:46.9122893Z Warning: cuda_complex_test does not exist. 2025-01-24T02:05:46.9123250Z + run_if_exists cuda_complex_math_test 2025-01-24T02:05:46.9123598Z + local test_name=cuda_complex_math_test 2025-01-24T02:05:46.9123984Z + [[ -x build/bin/cuda_complex_math_test ]] 2025-01-24T02:05:46.9124393Z + echo 'Warning: cuda_complex_math_test does not exist.' 2025-01-24T02:05:46.9124807Z Warning: cuda_complex_math_test does not exist. 2025-01-24T02:05:46.9125181Z + run_if_exists cuda_cub_test 2025-01-24T02:05:46.9125488Z + local test_name=cuda_cub_test 2025-01-24T02:05:46.9125797Z + [[ -x build/bin/cuda_cub_test ]] 2025-01-24T02:05:46.9126144Z + echo 'Warning: cuda_cub_test does not exist.' 2025-01-24T02:05:46.9126500Z Warning: cuda_cub_test does not exist. 2025-01-24T02:05:46.9126839Z + run_if_exists cuda_atomic_ops_test 2025-01-24T02:05:46.9127182Z + local test_name=cuda_atomic_ops_test 2025-01-24T02:05:46.9127543Z + [[ -x build/bin/cuda_atomic_ops_test ]] 2025-01-24T02:05:46.9127936Z + echo 'Warning: cuda_atomic_ops_test does not exist.' 2025-01-24T02:05:46.9128337Z Warning: cuda_atomic_ops_test does not exist. 2025-01-24T02:05:46.9128686Z + '[' ON == ON ']' 2025-01-24T02:05:46.9129402Z + valgrind --suppressions=/var/lib/jenkins/workspace/aten/tools/valgrind.sup --error-exitcode=1 build/bin/basic '--gtest_filter=-*CUDA' 2025-01-24T02:05:46.9417286Z ==6064== Memcheck, a memory error detector 2025-01-24T02:05:46.9417930Z ==6064== Copyright (C) 2002-2022, and GNU GPL'd, by Julian Seward et al. 2025-01-24T02:05:46.9418519Z ==6064== Using Valgrind-3.20.0 and LibVEX; rerun with -h for copyright info 2025-01-24T02:05:46.9419028Z ==6064== Command: build/bin/basic --gtest_filter=-*CUDA 2025-01-24T02:05:46.9419402Z ==6064== 2025-01-24T02:05:47.4407499Z ==6064== Warning: set address range perms: large range [0x4a08000, 0x155fc000) (defined) 2025-01-24T02:06:16.0247300Z Running main() from /var/lib/jenkins/workspace/third_party/googletest/googletest/src/gtest_main.cc 2025-01-24T02:06:16.0523029Z Note: Google Test filter = -*CUDA 2025-01-24T02:06:16.0569315Z [==========] Running 4 tests from 1 test suite. 2025-01-24T02:06:16.0596337Z [----------] Global test environment set-up. 2025-01-24T02:06:16.0666486Z [----------] 4 tests from BasicTest 2025-01-24T02:06:16.0690515Z [ RUN ] BasicTest.BasicTestCPU 2025-01-24T02:06:17.4964388Z 381 ms 2025-01-24T02:06:17.5803290Z 53 ms 2025-01-24T02:06:17.6531352Z 64 ms 2025-01-24T02:06:18.3398127Z [ OK ] BasicTest.BasicTestCPU (2268 ms) 2025-01-24T02:06:18.3406530Z [ RUN ] BasicTest.BasicTestHalfCPU 2025-01-24T02:06:18.4830101Z 94 ms 2025-01-24T02:06:18.5331672Z 45 ms 2025-01-24T02:06:18.5987021Z 63 ms 2025-01-24T02:06:18.6526339Z [ OK ] BasicTest.BasicTestHalfCPU (307 ms) 2025-01-24T02:06:18.6527076Z [ RUN ] BasicTest.FactoryMethodsTest 2025-01-24T02:06:18.6853878Z [ OK ] BasicTest.FactoryMethodsTest (32 ms) 2025-01-24T02:06:18.6854527Z [ RUN ] BasicTest.BasicStdTestCPU 2025-01-24T02:06:18.8171150Z Simple example: called once 2025-01-24T02:06:18.8674169Z throw: call_once will retry 2025-01-24T02:06:18.8688902Z Didn't throw, call_once will not attempt again 2025-01-24T02:06:18.9118180Z [ OK ] BasicTest.BasicStdTestCPU (226 ms) 2025-01-24T02:06:18.9142475Z [----------] 4 tests from BasicTest (2843 ms total) 2025-01-24T02:06:18.9142823Z 2025-01-24T02:06:18.9157322Z [----------] Global test environment tear-down 2025-01-24T02:06:18.9189568Z [==========] 4 tests from 1 test suite ran. (2869 ms total) 2025-01-24T02:06:18.9200078Z [ PASSED ] 4 tests. 2025-01-24T02:06:20.8156763Z ==6064== 2025-01-24T02:06:20.8160177Z ==6064== HEAP SUMMARY: 2025-01-24T02:06:20.8160558Z ==6064== in use at exit: 240,152 bytes in 3,996 blocks 2025-01-24T02:06:20.8168588Z ==6064== total heap usage: 747,563 allocs, 743,567 frees, 215,029,699 bytes allocated 2025-01-24T02:06:20.8169082Z ==6064== 2025-01-24T02:06:20.8542898Z ==6064== LEAK SUMMARY: 2025-01-24T02:06:20.8543330Z ==6064== definitely lost: 0 bytes in 0 blocks 2025-01-24T02:06:20.8543722Z ==6064== indirectly lost: 0 bytes in 0 blocks 2025-01-24T02:06:20.8544129Z ==6064== possibly lost: 0 bytes in 0 blocks 2025-01-24T02:06:20.8544544Z ==6064== still reachable: 240,152 bytes in 3,996 blocks 2025-01-24T02:06:20.8544960Z ==6064== suppressed: 0 bytes in 0 blocks 2025-01-24T02:06:20.8545425Z ==6064== Rerun with --leak-check=full to see details of leaked memory 2025-01-24T02:06:20.8545851Z ==6064== 2025-01-24T02:06:20.8546325Z ==6064== For lists of detected and suppressed errors, rerun with: -s 2025-01-24T02:06:20.8546885Z ==6064== ERROR SUMMARY: 0 errors from 0 contexts (suppressed: 0 from 0) 2025-01-24T02:06:20.9022115Z + [[ -x build/bin/tensor_interop_test ]] 2025-01-24T02:06:20.9023973Z + [[ -n '' ]] 2025-01-24T02:06:20.9024419Z + assert_git_not_dirty 2025-01-24T02:06:20.9024909Z + [[ linux-focal-py3.13-clang10 != *rocm* ]] 2025-01-24T02:06:20.9025356Z + [[ linux-focal-py3.13-clang10 != *xla* ]] 2025-01-24T02:06:20.9030943Z ++ git status --porcelain 2025-01-24T02:06:20.9032086Z ++ grep -v '?? third_party' 2025-01-24T02:06:21.1033976Z ++ true 2025-01-24T02:06:21.1035088Z + git_status= 2025-01-24T02:06:21.1035352Z + [[ -n '' ]] 2025-01-24T02:06:21.1036967Z + cleanup_workspace 2025-01-24T02:06:21.1037891Z + echo 'sudo may print the following warning message that can be ignored. The chown command will still run.' 2025-01-24T02:06:21.1038723Z sudo may print the following warning message that can be ignored. The chown command will still run. 2025-01-24T02:06:21.1039643Z + echo ' sudo: setrlimit(RLIMIT_STACK): Operation not permitted' 2025-01-24T02:06:21.1040454Z sudo: setrlimit(RLIMIT_STACK): Operation not permitted 2025-01-24T02:06:21.1041170Z + echo 'For more details refer to https://github.com/sudo-project/sudo/issues/42' 2025-01-24T02:06:21.1041785Z For more details refer to https://github.com/sudo-project/sudo/issues/42 2025-01-24T02:06:21.1042296Z + sudo chown -R 1000 /var/lib/jenkins/workspace 2025-01-24T02:06:23.7880956Z ##[group]Run pytorch/test-infra/.github/actions/upload-benchmark-results@main 2025-01-24T02:06:23.7881475Z with: 2025-01-24T02:06:23.7881753Z benchmark-results-dir: test/test-reports 2025-01-24T02:06:23.7882117Z dry-run: false 2025-01-24T02:06:23.7882387Z schema-version: v3 2025-01-24T02:06:23.7882925Z github-token: *** 2025-01-24T02:06:23.7883195Z env: 2025-01-24T02:06:23.7883430Z GIT_DEFAULT_BRANCH: main 2025-01-24T02:06:23.7883934Z DOCKER_CONTAINER_ID: 8336c83c461cac917c450a6b582f03790365573c87be1c4764650d0e39a45485 2025-01-24T02:06:23.7884460Z ##[endgroup] 2025-01-24T02:06:23.7914132Z ##[group]Run set -eux 2025-01-24T02:06:23.7914429Z set -eux 2025-01-24T02:06:23.7914725Z python3 -mpip install boto3==1.35.33 2025-01-24T02:06:23.7967627Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-01-24T02:06:23.7968041Z env: 2025-01-24T02:06:23.7968281Z GIT_DEFAULT_BRANCH: main 2025-01-24T02:06:23.7968821Z DOCKER_CONTAINER_ID: 8336c83c461cac917c450a6b582f03790365573c87be1c4764650d0e39a45485 2025-01-24T02:06:23.7969334Z ##[endgroup] 2025-01-24T02:06:23.7997281Z + python3 -mpip install boto3==1.35.33 2025-01-24T02:06:24.4655367Z Defaulting to user installation because normal site-packages is not writeable 2025-01-24T02:06:24.4961382Z Requirement already satisfied: boto3==1.35.33 in /home/ec2-user/.local/lib/python3.9/site-packages (1.35.33) 2025-01-24T02:06:24.5016735Z Requirement already satisfied: botocore<1.36.0,>=1.35.33 in /home/ec2-user/.local/lib/python3.9/site-packages (from boto3==1.35.33) (1.35.99) 2025-01-24T02:06:24.5021431Z Requirement already satisfied: s3transfer<0.11.0,>=0.10.0 in /home/ec2-user/.local/lib/python3.9/site-packages (from boto3==1.35.33) (0.10.4) 2025-01-24T02:06:24.5026827Z Requirement already satisfied: jmespath<2.0.0,>=0.7.1 in /usr/lib/python3.9/site-packages (from boto3==1.35.33) (0.10.0) 2025-01-24T02:06:24.5089331Z Requirement already satisfied: urllib3<1.27,>=1.25.4 in /usr/lib/python3.9/site-packages (from botocore<1.36.0,>=1.35.33->boto3==1.35.33) (1.25.10) 2025-01-24T02:06:24.5094654Z Requirement already satisfied: python-dateutil<3.0.0,>=2.1 in /usr/lib/python3.9/site-packages (from botocore<1.36.0,>=1.35.33->boto3==1.35.33) (2.8.1) 2025-01-24T02:06:24.5144105Z Requirement already satisfied: six>=1.5 in /usr/lib/python3.9/site-packages (from python-dateutil<3.0.0,>=2.1->botocore<1.36.0,>=1.35.33->boto3==1.35.33) (1.15.0) 2025-01-24T02:06:24.6672468Z ##[group]Run set -eux 2025-01-24T02:06:24.6672791Z set -eux 2025-01-24T02:06:24.6673033Z  2025-01-24T02:06:24.6673302Z if [[ -z "${GITHUB_TOKEN}" ]]; then 2025-01-24T02:06:24.6673682Z  echo "Missing github-token input" 2025-01-24T02:06:24.6674030Z  exit 1 2025-01-24T02:06:24.6674277Z fi 2025-01-24T02:06:24.6680956Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-01-24T02:06:24.6681381Z env: 2025-01-24T02:06:24.6681632Z GIT_DEFAULT_BRANCH: main 2025-01-24T02:06:24.6682147Z DOCKER_CONTAINER_ID: 8336c83c461cac917c450a6b582f03790365573c87be1c4764650d0e39a45485 2025-01-24T02:06:24.6682885Z GITHUB_TOKEN: *** 2025-01-24T02:06:24.6683150Z ##[endgroup] 2025-01-24T02:06:24.6706108Z + [[ -z *** ]] 2025-01-24T02:06:24.6767579Z ##[group]Run pytorch/test-infra/.github/actions/get-workflow-job-id@main 2025-01-24T02:06:24.6768058Z with: 2025-01-24T02:06:24.6768445Z github-token: *** 2025-01-24T02:06:24.6768694Z env: 2025-01-24T02:06:24.6768947Z GIT_DEFAULT_BRANCH: main 2025-01-24T02:06:24.6769441Z DOCKER_CONTAINER_ID: 8336c83c461cac917c450a6b582f03790365573c87be1c4764650d0e39a45485 2025-01-24T02:06:24.6769966Z ##[endgroup] 2025-01-24T02:06:24.6795983Z ##[group]Run set -eux 2025-01-24T02:06:24.6796277Z set -eux 2025-01-24T02:06:24.6796526Z  2025-01-24T02:06:24.6797025Z python3 "${GITHUB_ACTION_PATH}/../../scripts/get_workflow_job_id.py" "${GITHUB_RUN_ID}" "${RUNNER_NAME}" 2025-01-24T02:06:24.6802555Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-01-24T02:06:24.6802975Z env: 2025-01-24T02:06:24.6803221Z GIT_DEFAULT_BRANCH: main 2025-01-24T02:06:24.6803702Z DOCKER_CONTAINER_ID: 8336c83c461cac917c450a6b582f03790365573c87be1c4764650d0e39a45485 2025-01-24T02:06:24.6804380Z GITHUB_TOKEN: *** 2025-01-24T02:06:24.6804621Z ##[endgroup] 2025-01-24T02:06:24.6827276Z + python3 /home/ec2-user/actions-runner/_work/_actions/pytorch/test-infra/main/.github/actions/get-workflow-job-id/../../scripts/get_workflow_job_id.py 12940916563 i-0370698385eeb2f09 2025-01-24T02:06:26.7770232Z setting job-id=36096586663 2025-01-24T02:06:26.7770834Z setting job-name=linux-focal-py3.13-clang10 / test (dynamo_wrapped, 1, 3, lf.linux.2xlarge) 2025-01-24T02:06:26.7888266Z ##[group]Run set -eux 2025-01-24T02:06:26.7888573Z set -eux 2025-01-24T02:06:26.7888828Z  2025-01-24T02:06:26.7889232Z python3 "${GITHUB_ACTION_PATH}/../../scripts/benchmarks/gather_metadata.py" \ 2025-01-24T02:06:26.7889803Z  --schema-version "${SCHEMA_VERSION}" \ 2025-01-24T02:06:26.7890178Z  --repo "${REPO}" \ 2025-01-24T02:06:26.7890507Z  --head-branch "${HEAD_BRANCH}" \ 2025-01-24T02:06:26.7890859Z  --head-sha "${HEAD_SHA}" \ 2025-01-24T02:06:26.7891215Z  --workflow-id "${WORKFLOW_RUN_ID}" \ 2025-01-24T02:06:26.7891576Z  --run-attempt "${RUN_ATTEMPT}" \ 2025-01-24T02:06:26.7891921Z  --job-id "${JOB_ID}" \ 2025-01-24T02:06:26.7892344Z  --job-name "${JOB_NAME}" 2025-01-24T02:06:26.7897941Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-01-24T02:06:26.7898348Z env: 2025-01-24T02:06:26.7898582Z GIT_DEFAULT_BRANCH: main 2025-01-24T02:06:26.7899051Z DOCKER_CONTAINER_ID: 8336c83c461cac917c450a6b582f03790365573c87be1c4764650d0e39a45485 2025-01-24T02:06:26.7899569Z SCHEMA_VERSION: v3 2025-01-24T02:06:26.7899842Z REPO: pytorch/pytorch 2025-01-24T02:06:26.7900131Z HEAD_BRANCH: export-D68294638 2025-01-24T02:06:26.7900489Z HEAD_SHA: d087145b9259b4cfbcb758dc0bca6a6e5d75d08f 2025-01-24T02:06:26.7900944Z WORKFLOW_RUN_ID: 12940916563 2025-01-24T02:06:26.7901238Z RUN_ATTEMPT: 1 2025-01-24T02:06:26.7901485Z JOB_ID: 36096586663 2025-01-24T02:06:26.7901923Z JOB_NAME: linux-focal-py3.13-clang10 / test (dynamo_wrapped, 1, 3, lf.linux.2xlarge) 2025-01-24T02:06:26.7902425Z ##[endgroup] 2025-01-24T02:06:26.7927177Z + python3 /home/ec2-user/actions-runner/_work/_actions/pytorch/test-infra/main/.github/actions/upload-benchmark-results/../../scripts/benchmarks/gather_metadata.py --schema-version v3 --repo pytorch/pytorch --head-branch export-D68294638 --head-sha d087145b9259b4cfbcb758dc0bca6a6e5d75d08f --workflow-id 12940916563 --run-attempt 1 --job-id 36096586663 --job-name 'linux-focal-py3.13-clang10 / test (dynamo_wrapped, 1, 3, lf.linux.2xlarge)' 2025-01-24T02:06:26.8245008Z ##[group]Run set -eux 2025-01-24T02:06:26.8245297Z set -eux 2025-01-24T02:06:26.8245530Z  2025-01-24T02:06:26.8245805Z # TODO (huydhn): Implement this part 2025-01-24T02:06:26.8246230Z echo "runners=[]" >> "${GITHUB_OUTPUT}" 2025-01-24T02:06:26.8251468Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-01-24T02:06:26.8251877Z env: 2025-01-24T02:06:26.8252110Z GIT_DEFAULT_BRANCH: main 2025-01-24T02:06:26.8252579Z DOCKER_CONTAINER_ID: 8336c83c461cac917c450a6b582f03790365573c87be1c4764650d0e39a45485 2025-01-24T02:06:26.8253094Z ##[endgroup] 2025-01-24T02:06:26.8274509Z + echo 'runners=[]' 2025-01-24T02:06:26.8306308Z ##[group]Run set -eux 2025-01-24T02:06:26.8306600Z set -eux 2025-01-24T02:06:26.8306855Z  2025-01-24T02:06:26.8307123Z # TODO (huydhn): Implement this part 2025-01-24T02:06:26.8307532Z echo "dependencies={}" >> "${GITHUB_OUTPUT}" 2025-01-24T02:06:26.8312660Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-01-24T02:06:26.8313058Z env: 2025-01-24T02:06:26.8313287Z GIT_DEFAULT_BRANCH: main 2025-01-24T02:06:26.8313900Z DOCKER_CONTAINER_ID: 8336c83c461cac917c450a6b582f03790365573c87be1c4764650d0e39a45485 2025-01-24T02:06:26.8314424Z ##[endgroup] 2025-01-24T02:06:26.8335380Z + echo 'dependencies={}' 2025-01-24T02:06:26.8365594Z ##[group]Run set -eux 2025-01-24T02:06:26.8365886Z set -eux 2025-01-24T02:06:26.8366133Z  2025-01-24T02:06:26.8366413Z if [[ ! -d "${BENCHMARK_RESULTS_DIR}" ]]; then 2025-01-24T02:06:26.8366873Z  echo "${BENCHMARK_RESULTS_DIR} does not exist, skipping" 2025-01-24T02:06:26.8367381Z  # We don't want the job to fail if the directory doesn't exist 2025-01-24T02:06:26.8367794Z  exit 0 2025-01-24T02:06:26.8368034Z fi 2025-01-24T02:06:26.8368264Z  2025-01-24T02:06:26.8368516Z if [[ "${DRY_RUN}" == "true" ]]; then 2025-01-24T02:06:26.8369009Z  python3 "${GITHUB_ACTION_PATH}/../../scripts/upload_benchmark_results.py" \ 2025-01-24T02:06:26.8369599Z  --benchmark-results-dir "${BENCHMARK_RESULTS_DIR}" \ 2025-01-24T02:06:26.8370065Z  --metadata "${BENCHMARK_METADATA}" \ 2025-01-24T02:06:26.8370444Z  --runners "${RUNNER_INFO}" \ 2025-01-24T02:06:26.8370814Z  --dependencies "${DEPENDENCIES}" \ 2025-01-24T02:06:26.8371166Z  --dry-run 2025-01-24T02:06:26.8371420Z else 2025-01-24T02:06:26.8371822Z  python3 "${GITHUB_ACTION_PATH}/../../scripts/upload_benchmark_results.py" \ 2025-01-24T02:06:26.8372400Z  --benchmark-results-dir "${BENCHMARK_RESULTS_DIR}" \ 2025-01-24T02:06:26.8372936Z  --metadata "${BENCHMARK_METADATA}" \ 2025-01-24T02:06:26.8373316Z  --runners "${RUNNER_INFO}" \ 2025-01-24T02:06:26.8373672Z  --dependencies "${DEPENDENCIES}" 2025-01-24T02:06:26.8374018Z fi 2025-01-24T02:06:26.8378878Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-01-24T02:06:26.8379522Z env: 2025-01-24T02:06:26.8379761Z GIT_DEFAULT_BRANCH: main 2025-01-24T02:06:26.8380253Z DOCKER_CONTAINER_ID: 8336c83c461cac917c450a6b582f03790365573c87be1c4764650d0e39a45485 2025-01-24T02:06:26.8380888Z BENCHMARK_RESULTS_DIR: test/test-reports 2025-01-24T02:06:26.8381230Z DRY_RUN: false 2025-01-24T02:06:26.8395504Z BENCHMARK_METADATA: {"timestamp": 1737684386, "schema_version": "v3", "name": "linux-focal-py3.13-clang10 / test (dynamo_wrapped, 1, 3, lf.linux.2xlarge)", "repo": "pytorch/pytorch", "head_branch": "export-D68294638", "head_sha": "d087145b9259b4cfbcb758dc0bca6a6e5d75d08f", "workflow_id": 12940916563, "run_attempt": 1, "job_id": 36096586663} 2025-01-24T02:06:26.8396923Z RUNNER_INFO: [] 2025-01-24T02:06:26.8397163Z DEPENDENCIES: {} 2025-01-24T02:06:26.8397389Z ##[endgroup] 2025-01-24T02:06:26.8418310Z + [[ ! -d test/test-reports ]] 2025-01-24T02:06:26.8418830Z + [[ false == \t\r\u\e ]] 2025-01-24T02:06:26.8421482Z + python3 /home/ec2-user/actions-runner/_work/_actions/pytorch/test-infra/main/.github/actions/upload-benchmark-results/../../scripts/upload_benchmark_results.py --benchmark-results-dir test/test-reports --metadata '{"timestamp": 1737684386, "schema_version": "v3", "name": "linux-focal-py3.13-clang10 / test (dynamo_wrapped, 1, 3, lf.linux.2xlarge)", "repo": "pytorch/pytorch", "head_branch": "export-D68294638", "head_sha": "d087145b9259b4cfbcb758dc0bca6a6e5d75d08f", "workflow_id": 12940916563, "run_attempt": 1, "job_id": 36096586663}' --runners '[]' --dependencies '{}' 2025-01-24T02:06:27.1441684Z ##[group]Run cat test/**/*_toprint.log || true 2025-01-24T02:06:27.1442130Z cat test/**/*_toprint.log || true 2025-01-24T02:06:27.1447755Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-01-24T02:06:27.1448164Z env: 2025-01-24T02:06:27.1448399Z GIT_DEFAULT_BRANCH: main 2025-01-24T02:06:27.1448878Z DOCKER_CONTAINER_ID: 8336c83c461cac917c450a6b582f03790365573c87be1c4764650d0e39a45485 2025-01-24T02:06:27.1449391Z ##[endgroup] 2025-01-24T02:06:27.1515300Z cat: 'test/**/*_toprint.log': No such file or directory 2025-01-24T02:06:27.1553229Z ##[group]Run kill "$MONITOR_SCRIPT_PID" 2025-01-24T02:06:27.1553626Z kill "$MONITOR_SCRIPT_PID" 2025-01-24T02:06:27.1558720Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-01-24T02:06:27.1559140Z env: 2025-01-24T02:06:27.1559387Z GIT_DEFAULT_BRANCH: main 2025-01-24T02:06:27.1559877Z DOCKER_CONTAINER_ID: 8336c83c461cac917c450a6b582f03790365573c87be1c4764650d0e39a45485 2025-01-24T02:06:27.1560420Z MONITOR_SCRIPT_PID: 291790 2025-01-24T02:06:27.1560711Z ##[endgroup] 2025-01-24T02:06:27.1705025Z Prepare all required actions 2025-01-24T02:06:27.1705485Z Getting action download info 2025-01-24T02:06:27.3075813Z Download action repository 'actions/upload-artifact@v4' (SHA:65c4c4a1ddee5b72f698fdd19549f0f0fb45cf08) 2025-01-24T02:06:27.7784414Z ##[group]Run ./.github/actions/upload-test-artifacts 2025-01-24T02:06:27.7784806Z with: 2025-01-24T02:06:27.7785145Z file-suffix: test-dynamo_wrapped-1-3-lf.linux.2xlarge_36096586663 2025-01-24T02:06:27.7785598Z s3-bucket: gha-artifacts 2025-01-24T02:06:27.7785906Z env: 2025-01-24T02:06:27.7786140Z GIT_DEFAULT_BRANCH: main 2025-01-24T02:06:27.7786720Z DOCKER_CONTAINER_ID: 8336c83c461cac917c450a6b582f03790365573c87be1c4764650d0e39a45485 2025-01-24T02:06:27.7787224Z ##[endgroup] 2025-01-24T02:06:27.7820024Z ##[group]Run # Remove any previous test jsons if they exist 2025-01-24T02:06:27.7820523Z # Remove any previous test jsons if they exist 2025-01-24T02:06:27.7820923Z rm -f test-jsons-*.zip 2025-01-24T02:06:27.7821521Z zip -r "test-jsons-${FILE_SUFFIX}.zip" test/test-reports -i '*.json' 2025-01-24T02:06:27.7827260Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-01-24T02:06:27.7827652Z env: 2025-01-24T02:06:27.7842593Z GIT_DEFAULT_BRANCH: main 2025-01-24T02:06:27.7843098Z DOCKER_CONTAINER_ID: 8336c83c461cac917c450a6b582f03790365573c87be1c4764650d0e39a45485 2025-01-24T02:06:27.7843751Z FILE_SUFFIX: test-dynamo_wrapped-1-3-lf.linux.2xlarge_36096586663 2025-01-24T02:06:27.7844163Z ##[endgroup] 2025-01-24T02:06:27.7970129Z adding: test/test-reports/td_exclusions-965164e33e3856f36e0c.json (deflated 81%) 2025-01-24T02:06:27.7970833Z adding: test/test-reports/td_exclusions-4eaa9f4e90707100d593.json (deflated 73%) 2025-01-24T02:06:27.8002032Z ##[group]Run # Remove any previous test reports if they exist 2025-01-24T02:06:27.8002519Z # Remove any previous test reports if they exist 2025-01-24T02:06:27.8002922Z rm -f test-reports-*.zip 2025-01-24T02:06:27.8003423Z zip -r "test-reports-${FILE_SUFFIX}.zip" test/test-reports -i '*.xml' -i '*.csv' 2025-01-24T02:06:27.8009080Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-01-24T02:06:27.8009482Z env: 2025-01-24T02:06:27.8009718Z GIT_DEFAULT_BRANCH: main 2025-01-24T02:06:27.8010198Z DOCKER_CONTAINER_ID: 8336c83c461cac917c450a6b582f03790365573c87be1c4764650d0e39a45485 2025-01-24T02:06:27.8010838Z FILE_SUFFIX: test-dynamo_wrapped-1-3-lf.linux.2xlarge_36096586663 2025-01-24T02:06:27.8011276Z ##[endgroup] 2025-01-24T02:06:27.8167042Z adding: test/test-reports/python-pytest/test_transformers/test_transformers-735010e2522d8c95.xml (deflated 98%) 2025-01-24T02:06:27.8278534Z adding: test/test-reports/python-pytest/test_utils/test_utils-ddbb0386babde5f1.xml (deflated 99%) 2025-01-24T02:06:27.8313403Z adding: test/test-reports/python-pytest/test_torch/test_torch-531bcee730d0d71a.xml (deflated 95%) 2025-01-24T02:06:27.8341376Z adding: test/test-reports/python-pytest/test_nn/test_nn-131564154d5a55e2.xml (deflated 96%) 2025-01-24T02:06:27.8342238Z adding: test/test-reports/python-pytest/test_show_pickle/test_show_pickle-b9f601151b0b3c5a.xml (deflated 37%) 2025-01-24T02:06:27.8348374Z adding: test/test-reports/python-pytest/test_fake_tensor/test_fake_tensor-c12a071b09392b37.xml (deflated 93%) 2025-01-24T02:06:27.8350650Z adding: test/test-reports/python-pytest/test_multiprocessing/test_multiprocessing-f144cb2252b92655.xml (deflated 90%) 2025-01-24T02:06:27.8351619Z adding: test/test-reports/python-pytest/test_autocast/test_autocast-8404b2c7c78d0e35.xml (deflated 86%) 2025-01-24T02:06:27.8370777Z adding: test/test-reports/python-pytest/test_tensor_creation_ops/test_tensor_creation_ops-a133ba523bc0ca69.xml (deflated 94%) 2025-01-24T02:06:27.8371829Z adding: test/test-reports/python-pytest/test_extension_utils/test_extension_utils-aee4b69fe9348fbb.xml (deflated 51%) 2025-01-24T02:06:27.8373085Z adding: test/test-reports/python-pytest/test_mobile_optimizer/test_mobile_optimizer-6de1eb0e9659c419.xml (deflated 58%) 2025-01-24T02:06:27.8374187Z adding: test/test-reports/python-pytest/test_multiprocessing_spawn/test_multiprocessing_spawn-96143fab40d557fa.xml (deflated 94%) 2025-01-24T02:06:27.8381771Z adding: 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test/test-reports/python-unittest/test_autoload/TEST-TestDeviceBackendAutoload-20250124015222.xml (deflated 43%) 2025-01-24T02:06:27.9049431Z ##[group]Run # Remove any previous usage logs if they exist 2025-01-24T02:06:27.9049926Z # Remove any previous usage logs if they exist 2025-01-24T02:06:27.9050319Z rm -f logs-*.zip 2025-01-24T02:06:27.9050891Z # this workflow is also run in bazel build test, but we dont generate usage reports for it 2025-01-24T02:06:27.9051446Z # so check to see if the file exists first 2025-01-24T02:06:27.9051828Z if [ -f 'usage_log.txt' ]; then 2025-01-24T02:06:27.9052221Z  zip "logs-${FILE_SUFFIX}.zip" 'usage_log.txt' 2025-01-24T02:06:27.9052586Z fi 2025-01-24T02:06:27.9052978Z if find "test/test-reports" -name "*.log" 2>/dev/null | grep -q .; then 2025-01-24T02:06:27.9053603Z  zip -r "logs-${FILE_SUFFIX}.zip" test/test-reports -i '*.log' 2025-01-24T02:06:27.9054006Z fi 2025-01-24T02:06:27.9059358Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-01-24T02:06:27.9059763Z env: 2025-01-24T02:06:27.9059999Z GIT_DEFAULT_BRANCH: main 2025-01-24T02:06:27.9060478Z DOCKER_CONTAINER_ID: 8336c83c461cac917c450a6b582f03790365573c87be1c4764650d0e39a45485 2025-01-24T02:06:27.9061118Z FILE_SUFFIX: test-dynamo_wrapped-1-3-lf.linux.2xlarge_36096586663 2025-01-24T02:06:27.9061538Z ##[endgroup] 2025-01-24T02:06:27.9139255Z adding: usage_log.txt (deflated 95%) 2025-01-24T02:06:27.9281877Z adding: test/test-reports/test_transformers_1.2_f13409f1a9e61442_.log (deflated 98%) 2025-01-24T02:06:27.9388196Z adding: test/test-reports/test_utils_1.1_d2439f25a07aa688_.log (deflated 96%) 2025-01-24T02:06:27.9428196Z adding: test/test-reports/test_torch_1.1_fff4d454c50ae3ed_.log (deflated 92%) 2025-01-24T02:06:27.9461502Z adding: test/test-reports/test_nn_2.2_11fad3592f60cce8_.log (deflated 94%) 2025-01-24T02:06:27.9462757Z adding: test/test-reports/test_show_pickle_1.1_6ed5f46846c5929c_.log (deflated 51%) 2025-01-24T02:06:27.9470074Z adding: 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(deflated 49%) 2025-01-24T02:06:27.9648117Z adding: test/test-reports/test_mkldnn_1.1_dbdb82c8bdeb6283_.log (deflated 49%) 2025-01-24T02:06:27.9649411Z adding: test/test-reports/test_functional_autograd_benchmark_1.1_4b3ba4051dd447d7_.log (deflated 52%) 2025-01-24T02:06:27.9650822Z adding: test/test-reports/test_unary_ufuncs_1.1_202244ec3741e8f3_.log (deflated 50%) 2025-01-24T02:06:27.9652280Z adding: test/test-reports/test_jiterator_1.1_ccdc9f0f5af42781_.log (deflated 49%) 2025-01-24T02:06:27.9653482Z adding: test/test-reports/xpu.test_gemm_1.1_bec67091f920e33c_.log (deflated 49%) 2025-01-24T02:06:27.9654739Z adding: test/test-reports/test_matmul_cuda_1.1_c70609fc24125d99_.log (deflated 49%) 2025-01-24T02:06:27.9655988Z adding: test/test-reports/optim.test_lrscheduler_1.1_b99c93b345c20230_.log (deflated 7%) 2025-01-24T02:06:27.9657241Z adding: test/test-reports/optim.test_optim_1.1_d2c1c2192b688661_.log (deflated 7%) 2025-01-24T02:06:27.9658495Z adding: test/test-reports/cpp.wrapdim_test_1.1_0b515cfe8abca7f1_.log (deflated 50%) 2025-01-24T02:06:27.9659781Z adding: test/test-reports/dynamo.test_minifier_1.1_504b3cc3490ed2cb_.log (stored 0%) 2025-01-24T02:06:27.9661045Z adding: test/test-reports/dynamo.test_modules_1.1_4067876557a1bdfe_.log (stored 0%) 2025-01-24T02:06:27.9662178Z adding: test/test-reports/dynamo.test_backends_1.1_881f9bb5335fb57f_.log (stored 0%) 2025-01-24T02:06:27.9663423Z adding: test/test-reports/dynamo.test_unspec_1.1_4b328c9e76265156_.log (stored 0%) 2025-01-24T02:06:27.9664734Z adding: test/test-reports/dynamo.test_cudagraphs_1.1_13e998898c5f21d6_.log (stored 0%) 2025-01-24T02:06:27.9665996Z adding: test/test-reports/dynamo.test_config_1.1_cd5e147bb31760b5_.log (stored 0%) 2025-01-24T02:06:27.9667316Z adding: test/test-reports/dynamo.test_global_1.1_f29e8b4be586df2a_.log (stored 0%) 2025-01-24T02:06:27.9668490Z adding: test/test-reports/dynamo.test_subgraphs_1.1_c3e04b00612aacb7_.log (stored 0%) 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test/test-reports/dynamo.test_torchrec_1.1_56bd1ba11d8a6d83_.log (stored 0%) 2025-01-24T02:06:27.9691715Z adding: test/test-reports/dynamo.test_trace_rules_1.1_02e7e803bac3229e_.log (stored 0%) 2025-01-24T02:06:27.9693012Z adding: test/test-reports/dynamo.test_guard_manager_1.1_7f3401459110e525_.log (stored 0%) 2025-01-24T02:06:27.9694206Z adding: test/test-reports/test_tensorboard_1.1_20def0c821ee98a0_.log (deflated 79%) 2025-01-24T02:06:27.9695596Z adding: test/test-reports/test_functional_autograd_benchmark_1.1_8757b84f6788b2ca_.log (deflated 87%) 2025-01-24T02:06:27.9697099Z adding: test/test-reports/test_view_ops_1.1_6723bef76106e85c_.log (deflated 92%) 2025-01-24T02:06:27.9698267Z adding: test/test-reports/test_jiterator_1.1_0119591c427e919b_.log (deflated 49%) 2025-01-24T02:06:27.9699456Z adding: test/test-reports/xpu.test_gemm_1.1_8d9a07e61845f9b6_.log (deflated 49%) 2025-01-24T02:06:27.9700648Z adding: test/test-reports/test_matmul_cuda_1.1_d792935b30c15225_.log (deflated 49%) 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(deflated 49%) 2025-01-24T02:06:28.0117268Z adding: test/test-reports/cpp.basic_1.1_9e11980ce9684f8c_.log (deflated 49%) 2025-01-24T02:06:28.0118518Z adding: test/test-reports/cpp.broadcast_test_1.1_857c0ab311409371_.log (deflated 49%) 2025-01-24T02:06:28.0119960Z adding: test/test-reports/cpp.cpu_generator_test_1.1_6383c33391b1ae96_.log (deflated 49%) 2025-01-24T02:06:28.0121319Z adding: test/test-reports/cpp.dlconvertor_test_1.1_7745b32d362e009f_.log (deflated 49%) 2025-01-24T02:06:28.0122690Z adding: test/test-reports/cpp.extension_backend_test_1.1_5f660829fc884fee_.log (deflated 49%) 2025-01-24T02:06:28.0124095Z adding: test/test-reports/cpp.lazy_tensor_test_1.1_4b45d3f421d66473_.log (deflated 49%) 2025-01-24T02:06:28.0125415Z adding: test/test-reports/cpp.legacy_vmap_test_1.1_0a52569bd0f2d072_.log (deflated 49%) 2025-01-24T02:06:28.0126716Z adding: test/test-reports/cpp.native_test_1.1_fe29df51dbd53932_.log (deflated 49%) 2025-01-24T02:06:28.0128007Z adding: test/test-reports/cpp.operators_test_1.1_2a2713169e1f12bd_.log (deflated 49%) 2025-01-24T02:06:28.0129338Z adding: test/test-reports/cpp.scalar_tensor_test_1.1_07c03b147c455c87_.log (deflated 49%) 2025-01-24T02:06:28.0130642Z adding: test/test-reports/cpp.scalar_test_1.1_6b6087daeba61162_.log (deflated 49%) 2025-01-24T02:06:28.0131975Z adding: test/test-reports/cpp.tensor_iterator_test_1.1_c80c10ecf6d480cb_.log (deflated 49%) 2025-01-24T02:06:28.0133401Z adding: test/test-reports/cpp.undefined_tensor_test_1.1_3c714fb2c8b85adc_.log (deflated 49%) 2025-01-24T02:06:28.0134685Z adding: test/test-reports/cpp.wrapdim_test_1.1_2fdbba1931ad7b80_.log (deflated 49%) 2025-01-24T02:06:28.0135876Z adding: test/test-reports/cpp.Dimname_test_1.1_521a32cb4150017e_.log (deflated 59%) 2025-01-24T02:06:28.0136589Z adding: test/test-reports/cpp.NamedTensor_test_1.1_9d22701e230e27f6_.log (deflated 71%) 2025-01-24T02:06:28.0137276Z adding: test/test-reports/cpp.Dict_test_1.1_169319e185779e8c_.log (deflated 84%) 2025-01-24T02:06:28.0137989Z adding: test/test-reports/cpp.apply_utils_test_1.1_7dd39c5cf7cfbe72_.log (deflated 64%) 2025-01-24T02:06:28.0138670Z adding: test/test-reports/cpp.atest_1.1_4c55cfb0f1b6c0ac_.log (deflated 72%) 2025-01-24T02:06:28.0139346Z adding: test/test-reports/cpp.broadcast_test_1.1_6c071ba37f27a3a4_.log (deflated 50%) 2025-01-24T02:06:28.0140014Z adding: test/test-reports/cpp.basic_1.1_b664ed388566a321_.log (deflated 61%) 2025-01-24T02:06:28.0140707Z adding: test/test-reports/cpp.undefined_tensor_test_1.1_db344b305ba31456_.log (deflated 50%) 2025-01-24T02:06:28.0141606Z adding: test/test-reports/cpp.cpu_generator_test_1.1_6ff0c8a81218e9a0_.log (deflated 78%) 2025-01-24T02:06:28.0142331Z adding: test/test-reports/cpp.dlconvertor_test_1.1_8f2a3a2f0fb1abdd_.log (deflated 56%) 2025-01-24T02:06:28.0143092Z adding: test/test-reports/cpp.extension_backend_test_1.1_af39f1dc58a87c6f_.log (deflated 50%) 2025-01-24T02:06:28.0143842Z adding: test/test-reports/cpp.lazy_tensor_test_1.1_14ccebc02a61e9cb_.log (deflated 54%) 2025-01-24T02:06:28.0144542Z adding: test/test-reports/cpp.native_test_1.1_390f61fb11392ff6_.log (deflated 54%) 2025-01-24T02:06:28.0145232Z adding: test/test-reports/cpp.legacy_vmap_test_1.1_4e084f5d2c016643_.log (deflated 80%) 2025-01-24T02:06:28.0145945Z adding: test/test-reports/cpp.operators_test_1.1_b25e9c324f200d9b_.log (deflated 60%) 2025-01-24T02:06:28.0176564Z ##[group]Run # Remove any previous debugging artifacts if they exist 2025-01-24T02:06:28.0177124Z # Remove any previous debugging artifacts if they exist 2025-01-24T02:06:28.0177555Z rm -f debug-*.zip 2025-01-24T02:06:28.0177856Z if [ -d 'test/debug' ]; then 2025-01-24T02:06:28.0178286Z  zip -r "debug-${FILE_SUFFIX}.zip" test/debug 2025-01-24T02:06:28.0178655Z fi 2025-01-24T02:06:28.0184268Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-01-24T02:06:28.0184677Z env: 2025-01-24T02:06:28.0184916Z GIT_DEFAULT_BRANCH: main 2025-01-24T02:06:28.0185385Z DOCKER_CONTAINER_ID: 8336c83c461cac917c450a6b582f03790365573c87be1c4764650d0e39a45485 2025-01-24T02:06:28.0186021Z FILE_SUFFIX: test-dynamo_wrapped-1-3-lf.linux.2xlarge_36096586663 2025-01-24T02:06:28.0186630Z ##[endgroup] 2025-01-24T02:06:28.0270130Z ##[group]Run seemethere/upload-artifact-s3@v5 2025-01-24T02:06:28.0270489Z with: 2025-01-24T02:06:28.0270711Z s3-bucket: gha-artifacts 2025-01-24T02:06:28.0271056Z s3-prefix: pytorch/pytorch/12940916563/1/artifact 2025-01-24T02:06:28.0271435Z retention-days: 14 2025-01-24T02:06:28.0271711Z if-no-files-found: warn 2025-01-24T02:06:28.0272001Z path: test-jsons-*.zip 2025-01-24T02:06:28.0272276Z name: artifact 2025-01-24T02:06:28.0272521Z region: us-east-1 2025-01-24T02:06:28.0272764Z env: 2025-01-24T02:06:28.0272991Z GIT_DEFAULT_BRANCH: main 2025-01-24T02:06:28.0273466Z DOCKER_CONTAINER_ID: 8336c83c461cac917c450a6b582f03790365573c87be1c4764650d0e39a45485 2025-01-24T02:06:28.0273967Z ##[endgroup] 2025-01-24T02:06:28.4444262Z NOTE: s3-prefix specified, ignoring name parameter 2025-01-24T02:06:28.4445090Z With the provided path, there will be 1 file uploaded 2025-01-24T02:06:28.4445570Z Uploading to s3 prefix: pytorch/pytorch/12940916563/1/artifact 2025-01-24T02:06:28.6211309Z Starting upload of test-jsons-test-dynamo_wrapped-1-3-lf.linux.2xlarge_36096586663.zip 2025-01-24T02:06:28.7606934Z Finished upload of test-jsons-test-dynamo_wrapped-1-3-lf.linux.2xlarge_36096586663.zip 2025-01-24T02:06:28.7809217Z ##[group]Run seemethere/upload-artifact-s3@v5 2025-01-24T02:06:28.7809580Z with: 2025-01-24T02:06:28.7809806Z s3-bucket: gha-artifacts 2025-01-24T02:06:28.7810172Z s3-prefix: pytorch/pytorch/12940916563/1/artifact 2025-01-24T02:06:28.7810550Z retention-days: 14 2025-01-24T02:06:28.7810821Z if-no-files-found: error 2025-01-24T02:06:28.7811117Z path: test-reports-*.zip 2025-01-24T02:06:28.7811389Z name: artifact 2025-01-24T02:06:28.7811634Z region: us-east-1 2025-01-24T02:06:28.7811889Z env: 2025-01-24T02:06:28.7812117Z GIT_DEFAULT_BRANCH: main 2025-01-24T02:06:28.7812599Z DOCKER_CONTAINER_ID: 8336c83c461cac917c450a6b582f03790365573c87be1c4764650d0e39a45485 2025-01-24T02:06:28.7813112Z ##[endgroup] 2025-01-24T02:06:29.1099008Z NOTE: s3-prefix specified, ignoring name parameter 2025-01-24T02:06:29.1099521Z With the provided path, there will be 1 file uploaded 2025-01-24T02:06:29.1100006Z Uploading to s3 prefix: pytorch/pytorch/12940916563/1/artifact 2025-01-24T02:06:29.1138635Z Starting upload of test-reports-test-dynamo_wrapped-1-3-lf.linux.2xlarge_36096586663.zip 2025-01-24T02:06:29.3250080Z Finished upload of test-reports-test-dynamo_wrapped-1-3-lf.linux.2xlarge_36096586663.zip 2025-01-24T02:06:29.3460033Z ##[group]Run seemethere/upload-artifact-s3@v5 2025-01-24T02:06:29.3460536Z with: 2025-01-24T02:06:29.3460875Z s3-bucket: gha-artifacts 2025-01-24T02:06:29.3461363Z s3-prefix: pytorch/pytorch/12940916563/1/artifact 2025-01-24T02:06:29.3461928Z retention-days: 14 2025-01-24T02:06:29.3462220Z if-no-files-found: ignore 2025-01-24T02:06:29.3462497Z path: logs-*.zip 2025-01-24T02:06:29.3462763Z name: artifact 2025-01-24T02:06:29.3463019Z region: us-east-1 2025-01-24T02:06:29.3463277Z env: 2025-01-24T02:06:29.3463538Z GIT_DEFAULT_BRANCH: main 2025-01-24T02:06:29.3464007Z DOCKER_CONTAINER_ID: 8336c83c461cac917c450a6b582f03790365573c87be1c4764650d0e39a45485 2025-01-24T02:06:29.3464529Z ##[endgroup] 2025-01-24T02:06:29.6723828Z NOTE: s3-prefix specified, ignoring name parameter 2025-01-24T02:06:29.6724502Z With the provided path, there will be 1 file uploaded 2025-01-24T02:06:29.6724983Z Uploading to s3 prefix: pytorch/pytorch/12940916563/1/artifact 2025-01-24T02:06:29.6763157Z Starting upload of logs-test-dynamo_wrapped-1-3-lf.linux.2xlarge_36096586663.zip 2025-01-24T02:06:29.9057193Z Finished upload of logs-test-dynamo_wrapped-1-3-lf.linux.2xlarge_36096586663.zip 2025-01-24T02:06:29.9251626Z ##[group]Run seemethere/upload-artifact-s3@v5 2025-01-24T02:06:29.9251990Z with: 2025-01-24T02:06:29.9252231Z s3-bucket: gha-artifacts 2025-01-24T02:06:29.9252560Z s3-prefix: pytorch/pytorch/12940916563/1/artifact 2025-01-24T02:06:29.9252933Z retention-days: 14 2025-01-24T02:06:29.9253207Z if-no-files-found: ignore 2025-01-24T02:06:29.9253609Z path: debug-*.zip 2025-01-24T02:06:29.9253867Z name: artifact 2025-01-24T02:06:29.9254100Z region: us-east-1 2025-01-24T02:06:29.9254347Z env: 2025-01-24T02:06:29.9254579Z GIT_DEFAULT_BRANCH: main 2025-01-24T02:06:29.9255062Z DOCKER_CONTAINER_ID: 8336c83c461cac917c450a6b582f03790365573c87be1c4764650d0e39a45485 2025-01-24T02:06:29.9255586Z ##[endgroup] 2025-01-24T02:06:30.2545976Z No files were found with the provided path: debug-*.zip. No artifacts will be uploaded. 2025-01-24T02:06:30.2754662Z ##[group]Run # shellcheck disable=SC2156 2025-01-24T02:06:30.2755074Z # shellcheck disable=SC2156 2025-01-24T02:06:30.2755688Z find . -iname "core.[1-9]*" -exec docker exec "${DOCKER_CONTAINER_ID}" sh -c "gdb python {} -ex 'bt' -ex 'q'" \; 2025-01-24T02:06:30.2761623Z shell: /usr/bin/bash -e {0} 2025-01-24T02:06:30.2761920Z env: 2025-01-24T02:06:30.2762157Z GIT_DEFAULT_BRANCH: main 2025-01-24T02:06:30.2762638Z DOCKER_CONTAINER_ID: 8336c83c461cac917c450a6b582f03790365573c87be1c4764650d0e39a45485 2025-01-24T02:06:30.2763153Z ##[endgroup] 2025-01-24T02:06:30.4971408Z ##[group]Run pytorch/test-infra/.github/actions/teardown-linux@main 2025-01-24T02:06:30.4971869Z with: 2025-01-24T02:06:30.4972104Z env: 2025-01-24T02:06:30.4972341Z GIT_DEFAULT_BRANCH: main 2025-01-24T02:06:30.4972834Z DOCKER_CONTAINER_ID: 8336c83c461cac917c450a6b582f03790365573c87be1c4764650d0e39a45485 2025-01-24T02:06:30.4973358Z ##[endgroup] 2025-01-24T02:06:30.5000565Z ##[group]Run set -eou pipefail 2025-01-24T02:06:30.5000925Z set -eou pipefail 2025-01-24T02:06:30.5001228Z  2025-01-24T02:06:30.5001624Z echo "Holding runner for 2 hours until all ssh sessions have logged out" 2025-01-24T02:06:30.5002130Z for _ in $(seq 1440); do 2025-01-24T02:06:30.5002500Z  # Break if no ssh session exists anymore 2025-01-24T02:06:30.5002884Z  if [ "$(who)" = "" ]; then 2025-01-24T02:06:30.5003216Z  break 2025-01-24T02:06:30.5003481Z  fi 2025-01-24T02:06:30.5003784Z  echo "." 2025-01-24T02:06:30.5004061Z  sleep 5 2025-01-24T02:06:30.5004322Z done 2025-01-24T02:06:30.5009905Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-01-24T02:06:30.5010327Z env: 2025-01-24T02:06:30.5010578Z GIT_DEFAULT_BRANCH: main 2025-01-24T02:06:30.5011074Z DOCKER_CONTAINER_ID: 8336c83c461cac917c450a6b582f03790365573c87be1c4764650d0e39a45485 2025-01-24T02:06:30.5011596Z ##[endgroup] 2025-01-24T02:06:30.5034110Z Holding runner for 2 hours until all ssh sessions have logged out 2025-01-24T02:06:30.5123466Z ##[group]Run # ignore expansion of "docker ps -q" since it could be empty 2025-01-24T02:06:30.5124063Z # ignore expansion of "docker ps -q" since it could be empty 2025-01-24T02:06:30.5124519Z # shellcheck disable=SC2046 2025-01-24T02:06:30.5124865Z docker stop $(docker ps -q) || true 2025-01-24T02:06:30.5125237Z # Prune all of the docker images 2025-01-24T02:06:30.5125600Z docker system prune -af 2025-01-24T02:06:30.5130743Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-01-24T02:06:30.5131150Z env: 2025-01-24T02:06:30.5131391Z GIT_DEFAULT_BRANCH: main 2025-01-24T02:06:30.5131860Z DOCKER_CONTAINER_ID: 8336c83c461cac917c450a6b582f03790365573c87be1c4764650d0e39a45485 2025-01-24T02:06:30.5132373Z ##[endgroup] 2025-01-24T02:06:31.1906501Z 8336c83c461c 2025-01-24T02:06:31.7538462Z Deleted Containers: 2025-01-24T02:06:31.7538945Z 8336c83c461cac917c450a6b582f03790365573c87be1c4764650d0e39a45485 2025-01-24T02:06:31.7539288Z 2025-01-24T02:06:39.4614866Z Deleted Images: 2025-01-24T02:06:39.4615981Z untagged: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.13-clang10:bf84073365afe24d4dcffebc142d1c20de914163 2025-01-24T02:06:39.4617307Z untagged: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-py3.13-clang10@sha256:b18d6f46bf0b8cb413663001587c0d8163f78580e6b3a5bfd85f7e7cd2b2c872 2025-01-24T02:06:39.4618599Z deleted: sha256:4cf0e421f3ff055d4e789c93110b36c2fff474473cb047622f9ef66777a49f3e 2025-01-24T02:06:39.4619269Z deleted: sha256:245d62d48e5e56196873fd45e33156917e7993fd0172f93a37d26b0bcaab48e6 2025-01-24T02:06:39.4619933Z deleted: sha256:cb213e97af2088e6299f5968df41f51ea094c9615dc582db140299b2d9b89831 2025-01-24T02:06:39.4620612Z deleted: sha256:152569d5872ccbf7c09508ee05a5a1edcfe379a8d2fbc7d94c6ccd48a8a20e50 2025-01-24T02:06:39.4621313Z deleted: sha256:9d9c203baf96dddd6ff1fde3afe952bbdf69c83ac75fd73b23368899103e9e05 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overriding HOME='/home/ec2-user/actions-runner/_work/_temp/b3ba76b1-bbef-426c-9f5f-bce54bbff3b8' before making global git config changes 2025-01-24T02:06:39.6672838Z Adding repository directory to the temporary git global config as a safe directory 2025-01-24T02:06:39.6676793Z [command]/usr/bin/git config --global --add safe.directory /home/ec2-user/actions-runner/_work/pytorch/pytorch 2025-01-24T02:06:39.6723944Z [command]/usr/bin/git config --local --name-only --get-regexp core\.sshCommand 2025-01-24T02:06:39.6751743Z [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' || :" 2025-01-24T02:06:39.7123063Z Entering 'android/libs/fbjni' 2025-01-24T02:06:39.7173042Z Entering 'third_party/FP16' 2025-01-24T02:06:39.7224446Z Entering 'third_party/FXdiv' 2025-01-24T02:06:39.7275634Z Entering 'third_party/NNPACK' 2025-01-24T02:06:39.7325742Z Entering 'third_party/NVTX' 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'third_party/fbgemm/third_party/hipify_torch' 2025-01-24T02:06:39.8166845Z Entering 'third_party/flatbuffers' 2025-01-24T02:06:39.8219099Z Entering 'third_party/fmt' 2025-01-24T02:06:39.8268775Z Entering 'third_party/gemmlowp/gemmlowp' 2025-01-24T02:06:39.8324272Z Entering 'third_party/gloo' 2025-01-24T02:06:39.8375097Z Entering 'third_party/googletest' 2025-01-24T02:06:39.8426131Z Entering 'third_party/ideep' 2025-01-24T02:06:39.8475339Z Entering 'third_party/ideep/mkl-dnn' 2025-01-24T02:06:39.8532830Z Entering 'third_party/ittapi' 2025-01-24T02:06:39.8583993Z Entering 'third_party/kineto' 2025-01-24T02:06:39.8633202Z Entering 'third_party/kineto/libkineto/third_party/dynolog' 2025-01-24T02:06:39.8682114Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM' 2025-01-24T02:06:39.8732084Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr' 2025-01-24T02:06:39.8780577Z Entering 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'third_party/mimalloc' 2025-01-24T02:06:39.9329101Z Entering 'third_party/nccl/nccl' 2025-01-24T02:06:39.9378838Z Entering 'third_party/nlohmann' 2025-01-24T02:06:39.9430122Z Entering 'third_party/onnx' 2025-01-24T02:06:39.9501790Z Entering 'third_party/onnx/third_party/pybind11' 2025-01-24T02:06:39.9555734Z Entering 'third_party/opentelemetry-cpp' 2025-01-24T02:06:39.9607174Z Entering 'third_party/opentelemetry-cpp/third_party/benchmark' 2025-01-24T02:06:39.9655434Z Entering 'third_party/opentelemetry-cpp/third_party/googletest' 2025-01-24T02:06:39.9704654Z Entering 'third_party/opentelemetry-cpp/third_party/ms-gsl' 2025-01-24T02:06:39.9753282Z Entering 'third_party/opentelemetry-cpp/third_party/nlohmann-json' 2025-01-24T02:06:39.9805123Z Entering 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto' 2025-01-24T02:06:39.9852885Z Entering 'third_party/opentelemetry-cpp/third_party/opentracing-cpp' 2025-01-24T02:06:39.9901536Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp' 2025-01-24T02:06:39.9949634Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb' 2025-01-24T02:06:40.0000531Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest' 2025-01-24T02:06:40.0050804Z Entering 'third_party/opentelemetry-cpp/tools/vcpkg' 2025-01-24T02:06:40.0119332Z Entering 'third_party/pocketfft' 2025-01-24T02:06:40.0169028Z Entering 'third_party/protobuf' 2025-01-24T02:06:40.0223192Z Entering 'third_party/protobuf/third_party/benchmark' 2025-01-24T02:06:40.0272308Z Entering 'third_party/protobuf/third_party/googletest' 2025-01-24T02:06:40.0325456Z Entering 'third_party/psimd' 2025-01-24T02:06:40.0375282Z Entering 'third_party/pthreadpool' 2025-01-24T02:06:40.0425643Z Entering 'third_party/pybind11' 2025-01-24T02:06:40.0475421Z Entering 'third_party/python-peachpy' 2025-01-24T02:06:40.0525235Z Entering 'third_party/sleef' 2025-01-24T02:06:40.0575193Z Entering 'third_party/tensorpipe' 2025-01-24T02:06:40.0625563Z Entering 'third_party/tensorpipe/third_party/googletest' 2025-01-24T02:06:40.0673440Z Entering 'third_party/tensorpipe/third_party/libnop' 2025-01-24T02:06:40.0721778Z Entering 'third_party/tensorpipe/third_party/libuv' 2025-01-24T02:06:40.0769354Z Entering 'third_party/tensorpipe/third_party/pybind11' 2025-01-24T02:06:40.0816571Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang' 2025-01-24T02:06:40.0885296Z [command]/usr/bin/git config --local --name-only --get-regexp http\.https\:\/\/github\.com\/\.extraheader 2025-01-24T02:06:40.0903537Z http.https://github.com/.extraheader 2025-01-24T02:06:40.0912439Z [command]/usr/bin/git config --local --unset-all http.https://github.com/.extraheader 2025-01-24T02:06:40.0938830Z [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' || :" 2025-01-24T02:06:40.1207120Z Entering 'android/libs/fbjni' 2025-01-24T02:06:40.1241293Z http.https://github.com/.extraheader 2025-01-24T02:06:40.1275997Z Entering 'third_party/FP16' 2025-01-24T02:06:40.1310085Z http.https://github.com/.extraheader 2025-01-24T02:06:40.1340484Z Entering 'third_party/FXdiv' 2025-01-24T02:06:40.1374494Z http.https://github.com/.extraheader 2025-01-24T02:06:40.1405148Z Entering 'third_party/NNPACK' 2025-01-24T02:06:40.1437832Z http.https://github.com/.extraheader 2025-01-24T02:06:40.1468838Z Entering 'third_party/NVTX' 2025-01-24T02:06:40.1502024Z http.https://github.com/.extraheader 2025-01-24T02:06:40.1533066Z Entering 'third_party/VulkanMemoryAllocator' 2025-01-24T02:06:40.1565867Z http.https://github.com/.extraheader 2025-01-24T02:06:40.1597152Z Entering 'third_party/XNNPACK' 2025-01-24T02:06:40.1630080Z http.https://github.com/.extraheader 2025-01-24T02:06:40.1676292Z Entering 'third_party/benchmark' 2025-01-24T02:06:40.1710235Z http.https://github.com/.extraheader 2025-01-24T02:06:40.1741554Z Entering 'third_party/composable_kernel' 2025-01-24T02:06:40.1774824Z http.https://github.com/.extraheader 2025-01-24T02:06:40.1812446Z Entering 'third_party/cpp-httplib' 2025-01-24T02:06:40.1845551Z http.https://github.com/.extraheader 2025-01-24T02:06:40.1875895Z Entering 'third_party/cpuinfo' 2025-01-24T02:06:40.1909202Z http.https://github.com/.extraheader 2025-01-24T02:06:40.1939701Z Entering 'third_party/cudnn_frontend' 2025-01-24T02:06:40.1972966Z http.https://github.com/.extraheader 2025-01-24T02:06:40.2004185Z Entering 'third_party/cutlass' 2025-01-24T02:06:40.2036896Z http.https://github.com/.extraheader 2025-01-24T02:06:40.2076215Z Entering 'third_party/eigen' 2025-01-24T02:06:40.2108911Z http.https://github.com/.extraheader 2025-01-24T02:06:40.2141094Z Entering 'third_party/fbgemm' 2025-01-24T02:06:40.2173596Z http.https://github.com/.extraheader 2025-01-24T02:06:40.2205250Z Entering 'third_party/fbgemm/third_party/asmjit' 2025-01-24T02:06:40.2237555Z http.https://github.com/.extraheader 2025-01-24T02:06:40.2267129Z Entering 'third_party/fbgemm/third_party/cpuinfo' 2025-01-24T02:06:40.2299413Z http.https://github.com/.extraheader 2025-01-24T02:06:40.2329199Z Entering 'third_party/fbgemm/third_party/cutlass' 2025-01-24T02:06:40.2361707Z http.https://github.com/.extraheader 2025-01-24T02:06:40.2397886Z Entering 'third_party/fbgemm/third_party/googletest' 2025-01-24T02:06:40.2429953Z http.https://github.com/.extraheader 2025-01-24T02:06:40.2459623Z Entering 'third_party/fbgemm/third_party/hipify_torch' 2025-01-24T02:06:40.2492296Z http.https://github.com/.extraheader 2025-01-24T02:06:40.2522955Z Entering 'third_party/flatbuffers' 2025-01-24T02:06:40.2556314Z http.https://github.com/.extraheader 2025-01-24T02:06:40.2590485Z Entering 'third_party/fmt' 2025-01-24T02:06:40.2623858Z http.https://github.com/.extraheader 2025-01-24T02:06:40.2654273Z Entering 'third_party/gemmlowp/gemmlowp' 2025-01-24T02:06:40.2689598Z http.https://github.com/.extraheader 2025-01-24T02:06:40.2719831Z Entering 'third_party/gloo' 2025-01-24T02:06:40.2752436Z http.https://github.com/.extraheader 2025-01-24T02:06:40.2786837Z Entering 'third_party/googletest' 2025-01-24T02:06:40.2819300Z http.https://github.com/.extraheader 2025-01-24T02:06:40.2849705Z Entering 'third_party/ideep' 2025-01-24T02:06:40.2882828Z http.https://github.com/.extraheader 2025-01-24T02:06:40.2912818Z Entering 'third_party/ideep/mkl-dnn' 2025-01-24T02:06:40.2945900Z http.https://github.com/.extraheader 2025-01-24T02:06:40.2984294Z Entering 'third_party/ittapi' 2025-01-24T02:06:40.3018613Z http.https://github.com/.extraheader 2025-01-24T02:06:40.3048739Z Entering 'third_party/kineto' 2025-01-24T02:06:40.3083611Z http.https://github.com/.extraheader 2025-01-24T02:06:40.3114637Z Entering 'third_party/kineto/libkineto/third_party/dynolog' 2025-01-24T02:06:40.3148380Z http.https://github.com/.extraheader 2025-01-24T02:06:40.3178752Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM' 2025-01-24T02:06:40.3211796Z http.https://github.com/.extraheader 2025-01-24T02:06:40.3243889Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr' 2025-01-24T02:06:40.3276885Z http.https://github.com/.extraheader 2025-01-24T02:06:40.3308074Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt' 2025-01-24T02:06:40.3342431Z http.https://github.com/.extraheader 2025-01-24T02:06:40.3373437Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags' 2025-01-24T02:06:40.3406054Z http.https://github.com/.extraheader 2025-01-24T02:06:40.3436645Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc' 2025-01-24T02:06:40.3470138Z http.https://github.com/.extraheader 2025-01-24T02:06:40.3503209Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog' 2025-01-24T02:06:40.3537164Z http.https://github.com/.extraheader 2025-01-24T02:06:40.3568272Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest' 2025-01-24T02:06:40.3601374Z http.https://github.com/.extraheader 2025-01-24T02:06:40.3632615Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/json' 2025-01-24T02:06:40.3665639Z http.https://github.com/.extraheader 2025-01-24T02:06:40.3697297Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs' 2025-01-24T02:06:40.3730295Z http.https://github.com/.extraheader 2025-01-24T02:06:40.3762545Z Entering 'third_party/kineto/libkineto/third_party/fmt' 2025-01-24T02:06:40.3795576Z http.https://github.com/.extraheader 2025-01-24T02:06:40.3825705Z Entering 'third_party/kineto/libkineto/third_party/googletest' 2025-01-24T02:06:40.3858093Z http.https://github.com/.extraheader 2025-01-24T02:06:40.3891839Z Entering 'third_party/kleidiai' 2025-01-24T02:06:40.3925718Z http.https://github.com/.extraheader 2025-01-24T02:06:40.3956770Z Entering 'third_party/mimalloc' 2025-01-24T02:06:40.3991349Z http.https://github.com/.extraheader 2025-01-24T02:06:40.4022412Z Entering 'third_party/nccl/nccl' 2025-01-24T02:06:40.4056395Z http.https://github.com/.extraheader 2025-01-24T02:06:40.4088686Z Entering 'third_party/nlohmann' 2025-01-24T02:06:40.4123083Z http.https://github.com/.extraheader 2025-01-24T02:06:40.4155806Z Entering 'third_party/onnx' 2025-01-24T02:06:40.4188886Z http.https://github.com/.extraheader 2025-01-24T02:06:40.4235991Z Entering 'third_party/onnx/third_party/pybind11' 2025-01-24T02:06:40.4268733Z http.https://github.com/.extraheader 2025-01-24T02:06:40.4302753Z Entering 'third_party/opentelemetry-cpp' 2025-01-24T02:06:40.4337554Z http.https://github.com/.extraheader 2025-01-24T02:06:40.4369685Z Entering 'third_party/opentelemetry-cpp/third_party/benchmark' 2025-01-24T02:06:40.4403852Z http.https://github.com/.extraheader 2025-01-24T02:06:40.4433981Z Entering 'third_party/opentelemetry-cpp/third_party/googletest' 2025-01-24T02:06:40.4467133Z http.https://github.com/.extraheader 2025-01-24T02:06:40.4497351Z Entering 'third_party/opentelemetry-cpp/third_party/ms-gsl' 2025-01-24T02:06:40.4530075Z http.https://github.com/.extraheader 2025-01-24T02:06:40.4559672Z Entering 'third_party/opentelemetry-cpp/third_party/nlohmann-json' 2025-01-24T02:06:40.4592456Z http.https://github.com/.extraheader 2025-01-24T02:06:40.4623827Z Entering 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto' 2025-01-24T02:06:40.4656449Z http.https://github.com/.extraheader 2025-01-24T02:06:40.4687509Z Entering 'third_party/opentelemetry-cpp/third_party/opentracing-cpp' 2025-01-24T02:06:40.4720439Z http.https://github.com/.extraheader 2025-01-24T02:06:40.4750375Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp' 2025-01-24T02:06:40.4783352Z http.https://github.com/.extraheader 2025-01-24T02:06:40.4813214Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb' 2025-01-24T02:06:40.4846156Z http.https://github.com/.extraheader 2025-01-24T02:06:40.4880171Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest' 2025-01-24T02:06:40.4912410Z http.https://github.com/.extraheader 2025-01-24T02:06:40.4944271Z Entering 'third_party/opentelemetry-cpp/tools/vcpkg' 2025-01-24T02:06:40.4977406Z http.https://github.com/.extraheader 2025-01-24T02:06:40.5027491Z Entering 'third_party/pocketfft' 2025-01-24T02:06:40.5061371Z http.https://github.com/.extraheader 2025-01-24T02:06:40.5092431Z Entering 'third_party/protobuf' 2025-01-24T02:06:40.5126057Z http.https://github.com/.extraheader 2025-01-24T02:06:40.5160414Z Entering 'third_party/protobuf/third_party/benchmark' 2025-01-24T02:06:40.5193131Z http.https://github.com/.extraheader 2025-01-24T02:06:40.5222924Z Entering 'third_party/protobuf/third_party/googletest' 2025-01-24T02:06:40.5255434Z http.https://github.com/.extraheader 2025-01-24T02:06:40.5287971Z Entering 'third_party/psimd' 2025-01-24T02:06:40.5321034Z http.https://github.com/.extraheader 2025-01-24T02:06:40.5351780Z Entering 'third_party/pthreadpool' 2025-01-24T02:06:40.5385236Z http.https://github.com/.extraheader 2025-01-24T02:06:40.5416023Z Entering 'third_party/pybind11' 2025-01-24T02:06:40.5449165Z http.https://github.com/.extraheader 2025-01-24T02:06:40.5481359Z Entering 'third_party/python-peachpy' 2025-01-24T02:06:40.5513168Z http.https://github.com/.extraheader 2025-01-24T02:06:40.5544159Z Entering 'third_party/sleef' 2025-01-24T02:06:40.5577538Z http.https://github.com/.extraheader 2025-01-24T02:06:40.5607851Z Entering 'third_party/tensorpipe' 2025-01-24T02:06:40.5642269Z http.https://github.com/.extraheader 2025-01-24T02:06:40.5672496Z Entering 'third_party/tensorpipe/third_party/googletest' 2025-01-24T02:06:40.5704522Z http.https://github.com/.extraheader 2025-01-24T02:06:40.5735390Z Entering 'third_party/tensorpipe/third_party/libnop' 2025-01-24T02:06:40.5767436Z http.https://github.com/.extraheader 2025-01-24T02:06:40.5797756Z Entering 'third_party/tensorpipe/third_party/libuv' 2025-01-24T02:06:40.5831638Z http.https://github.com/.extraheader 2025-01-24T02:06:40.5861897Z Entering 'third_party/tensorpipe/third_party/pybind11' 2025-01-24T02:06:40.5894756Z http.https://github.com/.extraheader 2025-01-24T02:06:40.5924524Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang' 2025-01-24T02:06:40.5957128Z http.https://github.com/.extraheader 2025-01-24T02:06:40.6072703Z A job completed hook has been configured by the self-hosted runner administrator 2025-01-24T02:06:40.6098830Z ##[group]Run '/home/ec2-user/runner-scripts/after_job.sh' 2025-01-24T02:06:40.6103725Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-01-24T02:06:40.6104203Z ##[endgroup] 2025-01-24T02:06:48.3551443Z Cleaning up orphan processes