2025-09-07T06:24:34.5865505Z Current runner version: '2.328.0' 2025-09-07T06:24:34.5872301Z Runner name: 'i-09e16a84d31719099' 2025-09-07T06:24:34.5873267Z Runner group name: 'default' 2025-09-07T06:24:34.5874367Z Machine name: 'ip-10-0-6-53' 2025-09-07T06:24:34.5877448Z ##[group]GITHUB_TOKEN Permissions 2025-09-07T06:24:34.5880002Z Contents: read 2025-09-07T06:24:34.5880577Z Metadata: read 2025-09-07T06:24:34.5881120Z ##[endgroup] 2025-09-07T06:24:34.5883419Z Secret source: Actions 2025-09-07T06:24:34.5884111Z Prepare workflow directory 2025-09-07T06:24:34.6405433Z Prepare all required actions 2025-09-07T06:24:34.6446634Z Getting action download info 2025-09-07T06:24:34.9436223Z Download action repository 'pytorch/test-infra@main' (SHA:548a4bc624d43a01cdf165a63b041f0ae014ddbd) 2025-09-07T06:24:36.8881237Z Download action repository 'pytorch/pytorch@main' (SHA:93fb23d6fae7c4e82c4239a1033e522088742634) 2025-09-07T06:24:50.6134797Z Download action repository 'actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065' (SHA:a26af69be951a213d495a4c3e4e4022e16d87065) 2025-09-07T06:24:50.9138790Z Download action repository 'aws-actions/configure-aws-credentials@ececac1a45f3b08a01d2dd070d28d111c5fe6722' (SHA:ececac1a45f3b08a01d2dd070d28d111c5fe6722) 2025-09-07T06:24:51.1546476Z Download action repository 'aws-actions/amazon-ecr-login@062b18b96a7aff071d4dc91bc00c4c1a7945b076' (SHA:062b18b96a7aff071d4dc91bc00c4c1a7945b076) 2025-09-07T06:24:51.3854827Z Download action repository 'seemethere/upload-artifact-s3@baba72d0712b404f646cebe0730933554ebce96a' (SHA:baba72d0712b404f646cebe0730933554ebce96a) 2025-09-07T06:24:51.6740098Z Getting action download info 2025-09-07T06:24:51.7946271Z Download action repository 'actions/checkout@v4' (SHA:08eba0b27e820071cde6df949e0beb9ba4906955) 2025-09-07T06:24:52.0748477Z Getting action download info 2025-09-07T06:24:52.2086561Z Download action repository 'nick-fields/retry@v3.0.0' (SHA:7152eba30c6575329ac0576536151aca5a72780e) 2025-09-07T06:24:52.3887802Z Getting action download info 2025-09-07T06:24:52.4982847Z Download action repository 'nick-fields/retry@3e91a01664abd3c5cd539100d10d33b9c5b68482' (SHA:3e91a01664abd3c5cd539100d10d33b9c5b68482) 2025-09-07T06:24:52.7064723Z Getting action download info 2025-09-07T06:24:52.8491316Z Uses: pytorch/pytorch/.github/workflows/_linux-test.yml@refs/heads/main (93fb23d6fae7c4e82c4239a1033e522088742634) 2025-09-07T06:24:52.8495891Z ##[group] Inputs 2025-09-07T06:24:52.8496261Z build-environment: linux-jammy-py3.13-clang12 2025-09-07T06:24:52.8499286Z test-matrix: {"include": [{"config": "default", "shard": 1, "num_shards": 5, "runner": "linux.4xlarge"}, {"config": "default", "shard": 2, "num_shards": 5, "runner": "linux.4xlarge"}, {"config": "default", "shard": 3, "num_shards": 5, "runner": "linux.4xlarge"}, {"config": "default", "shard": 4, "num_shards": 5, "runner": "linux.4xlarge"}, {"config": "default", "shard": 5, "num_shards": 5, "runner": "linux.4xlarge"}, {"config": "crossref", "shard": 1, "num_shards": 2, "runner": "linux.2xlarge"}, {"config": "crossref", "shard": 2, "num_shards": 2, "runner": "linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 1, "num_shards": 3, "runner": "linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 2, "num_shards": 3, "runner": "linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 3, "num_shards": 3, "runner": "linux.2xlarge"}, {"config": "einops", "shard": 1, "num_shards": 1, "runner": "linux.2xlarge"}]} 2025-09-07T06:24:52.8502706Z docker-image: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-jammy-py3.13-clang12-ae53c6842aa4c2407d0ad976491ca941c2635c77 2025-09-07T06:24:52.8503535Z sync-tag: 2025-09-07T06:24:52.8504326Z timeout-minutes: 240 2025-09-07T06:24:52.8504592Z use-gha: 2025-09-07T06:24:52.8504825Z dashboard-tag: 2025-09-07T06:24:52.8505083Z s3-bucket: gha-artifacts 2025-09-07T06:24:52.8505351Z aws-role-to-assume: 2025-09-07T06:24:52.8505917Z disable-monitor: false 2025-09-07T06:24:52.8506233Z monitor-log-interval: 5 2025-09-07T06:24:52.8506795Z monitor-data-collect-interval: 1 2025-09-07T06:24:52.8507223Z ##[endgroup] 2025-09-07T06:24:52.8507640Z Complete job name: linux-jammy-py3.13-clang12 / test (default, 1, 5, linux.4xlarge) 2025-09-07T06:24:52.9022729Z A job started hook has been configured by the self-hosted runner administrator 2025-09-07T06:24:52.9130235Z ##[group]Run '/home/ec2-user/runner-scripts/before_job.sh' 2025-09-07T06:24:52.9139849Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-07T06:24:52.9140519Z ##[endgroup] 2025-09-07T06:24:54.1360170Z Runner Type: linux.4xlarge 2025-09-07T06:24:54.1360667Z Instance Type: c5.4xlarge 2025-09-07T06:24:54.1360931Z AMI Name: unknown 2025-09-07T06:24:54.1390764Z AMI ID: ami-05ffe3c48a9991133 2025-09-07T06:24:59.7278651Z ##[group]Run pytorch/test-infra/.github/actions/setup-ssh@main 2025-09-07T06:24:59.7279122Z with: 2025-09-07T06:24:59.7279711Z github-secret: *** 2025-09-07T06:24:59.7280447Z instructions: All testing is done inside the container, to start an interactive session run: docker exec -it $(docker container ps --format '{{.ID}}') bash 2025-09-07T06:24:59.7281281Z activate-with-label: false 2025-09-07T06:24:59.7281575Z label: with-ssh 2025-09-07T06:24:59.7281832Z remove-existing-keys: true 2025-09-07T06:24:59.7282122Z fail-silently: true 2025-09-07T06:24:59.7282376Z env: 2025-09-07T06:24:59.7282597Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:24:59.7282869Z ##[endgroup] 2025-09-07T06:24:59.8747204Z Please see https://github.com/pytorch/pytorch/wiki/Debugging-using-with-ssh-for-Github-Actions for more info. 2025-09-07T06:24:59.8748693Z Not on pull request and ciflow reference could not be extracted, skipping adding ssh keys 2025-09-07T06:24:59.9098342Z ##[group]Run pytorch/pytorch/.github/actions/checkout-pytorch@main 2025-09-07T06:24:59.9098813Z with: 2025-09-07T06:24:59.9099042Z no-sudo: true 2025-09-07T06:24:59.9099301Z submodules: recursive 2025-09-07T06:24:59.9099565Z fetch-depth: 0 2025-09-07T06:24:59.9099801Z env: 2025-09-07T06:24:59.9100018Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:24:59.9100303Z ##[endgroup] 2025-09-07T06:24:59.9185467Z ##[group]Run echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT" 2025-09-07T06:24:59.9186514Z echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT" 2025-09-07T06:24:59.9195002Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-07T06:24:59.9195415Z env: 2025-09-07T06:24:59.9195687Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:24:59.9195994Z ##[endgroup] 2025-09-07T06:24:59.9288373Z ##[group]Run # Use all available CPUs for fetching 2025-09-07T06:24:59.9288840Z # Use all available CPUs for fetching 2025-09-07T06:24:59.9289202Z cd "${GITHUB_WORKSPACE}" 2025-09-07T06:24:59.9289544Z git config --global fetch.parallel 0 2025-09-07T06:24:59.9289943Z git config --global submodule.fetchJobs 0 2025-09-07T06:24:59.9290298Z  2025-09-07T06:24:59.9290684Z # Clean workspace. The default checkout action should also do this, but 2025-09-07T06:24:59.9291161Z # do it here as well just in case 2025-09-07T06:24:59.9291490Z if [[ -d .git ]]; then 2025-09-07T06:24:59.9291852Z  if [ -z "${NO_SUDO}" ]; then 2025-09-07T06:24:59.9292170Z  sudo git clean -ffdx 2025-09-07T06:24:59.9292450Z  else 2025-09-07T06:24:59.9292692Z  git clean -ffdx 2025-09-07T06:24:59.9292964Z  fi 2025-09-07T06:24:59.9293186Z fi 2025-09-07T06:24:59.9298698Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-07T06:24:59.9299104Z env: 2025-09-07T06:24:59.9299402Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:24:59.9299708Z NO_SUDO: true 2025-09-07T06:24:59.9299941Z ##[endgroup] 2025-09-07T06:24:59.9455433Z ##[group]Run actions/checkout@v4 2025-09-07T06:24:59.9455757Z with: 2025-09-07T06:24:59.9456001Z ref: 93fb23d6fae7c4e82c4239a1033e522088742634 2025-09-07T06:24:59.9456548Z fetch-depth: 0 2025-09-07T06:24:59.9456803Z submodules: recursive 2025-09-07T06:24:59.9457079Z show-progress: false 2025-09-07T06:24:59.9457343Z repository: pytorch/pytorch 2025-09-07T06:24:59.9457798Z token: *** 2025-09-07T06:24:59.9458038Z ssh-strict: true 2025-09-07T06:24:59.9458279Z ssh-user: git 2025-09-07T06:24:59.9458517Z persist-credentials: true 2025-09-07T06:24:59.9458798Z clean: true 2025-09-07T06:24:59.9459069Z sparse-checkout-cone-mode: true 2025-09-07T06:24:59.9459380Z fetch-tags: false 2025-09-07T06:24:59.9459607Z lfs: false 2025-09-07T06:24:59.9459841Z set-safe-directory: true 2025-09-07T06:24:59.9460124Z env: 2025-09-07T06:24:59.9460347Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:24:59.9460601Z ##[endgroup] 2025-09-07T06:25:00.0574683Z Syncing repository: pytorch/pytorch 2025-09-07T06:25:00.0576548Z ##[group]Getting Git version info 2025-09-07T06:25:00.0577098Z Working directory is '/home/ec2-user/actions-runner/_work/pytorch/pytorch' 2025-09-07T06:25:00.0577868Z [command]/usr/bin/git version 2025-09-07T06:25:00.0749327Z git version 2.47.1 2025-09-07T06:25:00.0774271Z ##[endgroup] 2025-09-07T06:25:00.0784072Z Copying '/home/ec2-user/.gitconfig' to '/home/ec2-user/actions-runner/_work/_temp/8685d2ec-af29-47c8-869f-167836534466/.gitconfig' 2025-09-07T06:25:00.0802953Z Temporarily overriding HOME='/home/ec2-user/actions-runner/_work/_temp/8685d2ec-af29-47c8-869f-167836534466' before making global git config changes 2025-09-07T06:25:00.0804063Z Adding repository directory to the temporary git global config as a safe directory 2025-09-07T06:25:00.0807634Z [command]/usr/bin/git config --global --add safe.directory /home/ec2-user/actions-runner/_work/pytorch/pytorch 2025-09-07T06:25:00.0856204Z Deleting the contents of '/home/ec2-user/actions-runner/_work/pytorch/pytorch' 2025-09-07T06:25:00.0859107Z ##[group]Initializing the repository 2025-09-07T06:25:00.0863515Z [command]/usr/bin/git init /home/ec2-user/actions-runner/_work/pytorch/pytorch 2025-09-07T06:25:00.0920628Z hint: Using 'master' as the name for the initial branch. This default branch name 2025-09-07T06:25:00.0921275Z hint: is subject to change. To configure the initial branch name to use in all 2025-09-07T06:25:00.0921871Z hint: of your new repositories, which will suppress this warning, call: 2025-09-07T06:25:00.0922308Z hint: 2025-09-07T06:25:00.0922616Z hint: git config --global init.defaultBranch 2025-09-07T06:25:00.0923002Z hint: 2025-09-07T06:25:00.0923363Z hint: Names commonly chosen instead of 'master' are 'main', 'trunk' and 2025-09-07T06:25:00.0924216Z hint: 'development'. The just-created branch can be renamed via this command: 2025-09-07T06:25:00.0924685Z hint: 2025-09-07T06:25:00.0924910Z hint: git branch -m 2025-09-07T06:25:00.0940469Z Initialized empty Git repository in /home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/ 2025-09-07T06:25:00.0948719Z [command]/usr/bin/git remote add origin https://github.com/pytorch/pytorch 2025-09-07T06:25:00.0983150Z ##[endgroup] 2025-09-07T06:25:00.0983631Z ##[group]Disabling automatic garbage collection 2025-09-07T06:25:00.0986828Z [command]/usr/bin/git config --local gc.auto 0 2025-09-07T06:25:00.1010950Z ##[endgroup] 2025-09-07T06:25:00.1011352Z ##[group]Setting up auth 2025-09-07T06:25:00.1017178Z [command]/usr/bin/git config --local --name-only --get-regexp core\.sshCommand 2025-09-07T06:25:00.1042451Z [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' || :" 2025-09-07T06:25:00.1375984Z [command]/usr/bin/git config --local --name-only --get-regexp http\.https\:\/\/github\.com\/\.extraheader 2025-09-07T06:25:00.1401549Z [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' || :" 2025-09-07T06:25:00.1688603Z [command]/usr/bin/git config --local http.https://github.com/.extraheader AUTHORIZATION: basic *** 2025-09-07T06:25:00.1732235Z ##[endgroup] 2025-09-07T06:25:00.1733013Z ##[group]Fetching the repository 2025-09-07T06:25:00.1740970Z [command]/usr/bin/git -c protocol.version=2 fetch --prune --no-recurse-submodules origin +refs/heads/*:refs/remotes/origin/* +refs/tags/*:refs/tags/* 2025-09-07T06:25:45.3615791Z From https://github.com/pytorch/pytorch 2025-09-07T06:25:45.3616286Z * [new branch] 160583 -> origin/160583 2025-09-07T06:25:45.3616909Z * [new branch] 2.6.0.dev20241004+ -> origin/2.6.0.dev20241004+ 2025-09-07T06:25:45.3617461Z * [new branch] 5addvllmbuild -> origin/5addvllmbuild 2025-09-07T06:25:45.3618101Z * [new branch] AaronWang04_addmmfusion_perftest -> origin/AaronWang04_addmmfusion_perftest 2025-09-07T06:25:45.3618820Z * [new branch] HDCharles-2.6.0-release-notes -> origin/HDCharles-2.6.0-release-notes 2025-09-07T06:25:45.3619596Z * [new branch] ISSUE-154849 -> origin/ISSUE-154849 2025-09-07T06:25:45.3621426Z * [new branch] JackCaoG/dynamo_make_fx_non_core_aten_ops -> origin/JackCaoG/dynamo_make_fx_non_core_aten_ops 2025-09-07T06:25:45.3623022Z * [new branch] NicoshevSVE128 -> origin/NicoshevSVE128 2025-09-07T06:25:45.3624315Z * [new branch] PR-AOTInductorNoneBug -> origin/PR-AOTInductorNoneBug 2025-09-07T06:25:45.3625721Z * [new branch] PR-AOTInductorNoneBugFix -> origin/PR-AOTInductorNoneBugFix 2025-09-07T06:25:45.3627085Z * [new branch] PR-FixConfigsIssue -> origin/PR-FixConfigsIssue 2025-09-07T06:25:45.3627806Z * [new branch] PR-NoneBugFix-viable -> origin/PR-NoneBugFix-viable 2025-09-07T06:25:45.3628831Z * [new branch] PR-ResetToZero -> origin/PR-ResetToZero 2025-09-07T06:25:45.3629998Z * [new branch] Update-Flash-Packaging -> origin/Update-Flash-Packaging 2025-09-07T06:25:45.3631069Z * [new branch] VLA_exp -> origin/VLA_exp 2025-09-07T06:25:45.3632531Z * [new branch] actually-run-mps-aot-inductor -> origin/actually-run-mps-aot-inductor 2025-09-07T06:25:45.3634080Z * [new branch] add-missing-args-normalization -> origin/add-missing-args-normalization 2025-09-07T06:25:45.3635245Z * [new branch] add-user-guide-structure -> origin/add-user-guide-structure 2025-09-07T06:25:45.3636460Z * [new branch] add-vllm-nightly-build -> origin/add-vllm-nightly-build 2025-09-07T06:25:45.3637575Z * [new branch] add_compile_benchmarking -> origin/add_compile_benchmarking 2025-09-07T06:25:45.3638669Z * [new branch] addmm-heuristic -> origin/addmm-heuristic 2025-09-07T06:25:45.3639904Z * [new branch] addsimde -> origin/addsimde 2025-09-07T06:25:45.3640952Z * [new branch] addvllmtest -> origin/addvllmtest 2025-09-07T06:25:45.3642610Z * [new branch] adi/acl_upgrade -> origin/adi/acl_upgrade 2025-09-07T06:25:45.3643633Z * [new branch] adi/test -> origin/adi/test 2025-09-07T06:25:45.3644745Z * [new branch] adi/test_bgemm -> origin/adi/test_bgemm 2025-09-07T06:25:45.3645873Z * [new branch] adi/test_fusions -> origin/adi/test_fusions 2025-09-07T06:25:45.3647016Z * [new branch] adi/test_onednn_v3.9 -> origin/adi/test_onednn_v3.9 2025-09-07T06:25:45.3648183Z * [new branch] adi/test_presve_change -> origin/adi/test_presve_change 2025-09-07T06:25:45.3649073Z * [new branch] adi/test_timm -> origin/adi/test_timm 2025-09-07T06:25:45.3650652Z * [new branch] adi/testpresve_change -> origin/adi/testpresve_change 2025-09-07T06:25:45.3652494Z * [new branch] aditew01/test/vec_bf16 -> origin/aditew01/test/vec_bf16 2025-09-07T06:25:45.3653627Z * [new branch] ah-globalfeedback-hook -> origin/ah-globalfeedback-hook 2025-09-07T06:25:45.3654744Z * [new branch] alt-disable -> origin/alt-disable 2025-09-07T06:25:45.3656542Z * [new branch] angelayi/aoti_additional_files -> origin/angelayi/aoti_additional_files 2025-09-07T06:25:45.3657525Z * [new branch] angelayi/aoti_inductor_fx -> origin/angelayi/aoti_inductor_fx 2025-09-07T06:25:45.3658580Z * [new branch] angelayi/benchmark -> origin/angelayi/benchmark 2025-09-07T06:25:45.3659788Z * [new branch] angelayi/benchmark2 -> origin/angelayi/benchmark2 2025-09-07T06:25:45.3660974Z * [new branch] angelayi/change_pytree_serialization -> origin/angelayi/change_pytree_serialization 2025-09-07T06:25:45.3661988Z * [new branch] angelayi/cpp_loader -> origin/angelayi/cpp_loader 2025-09-07T06:25:45.3663483Z * [new branch] angelayi/custom_op_subgraph -> origin/angelayi/custom_op_subgraph 2025-09-07T06:25:45.3664812Z * [new branch] angelayi/customop -> origin/angelayi/customop 2025-09-07T06:25:45.3666230Z * [new branch] angelayi/fake_cache_empty -> origin/angelayi/fake_cache_empty 2025-09-07T06:25:45.3667328Z * [new branch] angelayi/is_symbolic_tracing -> origin/angelayi/is_symbolic_tracing 2025-09-07T06:25:45.3668367Z * [new branch] angelayi/item -> origin/angelayi/item 2025-09-07T06:25:45.3669639Z * [new branch] angelayi/no_so_weight -> origin/angelayi/no_so_weight 2025-09-07T06:25:45.3670667Z * [new branch] angelayi/opoverload -> origin/angelayi/opoverload 2025-09-07T06:25:45.3671736Z * [new branch] angelayi/pattern -> origin/angelayi/pattern 2025-09-07T06:25:45.3672890Z * [new branch] angelayi/pytree -> origin/angelayi/pytree 2025-09-07T06:25:45.3674033Z * [new branch] angelayi/scan_layers -> origin/angelayi/scan_layers 2025-09-07T06:25:45.3675290Z * [new branch] angelayi/symint_input -> origin/angelayi/symint_input 2025-09-07T06:25:45.3676382Z * [new branch] angelayi/test_cpp -> origin/angelayi/test_cpp 2025-09-07T06:25:45.3677439Z * [new branch] angelayi/torch_size -> origin/angelayi/torch_size 2025-09-07T06:25:45.3678596Z * [new branch] aoti-cuda-alloc -> origin/aoti-cuda-alloc 2025-09-07T06:25:45.3679746Z * [new branch] aoti_target_windows -> origin/aoti_target_windows 2025-09-07T06:25:45.3680818Z * [new branch] aoti_weight_sharing -> origin/aoti_weight_sharing 2025-09-07T06:25:45.3682178Z * [new branch] atalman-inductor-perf-cu124 -> origin/atalman-inductor-perf-cu124 2025-09-07T06:25:45.3683196Z * [new branch] atalman-inductor-perf-cu124.1 -> origin/atalman-inductor-perf-cu124.1 2025-09-07T06:25:45.3684261Z * [new branch] atalman-patch-1 -> origin/atalman-patch-1 2025-09-07T06:25:45.3685563Z * [new branch] atalman-patch-3 -> origin/atalman-patch-3 2025-09-07T06:25:45.3686557Z * [new branch] atalman-patch-4 -> origin/atalman-patch-4 2025-09-07T06:25:45.3687882Z * [new branch] atalman-patch-5 -> origin/atalman-patch-5 2025-09-07T06:25:45.3688979Z * [new branch] atalman-patch-6 -> origin/atalman-patch-6 2025-09-07T06:25:45.3690295Z * [new branch] atalman_inductor_2.3.0 -> origin/atalman_inductor_2.3.0 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origin/xmfan/ca_1051b93192 2025-09-07T06:25:45.9501203Z * [new branch] xmfan/ca_1a722f62c248391fc4a542e8851a5559aa356ae8 -> origin/xmfan/ca_1a722f62c248391fc4a542e8851a5559aa356ae8 2025-09-07T06:25:45.9501898Z * [new branch] xmfan/ca_5a2be192d1 -> origin/xmfan/ca_5a2be192d1 2025-09-07T06:25:45.9502966Z * [new branch] xmfan/ca_9d59b516e9 -> origin/xmfan/ca_9d59b516e9 2025-09-07T06:25:45.9504056Z * [new branch] xmfan/ca_api -> origin/xmfan/ca_api 2025-09-07T06:25:45.9505104Z * [new branch] xmfan/ca_apr8 -> origin/xmfan/ca_apr8 2025-09-07T06:25:45.9506479Z * [new branch] xmfan/ca_base -> origin/xmfan/ca_base 2025-09-07T06:25:45.9507894Z * [new branch] xmfan/ca_cudagraphs -> origin/xmfan/ca_cudagraphs 2025-09-07T06:25:45.9508922Z * [new branch] xmfan/ca_dynamic -> origin/xmfan/ca_dynamic 2025-09-07T06:25:45.9509985Z * [new branch] xmfan/ca_fix_dyn -> origin/xmfan/ca_fix_dyn 2025-09-07T06:25:45.9511048Z * [new branch] xmfan/ca_fix_lowering -> origin/xmfan/ca_fix_lowering 2025-09-07T06:25:45.9512032Z * [new branch] xmfan/ca_fix_polyfills -> origin/xmfan/ca_fix_polyfills 2025-09-07T06:25:45.9513316Z * [new branch] xmfan/ca_jan3 -> origin/xmfan/ca_jan3 2025-09-07T06:25:45.9514409Z * [new branch] xmfan/ca_jun18 -> origin/xmfan/ca_jun18 2025-09-07T06:25:45.9515598Z * [new branch] xmfan/ca_jun24 -> origin/xmfan/ca_jun24 2025-09-07T06:25:45.9516681Z * [new branch] xmfan/ca_mem_base -> origin/xmfan/ca_mem_base 2025-09-07T06:25:45.9517686Z * [new branch] xmfan/ca_mem_fix -> origin/xmfan/ca_mem_fix 2025-09-07T06:25:45.9518939Z * [new branch] xmfan/ca_memory_fix -> origin/xmfan/ca_memory_fix 2025-09-07T06:25:45.9520055Z * [new branch] xmfan/ca_memory_fix_rebased -> origin/xmfan/ca_memory_fix_rebased 2025-09-07T06:25:45.9521117Z * [new branch] xmfan/ca_memory_fix_rebased2 -> origin/xmfan/ca_memory_fix_rebased2 2025-09-07T06:25:45.9522054Z * [new branch] xmfan/ca_move_to_cuda -> origin/xmfan/ca_move_to_cuda 2025-09-07T06:25:45.9523204Z * [new branch] 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2025-09-07T06:25:45.9538085Z * [new branch] xmfan/disable_duck_shape -> origin/xmfan/disable_duck_shape 2025-09-07T06:25:45.9539295Z * [new branch] xmfan/fca_cpp_node_passthrough -> origin/xmfan/fca_cpp_node_passthrough 2025-09-07T06:25:45.9540235Z * [new branch] xmfan/issue_123374 -> origin/xmfan/issue_123374 2025-09-07T06:25:45.9541571Z * [new branch] xmfan/post_3945954741e2d37023c5d6954f9483008e0892f9 -> origin/xmfan/post_3945954741e2d37023c5d6954f9483008e0892f9 2025-09-07T06:25:45.9542519Z * [new branch] xmfan/pre_3945954741e2d37023c5d6954f9483008e0892f9 -> origin/xmfan/pre_3945954741e2d37023c5d6954f9483008e0892f9 2025-09-07T06:25:45.9543556Z * [new branch] xmfan/segfault_test -> origin/xmfan/segfault_test 2025-09-07T06:25:45.9544610Z * [new branch] xmfan/single_step -> origin/xmfan/single_step 2025-09-07T06:25:45.9545520Z * [new branch] xmfan/sth_0829 -> origin/xmfan/sth_0829 2025-09-07T06:25:45.9546695Z * [new branch] xmfan/test -> origin/xmfan/test 2025-09-07T06:25:45.9548265Z * [new branch] yguo/debug-0226-constexpr -> origin/yguo/debug-0226-constexpr 2025-09-07T06:25:45.9549233Z * [new branch] yguo/new_latest_changes -> origin/yguo/new_latest_changes 2025-09-07T06:25:45.9550269Z * [new branch] yguo/patch_constexpr_changes -> origin/yguo/patch_constexpr_changes 2025-09-07T06:25:45.9551368Z * [new branch] yihan_quantization -> origin/yihan_quantization 2025-09-07T06:25:45.9552942Z * [new branch] yiming/add_jit_trace_benchmark -> origin/yiming/add_jit_trace_benchmark 2025-09-07T06:25:45.9554007Z * [new branch] yiming/add_nativert_benchmark -> origin/yiming/add_nativert_benchmark 2025-09-07T06:25:45.9554850Z * [new branch] yiming/bootcamp -> origin/yiming/bootcamp 2025-09-07T06:25:45.9556808Z * [new branch] zainr/canary-test -> origin/zainr/canary-test 2025-09-07T06:25:45.9557999Z * [new branch] zainr/cleanup-gh-runners -> origin/zainr/cleanup-gh-runners 2025-09-07T06:25:45.9559092Z * [new branch] zainr/git-push-v2 -> origin/zainr/git-push-v2 2025-09-07T06:25:45.9559930Z * [new branch] zainr/pull-migration-c -> origin/zainr/pull-migration-c 2025-09-07T06:25:45.9561001Z * [new branch] zainr/test -> origin/zainr/test 2025-09-07T06:25:45.9562081Z * [new branch] zainr/test2 -> origin/zainr/test2 2025-09-07T06:25:45.9563073Z * [new branch] zainr/unstable -> origin/zainr/unstable 2025-09-07T06:25:45.9564045Z * [new branch] zainr/unstable-xla -> origin/zainr/unstable-xla 2025-09-07T06:25:45.9565714Z * [new branch] zasdfgbnm-patch-3 -> origin/zasdfgbnm-patch-3 2025-09-07T06:25:45.9566858Z * [new branch] zb2p -> origin/zb2p 2025-09-07T06:25:45.9568048Z * [new branch] zero_grad_optimization -> origin/zero_grad_optimization 2025-09-07T06:25:45.9569186Z * [new branch] zeros-and-scatter-part2 -> origin/zeros-and-scatter-part2 2025-09-07T06:25:45.9570887Z * [new branch] zhxchen17/scratch/0 -> origin/zhxchen17/scratch/0 2025-09-07T06:25:45.9572865Z * [new branch] zhxhcen17/moodycamel -> origin/zhxhcen17/moodycamel 2025-09-07T06:25:45.9574474Z * [new branch] zxiiro/main -> origin/zxiiro/main 2025-09-07T06:25:45.9575781Z * [new tag] bc2caa7fdf006894eff7af936babde69ab5a40f8-huydhn-debug -> bc2caa7fdf006894eff7af936babde69ab5a40f8-huydhn-debug 2025-09-07T06:25:45.9576488Z * [new tag] ci/binaries/77164 -> ci/binaries/77164 2025-09-07T06:25:45.9577550Z * [new tag] ciflow/binaries/156049 -> ciflow/binaries/156049 2025-09-07T06:25:45.9578383Z * [new tag] ciflow/binaries/156712 -> ciflow/binaries/156712 2025-09-07T06:25:45.9578845Z * [new tag] ciflow/binaries/157432 -> ciflow/binaries/157432 2025-09-07T06:25:45.9579610Z * [new tag] ciflow/binaries/157685 -> ciflow/binaries/157685 2025-09-07T06:25:45.9580181Z * [new tag] ciflow/binaries/157689 -> ciflow/binaries/157689 2025-09-07T06:25:45.9580907Z * [new tag] ciflow/binaries/158104 -> ciflow/binaries/158104 2025-09-07T06:25:45.9581706Z * [new tag] ciflow/binaries/160229 -> ciflow/binaries/160229 2025-09-07T06:25:45.9582464Z * [new tag] ciflow/binaries/160720 -> ciflow/binaries/160720 2025-09-07T06:25:45.9583124Z * [new tag] ciflow/binaries/162080 -> ciflow/binaries/162080 2025-09-07T06:25:45.9583813Z * [new tag] ciflow/binaries/162329 -> ciflow/binaries/162329 2025-09-07T06:25:45.9584727Z * [new tag] ciflow/binaries_libtorch/156049 -> ciflow/binaries_libtorch/156049 2025-09-07T06:25:45.9585418Z * [new tag] ciflow/binaries_libtorch/156711 -> ciflow/binaries_libtorch/156711 2025-09-07T06:25:45.9586091Z * [new tag] ciflow/binaries_libtorch/157432 -> ciflow/binaries_libtorch/157432 2025-09-07T06:25:45.9586855Z * [new tag] ciflow/binaries_wheel/156049 -> ciflow/binaries_wheel/156049 2025-09-07T06:25:45.9587544Z * [new tag] ciflow/binaries_wheel/156711 -> ciflow/binaries_wheel/156711 2025-09-07T06:25:45.9588213Z * [new tag] ciflow/binaries_wheel/157432 -> ciflow/binaries_wheel/157432 2025-09-07T06:25:45.9588896Z * [new tag] ciflow/binaries_wheel/162136 -> ciflow/binaries_wheel/162136 2025-09-07T06:25:45.9589601Z * [new tag] ciflow/binaries_wheel/162252 -> ciflow/binaries_wheel/162252 2025-09-07T06:25:45.9590302Z * [new tag] ciflow/binaries_wheel/162325 -> ciflow/binaries_wheel/162325 2025-09-07T06:25:45.9591384Z * [new tag] ciflow/h100-distributed/156703 -> ciflow/h100-distributed/156703 2025-09-07T06:25:45.9592177Z * [new tag] ciflow/h100-symm-mem/157635 -> ciflow/h100-symm-mem/157635 2025-09-07T06:25:45.9592851Z * [new tag] ciflow/h100-symm-mem/161984 -> ciflow/h100-symm-mem/161984 2025-09-07T06:25:45.9593525Z * [new tag] ciflow/h100-symm-mem/162003 -> ciflow/h100-symm-mem/162003 2025-09-07T06:25:45.9594346Z * [new tag] ciflow/h100-symm-mem/162011 -> ciflow/h100-symm-mem/162011 2025-09-07T06:25:45.9594965Z * [new tag] ciflow/h100-symm-mem/162026 -> ciflow/h100-symm-mem/162026 2025-09-07T06:25:45.9595652Z * [new tag] ciflow/h100-symm-mem/162033 -> ciflow/h100-symm-mem/162033 2025-09-07T06:25:45.9596331Z * [new tag] ciflow/h100-symm-mem/162040 -> ciflow/h100-symm-mem/162040 2025-09-07T06:25:45.9597052Z * [new tag] ciflow/h100-symm-mem/162041 -> ciflow/h100-symm-mem/162041 2025-09-07T06:25:45.9597737Z * [new tag] ciflow/h100-symm-mem/162142 -> ciflow/h100-symm-mem/162142 2025-09-07T06:25:45.9598352Z * [new tag] ciflow/h100-symm-mem/162150 -> ciflow/h100-symm-mem/162150 2025-09-07T06:25:45.9599024Z * [new tag] ciflow/h100-symm-mem/162243 -> ciflow/h100-symm-mem/162243 2025-09-07T06:25:45.9600064Z * [new tag] ciflow/h100-symm-mem/162320 -> ciflow/h100-symm-mem/162320 2025-09-07T06:25:45.9600793Z * [new tag] ciflow/h100/159158 -> ciflow/h100/159158 2025-09-07T06:25:45.9602048Z * [new tag] ciflow/h100/160480 -> ciflow/h100/160480 2025-09-07T06:25:45.9602782Z * [new tag] ciflow/h100/161749 -> ciflow/h100/161749 2025-09-07T06:25:45.9603533Z * [new tag] ciflow/h100/162022 -> ciflow/h100/162022 2025-09-07T06:25:45.9604214Z * [new tag] ciflow/h100/162278 -> ciflow/h100/162278 2025-09-07T06:25:45.9605459Z * [new tag] ciflow/inductor-perf-test-nightly-rocm/156592 -> ciflow/inductor-perf-test-nightly-rocm/156592 2025-09-07T06:25:45.9606334Z * [new tag] ciflow/inductor-perf-test-nightly/156592 -> ciflow/inductor-perf-test-nightly/156592 2025-09-07T06:25:45.9607130Z * [new tag] ciflow/inductor-periodic/162063 -> ciflow/inductor-periodic/162063 2025-09-07T06:25:45.9607827Z * [new tag] ciflow/inductor-periodic/162227 -> ciflow/inductor-periodic/162227 2025-09-07T06:25:45.9608598Z * [new tag] ciflow/inductor-periodic/162323 -> ciflow/inductor-periodic/162323 2025-09-07T06:25:45.9609604Z * [new tag] ciflow/inductor-rocm/154170 -> ciflow/inductor-rocm/154170 2025-09-07T06:25:45.9610394Z * [new tag] ciflow/inductor-rocm/159146 -> ciflow/inductor-rocm/159146 2025-09-07T06:25:45.9611062Z * [new tag] ciflow/inductor-rocm/159158 -> ciflow/inductor-rocm/159158 2025-09-07T06:25:45.9611874Z * [new tag] ciflow/inductor-rocm/161715 -> ciflow/inductor-rocm/161715 2025-09-07T06:25:45.9612760Z * [new tag] ciflow/inductor-rocm/162053 -> ciflow/inductor-rocm/162053 2025-09-07T06:25:45.9613789Z * [new tag] ciflow/inductor-rocm/162056 -> ciflow/inductor-rocm/162056 2025-09-07T06:25:45.9614538Z * [new tag] ciflow/inductor/137400 -> ciflow/inductor/137400 2025-09-07T06:25:45.9615229Z * [new tag] ciflow/inductor/148180 -> ciflow/inductor/148180 2025-09-07T06:25:45.9615877Z * [new tag] ciflow/inductor/148328 -> ciflow/inductor/148328 2025-09-07T06:25:45.9616546Z * [new tag] ciflow/inductor/148484 -> ciflow/inductor/148484 2025-09-07T06:25:45.9617221Z * [new tag] ciflow/inductor/148492 -> ciflow/inductor/148492 2025-09-07T06:25:45.9617880Z * [new tag] ciflow/inductor/152624 -> ciflow/inductor/152624 2025-09-07T06:25:45.9618570Z * [new tag] ciflow/inductor/154694 -> ciflow/inductor/154694 2025-09-07T06:25:45.9619220Z * [new tag] ciflow/inductor/156049 -> ciflow/inductor/156049 2025-09-07T06:25:45.9619949Z * [new tag] ciflow/inductor/156592 -> ciflow/inductor/156592 2025-09-07T06:25:45.9620616Z * [new tag] ciflow/inductor/157635 -> ciflow/inductor/157635 2025-09-07T06:25:45.9621332Z * [new tag] ciflow/inductor/157685 -> ciflow/inductor/157685 2025-09-07T06:25:45.9622272Z * [new tag] ciflow/inductor/157686 -> ciflow/inductor/157686 2025-09-07T06:25:45.9623216Z * [new tag] ciflow/inductor/157689 -> ciflow/inductor/157689 2025-09-07T06:25:45.9624206Z * [new tag] ciflow/inductor/157699 -> ciflow/inductor/157699 2025-09-07T06:25:45.9625108Z * [new tag] ciflow/inductor/157743 -> ciflow/inductor/157743 2025-09-07T06:25:45.9625859Z * [new tag] ciflow/inductor/157994 -> ciflow/inductor/157994 2025-09-07T06:25:45.9626590Z * [new tag] ciflow/inductor/158091 -> ciflow/inductor/158091 2025-09-07T06:25:45.9627318Z * [new tag] ciflow/inductor/158104 -> ciflow/inductor/158104 2025-09-07T06:25:45.9628117Z * [new tag] ciflow/inductor/158404 -> ciflow/inductor/158404 2025-09-07T06:25:45.9628860Z * [new tag] ciflow/inductor/158647 -> ciflow/inductor/158647 2025-09-07T06:25:45.9629778Z * [new tag] ciflow/inductor/158932 -> ciflow/inductor/158932 2025-09-07T06:25:45.9630429Z * [new tag] ciflow/inductor/159146 -> ciflow/inductor/159146 2025-09-07T06:25:45.9631137Z * [new tag] ciflow/inductor/159158 -> ciflow/inductor/159158 2025-09-07T06:25:45.9632084Z * [new tag] ciflow/inductor/159274 -> ciflow/inductor/159274 2025-09-07T06:25:45.9632750Z * [new tag] ciflow/inductor/159664 -> ciflow/inductor/159664 2025-09-07T06:25:45.9633646Z * [new tag] ciflow/inductor/159778 -> ciflow/inductor/159778 2025-09-07T06:25:45.9634351Z * [new tag] ciflow/inductor/159835 -> ciflow/inductor/159835 2025-09-07T06:25:45.9635375Z * [new tag] ciflow/inductor/159944 -> ciflow/inductor/159944 2025-09-07T06:25:45.9636264Z * [new tag] ciflow/inductor/160161 -> ciflow/inductor/160161 2025-09-07T06:25:45.9636920Z * [new tag] ciflow/inductor/160174 -> ciflow/inductor/160174 2025-09-07T06:25:45.9638023Z * [new tag] ciflow/inductor/160323 -> ciflow/inductor/160323 2025-09-07T06:25:45.9638979Z * [new tag] ciflow/inductor/160324 -> ciflow/inductor/160324 2025-09-07T06:25:45.9639813Z * [new tag] ciflow/inductor/160325 -> ciflow/inductor/160325 2025-09-07T06:25:45.9640789Z * [new tag] ciflow/inductor/160326 -> ciflow/inductor/160326 2025-09-07T06:25:45.9641545Z * [new tag] ciflow/inductor/160327 -> ciflow/inductor/160327 2025-09-07T06:25:45.9642292Z * [new tag] ciflow/inductor/160328 -> ciflow/inductor/160328 2025-09-07T06:25:45.9643202Z * [new tag] ciflow/inductor/160329 -> ciflow/inductor/160329 2025-09-07T06:25:45.9643826Z * [new tag] ciflow/inductor/160480 -> ciflow/inductor/160480 2025-09-07T06:25:45.9644845Z * [new tag] ciflow/inductor/160532 -> ciflow/inductor/160532 2025-09-07T06:25:45.9646151Z * [new tag] ciflow/inductor/160539 -> ciflow/inductor/160539 2025-09-07T06:25:45.9646879Z * [new tag] ciflow/inductor/160580 -> ciflow/inductor/160580 2025-09-07T06:25:45.9647538Z * [new tag] ciflow/inductor/160685 -> ciflow/inductor/160685 2025-09-07T06:25:45.9648265Z * [new tag] ciflow/inductor/160686 -> ciflow/inductor/160686 2025-09-07T06:25:45.9648985Z * [new tag] ciflow/inductor/160687 -> ciflow/inductor/160687 2025-09-07T06:25:45.9649670Z * [new tag] ciflow/inductor/160688 -> ciflow/inductor/160688 2025-09-07T06:25:45.9650492Z * [new tag] ciflow/inductor/160690 -> ciflow/inductor/160690 2025-09-07T06:25:45.9651122Z * [new tag] ciflow/inductor/160706 -> ciflow/inductor/160706 2025-09-07T06:25:45.9651955Z * [new tag] ciflow/inductor/160729 -> ciflow/inductor/160729 2025-09-07T06:25:45.9652708Z * [new tag] ciflow/inductor/160798 -> ciflow/inductor/160798 2025-09-07T06:25:45.9653622Z * [new tag] ciflow/inductor/160836 -> ciflow/inductor/160836 2025-09-07T06:25:45.9654241Z * [new tag] ciflow/inductor/160843 -> ciflow/inductor/160843 2025-09-07T06:25:45.9655386Z * [new tag] ciflow/inductor/160869 -> ciflow/inductor/160869 2025-09-07T06:25:45.9656074Z * [new tag] ciflow/inductor/160920 -> ciflow/inductor/160920 2025-09-07T06:25:45.9656709Z * [new tag] ciflow/inductor/160943 -> ciflow/inductor/160943 2025-09-07T06:25:45.9657395Z * [new tag] ciflow/inductor/161092 -> ciflow/inductor/161092 2025-09-07T06:25:45.9658093Z * [new tag] ciflow/inductor/161093 -> ciflow/inductor/161093 2025-09-07T06:25:45.9659035Z * [new tag] ciflow/inductor/161109 -> ciflow/inductor/161109 2025-09-07T06:25:45.9659641Z * [new tag] ciflow/inductor/161118 -> ciflow/inductor/161118 2025-09-07T06:25:45.9660655Z * [new tag] ciflow/inductor/161178 -> ciflow/inductor/161178 2025-09-07T06:25:45.9661341Z * [new tag] ciflow/inductor/161246 -> ciflow/inductor/161246 2025-09-07T06:25:45.9662054Z * [new tag] ciflow/inductor/161349 -> ciflow/inductor/161349 2025-09-07T06:25:45.9663336Z * [new tag] ciflow/inductor/161350 -> ciflow/inductor/161350 2025-09-07T06:25:45.9664045Z * [new tag] ciflow/inductor/161351 -> ciflow/inductor/161351 2025-09-07T06:25:45.9665011Z * [new tag] ciflow/inductor/161397 -> ciflow/inductor/161397 2025-09-07T06:25:45.9665663Z * [new tag] ciflow/inductor/161404 -> ciflow/inductor/161404 2025-09-07T06:25:45.9666356Z * [new tag] ciflow/inductor/161405 -> ciflow/inductor/161405 2025-09-07T06:25:45.9667091Z * [new tag] ciflow/inductor/161406 -> ciflow/inductor/161406 2025-09-07T06:25:45.9668119Z * [new tag] ciflow/inductor/161410 -> ciflow/inductor/161410 2025-09-07T06:25:45.9668742Z * [new tag] ciflow/inductor/161414 -> ciflow/inductor/161414 2025-09-07T06:25:45.9669810Z * [new tag] ciflow/inductor/161442 -> ciflow/inductor/161442 2025-09-07T06:25:45.9670423Z * [new tag] ciflow/inductor/161458 -> ciflow/inductor/161458 2025-09-07T06:25:45.9671160Z * [new tag] ciflow/inductor/161468 -> ciflow/inductor/161468 2025-09-07T06:25:45.9671840Z * [new tag] ciflow/inductor/161469 -> ciflow/inductor/161469 2025-09-07T06:25:45.9672778Z * [new tag] ciflow/inductor/161485 -> ciflow/inductor/161485 2025-09-07T06:25:45.9673418Z * [new tag] ciflow/inductor/161499 -> ciflow/inductor/161499 2025-09-07T06:25:45.9674117Z * [new tag] ciflow/inductor/161534 -> ciflow/inductor/161534 2025-09-07T06:25:45.9674921Z * [new tag] ciflow/inductor/161595 -> ciflow/inductor/161595 2025-09-07T06:25:45.9675622Z * [new tag] ciflow/inductor/161596 -> ciflow/inductor/161596 2025-09-07T06:25:45.9676961Z * [new tag] ciflow/inductor/161630 -> ciflow/inductor/161630 2025-09-07T06:25:45.9677579Z * [new tag] ciflow/inductor/161667 -> ciflow/inductor/161667 2025-09-07T06:25:45.9678333Z * [new tag] ciflow/inductor/161670 -> ciflow/inductor/161670 2025-09-07T06:25:45.9679017Z * [new tag] ciflow/inductor/161673 -> ciflow/inductor/161673 2025-09-07T06:25:45.9679746Z * [new tag] ciflow/inductor/161674 -> ciflow/inductor/161674 2025-09-07T06:25:45.9680562Z * [new tag] ciflow/inductor/161675 -> ciflow/inductor/161675 2025-09-07T06:25:45.9681180Z * [new tag] ciflow/inductor/161693 -> ciflow/inductor/161693 2025-09-07T06:25:45.9681881Z * [new tag] ciflow/inductor/161695 -> ciflow/inductor/161695 2025-09-07T06:25:45.9682578Z * [new tag] ciflow/inductor/161715 -> ciflow/inductor/161715 2025-09-07T06:25:45.9683274Z * [new tag] ciflow/inductor/161730 -> ciflow/inductor/161730 2025-09-07T06:25:45.9683984Z * [new tag] ciflow/inductor/161732 -> ciflow/inductor/161732 2025-09-07T06:25:45.9684934Z * [new tag] ciflow/inductor/161744 -> ciflow/inductor/161744 2025-09-07T06:25:45.9685612Z * [new tag] ciflow/inductor/161746 -> ciflow/inductor/161746 2025-09-07T06:25:45.9686263Z * [new tag] ciflow/inductor/161747 -> ciflow/inductor/161747 2025-09-07T06:25:45.9686997Z * [new tag] ciflow/inductor/161819 -> ciflow/inductor/161819 2025-09-07T06:25:45.9687703Z * [new tag] ciflow/inductor/161821 -> ciflow/inductor/161821 2025-09-07T06:25:45.9688493Z * [new tag] ciflow/inductor/161828 -> ciflow/inductor/161828 2025-09-07T06:25:45.9689189Z * [new tag] ciflow/inductor/161879 -> ciflow/inductor/161879 2025-09-07T06:25:45.9689914Z * [new tag] ciflow/inductor/161880 -> ciflow/inductor/161880 2025-09-07T06:25:45.9690638Z * [new tag] ciflow/inductor/161881 -> ciflow/inductor/161881 2025-09-07T06:25:45.9691569Z * [new tag] ciflow/inductor/161907 -> ciflow/inductor/161907 2025-09-07T06:25:45.9692222Z * [new tag] ciflow/inductor/161914 -> ciflow/inductor/161914 2025-09-07T06:25:45.9693025Z * [new tag] ciflow/inductor/161924 -> ciflow/inductor/161924 2025-09-07T06:25:45.9693962Z * [new tag] ciflow/inductor/161936 -> ciflow/inductor/161936 2025-09-07T06:25:45.9694580Z * [new tag] ciflow/inductor/161938 -> ciflow/inductor/161938 2025-09-07T06:25:45.9695346Z * [new tag] ciflow/inductor/161939 -> ciflow/inductor/161939 2025-09-07T06:25:45.9696035Z * [new tag] ciflow/inductor/161940 -> ciflow/inductor/161940 2025-09-07T06:25:45.9696789Z * [new tag] ciflow/inductor/161955 -> ciflow/inductor/161955 2025-09-07T06:25:45.9697463Z * [new tag] ciflow/inductor/161957 -> ciflow/inductor/161957 2025-09-07T06:25:45.9698179Z * [new tag] ciflow/inductor/161975 -> ciflow/inductor/161975 2025-09-07T06:25:45.9698905Z * [new tag] ciflow/inductor/161977 -> ciflow/inductor/161977 2025-09-07T06:25:45.9699584Z * [new tag] ciflow/inductor/161978 -> ciflow/inductor/161978 2025-09-07T06:25:45.9700283Z * [new tag] ciflow/inductor/161979 -> ciflow/inductor/161979 2025-09-07T06:25:45.9701006Z * [new tag] ciflow/inductor/161980 -> ciflow/inductor/161980 2025-09-07T06:25:45.9701720Z * [new tag] ciflow/inductor/161988 -> ciflow/inductor/161988 2025-09-07T06:25:45.9702432Z * [new tag] ciflow/inductor/161994 -> ciflow/inductor/161994 2025-09-07T06:25:45.9703140Z * [new tag] ciflow/inductor/162013 -> ciflow/inductor/162013 2025-09-07T06:25:45.9703862Z * [new tag] ciflow/inductor/162014 -> ciflow/inductor/162014 2025-09-07T06:25:45.9704579Z * [new tag] ciflow/inductor/162017 -> ciflow/inductor/162017 2025-09-07T06:25:45.9705262Z * [new tag] ciflow/inductor/162021 -> ciflow/inductor/162021 2025-09-07T06:25:45.9705986Z * [new tag] ciflow/inductor/162023 -> ciflow/inductor/162023 2025-09-07T06:25:45.9706688Z * [new tag] ciflow/inductor/162027 -> ciflow/inductor/162027 2025-09-07T06:25:45.9707488Z * [new tag] ciflow/inductor/162029 -> ciflow/inductor/162029 2025-09-07T06:25:45.9708193Z * [new tag] ciflow/inductor/162030 -> ciflow/inductor/162030 2025-09-07T06:25:45.9708874Z * [new tag] ciflow/inductor/162031 -> ciflow/inductor/162031 2025-09-07T06:25:45.9709586Z * [new tag] ciflow/inductor/162033 -> ciflow/inductor/162033 2025-09-07T06:25:45.9710573Z * [new tag] ciflow/inductor/162052 -> ciflow/inductor/162052 2025-09-07T06:25:45.9711684Z * [new tag] ciflow/inductor/162053 -> ciflow/inductor/162053 2025-09-07T06:25:45.9712444Z * [new tag] ciflow/inductor/162056 -> ciflow/inductor/162056 2025-09-07T06:25:45.9713171Z * [new tag] ciflow/inductor/162063 -> ciflow/inductor/162063 2025-09-07T06:25:45.9713877Z * [new tag] ciflow/inductor/162066 -> ciflow/inductor/162066 2025-09-07T06:25:45.9714689Z * [new tag] ciflow/inductor/162068 -> ciflow/inductor/162068 2025-09-07T06:25:45.9715699Z * [new tag] ciflow/inductor/162081 -> ciflow/inductor/162081 2025-09-07T06:25:45.9716384Z * [new tag] ciflow/inductor/162088 -> ciflow/inductor/162088 2025-09-07T06:25:45.9717075Z * [new tag] ciflow/inductor/162089 -> ciflow/inductor/162089 2025-09-07T06:25:45.9717823Z * [new tag] ciflow/inductor/162094 -> ciflow/inductor/162094 2025-09-07T06:25:45.9718510Z * [new tag] ciflow/inductor/162098 -> ciflow/inductor/162098 2025-09-07T06:25:45.9719235Z * [new tag] ciflow/inductor/162101 -> ciflow/inductor/162101 2025-09-07T06:25:45.9719951Z * [new tag] ciflow/inductor/162102 -> ciflow/inductor/162102 2025-09-07T06:25:45.9720646Z * [new tag] ciflow/inductor/162104 -> ciflow/inductor/162104 2025-09-07T06:25:45.9721400Z * [new tag] ciflow/inductor/162106 -> ciflow/inductor/162106 2025-09-07T06:25:45.9722110Z * [new tag] ciflow/inductor/162108 -> ciflow/inductor/162108 2025-09-07T06:25:45.9722858Z * [new tag] ciflow/inductor/162126 -> ciflow/inductor/162126 2025-09-07T06:25:45.9723615Z * [new tag] ciflow/inductor/162149 -> ciflow/inductor/162149 2025-09-07T06:25:45.9724547Z * [new tag] ciflow/inductor/162164 -> ciflow/inductor/162164 2025-09-07T06:25:45.9725249Z * [new tag] ciflow/inductor/162166 -> ciflow/inductor/162166 2025-09-07T06:25:45.9725975Z * [new tag] ciflow/inductor/162169 -> ciflow/inductor/162169 2025-09-07T06:25:45.9726685Z * [new tag] ciflow/inductor/162170 -> ciflow/inductor/162170 2025-09-07T06:25:45.9727390Z * [new tag] ciflow/inductor/162171 -> ciflow/inductor/162171 2025-09-07T06:25:45.9728129Z * [new tag] ciflow/inductor/162183 -> ciflow/inductor/162183 2025-09-07T06:25:45.9728894Z * [new tag] ciflow/inductor/162189 -> ciflow/inductor/162189 2025-09-07T06:25:45.9729611Z * [new tag] ciflow/inductor/162190 -> ciflow/inductor/162190 2025-09-07T06:25:45.9730289Z * [new tag] ciflow/inductor/162191 -> ciflow/inductor/162191 2025-09-07T06:25:45.9730969Z * [new tag] ciflow/inductor/162194 -> ciflow/inductor/162194 2025-09-07T06:25:45.9731977Z * [new tag] ciflow/inductor/162200 -> ciflow/inductor/162200 2025-09-07T06:25:45.9732647Z * [new tag] ciflow/inductor/162201 -> ciflow/inductor/162201 2025-09-07T06:25:45.9733306Z * [new tag] ciflow/inductor/162208 -> ciflow/inductor/162208 2025-09-07T06:25:45.9734225Z * [new tag] ciflow/inductor/162211 -> ciflow/inductor/162211 2025-09-07T06:25:45.9735055Z * [new tag] ciflow/inductor/162216 -> ciflow/inductor/162216 2025-09-07T06:25:45.9735672Z * [new tag] ciflow/inductor/162220 -> ciflow/inductor/162220 2025-09-07T06:25:45.9736658Z * [new tag] ciflow/inductor/162222 -> ciflow/inductor/162222 2025-09-07T06:25:45.9737312Z * [new tag] ciflow/inductor/162227 -> ciflow/inductor/162227 2025-09-07T06:25:45.9738376Z * [new tag] ciflow/inductor/162238 -> ciflow/inductor/162238 2025-09-07T06:25:45.9739002Z * [new tag] ciflow/inductor/162239 -> ciflow/inductor/162239 2025-09-07T06:25:45.9739764Z * [new tag] ciflow/inductor/162240 -> ciflow/inductor/162240 2025-09-07T06:25:45.9740453Z * [new tag] ciflow/inductor/162244 -> ciflow/inductor/162244 2025-09-07T06:25:45.9741217Z * [new tag] ciflow/inductor/162245 -> ciflow/inductor/162245 2025-09-07T06:25:45.9741877Z * [new tag] ciflow/inductor/162262 -> ciflow/inductor/162262 2025-09-07T06:25:45.9742602Z * [new tag] ciflow/inductor/162275 -> ciflow/inductor/162275 2025-09-07T06:25:45.9743287Z * [new tag] ciflow/inductor/162278 -> ciflow/inductor/162278 2025-09-07T06:25:45.9744038Z * [new tag] ciflow/inductor/162284 -> ciflow/inductor/162284 2025-09-07T06:25:45.9744736Z * [new tag] ciflow/inductor/162286 -> ciflow/inductor/162286 2025-09-07T06:25:45.9745451Z * [new tag] ciflow/inductor/162288 -> ciflow/inductor/162288 2025-09-07T06:25:45.9746171Z * [new tag] ciflow/inductor/162293 -> ciflow/inductor/162293 2025-09-07T06:25:45.9746892Z * [new tag] ciflow/inductor/162294 -> ciflow/inductor/162294 2025-09-07T06:25:45.9747593Z * [new tag] ciflow/inductor/162295 -> ciflow/inductor/162295 2025-09-07T06:25:45.9748288Z * [new tag] ciflow/inductor/162296 -> ciflow/inductor/162296 2025-09-07T06:25:45.9749016Z * [new tag] ciflow/inductor/162298 -> ciflow/inductor/162298 2025-09-07T06:25:45.9749714Z * [new tag] ciflow/inductor/162307 -> ciflow/inductor/162307 2025-09-07T06:25:45.9750457Z * [new tag] ciflow/inductor/162309 -> ciflow/inductor/162309 2025-09-07T06:25:45.9751143Z * [new tag] ciflow/inductor/162311 -> ciflow/inductor/162311 2025-09-07T06:25:45.9751952Z * [new tag] ciflow/inductor/162312 -> ciflow/inductor/162312 2025-09-07T06:25:45.9752603Z * [new tag] ciflow/inductor/162315 -> ciflow/inductor/162315 2025-09-07T06:25:45.9753371Z * [new tag] ciflow/inductor/162316 -> ciflow/inductor/162316 2025-09-07T06:25:45.9754080Z * [new tag] ciflow/inductor/162318 -> ciflow/inductor/162318 2025-09-07T06:25:45.9754918Z * [new tag] ciflow/inductor/162323 -> ciflow/inductor/162323 2025-09-07T06:25:45.9755652Z * [new tag] ciflow/inductor/162341 -> ciflow/inductor/162341 2025-09-07T06:25:45.9756335Z * [new tag] ciflow/inductor/162345 -> ciflow/inductor/162345 2025-09-07T06:25:45.9757391Z * [new tag] ciflow/inductor/3b9a386 -> ciflow/inductor/3b9a386 2025-09-07T06:25:45.9758205Z * [new tag] ciflow/inductor/3d4b92b -> ciflow/inductor/3d4b92b 2025-09-07T06:25:45.9759135Z * [new tag] ciflow/inductor/d224ac7 -> ciflow/inductor/d224ac7 2025-09-07T06:25:45.9760141Z * [new tag] ciflow/linux-aarch64/157994 -> ciflow/linux-aarch64/157994 2025-09-07T06:25:45.9760811Z * [new tag] ciflow/linux-aarch64/159737 -> ciflow/linux-aarch64/159737 2025-09-07T06:25:45.9761424Z * [new tag] ciflow/linux-aarch64/160078 -> ciflow/linux-aarch64/160078 2025-09-07T06:25:45.9762198Z * [new tag] ciflow/mps/157553 -> ciflow/mps/157553 2025-09-07T06:25:45.9762960Z * [new tag] ciflow/mps/157635 -> ciflow/mps/157635 2025-09-07T06:25:45.9763535Z * [new tag] ciflow/mps/161988 -> ciflow/mps/161988 2025-09-07T06:25:45.9764212Z * [new tag] ciflow/mps/162108 -> ciflow/mps/162108 2025-09-07T06:25:45.9764852Z * [new tag] ciflow/mps/162153 -> ciflow/mps/162153 2025-09-07T06:25:45.9765516Z * [new tag] ciflow/mps/162281 -> ciflow/mps/162281 2025-09-07T06:25:45.9766488Z * [new tag] ciflow/nightly/156049 -> ciflow/nightly/156049 2025-09-07T06:25:45.9767133Z * [new tag] ciflow/nightly/158104 -> ciflow/nightly/158104 2025-09-07T06:25:45.9767948Z * [new tag] ciflow/op-benchmark/157994 -> ciflow/op-benchmark/157994 2025-09-07T06:25:45.9769067Z * [new tag] ciflow/periodic-rocm-mi300/161529 -> ciflow/periodic-rocm-mi300/161529 2025-09-07T06:25:45.9769658Z * [new tag] ciflow/periodic-rocm-mi300/161715 -> ciflow/periodic-rocm-mi300/161715 2025-09-07T06:25:45.9770748Z * [new tag] ciflow/periodic/054a2fd -> ciflow/periodic/054a2fd 2025-09-07T06:25:45.9771274Z * [new tag] ciflow/periodic/156703 -> ciflow/periodic/156703 2025-09-07T06:25:45.9772003Z * [new tag] ciflow/periodic/161715 -> ciflow/periodic/161715 2025-09-07T06:25:45.9772658Z * [new tag] ciflow/periodic/162021 -> ciflow/periodic/162021 2025-09-07T06:25:45.9773336Z * [new tag] ciflow/periodic/162323 -> ciflow/periodic/162323 2025-09-07T06:25:45.9774286Z * [new tag] ciflow/periodic/2a6d37d -> ciflow/periodic/2a6d37d 2025-09-07T06:25:45.9775013Z * [new tag] ciflow/periodic/317eeb8 -> ciflow/periodic/317eeb8 2025-09-07T06:25:45.9775827Z * [new tag] ciflow/periodic/3c32 -> ciflow/periodic/3c32 2025-09-07T06:25:45.9776814Z * [new tag] ciflow/periodic/3e98831 -> ciflow/periodic/3e98831 2025-09-07T06:25:45.9777663Z * [new tag] ciflow/periodic/94512-point -> ciflow/periodic/94512-point 2025-09-07T06:25:45.9778857Z * [new tag] ciflow/periodic/csl/test87519 -> ciflow/periodic/csl/test87519 2025-09-07T06:25:45.9780000Z * [new tag] ciflow/periodic/csltest88275 -> ciflow/periodic/csltest88275 2025-09-07T06:25:45.9781120Z * [new tag] ciflow/periodic/csltest88761 -> ciflow/periodic/csltest88761 2025-09-07T06:25:45.9782056Z * [new tag] ciflow/periodic/release_1.12 -> ciflow/periodic/release_1.12 2025-09-07T06:25:45.9783085Z * [new tag] ciflow/periodic/release_1.12.0 -> ciflow/periodic/release_1.12.0 2025-09-07T06:25:45.9784080Z * [new tag] ciflow/periodic/sha-ec5b83 -> ciflow/periodic/sha-ec5b83 2025-09-07T06:25:45.9784816Z * [new tag] ciflow/rocm-mi300/154170 -> ciflow/rocm-mi300/154170 2025-09-07T06:25:45.9785713Z * [new tag] ciflow/rocm-mi300/158747 -> ciflow/rocm-mi300/158747 2025-09-07T06:25:45.9786371Z * [new tag] ciflow/rocm-mi300/159146 -> ciflow/rocm-mi300/159146 2025-09-07T06:25:45.9787039Z * [new tag] ciflow/rocm-mi300/159158 -> ciflow/rocm-mi300/159158 2025-09-07T06:25:45.9787725Z * [new tag] ciflow/rocm-mi300/161715 -> ciflow/rocm-mi300/161715 2025-09-07T06:25:45.9788382Z * [new tag] ciflow/rocm-mi300/161957 -> ciflow/rocm-mi300/161957 2025-09-07T06:25:45.9789077Z * [new tag] ciflow/rocm-mi300/162053 -> ciflow/rocm-mi300/162053 2025-09-07T06:25:45.9789721Z * [new tag] ciflow/rocm-mi300/162056 -> ciflow/rocm-mi300/162056 2025-09-07T06:25:45.9790513Z * [new tag] ciflow/rocm-mi300/162112 -> ciflow/rocm-mi300/162112 2025-09-07T06:25:45.9791168Z * [new tag] ciflow/rocm-mi300/162245 -> ciflow/rocm-mi300/162245 2025-09-07T06:25:45.9791965Z * [new tag] ciflow/rocm-mi300/162278 -> ciflow/rocm-mi300/162278 2025-09-07T06:25:45.9792967Z * [new tag] ciflow/rocm-mi300/162288 -> ciflow/rocm-mi300/162288 2025-09-07T06:25:45.9793683Z * [new tag] ciflow/rocm-mi355/162053 -> ciflow/rocm-mi355/162053 2025-09-07T06:25:45.9794387Z * [new tag] ciflow/rocm-mi355/162056 -> ciflow/rocm-mi355/162056 2025-09-07T06:25:45.9795358Z * [new tag] ciflow/rocm/148492 -> ciflow/rocm/148492 2025-09-07T06:25:45.9795917Z * [new tag] ciflow/rocm/154170 -> ciflow/rocm/154170 2025-09-07T06:25:45.9796900Z * [new tag] ciflow/rocm/156491 -> ciflow/rocm/156491 2025-09-07T06:25:45.9797466Z * [new tag] ciflow/rocm/156592 -> ciflow/rocm/156592 2025-09-07T06:25:45.9798164Z * [new tag] ciflow/rocm/158747 -> ciflow/rocm/158747 2025-09-07T06:25:45.9798860Z * [new tag] ciflow/rocm/159146 -> ciflow/rocm/159146 2025-09-07T06:25:45.9799898Z * [new tag] ciflow/rocm/159158 -> ciflow/rocm/159158 2025-09-07T06:25:45.9800588Z * [new tag] ciflow/rocm/161715 -> ciflow/rocm/161715 2025-09-07T06:25:45.9801282Z * [new tag] ciflow/rocm/161972 -> ciflow/rocm/161972 2025-09-07T06:25:45.9801971Z * [new tag] ciflow/rocm/162052 -> ciflow/rocm/162052 2025-09-07T06:25:45.9803107Z * [new tag] ciflow/rocm/162053 -> ciflow/rocm/162053 2025-09-07T06:25:45.9804128Z * [new tag] ciflow/rocm/162056 -> ciflow/rocm/162056 2025-09-07T06:25:45.9805050Z * [new tag] ciflow/rocm/162112 -> ciflow/rocm/162112 2025-09-07T06:25:45.9805844Z * [new tag] ciflow/rocm/162278 -> ciflow/rocm/162278 2025-09-07T06:25:45.9806563Z * [new tag] ciflow/rocm/162288 -> ciflow/rocm/162288 2025-09-07T06:25:45.9807278Z * [new tag] ciflow/rocm/162305 -> ciflow/rocm/162305 2025-09-07T06:25:45.9808374Z * [new tag] ciflow/slow/01c7106 -> ciflow/slow/01c7106 2025-09-07T06:25:45.9809133Z * [new tag] ciflow/slow/0577043 -> ciflow/slow/0577043 2025-09-07T06:25:45.9810484Z * [new tag] ciflow/slow/0d5b74da0cab798fbfdb9caa53fad816999c8386-sdym -> ciflow/slow/0d5b74da0cab798fbfdb9caa53fad816999c8386-sdym 2025-09-07T06:25:45.9810896Z * [new tag] ciflow/slow/0e81104 -> ciflow/slow/0e81104 2025-09-07T06:25:45.9811628Z * [new tag] ciflow/slow/161395 -> ciflow/slow/161395 2025-09-07T06:25:45.9812517Z * [new tag] ciflow/slow/1732077 -> ciflow/slow/1732077 2025-09-07T06:25:45.9813425Z * [new tag] ciflow/slow/187eb7c -> ciflow/slow/187eb7c 2025-09-07T06:25:45.9814164Z * [new tag] ciflow/slow/1faef89 -> ciflow/slow/1faef89 2025-09-07T06:25:45.9815334Z * [new tag] ciflow/slow/3920ec1 -> ciflow/slow/3920ec1 2025-09-07T06:25:45.9816299Z * [new tag] ciflow/slow/3b7c6b2 -> ciflow/slow/3b7c6b2 2025-09-07T06:25:45.9817197Z * [new tag] ciflow/slow/59a3759 -> ciflow/slow/59a3759 2025-09-07T06:25:45.9817981Z * [new tag] ciflow/slow/70ef0bb -> ciflow/slow/70ef0bb 2025-09-07T06:25:45.9818920Z * [new tag] ciflow/slow/788ff06 -> ciflow/slow/788ff06 2025-09-07T06:25:45.9820269Z * [new tag] ciflow/slow/8751002215790a3a88750faa8f4366933e296693-sdym -> ciflow/slow/8751002215790a3a88750faa8f4366933e296693-sdym 2025-09-07T06:25:45.9820707Z * [new tag] ciflow/slow/9d85864 -> ciflow/slow/9d85864 2025-09-07T06:25:45.9821639Z * [new tag] ciflow/slow/9ffad5b -> ciflow/slow/9ffad5b 2025-09-07T06:25:45.9822478Z * [new tag] ciflow/slow/a206e8b -> ciflow/slow/a206e8b 2025-09-07T06:25:45.9823395Z * [new tag] ciflow/slow/a837609 -> ciflow/slow/a837609 2025-09-07T06:25:45.9824478Z * [new tag] ciflow/slow/af841f3 -> ciflow/slow/af841f3 2025-09-07T06:25:45.9825781Z * [new tag] ciflow/slow/da3aba1e46157c4df504b067477cdf2b3c96b194-sdym -> ciflow/slow/da3aba1e46157c4df504b067477cdf2b3c96b194-sdym 2025-09-07T06:25:45.9826325Z * [new tag] ciflow/triton_binaries/162329 -> ciflow/triton_binaries/162329 2025-09-07T06:25:45.9827221Z * [new tag] ciflow/trunk/113258 -> ciflow/trunk/113258 2025-09-07T06:25:45.9827895Z * [new tag] ciflow/trunk/137400 -> ciflow/trunk/137400 2025-09-07T06:25:45.9828484Z * [new tag] ciflow/trunk/148180 -> ciflow/trunk/148180 2025-09-07T06:25:45.9829244Z * [new tag] ciflow/trunk/148328 -> ciflow/trunk/148328 2025-09-07T06:25:45.9829951Z * [new tag] ciflow/trunk/148492 -> ciflow/trunk/148492 2025-09-07T06:25:45.9830908Z * [new tag] ciflow/trunk/148919 -> ciflow/trunk/148919 2025-09-07T06:25:45.9831473Z * [new tag] ciflow/trunk/152624 -> ciflow/trunk/152624 2025-09-07T06:25:45.9832172Z * [new tag] ciflow/trunk/154170 -> ciflow/trunk/154170 2025-09-07T06:25:45.9832870Z * [new tag] ciflow/trunk/154694 -> ciflow/trunk/154694 2025-09-07T06:25:45.9833493Z * [new tag] ciflow/trunk/156049 -> ciflow/trunk/156049 2025-09-07T06:25:45.9834210Z * [new tag] ciflow/trunk/156703 -> ciflow/trunk/156703 2025-09-07T06:25:45.9835294Z * [new tag] ciflow/trunk/156711 -> ciflow/trunk/156711 2025-09-07T06:25:45.9836320Z * [new tag] ciflow/trunk/157432 -> ciflow/trunk/157432 2025-09-07T06:25:45.9837073Z * [new tag] ciflow/trunk/157685 -> ciflow/trunk/157685 2025-09-07T06:25:45.9837797Z * [new tag] ciflow/trunk/157689 -> ciflow/trunk/157689 2025-09-07T06:25:45.9838527Z * [new tag] ciflow/trunk/157699 -> ciflow/trunk/157699 2025-09-07T06:25:45.9839275Z * [new tag] ciflow/trunk/157813 -> ciflow/trunk/157813 2025-09-07T06:25:45.9839972Z * [new tag] ciflow/trunk/157994 -> ciflow/trunk/157994 2025-09-07T06:25:45.9840637Z * [new tag] ciflow/trunk/158091 -> ciflow/trunk/158091 2025-09-07T06:25:45.9841361Z * [new tag] ciflow/trunk/158104 -> ciflow/trunk/158104 2025-09-07T06:25:45.9842075Z * [new tag] ciflow/trunk/158404 -> ciflow/trunk/158404 2025-09-07T06:25:45.9842841Z * [new tag] ciflow/trunk/158647 -> ciflow/trunk/158647 2025-09-07T06:25:45.9843860Z * [new tag] ciflow/trunk/158846 -> ciflow/trunk/158846 2025-09-07T06:25:45.9844972Z * [new tag] ciflow/trunk/159158 -> ciflow/trunk/159158 2025-09-07T06:25:45.9845728Z * [new tag] ciflow/trunk/159682 -> ciflow/trunk/159682 2025-09-07T06:25:45.9846512Z * [new tag] ciflow/trunk/159835 -> ciflow/trunk/159835 2025-09-07T06:25:45.9847232Z * [new tag] ciflow/trunk/160161 -> ciflow/trunk/160161 2025-09-07T06:25:45.9847944Z * [new tag] ciflow/trunk/160236 -> ciflow/trunk/160236 2025-09-07T06:25:45.9848649Z * [new tag] ciflow/trunk/160329 -> ciflow/trunk/160329 2025-09-07T06:25:45.9849345Z * [new tag] ciflow/trunk/160480 -> ciflow/trunk/160480 2025-09-07T06:25:45.9850077Z * [new tag] ciflow/trunk/160532 -> ciflow/trunk/160532 2025-09-07T06:25:45.9850781Z * [new tag] ciflow/trunk/160836 -> ciflow/trunk/160836 2025-09-07T06:25:45.9851593Z * [new tag] ciflow/trunk/160843 -> ciflow/trunk/160843 2025-09-07T06:25:45.9852188Z * [new tag] ciflow/trunk/160869 -> ciflow/trunk/160869 2025-09-07T06:25:45.9853139Z * [new tag] ciflow/trunk/160940 -> ciflow/trunk/160940 2025-09-07T06:25:45.9853872Z * [new tag] ciflow/trunk/160943 -> ciflow/trunk/160943 2025-09-07T06:25:45.9854827Z * [new tag] ciflow/trunk/160953 -> ciflow/trunk/160953 2025-09-07T06:25:45.9855711Z * [new tag] ciflow/trunk/161035 -> ciflow/trunk/161035 2025-09-07T06:25:45.9856347Z * [new tag] ciflow/trunk/161178 -> ciflow/trunk/161178 2025-09-07T06:25:45.9857070Z * [new tag] ciflow/trunk/161349 -> ciflow/trunk/161349 2025-09-07T06:25:45.9857760Z * [new tag] ciflow/trunk/161350 -> ciflow/trunk/161350 2025-09-07T06:25:45.9858488Z * [new tag] ciflow/trunk/161351 -> ciflow/trunk/161351 2025-09-07T06:25:45.9859168Z * [new tag] ciflow/trunk/161395 -> ciflow/trunk/161395 2025-09-07T06:25:45.9859880Z * [new tag] ciflow/trunk/161405 -> ciflow/trunk/161405 2025-09-07T06:25:45.9860576Z * [new tag] ciflow/trunk/161406 -> ciflow/trunk/161406 2025-09-07T06:25:45.9861300Z * [new tag] ciflow/trunk/161410 -> ciflow/trunk/161410 2025-09-07T06:25:45.9862006Z * [new tag] ciflow/trunk/161468 -> ciflow/trunk/161468 2025-09-07T06:25:45.9862724Z * [new tag] ciflow/trunk/161499 -> ciflow/trunk/161499 2025-09-07T06:25:45.9863827Z * [new tag] ciflow/trunk/161527 -> ciflow/trunk/161527 2025-09-07T06:25:45.9864472Z * [new tag] ciflow/trunk/161534 -> ciflow/trunk/161534 2025-09-07T06:25:45.9865193Z * [new tag] ciflow/trunk/161591 -> ciflow/trunk/161591 2025-09-07T06:25:45.9865909Z * [new tag] ciflow/trunk/161595 -> ciflow/trunk/161595 2025-09-07T06:25:45.9866622Z * [new tag] ciflow/trunk/161596 -> ciflow/trunk/161596 2025-09-07T06:25:45.9867342Z * [new tag] ciflow/trunk/161633 -> ciflow/trunk/161633 2025-09-07T06:25:45.9868082Z * [new tag] ciflow/trunk/161634 -> ciflow/trunk/161634 2025-09-07T06:25:45.9868787Z * [new tag] ciflow/trunk/161635 -> ciflow/trunk/161635 2025-09-07T06:25:45.9869486Z * [new tag] ciflow/trunk/161667 -> ciflow/trunk/161667 2025-09-07T06:25:45.9870214Z * [new tag] ciflow/trunk/161670 -> ciflow/trunk/161670 2025-09-07T06:25:45.9870936Z * [new tag] ciflow/trunk/161692 -> ciflow/trunk/161692 2025-09-07T06:25:45.9871708Z * [new tag] ciflow/trunk/161693 -> ciflow/trunk/161693 2025-09-07T06:25:45.9872423Z * [new tag] ciflow/trunk/161695 -> ciflow/trunk/161695 2025-09-07T06:25:45.9873138Z * [new tag] ciflow/trunk/161730 -> ciflow/trunk/161730 2025-09-07T06:25:45.9873849Z * [new tag] ciflow/trunk/161744 -> ciflow/trunk/161744 2025-09-07T06:25:45.9874631Z * [new tag] ciflow/trunk/161749 -> ciflow/trunk/161749 2025-09-07T06:25:45.9875365Z * [new tag] ciflow/trunk/161881 -> ciflow/trunk/161881 2025-09-07T06:25:45.9876100Z * [new tag] ciflow/trunk/161924 -> ciflow/trunk/161924 2025-09-07T06:25:45.9877070Z * [new tag] ciflow/trunk/161926 -> ciflow/trunk/161926 2025-09-07T06:25:45.9877806Z * [new tag] ciflow/trunk/161936 -> ciflow/trunk/161936 2025-09-07T06:25:45.9878513Z * [new tag] ciflow/trunk/161952 -> ciflow/trunk/161952 2025-09-07T06:25:45.9879295Z * [new tag] ciflow/trunk/161955 -> ciflow/trunk/161955 2025-09-07T06:25:45.9879953Z * [new tag] ciflow/trunk/161957 -> ciflow/trunk/161957 2025-09-07T06:25:45.9880661Z * [new tag] ciflow/trunk/161959 -> ciflow/trunk/161959 2025-09-07T06:25:45.9881376Z * [new tag] ciflow/trunk/161977 -> ciflow/trunk/161977 2025-09-07T06:25:45.9882071Z * [new tag] ciflow/trunk/161988 -> ciflow/trunk/161988 2025-09-07T06:25:45.9882794Z * [new tag] ciflow/trunk/161994 -> ciflow/trunk/161994 2025-09-07T06:25:45.9883734Z * [new tag] ciflow/trunk/162007 -> ciflow/trunk/162007 2025-09-07T06:25:45.9884380Z * [new tag] ciflow/trunk/162013 -> ciflow/trunk/162013 2025-09-07T06:25:45.9885091Z * [new tag] ciflow/trunk/162017 -> ciflow/trunk/162017 2025-09-07T06:25:45.9885808Z * [new tag] ciflow/trunk/162021 -> ciflow/trunk/162021 2025-09-07T06:25:45.9886580Z * [new tag] ciflow/trunk/162022 -> ciflow/trunk/162022 2025-09-07T06:25:45.9887314Z * [new tag] ciflow/trunk/162040 -> ciflow/trunk/162040 2025-09-07T06:25:45.9888023Z * [new tag] ciflow/trunk/162041 -> ciflow/trunk/162041 2025-09-07T06:25:45.9888960Z * [new tag] ciflow/trunk/162062 -> ciflow/trunk/162062 2025-09-07T06:25:45.9889616Z * [new tag] ciflow/trunk/162066 -> ciflow/trunk/162066 2025-09-07T06:25:45.9890306Z * [new tag] ciflow/trunk/162089 -> ciflow/trunk/162089 2025-09-07T06:25:45.9891103Z * [new tag] ciflow/trunk/162099 -> ciflow/trunk/162099 2025-09-07T06:25:45.9891811Z * [new tag] ciflow/trunk/162104 -> ciflow/trunk/162104 2025-09-07T06:25:45.9893274Z * [new tag] ciflow/trunk/162106 -> ciflow/trunk/162106 2025-09-07T06:25:45.9893908Z * [new tag] ciflow/trunk/162112 -> ciflow/trunk/162112 2025-09-07T06:25:45.9894624Z * [new tag] ciflow/trunk/162119 -> ciflow/trunk/162119 2025-09-07T06:25:45.9895359Z * [new tag] ciflow/trunk/162142 -> ciflow/trunk/162142 2025-09-07T06:25:45.9896059Z * [new tag] ciflow/trunk/162169 -> ciflow/trunk/162169 2025-09-07T06:25:45.9896801Z * [new tag] ciflow/trunk/162183 -> ciflow/trunk/162183 2025-09-07T06:25:45.9897502Z * [new tag] ciflow/trunk/162190 -> ciflow/trunk/162190 2025-09-07T06:25:45.9898230Z * [new tag] ciflow/trunk/162194 -> ciflow/trunk/162194 2025-09-07T06:25:45.9898927Z * [new tag] ciflow/trunk/162200 -> ciflow/trunk/162200 2025-09-07T06:25:45.9899674Z * [new tag] ciflow/trunk/162206 -> ciflow/trunk/162206 2025-09-07T06:25:45.9900369Z * [new tag] ciflow/trunk/162208 -> ciflow/trunk/162208 2025-09-07T06:25:45.9901109Z * [new tag] ciflow/trunk/162222 -> ciflow/trunk/162222 2025-09-07T06:25:45.9902009Z * [new tag] ciflow/trunk/162238 -> ciflow/trunk/162238 2025-09-07T06:25:45.9902763Z * [new tag] ciflow/trunk/162244 -> ciflow/trunk/162244 2025-09-07T06:25:45.9903553Z * [new tag] ciflow/trunk/162267 -> ciflow/trunk/162267 2025-09-07T06:25:45.9904458Z * [new tag] ciflow/trunk/162269 -> ciflow/trunk/162269 2025-09-07T06:25:45.9905085Z * [new tag] ciflow/trunk/162278 -> ciflow/trunk/162278 2025-09-07T06:25:45.9905783Z * [new tag] ciflow/trunk/162286 -> ciflow/trunk/162286 2025-09-07T06:25:45.9906500Z * [new tag] ciflow/trunk/162288 -> ciflow/trunk/162288 2025-09-07T06:25:45.9907223Z * [new tag] ciflow/trunk/162293 -> ciflow/trunk/162293 2025-09-07T06:25:45.9908047Z * [new tag] ciflow/trunk/162310 -> ciflow/trunk/162310 2025-09-07T06:25:45.9908679Z * [new tag] ciflow/trunk/162311 -> ciflow/trunk/162311 2025-09-07T06:25:45.9909368Z * [new tag] ciflow/trunk/162315 -> ciflow/trunk/162315 2025-09-07T06:25:45.9910097Z * [new tag] ciflow/trunk/162325 -> ciflow/trunk/162325 2025-09-07T06:25:45.9911090Z * [new tag] ciflow/trunk/162328 -> ciflow/trunk/162328 2025-09-07T06:25:45.9911761Z * [new tag] ciflow/trunk/162329 -> ciflow/trunk/162329 2025-09-07T06:25:45.9912912Z * [new tag] ciflow/unstable/123 -> ciflow/unstable/123 2025-09-07T06:25:45.9913651Z * [new tag] ciflow/vllm/162292 -> ciflow/vllm/162292 2025-09-07T06:25:45.9914726Z * [new tag] ciflow/win-arm64/156049 -> ciflow/win-arm64/156049 2025-09-07T06:25:45.9915359Z * [new tag] ciflow/win-arm64/158104 -> ciflow/win-arm64/158104 2025-09-07T06:25:45.9916136Z * [new tag] ciflow/xpu/157699 -> ciflow/xpu/157699 2025-09-07T06:25:45.9916806Z * [new tag] ciflow/xpu/157994 -> ciflow/xpu/157994 2025-09-07T06:25:45.9917743Z * [new tag] ciflow/xpu/159459 -> ciflow/xpu/159459 2025-09-07T06:25:45.9918284Z * [new tag] ciflow/xpu/159718 -> ciflow/xpu/159718 2025-09-07T06:25:45.9919042Z * [new tag] ciflow/xpu/159944 -> ciflow/xpu/159944 2025-09-07T06:25:45.9919802Z * [new tag] ciflow/xpu/160867 -> ciflow/xpu/160867 2025-09-07T06:25:45.9920682Z * [new tag] ciflow/xpu/160938 -> ciflow/xpu/160938 2025-09-07T06:25:45.9921283Z * [new tag] ciflow/xpu/160940 -> ciflow/xpu/160940 2025-09-07T06:25:45.9921940Z * [new tag] ciflow/xpu/160953 -> ciflow/xpu/160953 2025-09-07T06:25:45.9922728Z * [new tag] ciflow/xpu/161045 -> ciflow/xpu/161045 2025-09-07T06:25:45.9923908Z * [new tag] ciflow/xpu/161058 -> ciflow/xpu/161058 2025-09-07T06:25:45.9928538Z * [new tag] ciflow/xpu/161246 -> ciflow/xpu/161246 2025-09-07T06:25:45.9929542Z * [new tag] ciflow/xpu/161397 -> ciflow/xpu/161397 2025-09-07T06:25:45.9930347Z * [new tag] ciflow/xpu/161485 -> ciflow/xpu/161485 2025-09-07T06:25:45.9931271Z * [new tag] ciflow/xpu/161988 -> ciflow/xpu/161988 2025-09-07T06:25:45.9931798Z * [new tag] ciflow/xpu/162062 -> ciflow/xpu/162062 2025-09-07T06:25:45.9932691Z * [new tag] cslpull75 -> cslpull75 2025-09-07T06:25:45.9933399Z * [new tag] cslpull76 -> cslpull76 2025-09-07T06:25:45.9934326Z * [new tag] cslpull77 -> cslpull77 2025-09-07T06:25:45.9935036Z * [new tag] cslpull78 -> cslpull78 2025-09-07T06:25:45.9936100Z * [new tag] cslpull79 -> cslpull79 2025-09-07T06:25:45.9937204Z * [new tag] cslpull80 -> cslpull80 2025-09-07T06:25:45.9937955Z * [new tag] cslpull81 -> cslpull81 2025-09-07T06:25:45.9938857Z * [new tag] cslpull82 -> cslpull82 2025-09-07T06:25:45.9939614Z * [new tag] cslpull83 -> cslpull83 2025-09-07T06:25:45.9940557Z * [new tag] cslpull84 -> cslpull84 2025-09-07T06:25:45.9941286Z * [new tag] cslpull85 -> cslpull85 2025-09-07T06:25:45.9942189Z * [new tag] cslpull86 -> cslpull86 2025-09-07T06:25:45.9942942Z * [new tag] cslpull87 -> cslpull87 2025-09-07T06:25:45.9943982Z * [new tag] cslpull88 -> cslpull88 2025-09-07T06:25:45.9944662Z * [new tag] cslpull89 -> cslpull89 2025-09-07T06:25:45.9945231Z * [new tag] cslpull90 -> cslpull90 2025-09-07T06:25:45.9946656Z * [new tag] cslpull91 -> cslpull91 2025-09-07T06:25:45.9947353Z * [new tag] cslpull92 -> cslpull92 2025-09-07T06:25:45.9948290Z * [new tag] flight_5 -> flight_5 2025-09-07T06:25:45.9949076Z * [new tag] flight_5.1 -> flight_5.1 2025-09-07T06:25:45.9950026Z * [new tag] flight_5.2 -> flight_5.2 2025-09-07T06:25:45.9950710Z * [new tag] flight_5.3 -> flight_5.3 2025-09-07T06:25:45.9951600Z * [new tag] forpull1 -> forpull1 2025-09-07T06:25:45.9952680Z * [new tag] malfet/tag-2ef5611 -> malfet/tag-2ef5611 2025-09-07T06:25:45.9953410Z * [new tag] malfet/tag-317b1a0 -> malfet/tag-317b1a0 2025-09-07T06:25:45.9954452Z * [new tag] malfet/tag-ec6f767 -> malfet/tag-ec6f767 2025-09-07T06:25:45.9955435Z * [new tag] nightly-binary -> nightly-binary 2025-09-07T06:25:45.9956513Z * [new tag] sqzhang_flight4_plus -> sqzhang_flight4_plus 2025-09-07T06:25:45.9957442Z * [new tag] sqzhang_flight_3 -> sqzhang_flight_3 2025-09-07T06:25:45.9958689Z * [new tag] trunk/00636e0171e7e733628c408084805442270cf608 -> trunk/00636e0171e7e733628c408084805442270cf608 2025-09-07T06:25:45.9959577Z * [new tag] trunk/019fed39aa6b2dd8c69347378d53423e5efae8d4 -> trunk/019fed39aa6b2dd8c69347378d53423e5efae8d4 2025-09-07T06:25:45.9960666Z * [new tag] trunk/01ab325cc2e0dc221af4d710974e1b9175066544 -> trunk/01ab325cc2e0dc221af4d710974e1b9175066544 2025-09-07T06:25:45.9961724Z * [new tag] trunk/01edcd4df8bf0c7b4cc2d3ec868bd2059eeea83b -> trunk/01edcd4df8bf0c7b4cc2d3ec868bd2059eeea83b 2025-09-07T06:25:45.9962549Z * [new tag] trunk/040d00af048967dde7938d358d7f5988cbd18388 -> trunk/040d00af048967dde7938d358d7f5988cbd18388 2025-09-07T06:25:45.9963494Z * [new tag] trunk/0447f2d99b4351b2ff129dce6eebb371024f73e5 -> trunk/0447f2d99b4351b2ff129dce6eebb371024f73e5 2025-09-07T06:25:45.9964363Z * [new tag] trunk/047603d35bdc70046216384838d6340feab79bf4 -> trunk/047603d35bdc70046216384838d6340feab79bf4 2025-09-07T06:25:45.9965276Z * [new tag] trunk/06da7c0730b3764f178ec3a90dedf4ffa4202d81 -> trunk/06da7c0730b3764f178ec3a90dedf4ffa4202d81 2025-09-07T06:25:45.9966370Z * [new tag] trunk/081cab045472ce045634548cc6c14a4870641e23 -> trunk/081cab045472ce045634548cc6c14a4870641e23 2025-09-07T06:25:45.9967191Z * [new tag] trunk/09587daf8c9f21f5340f73921ce5f23d1a4a4572 -> trunk/09587daf8c9f21f5340f73921ce5f23d1a4a4572 2025-09-07T06:25:45.9968032Z * [new tag] trunk/09be1890d72cc34fc946965dc4a27736bf0ca8c6 -> trunk/09be1890d72cc34fc946965dc4a27736bf0ca8c6 2025-09-07T06:25:45.9968897Z * [new tag] trunk/09d2f1b6315d6d416fbf452793d65795863ebc66 -> trunk/09d2f1b6315d6d416fbf452793d65795863ebc66 2025-09-07T06:25:45.9969719Z * [new tag] trunk/0af70e2353e1dcda83175fd4834ecb7b63e009e0 -> trunk/0af70e2353e1dcda83175fd4834ecb7b63e009e0 2025-09-07T06:25:45.9971316Z * [new tag] trunk/0c0e056a9e20c17271a6144dd32c0c7e3ba26736 -> trunk/0c0e056a9e20c17271a6144dd32c0c7e3ba26736 2025-09-07T06:25:45.9972078Z * [new tag] trunk/0cd6c56bdfa9178ff61be82ce3b178926ddb64a9 -> trunk/0cd6c56bdfa9178ff61be82ce3b178926ddb64a9 2025-09-07T06:25:45.9972961Z * [new tag] trunk/0d421ace32c1605ee8e452ee1eeb03bd243dd96c -> trunk/0d421ace32c1605ee8e452ee1eeb03bd243dd96c 2025-09-07T06:25:45.9974041Z * [new tag] trunk/0d71a9dd5b4b6d1dde58d91c9b71d96bc6a6a171 -> trunk/0d71a9dd5b4b6d1dde58d91c9b71d96bc6a6a171 2025-09-07T06:25:45.9974790Z * [new tag] trunk/0d84ff3b78f55492d3d4708458c92d776274939e -> trunk/0d84ff3b78f55492d3d4708458c92d776274939e 2025-09-07T06:25:45.9975656Z * [new tag] trunk/0f45aaf4414048b17d720d0915ce221a8de8ec63 -> trunk/0f45aaf4414048b17d720d0915ce221a8de8ec63 2025-09-07T06:25:45.9976499Z * [new tag] trunk/0ff8eabf1387de5acd6712a03bda61f1a3dfa27f -> trunk/0ff8eabf1387de5acd6712a03bda61f1a3dfa27f 2025-09-07T06:25:45.9977422Z * [new tag] trunk/104f2680e03d13a4765ca69f905d8f16fc0c822f -> trunk/104f2680e03d13a4765ca69f905d8f16fc0c822f 2025-09-07T06:25:45.9978247Z * [new tag] trunk/12814701555d3e41dfcdf8f9273af5821e322df0 -> trunk/12814701555d3e41dfcdf8f9273af5821e322df0 2025-09-07T06:25:45.9979141Z * [new tag] trunk/13b65196db422bdb394cb482e208c61ed448898c -> trunk/13b65196db422bdb394cb482e208c61ed448898c 2025-09-07T06:25:45.9979964Z * [new tag] trunk/13d66e2a66eceed14b8a8f5a971087df4f688a46 -> trunk/13d66e2a66eceed14b8a8f5a971087df4f688a46 2025-09-07T06:25:45.9980855Z * [new tag] trunk/145a3a7bda15e3963a33eb1b54bba5d4a270b225 -> trunk/145a3a7bda15e3963a33eb1b54bba5d4a270b225 2025-09-07T06:25:45.9981755Z * [new tag] trunk/146371483318e17929daefd37c8e459d9d6d47bb -> trunk/146371483318e17929daefd37c8e459d9d6d47bb 2025-09-07T06:25:45.9982640Z * [new tag] trunk/15c77a8cfd341e74fd124b077492ef2bfa51b339 -> trunk/15c77a8cfd341e74fd124b077492ef2bfa51b339 2025-09-07T06:25:45.9983645Z * [new tag] trunk/17fa8eec4a1e32939ab4d364ee6e75487a79b654 -> trunk/17fa8eec4a1e32939ab4d364ee6e75487a79b654 2025-09-07T06:25:45.9985187Z * [new tag] trunk/190c391a28845a14df26abb228d26aa813efb20c -> trunk/190c391a28845a14df26abb228d26aa813efb20c 2025-09-07T06:25:45.9985992Z * [new tag] trunk/1a588ace4667bde1331fbd8ed957157dca5cee68 -> trunk/1a588ace4667bde1331fbd8ed957157dca5cee68 2025-09-07T06:25:45.9987259Z * [new tag] trunk/1aa7476885e8f6e7b0ec3a5b6383aad9d3f343e7 -> trunk/1aa7476885e8f6e7b0ec3a5b6383aad9d3f343e7 2025-09-07T06:25:45.9987939Z * [new tag] trunk/1aeb421c342c9e9607842f4c87cb46e8e816ee53 -> trunk/1aeb421c342c9e9607842f4c87cb46e8e816ee53 2025-09-07T06:25:45.9988848Z * [new tag] trunk/1c1b28d5b6a942fafe23b2f09302d93c25226d4a -> trunk/1c1b28d5b6a942fafe23b2f09302d93c25226d4a 2025-09-07T06:25:45.9989626Z * [new tag] trunk/1ebd70d0c0d562d3be9abdee2a21906584af7d99 -> trunk/1ebd70d0c0d562d3be9abdee2a21906584af7d99 2025-09-07T06:25:45.9990544Z * [new tag] trunk/1ec2c15914da4ef7bd926ed9aebc8671c75fe965 -> trunk/1ec2c15914da4ef7bd926ed9aebc8671c75fe965 2025-09-07T06:25:45.9991367Z * [new tag] trunk/1f51056bd64e73d1aa81321bc3c098575b1bc78a -> trunk/1f51056bd64e73d1aa81321bc3c098575b1bc78a 2025-09-07T06:25:45.9992284Z * [new tag] trunk/1f820de639c75a1562d3fb03f160439f853ae07b -> trunk/1f820de639c75a1562d3fb03f160439f853ae07b 2025-09-07T06:25:45.9993126Z * [new tag] trunk/204697f0e695d82894c5010fbec664c4391f90cc -> trunk/204697f0e695d82894c5010fbec664c4391f90cc 2025-09-07T06:25:45.9993958Z * [new tag] trunk/20629b1619fe636227d01fc85ba221daa7185a05 -> trunk/20629b1619fe636227d01fc85ba221daa7185a05 2025-09-07T06:25:45.9994889Z * [new tag] trunk/20b47acef845e9c4f71da9429a396d293f50ebe7 -> trunk/20b47acef845e9c4f71da9429a396d293f50ebe7 2025-09-07T06:25:45.9995823Z * [new tag] trunk/20bfb2539d7c5250379648eda35f80b8a7d642dd -> trunk/20bfb2539d7c5250379648eda35f80b8a7d642dd 2025-09-07T06:25:45.9997338Z * [new tag] trunk/21fae99c180d17def562797ea0fb154d8fdf88e3 -> trunk/21fae99c180d17def562797ea0fb154d8fdf88e3 2025-09-07T06:25:45.9998142Z * [new tag] trunk/248355faf53f9f7ba2fd0a367d59600c6d991e7f -> trunk/248355faf53f9f7ba2fd0a367d59600c6d991e7f 2025-09-07T06:25:45.9998966Z * [new tag] trunk/25f4aaed9ec26f39c13862323ff8582006473d23 -> trunk/25f4aaed9ec26f39c13862323ff8582006473d23 2025-09-07T06:25:45.9999843Z * [new tag] trunk/261a84a1764412f8e659c956e3f81997ec3de9d5 -> trunk/261a84a1764412f8e659c956e3f81997ec3de9d5 2025-09-07T06:25:46.0000982Z * [new tag] trunk/28f4ab0737937858730f29f5c4e601e109cf9d5f -> trunk/28f4ab0737937858730f29f5c4e601e109cf9d5f 2025-09-07T06:25:46.0001772Z * [new tag] trunk/291cd11f2d5df6f48d348cce0e4e762f274f4dc4 -> trunk/291cd11f2d5df6f48d348cce0e4e762f274f4dc4 2025-09-07T06:25:46.0002647Z * [new tag] trunk/29280864d941e6108ab57f7298f520c0cf9696e9 -> trunk/29280864d941e6108ab57f7298f520c0cf9696e9 2025-09-07T06:25:46.0003555Z * [new tag] trunk/2a45837e98c63cae9d1a2e2133a727b829e549d5 -> trunk/2a45837e98c63cae9d1a2e2133a727b829e549d5 2025-09-07T06:25:46.0004479Z * [new tag] trunk/2a5c0785e2f975697fd7bdf1411de6e03dcaa1ef -> trunk/2a5c0785e2f975697fd7bdf1411de6e03dcaa1ef 2025-09-07T06:25:46.0005288Z * [new tag] trunk/2b8a83901c58a0858ea9e4ce00055f48e6ed164c -> trunk/2b8a83901c58a0858ea9e4ce00055f48e6ed164c 2025-09-07T06:25:46.0006131Z * [new tag] trunk/2ba65472dd54488a86a50326ea990195fc6732d6 -> trunk/2ba65472dd54488a86a50326ea990195fc6732d6 2025-09-07T06:25:46.0006956Z * [new tag] trunk/2c03f0acc53ed13fe8ebfe809129f25996e009a0 -> trunk/2c03f0acc53ed13fe8ebfe809129f25996e009a0 2025-09-07T06:25:46.0007797Z * [new tag] trunk/2dd529df0092799f68ee7afcf52338276906706a -> trunk/2dd529df0092799f68ee7afcf52338276906706a 2025-09-07T06:25:46.0008670Z * [new tag] trunk/2f6b4b1ad3f82bb3bd984f6e65744ea339ffb8b5 -> trunk/2f6b4b1ad3f82bb3bd984f6e65744ea339ffb8b5 2025-09-07T06:25:46.0009647Z * [new tag] trunk/2fa0520a64ed8aa734a56c4d124958f0b5711ca8 -> trunk/2fa0520a64ed8aa734a56c4d124958f0b5711ca8 2025-09-07T06:25:46.0010514Z * [new tag] trunk/302df2ac5dc4222294c09d48804a2dddb8f4bad8 -> trunk/302df2ac5dc4222294c09d48804a2dddb8f4bad8 2025-09-07T06:25:46.0011222Z * [new tag] trunk/33028597bfa2e0178e28c8cce33cb9b3800cac43 -> trunk/33028597bfa2e0178e28c8cce33cb9b3800cac43 2025-09-07T06:25:46.0012068Z * [new tag] trunk/34aa78274d6770086025a967fa63a86830e08176 -> trunk/34aa78274d6770086025a967fa63a86830e08176 2025-09-07T06:25:46.0013015Z * [new tag] trunk/3559c354ce6a14d11fe29fb12fa2747a2f2af449 -> trunk/3559c354ce6a14d11fe29fb12fa2747a2f2af449 2025-09-07T06:25:46.0013767Z * [new tag] trunk/36d207fcaaede0d1e58a5168084c307b32b6fd8b -> trunk/36d207fcaaede0d1e58a5168084c307b32b6fd8b 2025-09-07T06:25:46.0014489Z * [new tag] trunk/377033757ae5ca524ea842f1b0a5f446ed3d8fe0 -> trunk/377033757ae5ca524ea842f1b0a5f446ed3d8fe0 2025-09-07T06:25:46.0015342Z * [new tag] trunk/3771380f83fcac154a7c89ad679311d8c4818287 -> trunk/3771380f83fcac154a7c89ad679311d8c4818287 2025-09-07T06:25:46.0016200Z * [new tag] trunk/3a207816cc569f78863d86c01f2a3d265350e39f -> trunk/3a207816cc569f78863d86c01f2a3d265350e39f 2025-09-07T06:25:46.0017109Z * [new tag] trunk/3a20a20e7065ec927fdd216d4da3b04f879b3c67 -> trunk/3a20a20e7065ec927fdd216d4da3b04f879b3c67 2025-09-07T06:25:46.0018045Z * [new tag] trunk/3bbc2e3e4f025523eaa5dbff220b3e96bca608d0 -> trunk/3bbc2e3e4f025523eaa5dbff220b3e96bca608d0 2025-09-07T06:25:46.0018850Z * [new tag] trunk/3c0ff1b569c45cfa6935ad8031a9d4cf1551aa3f -> trunk/3c0ff1b569c45cfa6935ad8031a9d4cf1551aa3f 2025-09-07T06:25:46.0019695Z * [new tag] trunk/3c45af079afc92a03b03ddf4f9198902ffcf30cf -> trunk/3c45af079afc92a03b03ddf4f9198902ffcf30cf 2025-09-07T06:25:46.0020606Z * [new tag] trunk/3dde5d7f9bf80dd6623a712bc429e9e4302464b5 -> trunk/3dde5d7f9bf80dd6623a712bc429e9e4302464b5 2025-09-07T06:25:46.0021464Z * [new tag] trunk/403a3a393cda7e60f503f3b04b8805a845dcf45d -> trunk/403a3a393cda7e60f503f3b04b8805a845dcf45d 2025-09-07T06:25:46.0022336Z * [new tag] trunk/420c52ecf36f86d32da0853bfbe074b682b070aa -> trunk/420c52ecf36f86d32da0853bfbe074b682b070aa 2025-09-07T06:25:46.0023179Z * [new tag] trunk/43b7c86a2c0f91320f5c5f4827b111edff06fdb6 -> trunk/43b7c86a2c0f91320f5c5f4827b111edff06fdb6 2025-09-07T06:25:46.0024133Z * [new tag] trunk/451ed931562ec8b46d1f7e6c266a68132a119336 -> trunk/451ed931562ec8b46d1f7e6c266a68132a119336 2025-09-07T06:25:46.0025031Z * [new tag] trunk/480c7391126656154318fabf1d57ebc01e196e63 -> trunk/480c7391126656154318fabf1d57ebc01e196e63 2025-09-07T06:25:46.0025941Z * [new tag] trunk/48bedd753da22634aa94fbafeb731e82025404f3 -> trunk/48bedd753da22634aa94fbafeb731e82025404f3 2025-09-07T06:25:46.0026690Z * [new tag] trunk/494878a11b79071ada0b98f34042d47155be6d1c -> trunk/494878a11b79071ada0b98f34042d47155be6d1c 2025-09-07T06:25:46.0027696Z * [new tag] trunk/4ae57d448c0a7d37e4cfd5c27d977fad2cef4051 -> trunk/4ae57d448c0a7d37e4cfd5c27d977fad2cef4051 2025-09-07T06:25:46.0028569Z * [new tag] trunk/4cdaf8265d86f984254b62052da8c26ef61ef1cf -> trunk/4cdaf8265d86f984254b62052da8c26ef61ef1cf 2025-09-07T06:25:46.0029302Z * [new tag] trunk/4d4abec80f03cd8fdefe1d9cb3a60d3690cd777e -> trunk/4d4abec80f03cd8fdefe1d9cb3a60d3690cd777e 2025-09-07T06:25:46.0030225Z * [new tag] trunk/4e42aa8ffc44b8340eb0eeaf80a2cafc4763a186 -> trunk/4e42aa8ffc44b8340eb0eeaf80a2cafc4763a186 2025-09-07T06:25:46.0031074Z * [new tag] trunk/4f72d932feee0749397fec876dcd43994f50b215 -> trunk/4f72d932feee0749397fec876dcd43994f50b215 2025-09-07T06:25:46.0031997Z * [new tag] trunk/50fc22dedf3c4a27be61fa05551c4f320281b42d -> trunk/50fc22dedf3c4a27be61fa05551c4f320281b42d 2025-09-07T06:25:46.0032886Z * [new tag] trunk/5211f1f908907ffc064b56e43cf8659f7fc22aa9 -> trunk/5211f1f908907ffc064b56e43cf8659f7fc22aa9 2025-09-07T06:25:46.0033788Z * [new tag] trunk/524b78d4f67045b83bb69edc56ab16efe282971c -> trunk/524b78d4f67045b83bb69edc56ab16efe282971c 2025-09-07T06:25:46.0034679Z * [new tag] trunk/54e275e0d81fe1e1ccfa4fb5f2a5a9aaca00ca15 -> trunk/54e275e0d81fe1e1ccfa4fb5f2a5a9aaca00ca15 2025-09-07T06:25:46.0035547Z * [new tag] trunk/5561e45758d59c94605873d5db48ed459c004c3b -> trunk/5561e45758d59c94605873d5db48ed459c004c3b 2025-09-07T06:25:46.0036675Z * [new tag] trunk/57278d45f046d4f89f45d373b1af4dd56934ff24 -> trunk/57278d45f046d4f89f45d373b1af4dd56934ff24 2025-09-07T06:25:46.0037426Z * [new tag] trunk/5927a70934ccf7b70182d364c23245a7dd685503 -> trunk/5927a70934ccf7b70182d364c23245a7dd685503 2025-09-07T06:25:46.0038283Z * [new tag] trunk/5985e28912aeb40b103ebfcf2fd0665eb4a50599 -> trunk/5985e28912aeb40b103ebfcf2fd0665eb4a50599 2025-09-07T06:25:46.0041645Z * [new tag] trunk/5a2da090ed6db88bb657c4e51ec0b310cd08bff6 -> trunk/5a2da090ed6db88bb657c4e51ec0b310cd08bff6 2025-09-07T06:25:46.0042075Z * [new tag] trunk/5c473e9f5ee0ef0fc38e6cf34a95b547f8cdc8d5 -> trunk/5c473e9f5ee0ef0fc38e6cf34a95b547f8cdc8d5 2025-09-07T06:25:46.0042484Z * [new tag] trunk/5c67426d6847667a7c55a2dd01f470fa37238c18 -> trunk/5c67426d6847667a7c55a2dd01f470fa37238c18 2025-09-07T06:25:46.0042897Z * [new tag] trunk/5da573c42c332bc68d4b7946c69f690a876d951a -> trunk/5da573c42c332bc68d4b7946c69f690a876d951a 2025-09-07T06:25:46.0043302Z * [new tag] trunk/5e5870e858f60ff4bf87d03f3592097e934a9580 -> trunk/5e5870e858f60ff4bf87d03f3592097e934a9580 2025-09-07T06:25:46.0043708Z * [new tag] trunk/5f3cbc9442aa55b5afb29f4ac8ca9be569003e84 -> trunk/5f3cbc9442aa55b5afb29f4ac8ca9be569003e84 2025-09-07T06:25:46.0044672Z * [new tag] trunk/600c25e9a17fe56e3dee872be8854db08916ba0c -> trunk/600c25e9a17fe56e3dee872be8854db08916ba0c 2025-09-07T06:25:46.0045325Z * [new tag] trunk/601ae8e4831fc8123fffcfb8fd2e6b6381b42e14 -> trunk/601ae8e4831fc8123fffcfb8fd2e6b6381b42e14 2025-09-07T06:25:46.0046178Z * [new tag] trunk/6087ef41e54c2494b117ffd923faf20f515a6806 -> trunk/6087ef41e54c2494b117ffd923faf20f515a6806 2025-09-07T06:25:46.0047047Z * [new tag] trunk/626cb7df8161dd4ecb4fe43b60f37ce9076f56b1 -> trunk/626cb7df8161dd4ecb4fe43b60f37ce9076f56b1 2025-09-07T06:25:46.0047884Z * [new tag] trunk/62c3f9a97fd3dea7132a93066d32d893ffe101e6 -> trunk/62c3f9a97fd3dea7132a93066d32d893ffe101e6 2025-09-07T06:25:46.0048752Z * [new tag] trunk/63a9c23fe99eacfd09610c36dfe8f01b053c1a35 -> trunk/63a9c23fe99eacfd09610c36dfe8f01b053c1a35 2025-09-07T06:25:46.0049637Z * [new tag] trunk/65985937d97505f648b6ed852c3129f2dd08b251 -> trunk/65985937d97505f648b6ed852c3129f2dd08b251 2025-09-07T06:25:46.0051176Z * [new tag] trunk/66f3b4a682a6153517dd23369fdc3289b6494b07 -> trunk/66f3b4a682a6153517dd23369fdc3289b6494b07 2025-09-07T06:25:46.0051726Z * [new tag] trunk/6737e2c996990024187ba620d2764f3b6f6add2c -> trunk/6737e2c996990024187ba620d2764f3b6f6add2c 2025-09-07T06:25:46.0052650Z * [new tag] trunk/67c31dcd364f10072a55f4a30ffd1151c686283a -> trunk/67c31dcd364f10072a55f4a30ffd1151c686283a 2025-09-07T06:25:46.0054022Z * [new tag] trunk/68738beff73e9c3512e18b4edea811a897ce42db -> trunk/68738beff73e9c3512e18b4edea811a897ce42db 2025-09-07T06:25:46.0054840Z * [new tag] trunk/69a25f68884a168550695fdb1a7c310c54d29536 -> trunk/69a25f68884a168550695fdb1a7c310c54d29536 2025-09-07T06:25:46.0055744Z * [new tag] trunk/6b1900c22f1a07b9519346898d4c71d8a2b0f12f -> trunk/6b1900c22f1a07b9519346898d4c71d8a2b0f12f 2025-09-07T06:25:46.0056642Z * [new tag] trunk/6b8b3ac4403f771bd4a8f9a45d93347304148774 -> trunk/6b8b3ac4403f771bd4a8f9a45d93347304148774 2025-09-07T06:25:46.0057498Z * [new tag] trunk/6f7608d603834d6068b2e7a5d59bec3973b6bb1b -> trunk/6f7608d603834d6068b2e7a5d59bec3973b6bb1b 2025-09-07T06:25:46.0058430Z * [new tag] trunk/70d36e047dfb3488fd6335016711a784d810ebda -> trunk/70d36e047dfb3488fd6335016711a784d810ebda 2025-09-07T06:25:46.0059272Z * [new tag] trunk/71992dd805ff9d6763f77214dfe8b0465e88c87b -> trunk/71992dd805ff9d6763f77214dfe8b0465e88c87b 2025-09-07T06:25:46.0060148Z * [new tag] trunk/734ce8eba9c69381f187359bf0fef1d71d84cd20 -> trunk/734ce8eba9c69381f187359bf0fef1d71d84cd20 2025-09-07T06:25:46.0061022Z * [new tag] trunk/73eb4511fb863a37944342b7e92aae706de603c8 -> trunk/73eb4511fb863a37944342b7e92aae706de603c8 2025-09-07T06:25:46.0061959Z * [new tag] trunk/75bc23cfc345bd4c05e7f97c416c4b3d2d1fa64b -> trunk/75bc23cfc345bd4c05e7f97c416c4b3d2d1fa64b 2025-09-07T06:25:46.0062805Z * [new tag] trunk/771f369448321a387f2018535bc8b8b6e5f12fab -> trunk/771f369448321a387f2018535bc8b8b6e5f12fab 2025-09-07T06:25:46.0063745Z * [new tag] trunk/789d4942127143f2adcb53612c058ce4c9a2cf20 -> trunk/789d4942127143f2adcb53612c058ce4c9a2cf20 2025-09-07T06:25:46.0064459Z * [new tag] trunk/791eff96c85678c950888f9da24650083ee673fe -> trunk/791eff96c85678c950888f9da24650083ee673fe 2025-09-07T06:25:46.0065234Z * [new tag] trunk/793fc12aff1f69fbbf9f4278182fb52bbe350fc9 -> trunk/793fc12aff1f69fbbf9f4278182fb52bbe350fc9 2025-09-07T06:25:46.0066053Z * [new tag] trunk/79fcd5247a9a129eee526a14df30bfc6a22b3f01 -> trunk/79fcd5247a9a129eee526a14df30bfc6a22b3f01 2025-09-07T06:25:46.0066924Z * [new tag] trunk/7f4ff79210eb06924f223ae3a1941ee0e2635348 -> trunk/7f4ff79210eb06924f223ae3a1941ee0e2635348 2025-09-07T06:25:46.0067891Z * [new tag] trunk/8076a185c85112be62be292eb47409c88a585b1c -> trunk/8076a185c85112be62be292eb47409c88a585b1c 2025-09-07T06:25:46.0068678Z * [new tag] trunk/80dd397f1979371a5583fa3d5c7352029522a78d -> trunk/80dd397f1979371a5583fa3d5c7352029522a78d 2025-09-07T06:25:46.0069559Z * [new tag] trunk/8171d6052ec12628eb67e0040839314056014429 -> trunk/8171d6052ec12628eb67e0040839314056014429 2025-09-07T06:25:46.0070372Z * [new tag] trunk/81aeefa657b7ccc26b275c50a9f33b2f056e8071 -> trunk/81aeefa657b7ccc26b275c50a9f33b2f056e8071 2025-09-07T06:25:46.0071256Z * [new tag] trunk/81b7b16618bda250ce55982894a83dc0805eb64c -> trunk/81b7b16618bda250ce55982894a83dc0805eb64c 2025-09-07T06:25:46.0072136Z * [new tag] trunk/827f0d405448de31f79d1089f7d7fceab2f87895 -> trunk/827f0d405448de31f79d1089f7d7fceab2f87895 2025-09-07T06:25:46.0073100Z * [new tag] trunk/82f63c8f6de63c30132a8ac299b6e8c2fd0d3fe8 -> trunk/82f63c8f6de63c30132a8ac299b6e8c2fd0d3fe8 2025-09-07T06:25:46.0073975Z * [new tag] trunk/850e1382a9c56bfde18af09d3e72352d775e9435 -> trunk/850e1382a9c56bfde18af09d3e72352d775e9435 2025-09-07T06:25:46.0075024Z * [new tag] trunk/8678d831c48e616b717bff50f2d03141d2e9f965 -> trunk/8678d831c48e616b717bff50f2d03141d2e9f965 2025-09-07T06:25:46.0075922Z * [new tag] trunk/869cbcc16e489a4f5a14a93d5779b0ea86061c60 -> trunk/869cbcc16e489a4f5a14a93d5779b0ea86061c60 2025-09-07T06:25:46.0076869Z * [new tag] trunk/8703debf669bc2238211bfd039f4ecdd8228b7f7 -> trunk/8703debf669bc2238211bfd039f4ecdd8228b7f7 2025-09-07T06:25:46.0077759Z * [new tag] trunk/874069fbe46e82da5cfa405e6c0deb12e89ff608 -> trunk/874069fbe46e82da5cfa405e6c0deb12e89ff608 2025-09-07T06:25:46.0078684Z * [new tag] trunk/8875d6e394da2fffd04f31b28bf258c94d4776a3 -> trunk/8875d6e394da2fffd04f31b28bf258c94d4776a3 2025-09-07T06:25:46.0079581Z * [new tag] trunk/88d94d17e8c5155451393afa6eb3bab48ab61c16 -> trunk/88d94d17e8c5155451393afa6eb3bab48ab61c16 2025-09-07T06:25:46.0080523Z * [new tag] trunk/890626632def7e0ef95a2d01e87a0e4627824a9f -> trunk/890626632def7e0ef95a2d01e87a0e4627824a9f 2025-09-07T06:25:46.0081535Z * [new tag] trunk/8975cda2520b7b1b5bc3b4d8213edf261fa82570 -> trunk/8975cda2520b7b1b5bc3b4d8213edf261fa82570 2025-09-07T06:25:46.0082380Z * [new tag] trunk/89d41d3f61d04f14730ec26f008a59bef6624610 -> trunk/89d41d3f61d04f14730ec26f008a59bef6624610 2025-09-07T06:25:46.0083252Z * [new tag] trunk/8bb213b6d599ef1273fe52f9b1f6d476056c3a41 -> trunk/8bb213b6d599ef1273fe52f9b1f6d476056c3a41 2025-09-07T06:25:46.0084131Z * [new tag] trunk/8e23a1227b5fb2e39afaa7d57c075a75b640a5af -> trunk/8e23a1227b5fb2e39afaa7d57c075a75b640a5af 2025-09-07T06:25:46.0085514Z * [new tag] trunk/8ec551bb354ab2b85fbbba9d461740a20366d248 -> trunk/8ec551bb354ab2b85fbbba9d461740a20366d248 2025-09-07T06:25:46.0086371Z * [new tag] trunk/8fd3c9ce919c8d5c645fd348bba517e948cbc29d -> trunk/8fd3c9ce919c8d5c645fd348bba517e948cbc29d 2025-09-07T06:25:46.0087567Z * [new tag] trunk/90f50f7e68e120d9574e6e3189e37b4280010ad9 -> trunk/90f50f7e68e120d9574e6e3189e37b4280010ad9 2025-09-07T06:25:46.0088410Z * [new tag] trunk/91f0bcf43fc0bc743350d491ac63b77e92054ac9 -> trunk/91f0bcf43fc0bc743350d491ac63b77e92054ac9 2025-09-07T06:25:46.0089391Z * [new tag] trunk/92576a594b8121f6b0b1b5a3ea16d08792fc68ab -> trunk/92576a594b8121f6b0b1b5a3ea16d08792fc68ab 2025-09-07T06:25:46.0090262Z * [new tag] trunk/92a43025e0baa1f2ce345f28d22913b518a1ab9d -> trunk/92a43025e0baa1f2ce345f28d22913b518a1ab9d 2025-09-07T06:25:46.0091046Z * [new tag] trunk/93fb23d6fae7c4e82c4239a1033e522088742634 -> trunk/93fb23d6fae7c4e82c4239a1033e522088742634 2025-09-07T06:25:46.0091872Z * [new tag] trunk/9458d1ac3bd70c2af316a8ba95d2c6c9c1199c9c -> trunk/9458d1ac3bd70c2af316a8ba95d2c6c9c1199c9c 2025-09-07T06:25:46.0092861Z * [new tag] trunk/9480cdc0b61488c89a23c2f64f43b2dcedc8728e -> trunk/9480cdc0b61488c89a23c2f64f43b2dcedc8728e 2025-09-07T06:25:46.0093789Z * [new tag] trunk/9491d289b329e4ba4a9f5f5b1be7960671bb7840 -> trunk/9491d289b329e4ba4a9f5f5b1be7960671bb7840 2025-09-07T06:25:46.0094653Z * [new tag] trunk/9499c8761cd2067feb9877414e818f6fd00290f1 -> trunk/9499c8761cd2067feb9877414e818f6fd00290f1 2025-09-07T06:25:46.0095537Z * [new tag] trunk/95ee0bfea99d3d346d6502b91b497d2b35795504 -> trunk/95ee0bfea99d3d346d6502b91b497d2b35795504 2025-09-07T06:25:46.0096424Z * [new tag] trunk/98374612fc2febd686be20761e56bdc2424bc36a -> trunk/98374612fc2febd686be20761e56bdc2424bc36a 2025-09-07T06:25:46.0097559Z * [new tag] trunk/98efc9e93d8fc61eb53cb91378443617cb550500 -> trunk/98efc9e93d8fc61eb53cb91378443617cb550500 2025-09-07T06:25:46.0098391Z * [new tag] trunk/994f2a5dbcbdc915da39bf6f6ce4d1f5e74835c9 -> trunk/994f2a5dbcbdc915da39bf6f6ce4d1f5e74835c9 2025-09-07T06:25:46.0099315Z * [new tag] trunk/99f356fa58c8d726cef022d8710f5491291158f6 -> trunk/99f356fa58c8d726cef022d8710f5491291158f6 2025-09-07T06:25:46.0100231Z * [new tag] trunk/9a1c5c0a078b94d13ac5c1ae0d754d19fb73bf99 -> trunk/9a1c5c0a078b94d13ac5c1ae0d754d19fb73bf99 2025-09-07T06:25:46.0101103Z * [new tag] trunk/9a665ca3c472384e9d722bddba79e5a7680f1abd -> trunk/9a665ca3c472384e9d722bddba79e5a7680f1abd 2025-09-07T06:25:46.0102064Z * [new tag] trunk/9aedb3cd87b52160872173c177f61053d97bed57 -> trunk/9aedb3cd87b52160872173c177f61053d97bed57 2025-09-07T06:25:46.0102916Z * [new tag] trunk/9b81fe281da41f2421506339d26b027a468902f4 -> trunk/9b81fe281da41f2421506339d26b027a468902f4 2025-09-07T06:25:46.0103804Z * [new tag] trunk/9bdcee01f86e2969cff1140cdecfca13cb51816e -> trunk/9bdcee01f86e2969cff1140cdecfca13cb51816e 2025-09-07T06:25:46.0104695Z * [new tag] trunk/9c03d6be87eedc06e524e202e07a7e776551a839 -> trunk/9c03d6be87eedc06e524e202e07a7e776551a839 2025-09-07T06:25:46.0105550Z * [new tag] trunk/9c957723a0fedd9c637e63e023a613019e2cab60 -> trunk/9c957723a0fedd9c637e63e023a613019e2cab60 2025-09-07T06:25:46.0106458Z * [new tag] trunk/9e5247f51d81735e5f1e65e80588985fa93bccc5 -> trunk/9e5247f51d81735e5f1e65e80588985fa93bccc5 2025-09-07T06:25:46.0107423Z * [new tag] trunk/9eadb37cdd699f7e8e8177a5227bfeb16184ef26 -> trunk/9eadb37cdd699f7e8e8177a5227bfeb16184ef26 2025-09-07T06:25:46.0108352Z * [new tag] trunk/a00cdc1e4159db73c9ffb3f25e93e55877709a29 -> trunk/a00cdc1e4159db73c9ffb3f25e93e55877709a29 2025-09-07T06:25:46.0109194Z * [new tag] trunk/a02ee4a816d11380c6f564c1aba64d56af5ba705 -> trunk/a02ee4a816d11380c6f564c1aba64d56af5ba705 2025-09-07T06:25:46.0110060Z * [new tag] trunk/a3c7f77e50f900721817934120d60c2361b3c40d -> trunk/a3c7f77e50f900721817934120d60c2361b3c40d 2025-09-07T06:25:46.0110933Z * [new tag] trunk/a3d72b09ae12126a2b7d4a63a45ac100a882a802 -> trunk/a3d72b09ae12126a2b7d4a63a45ac100a882a802 2025-09-07T06:25:46.0112370Z * [new tag] trunk/a3e5466002791da609fcb069155d8ee347baee92 -> trunk/a3e5466002791da609fcb069155d8ee347baee92 2025-09-07T06:25:46.0113223Z * [new tag] trunk/a714437093ed196eee28f7de454cf4c41badc098 -> trunk/a714437093ed196eee28f7de454cf4c41badc098 2025-09-07T06:25:46.0114114Z * [new tag] trunk/a75e8cd27098f290de0b7439685d05ce02e91356 -> trunk/a75e8cd27098f290de0b7439685d05ce02e91356 2025-09-07T06:25:46.0114954Z * [new tag] trunk/a8d6943d36c1c2a5f90d3573460695bad4b623ae -> trunk/a8d6943d36c1c2a5f90d3573460695bad4b623ae 2025-09-07T06:25:46.0115934Z * [new tag] trunk/a918bbad6ab20649ff82eefb48417ecbe96bcb34 -> trunk/a918bbad6ab20649ff82eefb48417ecbe96bcb34 2025-09-07T06:25:46.0116853Z * [new tag] trunk/a99d8d39bc842d6ebc3e368b178e4884d24b056e -> trunk/a99d8d39bc842d6ebc3e368b178e4884d24b056e 2025-09-07T06:25:46.0117707Z * [new tag] trunk/aac1a50a191b4102d566c9c1ea22f06d6c2e3f02 -> trunk/aac1a50a191b4102d566c9c1ea22f06d6c2e3f02 2025-09-07T06:25:46.0118578Z * [new tag] trunk/aad96a202244c7d0d120c04ba8db593edd8c0f92 -> trunk/aad96a202244c7d0d120c04ba8db593edd8c0f92 2025-09-07T06:25:46.0119574Z * [new tag] trunk/ab643e4dbbaf7b663d4237514cbf01af9b11565c -> trunk/ab643e4dbbaf7b663d4237514cbf01af9b11565c 2025-09-07T06:25:46.0120448Z * [new tag] trunk/abc447174cd2cf8591edbc70a9f836f9a5779f47 -> trunk/abc447174cd2cf8591edbc70a9f836f9a5779f47 2025-09-07T06:25:46.0121348Z * [new tag] trunk/acece97c3a9dceb63194e314da93fdf37cf15a0d -> trunk/acece97c3a9dceb63194e314da93fdf37cf15a0d 2025-09-07T06:25:46.0122319Z * [new tag] trunk/adae7f66aacf3f248c3101b858cf98d5809119fa -> trunk/adae7f66aacf3f248c3101b858cf98d5809119fa 2025-09-07T06:25:46.0123245Z * [new tag] trunk/ae0edc133e61e3b16caf0b2ee0ff3f33ab72af4c -> trunk/ae0edc133e61e3b16caf0b2ee0ff3f33ab72af4c 2025-09-07T06:25:46.0124228Z * [new tag] trunk/aed33a8fcbd60b052d4559d261390c5797129c6d -> trunk/aed33a8fcbd60b052d4559d261390c5797129c6d 2025-09-07T06:25:46.0125224Z * [new tag] trunk/b04e922712080a3652e438d05e8bb74e0cd2d238 -> trunk/b04e922712080a3652e438d05e8bb74e0cd2d238 2025-09-07T06:25:46.0126260Z * [new tag] trunk/b0a3e58dd71c1a039ac0ef51e5bd8f704f632f6f -> trunk/b0a3e58dd71c1a039ac0ef51e5bd8f704f632f6f 2025-09-07T06:25:46.0127126Z * [new tag] trunk/b16d3f4c8c01d461c2f01064e9ca5fa2b33f5cf1 -> trunk/b16d3f4c8c01d461c2f01064e9ca5fa2b33f5cf1 2025-09-07T06:25:46.0128023Z * [new tag] trunk/b18bb6796f210a183e687d9d64984a5a9d13cf09 -> trunk/b18bb6796f210a183e687d9d64984a5a9d13cf09 2025-09-07T06:25:46.0129126Z * [new tag] trunk/b1bb98ddebdd3e41bf7987372409bdce96ae55de -> trunk/b1bb98ddebdd3e41bf7987372409bdce96ae55de 2025-09-07T06:25:46.0129955Z * [new tag] trunk/b2b4add0e754411372060e1d7b4057a66439172b -> trunk/b2b4add0e754411372060e1d7b4057a66439172b 2025-09-07T06:25:46.0130875Z * [new tag] trunk/b2c7b9ad2dc5a7c0b61febd307761bd5bc2f0f05 -> trunk/b2c7b9ad2dc5a7c0b61febd307761bd5bc2f0f05 2025-09-07T06:25:46.0131791Z * [new tag] trunk/b40d9432be44a6b5974ee62e7d19c3c61c5ece37 -> trunk/b40d9432be44a6b5974ee62e7d19c3c61c5ece37 2025-09-07T06:25:46.0132678Z * [new tag] trunk/b4ad38279b178b7bd14355123c1101e2e853e77b -> trunk/b4ad38279b178b7bd14355123c1101e2e853e77b 2025-09-07T06:25:46.0133588Z * [new tag] trunk/b67c41039835bd9b20b83cd6233e86baaa5f5dde -> trunk/b67c41039835bd9b20b83cd6233e86baaa5f5dde 2025-09-07T06:25:46.0134698Z * [new tag] trunk/b6d0a9ea9056ede4f7024dbf3bd6c43be3aff49c -> trunk/b6d0a9ea9056ede4f7024dbf3bd6c43be3aff49c 2025-09-07T06:25:46.0135537Z * [new tag] trunk/b7dad7dd49448c88d0751fa2e29c70afe985f734 -> trunk/b7dad7dd49448c88d0751fa2e29c70afe985f734 2025-09-07T06:25:46.0136383Z * [new tag] trunk/b7e207ca9f046ddd716076965a0cce403ba99052 -> trunk/b7e207ca9f046ddd716076965a0cce403ba99052 2025-09-07T06:25:46.0137338Z * [new tag] trunk/b919560c4a7010e2d89facee25586269a994746e -> trunk/b919560c4a7010e2d89facee25586269a994746e 2025-09-07T06:25:46.0138303Z * [new tag] trunk/b9ba612f7a968f7b27e121ca8f4d0a4d954f5354 -> trunk/b9ba612f7a968f7b27e121ca8f4d0a4d954f5354 2025-09-07T06:25:46.0139278Z * [new tag] trunk/ba7f546ccccb5e0b36d9070dc25f26a9647f89f8 -> trunk/ba7f546ccccb5e0b36d9070dc25f26a9647f89f8 2025-09-07T06:25:46.0140179Z * [new tag] trunk/bb950284c7e72905994bc25dd436c10e48088d85 -> trunk/bb950284c7e72905994bc25dd436c10e48088d85 2025-09-07T06:25:46.0141090Z * [new tag] trunk/bbedc71fd3267c639c38b4ec25eaa22f973d9c4d -> trunk/bbedc71fd3267c639c38b4ec25eaa22f973d9c4d 2025-09-07T06:25:46.0141950Z * [new tag] trunk/bc4db2c27fce6ff1648bdc5af31ec225d2a31f37 -> trunk/bc4db2c27fce6ff1648bdc5af31ec225d2a31f37 2025-09-07T06:25:46.0142796Z * [new tag] trunk/bc505977fb66677a09c31155c987330fbb18a865 -> trunk/bc505977fb66677a09c31155c987330fbb18a865 2025-09-07T06:25:46.0143722Z * [new tag] trunk/bd39e47feea7326afb5bbb67fcb1e69279239527 -> trunk/bd39e47feea7326afb5bbb67fcb1e69279239527 2025-09-07T06:25:46.0144665Z * [new tag] trunk/be5b03dde96638f25ffd732a4fed7e41b4cf40e1 -> trunk/be5b03dde96638f25ffd732a4fed7e41b4cf40e1 2025-09-07T06:25:46.0145594Z * [new tag] trunk/bffc7dd1f374d8408911cd22c6b3d6df39ded9b3 -> trunk/bffc7dd1f374d8408911cd22c6b3d6df39ded9b3 2025-09-07T06:25:46.0146525Z * [new tag] trunk/c024b1f5a18d5c5aee5cc2acdd4c52b24b93ffcf -> trunk/c024b1f5a18d5c5aee5cc2acdd4c52b24b93ffcf 2025-09-07T06:25:46.0147451Z * [new tag] trunk/c0983e6cc0acf71689e1851d12609e00b3f59371 -> trunk/c0983e6cc0acf71689e1851d12609e00b3f59371 2025-09-07T06:25:46.0148343Z * [new tag] trunk/c10195e723eeeedd099ed8b73eda7184ca618fad -> trunk/c10195e723eeeedd099ed8b73eda7184ca618fad 2025-09-07T06:25:46.0149266Z * [new tag] trunk/c157cf6488ade6a7ee2ce2d25b059e1335630a99 -> trunk/c157cf6488ade6a7ee2ce2d25b059e1335630a99 2025-09-07T06:25:46.0150156Z * [new tag] trunk/c2a30246172fd71d56529907ffd3c27b76b1f3a7 -> trunk/c2a30246172fd71d56529907ffd3c27b76b1f3a7 2025-09-07T06:25:46.0151105Z * [new tag] trunk/c32111149921b48bfef909293f1049e21619ed76 -> trunk/c32111149921b48bfef909293f1049e21619ed76 2025-09-07T06:25:46.0151865Z * [new tag] trunk/c37103234afc832dcad307e9016230810957c9d5 -> trunk/c37103234afc832dcad307e9016230810957c9d5 2025-09-07T06:25:46.0152823Z * [new tag] trunk/c3ceca2995cd35e1376c4b0704669bff1a81e836 -> trunk/c3ceca2995cd35e1376c4b0704669bff1a81e836 2025-09-07T06:25:46.0153732Z * [new tag] trunk/c3d54dea9febb1236d48d19e5d4876a63f2e20fd -> trunk/c3d54dea9febb1236d48d19e5d4876a63f2e20fd 2025-09-07T06:25:46.0154694Z * [new tag] trunk/c465b3d52c5687fe910d35a5c75341b77f821741 -> trunk/c465b3d52c5687fe910d35a5c75341b77f821741 2025-09-07T06:25:46.0155646Z * [new tag] trunk/c5b8a10be5e89396da916d1069ffcb7135f0372b -> trunk/c5b8a10be5e89396da916d1069ffcb7135f0372b 2025-09-07T06:25:46.0156451Z * [new tag] trunk/c7e41071a08f4045bc11ab60ec366d7357d56e30 -> trunk/c7e41071a08f4045bc11ab60ec366d7357d56e30 2025-09-07T06:25:46.0157385Z * [new tag] trunk/c98ddaca6d2e19ca37aff00c4ff0cda1e9a6ff65 -> trunk/c98ddaca6d2e19ca37aff00c4ff0cda1e9a6ff65 2025-09-07T06:25:46.0158602Z * [new tag] trunk/cb1e31362c7b53acf4ac95b9f8878064c184f03b -> trunk/cb1e31362c7b53acf4ac95b9f8878064c184f03b 2025-09-07T06:25:46.0159380Z * [new tag] trunk/cbfb005f7cce79974795b148e265f594f59477c8 -> trunk/cbfb005f7cce79974795b148e265f594f59477c8 2025-09-07T06:25:46.0160330Z * [new tag] trunk/cc5bdd12401bda835291d2f3cb297132ebdbf358 -> trunk/cc5bdd12401bda835291d2f3cb297132ebdbf358 2025-09-07T06:25:46.0161478Z * [new tag] trunk/cd529b686d54bbaa443f5b310140de48422d96c7 -> trunk/cd529b686d54bbaa443f5b310140de48422d96c7 2025-09-07T06:25:46.0162238Z * [new tag] trunk/cec0ff122815582af5302360aff03676558c5c87 -> trunk/cec0ff122815582af5302360aff03676558c5c87 2025-09-07T06:25:46.0163150Z * [new tag] trunk/d11720efdb563d02cf4f7d324311fb15a755268e -> trunk/d11720efdb563d02cf4f7d324311fb15a755268e 2025-09-07T06:25:46.0163996Z * [new tag] trunk/d1706d9128ae24d9048167e80d3fe5196d19035e -> trunk/d1706d9128ae24d9048167e80d3fe5196d19035e 2025-09-07T06:25:46.0164976Z * [new tag] trunk/d1a15abfdcaef138f2d9e93a9f46be44f30b766d -> trunk/d1a15abfdcaef138f2d9e93a9f46be44f30b766d 2025-09-07T06:25:46.0166214Z * [new tag] trunk/d232a95d4a79404ca05c1f52d37fde7339dcdf49 -> trunk/d232a95d4a79404ca05c1f52d37fde7339dcdf49 2025-09-07T06:25:46.0167023Z * [new tag] trunk/d2d4c8e9b2371c9aacfb771d9402ac7427b9778e -> trunk/d2d4c8e9b2371c9aacfb771d9402ac7427b9778e 2025-09-07T06:25:46.0167897Z * [new tag] trunk/d33840c542b387ab08ba49aa6c45aa9567fd9be7 -> trunk/d33840c542b387ab08ba49aa6c45aa9567fd9be7 2025-09-07T06:25:46.0168779Z * [new tag] trunk/d5643e8f3a648a99636bfa1f2a41d54bd3c0d0f1 -> trunk/d5643e8f3a648a99636bfa1f2a41d54bd3c0d0f1 2025-09-07T06:25:46.0169687Z * [new tag] trunk/d5b38410b5b6cf75c7a7389972777a6497926ee7 -> trunk/d5b38410b5b6cf75c7a7389972777a6497926ee7 2025-09-07T06:25:46.0170483Z * [new tag] trunk/d5e0f4202ba14632e4d14862ace096609e763462 -> trunk/d5e0f4202ba14632e4d14862ace096609e763462 2025-09-07T06:25:46.0171900Z * [new tag] trunk/d636c181f9140a7b59be10b36eae23039fc2bb72 -> trunk/d636c181f9140a7b59be10b36eae23039fc2bb72 2025-09-07T06:25:46.0173333Z * [new tag] trunk/d64718503728001a1e78168fd7f2d4ff23e57285 -> trunk/d64718503728001a1e78168fd7f2d4ff23e57285 2025-09-07T06:25:46.0174188Z * [new tag] trunk/d67c29ad22670320d676b02e394274af34e8e643 -> trunk/d67c29ad22670320d676b02e394274af34e8e643 2025-09-07T06:25:46.0175125Z * [new tag] trunk/d6b74568e2c98ce58ecc145b72ac66d4caf7ce95 -> trunk/d6b74568e2c98ce58ecc145b72ac66d4caf7ce95 2025-09-07T06:25:46.0176072Z * [new tag] trunk/d711f27845abd45007ccab6076649ebd896c2661 -> trunk/d711f27845abd45007ccab6076649ebd896c2661 2025-09-07T06:25:46.0176974Z * [new tag] trunk/d9d6dde0f42d4bcc8c97671ac50d5096c7e500ab -> trunk/d9d6dde0f42d4bcc8c97671ac50d5096c7e500ab 2025-09-07T06:25:46.0177851Z * [new tag] trunk/da4db4b33d1fdd046650cf19fdbac581a19bf2f9 -> trunk/da4db4b33d1fdd046650cf19fdbac581a19bf2f9 2025-09-07T06:25:46.0178628Z * [new tag] trunk/dac8a4b91c01c3bbc96f54e621b1ea4ffdbd29d1 -> trunk/dac8a4b91c01c3bbc96f54e621b1ea4ffdbd29d1 2025-09-07T06:25:46.0179600Z * [new tag] trunk/dbec08729fb9848bebed6048c63831b87170d061 -> trunk/dbec08729fb9848bebed6048c63831b87170d061 2025-09-07T06:25:46.0180409Z * [new tag] trunk/dcf385395d838f38c8dca25913578230dd43099a -> trunk/dcf385395d838f38c8dca25913578230dd43099a 2025-09-07T06:25:46.0181304Z * [new tag] trunk/dd2519abe83ec3c40d4797492434e41fe3b47e17 -> trunk/dd2519abe83ec3c40d4797492434e41fe3b47e17 2025-09-07T06:25:46.0182244Z * [new tag] trunk/dec72ea4b006dd0fbcaaaa106ad273d73807ab9d -> trunk/dec72ea4b006dd0fbcaaaa106ad273d73807ab9d 2025-09-07T06:25:46.0183115Z * [new tag] trunk/e0a62b266c021b910ce6dc02a6c9429210487717 -> trunk/e0a62b266c021b910ce6dc02a6c9429210487717 2025-09-07T06:25:46.0184029Z * [new tag] trunk/e19e02c84c9dcc408375e5cae3b0709c18b99228 -> trunk/e19e02c84c9dcc408375e5cae3b0709c18b99228 2025-09-07T06:25:46.0184976Z * [new tag] trunk/e304ea4e69d3a7deeb7e48c7450c214a4c953937 -> trunk/e304ea4e69d3a7deeb7e48c7450c214a4c953937 2025-09-07T06:25:46.0185963Z * [new tag] trunk/e3068cdb446adefb5a875616ba37a60235391439 -> trunk/e3068cdb446adefb5a875616ba37a60235391439 2025-09-07T06:25:46.0186845Z * [new tag] trunk/e381d4b0205d5f126c1de534f867ba776f7c3ee6 -> trunk/e381d4b0205d5f126c1de534f867ba776f7c3ee6 2025-09-07T06:25:46.0187818Z * [new tag] trunk/e4bd0ff4f8981b805df32ea5b3550621965ea4f2 -> trunk/e4bd0ff4f8981b805df32ea5b3550621965ea4f2 2025-09-07T06:25:46.0188711Z * [new tag] trunk/e532c9d4f1cdcbc1ea9628f55b9813e77847bdc7 -> trunk/e532c9d4f1cdcbc1ea9628f55b9813e77847bdc7 2025-09-07T06:25:46.0189606Z * [new tag] trunk/e92cd9415377403b6e90585e764639e2e0b5973b -> trunk/e92cd9415377403b6e90585e764639e2e0b5973b 2025-09-07T06:25:46.0190572Z * [new tag] trunk/e9481b6617b5576b099d8ca5798111592e9ad090 -> trunk/e9481b6617b5576b099d8ca5798111592e9ad090 2025-09-07T06:25:46.0191298Z * [new tag] trunk/ea1883dfd3e42defe37b11202b878bb76defa087 -> trunk/ea1883dfd3e42defe37b11202b878bb76defa087 2025-09-07T06:25:46.0192218Z * [new tag] trunk/eac3d6f04cfbbebe3d470dacd216da7d4b1f95a8 -> trunk/eac3d6f04cfbbebe3d470dacd216da7d4b1f95a8 2025-09-07T06:25:46.0193090Z * [new tag] trunk/eb18d32bda75189494d955aa001ade15f10333de -> trunk/eb18d32bda75189494d955aa001ade15f10333de 2025-09-07T06:25:46.0193841Z * [new tag] trunk/ef3be6726f7ff4b77c22db10cec5b686f9107ea9 -> trunk/ef3be6726f7ff4b77c22db10cec5b686f9107ea9 2025-09-07T06:25:46.0194812Z * [new tag] trunk/ef8aabd42422725026cb4dbf48aafa9efa226a04 -> trunk/ef8aabd42422725026cb4dbf48aafa9efa226a04 2025-09-07T06:25:46.0196002Z * [new tag] trunk/f00445b43eee57e20bb9316fa796ca23bf73373b -> trunk/f00445b43eee57e20bb9316fa796ca23bf73373b 2025-09-07T06:25:46.0196797Z * [new tag] trunk/f0c391102b754e3b145e8c59231d2df563487e37 -> trunk/f0c391102b754e3b145e8c59231d2df563487e37 2025-09-07T06:25:46.0197808Z * [new tag] trunk/f27985b7e796fb66a1b476284ba42d8cb360a751 -> trunk/f27985b7e796fb66a1b476284ba42d8cb360a751 2025-09-07T06:25:46.0198773Z * [new tag] trunk/f36f285953700f971552083a5da9d0ceacb63bbd -> trunk/f36f285953700f971552083a5da9d0ceacb63bbd 2025-09-07T06:25:46.0199681Z * [new tag] trunk/f3cebec39ebc110e1c8b06e741896585f7892dbb -> trunk/f3cebec39ebc110e1c8b06e741896585f7892dbb 2025-09-07T06:25:46.0200485Z * [new tag] trunk/f4c33cd44acac92c0b451a04da20ebe9370e5b0c -> trunk/f4c33cd44acac92c0b451a04da20ebe9370e5b0c 2025-09-07T06:25:46.0201432Z * [new tag] trunk/f612045ce105f008b2b675e2fc870163babeb2e8 -> trunk/f612045ce105f008b2b675e2fc870163babeb2e8 2025-09-07T06:25:46.0202393Z * [new tag] trunk/f8746b878dfc1e9639d42cbde832e9b9e792c86c -> trunk/f8746b878dfc1e9639d42cbde832e9b9e792c86c 2025-09-07T06:25:46.0203207Z * [new tag] trunk/f8ffa9194e26523e5f976d4a824d5cc58922727c -> trunk/f8ffa9194e26523e5f976d4a824d5cc58922727c 2025-09-07T06:25:46.0204089Z * [new tag] trunk/f981a7fa5230b98974291fdde32fe8488bc5d469 -> trunk/f981a7fa5230b98974291fdde32fe8488bc5d469 2025-09-07T06:25:46.0205015Z * [new tag] trunk/fbf3d2027daabbcb44d0af274b139be2a248a4f7 -> trunk/fbf3d2027daabbcb44d0af274b139be2a248a4f7 2025-09-07T06:25:46.0206276Z * [new tag] trunk/fca2601c9d628e1bd2d75c7318cd22c4e8c832aa -> trunk/fca2601c9d628e1bd2d75c7318cd22c4e8c832aa 2025-09-07T06:25:46.0207147Z * [new tag] trunk/fea20775ad96bdca972a1811d7d3372f368614ab -> trunk/fea20775ad96bdca972a1811d7d3372f368614ab 2025-09-07T06:25:46.0207874Z * [new tag] trunk/fefee081642f87419a21dc852f7167d4640443cd -> trunk/fefee081642f87419a21dc852f7167d4640443cd 2025-09-07T06:25:46.0208519Z * [new tag] v0.1.1 -> v0.1.1 2025-09-07T06:25:46.0209473Z * [new tag] v0.1.10 -> v0.1.10 2025-09-07T06:25:46.0210342Z * [new tag] v0.1.11 -> v0.1.11 2025-09-07T06:25:46.0211057Z * [new tag] v0.1.12 -> v0.1.12 2025-09-07T06:25:46.0211895Z * [new tag] v0.1.2 -> v0.1.2 2025-09-07T06:25:46.0212608Z * [new tag] v0.1.3 -> v0.1.3 2025-09-07T06:25:46.0213505Z * [new tag] v0.1.4 -> v0.1.4 2025-09-07T06:25:46.0214195Z * [new tag] v0.1.5 -> v0.1.5 2025-09-07T06:25:46.0215105Z * [new tag] v0.1.6 -> v0.1.6 2025-09-07T06:25:46.0215780Z * [new tag] v0.1.7 -> v0.1.7 2025-09-07T06:25:46.0216803Z * [new tag] v0.1.8 -> v0.1.8 2025-09-07T06:25:46.0217686Z * [new tag] v0.1.9 -> v0.1.9 2025-09-07T06:25:46.0218299Z * [new tag] v0.2.0 -> v0.2.0 2025-09-07T06:25:46.0219229Z * [new tag] v0.3.0 -> v0.3.0 2025-09-07T06:25:46.0220184Z * [new tag] v0.3.1 -> v0.3.1 2025-09-07T06:25:46.0220886Z * [new tag] v0.4.0 -> v0.4.0 2025-09-07T06:25:46.0221791Z * [new tag] v0.4.1 -> v0.4.1 2025-09-07T06:25:46.0222499Z * [new tag] v1.0.0 -> v1.0.0 2025-09-07T06:25:46.0223479Z * [new tag] v1.0.0a0 -> v1.0.0a0 2025-09-07T06:25:46.0224523Z * [new tag] v1.0.1 -> v1.0.1 2025-09-07T06:25:46.0225258Z * [new tag] v1.0rc0 -> v1.0rc0 2025-09-07T06:25:46.0225941Z * [new tag] v1.0rc1 -> v1.0rc1 2025-09-07T06:25:46.0226892Z * [new tag] v1.1.0 -> v1.1.0 2025-09-07T06:25:46.0227635Z * [new tag] v1.1.0a0 -> v1.1.0a0 2025-09-07T06:25:46.0228791Z * [new tag] v1.10.0 -> v1.10.0 2025-09-07T06:25:46.0229726Z * [new tag] v1.10.0-rc1 -> v1.10.0-rc1 2025-09-07T06:25:46.0230669Z * [new tag] v1.10.0-rc2 -> v1.10.0-rc2 2025-09-07T06:25:46.0231110Z * [new tag] v1.10.0-rc3 -> v1.10.0-rc3 2025-09-07T06:25:46.0232141Z * [new tag] v1.10.1 -> v1.10.1 2025-09-07T06:25:46.0232728Z * [new tag] v1.10.1-rc1 -> v1.10.1-rc1 2025-09-07T06:25:46.0233471Z * [new tag] v1.10.2 -> v1.10.2 2025-09-07T06:25:46.0234006Z * [new tag] v1.10.2-rc1 -> v1.10.2-rc1 2025-09-07T06:25:46.0235115Z * [new tag] v1.11.0 -> v1.11.0 2025-09-07T06:25:46.0236021Z * [new tag] v1.11.0-rc1 -> v1.11.0-rc1 2025-09-07T06:25:46.0236992Z * [new tag] v1.11.0-rc2 -> v1.11.0-rc2 2025-09-07T06:25:46.0237848Z * [new tag] v1.11.0-rc3 -> v1.11.0-rc3 2025-09-07T06:25:46.0238780Z * [new tag] v1.11.0-rc4 -> v1.11.0-rc4 2025-09-07T06:25:46.0239586Z * [new tag] v1.11.0-rc5 -> v1.11.0-rc5 2025-09-07T06:25:46.0240257Z * [new tag] v1.11.0-rc6 -> v1.11.0-rc6 2025-09-07T06:25:46.0240957Z * [new tag] v1.11.0-rc7 -> v1.11.0-rc7 2025-09-07T06:25:46.0241892Z * [new tag] v1.12.0 -> v1.12.0 2025-09-07T06:25:46.0242591Z * [new tag] v1.12.0-rc1 -> v1.12.0-rc1 2025-09-07T06:25:46.0243722Z * [new tag] v1.12.0-rc2 -> v1.12.0-rc2 2025-09-07T06:25:46.0244632Z * [new tag] v1.12.0-rc3 -> v1.12.0-rc3 2025-09-07T06:25:46.0245545Z * [new tag] v1.12.0-rc4 -> v1.12.0-rc4 2025-09-07T06:25:46.0246275Z * [new tag] v1.12.0-rc5 -> v1.12.0-rc5 2025-09-07T06:25:46.0247294Z * [new tag] v1.12.0-rc6 -> v1.12.0-rc6 2025-09-07T06:25:46.0247888Z * [new tag] v1.12.0-rc7 -> v1.12.0-rc7 2025-09-07T06:25:46.0248482Z * [new tag] v1.12.0-rc8 -> v1.12.0-rc8 2025-09-07T06:25:46.0249138Z * [new tag] v1.12.1 -> v1.12.1 2025-09-07T06:25:46.0250313Z * [new tag] v1.12.1-rc1 -> v1.12.1-rc1 2025-09-07T06:25:46.0251068Z * [new tag] v1.12.1-rc2 -> v1.12.1-rc2 2025-09-07T06:25:46.0252114Z * [new tag] v1.12.1-rc3 -> v1.12.1-rc3 2025-09-07T06:25:46.0253236Z * [new tag] v1.12.1-rc4 -> v1.12.1-rc4 2025-09-07T06:25:46.0253760Z * [new tag] v1.12.1-rc5 -> v1.12.1-rc5 2025-09-07T06:25:46.0254672Z * [new tag] v1.13.0 -> v1.13.0 2025-09-07T06:25:46.0255430Z * [new tag] v1.13.0-rc1 -> v1.13.0-rc1 2025-09-07T06:25:46.0256336Z * [new tag] v1.13.0-rc2 -> v1.13.0-rc2 2025-09-07T06:25:46.0257658Z * [new tag] v1.13.0-rc3 -> v1.13.0-rc3 2025-09-07T06:25:46.0258604Z * [new tag] v1.13.0-rc4 -> v1.13.0-rc4 2025-09-07T06:25:46.0259213Z * [new tag] v1.13.0-rc5 -> v1.13.0-rc5 2025-09-07T06:25:46.0259829Z * [new tag] v1.13.0-rc6 -> v1.13.0-rc6 2025-09-07T06:25:46.0261222Z * [new tag] v1.13.1 -> v1.13.1 2025-09-07T06:25:46.0261844Z * [new tag] v1.13.1-rc1 -> v1.13.1-rc1 2025-09-07T06:25:46.0262729Z * [new tag] v1.2.0 -> v1.2.0 2025-09-07T06:25:46.0263619Z * [new tag] v1.2.0a0 -> v1.2.0a0 2025-09-07T06:25:46.0264372Z * [new tag] v1.3.0 -> v1.3.0 2025-09-07T06:25:46.0265364Z * [new tag] v1.3.0a0 -> v1.3.0a0 2025-09-07T06:25:46.0265908Z * [new tag] v1.3.1 -> v1.3.1 2025-09-07T06:25:46.0266861Z * [new tag] v1.4.0 -> v1.4.0 2025-09-07T06:25:46.0267640Z * [new tag] v1.4.0a0 -> v1.4.0a0 2025-09-07T06:25:46.0268289Z * [new tag] v1.4.1 -> v1.4.1 2025-09-07T06:25:46.0269298Z * [new tag] v1.5.0 -> v1.5.0 2025-09-07T06:25:46.0270257Z * [new tag] v1.5.0-rc1 -> v1.5.0-rc1 2025-09-07T06:25:46.0271226Z * [new tag] v1.5.0-rc2 -> v1.5.0-rc2 2025-09-07T06:25:46.0272156Z * [new tag] v1.5.0-rc3 -> v1.5.0-rc3 2025-09-07T06:25:46.0272839Z * [new tag] v1.5.0-rc4 -> v1.5.0-rc4 2025-09-07T06:25:46.0273504Z * [new tag] v1.5.0-rc5 -> v1.5.0-rc5 2025-09-07T06:25:46.0274593Z * [new tag] v1.5.1 -> v1.5.1 2025-09-07T06:25:46.0275248Z * [new tag] v1.5.1-rc1 -> v1.5.1-rc1 2025-09-07T06:25:46.0275897Z * [new tag] v1.6.0 -> v1.6.0 2025-09-07T06:25:46.0276819Z * [new tag] v1.6.0-rc1 -> v1.6.0-rc1 2025-09-07T06:25:46.0277761Z * [new tag] v1.6.0-rc2 -> v1.6.0-rc2 2025-09-07T06:25:46.0278662Z * [new tag] v1.6.0-rc3 -> v1.6.0-rc3 2025-09-07T06:25:46.0279437Z * [new tag] v1.6.0-rc4 -> v1.6.0-rc4 2025-09-07T06:25:46.0280396Z * [new tag] v1.6.0-rc5 -> v1.6.0-rc5 2025-09-07T06:25:46.0281144Z * [new tag] v1.6.0-rc6 -> v1.6.0-rc6 2025-09-07T06:25:46.0281835Z * [new tag] v1.6.0-rc7 -> v1.6.0-rc7 2025-09-07T06:25:46.0282813Z * [new tag] v1.7.0 -> v1.7.0 2025-09-07T06:25:46.0283754Z * [new tag] v1.7.0-rc1 -> v1.7.0-rc1 2025-09-07T06:25:46.0284698Z * [new tag] v1.7.0-rc2 -> v1.7.0-rc2 2025-09-07T06:25:46.0285546Z * [new tag] v1.7.0-rc3 -> v1.7.0-rc3 2025-09-07T06:25:46.0286175Z * [new tag] v1.7.0-rc4 -> v1.7.0-rc4 2025-09-07T06:25:46.0287114Z * [new tag] v1.7.1 -> v1.7.1 2025-09-07T06:25:46.0288172Z * [new tag] v1.7.1-rc1 -> v1.7.1-rc1 2025-09-07T06:25:46.0288886Z * [new tag] v1.7.1-rc2 -> v1.7.1-rc2 2025-09-07T06:25:46.0289606Z * [new tag] v1.7.1-rc3 -> v1.7.1-rc3 2025-09-07T06:25:46.0290606Z * [new tag] v1.8.0 -> v1.8.0 2025-09-07T06:25:46.0291163Z * [new tag] v1.8.0-rc1 -> v1.8.0-rc1 2025-09-07T06:25:46.0292099Z * [new tag] v1.8.0-rc2 -> v1.8.0-rc2 2025-09-07T06:25:46.0292982Z * [new tag] v1.8.0-rc3 -> v1.8.0-rc3 2025-09-07T06:25:46.0293828Z * [new tag] v1.8.0-rc4 -> v1.8.0-rc4 2025-09-07T06:25:46.0294452Z * [new tag] v1.8.0-rc5 -> v1.8.0-rc5 2025-09-07T06:25:46.0295113Z * [new tag] v1.8.1 -> v1.8.1 2025-09-07T06:25:46.0296098Z * [new tag] v1.8.1-rc1 -> v1.8.1-rc1 2025-09-07T06:25:46.0296701Z * [new tag] v1.8.1-rc2 -> v1.8.1-rc2 2025-09-07T06:25:46.0297561Z * [new tag] v1.8.1-rc3 -> v1.8.1-rc3 2025-09-07T06:25:46.0298892Z * [new tag] v1.8.2 -> v1.8.2 2025-09-07T06:25:46.0299442Z * [new tag] v1.8.2-rc1 -> v1.8.2-rc1 2025-09-07T06:25:46.0300530Z * [new tag] v1.9.0 -> v1.9.0 2025-09-07T06:25:46.0301253Z * [new tag] v1.9.0-rc1 -> v1.9.0-rc1 2025-09-07T06:25:46.0302271Z * [new tag] v1.9.0-rc2 -> v1.9.0-rc2 2025-09-07T06:25:46.0303145Z * [new tag] v1.9.0-rc3 -> v1.9.0-rc3 2025-09-07T06:25:46.0303696Z * [new tag] v1.9.0-rc4 -> v1.9.0-rc4 2025-09-07T06:25:46.0304771Z * [new tag] v1.9.1 -> v1.9.1 2025-09-07T06:25:46.0305799Z * [new tag] v1.9.1-rc1 -> v1.9.1-rc1 2025-09-07T06:25:46.0306468Z * [new tag] v1.9.1-rc2 -> v1.9.1-rc2 2025-09-07T06:25:46.0307418Z * [new tag] v2.0.0 -> v2.0.0 2025-09-07T06:25:46.0308171Z * [new tag] v2.0.0-rc1 -> v2.0.0-rc1 2025-09-07T06:25:46.0309174Z * [new tag] v2.0.0-rc2 -> v2.0.0-rc2 2025-09-07T06:25:46.0310075Z * [new tag] v2.0.0-rc3 -> v2.0.0-rc3 2025-09-07T06:25:46.0311041Z * [new tag] v2.0.0-rc4 -> v2.0.0-rc4 2025-09-07T06:25:46.0311785Z * [new tag] v2.0.0-rc5 -> v2.0.0-rc5 2025-09-07T06:25:46.0312467Z * [new tag] v2.0.0-rc6 -> v2.0.0-rc6 2025-09-07T06:25:46.0313491Z * [new tag] v2.0.1 -> v2.0.1 2025-09-07T06:25:46.0314515Z * [new tag] v2.0.1-rc1 -> v2.0.1-rc1 2025-09-07T06:25:46.0315636Z * [new tag] v2.0.1-rc2 -> v2.0.1-rc2 2025-09-07T06:25:46.0316382Z * [new tag] v2.0.1-rc3 -> v2.0.1-rc3 2025-09-07T06:25:46.0317058Z * [new tag] v2.0.1-rc4 -> v2.0.1-rc4 2025-09-07T06:25:46.0318552Z * [new tag] v2.1.0 -> v2.1.0 2025-09-07T06:25:46.0319282Z * [new tag] v2.1.0-rc1 -> v2.1.0-rc1 2025-09-07T06:25:46.0320300Z * [new tag] v2.1.0-rc2 -> v2.1.0-rc2 2025-09-07T06:25:46.0321248Z * [new tag] v2.1.0-rc3 -> v2.1.0-rc3 2025-09-07T06:25:46.0322140Z * [new tag] v2.1.0-rc4 -> v2.1.0-rc4 2025-09-07T06:25:46.0323116Z * [new tag] v2.1.0-rc5 -> v2.1.0-rc5 2025-09-07T06:25:46.0323822Z * [new tag] v2.1.0-rc6 -> v2.1.0-rc6 2025-09-07T06:25:46.0327570Z * [new tag] v2.1.1 -> v2.1.1 2025-09-07T06:25:46.0328504Z * [new tag] v2.1.1-rc1 -> v2.1.1-rc1 2025-09-07T06:25:46.0329466Z * [new tag] v2.1.1-rc2 -> v2.1.1-rc2 2025-09-07T06:25:46.0330472Z * [new tag] v2.1.1-rc3 -> v2.1.1-rc3 2025-09-07T06:25:46.0331375Z * [new tag] v2.1.1-rc4 -> v2.1.1-rc4 2025-09-07T06:25:46.0332125Z * [new tag] v2.1.1-rc5 -> v2.1.1-rc5 2025-09-07T06:25:46.0332809Z * [new tag] v2.1.1-rc6 -> v2.1.1-rc6 2025-09-07T06:25:46.0333766Z * [new tag] v2.1.2 -> v2.1.2 2025-09-07T06:25:46.0334668Z * [new tag] v2.1.2-rc1 -> v2.1.2-rc1 2025-09-07T06:25:46.0335621Z * [new tag] v2.1.2-rc2 -> v2.1.2-rc2 2025-09-07T06:25:46.0336283Z * [new tag] v2.1.2-rc3 -> v2.1.2-rc3 2025-09-07T06:25:46.0337166Z * [new tag] v2.2.0 -> v2.2.0 2025-09-07T06:25:46.0338104Z * [new tag] v2.2.0-rc1 -> v2.2.0-rc1 2025-09-07T06:25:46.0348341Z * [new tag] v2.2.0-rc2 -> v2.2.0-rc2 2025-09-07T06:25:46.0348677Z * [new tag] v2.2.0-rc3 -> v2.2.0-rc3 2025-09-07T06:25:46.0348842Z * [new tag] v2.2.0-rc4 -> v2.2.0-rc4 2025-09-07T06:25:46.0349011Z * [new tag] v2.2.0-rc5 -> v2.2.0-rc5 2025-09-07T06:25:46.0349154Z * [new tag] v2.2.0-rc6 -> v2.2.0-rc6 2025-09-07T06:25:46.0349325Z * [new tag] v2.2.0-rc7 -> v2.2.0-rc7 2025-09-07T06:25:46.0349469Z * [new tag] v2.2.0-rc8 -> v2.2.0-rc8 2025-09-07T06:25:46.0349615Z * [new tag] v2.2.1 -> v2.2.1 2025-09-07T06:25:46.0349770Z * [new tag] v2.2.1-rc1 -> v2.2.1-rc1 2025-09-07T06:25:46.0349914Z * [new tag] v2.2.1-rc2 -> v2.2.1-rc2 2025-09-07T06:25:46.0350068Z * [new tag] v2.2.1-rc3 -> v2.2.1-rc3 2025-09-07T06:25:46.0350206Z * [new tag] v2.2.2 -> v2.2.2 2025-09-07T06:25:46.0350456Z * [new tag] v2.2.2-rc1 -> v2.2.2-rc1 2025-09-07T06:25:46.0350613Z * [new tag] v2.2.2-rc2 -> v2.2.2-rc2 2025-09-07T06:25:46.0350759Z * [new tag] v2.2.2-rc3 -> v2.2.2-rc3 2025-09-07T06:25:46.0350909Z * [new tag] v2.3.0 -> v2.3.0 2025-09-07T06:25:46.0351523Z * [new tag] v2.3.0-rc1 -> v2.3.0-rc1 2025-09-07T06:25:46.0352587Z * [new tag] v2.3.0-rc10 -> v2.3.0-rc10 2025-09-07T06:25:46.0353481Z * [new tag] v2.3.0-rc11 -> v2.3.0-rc11 2025-09-07T06:25:46.0354300Z * [new tag] v2.3.0-rc12 -> v2.3.0-rc12 2025-09-07T06:25:46.0355089Z * [new tag] v2.3.0-rc2 -> v2.3.0-rc2 2025-09-07T06:25:46.0356152Z * [new tag] v2.3.0-rc3 -> v2.3.0-rc3 2025-09-07T06:25:46.0356904Z * [new tag] v2.3.0-rc4 -> v2.3.0-rc4 2025-09-07T06:25:46.0357924Z * [new tag] v2.3.0-rc5 -> v2.3.0-rc5 2025-09-07T06:25:46.0358469Z * [new tag] v2.3.0-rc6 -> v2.3.0-rc6 2025-09-07T06:25:46.0359478Z * [new tag] v2.3.0-rc7 -> v2.3.0-rc7 2025-09-07T06:25:46.0360239Z * [new tag] v2.3.0-rc8 -> v2.3.0-rc8 2025-09-07T06:25:46.0360907Z * [new tag] v2.3.0-rc9 -> v2.3.0-rc9 2025-09-07T06:25:46.0361753Z * [new tag] v2.3.1 -> v2.3.1 2025-09-07T06:25:46.0362477Z * [new tag] v2.3.1-rc1 -> v2.3.1-rc1 2025-09-07T06:25:46.0363455Z * [new tag] v2.3.1-rc2 -> v2.3.1-rc2 2025-09-07T06:25:46.0364204Z * [new tag] v2.3.1-rc3 -> v2.3.1-rc3 2025-09-07T06:25:46.0365256Z * [new tag] v2.4.0 -> v2.4.0 2025-09-07T06:25:46.0366011Z * [new tag] v2.4.0-rc1 -> v2.4.0-rc1 2025-09-07T06:25:46.0366956Z * [new tag] v2.4.0-rc2 -> v2.4.0-rc2 2025-09-07T06:25:46.0367722Z * [new tag] v2.4.0-rc3 -> v2.4.0-rc3 2025-09-07T06:25:46.0368723Z * [new tag] v2.4.0-rc4 -> v2.4.0-rc4 2025-09-07T06:25:46.0369634Z * [new tag] v2.4.0-rc5 -> v2.4.0-rc5 2025-09-07T06:25:46.0370528Z * [new tag] v2.4.0-rc6 -> v2.4.0-rc6 2025-09-07T06:25:46.0371466Z * [new tag] v2.4.0-rc7 -> v2.4.0-rc7 2025-09-07T06:25:46.0372352Z * [new tag] v2.4.0-rc8 -> v2.4.0-rc8 2025-09-07T06:25:46.0373735Z * [new tag] v2.4.0-rc9 -> v2.4.0-rc9 2025-09-07T06:25:46.0374374Z * [new tag] v2.4.1 -> v2.4.1 2025-09-07T06:25:46.0375351Z * [new tag] v2.4.1-rc1 -> v2.4.1-rc1 2025-09-07T06:25:46.0376303Z * [new tag] v2.4.1-rc2 -> v2.4.1-rc2 2025-09-07T06:25:46.0377287Z * [new tag] v2.4.1-rc3 -> v2.4.1-rc3 2025-09-07T06:25:46.0378196Z * [new tag] v2.5.0 -> v2.5.0 2025-09-07T06:25:46.0379105Z * [new tag] v2.5.0-rc1 -> v2.5.0-rc1 2025-09-07T06:25:46.0379721Z * [new tag] v2.5.0-rc10 -> v2.5.0-rc10 2025-09-07T06:25:46.0380670Z * [new tag] v2.5.0-rc2 -> v2.5.0-rc2 2025-09-07T06:25:46.0381409Z * [new tag] v2.5.0-rc3 -> v2.5.0-rc3 2025-09-07T06:25:46.0382419Z * [new tag] v2.5.0-rc4 -> v2.5.0-rc4 2025-09-07T06:25:46.0383361Z * [new tag] v2.5.0-rc5 -> v2.5.0-rc5 2025-09-07T06:25:46.0384428Z * [new tag] v2.5.0-rc6 -> v2.5.0-rc6 2025-09-07T06:25:46.0385180Z * [new tag] v2.5.0-rc7 -> v2.5.0-rc7 2025-09-07T06:25:46.0386161Z * [new tag] v2.5.0-rc8 -> v2.5.0-rc8 2025-09-07T06:25:46.0387114Z * [new tag] v2.5.0-rc9 -> v2.5.0-rc9 2025-09-07T06:25:46.0387736Z * [new tag] v2.5.1 -> v2.5.1 2025-09-07T06:25:46.0388430Z * [new tag] v2.5.1-rc1 -> v2.5.1-rc1 2025-09-07T06:25:46.0388993Z * [new tag] v2.6.0 -> v2.6.0 2025-09-07T06:25:46.0390080Z * [new tag] v2.6.0-rc1 -> v2.6.0-rc1 2025-09-07T06:25:46.0391022Z * [new tag] v2.6.0-rc2 -> v2.6.0-rc2 2025-09-07T06:25:46.0391921Z * [new tag] v2.6.0-rc3 -> v2.6.0-rc3 2025-09-07T06:25:46.0392706Z * [new tag] v2.6.0-rc4 -> v2.6.0-rc4 2025-09-07T06:25:46.0393908Z * [new tag] v2.6.0-rc5 -> v2.6.0-rc5 2025-09-07T06:25:46.0395021Z * [new tag] v2.6.0-rc6 -> v2.6.0-rc6 2025-09-07T06:25:46.0396010Z * [new tag] v2.6.0-rc7 -> v2.6.0-rc7 2025-09-07T06:25:46.0397003Z * [new tag] v2.6.0-rc8 -> v2.6.0-rc8 2025-09-07T06:25:46.0397906Z * [new tag] v2.6.0-rc9 -> v2.6.0-rc9 2025-09-07T06:25:46.0399088Z * [new tag] v2.7.0 -> v2.7.0 2025-09-07T06:25:46.0399762Z * [new tag] v2.7.0-rc1 -> v2.7.0-rc1 2025-09-07T06:25:46.0400537Z * [new tag] v2.7.0-rc10 -> v2.7.0-rc10 2025-09-07T06:25:46.0401582Z * [new tag] v2.7.0-rc2 -> v2.7.0-rc2 2025-09-07T06:25:46.0402474Z * [new tag] v2.7.0-rc3 -> v2.7.0-rc3 2025-09-07T06:25:46.0403422Z * [new tag] v2.7.0-rc4 -> v2.7.0-rc4 2025-09-07T06:25:46.0404184Z * [new tag] v2.7.0-rc5 -> v2.7.0-rc5 2025-09-07T06:25:46.0405180Z * [new tag] v2.7.0-rc6 -> v2.7.0-rc6 2025-09-07T06:25:46.0406110Z * [new tag] v2.7.0-rc7 -> v2.7.0-rc7 2025-09-07T06:25:46.0407064Z * [new tag] v2.7.0-rc8 -> v2.7.0-rc8 2025-09-07T06:25:46.0408005Z * [new tag] v2.7.0-rc9 -> v2.7.0-rc9 2025-09-07T06:25:46.0408565Z * [new tag] v2.7.1 -> v2.7.1 2025-09-07T06:25:46.0409661Z * [new tag] v2.7.1-rc1 -> v2.7.1-rc1 2025-09-07T06:25:46.0410580Z * [new tag] v2.7.1-rc2 -> v2.7.1-rc2 2025-09-07T06:25:46.0411672Z * [new tag] v2.7.1-rc3 -> v2.7.1-rc3 2025-09-07T06:25:46.0412647Z * [new tag] v2.7.1-rc4 -> v2.7.1-rc4 2025-09-07T06:25:46.0413700Z * [new tag] v2.7.1-rc5 -> v2.7.1-rc5 2025-09-07T06:25:46.0414263Z * [new tag] v2.8.0 -> v2.8.0 2025-09-07T06:25:46.0415332Z * [new tag] v2.8.0-rc1 -> v2.8.0-rc1 2025-09-07T06:25:46.0416342Z * [new tag] v2.8.0-rc2 -> v2.8.0-rc2 2025-09-07T06:25:46.0417333Z * [new tag] v2.8.0-rc3 -> v2.8.0-rc3 2025-09-07T06:25:46.0418272Z * [new tag] v2.8.0-rc4 -> v2.8.0-rc4 2025-09-07T06:25:46.0419232Z * [new tag] v2.8.0-rc5 -> v2.8.0-rc5 2025-09-07T06:25:46.0420162Z * [new tag] v2.8.0-rc6 -> v2.8.0-rc6 2025-09-07T06:25:46.0421099Z * [new tag] v2.8.0-rc7 -> v2.8.0-rc7 2025-09-07T06:25:46.0421900Z * [new tag] v2.8.0-rc8 -> v2.8.0-rc8 2025-09-07T06:25:46.0422903Z * [new tag] whc_flight_1 -> whc_flight_1 2025-09-07T06:25:46.0423970Z * [new tag] whc_flight_2 -> whc_flight_2 2025-09-07T06:25:46.0424913Z * [new tag] whc_flight_4 -> whc_flight_4 2025-09-07T06:25:46.1086324Z [command]/usr/bin/git rev-parse --verify --quiet 93fb23d6fae7c4e82c4239a1033e522088742634^{object} 2025-09-07T06:25:46.1109883Z 93fb23d6fae7c4e82c4239a1033e522088742634 2025-09-07T06:25:46.1113593Z ##[endgroup] 2025-09-07T06:25:46.1114010Z ##[group]Determining the checkout info 2025-09-07T06:25:46.1115115Z ##[endgroup] 2025-09-07T06:25:46.1119045Z [command]/usr/bin/git sparse-checkout disable 2025-09-07T06:25:46.1157459Z [command]/usr/bin/git config --local --unset-all extensions.worktreeConfig 2025-09-07T06:25:46.1182288Z ##[group]Checking out the ref 2025-09-07T06:25:46.1185664Z [command]/usr/bin/git checkout --progress --force 93fb23d6fae7c4e82c4239a1033e522088742634 2025-09-07T06:25:47.1463453Z Updating files: 85% (16609/19405) 2025-09-07T06:25:47.1597048Z Updating files: 86% (16689/19405) 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to '93fb23d6fae7c4e82c4239a1033e522088742634'. 2025-09-07T06:25:47.3897366Z 2025-09-07T06:25:47.3897588Z You are in 'detached HEAD' state. You can look around, make experimental 2025-09-07T06:25:47.3898177Z changes and commit them, and you can discard any commits you make in this 2025-09-07T06:25:47.3898750Z state without impacting any branches by switching back to a branch. 2025-09-07T06:25:47.3899087Z 2025-09-07T06:25:47.3899316Z If you want to create a new branch to retain commits you create, you may 2025-09-07T06:25:47.3899840Z do so (now or later) by using -c with the switch command. Example: 2025-09-07T06:25:47.3900138Z 2025-09-07T06:25:47.3900276Z git switch -c 2025-09-07T06:25:47.3900478Z 2025-09-07T06:25:47.3900586Z Or undo this operation with: 2025-09-07T06:25:47.3900789Z 2025-09-07T06:25:47.3900880Z git switch - 2025-09-07T06:25:47.3901025Z 2025-09-07T06:25:47.3901265Z Turn off this advice by setting config variable advice.detachedHead to false 2025-09-07T06:25:47.3901621Z 2025-09-07T06:25:47.3901815Z HEAD is now at 93fb23d6fae Build vLLM nightly wheels (#162000) 2025-09-07T06:25:47.3962798Z ##[endgroup] 2025-09-07T06:25:47.3963244Z ##[group]Setting up auth for fetching submodules 2025-09-07T06:25:47.3968769Z [command]/usr/bin/git config --global http.https://github.com/.extraheader AUTHORIZATION: basic *** 2025-09-07T06:25:47.4016205Z [command]/usr/bin/git config --global --unset-all url.https://github.com/.insteadOf 2025-09-07T06:25:47.4043290Z [command]/usr/bin/git config --global --add url.https://github.com/.insteadOf git@github.com: 2025-09-07T06:25:47.4069376Z [command]/usr/bin/git config --global --add url.https://github.com/.insteadOf org-21003710@github.com: 2025-09-07T06:25:47.4091536Z ##[endgroup] 2025-09-07T06:25:47.4092120Z ##[group]Fetching submodules 2025-09-07T06:25:47.4095021Z [command]/usr/bin/git submodule sync --recursive 2025-09-07T06:25:47.4406611Z [command]/usr/bin/git -c protocol.version=2 submodule update --init --force --recursive 2025-09-07T06:25:47.4715462Z Submodule 'android/libs/fbjni' (https://github.com/facebookincubator/fbjni.git) registered for path 'android/libs/fbjni' 2025-09-07T06:25:47.4717616Z Submodule 'third_party/NNPACK_deps/FP16' (https://github.com/Maratyszcza/FP16.git) registered for path 'third_party/FP16' 2025-09-07T06:25:47.4721025Z Submodule 'third_party/NNPACK_deps/FXdiv' (https://github.com/Maratyszcza/FXdiv.git) registered for path 'third_party/FXdiv' 2025-09-07T06:25:47.4724733Z Submodule 'third_party/NNPACK' (https://github.com/Maratyszcza/NNPACK.git) registered for path 'third_party/NNPACK' 2025-09-07T06:25:47.4728227Z Submodule 'third_party/NVTX' (https://github.com/NVIDIA/NVTX.git) registered for path 'third_party/NVTX' 2025-09-07T06:25:47.4732405Z Submodule 'third_party/VulkanMemoryAllocator' (https://github.com/GPUOpen-LibrariesAndSDKs/VulkanMemoryAllocator.git) registered for path 'third_party/VulkanMemoryAllocator' 2025-09-07T06:25:47.4735874Z Submodule 'third_party/XNNPACK' (https://github.com/google/XNNPACK.git) registered for path 'third_party/XNNPACK' 2025-09-07T06:25:47.4740522Z Submodule 'third_party/aiter' (https://github.com/ROCm/aiter.git) registered for path 'third_party/aiter' 2025-09-07T06:25:47.4744753Z Submodule 'third_party/benchmark' (https://github.com/google/benchmark.git) registered for path 'third_party/benchmark' 2025-09-07T06:25:47.4757451Z Submodule 'third_party/composable_kernel' (https://github.com/ROCm/composable_kernel.git) registered for path 'third_party/composable_kernel' 2025-09-07T06:25:47.5139311Z Submodule 'third_party/cpp-httplib' (https://github.com/yhirose/cpp-httplib.git) registered for path 'third_party/cpp-httplib' 2025-09-07T06:25:47.5142881Z Submodule 'third_party/cpuinfo' (https://github.com/pytorch/cpuinfo.git) registered for path 'third_party/cpuinfo' 2025-09-07T06:25:47.5147293Z Submodule 'third_party/cudnn_frontend' (https://github.com/NVIDIA/cudnn-frontend.git) registered for path 'third_party/cudnn_frontend' 2025-09-07T06:25:47.5151670Z Submodule 'third_party/cutlass' (https://github.com/NVIDIA/cutlass.git) registered for path 'third_party/cutlass' 2025-09-07T06:25:47.5156416Z Submodule 'third_party/fbgemm' (https://github.com/pytorch/fbgemm) registered for path 'third_party/fbgemm' 2025-09-07T06:25:47.5161173Z Submodule 'third_party/flash-attention' (https://github.com/Dao-AILab/flash-attention.git) registered for path 'third_party/flash-attention' 2025-09-07T06:25:47.5165954Z Submodule 'third_party/flatbuffers' (https://github.com/google/flatbuffers.git) registered for path 'third_party/flatbuffers' 2025-09-07T06:25:47.5170592Z Submodule 'third_party/fmt' (https://github.com/fmtlib/fmt.git) registered for path 'third_party/fmt' 2025-09-07T06:25:47.5175762Z Submodule 'third_party/gemmlowp/gemmlowp' (https://github.com/google/gemmlowp.git) registered for path 'third_party/gemmlowp/gemmlowp' 2025-09-07T06:25:47.5180626Z Submodule 'third_party/gloo' (https://github.com/pytorch/gloo) registered for path 'third_party/gloo' 2025-09-07T06:25:47.5185847Z Submodule 'third_party/googletest' (https://github.com/google/googletest.git) registered for path 'third_party/googletest' 2025-09-07T06:25:47.5190968Z Submodule 'third_party/ideep' (https://github.com/intel/ideep) registered for path 'third_party/ideep' 2025-09-07T06:25:47.5196533Z Submodule 'third_party/ittapi' (https://github.com/intel/ittapi.git) registered for path 'third_party/ittapi' 2025-09-07T06:25:47.5212960Z Submodule 'third_party/kineto' (https://github.com/pytorch/kineto) registered for path 'third_party/kineto' 2025-09-07T06:25:47.5218460Z Submodule 'third_party/kleidiai' (https://github.com/ARM-software/kleidiai.git) registered for path 'third_party/kleidiai' 2025-09-07T06:25:47.5224155Z Submodule 'third_party/mimalloc' (https://github.com/microsoft/mimalloc.git) registered for path 'third_party/mimalloc' 2025-09-07T06:25:47.5231451Z Submodule 'third_party/nlohmann' (https://github.com/nlohmann/json.git) registered for path 'third_party/nlohmann' 2025-09-07T06:25:47.5237324Z Submodule 'third_party/onnx' (https://github.com/onnx/onnx.git) registered for path 'third_party/onnx' 2025-09-07T06:25:47.5243403Z Submodule 'third_party/opentelemetry-cpp' (https://github.com/open-telemetry/opentelemetry-cpp.git) registered for path 'third_party/opentelemetry-cpp' 2025-09-07T06:25:47.5249103Z Submodule 'third_party/pocketfft' (https://github.com/mreineck/pocketfft) registered for path 'third_party/pocketfft' 2025-09-07T06:25:47.5255164Z Submodule 'third_party/protobuf' (https://github.com/protocolbuffers/protobuf.git) registered for path 'third_party/protobuf' 2025-09-07T06:25:47.5261224Z Submodule 'third_party/NNPACK_deps/psimd' (https://github.com/Maratyszcza/psimd.git) registered for path 'third_party/psimd' 2025-09-07T06:25:47.5267492Z Submodule 'third_party/NNPACK_deps/pthreadpool' (https://github.com/Maratyszcza/pthreadpool.git) registered for path 'third_party/pthreadpool' 2025-09-07T06:25:47.5273643Z Submodule 'third_party/pybind11' (https://github.com/pybind/pybind11.git) registered for path 'third_party/pybind11' 2025-09-07T06:25:47.5280226Z Submodule 'third_party/python-peachpy' (https://github.com/malfet/PeachPy.git) registered for path 'third_party/python-peachpy' 2025-09-07T06:25:47.5286654Z Submodule 'third_party/sleef' (https://github.com/shibatch/sleef) registered for path 'third_party/sleef' 2025-09-07T06:25:47.5293408Z Submodule 'third_party/tensorpipe' (https://github.com/pytorch/tensorpipe.git) registered for path 'third_party/tensorpipe' 2025-09-07T06:25:47.5361596Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/android/libs/fbjni'... 2025-09-07T06:25:47.7595767Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/FXdiv'... 2025-09-07T06:25:47.7597196Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/FP16'... 2025-09-07T06:25:47.7628626Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fmt'... 2025-09-07T06:25:50.7222518Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/NNPACK'... 2025-09-07T06:25:50.7224508Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/NVTX'... 2025-09-07T06:25:50.7226170Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/benchmark'... 2025-09-07T06:25:50.7227900Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/gloo'... 2025-09-07T06:25:50.7229596Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/gemmlowp/gemmlowp'... 2025-09-07T06:25:50.7231351Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/cpp-httplib'... 2025-09-07T06:25:50.7233650Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/cpuinfo'... 2025-09-07T06:25:50.7235352Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/flash-attention'... 2025-09-07T06:25:50.7236875Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/ideep'... 2025-09-07T06:25:50.7238869Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/ittapi'... 2025-09-07T06:25:50.7240380Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kleidiai'... 2025-09-07T06:25:50.7241815Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/pocketfft'... 2025-09-07T06:25:50.7243315Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/cudnn_frontend'... 2025-09-07T06:25:50.7244755Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/psimd'... 2025-09-07T06:25:50.7246135Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/pthreadpool'... 2025-09-07T06:25:50.7358474Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/flatbuffers'... 2025-09-07T06:25:51.0545049Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/googletest'... 2025-09-07T06:25:51.0546870Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/mimalloc'... 2025-09-07T06:25:51.0548617Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/VulkanMemoryAllocator'... 2025-09-07T06:25:51.1546141Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp'... 2025-09-07T06:26:06.2574554Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/python-peachpy'... 2025-09-07T06:26:06.2576457Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/tensorpipe'... 2025-09-07T06:26:06.2578432Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto'... 2025-09-07T06:26:06.2579861Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/sleef'... 2025-09-07T06:26:06.2581283Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/pybind11'... 2025-09-07T06:26:06.2582738Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm'... 2025-09-07T06:26:06.2584155Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/cutlass'... 2025-09-07T06:26:06.2585544Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/onnx'... 2025-09-07T06:26:06.2587048Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/composable_kernel'... 2025-09-07T06:26:06.2588852Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/aiter'... 2025-09-07T06:26:06.2590289Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/nlohmann'... 2025-09-07T06:26:06.3576050Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/XNNPACK'... 2025-09-07T06:26:10.4064632Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/protobuf'... 2025-09-07T06:26:10.4223022Z Submodule path 'android/libs/fbjni': checked out '7e1e1fe3858c63c251c637ae41a20de425dde96f' 2025-09-07T06:26:10.4349929Z Submodule path 'third_party/FP16': checked out '4dfe081cf6bcd15db339cf2680b9281b8451eeb3' 2025-09-07T06:26:10.4445593Z Submodule path 'third_party/FXdiv': checked out 'b408327ac2a15ec3e43352421954f5b1967701d1' 2025-09-07T06:26:10.4700897Z Submodule path 'third_party/NNPACK': checked out 'c07e3a0400713d546e0dea2d5466dd22ea389c73' 2025-09-07T06:26:10.5476920Z Submodule path 'third_party/NVTX': checked out '2942f167cc30c5e3a44a2aecd5b0d9c07ff61a07' 2025-09-07T06:26:10.6048148Z Submodule path 'third_party/VulkanMemoryAllocator': checked out '1d8f600fd424278486eade7ed3e877c99f0846b1' 2025-09-07T06:26:11.3417820Z Submodule path 'third_party/XNNPACK': checked out '51a0103656eff6fc9bfd39a4597923c4b542c883' 2025-09-07T06:26:11.4989632Z Submodule path 'third_party/aiter': checked out '01aae101b9e5e94d6c16a9514c9fb8df99c93150' 2025-09-07T06:26:11.5010308Z Submodule '3rdparty/composable_kernel' (https://github.com/ROCm/composable_kernel.git) registered for path 'third_party/aiter/3rdparty/composable_kernel' 2025-09-07T06:26:11.5038554Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/aiter/3rdparty/composable_kernel'... 2025-09-07T06:26:15.1424998Z Submodule path 'third_party/aiter/3rdparty/composable_kernel': checked out 'cffe8fa2a442ac8e80dd236a1a5d24fe3d7e0cbf' 2025-09-07T06:26:15.1663260Z Submodule path 'third_party/benchmark': checked out '299e5928955cc62af9968370293b916f5130916f' 2025-09-07T06:26:15.4910045Z Submodule path 'third_party/composable_kernel': checked out '7fe50dc3da2069d6645d9deb8c017a876472a977' 2025-09-07T06:26:15.5377723Z Submodule path 'third_party/cpp-httplib': checked out '89c932f313c6437c38f2982869beacc89c2f2246' 2025-09-07T06:26:15.6355006Z Submodule path 'third_party/cpuinfo': checked out '5e3d2445e6a84d9599bee2bf78edbb4d80865e1d' 2025-09-07T06:26:15.6801079Z Submodule path 'third_party/cudnn_frontend': checked out 'f937055efc6d414d11f4c6577e3977fe74f35fb6' 2025-09-07T06:26:16.3112155Z Submodule path 'third_party/cutlass': checked out 'e51efbfe18fe4f4cbb66ab814c55bf4aa0185491' 2025-09-07T06:26:16.4627421Z Submodule path 'third_party/fbgemm': checked out '4b39c551efe15e6bbade20565b0ceb2d8ce3352d' 2025-09-07T06:26:16.4649033Z Submodule 'external/asmjit' (https://github.com/asmjit/asmjit.git) registered for path 'third_party/fbgemm/external/asmjit' 2025-09-07T06:26:16.4651688Z Submodule 'external/composable_kernel' (https://github.com/jwfromm/composable_kernel.git) registered for path 'third_party/fbgemm/external/composable_kernel' 2025-09-07T06:26:16.4655193Z Submodule 'external/cpuinfo' (https://github.com/pytorch/cpuinfo) registered for path 'third_party/fbgemm/external/cpuinfo' 2025-09-07T06:26:16.4657032Z Submodule 'external/cutlass' (https://github.com/jwfromm/cutlass) registered for path 'third_party/fbgemm/external/cutlass' 2025-09-07T06:26:16.4659838Z Submodule 'external/googletest' (https://github.com/google/googletest) registered for path 'third_party/fbgemm/external/googletest' 2025-09-07T06:26:16.4662830Z Submodule 'external/hipify_torch' (https://github.com/ROCmSoftwarePlatform/hipify_torch.git) registered for path 'third_party/fbgemm/external/hipify_torch' 2025-09-07T06:26:16.4665606Z Submodule 'external/json' (https://github.com/nlohmann/json.git) registered for path 'third_party/fbgemm/external/json' 2025-09-07T06:26:16.4696477Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm/external/asmjit'... 2025-09-07T06:26:17.8933162Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm/external/hipify_torch'... 2025-09-07T06:26:17.8934133Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm/external/cpuinfo'... 2025-09-07T06:26:17.9106923Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm/external/composable_kernel'... 2025-09-07T06:26:17.9305778Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm/external/googletest'... 2025-09-07T06:26:18.0307322Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm/external/cutlass'... 2025-09-07T06:26:18.8662052Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm/external/json'... 2025-09-07T06:26:23.7605133Z Submodule path 'third_party/fbgemm/external/asmjit': checked out 'a3199e8857792cd10b7589ff5d58343d2c9008ea' 2025-09-07T06:26:24.0224184Z Submodule path 'third_party/fbgemm/external/composable_kernel': checked out 'b1281b8b08d973a7064f864f47eeb30f3e2596e9' 2025-09-07T06:26:24.1230282Z Submodule path 'third_party/fbgemm/external/cpuinfo': checked out '6543fec09b2f04ac4a666882998b534afc9c1349' 2025-09-07T06:26:24.7491602Z Submodule path 'third_party/fbgemm/external/cutlass': checked out '311f3c8e51dc0eb56310cfc6980bf63d0fbd7917' 2025-09-07T06:26:24.7963863Z Submodule path 'third_party/fbgemm/external/googletest': checked out '52eb8108c5bdec04579160ae17225d66034bd723' 2025-09-07T06:26:24.8086088Z Submodule path 'third_party/fbgemm/external/hipify_torch': checked out '63b6a7b541fa7f08f8475ca7d74054db36ff2691' 2025-09-07T06:26:24.9190403Z Submodule path 'third_party/fbgemm/external/json': checked out '9cca280a4d0ccf0c08f47a99aa71d1b0e52f8d03' 2025-09-07T06:26:24.9930418Z Submodule path 'third_party/flash-attention': checked out '979702c87a8713a8e0a5e9fee122b90d2ef13be5' 2025-09-07T06:26:24.9950428Z Submodule 'csrc/composable_kernel' (https://github.com/ROCm/composable_kernel.git) registered for path 'third_party/flash-attention/csrc/composable_kernel' 2025-09-07T06:26:24.9952463Z Submodule 'csrc/cutlass' (https://github.com/NVIDIA/cutlass.git) registered for path 'third_party/flash-attention/csrc/cutlass' 2025-09-07T06:26:24.9980020Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/flash-attention/csrc/composable_kernel'... 2025-09-07T06:26:28.4366450Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/flash-attention/csrc/cutlass'... 2025-09-07T06:26:28.6789042Z Submodule path 'third_party/flash-attention/csrc/composable_kernel': checked out '888317e698e9803c62bd38568abc9e05d7709f33' 2025-09-07T06:26:29.2399535Z Submodule path 'third_party/flash-attention/csrc/cutlass': checked out 'c506e16788cb08416a4a57e11a9067beeee29420' 2025-09-07T06:26:29.3804286Z Submodule path 'third_party/flatbuffers': checked out 'a2cd1ea3b6d3fee220106b5fed3f7ce8da9eb757' 2025-09-07T06:26:29.4132411Z Submodule path 'third_party/fmt': checked out '40626af88bd7df9a5fb80be7b25ac85b122d6c21' 2025-09-07T06:26:29.4542893Z Submodule path 'third_party/gemmlowp/gemmlowp': checked out '3fb5c176c17c765a3492cd2f0321b0dab712f350' 2025-09-07T06:26:29.4798172Z Submodule path 'third_party/gloo': checked out 'c7b7b022c124d9643957d9bd55f57ac59fce8fa2' 2025-09-07T06:26:29.5257488Z Submodule path 'third_party/googletest': checked out '52eb8108c5bdec04579160ae17225d66034bd723' 2025-09-07T06:26:29.5392438Z Submodule path 'third_party/ideep': checked out '719d8e6cd7f7a0e01b155657526d693acf97c2b3' 2025-09-07T06:26:29.5409562Z Submodule 'mkl-dnn' (https://github.com/intel/mkl-dnn.git) registered for path 'third_party/ideep/mkl-dnn' 2025-09-07T06:26:29.5435707Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/ideep/mkl-dnn'... 2025-09-07T06:26:41.7622213Z Submodule path 'third_party/ideep/mkl-dnn': checked out '8d263e693366ef8db40acc569cc7d8edf644556d' 2025-09-07T06:26:41.7827418Z Submodule path 'third_party/ittapi': checked out 'dec1d23ca65ab069d225dfe40dea14f455170959' 2025-09-07T06:26:41.8705842Z Submodule path 'third_party/kineto': checked out '5e7501833f1021ce6f618572d3baf657b6319658' 2025-09-07T06:26:41.8725354Z Submodule 'libkineto/third_party/dynolog' (https://github.com/facebookincubator/dynolog.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog' 2025-09-07T06:26:41.8727205Z Submodule 'libkineto/third_party/fmt' (https://github.com/fmtlib/fmt.git) registered for path 'third_party/kineto/libkineto/third_party/fmt' 2025-09-07T06:26:41.8730170Z Submodule 'libkineto/third_party/googletest' (https://github.com/google/googletest.git) registered for path 'third_party/kineto/libkineto/third_party/googletest' 2025-09-07T06:26:41.8758500Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog'... 2025-09-07T06:26:42.5597574Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/fmt'... 2025-09-07T06:26:43.1230167Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/googletest'... 2025-09-07T06:26:43.2076307Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog': checked out '7d04a0053a845370ae06ce317a22a48e9edcc74e' 2025-09-07T06:26:43.2093600Z Submodule 'third_party/DCGM' (https://github.com/NVIDIA/DCGM.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM' 2025-09-07T06:26:43.2096333Z Submodule 'third_party/cpr' (https://github.com/libcpr/cpr.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr' 2025-09-07T06:26:43.2099044Z Submodule 'third_party/fmt' (https://github.com/fmtlib/fmt.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt' 2025-09-07T06:26:43.2101825Z Submodule 'third_party/gflags' (https://github.com/gflags/gflags.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags' 2025-09-07T06:26:43.2104668Z Submodule 'third_party/glog' (https://github.com/google/glog.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog' 2025-09-07T06:26:43.2107716Z Submodule 'third_party/googletest' (https://github.com/google/googletest.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest' 2025-09-07T06:26:43.2110697Z Submodule 'third_party/json' (https://github.com/nlohmann/json.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/json' 2025-09-07T06:26:43.2113836Z Submodule 'third_party/pfs' (https://github.com/dtrugman/pfs.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs' 2025-09-07T06:26:43.2143808Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM'... 2025-09-07T06:26:44.7947305Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/pfs'... 2025-09-07T06:26:44.7948543Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/gflags'... 2025-09-07T06:26:44.7949724Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/cpr'... 2025-09-07T06:26:44.7950898Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/glog'... 2025-09-07T06:26:44.7952113Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/googletest'... 2025-09-07T06:26:44.7953307Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/fmt'... 2025-09-07T06:26:44.8947996Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/json'... 2025-09-07T06:26:50.5360349Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM': checked out 'ffde4e54bc7249a6039a5e6b45b395141e1217f9' 2025-09-07T06:26:50.5542960Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr': checked out '871ed52d350214a034f6ef8a3b8f51c5ce1bd400' 2025-09-07T06:26:50.5919126Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt': checked out 'cd4af11efc9c622896a3e4cb599fa28668ca3d05' 2025-09-07T06:26:50.6061412Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags': checked out 'e171aa2d15ed9eb17054558e0b3a6a413bb01067' 2025-09-07T06:26:50.6078244Z Submodule 'doc' (https://github.com/gflags/gflags.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc' 2025-09-07T06:26:50.6103934Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc'... 2025-09-07T06:26:50.8826530Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc': checked out '8411df715cf522606e3b1aca386ddfc0b63d34b4' 2025-09-07T06:26:50.9019178Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog': checked out 'b33e3bad4c46c8a6345525fd822af355e5ef9446' 2025-09-07T06:26:50.9435874Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest': checked out '58d77fa8070e8cec2dc1ed015d66b454c8d78850' 2025-09-07T06:26:51.0458661Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/json': checked out '4f8fba14066156b73f1189a2b8bd568bde5284c5' 2025-09-07T06:26:51.0630516Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs': checked out 'f68a2fa8ea36c783bdd760371411fcb495aa3150' 2025-09-07T06:26:51.1031240Z Submodule path 'third_party/kineto/libkineto/third_party/fmt': checked out '0041a40c1350ba702d475b9c4ad62da77caea164' 2025-09-07T06:26:51.1629742Z Submodule path 'third_party/kineto/libkineto/third_party/googletest': checked out '7aca84427f224eeed3144123d5230d5871e93347' 2025-09-07T06:26:51.2065897Z Submodule path 'third_party/kleidiai': checked out 'cca02c2f69dd18e1f12647c1c0bdc8cf90e680c7' 2025-09-07T06:26:51.2458254Z Submodule path 'third_party/mimalloc': checked out 'fbd8b99c2b828428947d70fdc046bb55609be93e' 2025-09-07T06:26:51.3559959Z Submodule path 'third_party/nlohmann': checked out '55f93686c01528224f448c19128836e7df245f72' 2025-09-07T06:26:51.7608211Z Submodule path 'third_party/onnx': checked out 'e709452ef2bbc1d113faf678c24e6d3467696e83' 2025-09-07T06:26:51.7646855Z Submodule 'third_party/pybind11' (https://github.com/pybind/pybind11.git) registered for path 'third_party/onnx/third_party/pybind11' 2025-09-07T06:26:51.7673848Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/onnx/third_party/pybind11'... 2025-09-07T06:26:52.7345989Z Submodule path 'third_party/onnx/third_party/pybind11': checked out 'a2e59f0e7065404b44dfe92a28aca47ba1378dc4' 2025-09-07T06:26:52.8053492Z Submodule path 'third_party/opentelemetry-cpp': checked out 'a799f4aed9c94b765dcdaabaeab7d5e7e2310878' 2025-09-07T06:26:52.8073909Z Submodule 'third_party/benchmark' (https://github.com/google/benchmark) registered for path 'third_party/opentelemetry-cpp/third_party/benchmark' 2025-09-07T06:26:52.8075623Z Submodule 'third_party/googletest' (https://github.com/google/googletest) registered for path 'third_party/opentelemetry-cpp/third_party/googletest' 2025-09-07T06:26:52.8078197Z Submodule 'third_party/ms-gsl' (https://github.com/microsoft/GSL) registered for path 'third_party/opentelemetry-cpp/third_party/ms-gsl' 2025-09-07T06:26:52.8080977Z Submodule 'third_party/nlohmann-json' (https://github.com/nlohmann/json) registered for path 'third_party/opentelemetry-cpp/third_party/nlohmann-json' 2025-09-07T06:26:52.8084035Z Submodule 'third_party/opentelemetry-proto' (https://github.com/open-telemetry/opentelemetry-proto) registered for path 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto' 2025-09-07T06:26:52.8087028Z Submodule 'third_party/opentracing-cpp' (https://github.com/opentracing/opentracing-cpp.git) registered for path 'third_party/opentelemetry-cpp/third_party/opentracing-cpp' 2025-09-07T06:26:52.8089739Z Submodule 'third_party/prometheus-cpp' (https://github.com/jupp0r/prometheus-cpp) registered for path 'third_party/opentelemetry-cpp/third_party/prometheus-cpp' 2025-09-07T06:26:52.8092659Z Submodule 'tools/vcpkg' (https://github.com/Microsoft/vcpkg) registered for path 'third_party/opentelemetry-cpp/tools/vcpkg' 2025-09-07T06:26:52.8121802Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/third_party/benchmark'... 2025-09-07T06:26:53.2153971Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/third_party/opentracing-cpp'... 2025-09-07T06:26:53.2155288Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/third_party/opentelemetry-proto'... 2025-09-07T06:26:53.2156432Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/third_party/ms-gsl'... 2025-09-07T06:26:53.2157534Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/third_party/prometheus-cpp'... 2025-09-07T06:26:53.3154868Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/third_party/googletest'... 2025-09-07T06:26:53.9750223Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/third_party/nlohmann-json'... 2025-09-07T06:27:02.7297245Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/tools/vcpkg'... 2025-09-07T06:27:02.7496249Z Submodule path 'third_party/opentelemetry-cpp/third_party/benchmark': checked out 'd572f4777349d43653b21d6c2fc63020ab326db2' 2025-09-07T06:27:02.7907612Z Submodule path 'third_party/opentelemetry-cpp/third_party/googletest': checked out 'b796f7d44681514f58a683a3a71ff17c94edb0c1' 2025-09-07T06:27:02.8077416Z Submodule path 'third_party/opentelemetry-cpp/third_party/ms-gsl': checked out '6f4529395c5b7c2d661812257cd6780c67e54afa' 2025-09-07T06:27:02.9123959Z Submodule path 'third_party/opentelemetry-cpp/third_party/nlohmann-json': checked out 'bc889afb4c5bf1c0d8ee29ef35eaaf4c8bef8a5d' 2025-09-07T06:27:02.9266480Z Submodule path 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto': checked out '4ca4f0335c63cda7ab31ea7ed70d6553aee14dce' 2025-09-07T06:27:02.9416000Z Submodule path 'third_party/opentelemetry-cpp/third_party/opentracing-cpp': checked out '06b57f48ded1fa3bdd3d4346f6ef29e40e08eaf5' 2025-09-07T06:27:02.9579161Z Submodule path 'third_party/opentelemetry-cpp/third_party/prometheus-cpp': checked out 'c9ffcdda9086ffd9e1283ea7a0276d831f3c8a8d' 2025-09-07T06:27:02.9594958Z Submodule 'civetweb' (https://github.com/civetweb/civetweb.git) registered for path 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb' 2025-09-07T06:27:02.9597643Z Submodule 'googletest' (https://github.com/google/googletest.git) registered for path 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest' 2025-09-07T06:27:02.9624156Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb'... 2025-09-07T06:27:04.7479629Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest'... 2025-09-07T06:27:04.9999885Z Submodule path 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb': checked out 'eefb26f82b233268fc98577d265352720d477ba4' 2025-09-07T06:27:05.0474054Z Submodule path 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest': checked out 'e2239ee6043f73722e7aa812a459f54a28552929' 2025-09-07T06:27:05.5268902Z Submodule path 'third_party/opentelemetry-cpp/tools/vcpkg': checked out '8eb57355a4ffb410a2e94c07b4dca2dffbee8e50' 2025-09-07T06:27:05.5392353Z Submodule path 'third_party/pocketfft': 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2025-09-07T06:27:09.5455798Z [command]/usr/bin/git submodule foreach --recursive sh -c "git config --local 'http.https://github.com/.extraheader' 'AUTHORIZATION: basic ***' && git config --local --show-origin --name-only --get-regexp remote.origin.url" 2025-09-07T06:27:09.5757487Z Entering 'android/libs/fbjni' 2025-09-07T06:27:09.5809964Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/android/libs/fbjni/config remote.origin.url 2025-09-07T06:27:09.5826729Z Entering 'third_party/FP16' 2025-09-07T06:27:09.5879843Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/NNPACK_deps/FP16/config remote.origin.url 2025-09-07T06:27:09.5895825Z Entering 'third_party/FXdiv' 2025-09-07T06:27:09.5949181Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/NNPACK_deps/FXdiv/config remote.origin.url 2025-09-07T06:27:09.5964910Z Entering 'third_party/NNPACK' 2025-09-07T06:27:09.6017002Z 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file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/cpuinfo/config remote.origin.url 2025-09-07T06:27:09.6688441Z Entering 'third_party/cudnn_frontend' 2025-09-07T06:27:09.6740148Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/cudnn_frontend/config remote.origin.url 2025-09-07T06:27:09.6757103Z Entering 'third_party/cutlass' 2025-09-07T06:27:09.6810915Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/cutlass/config remote.origin.url 2025-09-07T06:27:09.6837589Z Entering 'third_party/fbgemm' 2025-09-07T06:27:09.6889719Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/fbgemm/config remote.origin.url 2025-09-07T06:27:09.6908126Z Entering 'third_party/fbgemm/external/asmjit' 2025-09-07T06:27:09.6959380Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/fbgemm/modules/external/asmjit/config remote.origin.url 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remote.origin.url 2025-09-07T06:27:09.7540321Z Entering 'third_party/flash-attention/csrc/cutlass' 2025-09-07T06:27:09.7590197Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/flash-attention/modules/csrc/cutlass/config remote.origin.url 2025-09-07T06:27:09.7614032Z Entering 'third_party/flatbuffers' 2025-09-07T06:27:09.7666101Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/flatbuffers/config remote.origin.url 2025-09-07T06:27:09.7685953Z Entering 'third_party/fmt' 2025-09-07T06:27:09.7737714Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/fmt/config remote.origin.url 2025-09-07T06:27:09.7754249Z Entering 'third_party/gemmlowp/gemmlowp' 2025-09-07T06:27:09.7805543Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/gemmlowp/gemmlowp/config remote.origin.url 2025-09-07T06:27:09.7821985Z Entering 'third_party/gloo' 2025-09-07T06:27:09.7872345Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/gloo/config remote.origin.url 2025-09-07T06:27:09.7888638Z Entering 'third_party/googletest' 2025-09-07T06:27:09.7940707Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/googletest/config remote.origin.url 2025-09-07T06:27:09.7957850Z Entering 'third_party/ideep' 2025-09-07T06:27:09.8009477Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/ideep/config remote.origin.url 2025-09-07T06:27:09.8025064Z Entering 'third_party/ideep/mkl-dnn' 2025-09-07T06:27:09.8075443Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/ideep/modules/mkl-dnn/config remote.origin.url 2025-09-07T06:27:09.8100183Z Entering 'third_party/ittapi' 2025-09-07T06:27:09.8152009Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/ittapi/config remote.origin.url 2025-09-07T06:27:09.8169079Z Entering 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file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/modules/libkineto/third_party/dynolog/modules/third_party/gflags/modules/doc/config remote.origin.url 2025-09-07T06:27:09.8657172Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog' 2025-09-07T06:27:09.8709165Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/modules/libkineto/third_party/dynolog/modules/third_party/glog/config remote.origin.url 2025-09-07T06:27:09.8725916Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest' 2025-09-07T06:27:09.8777356Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/modules/libkineto/third_party/dynolog/modules/third_party/googletest/config remote.origin.url 2025-09-07T06:27:09.8793349Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/json' 2025-09-07T06:27:09.8845843Z 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file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/modules/libkineto/third_party/googletest/config remote.origin.url 2025-09-07T06:27:09.9069573Z Entering 'third_party/kleidiai' 2025-09-07T06:27:09.9123306Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/kleidiai/config remote.origin.url 2025-09-07T06:27:09.9140634Z Entering 'third_party/mimalloc' 2025-09-07T06:27:09.9192321Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/mimalloc/config remote.origin.url 2025-09-07T06:27:09.9209431Z Entering 'third_party/nlohmann' 2025-09-07T06:27:09.9260551Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/nlohmann/config remote.origin.url 2025-09-07T06:27:09.9278506Z Entering 'third_party/onnx' 2025-09-07T06:27:09.9330935Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/onnx/config remote.origin.url 2025-09-07T06:27:09.9365922Z Entering 'third_party/onnx/third_party/pybind11' 2025-09-07T06:27:09.9418510Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/onnx/modules/third_party/pybind11/config remote.origin.url 2025-09-07T06:27:09.9437498Z Entering 'third_party/opentelemetry-cpp' 2025-09-07T06:27:09.9490019Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/config remote.origin.url 2025-09-07T06:27:09.9507485Z Entering 'third_party/opentelemetry-cpp/third_party/benchmark' 2025-09-07T06:27:09.9558826Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/modules/third_party/benchmark/config remote.origin.url 2025-09-07T06:27:09.9574200Z Entering 'third_party/opentelemetry-cpp/third_party/googletest' 2025-09-07T06:27:09.9626107Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/modules/third_party/googletest/config remote.origin.url 2025-09-07T06:27:09.9641382Z Entering 'third_party/opentelemetry-cpp/third_party/ms-gsl' 2025-09-07T06:27:09.9694363Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/modules/third_party/ms-gsl/config remote.origin.url 2025-09-07T06:27:09.9709956Z Entering 'third_party/opentelemetry-cpp/third_party/nlohmann-json' 2025-09-07T06:27:09.9764243Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/modules/third_party/nlohmann-json/config remote.origin.url 2025-09-07T06:27:09.9781054Z Entering 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto' 2025-09-07T06:27:09.9833264Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/modules/third_party/opentelemetry-proto/config remote.origin.url 2025-09-07T06:27:09.9848958Z Entering 'third_party/opentelemetry-cpp/third_party/opentracing-cpp' 2025-09-07T06:27:09.9899593Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/modules/third_party/opentracing-cpp/config remote.origin.url 2025-09-07T06:27:09.9915606Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp' 2025-09-07T06:27:09.9967816Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/modules/third_party/prometheus-cpp/config remote.origin.url 2025-09-07T06:27:09.9982331Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb' 2025-09-07T06:27:10.0033407Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/modules/third_party/prometheus-cpp/modules/civetweb/config remote.origin.url 2025-09-07T06:27:10.0051306Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest' 2025-09-07T06:27:10.0103653Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/modules/third_party/prometheus-cpp/modules/googletest/config remote.origin.url 2025-09-07T06:27:10.0121254Z Entering 'third_party/opentelemetry-cpp/tools/vcpkg' 2025-09-07T06:27:10.0172163Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/modules/tools/vcpkg/config remote.origin.url 2025-09-07T06:27:10.0209129Z Entering 'third_party/pocketfft' 2025-09-07T06:27:10.0262373Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/pocketfft/config remote.origin.url 2025-09-07T06:27:10.0279464Z Entering 'third_party/protobuf' 2025-09-07T06:27:10.0331323Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/protobuf/config remote.origin.url 2025-09-07T06:27:10.0351060Z Entering 'third_party/protobuf/third_party/benchmark' 2025-09-07T06:27:10.0402837Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/protobuf/modules/third_party/benchmark/config remote.origin.url 2025-09-07T06:27:10.0418847Z Entering 'third_party/protobuf/third_party/googletest' 2025-09-07T06:27:10.0470089Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/protobuf/modules/third_party/googletest/config remote.origin.url 2025-09-07T06:27:10.0487617Z Entering 'third_party/psimd' 2025-09-07T06:27:10.0539883Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/NNPACK_deps/psimd/config remote.origin.url 2025-09-07T06:27:10.0556030Z Entering 'third_party/pthreadpool' 2025-09-07T06:27:10.0607905Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/NNPACK_deps/pthreadpool/config remote.origin.url 2025-09-07T06:27:10.0624857Z Entering 'third_party/pybind11' 2025-09-07T06:27:10.0675941Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/pybind11/config remote.origin.url 2025-09-07T06:27:10.0692901Z Entering 'third_party/python-peachpy' 2025-09-07T06:27:10.0744997Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/python-peachpy/config remote.origin.url 2025-09-07T06:27:10.0761343Z Entering 'third_party/sleef' 2025-09-07T06:27:10.0812150Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/sleef/config remote.origin.url 2025-09-07T06:27:10.0828794Z Entering 'third_party/tensorpipe' 2025-09-07T06:27:10.0880346Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/tensorpipe/config remote.origin.url 2025-09-07T06:27:10.0896426Z Entering 'third_party/tensorpipe/third_party/googletest' 2025-09-07T06:27:10.0947224Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/tensorpipe/modules/third_party/googletest/config remote.origin.url 2025-09-07T06:27:10.0963343Z Entering 'third_party/tensorpipe/third_party/libnop' 2025-09-07T06:27:10.1014974Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/tensorpipe/modules/third_party/libnop/config remote.origin.url 2025-09-07T06:27:10.1031058Z Entering 'third_party/tensorpipe/third_party/libuv' 2025-09-07T06:27:10.1081492Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/tensorpipe/modules/third_party/libuv/config remote.origin.url 2025-09-07T06:27:10.1097789Z Entering 'third_party/tensorpipe/third_party/pybind11' 2025-09-07T06:27:10.1150372Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/tensorpipe/modules/third_party/pybind11/config remote.origin.url 2025-09-07T06:27:10.1165195Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang' 2025-09-07T06:27:10.1217266Z 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 2025-09-07T06:27:10.1771286Z [command]/usr/bin/git submodule foreach --recursive git config --local --add 'url.https://github.com/.insteadOf' 'git@github.com:' 2025-09-07T06:27:10.2073083Z Entering 'android/libs/fbjni' 2025-09-07T06:27:10.2116519Z Entering 'third_party/FP16' 2025-09-07T06:27:10.2159920Z Entering 'third_party/FXdiv' 2025-09-07T06:27:10.2203088Z Entering 'third_party/NNPACK' 2025-09-07T06:27:10.2246235Z Entering 'third_party/NVTX' 2025-09-07T06:27:10.2289213Z Entering 'third_party/VulkanMemoryAllocator' 2025-09-07T06:27:10.2331955Z Entering 'third_party/XNNPACK' 2025-09-07T06:27:10.2389543Z Entering 'third_party/aiter' 2025-09-07T06:27:10.2433173Z Entering 'third_party/aiter/3rdparty/composable_kernel' 2025-09-07T06:27:10.2483127Z Entering 'third_party/benchmark' 2025-09-07T06:27:10.2525922Z Entering 'third_party/composable_kernel' 2025-09-07T06:27:10.2577083Z Entering 'third_party/cpp-httplib' 2025-09-07T06:27:10.2619264Z Entering 'third_party/cpuinfo' 2025-09-07T06:27:10.2662475Z Entering 'third_party/cudnn_frontend' 2025-09-07T06:27:10.2704326Z Entering 'third_party/cutlass' 2025-09-07T06:27:10.2755822Z Entering 'third_party/fbgemm' 2025-09-07T06:27:10.2800410Z Entering 'third_party/fbgemm/external/asmjit' 2025-09-07T06:27:10.2842380Z Entering 'third_party/fbgemm/external/composable_kernel' 2025-09-07T06:27:10.2891407Z Entering 'third_party/fbgemm/external/cpuinfo' 2025-09-07T06:27:10.2933694Z Entering 'third_party/fbgemm/external/cutlass' 2025-09-07T06:27:10.2982771Z Entering 'third_party/fbgemm/external/googletest' 2025-09-07T06:27:10.3023576Z Entering 'third_party/fbgemm/external/hipify_torch' 2025-09-07T06:27:10.3064529Z Entering 'third_party/fbgemm/external/json' 2025-09-07T06:27:10.3107989Z Entering 'third_party/flash-attention' 2025-09-07T06:27:10.3153295Z Entering 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2025-09-07T06:27:10.3769966Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt' 2025-09-07T06:27:10.3811299Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags' 2025-09-07T06:27:10.3851660Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc' 2025-09-07T06:27:10.3895335Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog' 2025-09-07T06:27:10.3942079Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest' 2025-09-07T06:27:10.3983344Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/json' 2025-09-07T06:27:10.4026472Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs' 2025-09-07T06:27:10.4070613Z Entering 'third_party/kineto/libkineto/third_party/fmt' 2025-09-07T06:27:10.4116118Z Entering 'third_party/kineto/libkineto/third_party/googletest' 2025-09-07T06:27:10.4158476Z Entering 'third_party/kleidiai' 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2025-09-07T06:27:10.4726092Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb' 2025-09-07T06:27:10.4768023Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest' 2025-09-07T06:27:10.4810850Z Entering 'third_party/opentelemetry-cpp/tools/vcpkg' 2025-09-07T06:27:10.4873327Z Entering 'third_party/pocketfft' 2025-09-07T06:27:10.4916789Z Entering 'third_party/protobuf' 2025-09-07T06:27:10.4963846Z Entering 'third_party/protobuf/third_party/benchmark' 2025-09-07T06:27:10.5005268Z Entering 'third_party/protobuf/third_party/googletest' 2025-09-07T06:27:10.5051886Z Entering 'third_party/psimd' 2025-09-07T06:27:10.5094756Z Entering 'third_party/pthreadpool' 2025-09-07T06:27:10.5141287Z Entering 'third_party/pybind11' 2025-09-07T06:27:10.5183995Z Entering 'third_party/python-peachpy' 2025-09-07T06:27:10.5227316Z Entering 'third_party/sleef' 2025-09-07T06:27:10.5269492Z Entering 'third_party/tensorpipe' 2025-09-07T06:27:10.5311928Z Entering 'third_party/tensorpipe/third_party/googletest' 2025-09-07T06:27:10.5353158Z Entering 'third_party/tensorpipe/third_party/libnop' 2025-09-07T06:27:10.5395453Z Entering 'third_party/tensorpipe/third_party/libuv' 2025-09-07T06:27:10.5437563Z Entering 'third_party/tensorpipe/third_party/pybind11' 2025-09-07T06:27:10.5477058Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang' 2025-09-07T06:27:10.5536398Z [command]/usr/bin/git submodule foreach --recursive git config --local --add 'url.https://github.com/.insteadOf' 'org-21003710@github.com:' 2025-09-07T06:27:10.5843442Z Entering 'android/libs/fbjni' 2025-09-07T06:27:10.5885471Z Entering 'third_party/FP16' 2025-09-07T06:27:10.5928572Z Entering 'third_party/FXdiv' 2025-09-07T06:27:10.5970460Z Entering 'third_party/NNPACK' 2025-09-07T06:27:10.6013588Z Entering 'third_party/NVTX' 2025-09-07T06:27:10.6057234Z Entering 'third_party/VulkanMemoryAllocator' 2025-09-07T06:27:10.6100340Z Entering 'third_party/XNNPACK' 2025-09-07T06:27:10.6158360Z Entering 'third_party/aiter' 2025-09-07T06:27:10.6228584Z Entering 'third_party/aiter/3rdparty/composable_kernel' 2025-09-07T06:27:10.6281504Z Entering 'third_party/benchmark' 2025-09-07T06:27:10.6323364Z Entering 'third_party/composable_kernel' 2025-09-07T06:27:10.6373280Z Entering 'third_party/cpp-httplib' 2025-09-07T06:27:10.6415737Z Entering 'third_party/cpuinfo' 2025-09-07T06:27:10.6458847Z Entering 'third_party/cudnn_frontend' 2025-09-07T06:27:10.6500787Z Entering 'third_party/cutlass' 2025-09-07T06:27:10.6553027Z Entering 'third_party/fbgemm' 2025-09-07T06:27:10.6599072Z Entering 'third_party/fbgemm/external/asmjit' 2025-09-07T06:27:10.6640139Z Entering 'third_party/fbgemm/external/composable_kernel' 2025-09-07T06:27:10.6689746Z Entering 'third_party/fbgemm/external/cpuinfo' 2025-09-07T06:27:10.6732586Z Entering 'third_party/fbgemm/external/cutlass' 2025-09-07T06:27:10.6786185Z Entering 'third_party/fbgemm/external/googletest' 2025-09-07T06:27:10.6828481Z Entering 'third_party/fbgemm/external/hipify_torch' 2025-09-07T06:27:10.6869401Z Entering 'third_party/fbgemm/external/json' 2025-09-07T06:27:10.6913951Z Entering 'third_party/flash-attention' 2025-09-07T06:27:10.6958997Z Entering 'third_party/flash-attention/csrc/composable_kernel' 2025-09-07T06:27:10.7006403Z Entering 'third_party/flash-attention/csrc/cutlass' 2025-09-07T06:27:10.7059039Z Entering 'third_party/flatbuffers' 2025-09-07T06:27:10.7104530Z Entering 'third_party/fmt' 2025-09-07T06:27:10.7148027Z Entering 'third_party/gemmlowp/gemmlowp' 2025-09-07T06:27:10.7190487Z Entering 'third_party/gloo' 2025-09-07T06:27:10.7233559Z Entering 'third_party/googletest' 2025-09-07T06:27:10.7275520Z Entering 'third_party/ideep' 2025-09-07T06:27:10.7317462Z Entering 'third_party/ideep/mkl-dnn' 2025-09-07T06:27:10.7368110Z Entering 'third_party/ittapi' 2025-09-07T06:27:10.7411081Z Entering 'third_party/kineto' 2025-09-07T06:27:10.7457315Z Entering 'third_party/kineto/libkineto/third_party/dynolog' 2025-09-07T06:27:10.7498626Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM' 2025-09-07T06:27:10.7541962Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr' 2025-09-07T06:27:10.7584159Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt' 2025-09-07T06:27:10.7626048Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags' 2025-09-07T06:27:10.7666927Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc' 2025-09-07T06:27:10.7713805Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog' 2025-09-07T06:27:10.7757033Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest' 2025-09-07T06:27:10.7799282Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/json' 2025-09-07T06:27:10.7842570Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs' 2025-09-07T06:27:10.7886155Z Entering 'third_party/kineto/libkineto/third_party/fmt' 2025-09-07T06:27:10.7929926Z Entering 'third_party/kineto/libkineto/third_party/googletest' 2025-09-07T06:27:10.7973139Z Entering 'third_party/kleidiai' 2025-09-07T06:27:10.8016495Z Entering 'third_party/mimalloc' 2025-09-07T06:27:10.8059755Z Entering 'third_party/nlohmann' 2025-09-07T06:27:10.8103993Z Entering 'third_party/onnx' 2025-09-07T06:27:10.8165125Z Entering 'third_party/onnx/third_party/pybind11' 2025-09-07T06:27:10.8211367Z Entering 'third_party/opentelemetry-cpp' 2025-09-07T06:27:10.8257553Z Entering 'third_party/opentelemetry-cpp/third_party/benchmark' 2025-09-07T06:27:10.8298528Z Entering 'third_party/opentelemetry-cpp/third_party/googletest' 2025-09-07T06:27:10.8341224Z Entering 'third_party/opentelemetry-cpp/third_party/ms-gsl' 2025-09-07T06:27:10.8381439Z Entering 'third_party/opentelemetry-cpp/third_party/nlohmann-json' 2025-09-07T06:27:10.8424158Z Entering 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto' 2025-09-07T06:27:10.8465318Z Entering 'third_party/opentelemetry-cpp/third_party/opentracing-cpp' 2025-09-07T06:27:10.8506584Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp' 2025-09-07T06:27:10.8548882Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb' 2025-09-07T06:27:10.8592180Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest' 2025-09-07T06:27:10.8635324Z Entering 'third_party/opentelemetry-cpp/tools/vcpkg' 2025-09-07T06:27:10.8697610Z Entering 'third_party/pocketfft' 2025-09-07T06:27:10.8740934Z Entering 'third_party/protobuf' 2025-09-07T06:27:10.8787267Z Entering 'third_party/protobuf/third_party/benchmark' 2025-09-07T06:27:10.8829152Z Entering 'third_party/protobuf/third_party/googletest' 2025-09-07T06:27:10.8872146Z Entering 'third_party/psimd' 2025-09-07T06:27:10.8918756Z Entering 'third_party/pthreadpool' 2025-09-07T06:27:10.8960525Z Entering 'third_party/pybind11' 2025-09-07T06:27:10.9003848Z Entering 'third_party/python-peachpy' 2025-09-07T06:27:10.9046177Z Entering 'third_party/sleef' 2025-09-07T06:27:10.9089166Z Entering 'third_party/tensorpipe' 2025-09-07T06:27:10.9133536Z Entering 'third_party/tensorpipe/third_party/googletest' 2025-09-07T06:27:10.9174782Z Entering 'third_party/tensorpipe/third_party/libnop' 2025-09-07T06:27:10.9216189Z Entering 'third_party/tensorpipe/third_party/libuv' 2025-09-07T06:27:10.9258089Z Entering 'third_party/tensorpipe/third_party/pybind11' 2025-09-07T06:27:10.9298911Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang' 2025-09-07T06:27:10.9353438Z ##[endgroup] 2025-09-07T06:27:10.9387580Z [command]/usr/bin/git log -1 --format=%H 2025-09-07T06:27:10.9410633Z 93fb23d6fae7c4e82c4239a1033e522088742634 2025-09-07T06:27:10.9504930Z ##[group]Run cd "${GITHUB_WORKSPACE}" 2025-09-07T06:27:10.9505298Z cd "${GITHUB_WORKSPACE}" 2025-09-07T06:27:10.9505619Z # Clean stale submodule dirs 2025-09-07T06:27:10.9505954Z if [ -z "${NO_SUDO}" ]; then 2025-09-07T06:27:10.9506344Z  sudo git submodule foreach --recursive git clean -ffdx 2025-09-07T06:27:10.9506744Z else 2025-09-07T06:27:10.9507197Z  git submodule foreach --recursive git clean -ffdx 2025-09-07T06:27:10.9507574Z fi 2025-09-07T06:27:10.9516380Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-07T06:27:10.9516760Z env: 2025-09-07T06:27:10.9516983Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:27:10.9517250Z NO_SUDO: true 2025-09-07T06:27:10.9517468Z ##[endgroup] 2025-09-07T06:27:10.9839462Z Entering 'android/libs/fbjni' 2025-09-07T06:27:10.9871436Z Entering 'third_party/FP16' 2025-09-07T06:27:10.9903660Z Entering 'third_party/FXdiv' 2025-09-07T06:27:10.9935359Z Entering 'third_party/NNPACK' 2025-09-07T06:27:10.9970206Z Entering 'third_party/NVTX' 2025-09-07T06:27:11.0007493Z Entering 'third_party/VulkanMemoryAllocator' 2025-09-07T06:27:11.0040925Z Entering 'third_party/XNNPACK' 2025-09-07T06:27:11.0158650Z Entering 'third_party/aiter' 2025-09-07T06:27:11.0201449Z Entering 'third_party/aiter/3rdparty/composable_kernel' 2025-09-07T06:27:11.0307111Z Entering 'third_party/benchmark' 2025-09-07T06:27:11.0339416Z Entering 'third_party/composable_kernel' 2025-09-07T06:27:11.0454345Z Entering 'third_party/cpp-httplib' 2025-09-07T06:27:11.0490122Z Entering 'third_party/cpuinfo' 2025-09-07T06:27:11.0526194Z Entering 'third_party/cudnn_frontend' 2025-09-07T06:27:11.0560168Z Entering 'third_party/cutlass' 2025-09-07T06:27:11.0655177Z Entering 'third_party/fbgemm' 2025-09-07T06:27:11.0713658Z Entering 'third_party/fbgemm/external/asmjit' 2025-09-07T06:27:11.0745317Z Entering 'third_party/fbgemm/external/composable_kernel' 2025-09-07T06:27:11.0851013Z Entering 'third_party/fbgemm/external/cpuinfo' 2025-09-07T06:27:11.0885578Z Entering 'third_party/fbgemm/external/cutlass' 2025-09-07T06:27:11.0981747Z Entering 'third_party/fbgemm/external/googletest' 2025-09-07T06:27:11.1014122Z Entering 'third_party/fbgemm/external/hipify_torch' 2025-09-07T06:27:11.1042882Z Entering 'third_party/fbgemm/external/json' 2025-09-07T06:27:11.1085633Z Entering 'third_party/flash-attention' 2025-09-07T06:27:11.1125952Z Entering 'third_party/flash-attention/csrc/composable_kernel' 2025-09-07T06:27:11.1222490Z Entering 'third_party/flash-attention/csrc/cutlass' 2025-09-07T06:27:11.1310281Z Entering 'third_party/flatbuffers' 2025-09-07T06:27:11.1380041Z Entering 'third_party/fmt' 2025-09-07T06:27:11.1413027Z Entering 'third_party/gemmlowp/gemmlowp' 2025-09-07T06:27:11.1445278Z Entering 'third_party/gloo' 2025-09-07T06:27:11.1478737Z Entering 'third_party/googletest' 2025-09-07T06:27:11.1512102Z Entering 'third_party/ideep' 2025-09-07T06:27:11.1543610Z Entering 'third_party/ideep/mkl-dnn' 2025-09-07T06:27:11.1624811Z Entering 'third_party/ittapi' 2025-09-07T06:27:11.1659832Z Entering 'third_party/kineto' 2025-09-07T06:27:11.1694346Z Entering 'third_party/kineto/libkineto/third_party/dynolog' 2025-09-07T06:27:11.1728738Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM' 2025-09-07T06:27:11.1772904Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr' 2025-09-07T06:27:11.1804264Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt' 2025-09-07T06:27:11.1836763Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags' 2025-09-07T06:27:11.1865247Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc' 2025-09-07T06:27:11.1895791Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog' 2025-09-07T06:27:11.1928787Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest' 2025-09-07T06:27:11.1961373Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/json' 2025-09-07T06:27:11.2000458Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs' 2025-09-07T06:27:11.2034243Z Entering 'third_party/kineto/libkineto/third_party/fmt' 2025-09-07T06:27:11.2064963Z Entering 'third_party/kineto/libkineto/third_party/googletest' 2025-09-07T06:27:11.2097918Z Entering 'third_party/kleidiai' 2025-09-07T06:27:11.2134762Z Entering 'third_party/mimalloc' 2025-09-07T06:27:11.2168216Z Entering 'third_party/nlohmann' 2025-09-07T06:27:11.2213737Z Entering 'third_party/onnx' 2025-09-07T06:27:11.2530424Z Entering 'third_party/onnx/third_party/pybind11' 2025-09-07T06:27:11.2566549Z Entering 'third_party/opentelemetry-cpp' 2025-09-07T06:27:11.2621170Z Entering 'third_party/opentelemetry-cpp/third_party/benchmark' 2025-09-07T06:27:11.2651580Z Entering 'third_party/opentelemetry-cpp/third_party/googletest' 2025-09-07T06:27:11.2684297Z Entering 'third_party/opentelemetry-cpp/third_party/ms-gsl' 2025-09-07T06:27:11.2714341Z Entering 'third_party/opentelemetry-cpp/third_party/nlohmann-json' 2025-09-07T06:27:11.2756533Z Entering 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto' 2025-09-07T06:27:11.2786683Z Entering 'third_party/opentelemetry-cpp/third_party/opentracing-cpp' 2025-09-07T06:27:11.2816488Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp' 2025-09-07T06:27:11.2846746Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb' 2025-09-07T06:27:11.2891980Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest' 2025-09-07T06:27:11.2926331Z Entering 'third_party/opentelemetry-cpp/tools/vcpkg' 2025-09-07T06:27:11.3185472Z Entering 'third_party/pocketfft' 2025-09-07T06:27:11.3217005Z Entering 'third_party/protobuf' 2025-09-07T06:27:11.3291613Z Entering 'third_party/protobuf/third_party/benchmark' 2025-09-07T06:27:11.3322186Z Entering 'third_party/protobuf/third_party/googletest' 2025-09-07T06:27:11.3357034Z Entering 'third_party/psimd' 2025-09-07T06:27:11.3386945Z Entering 'third_party/pthreadpool' 2025-09-07T06:27:11.3417309Z Entering 'third_party/pybind11' 2025-09-07T06:27:11.3451563Z Entering 'third_party/python-peachpy' 2025-09-07T06:27:11.3484749Z Entering 'third_party/sleef' 2025-09-07T06:27:11.3517680Z Entering 'third_party/tensorpipe' 2025-09-07T06:27:11.3551402Z Entering 'third_party/tensorpipe/third_party/googletest' 2025-09-07T06:27:11.3583495Z Entering 'third_party/tensorpipe/third_party/libnop' 2025-09-07T06:27:11.3613103Z Entering 'third_party/tensorpipe/third_party/libuv' 2025-09-07T06:27:11.3647585Z Entering 'third_party/tensorpipe/third_party/pybind11' 2025-09-07T06:27:11.3676882Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang' 2025-09-07T06:27:11.3839447Z Prepare all required actions 2025-09-07T06:27:11.3840020Z Getting action download info 2025-09-07T06:27:11.5097246Z ##[group]Run ./.github/actions/setup-linux 2025-09-07T06:27:11.5097738Z env: 2025-09-07T06:27:11.5097964Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:27:11.5098218Z ##[endgroup] 2025-09-07T06:27:11.5146897Z ##[group]Run set -euo pipefail 2025-09-07T06:27:11.5147275Z set -euo pipefail 2025-09-07T06:27:11.5147580Z function get_ec2_metadata() { 2025-09-07T06:27:11.5147961Z  # Pulled from instance metadata endpoint for EC2 2025-09-07T06:27:11.5148621Z  # see https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/instancedata-data-retrieval.html 2025-09-07T06:27:11.5149211Z  category=$1 2025-09-07T06:27:11.5149583Z  # If it is GCP runner (runner name contains gcp), do not run this 2025-09-07T06:27:11.5150025Z  runner_name_str=i-09e16a84d31719099 2025-09-07T06:27:11.5150437Z  if [[ -f /.inarc ]]; then 2025-09-07T06:27:11.5150795Z  echo "ARC Runner, no info on ec2 metadata" 2025-09-07T06:27:11.5151183Z  elif [[ $runner_name_str == *"gcp"* ]]; then 2025-09-07T06:27:11.5151681Z  echo "Runner is from Google Cloud Platform, No info on ec2 metadata" 2025-09-07T06:27:11.5152123Z  else 2025-09-07T06:27:11.5153009Z  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}" 2025-09-07T06:27:11.5153951Z  fi 2025-09-07T06:27:11.5154265Z } 2025-09-07T06:27:11.5154535Z echo "ami-id: $(get_ec2_metadata ami-id)" 2025-09-07T06:27:11.5154975Z echo "instance-id: $(get_ec2_metadata instance-id)" 2025-09-07T06:27:11.5155470Z echo "instance-type: $(get_ec2_metadata instance-type)" 2025-09-07T06:27:11.5155888Z echo "system info $(uname -a)" 2025-09-07T06:27:11.5162715Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-07T06:27:11.5163107Z env: 2025-09-07T06:27:11.5163333Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:27:11.5163600Z ##[endgroup] 2025-09-07T06:27:11.5312022Z ami-id: ami-05ffe3c48a9991133 2025-09-07T06:27:11.5418956Z instance-id: i-09e16a84d31719099 2025-09-07T06:27:11.5537602Z instance-type: c5.4xlarge 2025-09-07T06:27:11.5548704Z system info Linux ip-10-0-6-53.ec2.internal 6.1.141-155.222.amzn2023.x86_64 #1 SMP PREEMPT_DYNAMIC Tue Jun 17 10:29:47 UTC 2025 x86_64 x86_64 x86_64 GNU/Linux 2025-09-07T06:27:11.5579137Z ##[group]Run echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT" 2025-09-07T06:27:11.5580119Z echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT" 2025-09-07T06:27:11.5586255Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-07T06:27:11.5586666Z env: 2025-09-07T06:27:11.5586888Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:27:11.5587156Z ##[endgroup] 2025-09-07T06:27:11.5646682Z ##[group]Run if systemctl is-active --quiet docker; then 2025-09-07T06:27:11.5647166Z if systemctl is-active --quiet docker; then 2025-09-07T06:27:11.5647562Z  echo "Docker daemon is running..."; 2025-09-07T06:27:11.5647903Z else 2025-09-07T06:27:11.5648254Z  echo "Starting docker daemon..." && sudo systemctl start docker; 2025-09-07T06:27:11.5648687Z fi 2025-09-07T06:27:11.5654215Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-07T06:27:11.5654604Z env: 2025-09-07T06:27:11.5654810Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:27:11.5655081Z ##[endgroup] 2025-09-07T06:27:11.5764653Z Docker daemon is running... 2025-09-07T06:27:11.5875038Z ##[group]Run nick-fields/retry@v3.0.0 2025-09-07T06:27:11.5875341Z with: 2025-09-07T06:27:11.5875552Z shell: bash 2025-09-07T06:27:11.5875957Z timeout_minutes: 5 2025-09-07T06:27:11.5876205Z max_attempts: 3 2025-09-07T06:27:11.5876435Z retry_wait_seconds: 30 2025-09-07T06:27:11.5878806Z command: AWS_ACCOUNT_ID=$(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 2025-09-07T06:27:11.5881368Z polling_interval_seconds: 1 2025-09-07T06:27:11.5881650Z warning_on_retry: true 2025-09-07T06:27:11.5881927Z continue_on_error: false 2025-09-07T06:27:11.5882192Z env: 2025-09-07T06:27:11.5882415Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:27:11.5882673Z AWS_RETRY_MODE: standard 2025-09-07T06:27:11.5882939Z AWS_MAX_ATTEMPTS: 5 2025-09-07T06:27:11.5883201Z AWS_DEFAULT_REGION: us-east-1 2025-09-07T06:27:11.5883486Z ##[endgroup] 2025-09-07T06:27:12.8302734Z WARNING! Your password will be stored unencrypted in /home/ec2-user/.docker/config.json. 2025-09-07T06:27:12.8303379Z Configure a credential helper to remove this warning. See 2025-09-07T06:27:12.8303987Z https://docs.docker.com/engine/reference/commandline/login/#credentials-store 2025-09-07T06:27:12.8304404Z 2025-09-07T06:27:12.8304509Z Login Succeeded 2025-09-07T06:27:13.7521031Z Command completed after 1 attempt(s). 2025-09-07T06:27:13.7582581Z ##[group]Run env | grep '^GITHUB' >> "/tmp/github_env_${GITHUB_RUN_ID}" 2025-09-07T06:27:13.7583139Z env | grep '^GITHUB' >> "/tmp/github_env_${GITHUB_RUN_ID}" 2025-09-07T06:27:13.7583613Z env | grep '^CI' >> "/tmp/github_env_${GITHUB_RUN_ID}" 2025-09-07T06:27:13.7592300Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-07T06:27:13.7592684Z env: 2025-09-07T06:27:13.7592907Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:27:13.7593177Z ##[endgroup] 2025-09-07T06:27:13.7678176Z ##[group]Run # ignore expansion of "docker ps -q" since it could be empty 2025-09-07T06:27:13.7678761Z # ignore expansion of "docker ps -q" since it could be empty 2025-09-07T06:27:13.7679209Z # shellcheck disable=SC2046 2025-09-07T06:27:13.7679553Z docker stop $(docker ps -q) || true 2025-09-07T06:27:13.7679898Z # Prune all of the docker images 2025-09-07T06:27:13.7680239Z docker system prune -af 2025-09-07T06:27:13.7686192Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-07T06:27:13.7686584Z env: 2025-09-07T06:27:13.7686795Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:27:13.7687092Z ##[endgroup] 2025-09-07T06:27:13.8213600Z "docker stop" requires at least 1 argument. 2025-09-07T06:27:13.8214035Z See 'docker stop --help'. 2025-09-07T06:27:13.8214216Z 2025-09-07T06:27:13.8214432Z Usage: docker stop [OPTIONS] CONTAINER [CONTAINER...] 2025-09-07T06:27:13.8214707Z 2025-09-07T06:27:13.8214819Z Stop one or more running containers 2025-09-07T06:27:13.8531221Z Total reclaimed space: 0B 2025-09-07T06:27:13.8568439Z ##[group]Run set +e 2025-09-07T06:27:13.8568730Z set +e 2025-09-07T06:27:13.8568970Z set -x 2025-09-07T06:27:13.8569201Z  2025-09-07T06:27:13.8569457Z PT_DOMAIN=download.pytorch.org 2025-09-07T06:27:13.8570046Z # TODO: Flaky access to download.pytorch.org https://github.com/pytorch/pytorch/issues/100400, 2025-09-07T06:27:13.8570835Z # cleaning this up once the issue is fixed. There are more than one resolved IP here, the last 2025-09-07T06:27:13.8571381Z # one is returned at random 2025-09-07T06:27:13.8571787Z RESOLVED_IP=$(dig -4 +short "${PT_DOMAIN}" | tail -n1) 2025-09-07T06:27:13.8572163Z  2025-09-07T06:27:13.8572543Z if [ -z "${RESOLVED_IP}" ]; then 2025-09-07T06:27:13.8572993Z  echo "Couldn't resolve ${PT_DOMAIN}, retrying with Google DNS..." 2025-09-07T06:27:13.8573540Z  RESOLVED_IP=$(dig -4 +short "${PT_DOMAIN}" @8.8.8.8 | tail -n1) 2025-09-07T06:27:13.8574062Z  2025-09-07T06:27:13.8574290Z  if [ -z "${RESOLVED_IP}" ]; then 2025-09-07T06:27:13.8574689Z  echo "Couldn't resolve ${PT_DOMAIN}, exiting..." 2025-09-07T06:27:13.8575063Z  exit 1 2025-09-07T06:27:13.8575306Z  fi 2025-09-07T06:27:13.8575511Z fi 2025-09-07T06:27:13.8575720Z  2025-09-07T06:27:13.8575983Z if grep -r "${PT_DOMAIN}" /etc/hosts; then 2025-09-07T06:27:13.8576358Z  # Clean up any old records first 2025-09-07T06:27:13.8576713Z  sudo sed -i "/${PT_DOMAIN}/d" /etc/hosts 2025-09-07T06:27:13.8577043Z fi 2025-09-07T06:27:13.8577252Z  2025-09-07T06:27:13.8577569Z echo "${RESOLVED_IP} ${PT_DOMAIN}" | sudo tee -a /etc/hosts 2025-09-07T06:27:13.8577963Z cat /etc/hosts 2025-09-07T06:27:13.8584138Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-07T06:27:13.8584543Z env: 2025-09-07T06:27:13.8584766Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:27:13.8585030Z ##[endgroup] 2025-09-07T06:27:13.8609202Z + PT_DOMAIN=download.pytorch.org 2025-09-07T06:27:13.8615288Z ++ dig -4 +short download.pytorch.org 2025-09-07T06:27:13.8615956Z ++ tail -n1 2025-09-07T06:27:13.9181599Z + RESOLVED_IP=18.160.10.76 2025-09-07T06:27:13.9181959Z + '[' -z 18.160.10.76 ']' 2025-09-07T06:27:13.9182272Z + grep -r download.pytorch.org /etc/hosts 2025-09-07T06:27:13.9198383Z + echo '18.160.10.76 download.pytorch.org' 2025-09-07T06:27:13.9199118Z + sudo tee -a /etc/hosts 2025-09-07T06:27:14.2235317Z 18.160.10.76 download.pytorch.org 2025-09-07T06:27:14.2250965Z + cat /etc/hosts 2025-09-07T06:27:14.2259820Z 127.0.0.1 localhost localhost.localdomain localhost4 localhost4.localdomain4 2025-09-07T06:27:14.2265349Z ::1 localhost6 localhost6.localdomain6 2025-09-07T06:27:14.2265744Z 18.160.10.76 download.pytorch.org 2025-09-07T06:27:14.2445248Z ##[group]Run pytorch/test-infra/.github/actions/calculate-docker-image@main 2025-09-07T06:27:14.2445772Z with: 2025-09-07T06:27:14.2446531Z docker-image-name: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-jammy-py3.13-clang12-ae53c6842aa4c2407d0ad976491ca941c2635c77 2025-09-07T06:27:14.2447390Z use-custom-docker-registry: true 2025-09-07T06:27:14.2447699Z docker-build-dir: .ci/docker 2025-09-07T06:27:14.2448003Z docker-build-script: ./build.sh 2025-09-07T06:27:14.2448308Z working-directory: . 2025-09-07T06:27:14.2448666Z docker-registry: 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-09-07T06:27:14.2449060Z force-push: false 2025-09-07T06:27:14.2449335Z env: 2025-09-07T06:27:14.2449553Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:27:14.2449814Z ##[endgroup] 2025-09-07T06:27:14.2477866Z ##[group]Run set -ex 2025-09-07T06:27:14.2478188Z set -ex 2025-09-07T06:27:14.2478420Z  2025-09-07T06:27:14.2478873Z # If the docker build directory or the build script doesn't exist, the action will 2025-09-07T06:27:14.2479579Z # gracefully return the docker image name as it is. Pulling docker image in Linux 2025-09-07T06:27:14.2480158Z # job could then download the pre-built image as usual 2025-09-07T06:27:14.2480869Z if [[ -d "${DOCKER_BUILD_DIR}" ]] && [[ -f "${DOCKER_BUILD_DIR}/${DOCKER_BUILD_SCRIPT}" ]] && [[ "${USE_CUSTOM_DOCKER_REGISTRY}" == "true" ]]; then 2025-09-07T06:27:14.2481538Z  echo "skip=false" >> "${GITHUB_OUTPUT}" 2025-09-07T06:27:14.2481875Z else 2025-09-07T06:27:14.2482140Z  echo "skip=true" >> "${GITHUB_OUTPUT}" 2025-09-07T06:27:14.2482585Z  echo "docker-image=${DOCKER_IMAGE_NAME}" >> "${GITHUB_OUTPUT}" 2025-09-07T06:27:14.2483004Z  2025-09-07T06:27:14.2483573Z  echo "Not using custom ECR registry. Either it was not requested or there is no Docker build script in the ${REPO_NAME} repo..." 2025-09-07T06:27:14.2484234Z  exit 0 2025-09-07T06:27:14.2484461Z fi 2025-09-07T06:27:14.2484790Z  2025-09-07T06:27:14.2485144Z if [[ "${DOCKER_IMAGE_NAME}" == *"${DOCKER_REGISTRY}/${REPO_NAME}"* ]]; then 2025-09-07T06:27:14.2485768Z  # The docker image name already includes the ECR prefix and tag, so we can just 2025-09-07T06:27:14.2486329Z  # use it as it is, but first let's extract the tag 2025-09-07T06:27:14.2486830Z  DOCKER_TAG=$(echo "${DOCKER_IMAGE_NAME}" | awk -F '[:,]' '{print $2}') 2025-09-07T06:27:14.2487339Z  echo "docker-tag=${DOCKER_TAG}" >> "${GITHUB_OUTPUT}" 2025-09-07T06:27:14.2487840Z  echo "docker-image=${DOCKER_IMAGE_NAME}" >> "${GITHUB_OUTPUT}" 2025-09-07T06:27:14.2488259Z else 2025-09-07T06:27:14.2488526Z  if [[ "${DOCKER_IMAGE_NAME}" == *:* ]]; then 2025-09-07T06:27:14.2488908Z  CUSTOM_TAG_PREFIX=${DOCKER_IMAGE_NAME#*:} 2025-09-07T06:27:14.2489316Z  DOCKER_IMAGE_NAME=${DOCKER_IMAGE_NAME%%:*} 2025-09-07T06:27:14.2489665Z  fi 2025-09-07T06:27:14.2490132Z  DOCKER_TAG=${CUSTOM_TAG_PREFIX:+${CUSTOM_TAG_PREFIX}-}$(git rev-parse HEAD:"${DOCKER_BUILD_DIR}") 2025-09-07T06:27:14.2490762Z  echo "docker-tag=${DOCKER_TAG}" >> "${GITHUB_OUTPUT}" 2025-09-07T06:27:14.2491412Z  echo "docker-image=${DOCKER_REGISTRY}/${REPO_NAME}/${DOCKER_IMAGE_NAME}:${DOCKER_TAG}" >> "${GITHUB_OUTPUT}" 2025-09-07T06:27:14.2492202Z  echo "custom-tag-prefix=${CUSTOM_TAG_PREFIX}" >> "${GITHUB_OUTPUT}" 2025-09-07T06:27:14.2492646Z fi 2025-09-07T06:27:14.2500593Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-07T06:27:14.2501043Z env: 2025-09-07T06:27:14.2501263Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:27:14.2501541Z REPO_NAME: pytorch 2025-09-07T06:27:14.2502425Z DOCKER_IMAGE_NAME: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-jammy-py3.13-clang12-ae53c6842aa4c2407d0ad976491ca941c2635c77 2025-09-07T06:27:14.2503265Z DOCKER_BUILD_DIR: .ci/docker 2025-09-07T06:27:14.2503577Z DOCKER_BUILD_SCRIPT: ./build.sh 2025-09-07T06:27:14.2503969Z DOCKER_REGISTRY: 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-09-07T06:27:14.2504387Z USE_CUSTOM_DOCKER_REGISTRY: true 2025-09-07T06:27:14.2504676Z CUSTOM_TAG_PREFIX: 2025-09-07T06:27:14.2504923Z ##[endgroup] 2025-09-07T06:27:14.2531376Z + [[ -d .ci/docker ]] 2025-09-07T06:27:14.2531669Z + [[ -f .ci/docker/./build.sh ]] 2025-09-07T06:27:14.2531968Z + [[ true == \t\r\u\e ]] 2025-09-07T06:27:14.2532225Z + echo skip=false 2025-09-07T06:27:14.2533568Z + [[ 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-jammy-py3.13-clang12-ae53c6842aa4c2407d0ad976491ca941c2635c77 == *\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* ]] 2025-09-07T06:27:14.2539137Z ++ echo 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-jammy-py3.13-clang12-ae53c6842aa4c2407d0ad976491ca941c2635c77 2025-09-07T06:27:14.2540304Z ++ awk -F '[:,]' '{print $2}' 2025-09-07T06:27:14.2625119Z + DOCKER_TAG=pytorch-linux-jammy-py3.13-clang12-ae53c6842aa4c2407d0ad976491ca941c2635c77 2025-09-07T06:27:14.2626167Z + echo docker-tag=pytorch-linux-jammy-py3.13-clang12-ae53c6842aa4c2407d0ad976491ca941c2635c77 2025-09-07T06:27:14.2627366Z + echo docker-image=308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-jammy-py3.13-clang12-ae53c6842aa4c2407d0ad976491ca941c2635c77 2025-09-07T06:27:14.2659881Z ##[group]Run set +e 2025-09-07T06:27:14.2660198Z set +e 2025-09-07T06:27:14.2660477Z set -x 2025-09-07T06:27:14.2660706Z  2025-09-07T06:27:14.2660913Z login() { 2025-09-07T06:27:14.2661404Z  aws ecr get-login-password --region us-east-1 | docker login -u AWS --password-stdin "$1" 2025-09-07T06:27:14.2661938Z } 2025-09-07T06:27:14.2662151Z  2025-09-07T06:27:14.2662360Z retry () { 2025-09-07T06:27:14.2662638Z  $* || (sleep 1 && $*) || (sleep 2 && $*) 2025-09-07T06:27:14.2663109Z } 2025-09-07T06:27:14.2663323Z  2025-09-07T06:27:14.2663547Z retry login "${DOCKER_REGISTRY}" 2025-09-07T06:27:14.2663869Z  2025-09-07T06:27:14.2664094Z START_TIME=$(date +%s) 2025-09-07T06:27:14.2664396Z # Wait up to 120 minutes 2025-09-07T06:27:14.2664768Z while [[ $(( $(date +%s) - 7200 )) -lt $START_TIME ]]; do 2025-09-07T06:27:14.2665261Z  # Check if image already exists, if it does then skip building it 2025-09-07T06:27:14.2665770Z  if docker manifest inspect "${DOCKER_IMAGE}"; then 2025-09-07T06:27:14.2666145Z  exit 0 2025-09-07T06:27:14.2666382Z  fi 2025-09-07T06:27:14.2666586Z  2025-09-07T06:27:14.2666975Z  # NB: This flag is used by Docker build workflow to push the image to ECR, so we can 2025-09-07T06:27:14.2667666Z  # use this to differentiate between the Docker build and regular build jobs. For the 2025-09-07T06:27:14.2668356Z  # latter, it will wait for the Docker images to become available before continuing 2025-09-07T06:27:14.2668892Z  if [ "${DOCKER_PUSH:-false}" == "true" ]; then 2025-09-07T06:27:14.2669293Z  # It's a Docker build job, let's build the image 2025-09-07T06:27:14.2669651Z  break 2025-09-07T06:27:14.2669887Z  else 2025-09-07T06:27:14.2670238Z  # It's a regular build job, wait for the image to become available 2025-09-07T06:27:14.2670652Z  sleep 300 2025-09-07T06:27:14.2670900Z  fi 2025-09-07T06:27:14.2671121Z done 2025-09-07T06:27:14.2671337Z  2025-09-07T06:27:14.2671686Z # NB: This part requires a full checkout. Otherwise, the merge base will 2025-09-07T06:27:14.2672279Z # be empty. The default action would be to continue rebuild the image 2025-09-07T06:27:14.2672926Z if [[ "$BASE_REVISION" = "$(git rev-parse HEAD)" ]]; then 2025-09-07T06:27:14.2673403Z  # if we're on the base branch then use the parent commit 2025-09-07T06:27:14.2673819Z  MERGE_BASE=$(git rev-parse HEAD~) 2025-09-07T06:27:14.2674216Z else 2025-09-07T06:27:14.2674552Z  # otherwise we're on a PR, so use the most recent base commit 2025-09-07T06:27:14.2675042Z  MERGE_BASE=$(git merge-base HEAD "$BASE_REVISION") 2025-09-07T06:27:14.2675411Z fi 2025-09-07T06:27:14.2675611Z  2025-09-07T06:27:14.2675851Z if [[ -z "${MERGE_BASE}" ]]; then 2025-09-07T06:27:14.2676220Z  echo "rebuild=true" >> "${GITHUB_OUTPUT}" 2025-09-07T06:27:14.2676560Z  2025-09-07T06:27:14.2677027Z  echo "Finding merge base only works with full checkout, please set fetch-depth to 0, continuing ..." 2025-09-07T06:27:14.2677596Z  exit 0 2025-09-07T06:27:14.2677823Z fi 2025-09-07T06:27:14.2678038Z  2025-09-07T06:27:14.2678362Z if ! git rev-parse "${MERGE_BASE}:${DOCKER_BUILD_DIR}"; then 2025-09-07T06:27:14.2679059Z  echo "Directory '${DOCKER_BUILD_DIR}' not found in commit $MERGE_BASE, you should rebase onto a more recent commit" 2025-09-07T06:27:14.2679671Z  exit 1 2025-09-07T06:27:14.2679902Z fi 2025-09-07T06:27:14.2680115Z  2025-09-07T06:27:14.2680473Z PREVIOUS_DOCKER_TAG=$(git rev-parse "${MERGE_BASE}:${DOCKER_BUILD_DIR}") 2025-09-07T06:27:14.2681160Z # If no image exists but the hash is the same as the previous hash then we should error out here 2025-09-07T06:27:14.2681778Z if [[ "${PREVIOUS_DOCKER_TAG}" == "${DOCKER_TAG}" ]]; then 2025-09-07T06:27:14.2682485Z  echo "WARNING: Something has gone wrong and the previous image isn't available for the merge-base of your branch" 2025-09-07T06:27:14.2683291Z  echo " Will re-build docker image to store in local cache, TTS may be longer" 2025-09-07T06:27:14.2683771Z fi 2025-09-07T06:27:14.2684045Z  2025-09-07T06:27:14.2684313Z echo "rebuild=true" >> "${GITHUB_OUTPUT}" 2025-09-07T06:27:14.2690022Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-07T06:27:14.2690412Z env: 2025-09-07T06:27:14.2690623Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:27:14.2690903Z DOCKER_BUILD_DIR: .ci/docker 2025-09-07T06:27:14.2691252Z BASE_REVISION: 93fb23d6fae7c4e82c4239a1033e522088742634 2025-09-07T06:27:14.2692131Z DOCKER_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-jammy-py3.13-clang12-ae53c6842aa4c2407d0ad976491ca941c2635c77 2025-09-07T06:27:14.2693143Z DOCKER_TAG: pytorch-linux-jammy-py3.13-clang12-ae53c6842aa4c2407d0ad976491ca941c2635c77 2025-09-07T06:27:14.2693768Z DOCKER_REGISTRY: 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-09-07T06:27:14.2694164Z DOCKER_PUSH: 2025-09-07T06:27:14.2694394Z ##[endgroup] 2025-09-07T06:27:14.2718221Z + retry login 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-09-07T06:27:14.2718907Z + login 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-09-07T06:27:14.2721203Z + aws ecr get-login-password --region us-east-1 2025-09-07T06:27:14.2722431Z + docker login -u AWS --password-stdin 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-09-07T06:27:14.8094234Z WARNING! Your password will be stored unencrypted in /home/ec2-user/.docker/config.json. 2025-09-07T06:27:14.8094878Z Configure a credential helper to remove this warning. See 2025-09-07T06:27:14.8095839Z https://docs.docker.com/engine/reference/commandline/login/#credentials-store 2025-09-07T06:27:14.8096295Z 2025-09-07T06:27:14.8096403Z Login Succeeded 2025-09-07T06:27:14.8109611Z ++ date +%s 2025-09-07T06:27:14.8119506Z + START_TIME=1757226434 2025-09-07T06:27:14.8122798Z ++ date +%s 2025-09-07T06:27:14.8132227Z + [[ 1757219234 -lt 1757226434 ]] 2025-09-07T06:27:14.8133346Z + docker manifest inspect 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-jammy-py3.13-clang12-ae53c6842aa4c2407d0ad976491ca941c2635c77 2025-09-07T06:27:15.0520648Z { 2025-09-07T06:27:15.0521008Z "schemaVersion": 2, 2025-09-07T06:27:15.0521652Z "mediaType": "application/vnd.docker.distribution.manifest.v2+json", 2025-09-07T06:27:15.0522343Z "config": { 2025-09-07T06:27:15.0522833Z "mediaType": "application/vnd.docker.container.image.v1+json", 2025-09-07T06:27:15.0523474Z "size": 30147, 2025-09-07T06:27:15.0524345Z "digest": "sha256:d68dea278b660b539496dbad92d5230006940a736c3a0dcc39d6f72863a5aaa0" 2025-09-07T06:27:15.0525146Z }, 2025-09-07T06:27:15.0525449Z "layers": [ 2025-09-07T06:27:15.0525806Z { 2025-09-07T06:27:15.0526365Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-09-07T06:27:15.0546885Z "size": 30448359, 2025-09-07T06:27:15.0547506Z "digest": "sha256:e6fdc8487bfe6d764301ef3634bc6c043841dc3ab05ca14f81e69c0f92562d46" 2025-09-07T06:27:15.0548043Z }, 2025-09-07T06:27:15.0548232Z { 2025-09-07T06:27:15.0548593Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-09-07T06:27:15.0549036Z "size": 1554, 2025-09-07T06:27:15.0549466Z "digest": "sha256:efc45b9044a6cbae9d1981fa8f749b3b24e14bf1e2227b92e3e19d9f6f73f452" 2025-09-07T06:27:15.0549944Z }, 2025-09-07T06:27:15.0550138Z { 2025-09-07T06:27:15.0550472Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-09-07T06:27:15.0550900Z "size": 344022769, 2025-09-07T06:27:15.0551321Z "digest": 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"sha256:25069f524b4c945f72a7babca5cc2d9f555e3ec4352030b5bbd67ba8ac4b0f74" 2025-09-07T06:27:15.0641110Z }, 2025-09-07T06:27:15.0641276Z { 2025-09-07T06:27:15.0641599Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-09-07T06:27:15.0642012Z "size": 1012, 2025-09-07T06:27:15.0642409Z "digest": "sha256:c576014b13e169b13387245f818ac901197b9d33206aacca506b88b26ad9a1fb" 2025-09-07T06:27:15.0642852Z }, 2025-09-07T06:27:15.0643030Z { 2025-09-07T06:27:15.0643345Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-09-07T06:27:15.0643747Z "size": 724, 2025-09-07T06:27:15.0644151Z "digest": "sha256:11edb6ea0bca3be307ef836b0bd07999ff562bcb7a807f5e6c9f7d4d5f976b5d" 2025-09-07T06:27:15.0644631Z }, 2025-09-07T06:27:15.0644815Z { 2025-09-07T06:27:15.0645128Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-09-07T06:27:15.0645526Z "size": 135, 2025-09-07T06:27:15.0645929Z "digest": "sha256:cd8bd7e8fa9445552b41b40d0d4b59943007a6e907837d8e14bf638363154f90" 2025-09-07T06:27:15.0646410Z }, 2025-09-07T06:27:15.0646603Z { 2025-09-07T06:27:15.0646919Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-09-07T06:27:15.0647338Z "size": 32, 2025-09-07T06:27:15.0647816Z "digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1" 2025-09-07T06:27:15.0648301Z }, 2025-09-07T06:27:15.0648484Z { 2025-09-07T06:27:15.0648815Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-09-07T06:27:15.0649292Z "size": 158, 2025-09-07T06:27:15.0649712Z "digest": "sha256:f05371ab6d70bb3df829f342267e16fb3ea0d7102f6ae1c52897cdf720d82a81" 2025-09-07T06:27:15.0650181Z }, 2025-09-07T06:27:15.0650440Z { 2025-09-07T06:27:15.0650767Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-09-07T06:27:15.0651189Z "size": 1369, 2025-09-07T06:27:15.0651591Z "digest": "sha256:2ea1f5444bf63e47215d2ce9bfc3d2185dd9bf861cc250e34632216f5814b8da" 2025-09-07T06:27:15.0652079Z }, 2025-09-07T06:27:15.0652269Z { 2025-09-07T06:27:15.0652598Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-09-07T06:27:15.0653014Z "size": 32, 2025-09-07T06:27:15.0653429Z "digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1" 2025-09-07T06:27:15.0653920Z }, 2025-09-07T06:27:15.0654113Z { 2025-09-07T06:27:15.0654428Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-09-07T06:27:15.0654851Z "size": 137, 2025-09-07T06:27:15.0655265Z "digest": "sha256:dc3473b7f9639490137bc3cf54b339baf944c9e1ebf417eb8cb2fa6191480a11" 2025-09-07T06:27:15.0655746Z }, 2025-09-07T06:27:15.0655929Z { 2025-09-07T06:27:15.0656271Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-09-07T06:27:15.0656750Z "size": 381, 2025-09-07T06:27:15.0657164Z "digest": "sha256:3258a00bf4d8c01ff6239aa16d98c260e208b7fd6fe393c70ef0367a0a44b318" 2025-09-07T06:27:15.0657630Z }, 2025-09-07T06:27:15.0657820Z { 2025-09-07T06:27:15.0658144Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-09-07T06:27:15.0658563Z "size": 32, 2025-09-07T06:27:15.0659045Z "digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1" 2025-09-07T06:27:15.0659538Z }, 2025-09-07T06:27:15.0659732Z { 2025-09-07T06:27:15.0660061Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-09-07T06:27:15.0660470Z "size": 104, 2025-09-07T06:27:15.0660889Z "digest": "sha256:a5e2ce08f721a164ec5ae3e23221cbd32361ce97a5104e02c9151f9a768d3ac8" 2025-09-07T06:27:15.0661377Z }, 2025-09-07T06:27:15.0661572Z { 2025-09-07T06:27:15.0661886Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-09-07T06:27:15.0662303Z "size": 409, 2025-09-07T06:27:15.0662709Z "digest": "sha256:0620806a0206044e85ebed03275b53eb24d015f4b21fd4ca32480d1a8150c7cf" 2025-09-07T06:27:15.0663179Z }, 2025-09-07T06:27:15.0663357Z { 2025-09-07T06:27:15.0663683Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-09-07T06:27:15.0664101Z "size": 32, 2025-09-07T06:27:15.0664519Z "digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1" 2025-09-07T06:27:15.0664993Z }, 2025-09-07T06:27:15.0665185Z { 2025-09-07T06:27:15.0665515Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-09-07T06:27:15.0665940Z "size": 109, 2025-09-07T06:27:15.0666332Z "digest": "sha256:86243afc698eb6983eb8ab0e82e3172878b440c78788227107d0221c83d5876e" 2025-09-07T06:27:15.0666808Z }, 2025-09-07T06:27:15.0667002Z { 2025-09-07T06:27:15.0667327Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-09-07T06:27:15.0667732Z "size": 1897, 2025-09-07T06:27:15.0668151Z "digest": "sha256:81d56f8c0e9e96289a7250e45d9cc29663b554c56251c1fee391de9bebf0c201" 2025-09-07T06:27:15.0668629Z }, 2025-09-07T06:27:15.0668819Z { 2025-09-07T06:27:15.0669132Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-09-07T06:27:15.0669552Z "size": 243408560, 2025-09-07T06:27:15.0669991Z "digest": "sha256:583d3e85f6ec4fbf3276a0e7c69498705dc09173a39bbcebe5a1b8fd13d7e225" 2025-09-07T06:27:15.0670476Z }, 2025-09-07T06:27:15.0670728Z { 2025-09-07T06:27:15.0671057Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-09-07T06:27:15.0671479Z "size": 106, 2025-09-07T06:27:15.0671893Z "digest": "sha256:1d2c6772b50878424206ff920f681eb7943f9e7eedb6e31fdfd6fa6a38de8e33" 2025-09-07T06:27:15.0672358Z }, 2025-09-07T06:27:15.0672553Z { 2025-09-07T06:27:15.0672885Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-09-07T06:27:15.0673304Z "size": 164, 2025-09-07T06:27:15.0673703Z "digest": "sha256:2c149ad240dfccc069a636e2b6bf13397ae1918c01381975dd069948e5ea471d" 2025-09-07T06:27:15.0674268Z }, 2025-09-07T06:27:15.0674460Z { 2025-09-07T06:27:15.0674783Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-09-07T06:27:15.0675193Z "size": 7943, 2025-09-07T06:27:15.0675611Z "digest": "sha256:77d8ceec47985080ac194cf4eeeafaf70595896cc4bf664906c20422325d5ddd" 2025-09-07T06:27:15.0676097Z }, 2025-09-07T06:27:15.0676288Z { 2025-09-07T06:27:15.0676603Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-09-07T06:27:15.0677029Z "size": 8074, 2025-09-07T06:27:15.0677436Z "digest": "sha256:2be2d278705b612946c28177f8684dc07e44a27248c228732a50c62716acfe4b" 2025-09-07T06:27:15.0677907Z }, 2025-09-07T06:27:15.0678088Z { 2025-09-07T06:27:15.0678417Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-09-07T06:27:15.0678842Z "size": 302, 2025-09-07T06:27:15.0679247Z "digest": "sha256:ca8d286566c546411a1541f9e11d22786fae23a1d8004f8e69e283ce89ad2916" 2025-09-07T06:27:15.0679707Z }, 2025-09-07T06:27:15.0679897Z { 2025-09-07T06:27:15.0680225Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-09-07T06:27:15.0680646Z "size": 32, 2025-09-07T06:27:15.0681046Z "digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1" 2025-09-07T06:27:15.0681530Z }, 2025-09-07T06:27:15.0681721Z { 2025-09-07T06:27:15.0682139Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-09-07T06:27:15.0682577Z "size": 108, 2025-09-07T06:27:15.0682985Z "digest": "sha256:7461eb1803b5c82f085c3d8caf6b92b6e01c24e55edc778d53c6bb4992fada35" 2025-09-07T06:27:15.0683471Z }, 2025-09-07T06:27:15.0683668Z { 2025-09-07T06:27:15.0683984Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-09-07T06:27:15.0684415Z "size": 54145653, 2025-09-07T06:27:15.0684848Z "digest": "sha256:08bab7160595f46d42c82fd0253b4294dacaefe59cdba6772e375da23e62c0ed" 2025-09-07T06:27:15.0685338Z }, 2025-09-07T06:27:15.0685536Z { 2025-09-07T06:27:15.0685856Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-09-07T06:27:15.0686282Z "size": 32, 2025-09-07T06:27:15.0686701Z "digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1" 2025-09-07T06:27:15.0687191Z } 2025-09-07T06:27:15.0687373Z ] 2025-09-07T06:27:15.0687569Z } 2025-09-07T06:27:15.0723487Z ##[group]Run set -eux 2025-09-07T06:27:15.0724000Z set -eux 2025-09-07T06:27:15.0724448Z # It's ok if this steps fails, it would then be an anonymous user like what we used to have 2025-09-07T06:27:15.0725642Z 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 || true 2025-09-07T06:27:15.0732324Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-07T06:27:15.0732694Z env: 2025-09-07T06:27:15.0732917Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:27:15.0733180Z ##[endgroup] 2025-09-07T06:27:15.0762075Z + aws secretsmanager get-secret-value --secret-id docker_hub_readonly_token 2025-09-07T06:27:15.0762794Z + jq --raw-output .SecretString 2025-09-07T06:27:15.0763999Z + jq -r .docker_hub_readonly_token 2025-09-07T06:27:15.0764936Z + docker login --username pytorchbot --password-stdin 2025-09-07T06:27:15.6839431Z WARNING! Your password will be stored unencrypted in /home/ec2-user/.docker/config.json. 2025-09-07T06:27:15.6840347Z Configure a credential helper to remove this warning. See 2025-09-07T06:27:15.6840943Z https://docs.docker.com/engine/reference/commandline/login/#credentials-store 2025-09-07T06:27:15.6841339Z 2025-09-07T06:27:15.6841455Z Login Succeeded 2025-09-07T06:27:15.6929841Z ##[group]Run tag=${ECR_DOCKER_IMAGE##*:} 2025-09-07T06:27:15.6930229Z tag=${ECR_DOCKER_IMAGE##*:} 2025-09-07T06:27:15.6930630Z echo "docker pull ghcr.io/pytorch/ci-image:${tag/:/-}" 2025-09-07T06:27:15.6936663Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-07T06:27:15.6937050Z env: 2025-09-07T06:27:15.6937272Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:27:15.6938061Z ECR_DOCKER_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-jammy-py3.13-clang12-ae53c6842aa4c2407d0ad976491ca941c2635c77 2025-09-07T06:27:15.6938883Z ##[endgroup] 2025-09-07T06:27:15.6965595Z docker pull ghcr.io/pytorch/ci-image:pytorch-linux-jammy-py3.13-clang12-ae53c6842aa4c2407d0ad976491ca941c2635c77 2025-09-07T06:27:15.7041977Z ##[group]Run pytorch/test-infra/.github/actions/pull-docker-image@main 2025-09-07T06:27:15.7042430Z with: 2025-09-07T06:27:15.7043155Z docker-image: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-jammy-py3.13-clang12-ae53c6842aa4c2407d0ad976491ca941c2635c77 2025-09-07T06:27:15.7044057Z docker-registry: 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-09-07T06:27:15.7044454Z env: 2025-09-07T06:27:15.7044669Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:27:15.7044937Z ##[endgroup] 2025-09-07T06:27:15.7068524Z ##[group]Run set -x 2025-09-07T06:27:15.7068805Z set -x 2025-09-07T06:27:15.7069044Z set +e 2025-09-07T06:27:15.7069256Z  2025-09-07T06:27:15.7069478Z login() { 2025-09-07T06:27:15.7069972Z  aws ecr get-login-password --region us-east-1 | docker login -u AWS --password-stdin "$1" 2025-09-07T06:27:15.7070508Z } 2025-09-07T06:27:15.7070718Z  2025-09-07T06:27:15.7070979Z retry () { 2025-09-07T06:27:15.7071258Z  $* || (sleep 1 && $*) || (sleep 2 && $*) 2025-09-07T06:27:15.7071569Z } 2025-09-07T06:27:15.7071779Z  2025-09-07T06:27:15.7072018Z retry login "${DOCKER_REGISTRY}" 2025-09-07T06:27:15.7072334Z  2025-09-07T06:27:15.7072824Z IMAGE_SIZE=$(docker manifest inspect "${DOCKER_IMAGE}" | jq '[.layers[].size, .config.size] | add / 1024 / 1024') 2025-09-07T06:27:15.7073502Z echo "Compressed size of image in MB: ${IMAGE_SIZE}" 2025-09-07T06:27:15.7073885Z  2025-09-07T06:27:15.7074214Z set -e 2025-09-07T06:27:15.7074555Z # ignore output since only exit code is used for conditional 2025-09-07T06:27:15.7075065Z # only pull docker image if it's not available locally 2025-09-07T06:27:15.7075634Z if ! docker inspect --type=image "${DOCKER_IMAGE}" >/dev/null 2>/dev/null; then 2025-09-07T06:27:15.7076170Z  retry docker pull "${DOCKER_IMAGE}" 2025-09-07T06:27:15.7076510Z fi 2025-09-07T06:27:15.7081949Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-07T06:27:15.7082327Z env: 2025-09-07T06:27:15.7082548Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:27:15.7083602Z DOCKER_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-jammy-py3.13-clang12-ae53c6842aa4c2407d0ad976491ca941c2635c77 2025-09-07T06:27:15.7084518Z DOCKER_REGISTRY: 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-09-07T06:27:15.7084909Z ##[endgroup] 2025-09-07T06:27:15.7108452Z + set +e 2025-09-07T06:27:15.7108811Z + retry login 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-09-07T06:27:15.7109262Z + login 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-09-07T06:27:15.7111991Z + aws ecr get-login-password --region us-east-1 2025-09-07T06:27:15.7113319Z + docker login -u AWS --password-stdin 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-09-07T06:27:16.2516892Z WARNING! Your password will be stored unencrypted in /home/ec2-user/.docker/config.json. 2025-09-07T06:27:16.2517759Z Configure a credential helper to remove this warning. See 2025-09-07T06:27:16.2518489Z https://docs.docker.com/engine/reference/commandline/login/#credentials-store 2025-09-07T06:27:16.2518915Z 2025-09-07T06:27:16.2519014Z Login Succeeded 2025-09-07T06:27:16.2536667Z ++ docker manifest inspect 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-jammy-py3.13-clang12-ae53c6842aa4c2407d0ad976491ca941c2635c77 2025-09-07T06:27:16.2537650Z ++ jq '[.layers[].size, .config.size] | add / 1024 / 1024' 2025-09-07T06:27:16.4604471Z + IMAGE_SIZE=4409.002216339111 2025-09-07T06:27:16.4604904Z + echo 'Compressed size of image in MB: 4409.002216339111' 2025-09-07T06:27:16.4605307Z + set -e 2025-09-07T06:27:16.4606109Z + docker inspect --type=image 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-jammy-py3.13-clang12-ae53c6842aa4c2407d0ad976491ca941c2635c77 2025-09-07T06:27:16.4607363Z Compressed size of image in MB: 4409.002216339111 2025-09-07T06:27:16.4730850Z + retry docker pull 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-jammy-py3.13-clang12-ae53c6842aa4c2407d0ad976491ca941c2635c77 2025-09-07T06:27:16.4732213Z + docker pull 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-jammy-py3.13-clang12-ae53c6842aa4c2407d0ad976491ca941c2635c77 2025-09-07T06:27:16.7181400Z pytorch-linux-jammy-py3.13-clang12-ae53c6842aa4c2407d0ad976491ca941c2635c77: Pulling from pytorch/ci-image 2025-09-07T06:27:16.7182438Z e6fdc8487bfe: Pulling fs layer 2025-09-07T06:27:16.7182882Z efc45b9044a6: Pulling fs layer 2025-09-07T06:27:16.7183358Z fad5f8e61058: Pulling fs layer 2025-09-07T06:27:16.7183839Z c0da146487b6: Pulling fs layer 2025-09-07T06:27:16.7184309Z 67db20671424: Pulling fs layer 2025-09-07T06:27:16.7184651Z 673cf5ffa968: Pulling fs layer 2025-09-07T06:27:16.7184986Z 41f32a933100: Pulling fs layer 2025-09-07T06:27:16.7185468Z ed25a020f194: Pulling fs layer 2025-09-07T06:27:16.7185879Z d242bd81fee3: Pulling fs layer 2025-09-07T06:27:16.7186342Z 8c15b80c2064: Pulling fs layer 2025-09-07T06:27:16.7198393Z 4d141034e9db: Pulling fs layer 2025-09-07T06:27:16.7198735Z 11edb6ea0bca: Pulling fs layer 2025-09-07T06:27:16.7199018Z a51285558384: Pulling fs layer 2025-09-07T06:27:16.7199310Z f3371e22a442: Pulling fs layer 2025-09-07T06:27:16.7199599Z 4f4fb700ef54: Pulling fs layer 2025-09-07T06:27:16.7199878Z c0da146487b6: Waiting 2025-09-07T06:27:16.7200116Z d242bd81fee3: Waiting 2025-09-07T06:27:16.7200375Z 829c85269cfc: Pulling fs layer 2025-09-07T06:27:16.7200664Z a0228ccab2ac: Pulling fs layer 2025-09-07T06:27:16.7200953Z 1c71c7358f5b: Pulling fs layer 2025-09-07T06:27:16.7201224Z 059f21cc5727: Pulling fs layer 2025-09-07T06:27:16.7201503Z a51285558384: Waiting 2025-09-07T06:27:16.7201731Z 67db20671424: Waiting 2025-09-07T06:27:16.7201973Z f3371e22a442: Waiting 2025-09-07T06:27:16.7202216Z 7f8e86c840b7: Pulling fs layer 2025-09-07T06:27:16.7202514Z 4f4fb700ef54: Waiting 2025-09-07T06:27:16.7202818Z 829c85269cfc: Waiting 2025-09-07T06:27:16.7203195Z 0f520396671f: Pulling fs layer 2025-09-07T06:27:16.7203459Z 4d141034e9db: Waiting 2025-09-07T06:27:16.7203755Z a0228ccab2ac: Waiting 2025-09-07T06:27:16.7204009Z edeb483a9b38: Pulling fs layer 2025-09-07T06:27:16.7204281Z 1c71c7358f5b: Waiting 2025-09-07T06:27:16.7204517Z 0f520396671f: Waiting 2025-09-07T06:27:16.7204764Z 8608e3e91e31: Pulling fs layer 2025-09-07T06:27:16.7205038Z 059f21cc5727: Waiting 2025-09-07T06:27:16.7205265Z 673cf5ffa968: Waiting 2025-09-07T06:27:16.7205516Z 809a30f3be95: Pulling fs layer 2025-09-07T06:27:16.7205798Z 90710e19b430: Pulling fs layer 2025-09-07T06:27:16.7206075Z 8608e3e91e31: Waiting 2025-09-07T06:27:16.7206302Z edeb483a9b38: Waiting 2025-09-07T06:27:16.7206557Z a65dff48674f: Pulling fs layer 2025-09-07T06:27:16.7206835Z 809a30f3be95: Waiting 2025-09-07T06:27:16.7207078Z 11edb6ea0bca: Waiting 2025-09-07T06:27:16.7207319Z 720fb67e397f: Pulling fs layer 2025-09-07T06:27:16.7207809Z 90710e19b430: Waiting 2025-09-07T06:27:16.7208054Z a65dff48674f: Waiting 2025-09-07T06:27:16.7208294Z c469b381684d: Pulling fs layer 2025-09-07T06:27:16.7208577Z c98637e362e4: Pulling fs layer 2025-09-07T06:27:16.7208849Z 720fb67e397f: Waiting 2025-09-07T06:27:16.7209091Z 7f8e86c840b7: Waiting 2025-09-07T06:27:16.7209325Z 7988968c85d3: Pulling fs layer 2025-09-07T06:27:16.7209595Z c469b381684d: Waiting 2025-09-07T06:27:16.7209830Z c98637e362e4: Waiting 2025-09-07T06:27:16.7210081Z 7741a1f97c0d: Pulling fs layer 2025-09-07T06:27:16.7210355Z aa3590d8b0d4: Pulling fs layer 2025-09-07T06:27:16.7210630Z 7741a1f97c0d: Waiting 2025-09-07T06:27:16.7210869Z 7988968c85d3: Waiting 2025-09-07T06:27:16.7211104Z ed25a020f194: Waiting 2025-09-07T06:27:16.7211341Z 91927b708b6a: Pulling fs layer 2025-09-07T06:27:16.7211615Z aa3590d8b0d4: Waiting 2025-09-07T06:27:16.7211868Z ed7eb4cd91c6: Pulling fs layer 2025-09-07T06:27:16.7212152Z 7f422bd7611b: Pulling fs layer 2025-09-07T06:27:16.7212413Z 7f422bd7611b: Waiting 2025-09-07T06:27:16.7212779Z 41f32a933100: Waiting 2025-09-07T06:27:16.7213036Z 8e1167399aca: Pulling fs layer 2025-09-07T06:27:16.7213392Z d157c63b23dc: Pulling fs layer 2025-09-07T06:27:16.7213652Z 91927b708b6a: Waiting 2025-09-07T06:27:16.7213900Z ec0c7fb1e287: Pulling fs layer 2025-09-07T06:27:16.7214173Z 8e1167399aca: Waiting 2025-09-07T06:27:16.7214420Z d157c63b23dc: Waiting 2025-09-07T06:27:16.7214748Z ec0c7fb1e287: Waiting 2025-09-07T06:27:16.7215002Z 97d08cfe0fea: Pulling fs layer 2025-09-07T06:27:16.7215274Z 8c15b80c2064: Waiting 2025-09-07T06:27:16.7215521Z 9e37f0a1d024: Pulling fs layer 2025-09-07T06:27:16.7215782Z ed7eb4cd91c6: Waiting 2025-09-07T06:27:16.7216033Z 6261a3bd7fa1: Pulling fs layer 2025-09-07T06:27:16.7216306Z 97d08cfe0fea: Waiting 2025-09-07T06:27:16.7216556Z 7025d6c8fff9: Pulling fs layer 2025-09-07T06:27:16.7216868Z 9e37f0a1d024: Waiting 2025-09-07T06:27:16.7217119Z a3f895aec88a: Pulling fs layer 2025-09-07T06:27:16.7217395Z 6261a3bd7fa1: Waiting 2025-09-07T06:27:16.7217636Z 7025d6c8fff9: Waiting 2025-09-07T06:27:16.7217887Z 25069f524b4c: Pulling fs layer 2025-09-07T06:27:16.7218171Z c576014b13e1: Pulling fs layer 2025-09-07T06:27:16.7218447Z a3f895aec88a: Waiting 2025-09-07T06:27:16.7218701Z cd8bd7e8fa94: Pulling fs layer 2025-09-07T06:27:16.7218974Z f05371ab6d70: Pulling fs layer 2025-09-07T06:27:16.7219262Z 2ea1f5444bf6: Pulling fs layer 2025-09-07T06:27:16.7219534Z 25069f524b4c: Waiting 2025-09-07T06:27:16.7219773Z cd8bd7e8fa94: Waiting 2025-09-07T06:27:16.7220003Z c576014b13e1: Waiting 2025-09-07T06:27:16.7220238Z f05371ab6d70: Waiting 2025-09-07T06:27:16.7220487Z dc3473b7f963: Pulling fs layer 2025-09-07T06:27:16.7220757Z 3258a00bf4d8: Pulling fs layer 2025-09-07T06:27:16.7221029Z 2ea1f5444bf6: Waiting 2025-09-07T06:27:16.7221283Z a5e2ce08f721: Pulling fs layer 2025-09-07T06:27:16.7221564Z 0620806a0206: Pulling fs layer 2025-09-07T06:27:16.7221822Z dc3473b7f963: Waiting 2025-09-07T06:27:16.7222064Z a5e2ce08f721: Waiting 2025-09-07T06:27:16.7222319Z 86243afc698e: Pulling fs layer 2025-09-07T06:27:16.7222600Z 0620806a0206: Waiting 2025-09-07T06:27:16.7222839Z 81d56f8c0e9e: Pulling fs layer 2025-09-07T06:27:16.7223114Z 3258a00bf4d8: Waiting 2025-09-07T06:27:16.7223363Z 583d3e85f6ec: Pulling fs layer 2025-09-07T06:27:16.7223842Z 81d56f8c0e9e: Waiting 2025-09-07T06:27:16.7224098Z 1d2c6772b508: Pulling fs layer 2025-09-07T06:27:16.7224429Z 583d3e85f6ec: Waiting 2025-09-07T06:27:16.7224668Z 86243afc698e: Waiting 2025-09-07T06:27:16.7224915Z 2c149ad240df: Pulling fs layer 2025-09-07T06:27:16.7225187Z 77d8ceec4798: Pulling fs layer 2025-09-07T06:27:16.7225470Z 2be2d278705b: Pulling fs layer 2025-09-07T06:27:16.7225752Z ca8d286566c5: Pulling fs layer 2025-09-07T06:27:16.7226068Z 2be2d278705b: Waiting 2025-09-07T06:27:16.7226297Z 1d2c6772b508: Waiting 2025-09-07T06:27:16.7226547Z 7461eb1803b5: Pulling fs layer 2025-09-07T06:27:16.7226836Z 08bab7160595: Pulling fs layer 2025-09-07T06:27:16.7227113Z ca8d286566c5: Waiting 2025-09-07T06:27:16.7227383Z 08bab7160595: Waiting 2025-09-07T06:27:16.7227791Z 7461eb1803b5: Waiting 2025-09-07T06:27:16.7228038Z 77d8ceec4798: Waiting 2025-09-07T06:27:16.7228282Z 2c149ad240df: Waiting 2025-09-07T06:27:16.8027978Z efc45b9044a6: Download complete 2025-09-07T06:27:16.8688819Z c0da146487b6: Verifying Checksum 2025-09-07T06:27:16.8689188Z c0da146487b6: Download complete 2025-09-07T06:27:17.0631381Z e6fdc8487bfe: Verifying Checksum 2025-09-07T06:27:17.0631818Z e6fdc8487bfe: Download complete 2025-09-07T06:27:17.1559701Z 673cf5ffa968: Verifying Checksum 2025-09-07T06:27:17.1560091Z 673cf5ffa968: Download complete 2025-09-07T06:27:17.2289316Z 41f32a933100: Verifying Checksum 2025-09-07T06:27:17.2289951Z 41f32a933100: Download complete 2025-09-07T06:27:17.2991479Z ed25a020f194: Verifying Checksum 2025-09-07T06:27:17.2992021Z ed25a020f194: Download complete 2025-09-07T06:27:17.3714381Z d242bd81fee3: Verifying Checksum 2025-09-07T06:27:17.3714936Z d242bd81fee3: Download complete 2025-09-07T06:27:17.4581676Z 8c15b80c2064: Download complete 2025-09-07T06:27:17.5489853Z 4d141034e9db: Verifying Checksum 2025-09-07T06:27:17.5490692Z 4d141034e9db: Download complete 2025-09-07T06:27:17.5655906Z 67db20671424: Verifying Checksum 2025-09-07T06:27:17.5656270Z 67db20671424: Download complete 2025-09-07T06:27:17.6303193Z 11edb6ea0bca: Verifying Checksum 2025-09-07T06:27:17.6303566Z 11edb6ea0bca: Download complete 2025-09-07T06:27:17.6470372Z a51285558384: Verifying Checksum 2025-09-07T06:27:17.6470719Z a51285558384: Download complete 2025-09-07T06:27:17.6543459Z 4f4fb700ef54: Verifying Checksum 2025-09-07T06:27:17.6543790Z 4f4fb700ef54: Download complete 2025-09-07T06:27:17.7587828Z 829c85269cfc: Verifying Checksum 2025-09-07T06:27:17.7588207Z 829c85269cfc: Download complete 2025-09-07T06:27:17.8273908Z a0228ccab2ac: Verifying Checksum 2025-09-07T06:27:17.8274593Z a0228ccab2ac: Download complete 2025-09-07T06:27:17.8950274Z 1c71c7358f5b: Verifying Checksum 2025-09-07T06:27:17.8950660Z 1c71c7358f5b: Download complete 2025-09-07T06:27:17.9068860Z e6fdc8487bfe: Pull complete 2025-09-07T06:27:17.9280179Z efc45b9044a6: Pull complete 2025-09-07T06:27:17.9987708Z 059f21cc5727: Verifying Checksum 2025-09-07T06:27:17.9988090Z 059f21cc5727: Download complete 2025-09-07T06:27:18.0677961Z 7f8e86c840b7: Verifying Checksum 2025-09-07T06:27:18.0678345Z 7f8e86c840b7: Download complete 2025-09-07T06:27:18.1710618Z 0f520396671f: Download complete 2025-09-07T06:27:18.2339833Z edeb483a9b38: Download complete 2025-09-07T06:27:18.3050101Z 8608e3e91e31: Verifying Checksum 2025-09-07T06:27:18.3050460Z 8608e3e91e31: Download complete 2025-09-07T06:27:18.3796909Z 809a30f3be95: Download complete 2025-09-07T06:27:18.4396048Z 90710e19b430: Download complete 2025-09-07T06:27:18.5203144Z a65dff48674f: Verifying Checksum 2025-09-07T06:27:18.5203513Z a65dff48674f: Download complete 2025-09-07T06:27:18.5920529Z 720fb67e397f: Verifying Checksum 2025-09-07T06:27:18.5920904Z 720fb67e397f: Download complete 2025-09-07T06:27:20.2096140Z fad5f8e61058: Verifying Checksum 2025-09-07T06:27:20.2096775Z fad5f8e61058: Download complete 2025-09-07T06:27:20.3217401Z c98637e362e4: Verifying Checksum 2025-09-07T06:27:20.3217841Z c98637e362e4: Download complete 2025-09-07T06:27:20.4118002Z 7988968c85d3: Verifying Checksum 2025-09-07T06:27:20.4118857Z 7988968c85d3: Download complete 2025-09-07T06:27:20.4842808Z 7741a1f97c0d: Download complete 2025-09-07T06:27:20.5563487Z aa3590d8b0d4: Download complete 2025-09-07T06:27:20.7870036Z 91927b708b6a: Verifying Checksum 2025-09-07T06:27:20.7870450Z 91927b708b6a: Download complete 2025-09-07T06:27:20.8575983Z ed7eb4cd91c6: Verifying Checksum 2025-09-07T06:27:20.8576396Z ed7eb4cd91c6: Download complete 2025-09-07T06:27:20.9229863Z 7f422bd7611b: Verifying Checksum 2025-09-07T06:27:20.9230236Z 7f422bd7611b: Download complete 2025-09-07T06:27:21.0051132Z 8e1167399aca: Verifying Checksum 2025-09-07T06:27:21.0051666Z 8e1167399aca: Download complete 2025-09-07T06:27:21.0907357Z d157c63b23dc: Download complete 2025-09-07T06:27:21.1624194Z ec0c7fb1e287: Download complete 2025-09-07T06:27:21.2466636Z 97d08cfe0fea: Verifying Checksum 2025-09-07T06:27:21.2467268Z 97d08cfe0fea: Download complete 2025-09-07T06:27:21.3371080Z 9e37f0a1d024: Verifying Checksum 2025-09-07T06:27:21.3371615Z 9e37f0a1d024: Download complete 2025-09-07T06:27:21.4353252Z 6261a3bd7fa1: Verifying Checksum 2025-09-07T06:27:21.4353816Z 6261a3bd7fa1: Download complete 2025-09-07T06:27:21.5071155Z 7025d6c8fff9: Download complete 2025-09-07T06:27:21.5853354Z a3f895aec88a: Verifying Checksum 2025-09-07T06:27:21.5853884Z a3f895aec88a: Download complete 2025-09-07T06:27:21.6563137Z 25069f524b4c: Download complete 2025-09-07T06:27:21.7262544Z c576014b13e1: Download complete 2025-09-07T06:27:21.8024670Z cd8bd7e8fa94: Verifying Checksum 2025-09-07T06:27:21.8025303Z cd8bd7e8fa94: Download complete 2025-09-07T06:27:21.8871032Z f05371ab6d70: Verifying Checksum 2025-09-07T06:27:21.8871454Z f05371ab6d70: Download complete 2025-09-07T06:27:21.9788024Z 2ea1f5444bf6: Download complete 2025-09-07T06:27:22.0488241Z dc3473b7f963: Download complete 2025-09-07T06:27:22.1284669Z 3258a00bf4d8: Verifying Checksum 2025-09-07T06:27:22.1285243Z 3258a00bf4d8: Download complete 2025-09-07T06:27:22.2034866Z a5e2ce08f721: Verifying Checksum 2025-09-07T06:27:22.2035240Z a5e2ce08f721: Download complete 2025-09-07T06:27:22.2928747Z 0620806a0206: Verifying Checksum 2025-09-07T06:27:22.2929117Z 0620806a0206: Download complete 2025-09-07T06:27:22.3800131Z 86243afc698e: Verifying Checksum 2025-09-07T06:27:22.3800703Z 86243afc698e: Download complete 2025-09-07T06:27:22.4458646Z 81d56f8c0e9e: Verifying Checksum 2025-09-07T06:27:22.4459026Z 81d56f8c0e9e: Download complete 2025-09-07T06:27:23.1945437Z c469b381684d: Verifying Checksum 2025-09-07T06:27:23.1945835Z c469b381684d: Download complete 2025-09-07T06:27:23.2827499Z 1d2c6772b508: Verifying Checksum 2025-09-07T06:27:23.2827966Z 1d2c6772b508: Download complete 2025-09-07T06:27:23.3579344Z 2c149ad240df: Verifying Checksum 2025-09-07T06:27:23.3579892Z 2c149ad240df: Download complete 2025-09-07T06:27:23.4355360Z 77d8ceec4798: Verifying Checksum 2025-09-07T06:27:23.4355821Z 77d8ceec4798: Download complete 2025-09-07T06:27:23.5166773Z 2be2d278705b: Verifying Checksum 2025-09-07T06:27:23.5167225Z 2be2d278705b: Download complete 2025-09-07T06:27:23.5979402Z ca8d286566c5: Verifying Checksum 2025-09-07T06:27:23.5979995Z ca8d286566c5: Download complete 2025-09-07T06:27:23.6741679Z 7461eb1803b5: Verifying Checksum 2025-09-07T06:27:23.6742275Z 7461eb1803b5: Download complete 2025-09-07T06:27:24.2694350Z 08bab7160595: Verifying Checksum 2025-09-07T06:27:24.2694939Z 08bab7160595: Download complete 2025-09-07T06:27:24.9242174Z 583d3e85f6ec: Verifying Checksum 2025-09-07T06:27:24.9242560Z 583d3e85f6ec: Download complete 2025-09-07T06:27:28.0147848Z fad5f8e61058: Pull complete 2025-09-07T06:27:28.1436355Z c0da146487b6: Pull complete 2025-09-07T06:27:29.4247359Z 67db20671424: Pull complete 2025-09-07T06:27:29.5455148Z 673cf5ffa968: Pull complete 2025-09-07T06:27:29.6789983Z 41f32a933100: Pull complete 2025-09-07T06:27:29.8049511Z ed25a020f194: Pull complete 2025-09-07T06:27:29.9238636Z d242bd81fee3: Pull complete 2025-09-07T06:27:30.0459133Z 8c15b80c2064: Pull complete 2025-09-07T06:27:30.1660818Z 4d141034e9db: Pull complete 2025-09-07T06:27:30.2973676Z 11edb6ea0bca: Pull complete 2025-09-07T06:27:30.4315360Z a51285558384: Pull complete 2025-09-07T06:27:51.7821651Z f3371e22a442: Verifying Checksum 2025-09-07T06:27:51.7822023Z f3371e22a442: Download complete 2025-09-07T06:28:36.9693510Z f3371e22a442: Pull complete 2025-09-07T06:28:37.2057656Z 4f4fb700ef54: Pull complete 2025-09-07T06:28:37.3654892Z 829c85269cfc: Pull complete 2025-09-07T06:28:37.4611717Z a0228ccab2ac: Pull complete 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2025-09-07T06:28:51.1747992Z 86243afc698e: Pull complete 2025-09-07T06:28:51.4021208Z 81d56f8c0e9e: Pull complete 2025-09-07T06:28:56.8521305Z 583d3e85f6ec: Pull complete 2025-09-07T06:28:56.9866986Z 1d2c6772b508: Pull complete 2025-09-07T06:28:57.1229195Z 2c149ad240df: Pull complete 2025-09-07T06:28:57.2537031Z 77d8ceec4798: Pull complete 2025-09-07T06:28:57.3664272Z 2be2d278705b: Pull complete 2025-09-07T06:28:57.4695896Z ca8d286566c5: Pull complete 2025-09-07T06:28:57.7367469Z 7461eb1803b5: Pull complete 2025-09-07T06:28:59.3552992Z 08bab7160595: Pull complete 2025-09-07T06:28:59.6632838Z Digest: sha256:91b756ef2bd4983cfdb00ea40fdbe492b4a29b8a15826aa577c257c337fe768e 2025-09-07T06:28:59.7087131Z Status: Downloaded newer image for 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-jammy-py3.13-clang12-ae53c6842aa4c2407d0ad976491ca941c2635c77 2025-09-07T06:28:59.7337024Z 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-jammy-py3.13-clang12-ae53c6842aa4c2407d0ad976491ca941c2635c77 2025-09-07T06:28:59.7383579Z ##[group]Run echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT" 2025-09-07T06:28:59.7384973Z echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT" 2025-09-07T06:28:59.7394912Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-07T06:28:59.7395305Z env: 2025-09-07T06:28:59.7395531Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:28:59.7395799Z ##[endgroup] 2025-09-07T06:28:59.7484816Z Prepare all required actions 2025-09-07T06:28:59.7555670Z ##[group]Run ./.github/actions/get-workflow-job-id 2025-09-07T06:28:59.7556012Z with: 2025-09-07T06:28:59.7556818Z github-token: *** 2025-09-07T06:28:59.7557069Z env: 2025-09-07T06:28:59.7557291Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:28:59.7557551Z ##[endgroup] 2025-09-07T06:28:59.7658223Z ##[group]Run set -eux 2025-09-07T06:28:59.7658516Z set -eux 2025-09-07T06:28:59.7658977Z python3 .github/scripts/get_workflow_job_id.py "${GITHUB_RUN_ID}" "${RUNNER_NAME}" 2025-09-07T06:28:59.7665838Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-07T06:28:59.7666238Z env: 2025-09-07T06:28:59.7666463Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:28:59.7666978Z GITHUB_TOKEN: *** 2025-09-07T06:28:59.7667225Z ##[endgroup] 2025-09-07T06:28:59.7696497Z + python3 .github/scripts/get_workflow_job_id.py 17524754568 i-09e16a84d31719099 2025-09-07T06:29:01.5420293Z Setting output job-id=49774041675 2025-09-07T06:29:01.5420951Z Setting output job-name=linux-jammy-py3.13-clang12 / test (default, 1, 5, linux.4xlarge) 2025-09-07T06:29:01.5630913Z ##[group]Run python3 -m pip install psutil==5.9.8 dataclasses_json==0.6.7 nvidia-ml-py==11.525.84 2025-09-07T06:29:01.5631685Z python3 -m pip install psutil==5.9.8 dataclasses_json==0.6.7 nvidia-ml-py==11.525.84 2025-09-07T06:29:01.5632679Z python3 -m tools.stats.monitor --log-interval "$MONITOR_LOG_INTERVAL" --data-collect-interval "$MONITOR_DATA_COLLECT_INTERVAL" > usage_log.txt 2>&1 & 2025-09-07T06:29:01.5633561Z echo "monitor-script-pid=${!}" >> "${GITHUB_OUTPUT}" 2025-09-07T06:29:01.5640400Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-07T06:29:01.5640796Z env: 2025-09-07T06:29:01.5641022Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:29:01.5641290Z JOB_ID: 49774041675 2025-09-07T06:29:01.5641684Z JOB_NAME: linux-jammy-py3.13-clang12 / test (default, 1, 5, linux.4xlarge) 2025-09-07T06:29:01.5642144Z WORKFLOW_NAME: pull 2025-09-07T06:29:01.5642406Z WORKFLOW_RUN_ID: 17524754568 2025-09-07T06:29:01.5642729Z MONITOR_LOG_INTERVAL: 5 2025-09-07T06:29:01.5643010Z MONITOR_DATA_COLLECT_INTERVAL: 1 2025-09-07T06:29:01.5643308Z ##[endgroup] 2025-09-07T06:29:02.2694769Z Defaulting to user installation because normal site-packages is not writeable 2025-09-07T06:29:02.6660356Z Collecting psutil==5.9.8 2025-09-07T06:29:02.6838154Z Downloading psutil-5.9.8-cp36-abi3-manylinux_2_12_x86_64.manylinux2010_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (288 kB) 2025-09-07T06:29:02.7631145Z Collecting dataclasses_json==0.6.7 2025-09-07T06:29:02.7670843Z Downloading dataclasses_json-0.6.7-py3-none-any.whl (28 kB) 2025-09-07T06:29:02.8009419Z Collecting nvidia-ml-py==11.525.84 2025-09-07T06:29:02.8046274Z Downloading nvidia_ml_py-11.525.84-py3-none-any.whl (34 kB) 2025-09-07T06:29:02.8444537Z Collecting typing-inspect<1,>=0.4.0 2025-09-07T06:29:02.8480666Z Downloading typing_inspect-0.9.0-py3-none-any.whl (8.8 kB) 2025-09-07T06:29:02.9621190Z Collecting marshmallow<4.0.0,>=3.18.0 2025-09-07T06:29:02.9659933Z Downloading marshmallow-3.26.1-py3-none-any.whl (50 kB) 2025-09-07T06:29:03.0276465Z Collecting packaging>=17.0 2025-09-07T06:29:03.0313411Z Downloading packaging-25.0-py3-none-any.whl (66 kB) 2025-09-07T06:29:03.0891654Z Collecting typing-extensions>=3.7.4 2025-09-07T06:29:03.0929971Z Downloading typing_extensions-4.15.0-py3-none-any.whl (44 kB) 2025-09-07T06:29:03.1199695Z Collecting mypy-extensions>=0.3.0 2025-09-07T06:29:03.1237303Z Downloading mypy_extensions-1.1.0-py3-none-any.whl (5.0 kB) 2025-09-07T06:29:03.2144976Z Installing collected packages: typing-extensions, packaging, mypy-extensions, typing-inspect, marshmallow, psutil, nvidia-ml-py, dataclasses-json 2025-09-07T06:29:03.4949204Z Successfully installed dataclasses-json-0.6.7 marshmallow-3.26.1 mypy-extensions-1.1.0 nvidia-ml-py-11.525.84 packaging-25.0 psutil-5.9.8 typing-extensions-4.15.0 typing-inspect-0.9.0 2025-09-07T06:29:03.6800811Z Prepare all required actions 2025-09-07T06:29:03.6801228Z Getting action download info 2025-09-07T06:29:03.8396560Z Download action repository 'seemethere/download-artifact-s3@v4' (SHA:1da556a7aa0a088e3153970611f6c432d58e80e6) 2025-09-07T06:29:04.1396813Z Download action repository 'actions/download-artifact@v4' (SHA:d3f86a106a0bac45b974a628896c90dbdf5c8093) 2025-09-07T06:29:04.4505925Z ##[group]Run ./.github/actions/download-build-artifacts 2025-09-07T06:29:04.4506320Z with: 2025-09-07T06:29:04.4506560Z name: linux-jammy-py3.13-clang12 2025-09-07T06:29:04.4506878Z s3-bucket: gha-artifacts 2025-09-07T06:29:04.4507133Z env: 2025-09-07T06:29:04.4507370Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:29:04.4507636Z ##[endgroup] 2025-09-07T06:29:04.4541122Z ##[group]Run seemethere/download-artifact-s3@v4 2025-09-07T06:29:04.4541464Z with: 2025-09-07T06:29:04.4541707Z name: linux-jammy-py3.13-clang12 2025-09-07T06:29:04.4542187Z s3-bucket: gha-artifacts 2025-09-07T06:29:04.4542504Z region: us-east-1 2025-09-07T06:29:04.4542734Z env: 2025-09-07T06:29:04.4542937Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:29:04.4543199Z ##[endgroup] 2025-09-07T06:29:04.9480179Z (node:44231) NOTE: We are formalizing our plans to enter AWS SDK for JavaScript (v2) into maintenance mode in 2023. 2025-09-07T06:29:04.9480707Z 2025-09-07T06:29:04.9480924Z Please migrate your code to use AWS SDK for JavaScript (v3). 2025-09-07T06:29:04.9481485Z For more information, check the migration guide at https://a.co/7PzMCcy 2025-09-07T06:29:04.9482066Z (Use `node --trace-warnings ...` to show where the warning was created) 2025-09-07T06:29:05.2410901Z Found 1 objects with prefix pytorch/pytorch/17524754568/linux-jammy-py3.13-clang12/ 2025-09-07T06:29:05.2411675Z Starting download (1/1): /home/ec2-user/actions-runner/_work/pytorch/pytorch/artifacts.zip 2025-09-07T06:29:09.9035120Z Finished download (1/1): /home/ec2-user/actions-runner/_work/pytorch/pytorch/artifacts.zip 2025-09-07T06:29:09.9041628Z Artifact download has finished successfully 2025-09-07T06:29:09.9204558Z ##[group]Run unzip -o artifacts.zip 2025-09-07T06:29:09.9204908Z unzip -o artifacts.zip 2025-09-07T06:29:09.9210692Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-07T06:29:09.9211082Z env: 2025-09-07T06:29:09.9211309Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:29:09.9211581Z ##[endgroup] 2025-09-07T06:29:09.9466979Z Archive: artifacts.zip 2025-09-07T06:29:09.9467664Z creating: dist/ 2025-09-07T06:29:11.0925455Z inflating: dist/torch-2.9.0a0+git93fb23d-cp313-cp313-linux_x86_64.whl 2025-09-07T06:29:11.1041899Z inflating: dist/.ninja_log 2025-09-07T06:29:11.1042328Z creating: build/custom_test_artifacts/ 2025-09-07T06:29:11.1043065Z creating: build/custom_test_artifacts/custom-op-build/ 2025-09-07T06:29:11.1043622Z creating: build/custom_test_artifacts/custom-op-build/CMakeFiles/ 2025-09-07T06:29:11.1044249Z creating: build/custom_test_artifacts/custom-op-build/CMakeFiles/pkgRedirects/ 2025-09-07T06:29:11.1046644Z inflating: build/custom_test_artifacts/custom-op-build/CMakeFiles/CMakeConfigureLog.yaml 2025-09-07T06:29:11.1047341Z creating: build/custom_test_artifacts/custom-op-build/CMakeFiles/4.0.0/ 2025-09-07T06:29:11.1048113Z inflating: 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build/bin/IListRef_test 2025-09-07T06:29:16.8850575Z inflating: build/bin/List_test 2025-09-07T06:29:16.8911674Z inflating: build/bin/KernelFunction_test 2025-09-07T06:29:16.9035793Z inflating: build/bin/kernel_function_legacy_test 2025-09-07T06:29:16.9128552Z inflating: build/bin/kernel_function_test 2025-09-07T06:29:16.9253965Z inflating: build/bin/kernel_lambda_legacy_test 2025-09-07T06:29:16.9353565Z inflating: build/bin/kernel_lambda_test 2025-09-07T06:29:16.9411370Z inflating: build/bin/kernel_stackbased_test 2025-09-07T06:29:16.9505635Z inflating: build/bin/make_boxed_from_unboxed_functor_test 2025-09-07T06:29:16.9554573Z inflating: build/bin/CppSignature_test 2025-09-07T06:29:16.9606762Z inflating: build/bin/backend_fallback_test 2025-09-07T06:29:16.9654501Z inflating: build/bin/op_allowlist_test 2025-09-07T06:29:16.9952793Z inflating: build/bin/op_registration_test 2025-09-07T06:29:17.0014113Z inflating: build/bin/inline_container_test 2025-09-07T06:29:17.0335841Z inflating: build/bin/test_nativert 2025-09-07T06:29:17.0385690Z inflating: build/bin/HashStoreTest 2025-09-07T06:29:17.0450397Z inflating: build/bin/ProcessGroupGlooTest 2025-09-07T06:29:17.0453218Z inflating: build/bin/example_allreduce 2025-09-07T06:29:17.1498919Z inflating: build/bin/test_jit 2025-09-07T06:29:17.1551496Z inflating: build/bin/test_dist_autograd 2025-09-07T06:29:17.1616292Z inflating: build/bin/test_cpp_rpc 2025-09-07T06:29:17.1618470Z inflating: build/bin/parallel_benchmark 2025-09-07T06:29:17.2880435Z inflating: build/bin/test_api 2025-09-07T06:29:17.3192551Z inflating: build/bin/test_lazy 2025-09-07T06:29:17.3195909Z inflating: build/bin/torch_shm_manager 2025-09-07T06:29:17.3196272Z creating: .additional_ci_files/ 2025-09-07T06:29:17.3283272Z inflating: .additional_ci_files/test-times.json 2025-09-07T06:29:17.3612112Z inflating: .additional_ci_files/test-class-times.json 2025-09-07T06:29:17.3667010Z ##[group]Run rm artifacts.zip 2025-09-07T06:29:17.3667511Z rm artifacts.zip 2025-09-07T06:29:17.3673912Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-07T06:29:17.3674298Z env: 2025-09-07T06:29:17.3674583Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:29:17.3674852Z ##[endgroup] 2025-09-07T06:29:17.4086752Z ##[group]Run df -H 2025-09-07T06:29:17.4087020Z df -H 2025-09-07T06:29:17.4092638Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-07T06:29:17.4093037Z env: 2025-09-07T06:29:17.4093265Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:29:17.4093539Z ##[endgroup] 2025-09-07T06:29:17.4134230Z Filesystem Size Used Avail Use% Mounted on 2025-09-07T06:29:17.4134687Z devtmpfs 4.2M 0 4.2M 0% /dev 2025-09-07T06:29:17.4135048Z tmpfs 17G 0 17G 0% /dev/shm 2025-09-07T06:29:17.4135397Z tmpfs 6.6G 521k 6.6G 1% /run 2025-09-07T06:29:17.4135753Z /dev/nvme0n1p1 161G 27G 135G 17% / 2025-09-07T06:29:17.4136116Z tmpfs 17G 13k 17G 1% /tmp 2025-09-07T06:29:17.4136623Z /dev/nvme0n1p128 11M 1.4M 9.2M 13% /boot/efi 2025-09-07T06:29:17.4204179Z Prepare all required actions 2025-09-07T06:29:17.4204704Z Getting action download info 2025-09-07T06:29:17.5557295Z ##[group]Run ./.github/actions/download-td-artifacts 2025-09-07T06:29:17.5557656Z with: 2025-09-07T06:29:17.5557865Z env: 2025-09-07T06:29:17.5558084Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:29:17.5558351Z ##[endgroup] 2025-09-07T06:29:17.5606360Z ##[group]Run seemethere/download-artifact-s3@v4 2025-09-07T06:29:17.5606720Z with: 2025-09-07T06:29:17.5606921Z name: td_results 2025-09-07T06:29:17.5607169Z s3-bucket: gha-artifacts 2025-09-07T06:29:17.5607444Z region: us-east-1 2025-09-07T06:29:17.5607676Z env: 2025-09-07T06:29:17.5607879Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:29:17.5608144Z ##[endgroup] 2025-09-07T06:29:18.0170699Z (node:44252) NOTE: We are formalizing our plans to enter AWS SDK for JavaScript (v2) into maintenance mode in 2023. 2025-09-07T06:29:18.0171262Z 2025-09-07T06:29:18.0171527Z Please migrate your code to use AWS SDK for JavaScript (v3). 2025-09-07T06:29:18.0172083Z For more information, check the migration guide at https://a.co/7PzMCcy 2025-09-07T06:29:18.0172654Z (Use `node --trace-warnings ...` to show where the warning was created) 2025-09-07T06:29:18.1696194Z Found 1 objects with prefix pytorch/pytorch/17524754568/td_results/ 2025-09-07T06:29:18.1696873Z Starting download (1/1): /home/ec2-user/actions-runner/_work/pytorch/pytorch/td_results.json 2025-09-07T06:29:18.2495538Z Finished download (1/1): /home/ec2-user/actions-runner/_work/pytorch/pytorch/td_results.json 2025-09-07T06:29:18.2500794Z Artifact download has finished successfully 2025-09-07T06:29:18.2654814Z ##[group]Run mkdir -p .additional_ci_files 2025-09-07T06:29:18.2655197Z mkdir -p .additional_ci_files 2025-09-07T06:29:18.2655645Z mv td_results.json .additional_ci_files/td_results.json || true 2025-09-07T06:29:18.2662670Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-07T06:29:18.2663045Z env: 2025-09-07T06:29:18.2663282Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:29:18.2663549Z ##[endgroup] 2025-09-07T06:29:18.2782742Z ##[group]Run .github/scripts/parse_ref.py 2025-09-07T06:29:18.2783112Z .github/scripts/parse_ref.py 2025-09-07T06:29:18.2788804Z shell: /usr/bin/bash -e {0} 2025-09-07T06:29:18.2789095Z env: 2025-09-07T06:29:18.2789325Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:29:18.2789583Z ##[endgroup] 2025-09-07T06:29:18.3069612Z Setting output branch=main 2025-09-07T06:29:18.3201196Z Prepare all required actions 2025-09-07T06:29:18.3201712Z Getting action download info 2025-09-07T06:29:18.4558478Z ##[group]Run ./.github/actions/filter-test-configs 2025-09-07T06:29:18.4558843Z with: 2025-09-07T06:29:18.4559255Z github-token: *** 2025-09-07T06:29:18.4562165Z test-matrix: {"include": [{"config": "default", "shard": 1, "num_shards": 5, "runner": "linux.4xlarge"}, {"config": "default", "shard": 2, "num_shards": 5, "runner": "linux.4xlarge"}, {"config": "default", "shard": 3, "num_shards": 5, "runner": "linux.4xlarge"}, {"config": "default", "shard": 4, "num_shards": 5, "runner": "linux.4xlarge"}, {"config": "default", "shard": 5, "num_shards": 5, "runner": "linux.4xlarge"}, {"config": "crossref", "shard": 1, "num_shards": 2, "runner": "linux.2xlarge"}, {"config": "crossref", "shard": 2, "num_shards": 2, "runner": "linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 1, "num_shards": 3, "runner": "linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 2, "num_shards": 3, "runner": "linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 3, "num_shards": 3, "runner": "linux.2xlarge"}, {"config": "einops", "shard": 1, "num_shards": 1, "runner": "linux.2xlarge"}]} 2025-09-07T06:29:18.4565265Z job-name: linux-jammy-py3.13-clang12 / test (default, 1, 5, linux.4xlarge) 2025-09-07T06:29:18.4565732Z env: 2025-09-07T06:29:18.4565956Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:29:18.4566229Z ##[endgroup] 2025-09-07T06:29:18.4706535Z ##[group]Run nick-fields/retry@v3.0.0 2025-09-07T06:29:18.4706861Z with: 2025-09-07T06:29:18.4707063Z shell: bash 2025-09-07T06:29:18.4707294Z timeout_minutes: 10 2025-09-07T06:29:18.4707545Z max_attempts: 5 2025-09-07T06:29:18.4707786Z retry_wait_seconds: 30 2025-09-07T06:29:18.4708594Z 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==6.0.2 2025-09-07T06:29:18.4709475Z polling_interval_seconds: 1 2025-09-07T06:29:18.4709768Z warning_on_retry: true 2025-09-07T06:29:18.4710036Z continue_on_error: false 2025-09-07T06:29:18.4710300Z env: 2025-09-07T06:29:18.4710507Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:29:18.4710957Z GITHUB_TOKEN: *** 2025-09-07T06:29:18.4711197Z ##[endgroup] 2025-09-07T06:29:18.6253875Z + python3 -m pip install requests==2.27.1 pyyaml==6.0.2 2025-09-07T06:29:18.8689176Z Defaulting to user installation because normal site-packages is not writeable 2025-09-07T06:29:19.4725077Z Collecting requests==2.27.1 2025-09-07T06:29:19.4890889Z Downloading requests-2.27.1-py2.py3-none-any.whl (63 kB) 2025-09-07T06:29:19.6988795Z Collecting pyyaml==6.0.2 2025-09-07T06:29:19.7026915Z Downloading PyYAML-6.0.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (737 kB) 2025-09-07T06:29:19.7533214Z Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/lib/python3.9/site-packages (from requests==2.27.1) (1.25.10) 2025-09-07T06:29:19.8272566Z Collecting certifi>=2017.4.17 2025-09-07T06:29:19.8310076Z Downloading certifi-2025.8.3-py3-none-any.whl (161 kB) 2025-09-07T06:29:20.2438146Z Collecting charset-normalizer~=2.0.0 2025-09-07T06:29:20.2475960Z Downloading charset_normalizer-2.0.12-py3-none-any.whl (39 kB) 2025-09-07T06:29:20.2722308Z Requirement already satisfied: idna<4,>=2.5 in /usr/lib/python3.9/site-packages (from requests==2.27.1) (2.10) 2025-09-07T06:29:20.3564331Z Installing collected packages: charset-normalizer, certifi, requests, pyyaml 2025-09-07T06:29:20.6752697Z Successfully installed certifi-2025.8.3 charset-normalizer-2.0.12 pyyaml-6.0.2 requests-2.27.1 2025-09-07T06:29:21.5500312Z Command completed after 1 attempt(s). 2025-09-07T06:29:21.5545479Z ##[group]Run set -x 2025-09-07T06:29:21.5545758Z set -x 2025-09-07T06:29:21.5545991Z  2025-09-07T06:29:21.5546371Z # Use relative path here as this could be checked out anywhere, not necessarily 2025-09-07T06:29:21.5546871Z # in runner workspace 2025-09-07T06:29:21.5547434Z python3 "${GITHUB_ACTION_PATH}/../../scripts/parse_ref.py" 2025-09-07T06:29:21.5553353Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-07T06:29:21.5553742Z env: 2025-09-07T06:29:21.5554063Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:29:21.5554337Z ##[endgroup] 2025-09-07T06:29:21.5579667Z + python3 /home/ec2-user/actions-runner/_work/pytorch/pytorch/./.github/actions/filter-test-configs/../../scripts/parse_ref.py 2025-09-07T06:29:21.5760112Z Setting output branch=main 2025-09-07T06:29:21.5921455Z ##[group]Run echo "Workflow: ${GITHUB_WORKFLOW}" 2025-09-07T06:29:21.5921880Z echo "Workflow: ${GITHUB_WORKFLOW}" 2025-09-07T06:29:21.5922274Z echo "Job name: ${JOB_NAME}" 2025-09-07T06:29:21.5922575Z  2025-09-07T06:29:21.5922988Z # Use relative path here as this could be checked out anywhere, not necessarily 2025-09-07T06:29:21.5923486Z # in runner workspace 2025-09-07T06:29:21.5924125Z python3 "${GITHUB_ACTION_PATH}/../../scripts/filter_test_configs.py" \ 2025-09-07T06:29:21.5924643Z  --workflow "${GITHUB_WORKFLOW}" \ 2025-09-07T06:29:21.5924974Z  --job-name "${JOB_NAME}" \ 2025-09-07T06:29:21.5927925Z  --test-matrix "{"include": [{"config": "default", "shard": 1, "num_shards": 5, "runner": "linux.4xlarge"}, {"config": "default", "shard": 2, "num_shards": 5, "runner": "linux.4xlarge"}, {"config": "default", "shard": 3, "num_shards": 5, "runner": "linux.4xlarge"}, {"config": "default", "shard": 4, "num_shards": 5, "runner": "linux.4xlarge"}, {"config": "default", "shard": 5, "num_shards": 5, "runner": "linux.4xlarge"}, {"config": "crossref", "shard": 1, "num_shards": 2, "runner": "linux.2xlarge"}, {"config": "crossref", "shard": 2, "num_shards": 2, "runner": "linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 1, "num_shards": 3, "runner": "linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 2, "num_shards": 3, "runner": "linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 3, "num_shards": 3, "runner": "linux.2xlarge"}, {"config": "einops", "shard": 1, "num_shards": 1, "runner": "linux.2xlarge"}]}" \ 2025-09-07T06:29:21.5930952Z  --selected-test-configs "" \ 2025-09-07T06:29:21.5931299Z  --pr-number "${PR_NUMBER}" \ 2025-09-07T06:29:21.5931609Z  --tag "${TAG}" \ 2025-09-07T06:29:21.5931913Z  --event-name "${EVENT_NAME}" \ 2025-09-07T06:29:21.5932252Z  --schedule "${SCHEDULE}" \ 2025-09-07T06:29:21.5932574Z  --branch "${HEAD_BRANCH}" 2025-09-07T06:29:21.5938430Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-07T06:29:21.5938812Z env: 2025-09-07T06:29:21.5939033Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:29:21.5939607Z GITHUB_TOKEN: *** 2025-09-07T06:29:21.5939997Z JOB_NAME: linux-jammy-py3.13-clang12 / test (default, 1, 5, linux.4xlarge) 2025-09-07T06:29:21.5940459Z PR_NUMBER: 2025-09-07T06:29:21.5940681Z TAG: 2025-09-07T06:29:21.5940884Z EVENT_NAME: push 2025-09-07T06:29:21.5941129Z SCHEDULE: 2025-09-07T06:29:21.5941336Z HEAD_BRANCH: main 2025-09-07T06:29:21.5941573Z ##[endgroup] 2025-09-07T06:29:21.5965907Z Workflow: pull 2025-09-07T06:29:21.5966333Z Job name: linux-jammy-py3.13-clang12 / test (default, 1, 5, linux.4xlarge) 2025-09-07T06:29:21.7696002Z Setting output keep-going=True 2025-09-07T06:29:21.7696408Z Setting output ci-verbose-test-logs=False 2025-09-07T06:29:21.7697020Z Setting output ci-test-showlocals=False 2025-09-07T06:29:21.7697375Z Setting output ci-no-test-timeout=False 2025-09-07T06:29:21.7697715Z Setting output ci-no-td=False 2025-09-07T06:29:21.7698035Z Setting output ci-td-distributed=False 2025-09-07T06:29:21.7698364Z Setting output is-unstable=False 2025-09-07T06:29:21.7698683Z Setting output reenabled-issues= 2025-09-07T06:29:21.7701825Z Setting output test-matrix={"include": [{"config": "default", "shard": 1, "num_shards": 5, "runner": "linux.4xlarge"}, {"config": "default", "shard": 2, "num_shards": 5, "runner": "linux.4xlarge"}, {"config": "default", "shard": 3, "num_shards": 5, "runner": "linux.4xlarge"}, {"config": "default", "shard": 4, "num_shards": 5, "runner": "linux.4xlarge"}, {"config": "default", "shard": 5, "num_shards": 5, "runner": "linux.4xlarge"}, {"config": "crossref", "shard": 1, "num_shards": 2, "runner": "linux.2xlarge"}, {"config": "crossref", "shard": 2, "num_shards": 2, "runner": "linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 1, "num_shards": 3, "runner": "linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 2, "num_shards": 3, "runner": "linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 3, "num_shards": 3, "runner": "linux.2xlarge"}, {"config": "einops", "shard": 1, "num_shards": 1, "runner": "linux.2xlarge"}]} 2025-09-07T06:29:21.7704866Z Setting output is-test-matrix-empty=False 2025-09-07T06:29:21.7949579Z ##[group]Run echo "Filtered matrix:" 2025-09-07T06:29:21.7950042Z echo "Filtered matrix:" 2025-09-07T06:29:21.7952958Z echo "{"include": [{"config": "default", "shard": 1, "num_shards": 5, "runner": "linux.4xlarge"}, {"config": "default", "shard": 2, "num_shards": 5, "runner": "linux.4xlarge"}, {"config": "default", "shard": 3, "num_shards": 5, "runner": "linux.4xlarge"}, {"config": "default", "shard": 4, "num_shards": 5, "runner": "linux.4xlarge"}, {"config": "default", "shard": 5, "num_shards": 5, "runner": "linux.4xlarge"}, {"config": "crossref", "shard": 1, "num_shards": 2, "runner": "linux.2xlarge"}, {"config": "crossref", "shard": 2, "num_shards": 2, "runner": "linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 1, "num_shards": 3, "runner": "linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 2, "num_shards": 3, "runner": "linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 3, "num_shards": 3, "runner": "linux.2xlarge"}, {"config": "einops", "shard": 1, "num_shards": 1, "runner": "linux.2xlarge"}]}" 2025-09-07T06:29:21.7955945Z  2025-09-07T06:29:21.7956160Z echo 2025-09-07T06:29:21.7956445Z echo "Is the current job unstable? False" 2025-09-07T06:29:21.7956785Z  2025-09-07T06:29:21.7956993Z echo 2025-09-07T06:29:21.7957254Z echo "Is keep-going label set? True" 2025-09-07T06:29:21.7957581Z  2025-09-07T06:29:21.7957776Z echo 2025-09-07T06:29:21.7958014Z echo "Reenabled issues? " 2025-09-07T06:29:21.7963967Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-07T06:29:21.7964370Z env: 2025-09-07T06:29:21.7964578Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:29:21.7964844Z ##[endgroup] 2025-09-07T06:29:21.7988406Z Filtered matrix: 2025-09-07T06:29:21.7992317Z {include: [{config: default, shard: 1, num_shards: 5, runner: linux.4xlarge}, {config: default, shard: 2, num_shards: 5, runner: linux.4xlarge}, {config: default, shard: 3, num_shards: 5, runner: linux.4xlarge}, {config: default, shard: 4, num_shards: 5, runner: linux.4xlarge}, {config: default, shard: 5, num_shards: 5, runner: linux.4xlarge}, {config: crossref, shard: 1, num_shards: 2, runner: linux.2xlarge}, {config: crossref, shard: 2, num_shards: 2, runner: linux.2xlarge}, {config: dynamo_wrapped, shard: 1, num_shards: 3, runner: linux.2xlarge}, {config: dynamo_wrapped, shard: 2, num_shards: 3, runner: linux.2xlarge}, {config: dynamo_wrapped, shard: 3, num_shards: 3, runner: linux.2xlarge}, {config: einops, shard: 1, num_shards: 1, runner: linux.2xlarge}]} 2025-09-07T06:29:21.7995156Z 2025-09-07T06:29:21.7995451Z Is the current job unstable? False 2025-09-07T06:29:21.7995660Z 2025-09-07T06:29:21.7995786Z Is keep-going label set? True 2025-09-07T06:29:21.7995980Z 2025-09-07T06:29:21.7996078Z Reenabled issues? 2025-09-07T06:29:21.8131947Z ##[group]Run echo "timeout=$((JOB_TIMEOUT-30))" >> "${GITHUB_OUTPUT}" 2025-09-07T06:29:21.8132493Z echo "timeout=$((JOB_TIMEOUT-30))" >> "${GITHUB_OUTPUT}" 2025-09-07T06:29:21.8138083Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-07T06:29:21.8138475Z env: 2025-09-07T06:29:21.8138695Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:29:21.8138969Z JOB_TIMEOUT: 240 2025-09-07T06:29:21.8139193Z ##[endgroup] 2025-09-07T06:29:21.8311039Z ##[group]Run env | grep '^GITHUB' >> "/tmp/github_env_${GITHUB_RUN_ID}" 2025-09-07T06:29:21.8311584Z env | grep '^GITHUB' >> "/tmp/github_env_${GITHUB_RUN_ID}" 2025-09-07T06:29:21.8312050Z env | grep '^CI' >> "/tmp/github_env_${GITHUB_RUN_ID}" 2025-09-07T06:29:21.8317703Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-07T06:29:21.8318109Z env: 2025-09-07T06:29:21.8318335Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:29:21.8318604Z ##[endgroup] 2025-09-07T06:29:21.8542311Z ##[group]Run set -x 2025-09-07T06:29:21.8542675Z set -x 2025-09-07T06:29:21.8542895Z  2025-09-07T06:29:21.8543163Z if [[ $TEST_CONFIG == 'multigpu' ]]; then 2025-09-07T06:29:21.8543578Z  TEST_COMMAND=.ci/pytorch/multigpu-test.sh 2025-09-07T06:29:21.8544000Z elif [[ $BUILD_ENVIRONMENT == *onnx* ]]; then 2025-09-07T06:29:21.8544383Z  TEST_COMMAND=.ci/onnx/test.sh 2025-09-07T06:29:21.8544710Z else 2025-09-07T06:29:21.8544963Z  TEST_COMMAND=.ci/pytorch/test.sh 2025-09-07T06:29:21.8545285Z fi 2025-09-07T06:29:21.8545505Z  2025-09-07T06:29:21.8545782Z # Leaving 1GB for the runner and other things 2025-09-07T06:29:21.8546375Z TOTAL_AVAILABLE_MEMORY_IN_GB=$(awk '/MemTotal/ { printf "%.3f \n", $2/1024/1024 - 1 }' /proc/meminfo) 2025-09-07T06:29:21.8547307Z # https://docs.docker.com/engine/containers/resource_constraints/#--memory-swap-details, the 3GB swap 2025-09-07T06:29:21.8548047Z # comes from https://github.com/pytorch/test-infra/pull/6058 2025-09-07T06:29:21.8548591Z TOTAL_MEMORY_WITH_SWAP=$(("${TOTAL_AVAILABLE_MEMORY_IN_GB%.*}" + 3)) 2025-09-07T06:29:21.8549025Z  2025-09-07T06:29:21.8549300Z if [[ ${BUILD_ENVIRONMENT} == *"s390x"* ]]; then 2025-09-07T06:29:21.8549659Z  SHM_OPTS= 2025-09-07T06:29:21.8549907Z  JENKINS_USER= 2025-09-07T06:29:21.8550272Z  # ensure that docker container cleanly exits in 12 hours 2025-09-07T06:29:21.8550771Z  # if for some reason cleanup action doesn't stop container 2025-09-07T06:29:21.8551186Z  # when job is cancelled 2025-09-07T06:29:21.8551505Z  DOCKER_SHELL_CMD="sleep 12h" 2025-09-07T06:29:21.8551807Z else 2025-09-07T06:29:21.8552075Z  SHM_OPTS="--shm-size=${SHM_SIZE}" 2025-09-07T06:29:21.8552429Z  JENKINS_USER="--user jenkins" 2025-09-07T06:29:21.8552761Z  DOCKER_SHELL_CMD= 2025-09-07T06:29:21.8553022Z fi 2025-09-07T06:29:21.8553234Z  2025-09-07T06:29:21.8553582Z # detached container should get cleaned up by teardown_ec2_linux 2025-09-07T06:29:21.8554233Z # TODO: Stop building test binaries as part of the build phase 2025-09-07T06:29:21.8554861Z # Used for GPU_FLAG, SHM_OPTS, JENKINS_USER and DOCKER_SHELL_CMD since that doesn't play nice 2025-09-07T06:29:21.8555428Z # shellcheck disable=SC2086,SC2090 2025-09-07T06:29:21.8555782Z container_name=$(docker run \ 2025-09-07T06:29:21.8556109Z  ${GPU_FLAG:-} \ 2025-09-07T06:29:21.8556426Z  ${SCCACHE_SERVER_PORT_DOCKER_FLAG:-} \ 2025-09-07T06:29:21.8556771Z  -e BUILD_ENVIRONMENT \ 2025-09-07T06:29:21.8557105Z  -e PR_NUMBER \ 2025-09-07T06:29:21.8557596Z  -e GITHUB_ACTIONS \ 2025-09-07T06:29:21.8557928Z  -e GITHUB_REPOSITORY \ 2025-09-07T06:29:21.8558276Z  -e GITHUB_WORKFLOW \ 2025-09-07T06:29:21.8558574Z  -e GITHUB_JOB \ 2025-09-07T06:29:21.8558854Z  -e GITHUB_RUN_ID \ 2025-09-07T06:29:21.8559149Z  -e GITHUB_RUN_NUMBER \ 2025-09-07T06:29:21.8559443Z  -e GITHUB_RUN_ATTEMPT \ 2025-09-07T06:29:21.8559745Z  -e JOB_ID \ 2025-09-07T06:29:21.8560005Z  -e JOB_NAME \ 2025-09-07T06:29:21.8560275Z  -e BASE_SHA \ 2025-09-07T06:29:21.8560523Z  -e BRANCH \ 2025-09-07T06:29:21.8560776Z  -e SHA1 \ 2025-09-07T06:29:21.8561036Z  -e AWS_DEFAULT_REGION \ 2025-09-07T06:29:21.8561342Z  -e IN_WHEEL_TEST \ 2025-09-07T06:29:21.8561615Z  -e SHARD_NUMBER \ 2025-09-07T06:29:21.8561894Z  -e TEST_CONFIG \ 2025-09-07T06:29:21.8562175Z  -e NUM_TEST_SHARDS \ 2025-09-07T06:29:21.8562479Z  -e REENABLED_ISSUES \ 2025-09-07T06:29:21.8562777Z  -e CONTINUE_THROUGH_ERROR \ 2025-09-07T06:29:21.8563188Z  -e VERBOSE_TEST_LOGS \ 2025-09-07T06:29:21.8563500Z  -e TEST_SHOWLOCALS \ 2025-09-07T06:29:21.8563850Z  -e NO_TEST_TIMEOUT \ 2025-09-07T06:29:21.8564124Z  -e NO_TD \ 2025-09-07T06:29:21.8564387Z  -e TD_DISTRIBUTED \ 2025-09-07T06:29:21.8564680Z  -e PR_LABELS \ 2025-09-07T06:29:21.8565014Z  -e MAX_JOBS="$(nproc --ignore=2)" \ 2025-09-07T06:29:21.8565352Z  -e SCCACHE_BUCKET \ 2025-09-07T06:29:21.8565646Z  -e SCCACHE_REGION \ 2025-09-07T06:29:21.8565938Z  -e XLA_CUDA \ 2025-09-07T06:29:21.8566238Z  -e XLA_CLANG_CACHE_S3_BUCKET_NAME \ 2025-09-07T06:29:21.8566612Z  -e PYTORCH_TEST_CUDA_MEM_LEAK_CHECK \ 2025-09-07T06:29:21.8566985Z  -e PYTORCH_TEST_RERUN_DISABLED_TESTS \ 2025-09-07T06:29:21.8567362Z  -e SKIP_SCCACHE_INITIALIZATION=1 \ 2025-09-07T06:29:21.8567721Z  -e HUGGING_FACE_HUB_TOKEN \ 2025-09-07T06:29:21.8568067Z  -e VLLM_TEST_HUGGING_FACE_TOKEN \ 2025-09-07T06:29:21.8568411Z  -e SCRIBE_GRAPHQL_ACCESS_TOKEN \ 2025-09-07T06:29:21.8568748Z  -e DASHBOARD_TAG \ 2025-09-07T06:29:21.8569046Z  -e ARTIFACTS_FILE_SUFFIX \ 2025-09-07T06:29:21.8569420Z  --memory="${TOTAL_AVAILABLE_MEMORY_IN_GB%.*}g" \ 2025-09-07T06:29:21.8569841Z  --memory-swap="${TOTAL_MEMORY_WITH_SWAP}g" \ 2025-09-07T06:29:21.8570275Z  --env-file="/tmp/github_env_${GITHUB_RUN_ID}" \ 2025-09-07T06:29:21.8570686Z  --security-opt seccomp=unconfined \ 2025-09-07T06:29:21.8571043Z  --cap-add=SYS_PTRACE \ 2025-09-07T06:29:21.8571350Z  --ipc=host \ 2025-09-07T06:29:21.8571604Z  ${SHM_OPTS} \ 2025-09-07T06:29:21.8571865Z  --tty \ 2025-09-07T06:29:21.8572108Z  --detach \ 2025-09-07T06:29:21.8572386Z  --name="${container_name}" \ 2025-09-07T06:29:21.8572697Z  ${JENKINS_USER} \ 2025-09-07T06:29:21.8573060Z  -v "${GITHUB_WORKSPACE}:/var/lib/jenkins/workspace" \ 2025-09-07T06:29:21.8573471Z  -w /var/lib/jenkins/workspace \ 2025-09-07T06:29:21.8573803Z  "${DOCKER_IMAGE}" \ 2025-09-07T06:29:21.8574081Z  ${DOCKER_SHELL_CMD} 2025-09-07T06:29:21.8574356Z ) 2025-09-07T06:29:21.8574656Z # Propagate download.pytorch.org IP to container 2025-09-07T06:29:21.8575351Z grep download.pytorch.org /etc/hosts | docker exec -i "${container_name}" sudo bash -c "/bin/cat >> /etc/hosts" 2025-09-07T06:29:21.8576095Z echo "DOCKER_CONTAINER_ID=${container_name}" >> "${GITHUB_ENV}" 2025-09-07T06:29:21.8576510Z  2025-09-07T06:29:21.8576783Z if [[ ${BUILD_ENVIRONMENT} == *"s390x"* ]]; then 2025-09-07T06:29:21.8577389Z  docker exec -t "${container_name}" sh -c "python3 -m pip install -r .ci/docker/requirements-ci.txt" 2025-09-07T06:29:21.8578018Z fi 2025-09-07T06:29:21.8578222Z  2025-09-07T06:29:21.8578735Z docker exec -t "${container_name}" sh -c "python3 -m pip install $(echo dist/*.whl)[opt-einsum] && ${TEST_COMMAND}" 2025-09-07T06:29:21.8584386Z shell: /usr/bin/bash -e {0} 2025-09-07T06:29:21.8584664Z env: 2025-09-07T06:29:21.8584874Z GIT_DEFAULT_BRANCH: main 2025-09-07T06:29:21.8585195Z BUILD_ENVIRONMENT: linux-jammy-py3.13-clang12 2025-09-07T06:29:21.8585544Z PR_NUMBER: 2025-09-07T06:29:21.8585792Z GITHUB_REPOSITORY: pytorch/pytorch 2025-09-07T06:29:21.8586097Z GITHUB_WORKFLOW: pull 2025-09-07T06:29:21.8586354Z GITHUB_JOB: test 2025-09-07T06:29:21.8586601Z GITHUB_RUN_ID: 17524754568 2025-09-07T06:29:21.8586879Z GITHUB_RUN_NUMBER: 353985 2025-09-07T06:29:21.8587150Z GITHUB_RUN_ATTEMPT: 1 2025-09-07T06:29:21.8587403Z JOB_ID: 49774041675 2025-09-07T06:29:21.8587794Z JOB_NAME: linux-jammy-py3.13-clang12 / test (default, 1, 5, linux.4xlarge) 2025-09-07T06:29:21.8588252Z BRANCH: main 2025-09-07T06:29:21.8588520Z SHA1: 93fb23d6fae7c4e82c4239a1033e522088742634 2025-09-07T06:29:21.8588987Z BASE_SHA: 93fb23d6fae7c4e82c4239a1033e522088742634 2025-09-07T06:29:21.8589345Z TEST_CONFIG: default 2025-09-07T06:29:21.8589597Z SHARD_NUMBER: 1 2025-09-07T06:29:21.8589837Z NUM_TEST_SHARDS: 5 2025-09-07T06:29:21.8590075Z REENABLED_ISSUES: 2025-09-07T06:29:21.8590342Z CONTINUE_THROUGH_ERROR: True 2025-09-07T06:29:21.8590637Z VERBOSE_TEST_LOGS: False 2025-09-07T06:29:21.8590915Z TEST_SHOWLOCALS: False 2025-09-07T06:29:21.8591170Z NO_TEST_TIMEOUT: False 2025-09-07T06:29:21.8591427Z NO_TD: False 2025-09-07T06:29:21.8591660Z TD_DISTRIBUTED: False 2025-09-07T06:29:21.8591977Z SCCACHE_BUCKET: ossci-compiler-cache-circleci-v2 2025-09-07T06:29:21.8592338Z SCCACHE_REGION: us-east-1 2025-09-07T06:29:21.8592607Z SHM_SIZE: 1g 2025-09-07T06:29:21.8593341Z DOCKER_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-jammy-py3.13-clang12-ae53c6842aa4c2407d0ad976491ca941c2635c77 2025-09-07T06:29:21.8594253Z XLA_CUDA: 2025-09-07T06:29:21.8594613Z XLA_CLANG_CACHE_S3_BUCKET_NAME: ossci-compiler-clang-cache-circleci-xla 2025-09-07T06:29:21.8595089Z PYTORCH_TEST_CUDA_MEM_LEAK_CHECK: 0 2025-09-07T06:29:21.8595420Z PYTORCH_TEST_RERUN_DISABLED_TESTS: 0 2025-09-07T06:29:21.8595733Z DASHBOARD_TAG: 2025-09-07T06:29:21.8596196Z VLLM_TEST_HUGGING_FACE_TOKEN: *** 2025-09-07T06:29:21.8596601Z HUGGING_FACE_HUB_TOKEN: *** 2025-09-07T06:29:21.8597024Z SCRIBE_GRAPHQL_ACCESS_TOKEN: *** 2025-09-07T06:29:21.8597439Z ARTIFACTS_FILE_SUFFIX: test-default-1-5-linux.4xlarge_49774041675 2025-09-07T06:29:21.8597862Z ##[endgroup] 2025-09-07T06:29:21.8620779Z + [[ default == \m\u\l\t\i\g\p\u ]] 2025-09-07T06:29:21.8621417Z + [[ linux-jammy-py3.13-clang12 == *onnx* ]] 2025-09-07T06:29:21.8621765Z + TEST_COMMAND=.ci/pytorch/test.sh 2025-09-07T06:29:21.8624281Z ++ awk '/MemTotal/ { printf "%.3f \n", $2/1024/1024 - 1 }' /proc/meminfo 2025-09-07T06:29:21.8644369Z + TOTAL_AVAILABLE_MEMORY_IN_GB='29.641 ' 2025-09-07T06:29:21.8644940Z + TOTAL_MEMORY_WITH_SWAP=32 2025-09-07T06:29:21.8645545Z + [[ linux-jammy-py3.13-clang12 == *\s\3\9\0\x* ]] 2025-09-07T06:29:21.8645943Z + SHM_OPTS=--shm-size=1g 2025-09-07T06:29:21.8646221Z + JENKINS_USER='--user jenkins' 2025-09-07T06:29:21.8646509Z + DOCKER_SHELL_CMD= 2025-09-07T06:29:21.8653398Z +++ nproc --ignore=2 2025-09-07T06:29:21.8874106Z ++ 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=14 -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 VLLM_TEST_HUGGING_FACE_TOKEN -e SCRIBE_GRAPHQL_ACCESS_TOKEN -e DASHBOARD_TAG -e ARTIFACTS_FILE_SUFFIX --memory=29g --memory-swap=32g --env-file=/tmp/github_env_17524754568 --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/ci-image:pytorch-linux-jammy-py3.13-clang12-ae53c6842aa4c2407d0ad976491ca941c2635c77 2025-09-07T06:29:41.0180670Z + container_name=2a1e06725f9987eb4a7a91656c15a654e902febdeb1d85d79899892d5c9f5783 2025-09-07T06:29:41.0183664Z + grep download.pytorch.org /etc/hosts 2025-09-07T06:29:41.0184983Z + docker exec -i 2a1e06725f9987eb4a7a91656c15a654e902febdeb1d85d79899892d5c9f5783 sudo bash -c '/bin/cat >> /etc/hosts' 2025-09-07T06:29:41.1780598Z + echo DOCKER_CONTAINER_ID=2a1e06725f9987eb4a7a91656c15a654e902febdeb1d85d79899892d5c9f5783 2025-09-07T06:29:41.1781646Z + [[ linux-jammy-py3.13-clang12 == *\s\3\9\0\x* ]] 2025-09-07T06:29:41.1785165Z ++ echo dist/torch-2.9.0a0+git93fb23d-cp313-cp313-linux_x86_64.whl 2025-09-07T06:29:41.1787567Z + docker exec -t 2a1e06725f9987eb4a7a91656c15a654e902febdeb1d85d79899892d5c9f5783 sh -c 'python3 -m pip install dist/torch-2.9.0a0+git93fb23d-cp313-cp313-linux_x86_64.whl[opt-einsum] && .ci/pytorch/test.sh' 2025-09-07T06:29:41.5416739Z Processing ./dist/torch-2.9.0a0+git93fb23d-cp313-cp313-linux_x86_64.whl (from torch==2.9.0a0+git93fb23d) 2025-09-07T06:29:42.0377407Z Requirement already satisfied: filelock in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch==2.9.0a0+git93fb23d->torch==2.9.0a0+git93fb23d) (3.19.1) 2025-09-07T06:29:42.0378854Z Requirement already satisfied: typing-extensions>=4.10.0 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch==2.9.0a0+git93fb23d->torch==2.9.0a0+git93fb23d) (4.15.0) 2025-09-07T06:29:42.0381998Z Requirement already satisfied: setuptools in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch==2.9.0a0+git93fb23d->torch==2.9.0a0+git93fb23d) (80.9.0) 2025-09-07T06:29:42.0385453Z Requirement already satisfied: sympy>=1.13.3 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch==2.9.0a0+git93fb23d->torch==2.9.0a0+git93fb23d) (1.13.3) 2025-09-07T06:29:42.0388982Z Requirement already satisfied: networkx>=2.5.1 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch==2.9.0a0+git93fb23d->torch==2.9.0a0+git93fb23d) (2.8.8) 2025-09-07T06:29:42.0391786Z Requirement already satisfied: jinja2 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch==2.9.0a0+git93fb23d->torch==2.9.0a0+git93fb23d) (3.1.6) 2025-09-07T06:29:42.0395424Z Requirement already satisfied: fsspec>=0.8.5 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch==2.9.0a0+git93fb23d->torch==2.9.0a0+git93fb23d) (2025.7.0) 2025-09-07T06:29:42.0408557Z Requirement already satisfied: opt-einsum>=3.3 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch==2.9.0a0+git93fb23d->torch==2.9.0a0+git93fb23d) (3.3.0) 2025-09-07T06:29:42.0513392Z Requirement already satisfied: numpy>=1.7 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from opt-einsum>=3.3->torch==2.9.0a0+git93fb23d->torch==2.9.0a0+git93fb23d) (2.1.2) 2025-09-07T06:29:42.0559571Z Requirement already satisfied: mpmath<1.4,>=1.1.0 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from sympy>=1.13.3->torch==2.9.0a0+git93fb23d->torch==2.9.0a0+git93fb23d) (1.3.0) 2025-09-07T06:29:42.0591997Z Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from jinja2->torch==2.9.0a0+git93fb23d->torch==2.9.0a0+git93fb23d) (3.0.2) 2025-09-07T06:29:42.2599980Z Installing collected packages: torch 2025-09-07T06:29:52.0139321Z Successfully installed torch-2.9.0a0+git93fb23d 2025-09-07T06:29:52.0853350Z + export TERM=vt100 2025-09-07T06:29:52.0853929Z + TERM=vt100 2025-09-07T06:29:52.0856243Z ++ dirname .ci/pytorch/test.sh 2025-09-07T06:29:52.0863760Z + source .ci/pytorch/common.sh 2025-09-07T06:29:52.0866714Z +++ dirname .ci/pytorch/common.sh 2025-09-07T06:29:52.0873115Z ++ source .ci/pytorch/common_utils.sh 2025-09-07T06:29:52.0874785Z +++ declare -f -t trap_add 2025-09-07T06:29:52.0879004Z ++ set -ex -o pipefail 2025-09-07T06:29:52.0879380Z ++ [[ linux-jammy-py3.13-clang12 == *rocm* ]] 2025-09-07T06:29:52.0879709Z ++ BUILD_TEST_LIBTORCH=0 2025-09-07T06:29:52.0882930Z ++ dirname .ci/pytorch/test.sh 2025-09-07T06:29:52.0889256Z + source .ci/pytorch/common-build.sh 2025-09-07T06:29:52.0890699Z ++ [[ linux-jammy-py3.13-clang12 != *win-* ]] 2025-09-07T06:29:52.0896350Z ++++ dirname .ci/pytorch/common-build.sh 2025-09-07T06:29:52.0903649Z +++ cd .ci/pytorch 2025-09-07T06:29:52.0903937Z +++ pwd -P 2025-09-07T06:29:52.0906300Z ++ script_dir=/var/lib/jenkins/workspace/.ci/pytorch 2025-09-07T06:29:52.0906734Z ++ [[ linux-jammy-py3.13-clang12 == *-pch* ]] 2025-09-07T06:29:52.0907072Z ++ which sccache 2025-09-07T06:29:52.0922654Z ++ [[ -z ossci-compiler-cache-circleci-v2 ]] 2025-09-07T06:29:52.0923475Z ++ sccache --stop-server 2025-09-07T06:29:52.0946572Z ++ true 2025-09-07T06:29:52.0946821Z ++ rm -f /var/lib/jenkins/sccache_error.log 2025-09-07T06:29:52.0955416Z ++ trap_add sccache_epilogue EXIT 2025-09-07T06:29:52.0955875Z ++ trap_add_cmd=sccache_epilogue 2025-09-07T06:29:52.0956326Z ++ shift 2025-09-07T06:29:52.0956680Z ++ for trap_add_name in "$@" 2025-09-07T06:29:52.0962776Z ++++ trap -p EXIT 2025-09-07T06:29:52.0964624Z +++ eval 'extract_trap_cmd ' 2025-09-07T06:29:52.0965114Z ++++ extract_trap_cmd 2025-09-07T06:29:52.0965511Z ++++ printf '%s\n' '' 2025-09-07T06:29:52.0966015Z +++ printf '%s\n' sccache_epilogue 2025-09-07T06:29:52.0966433Z ++ trap -- ' 2025-09-07T06:29:52.0966806Z sccache_epilogue' EXIT 2025-09-07T06:29:52.0967056Z ++ [[ -n 1 ]] 2025-09-07T06:29:52.0967526Z ++ echo 'Skipping sccache server initialization, setting environment variables' 2025-09-07T06:29:52.0968171Z Skipping sccache server initialization, setting environment variables 2025-09-07T06:29:52.0968658Z ++ export SCCACHE_IDLE_TIMEOUT=0 2025-09-07T06:29:52.0968947Z ++ SCCACHE_IDLE_TIMEOUT=0 2025-09-07T06:29:52.0969309Z ++ export SCCACHE_ERROR_LOG=/var/lib/jenkins/sccache_error.log 2025-09-07T06:29:52.0969768Z ++ SCCACHE_ERROR_LOG=/var/lib/jenkins/sccache_error.log 2025-09-07T06:29:52.0970186Z ++ export RUST_LOG=sccache::server=error 2025-09-07T06:29:52.0970509Z ++ RUST_LOG=sccache::server=error 2025-09-07T06:29:52.0970840Z ++ sccache --zero-stats 2025-09-07T06:29:52.2104237Z Statistics zeroed. 2025-09-07T06:29:52.2108611Z ++ which ccache 2025-09-07T06:29:52.2131045Z + [[ linux-jammy-py3.13-clang12 != *rocm* ]] 2025-09-07T06:29:52.2131450Z + [[ linux-jammy-py3.13-clang12 != *s390x* ]] 2025-09-07T06:29:52.2131811Z + [[ -d /var/lib/jenkins/workspace ]] 2025-09-07T06:29:52.2133928Z ++ stat -c %u /var/lib/jenkins/workspace 2025-09-07T06:29:52.2144467Z + WORKSPACE_ORIGINAL_OWNER_ID=1000 2025-09-07T06:29:52.2144815Z + trap_add cleanup_workspace EXIT 2025-09-07T06:29:52.2145139Z + trap_add_cmd=cleanup_workspace 2025-09-07T06:29:52.2145433Z + shift 2025-09-07T06:29:52.2145659Z + for trap_add_name in "$@" 2025-09-07T06:29:52.2152401Z +++ trap -p EXIT 2025-09-07T06:29:52.2154720Z ++ eval 'extract_trap_cmd trap -- '\'' 2025-09-07T06:29:52.2155087Z sccache_epilogue'\'' EXIT' 2025-09-07T06:29:52.2155387Z +++ extract_trap_cmd trap -- ' 2025-09-07T06:29:52.2155680Z sccache_epilogue' EXIT 2025-09-07T06:29:52.2155928Z +++ printf '%s\n' ' 2025-09-07T06:29:52.2156171Z sccache_epilogue' 2025-09-07T06:29:52.2156435Z ++ printf '%s\n' cleanup_workspace 2025-09-07T06:29:52.2157658Z + trap -- ' 2025-09-07T06:29:52.2157908Z sccache_epilogue 2025-09-07T06:29:52.2158159Z cleanup_workspace' EXIT 2025-09-07T06:29:52.2158477Z + sudo chown -R jenkins /var/lib/jenkins/workspace 2025-09-07T06:29:52.9112439Z + git config --global --add safe.directory /var/lib/jenkins/workspace 2025-09-07T06:29:52.9131816Z + echo 'Environment variables:' 2025-09-07T06:29:52.9132133Z Environment variables: 2025-09-07T06:29:52.9132389Z + env 2025-09-07T06:29:52.9140764Z GITHUB_WORKSPACE=/home/ec2-user/actions-runner/_work/pytorch/pytorch 2025-09-07T06:29:52.9141303Z CONTINUE_THROUGH_ERROR=True 2025-09-07T06:29:52.9141632Z BUILD_ENVIRONMENT=linux-jammy-py3.13-clang12 2025-09-07T06:29:52.9142235Z VLLM_TEST_HUGGING_FACE_TOKEN=*** 2025-09-07T06:29:52.9142523Z HOSTNAME=2a1e06725f99 2025-09-07T06:29:52.9143133Z GITHUB_PATH=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/add_path_ef0760a7-8e14-432a-97a4-317cb80db868 2025-09-07T06:29:52.9143796Z GITHUB_ACTION=__run_2 2025-09-07T06:29:52.9144072Z PYTORCH_TEST_CUDA_MEM_LEAK_CHECK=0 2025-09-07T06:29:52.9144367Z GITHUB_RUN_NUMBER=353985 2025-09-07T06:29:52.9144632Z TEST_CONFIG=default 2025-09-07T06:29:52.9144892Z GITHUB_REPOSITORY_OWNER_ID=21003710 2025-09-07T06:29:52.9145227Z TORCH_NVCC_FLAGS=-Xfatbin -compress-all 2025-09-07T06:29:52.9145610Z SCCACHE_IDLE_TIMEOUT=0 2025-09-07T06:29:52.9146021Z SCRIBE_GRAPHQL_ACCESS_TOKEN=*** 2025-09-07T06:29:52.9146336Z GITHUB_TRIGGERING_ACTOR=pytorchmergebot 2025-09-07T06:29:52.9146837Z GITHUB_REF_TYPE=branch 2025-09-07T06:29:52.9147126Z BASE_SHA=93fb23d6fae7c4e82c4239a1033e522088742634 2025-09-07T06:29:52.9147469Z XLA_CUDA= 2025-09-07T06:29:52.9147701Z NCCL_LIB_DIR=/usr/local/cuda/lib64/ 2025-09-07T06:29:52.9148112Z HUGGING_FACE_HUB_TOKEN=*** 2025-09-07T06:29:52.9148552Z *** 2025-09-07T06:29:52.9148757Z GITHUB_REPOSITORY_ID=65600975 2025-09-07T06:29:52.9149042Z GITHUB_ACTIONS=true 2025-09-07T06:29:52.9149341Z SCCACHE_ERROR_LOG=/var/lib/jenkins/sccache_error.log 2025-09-07T06:29:52.9149739Z SHA1=93fb23d6fae7c4e82c4239a1033e522088742634 2025-09-07T06:29:52.9150105Z GITHUB_SHA=93fb23d6fae7c4e82c4239a1033e522088742634 2025-09-07T06:29:52.9150625Z GITHUB_WORKFLOW_REF=pytorch/pytorch/.github/workflows/pull.yml@refs/heads/main 2025-09-07T06:29:52.9151108Z UCC_HOME=/usr 2025-09-07T06:29:52.9151339Z VERBOSE_TEST_LOGS=False 2025-09-07T06:29:52.9151599Z GITHUB_REF=refs/heads/main 2025-09-07T06:29:52.9151866Z SHARD_NUMBER=1 2025-09-07T06:29:52.9152111Z GITHUB_REF_PROTECTED=true 2025-09-07T06:29:52.9152391Z HOME=/var/lib/jenkins 2025-09-07T06:29:52.9152668Z GITHUB_API_URL=https://api.github.com 2025-09-07T06:29:52.9153006Z PYTORCH_TEST_RERUN_DISABLED_TESTS=0 2025-09-07T06:29:52.9153303Z UCX_COMMIT= 2025-09-07T06:29:52.9153523Z USE_SYSTEM_NCCL=1 2025-09-07T06:29:52.9153746Z NUM_TEST_SHARDS=5 2025-09-07T06:29:52.9153976Z UCX_HOME=/usr 2025-09-07T06:29:52.9154640Z GITHUB_STATE=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/save_state_ef0760a7-8e14-432a-97a4-317cb80db868 2025-09-07T06:29:52.9155442Z JOB_NAME=linux-jammy-py3.13-clang12 / test (default, 1, 5, linux.4xlarge) 2025-09-07T06:29:52.9156206Z GITHUB_ENV=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/set_env_ef0760a7-8e14-432a-97a4-317cb80db868 2025-09-07T06:29:52.9157060Z GITHUB_EVENT_PATH=/home/ec2-user/actions-runner/_work/_temp/_github_workflow/event.json 2025-09-07T06:29:52.9157586Z GITHUB_EVENT_NAME=push 2025-09-07T06:29:52.9157846Z DASHBOARD_TAG= 2025-09-07T06:29:52.9158071Z GITHUB_RUN_ID=17524754568 2025-09-07T06:29:52.9158348Z INSTALLED_OPENBLAS= 2025-09-07T06:29:52.9158985Z GITHUB_STEP_SUMMARY=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/step_summary_ef0760a7-8e14-432a-97a4-317cb80db868 2025-09-07T06:29:52.9159725Z GITHUB_ACTOR=pytorchmergebot 2025-09-07T06:29:52.9160008Z PR_NUMBER= 2025-09-07T06:29:52.9160217Z DESIRED_CUDA= 2025-09-07T06:29:52.9160451Z GITHUB_RUN_ATTEMPT=1 2025-09-07T06:29:52.9160716Z ANACONDA_PYTHON_VERSION=3.13 2025-09-07T06:29:52.9161049Z GITHUB_GRAPHQL_URL=https://api.github.com/graphql 2025-09-07T06:29:52.9161413Z TERM=vt100 2025-09-07T06:29:52.9161638Z INSTALLED_VISION=yes 2025-09-07T06:29:52.9161885Z BRANCH=main 2025-09-07T06:29:52.9162100Z SCCACHE_REGION=us-east-1 2025-09-07T06:29:52.9162379Z OPENSSL_ROOT_DIR=/opt/openssl 2025-09-07T06:29:52.9162669Z CUDA_PATH=/usr/local/cuda 2025-09-07T06:29:52.9163345Z GITHUB_ACTION_PATH=/home/ec2-user/actions-runner/_work/pytorch/pytorch/./.github/actions/setup-linux 2025-09-07T06:29:52.9163931Z GITHUB_SERVER_URL=https://github.com 2025-09-07T06:29:52.9164245Z UCC_COMMIT= 2025-09-07T06:29:52.9164467Z REENABLED_ISSUES= 2025-09-07T06:29:52.9164699Z DOCS= 2025-09-07T06:29:52.9164887Z SHLVL=1 2025-09-07T06:29:52.9165133Z MAX_JOBS=14 2025-09-07T06:29:52.9165356Z GITHUB_ACTOR_ID=97764156 2025-09-07T06:29:52.9165703Z GITHUB_WORKFLOW_SHA=93fb23d6fae7c4e82c4239a1033e522088742634 2025-09-07T06:29:52.9166081Z GITHUB_REF_NAME=main 2025-09-07T06:29:52.9166501Z XLA_CLANG_CACHE_S3_BUCKET_NAME=ossci-compiler-clang-cache-circleci-xla 2025-09-07T06:29:52.9166941Z GITHUB_JOB=test 2025-09-07T06:29:52.9167176Z NO_TEST_TIMEOUT=False 2025-09-07T06:29:52.9167421Z TD_DISTRIBUTED=False 2025-09-07T06:29:52.9167691Z GITHUB_REPOSITORY=pytorch/pytorch 2025-09-07T06:29:52.9168002Z GITHUB_RETENTION_DAYS=90 2025-09-07T06:29:52.9168275Z OPENSSL_DIR=/opt/openssl 2025-09-07T06:29:52.9168535Z GITHUB_ACTION_REPOSITORY= 2025-09-07T06:29:52.9169432Z 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 2025-09-07T06:29:52.9170272Z GITHUB_BASE_REF= 2025-09-07T06:29:52.9170510Z INSTALLED_ACL= 2025-09-07T06:29:52.9170844Z ARTIFACTS_FILE_SUFFIX=test-default-1-5-linux.4xlarge_49774041675 2025-09-07T06:29:52.9171256Z CI=true 2025-09-07T06:29:52.9171508Z GITHUB_REPOSITORY_OWNER=pytorch 2025-09-07T06:29:52.9171840Z RUST_LOG=sccache::server=error 2025-09-07T06:29:52.9172103Z JOB_ID=49774041675 2025-09-07T06:29:52.9172335Z GITHUB_HEAD_REF= 2025-09-07T06:29:52.9172570Z GITHUB_ACTION_REF= 2025-09-07T06:29:52.9172864Z SCCACHE_BUCKET=ossci-compiler-cache-circleci-v2 2025-09-07T06:29:52.9173223Z TEST_SHOWLOCALS=False 2025-09-07T06:29:52.9173486Z GITHUB_WORKFLOW=pull 2025-09-07T06:29:52.9173775Z DEBIAN_FRONTEND=noninteractive 2025-09-07T06:29:52.9174421Z GITHUB_OUTPUT=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/set_output_ef0760a7-8e14-432a-97a4-317cb80db868 2025-09-07T06:29:52.9175087Z NO_TD=False 2025-09-07T06:29:52.9175315Z SKIP_SCCACHE_INITIALIZATION=1 2025-09-07T06:29:52.9175641Z NCCL_INCLUDE_DIR=/usr/local/cuda/include/ 2025-09-07T06:29:52.9175968Z _=/usr/bin/env 2025-09-07T06:29:52.9176278Z ++ python -c 'import site; print(site.getsitepackages()[0])' 2025-09-07T06:29:53.0031636Z + TORCH_INSTALL_DIR=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch 2025-09-07T06:29:53.0032412Z + TORCH_BIN_DIR=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/bin 2025-09-07T06:29:53.0033021Z + TORCH_LIB_DIR=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib 2025-09-07T06:29:53.0033634Z + TORCH_TEST_DIR=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/test 2025-09-07T06:29:53.0034095Z + BUILD_DIR=build 2025-09-07T06:29:53.0034405Z + BUILD_RENAMED_DIR=build_renamed 2025-09-07T06:29:53.0034715Z + BUILD_BIN_DIR=build/bin 2025-09-07T06:29:53.0034981Z + SHARD_NUMBER=1 2025-09-07T06:29:53.0035235Z + NUM_TEST_SHARDS=5 2025-09-07T06:29:53.0035503Z + export TORCH_SERIALIZATION_DEBUG=1 2025-09-07T06:29:53.0035824Z + TORCH_SERIALIZATION_DEBUG=1 2025-09-07T06:29:53.0036120Z + export VALGRIND=ON 2025-09-07T06:29:53.0036435Z + VALGRIND=ON 2025-09-07T06:29:53.0036726Z + [[ linux-jammy-py3.13-clang12 == *clang9* ]] 2025-09-07T06:29:53.0037088Z + [[ linux-jammy-py3.13-clang12 == *xpu* ]] 2025-09-07T06:29:53.0037412Z + detect_cuda_arch 2025-09-07T06:29:53.0037668Z + [[ linux-jammy-py3.13-clang12 == *cuda* ]] 2025-09-07T06:29:53.0038021Z + [[ linux-jammy-py3.13-clang12 == *s390x* ]] 2025-09-07T06:29:53.0038340Z + [[ 0 == \1 ]] 2025-09-07T06:29:53.0038562Z + [[ True == \1 ]] 2025-09-07T06:29:53.0038812Z + [[ linux-jammy-py3.13-clang12 != *bazel* ]] 2025-09-07T06:29:53.0039163Z ++ realpath build/custom_test_artifacts 2025-09-07T06:29:53.0045967Z + CUSTOM_TEST_ARTIFACT_BUILD_DIR=/var/lib/jenkins/workspace/build/custom_test_artifacts 2025-09-07T06:29:53.0046487Z + [[ -n '' ]] 2025-09-07T06:29:53.0046953Z + echo 'Environment variables' 2025-09-07T06:29:53.0047237Z Environment variables 2025-09-07T06:29:53.0047484Z + env 2025-09-07T06:29:53.0129111Z GITHUB_WORKSPACE=/home/ec2-user/actions-runner/_work/pytorch/pytorch 2025-09-07T06:29:53.0129987Z CONTINUE_THROUGH_ERROR=True 2025-09-07T06:29:53.0130566Z BUILD_ENVIRONMENT=linux-jammy-py3.13-clang12 2025-09-07T06:29:53.0131546Z VLLM_TEST_HUGGING_FACE_TOKEN=*** 2025-09-07T06:29:53.0131855Z HOSTNAME=2a1e06725f99 2025-09-07T06:29:53.0132761Z GITHUB_PATH=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/add_path_ef0760a7-8e14-432a-97a4-317cb80db868 2025-09-07T06:29:53.0133542Z GITHUB_ACTION=__run_2 2025-09-07T06:29:53.0133804Z PYTORCH_TEST_CUDA_MEM_LEAK_CHECK=0 2025-09-07T06:29:53.0134120Z GITHUB_RUN_NUMBER=353985 2025-09-07T06:29:53.0134390Z TEST_CONFIG=default 2025-09-07T06:29:53.0134652Z GITHUB_REPOSITORY_OWNER_ID=21003710 2025-09-07T06:29:53.0134985Z TORCH_NVCC_FLAGS=-Xfatbin -compress-all 2025-09-07T06:29:53.0135316Z SCCACHE_IDLE_TIMEOUT=0 2025-09-07T06:29:53.0135731Z SCRIBE_GRAPHQL_ACCESS_TOKEN=*** 2025-09-07T06:29:53.0136089Z GITHUB_TRIGGERING_ACTOR=pytorchmergebot 2025-09-07T06:29:53.0136995Z GITHUB_REF_TYPE=branch 2025-09-07T06:29:53.0137522Z BASE_SHA=93fb23d6fae7c4e82c4239a1033e522088742634 2025-09-07T06:29:53.0138142Z XLA_CUDA= 2025-09-07T06:29:53.0138585Z NCCL_LIB_DIR=/usr/local/cuda/lib64/ 2025-09-07T06:29:53.0139411Z HUGGING_FACE_HUB_TOKEN=*** 2025-09-07T06:29:53.0139734Z *** 2025-09-07T06:29:53.0139963Z GITHUB_REPOSITORY_ID=65600975 2025-09-07T06:29:53.0140259Z GITHUB_ACTIONS=true 2025-09-07T06:29:53.0140566Z SCCACHE_ERROR_LOG=/var/lib/jenkins/sccache_error.log 2025-09-07T06:29:53.0140953Z SHA1=93fb23d6fae7c4e82c4239a1033e522088742634 2025-09-07T06:29:53.0141337Z GITHUB_SHA=93fb23d6fae7c4e82c4239a1033e522088742634 2025-09-07T06:29:53.0141860Z GITHUB_WORKFLOW_REF=pytorch/pytorch/.github/workflows/pull.yml@refs/heads/main 2025-09-07T06:29:53.0142340Z UCC_HOME=/usr 2025-09-07T06:29:53.0142566Z TORCH_SERIALIZATION_DEBUG=1 2025-09-07T06:29:53.0142863Z VERBOSE_TEST_LOGS=False 2025-09-07T06:29:53.0143128Z GITHUB_REF=refs/heads/main 2025-09-07T06:29:53.0143390Z SHARD_NUMBER=1 2025-09-07T06:29:53.0143625Z GITHUB_REF_PROTECTED=true 2025-09-07T06:29:53.0143929Z HOME=/var/lib/jenkins 2025-09-07T06:29:53.0144217Z GITHUB_API_URL=https://api.github.com 2025-09-07T06:29:53.0144556Z PYTORCH_TEST_RERUN_DISABLED_TESTS=0 2025-09-07T06:29:53.0144857Z UCX_COMMIT= 2025-09-07T06:29:53.0158766Z USE_SYSTEM_NCCL=1 2025-09-07T06:29:53.0159121Z NUM_TEST_SHARDS=5 2025-09-07T06:29:53.0159367Z UCX_HOME=/usr 2025-09-07T06:29:53.0159972Z GITHUB_STATE=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/save_state_ef0760a7-8e14-432a-97a4-317cb80db868 2025-09-07T06:29:53.0160840Z JOB_NAME=linux-jammy-py3.13-clang12 / test (default, 1, 5, linux.4xlarge) 2025-09-07T06:29:53.0161627Z GITHUB_ENV=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/set_env_ef0760a7-8e14-432a-97a4-317cb80db868 2025-09-07T06:29:53.0162464Z GITHUB_EVENT_PATH=/home/ec2-user/actions-runner/_work/_temp/_github_workflow/event.json 2025-09-07T06:29:53.0163009Z GITHUB_EVENT_NAME=push 2025-09-07T06:29:53.0163258Z DASHBOARD_TAG= 2025-09-07T06:29:53.0163505Z GITHUB_RUN_ID=17524754568 2025-09-07T06:29:53.0163763Z INSTALLED_OPENBLAS= 2025-09-07T06:29:53.0164403Z GITHUB_STEP_SUMMARY=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/step_summary_ef0760a7-8e14-432a-97a4-317cb80db868 2025-09-07T06:29:53.0165121Z GITHUB_ACTOR=pytorchmergebot 2025-09-07T06:29:53.0165389Z PR_NUMBER= 2025-09-07T06:29:53.0165605Z DESIRED_CUDA= 2025-09-07T06:29:53.0165890Z GITHUB_RUN_ATTEMPT=1 2025-09-07T06:29:53.0166152Z VALGRIND=ON 2025-09-07T06:29:53.0166388Z ANACONDA_PYTHON_VERSION=3.13 2025-09-07T06:29:53.0166743Z GITHUB_GRAPHQL_URL=https://api.github.com/graphql 2025-09-07T06:29:53.0167105Z TERM=vt100 2025-09-07T06:29:53.0167314Z INSTALLED_VISION=yes 2025-09-07T06:29:53.0167559Z BRANCH=main 2025-09-07T06:29:53.0167786Z SCCACHE_REGION=us-east-1 2025-09-07T06:29:53.0168062Z OPENSSL_ROOT_DIR=/opt/openssl 2025-09-07T06:29:53.0169457Z CUDA_PATH=/usr/local/cuda 2025-09-07T06:29:53.0170005Z GITHUB_ACTION_PATH=/home/ec2-user/actions-runner/_work/pytorch/pytorch/./.github/actions/setup-linux 2025-09-07T06:29:53.0170608Z GITHUB_SERVER_URL=https://github.com 2025-09-07T06:29:53.0170920Z UCC_COMMIT= 2025-09-07T06:29:53.0171129Z REENABLED_ISSUES= 2025-09-07T06:29:53.0171359Z DOCS= 2025-09-07T06:29:53.0171560Z SHLVL=1 2025-09-07T06:29:53.0171768Z MAX_JOBS=14 2025-09-07T06:29:53.0171977Z GITHUB_ACTOR_ID=97764156 2025-09-07T06:29:53.0172329Z GITHUB_WORKFLOW_SHA=93fb23d6fae7c4e82c4239a1033e522088742634 2025-09-07T06:29:53.0172722Z GITHUB_REF_NAME=main 2025-09-07T06:29:53.0173108Z XLA_CLANG_CACHE_S3_BUCKET_NAME=ossci-compiler-clang-cache-circleci-xla 2025-09-07T06:29:53.0173538Z GITHUB_JOB=test 2025-09-07T06:29:53.0173776Z NO_TEST_TIMEOUT=False 2025-09-07T06:29:53.0174031Z TD_DISTRIBUTED=False 2025-09-07T06:29:53.0174302Z GITHUB_REPOSITORY=pytorch/pytorch 2025-09-07T06:29:53.0174600Z GITHUB_RETENTION_DAYS=90 2025-09-07T06:29:53.0174876Z OPENSSL_DIR=/opt/openssl 2025-09-07T06:29:53.0175152Z GITHUB_ACTION_REPOSITORY= 2025-09-07T06:29:53.0176045Z 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 2025-09-07T06:29:53.0176872Z GITHUB_BASE_REF= 2025-09-07T06:29:53.0177114Z INSTALLED_ACL= 2025-09-07T06:29:53.0177463Z ARTIFACTS_FILE_SUFFIX=test-default-1-5-linux.4xlarge_49774041675 2025-09-07T06:29:53.0177884Z CI=true 2025-09-07T06:29:53.0178128Z GITHUB_REPOSITORY_OWNER=pytorch 2025-09-07T06:29:53.0178473Z RUST_LOG=sccache::server=error 2025-09-07T06:29:53.0178755Z JOB_ID=49774041675 2025-09-07T06:29:53.0178991Z GITHUB_HEAD_REF= 2025-09-07T06:29:53.0179215Z GITHUB_ACTION_REF= 2025-09-07T06:29:53.0179512Z SCCACHE_BUCKET=ossci-compiler-cache-circleci-v2 2025-09-07T06:29:53.0179882Z TEST_SHOWLOCALS=False 2025-09-07T06:29:53.0180141Z GITHUB_WORKFLOW=pull 2025-09-07T06:29:53.0180399Z DEBIAN_FRONTEND=noninteractive 2025-09-07T06:29:53.0181056Z GITHUB_OUTPUT=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/set_output_ef0760a7-8e14-432a-97a4-317cb80db868 2025-09-07T06:29:53.0181722Z NO_TD=False 2025-09-07T06:29:53.0181960Z SKIP_SCCACHE_INITIALIZATION=1 2025-09-07T06:29:53.0182261Z NCCL_INCLUDE_DIR=/usr/local/cuda/include/ 2025-09-07T06:29:53.0182584Z _=/usr/bin/env 2025-09-07T06:29:53.0182816Z + echo 'Testing pytorch' 2025-09-07T06:29:53.0183081Z Testing pytorch 2025-09-07T06:29:53.0183306Z + export LANG=C.UTF-8 2025-09-07T06:29:53.0183554Z + LANG=C.UTF-8 2025-09-07T06:29:53.0183778Z + PR_NUMBER= 2025-09-07T06:29:53.0184013Z + [[ default == \d\e\f\a\u\l\t ]] 2025-09-07T06:29:53.0184307Z + export CUDA_VISIBLE_DEVICES=0 2025-09-07T06:29:53.0184600Z + CUDA_VISIBLE_DEVICES=0 2025-09-07T06:29:53.0184873Z + export HIP_VISIBLE_DEVICES=0 2025-09-07T06:29:53.0185162Z + HIP_VISIBLE_DEVICES=0 2025-09-07T06:29:53.0185420Z + [[ default == \d\i\s\t\r\i\b\u\t\e\d ]] 2025-09-07T06:29:53.0185736Z + [[ default == \s\l\o\w ]] 2025-09-07T06:29:53.0186070Z + [[ linux-jammy-py3.13-clang12 == *slow-gradcheck* ]] 2025-09-07T06:29:53.0186513Z + [[ linux-jammy-py3.13-clang12 == *cuda* ]] 2025-09-07T06:29:53.0186859Z + [[ linux-jammy-py3.13-clang12 == *rocm* ]] 2025-09-07T06:29:53.0187208Z + [[ linux-jammy-py3.13-clang12 == *xpu* ]] 2025-09-07T06:29:53.0187541Z + [[ default == *crossref* ]] 2025-09-07T06:29:53.0187847Z + [[ linux-jammy-py3.13-clang12 == *rocm* ]] 2025-09-07T06:29:53.0188180Z + [[ linux-jammy-py3.13-clang12 == *xpu* ]] 2025-09-07T06:29:53.0188539Z + [[ linux-jammy-py3.13-clang12 != *-bazel-* ]] 2025-09-07T06:29:53.0188889Z + pip_install ninja==1.10.2 2025-09-07T06:29:53.0189268Z + pip_install_pkg='python3 -m pip install --progress-bar off' 2025-09-07T06:29:53.0189754Z + python3 -m pip install --progress-bar off ninja==1.10.2 2025-09-07T06:29:53.5920298Z Collecting ninja==1.10.2 2025-09-07T06:29:53.6144845Z Downloading ninja-1.10.2-py2.py3-none-manylinux_2_5_x86_64.manylinux1_x86_64.whl.metadata (5.0 kB) 2025-09-07T06:29:53.6283296Z Downloading ninja-1.10.2-py2.py3-none-manylinux_2_5_x86_64.manylinux1_x86_64.whl (108 kB) 2025-09-07T06:29:53.8016764Z Installing collected packages: ninja 2025-09-07T06:29:53.8017365Z Attempting uninstall: ninja 2025-09-07T06:29:53.8050153Z Found existing installation: ninja 1.11.1.3 2025-09-07T06:29:53.8070616Z Uninstalling ninja-1.11.1.3: 2025-09-07T06:29:53.8176973Z Successfully uninstalled ninja-1.11.1.3 2025-09-07T06:29:53.8571909Z Successfully installed ninja-1.10.2 2025-09-07T06:29:53.9212814Z + 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 2025-09-07T06:29:53.9214492Z + 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 2025-09-07T06:29:53.9215527Z + [[ linux-jammy-py3.13-clang12 == *aarch64* ]] 2025-09-07T06:29:53.9216277Z + [[ linux-jammy-py3.13-clang12 == *asan* ]] 2025-09-07T06:29:53.9216863Z + [[ linux-jammy-py3.13-clang12 == *-debug* ]] 2025-09-07T06:29:53.9217535Z + [[ linux-jammy-py3.13-clang12 != *-bazel-* ]] 2025-09-07T06:29:53.9218074Z + echo 'We are not in debug mode: linux-jammy-py3.13-clang12. Expect the assertion to pass' 2025-09-07T06:29:53.9218731Z We are not in debug mode: linux-jammy-py3.13-clang12. Expect the assertion to pass 2025-09-07T06:29:53.9219206Z + cd test 2025-09-07T06:29:53.9219566Z + python -c 'import torch; torch._C._crash_if_debug_asserts_fail(424242)' 2025-09-07T06:29:55.2864486Z + [[ default == \n\o\g\p\u\_\N\O\_\A\V\X\2 ]] 2025-09-07T06:29:55.2864909Z + [[ default == \n\o\g\p\u\_\A\V\X\5\1\2 ]] 2025-09-07T06:29:55.2865292Z + [[ default == \l\e\g\a\c\y\_\n\v\i\d\i\a\_\d\r\i\v\e\r ]] 2025-09-07T06:29:55.2868152Z + DYNAMO_BENCHMARK_FLAGS=() 2025-09-07T06:29:55.2869177Z + [[ default == *pr_time_benchmarks* ]] 2025-09-07T06:29:55.2869794Z + [[ default == *dynamo_eager* ]] 2025-09-07T06:29:55.2870179Z + [[ default == *aot_eager* ]] 2025-09-07T06:29:55.2870524Z + [[ default == *aot_inductor* ]] 2025-09-07T06:29:55.2870858Z + [[ default == *max_autotune_inductor* ]] 2025-09-07T06:29:55.2871173Z + [[ default == *inductor* ]] 2025-09-07T06:29:55.2871462Z + [[ default == *dynamic* ]] 2025-09-07T06:29:55.2871747Z + [[ default == *cpu* ]] 2025-09-07T06:29:55.2872047Z + DYNAMO_BENCHMARK_FLAGS+=(--device cuda) 2025-09-07T06:29:55.2958722Z + [[ linux-jammy-py3.13-clang12 == *libtorch* ]] 2025-09-07T06:29:55.2959447Z + [[ linux-jammy-py3.13-clang12 == *-bazel-* ]] 2025-09-07T06:29:55.2961802Z + cd test 2025-09-07T06:29:55.2962719Z + python -c 'import torch; print(torch.__config__.show())' 2025-09-07T06:29:56.3901396Z PyTorch built with: 2025-09-07T06:29:56.3901795Z - GCC 4.2 2025-09-07T06:29:56.3902031Z - C++ Version: 201703 2025-09-07T06:29:56.3902313Z - clang 12.0.1 2025-09-07T06:29:56.3902873Z - Intel(R) oneAPI Math Kernel Library Version 2024.2-Product Build 20240605 for Intel(R) 64 architecture applications 2025-09-07T06:29:56.3903670Z - Intel(R) MKL-DNN v3.7.1 (Git Hash 8d263e693366ef8db40acc569cc7d8edf644556d) 2025-09-07T06:29:56.3904127Z - OpenMP 201811 2025-09-07T06:29:56.3904418Z - LAPACK is enabled (usually provided by MKL) 2025-09-07T06:29:56.3904768Z - NNPACK is enabled 2025-09-07T06:29:56.3905034Z - CPU capability usage: AVX512 2025-09-07T06:29:56.3910791Z - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, COMMIT_SHA=93fb23d6fae7c4e82c4239a1033e522088742634, CXX_COMPILER=/opt/cache/bin/clang++, CXX_FLAGS= -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 -DC10_NODEPRECATED -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 -Wsuggest-override -Wnewline-eof -Winconsistent-missing-override -Winconsistent-missing-destructor-override -Wno-pass-failed -Wno-error=old-style-cast -Wconstant-conversion -Qunused-arguments -faligned-new -Werror -fno-math-errno -fno-trapping-math -Werror=format, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, TORCH_VERSION=2.9.0, USE_CUDA=OFF, USE_CUDNN=OFF, USE_CUSPARSELT=OFF, 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, USE_XCCL=OFF, USE_XPU=OFF, 2025-09-07T06:29:56.3917020Z 2025-09-07T06:29:56.6541861Z + cd test 2025-09-07T06:29:56.6542351Z + python -c 'import torch; print(torch.__config__.parallel_info())' 2025-09-07T06:29:57.7665217Z ATen/Parallel: 2025-09-07T06:29:57.7665565Z at::get_num_threads() : 8 2025-09-07T06:29:57.7665890Z at::get_num_interop_threads() : 8 2025-09-07T06:29:57.7666188Z OpenMP 201811 2025-09-07T06:29:57.7666465Z omp_get_max_threads() : 8 2025-09-07T06:29:57.7667359Z Intel(R) oneAPI Math Kernel Library Version 2024.2-Product Build 20240605 for Intel(R) 64 architecture applications 2025-09-07T06:29:57.7667995Z mkl_get_max_threads() : 8 2025-09-07T06:29:57.7668384Z Intel(R) MKL-DNN v3.7.1 (Git Hash 8d263e693366ef8db40acc569cc7d8edf644556d) 2025-09-07T06:29:57.7668856Z std::thread::hardware_concurrency() : 16 2025-09-07T06:29:57.7669208Z Environment variables: 2025-09-07T06:29:57.7669479Z OMP_NUM_THREADS : [not set] 2025-09-07T06:29:57.7669751Z MKL_NUM_THREADS : [not set] 2025-09-07T06:29:57.7670041Z ATen parallel backend: OpenMP 2025-09-07T06:29:57.7670242Z 2025-09-07T06:29:58.0266843Z + [[ default == *numpy_2* ]] 2025-09-07T06:29:58.0267386Z + [[ linux-jammy-py3.13-clang12 == *aarch64* ]] 2025-09-07T06:29:58.0267768Z + [[ default == *backward* ]] 2025-09-07T06:29:58.0268055Z + [[ default == *xla* ]] 2025-09-07T06:29:58.0268314Z + [[ default == *vllm* ]] 2025-09-07T06:29:58.0268603Z + [[ default == *executorch* ]] 2025-09-07T06:29:58.0268897Z + [[ default == \j\i\t\_\l\e\g\a\c\y ]] 2025-09-07T06:29:58.0269274Z + [[ linux-jammy-py3.13-clang12 == *libtorch* ]] 2025-09-07T06:29:58.0269630Z + [[ default == distributed ]] 2025-09-07T06:29:58.0269931Z + [[ default == *operator_benchmark* ]] 2025-09-07T06:29:58.0270251Z + [[ default == *inductor_distributed* ]] 2025-09-07T06:29:58.0270586Z + [[ default == *inductor-halide* ]] 2025-09-07T06:29:58.0270910Z + [[ default == *inductor-triton-cpu* ]] 2025-09-07T06:29:58.0271261Z + [[ default == *inductor-micro-benchmark* ]] 2025-09-07T06:29:58.0271631Z + [[ default == *huggingface* ]] 2025-09-07T06:29:58.0271920Z + [[ default == *timm* ]] 2025-09-07T06:29:58.0272187Z + [[ default == cachebench ]] 2025-09-07T06:29:58.0272476Z + [[ default == verify_cachebench ]] 2025-09-07T06:29:58.0272774Z + [[ default == *torchbench* ]] 2025-09-07T06:29:58.0273084Z + [[ default == *inductor_cpp_wrapper* ]] 2025-09-07T06:29:58.0273411Z + [[ default == *inductor* ]] 2025-09-07T06:29:58.0273696Z + [[ default == *einops* ]] 2025-09-07T06:29:58.0273969Z + [[ default == *dynamo_wrapped* ]] 2025-09-07T06:29:58.0274381Z + [[ linux-jammy-py3.13-clang12 == *rocm* ]] 2025-09-07T06:29:58.0274699Z + [[ 1 == 1 ]] 2025-09-07T06:29:58.0274931Z + [[ 5 -gt 1 ]] 2025-09-07T06:29:58.0275180Z + test_lazy_tensor_meta_reference_disabled 2025-09-07T06:29:58.0275584Z + export TORCH_DISABLE_FUNCTIONALIZATION_META_REFERENCE=1 2025-09-07T06:29:58.0276028Z + TORCH_DISABLE_FUNCTIONALIZATION_META_REFERENCE=1 2025-09-07T06:29:58.0276477Z + echo 'Testing lazy tensor operations without meta reference' 2025-09-07T06:29:58.0276928Z Testing lazy tensor operations without meta reference 2025-09-07T06:29:58.0277418Z + python test/run_test.py --include lazy/test_ts_opinfo.py --verbose 2025-09-07T06:30:00.0299316Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T06:30:00.0301329Z import pkg_resources 2025-09-07T06:30:01.5449016Z Downloading https://ossci-metrics.s3.amazonaws.com/disabled-tests-condensed.json to /var/lib/jenkins/workspace/test/.pytorch-disabled-tests.json 2025-09-07T06:30:01.5924827Z Ignoring disabled issues: [''] 2025-09-07T06:30:01.6064462Z Found test times from artifacts 2025-09-07T06:30:01.6609073Z Found test times from artifacts 2025-09-07T06:30:01.6626256Z Running all tests 2025-09-07T06:30:01.6629660Z Running parallel tests on 3 processes 2025-09-07T06:30:01.6630002Z Name: tests to run (est. time: 0.41min) 2025-09-07T06:30:01.6630319Z Serial tests (0): 2025-09-07T06:30:01.6630574Z Parallel tests (1): 2025-09-07T06:30:01.6630837Z lazy/test_ts_opinfo 1/1 2025-09-07T06:30:01.6631114Z Name: excluded (est. time: 0.0min) 2025-09-07T06:30:01.6631490Z Serial tests (0): 2025-09-07T06:30:01.6631735Z Parallel tests (0): 2025-09-07T06:30:01.6632119Z Running lazy/test_ts_opinfo 1/1 ... [2025-09-07 06:30:01.663093] 2025-09-07T06:30:01.6632722Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T06:30:01.6636306Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'lazy/test_ts_opinfo.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'] ... [2025-09-07 06:30:01.663404] 2025-09-07T06:30:05.4330288Z 2025-09-07T06:30:05.4331242Z lazy/test_ts_opinfo 1/1 was successful, full logs can be found in artifacts with path test/test-reports/lazy.test_ts_opinfo_1.1_2cd0e0606c13e732_.log 2025-09-07T06:30:05.4332035Z Running 0 items in this shard: 2025-09-07T06:30:05.4332227Z 2025-09-07T06:30:05.8426033Z Uploading artifacts took 0.41 seconds 2025-09-07T06:30:08.0030335Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T06:30:08.0031908Z import pkg_resources 2025-09-07T06:30:08.0033355Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T06:30:08.0034900Z import pkg_resources 2025-09-07T06:30:08.0218004Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T06:30:08.0219540Z import pkg_resources 2025-09-07T06:30:08.1201584Z Running lazy/test_ts_opinfo 1/1 ... [2025-09-07 06:30:08.119830] 2025-09-07T06:30:08.1202093Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T06:30:08.1203676Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'lazy/test_ts_opinfo.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'] ... [2025-09-07 06:30:08.120161] 2025-09-07T06:30:23.0542260Z 2025-09-07T06:30:23.0543391Z lazy/test_ts_opinfo 1/1 was successful, full logs can be found in artifacts with path test/test-reports/lazy.test_ts_opinfo_1.1_8806d00b2815e162_.log 2025-09-07T06:30:23.0662679Z Running 292 items in this shard: test/lazy/test_ts_opinfo.py::TestLazyTensor::testConvolutionBackward, test/lazy/test_ts_opinfo.py::TestLazyTensor::test_tensor_ctr, test/lazy/test_ts_opinfo.py::TestLazyTensor::test_view_mark_step_preserved, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness__softmax_backward_data_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_abs_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_add_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_addcmul_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_addmm_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_addmm_decomposed_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_alias_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_arange_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_as_strided_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_as_strided_scatter_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_baddbmm_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_block_diag_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_bmm_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_cat_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_clamp_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_clamp_min_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_constant_pad_nd_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_cos_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_cumsum_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_diag_embed_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_diagonal_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_diagonal_scatter_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_div_floor_rounding_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_div_no_rounding_mode_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_div_trunc_rounding_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_empty_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_empty_strided_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_eq_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_exp_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_expand_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_flip_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_floor_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_frac_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_gather_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_ge_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_grid_sampler_2d_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_gt_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_le_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_log2_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_log_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_logsumexp_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_lt_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_masked_fill_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_max_binary_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_max_pool2d_with_indices_backward_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_max_reduction_no_dim_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_max_reduction_with_dim_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_maximum_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_mean_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_min_binary_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_min_reduction_no_dim_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_min_reduction_with_dim_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_minimum_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_mm_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_mul_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_mv_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_narrow_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_native_batch_norm_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_native_dropout_backward_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_native_layer_norm_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_ne_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_neg_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_new_empty_strided_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_norm_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_norm_fro_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_norm_inf_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_norm_nuc_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_permute_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_reciprocal_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_remainder_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_repeat_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_rsqrt_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_scatter_add_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_select_scatter_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_sgn_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_sigmoid_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_slice_scatter_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_sort_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_sqrt_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_squeeze_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_stack_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_std_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_std_unbiased_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_sub_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_sum_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_t_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_tanh_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_topk_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_trace_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_transpose_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_tril_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_triu_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_trunc_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_unfold_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_uniform_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_unsqueeze_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_view_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir__softmax_backward_data_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_abs_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_add_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_addcmul_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_addmm_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_addmm_decomposed_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_alias_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_arange_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_as_strided_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_as_strided_scatter_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_baddbmm_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_block_diag_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_bmm_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_cat_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_clamp_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_clamp_min_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_constant_pad_nd_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_cos_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_cumsum_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_diag_embed_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_diagonal_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_diagonal_scatter_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_div_floor_rounding_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_div_no_rounding_mode_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_div_trunc_rounding_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_empty_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_empty_strided_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_eq_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_exp_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_expand_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_flip_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_floor_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_frac_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_gather_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_ge_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_grid_sampler_2d_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_gt_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_le_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_log2_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_log_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_logsumexp_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_lt_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_masked_fill_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_max_binary_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_max_pool2d_with_indices_backward_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_max_reduction_no_dim_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_max_reduction_with_dim_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_maximum_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_mean_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_min_binary_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_min_reduction_no_dim_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_min_reduction_with_dim_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_minimum_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_mm_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_mul_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_mv_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_narrow_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_native_batch_norm_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_native_dropout_backward_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_native_layer_norm_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_ne_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_neg_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_new_empty_strided_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_norm_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_norm_fro_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_norm_inf_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_norm_nuc_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_permute_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_reciprocal_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_remainder_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_repeat_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_rsqrt_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_scatter_add_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_select_scatter_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_sgn_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_sigmoid_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_slice_scatter_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_sort_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_sqrt_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_squeeze_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_stack_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_std_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_std_unbiased_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_sub_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_sum_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_t_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_tanh_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_topk_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_trace_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_transpose_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_tril_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_triu_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_trunc_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_unfold_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_uniform_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_unsqueeze_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_view_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy__softmax_backward_data_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_abs_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_add_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_addcdiv_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_addcmul_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_addmm_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_addmm_decomposed_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_alias_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_arange_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_as_strided_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_as_strided_scatter_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_baddbmm_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_bernoulli_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_bmm_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_cat_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_clamp_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_clamp_min_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_constant_pad_nd_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_cos_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_cumsum_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_diagonal_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_diagonal_scatter_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_div_floor_rounding_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_div_no_rounding_mode_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_div_trunc_rounding_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_empty_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_empty_strided_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_eq_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_exp_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_expand_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_flip_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_floor_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_frac_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_gather_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_ge_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_grid_sampler_2d_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_gt_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_le_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_log2_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_log_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_lt_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_masked_fill_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_max_binary_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_max_pool2d_with_indices_backward_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_max_reduction_no_dim_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_max_reduction_with_dim_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_maximum_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_mean_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_min_binary_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_min_reduction_no_dim_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_minimum_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_mm_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_mul_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_mv_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_native_batch_norm_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_native_dropout_backward_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_native_layer_norm_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_ne_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_neg_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_norm_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_norm_fro_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_norm_inf_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_permute_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_pow_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_reciprocal_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_remainder_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_repeat_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_rsqrt_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_scatter_add_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_select_scatter_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_sgn_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_sigmoid_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_slice_scatter_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_sort_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_sqrt_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_squeeze_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_stack_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_std_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_std_unbiased_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_sub_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_sum_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_t_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_tanh_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_topk_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_trace_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_transpose_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_tril_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_triu_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_trunc_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_unfold_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_uniform_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_unsqueeze_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_view_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyDynamicOps::test_adaptiveavgpool3d_dynamic, test/lazy/test_ts_opinfo.py::TestLazyDynamicOps::test_nonzero_dynamic 2025-09-07T06:30:23.0777537Z 2025-09-07T06:30:23.6776701Z Running test batch 'tests to run' cost 22.01 seconds 2025-09-07T06:30:24.2939545Z 2025-09-07T06:30:24.2939847Z real 0m26.267s 2025-09-07T06:30:24.2940100Z user 1m26.337s 2025-09-07T06:30:24.2940329Z sys 0m4.201s 2025-09-07T06:30:24.2940682Z + export -n TORCH_DISABLE_FUNCTIONALIZATION_META_REFERENCE 2025-09-07T06:30:24.2941080Z + test_without_numpy 2025-09-07T06:30:24.2944147Z ++ dirname .ci/pytorch/test.sh 2025-09-07T06:30:24.2963896Z + pushd .ci/pytorch 2025-09-07T06:30:24.2964268Z ~/workspace/.ci/pytorch ~/workspace 2025-09-07T06:30:24.2965129Z + python -c 'import sys;sys.path.insert(0, '\''fake_numpy'\'');from unittest import TestCase;import torch;x=torch.randn(3,3);TestCase().assertRaises(RuntimeError, lambda: x.numpy())' 2025-09-07T06:30:24.9246624Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_subclasses/functional_tensor.py:279: UserWarning: Failed to initialize NumPy: Sorry PyTorch, but our NumPy is in the other folder (Triggered internally at /var/lib/jenkins/workspace/torch/csrc/utils/tensor_numpy.cpp:84.) 2025-09-07T06:30:24.9248102Z cpu = _conversion_method_template(device=torch.device("cpu")) 2025-09-07T06:30:25.5505242Z + python -c 'import sys;sys.path.insert(0, '\''fake_numpy'\'');import torch;print(torch.tensor([torch.tensor(0.), torch.tensor(1.)]))' 2025-09-07T06:30:26.1809379Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_subclasses/functional_tensor.py:279: UserWarning: Failed to initialize NumPy: Sorry PyTorch, but our NumPy is in the other folder (Triggered internally at /var/lib/jenkins/workspace/torch/csrc/utils/tensor_numpy.cpp:84.) 2025-09-07T06:30:26.1810881Z cpu = _conversion_method_template(device=torch.device("cpu")) 2025-09-07T06:30:26.5597415Z tensor([0., 1.]) 2025-09-07T06:30:26.8068202Z + [[ default == *dynamo_wrapped* ]] 2025-09-07T06:30:26.8069070Z + python -c 'import sys;sys.path.insert(0, '\''fake_numpy'\'');import torch; import torch.onnx' 2025-09-07T06:30:27.4376166Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_subclasses/functional_tensor.py:279: UserWarning: Failed to initialize NumPy: Sorry PyTorch, but our NumPy is in the other folder (Triggered internally at /var/lib/jenkins/workspace/torch/csrc/utils/tensor_numpy.cpp:84.) 2025-09-07T06:30:27.4377984Z cpu = _conversion_method_template(device=torch.device("cpu")) 2025-09-07T06:30:28.0915754Z + popd 2025-09-07T06:30:28.0916024Z ~/workspace 2025-09-07T06:30:28.0916254Z + install_torchvision 2025-09-07T06:30:28.0916511Z + local orig_preload 2025-09-07T06:30:28.0916761Z + local commit 2025-09-07T06:30:28.0919115Z ++ get_pinned_commit vision 2025-09-07T06:30:28.0919436Z ++ cat .github/ci_commit_pins/vision.txt 2025-09-07T06:30:28.0940934Z + commit=966da7e46f65d6d49df3e31214470a4fe5cc8e66 2025-09-07T06:30:28.0941415Z + orig_preload= 2025-09-07T06:30:28.0941719Z + '[' -n '' ']' 2025-09-07T06:30:28.0942112Z + [[ linux-jammy-py3.13-clang12 == *cuda* ]] 2025-09-07T06:30:28.0942810Z + pip_build_and_install git+https://github.com/pytorch/vision.git@966da7e46f65d6d49df3e31214470a4fe5cc8e66 dist/vision 2025-09-07T06:30:28.0943735Z + local build_target=git+https://github.com/pytorch/vision.git@966da7e46f65d6d49df3e31214470a4fe5cc8e66 2025-09-07T06:30:28.0944353Z + local wheel_dir=dist/vision 2025-09-07T06:30:28.0944648Z + local found_whl=0 2025-09-07T06:30:28.0944893Z + for file in "${wheel_dir}"/*.whl 2025-09-07T06:30:28.0945203Z + [[ -f dist/vision/*.whl ]] 2025-09-07T06:30:28.0945478Z + '[' 0 == 0 ']' 2025-09-07T06:30:28.0946249Z + python3 -m pip wheel --no-build-isolation --no-deps --no-use-pep517 -w dist/vision git+https://github.com/pytorch/vision.git@966da7e46f65d6d49df3e31214470a4fe5cc8e66 2025-09-07T06:30:28.4018219Z Collecting git+https://github.com/pytorch/vision.git@966da7e46f65d6d49df3e31214470a4fe5cc8e66 2025-09-07T06:30:28.4022386Z Cloning https://github.com/pytorch/vision.git (to revision 966da7e46f65d6d49df3e31214470a4fe5cc8e66) to /tmp/pip-req-build-jyq313xr 2025-09-07T06:30:28.4041316Z Running command git clone --filter=blob:none --quiet https://github.com/pytorch/vision.git /tmp/pip-req-build-jyq313xr 2025-09-07T06:30:29.9421946Z Running command git rev-parse -q --verify 'sha^966da7e46f65d6d49df3e31214470a4fe5cc8e66' 2025-09-07T06:30:29.9441179Z Running command git fetch -q https://github.com/pytorch/vision.git 966da7e46f65d6d49df3e31214470a4fe5cc8e66 2025-09-07T06:30:30.0678546Z Running command git checkout -q 966da7e46f65d6d49df3e31214470a4fe5cc8e66 2025-09-07T06:30:30.3820557Z Resolved https://github.com/pytorch/vision.git to commit 966da7e46f65d6d49df3e31214470a4fe5cc8e66 2025-09-07T06:30:31.9554857Z Preparing metadata (setup.py) ... [?25l- \ done 2025-09-07T06:30:31.9594050Z [?25hBuilding wheels for collected packages: torchvision 2025-09-07T06:30:31.9642907Z  DEPRECATION: Building 'torchvision' using the legacy setup.py bdist_wheel mechanism, which will be removed in a future version. pip 25.3 will enforce this behaviour change. A possible replacement is to use the standardized build interface by setting the `--use-pep517` option, (possibly combined with `--no-build-isolation`), or adding a `pyproject.toml` file to the source tree of 'torchvision'. Discussion can be found at https://github.com/pypa/pip/issues/6334 2025-09-07T06:31:25.9823027Z  Building wheel for torchvision (setup.py) ... [?25l- \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ done 2025-09-07T06:31:25.9862223Z [?25h Created wheel for torchvision: filename=torchvision-0.22.0a0+966da7e-cp313-cp313-linux_x86_64.whl size=1199432 sha256=f9ccf795b0744b136603bf5f74bb8bef5de374d05a29821f468cfd7fbb562715 2025-09-07T06:31:25.9864522Z Stored in directory: /var/lib/jenkins/.cache/pip/wheels/89/53/07/16d05bff0990e585bfa232438657da6e5e4f6d0cea71bd3e09 2025-09-07T06:31:25.9899822Z Successfully built torchvision 2025-09-07T06:31:26.0761233Z + for file in "${wheel_dir}"/*.whl 2025-09-07T06:31:26.0761826Z + pip_install_whl dist/vision/torchvision-0.22.0a0+966da7e-cp313-cp313-linux_x86_64.whl 2025-09-07T06:31:26.0762517Z + args=('dist/vision/torchvision-0.22.0a0+966da7e-cp313-cp313-linux_x86_64.whl') 2025-09-07T06:31:26.0763023Z + local args 2025-09-07T06:31:26.0763694Z + [[ dist/vision/torchvision-0.22.0a0+966da7e-cp313-cp313-linux_x86_64.whl == *\ * ]] 2025-09-07T06:31:26.0764213Z + for path in "${args[@]}" 2025-09-07T06:31:26.0764700Z + echo 'Installing dist/vision/torchvision-0.22.0a0+966da7e-cp313-cp313-linux_x86_64.whl' 2025-09-07T06:31:26.0765424Z Installing dist/vision/torchvision-0.22.0a0+966da7e-cp313-cp313-linux_x86_64.whl 2025-09-07T06:31:26.0766255Z + python3 -mpip install --no-index --no-deps dist/vision/torchvision-0.22.0a0+966da7e-cp313-cp313-linux_x86_64.whl 2025-09-07T06:31:26.3978299Z Processing ./dist/vision/torchvision-0.22.0a0+966da7e-cp313-cp313-linux_x86_64.whl 2025-09-07T06:31:26.4077162Z Installing collected packages: torchvision 2025-09-07T06:31:26.8731255Z Successfully installed torchvision-0.22.0a0+966da7e 2025-09-07T06:31:26.9200989Z + '[' -n '' ']' 2025-09-07T06:31:26.9201300Z + test_python_shard 1 2025-09-07T06:31:26.9201568Z + [[ -z 5 ]] 2025-09-07T06:31:26.9202236Z + python test/run_test.py --exclude-jit-executor --exclude-distributed-tests --shard 1 5 --verbose --upload-artifacts-while-running 2025-09-07T06:31:28.9086584Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T06:31:28.9088220Z import pkg_resources 2025-09-07T06:31:30.3458062Z Downloading https://ossci-metrics.s3.amazonaws.com/disabled-tests-condensed.json to /var/lib/jenkins/workspace/test/.pytorch-disabled-tests.json 2025-09-07T06:31:30.3591974Z Found test times from artifacts 2025-09-07T06:31:30.4144637Z Found test times from artifacts 2025-09-07T06:31:30.4162137Z Running all tests 2025-09-07T06:31:30.4538204Z Running parallel tests on 3 processes 2025-09-07T06:31:30.4542681Z Name: tests to run (est. time: 87.79min) 2025-09-07T06:31:30.4543075Z Serial tests (51): 2025-09-07T06:31:30.4543332Z test_reductions 1/1 2025-09-07T06:31:30.4543622Z test_ci_sanity_check_fail 1/1 2025-09-07T06:31:30.4543919Z test_openreg 1/1 2025-09-07T06:31:30.4544194Z test_cpp_extensions_mtia_backend 1/1 2025-09-07T06:31:30.4544530Z test_cpp_extensions_stream_and_event 1/1 2025-09-07T06:31:30.4544868Z test_python_dispatch 1/1 2025-09-07T06:31:30.4545160Z test_tensor_creation_ops 1/1 2025-09-07T06:31:30.4545461Z test_cpp_extensions_jit 1/1 2025-09-07T06:31:30.4545729Z test_nn 1/1 2025-09-07T06:31:30.4545964Z test_overrides 1/1 2025-09-07T06:31:30.4546227Z test_extension_utils 1/1 2025-09-07T06:31:30.4546519Z inductor/test_flex_attention 1/5 2025-09-07T06:31:30.4546830Z inductor/test_flex_attention 2/5 2025-09-07T06:31:30.4547148Z inductor/test_flex_attention 3/5 2025-09-07T06:31:30.4547465Z inductor/test_flex_attention 4/5 2025-09-07T06:31:30.4547778Z inductor/test_flex_attention 5/5 2025-09-07T06:31:30.4548348Z inductor/test_cutlass_backend 1/1 2025-09-07T06:31:30.4548672Z test_cpp_api_parity 1/1 2025-09-07T06:31:30.4548953Z test_fx 1/1 2025-09-07T06:31:30.4549213Z test_transformers_privateuse1 1/1 2025-09-07T06:31:30.4549531Z inductor/test_benchmark_fusion 1/1 2025-09-07T06:31:30.4549847Z test_show_pickle 1/1 2025-09-07T06:31:30.4550104Z test_utils 1/1 2025-09-07T06:31:30.4550347Z test_tensorexpr 1/1 2025-09-07T06:31:30.4550608Z inductor/test_max_autotune 1/1 2025-09-07T06:31:30.4550919Z test_multiprocessing 1/1 2025-09-07T06:31:30.4551198Z test_dispatch 1/1 2025-09-07T06:31:30.4551462Z test_namedtuple_return_api 1/1 2025-09-07T06:31:30.4551752Z test_jit_disabled 1/1 2025-09-07T06:31:30.4552016Z test_fake_tensor 1/1 2025-09-07T06:31:30.4552276Z test_cuda_trace 1/1 2025-09-07T06:31:30.4552529Z test_cuda_nvml_based_avail 1/1 2025-09-07T06:31:30.4552833Z test_autograd_fallback 1/1 2025-09-07T06:31:30.4553131Z dynamo/test_fake_distributed 1/1 2025-09-07T06:31:30.4553525Z inductor/test_distributed_patterns 1/1 2025-09-07T06:31:30.4554011Z test_autocast 1/1 2025-09-07T06:31:30.4554263Z test_torch 1/1 2025-09-07T06:31:30.4554593Z test_sort_and_select 1/1 2025-09-07T06:31:30.4554868Z test_native_mha 1/1 2025-09-07T06:31:30.4555119Z test_cuda_primary_ctx 1/1 2025-09-07T06:31:30.4555403Z nn/test_pooling 1/1 2025-09-07T06:31:30.4555678Z test_multiprocessing_spawn 1/1 2025-09-07T06:31:30.4555987Z nn/test_convolution 1/1 2025-09-07T06:31:30.4556259Z test_mobile_optimizer 1/1 2025-09-07T06:31:30.4556550Z test_spectral_ops 1/1 2025-09-07T06:31:30.4556842Z distributions/test_distributions 1/1 2025-09-07T06:31:30.4557230Z doctests 1/1 2025-09-07T06:31:30.4557464Z test_autoload_disable 1/1 2025-09-07T06:31:30.4557757Z test_autoload_enable 1/1 2025-09-07T06:31:30.4558053Z test_cpp_extensions_aot_ninja 1/1 2025-09-07T06:31:30.4558387Z test_cpp_extensions_aot_no_ninja 1/1 2025-09-07T06:31:30.4558706Z Parallel tests (40): 2025-09-07T06:31:30.4558970Z inductor/test_memory 1/1 2025-09-07T06:31:30.4559265Z inductor/test_inductor_utils 1/1 2025-09-07T06:31:30.4559580Z inductor/test_best_config 1/1 2025-09-07T06:31:30.4559879Z inductor/test_cudagraph_trees 1/1 2025-09-07T06:31:30.4560195Z inductor/test_metrics 1/1 2025-09-07T06:31:30.4560485Z inductor/test_cuda_repro 1/1 2025-09-07T06:31:30.4560788Z inductor/test_profiler 1/1 2025-09-07T06:31:30.4561093Z inductor/test_cooperative_reductions 1/1 2025-09-07T06:31:30.4561456Z higher_order_ops/test_invoke_quant 1/1 2025-09-07T06:31:30.4561794Z inductor/test_online_softmax 1/1 2025-09-07T06:31:30.4562110Z dynamo/test_unittest 1/1 2025-09-07T06:31:30.4562398Z dynamo/test_guard_serialization 1/1 2025-09-07T06:31:30.4562727Z functorch/test_minifier 1/1 2025-09-07T06:31:30.4563020Z test_legacy_vmap 1/1 2025-09-07T06:31:30.4563317Z torch_np/numpy_tests/core/test_einsum 1/1 2025-09-07T06:31:30.4563662Z inductor/test_benchmarking 1/1 2025-09-07T06:31:30.4563980Z dynamo/test_model_output 1/1 2025-09-07T06:31:30.4564284Z torch_np/test_basic 1/1 2025-09-07T06:31:30.4564573Z test_segment_reductions 1/1 2025-09-07T06:31:30.4564857Z test_ops_fwd_gradients 1/2 2025-09-07T06:31:30.4565175Z inductor/test_minifier_isolate 1/1 2025-09-07T06:31:30.4565484Z xpu/test_conv 1/1 2025-09-07T06:31:30.4565749Z dynamo/test_flat_apply 1/1 2025-09-07T06:31:30.4566026Z export/test_unflatten 1/1 2025-09-07T06:31:30.4566317Z lazy/test_step_closures 1/1 2025-09-07T06:31:30.4566597Z test_monitor 1/1 2025-09-07T06:31:30.4566860Z functorch/test_logging 1/1 2025-09-07T06:31:30.4567130Z test_stateless 1/1 2025-09-07T06:31:30.4567412Z torch_np/numpy_tests/core/test_numeric 1/1 2025-09-07T06:31:30.4567743Z test_weak 1/1 2025-09-07T06:31:30.4568007Z inductor/test_mkldnn_pattern_matcher 1/3 2025-09-07T06:31:30.4568324Z functorch/test_ac 1/1 2025-09-07T06:31:30.4568725Z dynamo/test_cudagraphs 1/1 2025-09-07T06:31:30.4569009Z test_decomp 2/12 2025-09-07T06:31:30.4569259Z test_decomp 5/12 2025-09-07T06:31:30.4569489Z test_decomp 12/12 2025-09-07T06:31:30.4569753Z inductor/test_fxir_backend 1/1 2025-09-07T06:31:30.4570063Z test_accelerator 1/1 2025-09-07T06:31:30.4570321Z test_ops 3/6 2025-09-07T06:31:30.4570542Z test_ops 6/6 2025-09-07T06:31:30.4570798Z Name: excluded (est. time: 0.0min) 2025-09-07T06:31:30.4571094Z Serial tests (0): 2025-09-07T06:31:30.4571339Z Parallel tests (0): 2025-09-07T06:31:30.4571670Z Running test_reductions 1/1 ... [2025-09-07 06:31:30.455272] 2025-09-07T06:31:30.4572075Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T06:31:30.4573129Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_reductions.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 06:31:30.455633] 2025-09-07T06:34:41.2006520Z 2025-09-07T06:34:41.2007759Z test_reductions 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_reductions_1.1_685544636143555d_.log 2025-09-07T06:34:41.3788874Z Running 4673 items in this shard: test/test_reductions.py::TestReductionsCPU::test_accreal_type_cpu, test/test_reductions.py::TestReductionsCPU::test_all_any_cpu, test/test_reductions.py::TestReductionsCPU::test_all_any_empty_cpu, test/test_reductions.py::TestReductionsCPU::test_all_any_vs_numpy_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_all_any_vs_numpy_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_all_any_vs_numpy_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_all_any_vs_numpy_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_all_any_vs_numpy_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_all_any_vs_numpy_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_all_any_vs_numpy_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_all_any_vs_numpy_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_all_any_vs_numpy_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_all_any_vs_numpy_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_all_any_vs_numpy_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_all_any_with_dim_cpu, test/test_reductions.py::TestReductionsCPU::test_all_issue117215_cpu, test/test_reductions.py::TestReductionsCPU::test_amax_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_amax_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_amax_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_amax_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_amax_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_amax_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_amin_amax_some_dims_cpu, test/test_reductions.py::TestReductionsCPU::test_amin_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_amin_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_amin_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_amin_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_amin_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_amin_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_aminmax_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_aminmax_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_aminmax_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_aminmax_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_argminmax_axis_with_dim_one_cpu, test/test_reductions.py::TestReductionsCPU::test_argminmax_large_axis_cpu, test/test_reductions.py::TestReductionsCPU::test_argminmax_multiple_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_argminmax_multiple_cpu_float32, 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test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_prod_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_prod_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_prod_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_prod_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_prod_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_prod_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_std_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_std_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_std_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_std_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_std_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_std_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_std_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_std_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_std_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_std_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_std_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_sum_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_sum_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_sum_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_sum_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_sum_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_sum_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_sum_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_sum_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_sum_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_sum_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_sum_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_sum_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_var_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_var_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_var_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_var_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_var_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_var_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_var_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_var_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_var_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_var_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_var_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_result_dtype_mean_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_mean_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_result_dtype_mean_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_mean_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_mean_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_mean_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_nanmean_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_nanmean_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_nanmean_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_nanmean_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_nansum_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_nansum_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_result_dtype_nansum_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_nansum_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_nansum_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_nansum_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_nansum_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_nansum_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_nansum_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_result_dtype_nansum_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_result_dtype_prod_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_prod_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_result_dtype_prod_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_result_dtype_prod_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_prod_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_prod_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_prod_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_prod_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_prod_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_prod_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_prod_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_result_dtype_prod_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_result_dtype_std_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_std_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_result_dtype_std_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_std_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_std_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_std_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_std_unbiased_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_std_unbiased_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_result_dtype_std_unbiased_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_std_unbiased_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_std_unbiased_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_std_unbiased_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_sum_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_sum_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_result_dtype_sum_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_result_dtype_sum_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_sum_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_sum_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_sum_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_sum_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_sum_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_sum_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_sum_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_result_dtype_sum_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_result_dtype_var_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_var_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_result_dtype_var_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_var_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_var_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_var_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_var_unbiased_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_var_unbiased_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_result_dtype_var_unbiased_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_var_unbiased_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_var_unbiased_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_var_unbiased_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_std_correction_vs_numpy_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_std_correction_vs_numpy_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_std_correction_vs_numpy_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_std_correction_vs_numpy_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_std_dim_cpu, test/test_reductions.py::TestReductionsCPU::test_std_mean_all_dims_cpu, test/test_reductions.py::TestReductionsCPU::test_std_mean_correction_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_std_mean_correction_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_std_mean_correction_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_std_mean_correction_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_std_mean_cpu, test/test_reductions.py::TestReductionsCPU::test_std_mean_some_dims_cpu, test/test_reductions.py::TestReductionsCPU::test_std_vs_numpy_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_std_vs_numpy_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_std_vs_numpy_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_std_vs_numpy_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_sum_all_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_sum_all_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_sum_cpu_device_mismatch_cpu, test/test_reductions.py::TestReductionsCPU::test_sum_dim_cpu, test/test_reductions.py::TestReductionsCPU::test_sum_dim_reduction_uint8_overflow_cpu, test/test_reductions.py::TestReductionsCPU::test_sum_integer_upcast_cpu, test/test_reductions.py::TestReductionsCPU::test_sum_noncontig_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_sum_noncontig_lowp_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_sum_noncontig_lowp_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_sum_out_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_sum_parallel_cpu, test/test_reductions.py::TestReductionsCPU::test_sum_vs_numpy_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_sum_vs_numpy_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_sum_vs_numpy_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_sum_vs_numpy_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_sum_vs_numpy_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_sum_vs_numpy_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_sum_vs_numpy_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_tensor_compare_ops_argmax_argmix_kthvalue_dim_empty_cpu, test/test_reductions.py::TestReductionsCPU::test_tensor_compare_ops_empty_cpu, test/test_reductions.py::TestReductionsCPU::test_tensor_reduce_ops_empty_cpu, test/test_reductions.py::TestReductionsCPU::test_var_correction_vs_numpy_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_var_correction_vs_numpy_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_var_correction_vs_numpy_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_var_correction_vs_numpy_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_var_cpu, test/test_reductions.py::TestReductionsCPU::test_var_dim_cpu, test/test_reductions.py::TestReductionsCPU::test_var_large_input_cpu, test/test_reductions.py::TestReductionsCPU::test_var_mean_all_dims_cpu, test/test_reductions.py::TestReductionsCPU::test_var_mean_correction_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_var_mean_correction_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_var_mean_correction_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_var_mean_correction_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_var_mean_cpu, test/test_reductions.py::TestReductionsCPU::test_var_mean_some_dims_cpu, test/test_reductions.py::TestReductionsCPU::test_var_stability2_cpu, test/test_reductions.py::TestReductionsCPU::test_var_stability_cpu, test/test_reductions.py::TestReductionsCPU::test_var_unbiased_cpu, test/test_reductions.py::TestReductionsCPU::test_var_vs_numpy_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_var_vs_numpy_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_var_vs_numpy_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_var_vs_numpy_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_warn_invalid_degrees_of_freedom_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_warn_invalid_degrees_of_freedom_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_warn_invalid_degrees_of_freedom_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_warn_invalid_degrees_of_freedom_cpu_float64 2025-09-07T06:34:41.5544650Z 2025-09-07T06:34:41.7075526Z Uploading artifacts took 0.50 seconds 2025-09-07T06:34:41.7077723Z Running test_ci_sanity_check_fail 1/1 ... [2025-09-07 06:34:41.707592] 2025-09-07T06:34:41.7078438Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T06:34:41.7081860Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_ci_sanity_check_fail.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 06:34:41.707947] 2025-09-07T06:34:52.8605812Z Running test_openreg 1/1 ... [2025-09-07 06:34:52.860260] 2025-09-07T06:34:53.1745695Z Processing /var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/torch_openreg 2025-09-07T06:34:53.4397869Z Preparing metadata (pyproject.toml) ... [?25l- done 2025-09-07T06:34:53.4426242Z [?25hRequirement already satisfied: torch in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch_openreg==0.0.1) (2.9.0a0+git93fb23d) 2025-09-07T06:34:53.4438784Z Requirement already satisfied: filelock in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch->torch_openreg==0.0.1) (3.19.1) 2025-09-07T06:34:53.4443055Z Requirement already satisfied: typing-extensions>=4.10.0 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch->torch_openreg==0.0.1) (4.15.0) 2025-09-07T06:34:53.4448955Z Requirement already satisfied: setuptools in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch->torch_openreg==0.0.1) (80.9.0) 2025-09-07T06:34:53.4453272Z Requirement already satisfied: sympy>=1.13.3 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch->torch_openreg==0.0.1) (1.13.3) 2025-09-07T06:34:53.4457667Z Requirement already satisfied: networkx>=2.5.1 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch->torch_openreg==0.0.1) (2.8.8) 2025-09-07T06:34:53.4462953Z Requirement already satisfied: jinja2 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch->torch_openreg==0.0.1) (3.1.6) 2025-09-07T06:34:53.4467494Z Requirement already satisfied: fsspec>=0.8.5 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch->torch_openreg==0.0.1) (2025.7.0) 2025-09-07T06:34:53.4578065Z Requirement already satisfied: mpmath<1.4,>=1.1.0 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from sympy>=1.13.3->torch->torch_openreg==0.0.1) (1.3.0) 2025-09-07T06:34:53.4607243Z Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from jinja2->torch->torch_openreg==0.0.1) (3.0.2) 2025-09-07T06:34:53.4670707Z Building wheels for collected packages: torch_openreg 2025-09-07T06:34:58.4943327Z Building wheel for torch_openreg (pyproject.toml) ... [?25l- \ | / - \ | / - \ | / - \ | done 2025-09-07T06:34:58.4957283Z [?25h Created wheel for torch_openreg: filename=torch_openreg-0.0.1-cp313-cp313-linux_x86_64.whl size=277882 sha256=32b22f50ac61498165fb4b1ab8030d4314cd9a75588cebc065080900d60959e5 2025-09-07T06:34:58.4958614Z Stored in directory: /tmp/pip-ephem-wheel-cache-ukn56ax_/wheels/bc/4f/31/9af65770c0a69187e95f1d791df9c71156b2b3f469bce9d735 2025-09-07T06:34:58.4982575Z Successfully built torch_openreg 2025-09-07T06:34:58.6420960Z Installing collected packages: torch_openreg 2025-09-07T06:34:58.6606863Z Successfully installed torch_openreg-0.0.1 2025-09-07T06:34:58.7212708Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T06:34:58.7215875Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_openreg.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 06:34:58.721368] 2025-09-07T06:35:03.2416970Z 2025-09-07T06:35:03.2417883Z test_openreg 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_openreg_1.1_b378819a36897a44_.log 2025-09-07T06:35:03.2431127Z Running 44 items in this shard: test/test_openreg.py::TestPrivateUse1::test_backend_dispatchstub, test/test_openreg.py::TestPrivateUse1::test_backend_generate_methods, test/test_openreg.py::TestPrivateUse1::test_backend_module_function, test/test_openreg.py::TestPrivateUse1::test_backend_module_methods, test/test_openreg.py::TestPrivateUse1::test_backend_module_registration, test/test_openreg.py::TestPrivateUse1::test_backend_name, test/test_openreg.py::TestPrivateUse1::test_backend_operator_registration, test/test_openreg.py::TestPrivateUse1::test_backend_packed_sequence_methods, test/test_openreg.py::TestPrivateUse1::test_backend_storage_methods, test/test_openreg.py::TestPrivateUse1::test_backend_tensor_methods, test/test_openreg.py::TestPrivateUse1::test_backend_tensor_type, test/test_openreg.py::TestPrivateUse1::test_backend_type_methods, test/test_openreg.py::TestOpenReg::test_autograd_init, test/test_openreg.py::TestOpenReg::test_compile_autograd_function_aliasing, test/test_openreg.py::TestOpenReg::test_compile_autograd_function_returns_self, test/test_openreg.py::TestOpenReg::test_copy_same_device, test/test_openreg.py::TestOpenReg::test_cross_device_copy, test/test_openreg.py::TestOpenReg::test_cross_diff_devices_copy, test/test_openreg.py::TestOpenReg::test_data_dependent_output, test/test_openreg.py::TestOpenReg::test_event_elapsed_time, test/test_openreg.py::TestOpenReg::test_event_wait_stream, test/test_openreg.py::TestOpenReg::test_expand, test/test_openreg.py::TestOpenReg::test_factory, test/test_openreg.py::TestOpenReg::test_fake_tensor, test/test_openreg.py::TestOpenReg::test_generator, test/test_openreg.py::TestOpenReg::test_manual_seed, test/test_openreg.py::TestOpenReg::test_named_tensor, test/test_openreg.py::TestOpenReg::test_open_device_cpu_serialization, test/test_openreg.py::TestOpenReg::test_open_device_dlpack, test/test_openreg.py::TestOpenReg::test_open_device_numpy_serialization, test/test_openreg.py::TestOpenReg::test_pin_memory, test/test_openreg.py::TestOpenReg::test_printing, test/test_openreg.py::TestOpenReg::test_quantize, test/test_openreg.py::TestOpenReg::test_record_event, test/test_openreg.py::TestOpenReg::test_resize, test/test_openreg.py::TestOpenReg::test_rewrapped_storage, test/test_openreg.py::TestOpenReg::test_rng_state, test/test_openreg.py::TestOpenReg::test_scalar_type_fallback, test/test_openreg.py::TestOpenReg::test_serialization, test/test_openreg.py::TestOpenReg::test_stream_synchronize, test/test_openreg.py::TestOpenReg::test_stream_wait_event, test/test_openreg.py::TestOpenReg::test_stream_wait_stream, test/test_openreg.py::TestOpenReg::test_tensor_type_fallback, test/test_openreg.py::TestOpenReg::test_tensorlist_type_fallback 2025-09-07T06:35:03.2443620Z 2025-09-07T06:35:03.2443882Z Running test_cpp_extensions_mtia_backend 1/1 ... [2025-09-07 06:35:03.241942] 2025-09-07T06:35:03.2444424Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T06:35:03.2445655Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_cpp_extensions_mtia_backend.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 06:35:03.242229] 2025-09-07T06:35:06.9114623Z 2025-09-07T06:35:06.9115652Z test_cpp_extensions_mtia_backend 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_cpp_extensions_mtia_backend_1.1_5c365dac92ada751_.log 2025-09-07T06:35:06.9118709Z Running 5 items in this shard: test/test_cpp_extensions_mtia_backend.py::TestCppExtensionMTIABackend::test_device_context, test/test_cpp_extensions_mtia_backend.py::TestCppExtensionMTIABackend::test_get_device_module, test/test_cpp_extensions_mtia_backend.py::TestCppExtensionMTIABackend::test_stream_basic, test/test_cpp_extensions_mtia_backend.py::TestCppExtensionMTIABackend::test_stream_context, test/test_cpp_extensions_mtia_backend.py::TestCppExtensionMTIABackend::test_stream_context_different_device 2025-09-07T06:35:06.9121016Z 2025-09-07T06:35:06.9121296Z Running test_cpp_extensions_stream_and_event 1/1 ... [2025-09-07 06:35:06.911662] 2025-09-07T06:35:06.9122048Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T06:35:06.9123175Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_cpp_extensions_stream_and_event.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 06:35:06.911968] 2025-09-07T06:35:10.5812081Z 2025-09-07T06:35:10.5813122Z test_cpp_extensions_stream_and_event 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_cpp_extensions_stream_and_event_1.1_0eb2261fc332cb0e_.log 2025-09-07T06:35:10.5814419Z Running 1 items in this shard: test/test_cpp_extensions_stream_and_event.py::TestCppExtensionStreamAndEvent::test_stream_event 2025-09-07T06:35:10.5814995Z 2025-09-07T06:35:10.5815496Z Running test_python_dispatch 1/1 ... [2025-09-07 06:35:10.581405] 2025-09-07T06:35:10.5815918Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T06:35:10.5819142Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_python_dispatch.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 06:35:10.581697] 2025-09-07T06:35:15.3022549Z 2025-09-07T06:35:15.3024026Z test_python_dispatch 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_python_dispatch_1.1_a78df30a800fc75e_.log 2025-09-07T06:35:15.3071713Z Running 119 items in this shard: test/test_python_dispatch.py::TestDispatcherPythonBindings::test_call_boxed, test/test_python_dispatch.py::TestPythonRegistration::test_alias_analysis, test/test_python_dispatch.py::TestPythonRegistration::test_create_new_library, test/test_python_dispatch.py::TestPythonRegistration::test_create_new_library_fragment_no_existing, test/test_python_dispatch.py::TestPythonRegistration::test_create_new_library_fragment_with_existing, test/test_python_dispatch.py::TestPythonRegistration::test_dispatcher_error_filenames, test/test_python_dispatch.py::TestPythonRegistration::test_dispatchkeyset_eq, test/test_python_dispatch.py::TestPythonRegistration::test_dispatchkeyset_pickle, test/test_python_dispatch.py::TestPythonRegistration::test_error_for_unsupported_ns_or_kind, test/test_python_dispatch.py::TestPythonRegistration::test_error_if_fn_not_callable, test/test_python_dispatch.py::TestPythonRegistration::test_extend_library_with_dispatch_key_arg, test/test_python_dispatch.py::TestPythonRegistration::test_fallback, test/test_python_dispatch.py::TestPythonRegistration::test_fallback_fallthrough, test/test_python_dispatch.py::TestPythonRegistration::test_fallback_keyset, test/test_python_dispatch.py::TestPythonRegistration::test_fallthrough_for_dense_key_with_meta_in_tls, test/test_python_dispatch.py::TestPythonRegistration::test_finalizer, test/test_python_dispatch.py::TestPythonRegistration::test_override_aten_ops_with_multiple_libraries, test/test_python_dispatch.py::TestPythonRegistration::test_override_cpu_sum, test/test_python_dispatch.py::TestPythonRegistration::test_override_cuda_with_jiterator, test/test_python_dispatch.py::TestPythonRegistration::test_register_fallthrough, test/test_python_dispatch.py::TestPythonRegistration::test_returning_symint, test/test_python_dispatch.py::TestPythonDispatch::test_all_same_mode, test/test_python_dispatch.py::TestPythonDispatch::test_autograd_in_attr, test/test_python_dispatch.py::TestPythonDispatch::test_basic, test/test_python_dispatch.py::TestPythonDispatch::test_capture_logs_with_torch_dispatch_mode, test/test_python_dispatch.py::TestPythonDispatch::test_construct_int_tensor, test/test_python_dispatch.py::TestPythonDispatch::test_custom_autograd, test/test_python_dispatch.py::TestPythonDispatch::test_custom_dispatch_mode_not_supports_higher_order_operators, test/test_python_dispatch.py::TestPythonDispatch::test_custom_dispatch_mode_supports_higher_order_operators, test/test_python_dispatch.py::TestPythonDispatch::test_custom_size_policy_dynamic_shapes, test/test_python_dispatch.py::TestPythonDispatch::test_data_ptr_respects_numel_slow_path, test/test_python_dispatch.py::TestPythonDispatch::test_deepcopy_non_wrapper_subclass, test/test_python_dispatch.py::TestPythonDispatch::test_deepcopy_wrapper_subclass, test/test_python_dispatch.py::TestPythonDispatch::test_deepcopy_wrapper_subclass_with_clone_returning_different_type, test/test_python_dispatch.py::TestPythonDispatch::test_detach_appears_twice_when_called_once, test/test_python_dispatch.py::TestPythonDispatch::test_device_slowpath, test/test_python_dispatch.py::TestPythonDispatch::test_dim_slowpath, test/test_python_dispatch.py::TestPythonDispatch::test_dispatch_super_call, test/test_python_dispatch.py::TestPythonDispatch::test_dispatch_super_call_list_arg, test/test_python_dispatch.py::TestPythonDispatch::test_dispatch_super_dont_autograd, test/test_python_dispatch.py::TestPythonDispatch::test_dispatch_uint64, test/test_python_dispatch.py::TestPythonDispatch::test_error_using_class_method_on_mode, test/test_python_dispatch.py::TestPythonDispatch::test_exception_handling, test/test_python_dispatch.py::TestPythonDispatch::test_fancy_strides, test/test_python_dispatch.py::TestPythonDispatch::test_format, test/test_python_dispatch.py::TestPythonDispatch::test_get_cur_mode, test/test_python_dispatch.py::TestPythonDispatch::test_get_mode_stack, test/test_python_dispatch.py::TestPythonDispatch::test_index_put_where_only_index_is_subclass, test/test_python_dispatch.py::TestPythonDispatch::test_invalid_ret, test/test_python_dispatch.py::TestPythonDispatch::test_is_contiguous_slow_path, test/test_python_dispatch.py::TestPythonDispatch::test_kwarg_only, test/test_python_dispatch.py::TestPythonDispatch::test_kwarg_only_and_positional_default, test/test_python_dispatch.py::TestPythonDispatch::test_layout_slow_path, test/test_python_dispatch.py::TestPythonDispatch::test_like, test/test_python_dispatch.py::TestPythonDispatch::test_list_ret, test/test_python_dispatch.py::TestPythonDispatch::test_make_fx_with_subclass, test/test_python_dispatch.py::TestPythonDispatch::test_make_subclass_with_modes, test/test_python_dispatch.py::TestPythonDispatch::test_make_wrapper_subclass_noalloc, test/test_python_dispatch.py::TestPythonDispatch::test_make_wrapper_subclass_propagates_metadata, test/test_python_dispatch.py::TestPythonDispatch::test_maybe_tuple_bug, test/test_python_dispatch.py::TestPythonDispatch::test_mode_detection, test/test_python_dispatch.py::TestPythonDispatch::test_mode_with_make_subclass, test/test_python_dispatch.py::TestPythonDispatch::test_multiple_ops_subclass, test/test_python_dispatch.py::TestPythonDispatch::test_nested_push_logging_tensor_mode, test/test_python_dispatch.py::TestPythonDispatch::test_nesting_same_mode, test/test_python_dispatch.py::TestPythonDispatch::test_new_ones, test/test_python_dispatch.py::TestPythonDispatch::test_none_wrapping, test/test_python_dispatch.py::TestPythonDispatch::test_notimplemented_mode, test/test_python_dispatch.py::TestPythonDispatch::test_optional_tensor_list, test/test_python_dispatch.py::TestPythonDispatch::test_out, test/test_python_dispatch.py::TestPythonDispatch::test_produce_real_type, test/test_python_dispatch.py::TestPythonDispatch::test_record_stream, test/test_python_dispatch.py::TestPythonDispatch::test_return_and_correct_aliasing_gives_correct_stride, test/test_python_dispatch.py::TestPythonDispatch::test_return_stream, test/test_python_dispatch.py::TestPythonDispatch::test_set_data, test/test_python_dispatch.py::TestPythonDispatch::test_shallow_copy_and_detach, test/test_python_dispatch.py::TestPythonDispatch::test_sizes_slow_path, test/test_python_dispatch.py::TestPythonDispatch::test_standard_is_not_subclass, test/test_python_dispatch.py::TestPythonDispatch::test_storage, test/test_python_dispatch.py::TestPythonDispatch::test_storage_can_be_converted_to_python_object, test/test_python_dispatch.py::TestPythonDispatch::test_strides_slow_path, test/test_python_dispatch.py::TestPythonDispatch::test_subclass_creation, test/test_python_dispatch.py::TestPythonDispatch::test_subclass_priority, test/test_python_dispatch.py::TestPythonDispatch::test_sym_sizes_strides_slow_path, test/test_python_dispatch.py::TestPythonDispatch::test_tolist_numpy_with_torch_dispatch_mode, test/test_python_dispatch.py::TestPythonDispatch::test_torch_dispatch_mode_basic, test/test_python_dispatch.py::TestPythonDispatch::test_torch_dispatch_mode_respects_no_dispatch, test/test_python_dispatch.py::TestPythonDispatch::test_torch_dispatch_mode_subclass_priority, test/test_python_dispatch.py::TestPythonDispatch::test_torch_dispatch_mode_unrelated_tensors, test/test_python_dispatch.py::TestPythonDispatch::test_version, test/test_python_dispatch.py::TestPythonDispatch::test_view_returns_alias_under_torch_dispatch, test/test_python_dispatch.py::TestPythonDispatch::test_with_mode_created_separately, test/test_python_dispatch.py::TestPythonDispatch::test_with_nested_modes, test/test_python_dispatch.py::TestPythonDispatch::test_wrapper_subclass_extra_dispatch_keys, test/test_python_dispatch.py::TestPythonDispatch::test_wrapper_subclass_multiprocessing_preserves_dtype, test/test_python_dispatch.py::TestPythonDispatch::test_wrapper_subclass_reentrant_dispatch_with_mode, test/test_python_dispatch.py::TestPythonDispatch::test_wrapper_subclass_serializes, test/test_python_dispatch.py::TestPythonDispatcher::test_basic, test/test_python_dispatch.py::TestPythonDispatcher::test_lstsq, test/test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_cat_cpu_float32, test/test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_conv2d_cpu, test/test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_custom_NumpyCatCustomOp_cpu_float32, test/test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_custom_NumpyCubeCustomOp_cpu_float32, test/test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_custom_NumpyMulCustomOp_cpu_float32, test/test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_custom_NumpyMulScalarCustomOp_cpu_float32, test/test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_custom_NumpyNMSCustomOp_cpu_float32, test/test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_custom_NumpyNonzeroCustomOp_cpu_float32, test/test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_custom_NumpySortCustomOp_cpu_float32, test/test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_custom_NumpySplitCopyCustomOp_cpu_float32, test/test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_custom_NumpySplitCopyWithIntCustomOp_cpu_float32, test/test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_custom_NumpyTakeCustomOp_cpu_float32, test/test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_custom_NumpyViewCopyCustomOp_cpu_float32, test/test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_fft_fft2_cpu, test/test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_mul_cpu_float32, test/test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_native_batch_norm_cpu_float32, test/test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_out_op_cpu, test/test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_split_cpu_float32, test/test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_split_list_args_cpu_float32, test/test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_view_cpu_float32 2025-09-07T06:35:15.3117410Z 2025-09-07T06:35:15.3117661Z Running test_tensor_creation_ops 1/1 ... [2025-09-07 06:35:15.302571] 2025-09-07T06:35:15.3118113Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T06:35:15.3119258Z 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'] ... [2025-09-07 06:35:15.302864] 2025-09-07T06:35:34.4929130Z 2025-09-07T06:35:34.4930231Z 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_cfff160456a18412_.log 2025-09-07T06:35:34.5174969Z Running 646 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_misaligned_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_multi_batch_cpu_float32, 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_size1_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_stack_cross_devices_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_cat_trailing_dim_cpu_float32, 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_concat_empty_list_error_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_new_tensor_device_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_bounds_checking_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_zeros_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_zeros_dtype_layout_device_match_cpu_bool, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_zeros_dtype_layout_device_match_cpu_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_zeros_dtype_layout_device_match_cpu_float16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_zeros_dtype_layout_device_match_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_zeros_dtype_layout_device_match_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_zeros_dtype_layout_device_match_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_zeros_dtype_layout_device_match_cpu_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_zeros_out_cpu, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_normal_cpu_float32, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_normal_cpu_float64, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_normal_std_error_cpu, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_rand_cpu_complex128, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_rand_cpu_complex32, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_rand_cpu_complex64, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_rand_cpu_float32, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_rand_cpu_float64, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_randint_cpu, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_randint_distribution_cpu, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_randint_inference_cpu, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_randn_cpu_bfloat16, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_randn_cpu_complex128, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_randn_cpu_complex32, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_randn_cpu_complex64, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_randn_cpu_float16, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_randn_cpu_float32, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_randn_cpu_float64, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_random_neg_values_cpu, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_randperm_cpu, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_randperm_device_compatibility_cpu, test/test_tensor_creation_ops.py::TestRandomTensorCreationCPU::test_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 2025-09-07T06:35:34.5415204Z 2025-09-07T06:35:34.5415429Z Running test_cpp_extensions_jit 1/1 ... [2025-09-07 06:35:34.493935] 2025-09-07T06:35:34.5415885Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T06:35:34.5416994Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_cpp_extensions_jit.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 06:35:34.494235] 2025-09-07T06:36:10.3575756Z 2025-09-07T06:36:10.3576750Z test_cpp_extensions_jit 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_cpp_extensions_jit_1.1_5f37f73aa56d913b_.log 2025-09-07T06:36:10.3590829Z Running 34 items in this shard: test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_aoti_torch_call_dispatcher, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_autograd_from_cpp, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_compilation_error_formatting, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_cpp_frontend_module_has_same_output_as_python, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_cpp_frontend_module_has_up_to_date_attributes, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_cpp_frontend_module_python_inter_op, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_cpp_frontend_module_python_inter_op_with_cuda, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_cuda_arch_flags_default_gencode, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_cuda_arch_flags_non_default_gencode, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_cuda_pluggable_allocator_include, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_custom_compound_op_autograd, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_custom_functorch_error, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_gen_extension_h_pch, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_half_support, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_inline_jit_compile_custom_op_cuda, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_inline_jit_compile_extension_cuda, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_inline_jit_compile_extension_multiple_sources_and_no_functions, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_inline_jit_compile_extension_throws_when_functions_is_bad, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_inline_jit_compile_extension_with_functions_as_dict, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_inline_jit_compile_extension_with_functions_as_list, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_inline_jit_compile_extension_xpu, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_jit_compile_extension, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_jit_cuda_archflags, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_jit_cuda_extension, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_jit_cudnn_extension, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_jit_xpu_archlists, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_jit_xpu_extension, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_lenient_flag_handling_in_jit_extensions, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_load_with_non_platform_default_encoding, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_mps_extension, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_reload_jit_extension, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_returns_shared_library_path_when_is_python_module_is_true, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_set_default_type_also_changes_aten_default_type, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_warning 2025-09-07T06:36:10.3605308Z 2025-09-07T06:36:10.3605599Z Running test_nn 1/1 ... [2025-09-07 06:36:10.357813] 2025-09-07T06:36:10.3605968Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T06:36:10.3606981Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_nn.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 06:36:10.358108] 2025-09-07T06:38:21.4919432Z 2025-09-07T06:38:21.4920272Z test_nn 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_nn_1.1_5717fec024d7050c_.log 2025-09-07T06:38:21.6122611Z Running 2231 items in this shard: test/test_nn.py::TestNN::test_AdaptiveLogSoftmax, test/test_nn.py::TestNN::test_AdaptiveLogSoftmax_cuda, test/test_nn.py::TestNN::test_BCELoss_no_batch_dim_mean, test/test_nn.py::TestNN::test_BCELoss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_BCELoss_no_batch_dim_mean_cuda_float, test/test_nn.py::TestNN::test_BCELoss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_BCELoss_no_batch_dim_none, test/test_nn.py::TestNN::test_BCELoss_no_batch_dim_none_cuda_double, test/test_nn.py::TestNN::test_BCELoss_no_batch_dim_none_cuda_float, test/test_nn.py::TestNN::test_BCELoss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_BCELoss_no_batch_dim_sum, test/test_nn.py::TestNN::test_BCELoss_no_batch_dim_sum_cuda_double, test/test_nn.py::TestNN::test_BCELoss_no_batch_dim_sum_cuda_float, test/test_nn.py::TestNN::test_BCELoss_no_batch_dim_sum_cuda_half, test/test_nn.py::TestNN::test_BCELoss_no_reduce, test/test_nn.py::TestNN::test_BCELoss_no_reduce_cuda, test/test_nn.py::TestNN::test_BCELoss_no_reduce_scalar, test/test_nn.py::TestNN::test_BCELoss_no_reduce_scalar_cuda, test/test_nn.py::TestNN::test_BCELoss_weights_no_reduce, test/test_nn.py::TestNN::test_BCELoss_weights_no_reduce_cuda, test/test_nn.py::TestNN::test_BCELoss_weights_no_reduce_scalar, test/test_nn.py::TestNN::test_BCELoss_weights_no_reduce_scalar_cuda, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_legacy_enum, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_legacy_enum_cuda, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_batch_dim_mean, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_batch_dim_mean_cuda_float, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_batch_dim_none, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_batch_dim_none_cuda_double, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_batch_dim_none_cuda_float, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_batch_dim_sum, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_batch_dim_sum_cuda_double, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_batch_dim_sum_cuda_float, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_batch_dim_sum_cuda_half, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_reduce, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_reduce_cuda, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_reduce_scalar, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_reduce_scalar_cuda, test/test_nn.py::TestNN::test_CELU_no_batch_dim, test/test_nn.py::TestNN::test_CELU_no_batch_dim_cuda, test/test_nn.py::TestNN::test_CTCLoss_critical_target_len, test/test_nn.py::TestNN::test_CTCLoss_lengthchecks_cpu, test/test_nn.py::TestNN::test_CTCLoss_lengthchecks_cuda, test/test_nn.py::TestNN::test_CTCLoss_long_targets, test/test_nn.py::TestNN::test_CTCLoss_typechecks, test/test_nn.py::TestNN::test_CTCLoss_zero_infinity, test/test_nn.py::TestNN::test_CTCLoss_zero_lengths, test/test_nn.py::TestNN::test_Conv1d, test/test_nn.py::TestNN::test_Conv1d_circular_stride2_pad2, test/test_nn.py::TestNN::test_Conv1d_circular_stride2_pad2_cuda, test/test_nn.py::TestNN::test_Conv1d_cuda, test/test_nn.py::TestNN::test_Conv1d_dilated, test/test_nn.py::TestNN::test_Conv1d_dilated_cuda, test/test_nn.py::TestNN::test_Conv1d_groups, test/test_nn.py::TestNN::test_Conv1d_groups_cuda, test/test_nn.py::TestNN::test_Conv1d_pad1, test/test_nn.py::TestNN::test_Conv1d_pad1_cuda, test/test_nn.py::TestNN::test_Conv1d_pad1size1, test/test_nn.py::TestNN::test_Conv1d_pad1size1_cuda, test/test_nn.py::TestNN::test_Conv1d_pad2, test/test_nn.py::TestNN::test_Conv1d_pad2_cuda, test/test_nn.py::TestNN::test_Conv1d_pad2size1, test/test_nn.py::TestNN::test_Conv1d_pad2size1_cuda, test/test_nn.py::TestNN::test_Conv1d_pad_same, test/test_nn.py::TestNN::test_Conv1d_pad_same2, test/test_nn.py::TestNN::test_Conv1d_pad_same2_cuda, test/test_nn.py::TestNN::test_Conv1d_pad_same_cuda, test/test_nn.py::TestNN::test_Conv1d_pad_same_dilated, test/test_nn.py::TestNN::test_Conv1d_pad_same_dilated_cuda, test/test_nn.py::TestNN::test_Conv1d_pad_valid, test/test_nn.py::TestNN::test_Conv1d_pad_valid_cuda, test/test_nn.py::TestNN::test_Conv1d_reflect_stride2_pad2, test/test_nn.py::TestNN::test_Conv1d_reflect_stride2_pad2_cuda, test/test_nn.py::TestNN::test_Conv1d_replicate_stride2_pad2, test/test_nn.py::TestNN::test_Conv1d_replicate_stride2_pad2_cuda, test/test_nn.py::TestNN::test_Conv1d_stride, test/test_nn.py::TestNN::test_Conv1d_stride_cuda, test/test_nn.py::TestNN::test_Conv1d_zero_batch, test/test_nn.py::TestNN::test_Conv1d_zero_batch_cuda, test/test_nn.py::TestNN::test_Conv1d_zeros_stride2_pad2, test/test_nn.py::TestNN::test_Conv1d_zeros_stride2_pad2_cuda, test/test_nn.py::TestNN::test_Conv2d, test/test_nn.py::TestNN::test_Conv2d_circular_stride2_pad2, test/test_nn.py::TestNN::test_Conv2d_circular_stride2_pad2_cuda, test/test_nn.py::TestNN::test_Conv2d_cuda, test/test_nn.py::TestNN::test_Conv2d_depthwise, test/test_nn.py::TestNN::test_Conv2d_depthwise_cuda, test/test_nn.py::TestNN::test_Conv2d_depthwise_dilated, test/test_nn.py::TestNN::test_Conv2d_depthwise_dilated_cuda, test/test_nn.py::TestNN::test_Conv2d_depthwise_padded, test/test_nn.py::TestNN::test_Conv2d_depthwise_padded_cuda, test/test_nn.py::TestNN::test_Conv2d_depthwise_strided, test/test_nn.py::TestNN::test_Conv2d_depthwise_strided_cuda, test/test_nn.py::TestNN::test_Conv2d_depthwise_with_multiplier, test/test_nn.py::TestNN::test_Conv2d_depthwise_with_multiplier_cuda, test/test_nn.py::TestNN::test_Conv2d_dilated, test/test_nn.py::TestNN::test_Conv2d_dilated_cuda, test/test_nn.py::TestNN::test_Conv2d_dilated_with_long_tensor, test/test_nn.py::TestNN::test_Conv2d_dilated_with_long_tensor_cuda, test/test_nn.py::TestNN::test_Conv2d_groups, test/test_nn.py::TestNN::test_Conv2d_groups_cuda, test/test_nn.py::TestNN::test_Conv2d_groups_thnn, test/test_nn.py::TestNN::test_Conv2d_groups_thnn_cuda, test/test_nn.py::TestNN::test_Conv2d_groups_thnn_with_long_tensor, test/test_nn.py::TestNN::test_Conv2d_groups_thnn_with_long_tensor_cuda, test/test_nn.py::TestNN::test_Conv2d_groups_with_long_tensor, test/test_nn.py::TestNN::test_Conv2d_groups_with_long_tensor_cuda, test/test_nn.py::TestNN::test_Conv2d_no_bias, test/test_nn.py::TestNN::test_Conv2d_no_bias_cuda, test/test_nn.py::TestNN::test_Conv2d_no_bias_with_long_tensor, test/test_nn.py::TestNN::test_Conv2d_no_bias_with_long_tensor_cuda, test/test_nn.py::TestNN::test_Conv2d_pad_same, test/test_nn.py::TestNN::test_Conv2d_pad_same_cuda, test/test_nn.py::TestNN::test_Conv2d_pad_same_dilated, test/test_nn.py::TestNN::test_Conv2d_pad_same_dilated_cuda, test/test_nn.py::TestNN::test_Conv2d_pad_valid, test/test_nn.py::TestNN::test_Conv2d_pad_valid_cuda, test/test_nn.py::TestNN::test_Conv2d_padding, test/test_nn.py::TestNN::test_Conv2d_padding_cuda, test/test_nn.py::TestNN::test_Conv2d_padding_with_long_tensor, test/test_nn.py::TestNN::test_Conv2d_padding_with_long_tensor_cuda, test/test_nn.py::TestNN::test_Conv2d_reflect_stride2_pad2, test/test_nn.py::TestNN::test_Conv2d_reflect_stride2_pad2_cuda, test/test_nn.py::TestNN::test_Conv2d_replicate_stride2_pad2, test/test_nn.py::TestNN::test_Conv2d_replicate_stride2_pad2_cuda, test/test_nn.py::TestNN::test_Conv2d_strided, test/test_nn.py::TestNN::test_Conv2d_strided_cuda, test/test_nn.py::TestNN::test_Conv2d_strided_with_long_tensor, test/test_nn.py::TestNN::test_Conv2d_strided_with_long_tensor_cuda, test/test_nn.py::TestNN::test_Conv2d_with_long_tensor, test/test_nn.py::TestNN::test_Conv2d_with_long_tensor_cuda, test/test_nn.py::TestNN::test_Conv2d_zero_batch, test/test_nn.py::TestNN::test_Conv2d_zero_batch_cuda, test/test_nn.py::TestNN::test_Conv2d_zero_batch_with_long_tensor, test/test_nn.py::TestNN::test_Conv2d_zero_batch_with_long_tensor_cuda, test/test_nn.py::TestNN::test_Conv2d_zeros_stride2_pad2, test/test_nn.py::TestNN::test_Conv2d_zeros_stride2_pad2_cuda, test/test_nn.py::TestNN::test_Conv3d, test/test_nn.py::TestNN::test_Conv3d_1x1x1_no_bias, test/test_nn.py::TestNN::test_Conv3d_1x1x1_no_bias_cuda, test/test_nn.py::TestNN::test_Conv3d_1x1x1_no_bias_with_long_tensor, test/test_nn.py::TestNN::test_Conv3d_1x1x1_no_bias_with_long_tensor_cuda, test/test_nn.py::TestNN::test_Conv3d_circular_stride2_pad2, test/test_nn.py::TestNN::test_Conv3d_circular_stride2_pad2_cuda, test/test_nn.py::TestNN::test_Conv3d_cuda, test/test_nn.py::TestNN::test_Conv3d_dilated, test/test_nn.py::TestNN::test_Conv3d_dilated_cuda, test/test_nn.py::TestNN::test_Conv3d_dilated_strided, test/test_nn.py::TestNN::test_Conv3d_dilated_strided_cuda, test/test_nn.py::TestNN::test_Conv3d_groups, test/test_nn.py::TestNN::test_Conv3d_groups_cuda, test/test_nn.py::TestNN::test_Conv3d_groups_with_long_tensor, test/test_nn.py::TestNN::test_Conv3d_groups_with_long_tensor_cuda, test/test_nn.py::TestNN::test_Conv3d_no_bias, test/test_nn.py::TestNN::test_Conv3d_no_bias_cuda, test/test_nn.py::TestNN::test_Conv3d_no_bias_with_long_tensor, test/test_nn.py::TestNN::test_Conv3d_no_bias_with_long_tensor_cuda, test/test_nn.py::TestNN::test_Conv3d_pad_same, test/test_nn.py::TestNN::test_Conv3d_pad_same_cuda, test/test_nn.py::TestNN::test_Conv3d_pad_same_dilated, test/test_nn.py::TestNN::test_Conv3d_pad_same_dilated_cuda, test/test_nn.py::TestNN::test_Conv3d_pad_valid, test/test_nn.py::TestNN::test_Conv3d_pad_valid_cuda, test/test_nn.py::TestNN::test_Conv3d_replicate_stride2_pad2, test/test_nn.py::TestNN::test_Conv3d_replicate_stride2_pad2_cuda, test/test_nn.py::TestNN::test_Conv3d_stride, test/test_nn.py::TestNN::test_Conv3d_stride_cuda, test/test_nn.py::TestNN::test_Conv3d_stride_padding, test/test_nn.py::TestNN::test_Conv3d_stride_padding_cuda, test/test_nn.py::TestNN::test_Conv3d_stride_padding_with_long_tensor, test/test_nn.py::TestNN::test_Conv3d_stride_padding_with_long_tensor_cuda, test/test_nn.py::TestNN::test_Conv3d_stride_with_long_tensor, test/test_nn.py::TestNN::test_Conv3d_stride_with_long_tensor_cuda, test/test_nn.py::TestNN::test_Conv3d_with_long_tensor, test/test_nn.py::TestNN::test_Conv3d_with_long_tensor_cuda, test/test_nn.py::TestNN::test_Conv3d_zero_batch, test/test_nn.py::TestNN::test_Conv3d_zero_batch_cuda, test/test_nn.py::TestNN::test_Conv3d_zero_batch_with_long_tensor, test/test_nn.py::TestNN::test_Conv3d_zero_batch_with_long_tensor_cuda, test/test_nn.py::TestNN::test_Conv3d_zeros_stride2_pad2, test/test_nn.py::TestNN::test_Conv3d_zeros_stride2_pad2_cuda, test/test_nn.py::TestNN::test_ConvTranspose1d, test/test_nn.py::TestNN::test_ConvTranspose1d_cuda, test/test_nn.py::TestNN::test_ConvTranspose1d_dilated, test/test_nn.py::TestNN::test_ConvTranspose1d_dilated_cuda, test/test_nn.py::TestNN::test_ConvTranspose1d_groups, test/test_nn.py::TestNN::test_ConvTranspose1d_groups_cuda, test/test_nn.py::TestNN::test_ConvTranspose1d_no_bias, test/test_nn.py::TestNN::test_ConvTranspose1d_no_bias_cuda, test/test_nn.py::TestNN::test_ConvTranspose2d, test/test_nn.py::TestNN::test_ConvTranspose2d_cuda, test/test_nn.py::TestNN::test_ConvTranspose2d_dilated, test/test_nn.py::TestNN::test_ConvTranspose2d_dilated_cuda, test/test_nn.py::TestNN::test_ConvTranspose2d_dilated_with_long_tensor, test/test_nn.py::TestNN::test_ConvTranspose2d_dilated_with_long_tensor_cuda, test/test_nn.py::TestNN::test_ConvTranspose2d_groups, test/test_nn.py::TestNN::test_ConvTranspose2d_groups_cuda, test/test_nn.py::TestNN::test_ConvTranspose2d_groups_with_long_tensor, test/test_nn.py::TestNN::test_ConvTranspose2d_groups_with_long_tensor_cuda, test/test_nn.py::TestNN::test_ConvTranspose2d_no_bias, test/test_nn.py::TestNN::test_ConvTranspose2d_no_bias_cuda, test/test_nn.py::TestNN::test_ConvTranspose2d_no_bias_with_long_tensor, test/test_nn.py::TestNN::test_ConvTranspose2d_no_bias_with_long_tensor_cuda, test/test_nn.py::TestNN::test_ConvTranspose2d_with_long_tensor, test/test_nn.py::TestNN::test_ConvTranspose2d_with_long_tensor_cuda, test/test_nn.py::TestNN::test_ConvTranspose3d, test/test_nn.py::TestNN::test_ConvTranspose3d_cuda, test/test_nn.py::TestNN::test_ConvTranspose3d_dilated, test/test_nn.py::TestNN::test_ConvTranspose3d_dilated_cuda, test/test_nn.py::TestNN::test_CosineEmbeddingLoss_no_batch_dim_mean, test/test_nn.py::TestNN::test_CosineEmbeddingLoss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_CosineEmbeddingLoss_no_batch_dim_mean_cuda_float, test/test_nn.py::TestNN::test_CosineEmbeddingLoss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_CosineEmbeddingLoss_no_batch_dim_none, test/test_nn.py::TestNN::test_CosineEmbeddingLoss_no_batch_dim_none_cuda_double, test/test_nn.py::TestNN::test_CosineEmbeddingLoss_no_batch_dim_none_cuda_float, test/test_nn.py::TestNN::test_CosineEmbeddingLoss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_CosineEmbeddingLoss_no_batch_dim_sum, test/test_nn.py::TestNN::test_CosineEmbeddingLoss_no_batch_dim_sum_cuda_double, test/test_nn.py::TestNN::test_CosineEmbeddingLoss_no_batch_dim_sum_cuda_float, test/test_nn.py::TestNN::test_CosineEmbeddingLoss_no_batch_dim_sum_cuda_half, test/test_nn.py::TestNN::test_CrossMapLRN2d, test/test_nn.py::TestNN::test_CrossMapLRN2d_cuda, test/test_nn.py::TestNN::test_ELU_no_batch_dim, test/test_nn.py::TestNN::test_ELU_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Embedding, test/test_nn.py::TestNN::test_EmbeddingBag_discontiguous, test/test_nn.py::TestNN::test_EmbeddingBag_discontiguous_cuda, test/test_nn.py::TestNN::test_EmbeddingBag_max, test/test_nn.py::TestNN::test_EmbeddingBag_max_cuda, test/test_nn.py::TestNN::test_EmbeddingBag_max_padding_idx, test/test_nn.py::TestNN::test_EmbeddingBag_max_padding_idx_cuda, test/test_nn.py::TestNN::test_EmbeddingBag_mean, test/test_nn.py::TestNN::test_EmbeddingBag_mean_cuda, test/test_nn.py::TestNN::test_EmbeddingBag_mean_padding_idx, test/test_nn.py::TestNN::test_EmbeddingBag_mean_padding_idx_cuda, test/test_nn.py::TestNN::test_EmbeddingBag_sparse, test/test_nn.py::TestNN::test_EmbeddingBag_sparse_cuda, test/test_nn.py::TestNN::test_EmbeddingBag_sum, test/test_nn.py::TestNN::test_EmbeddingBag_sum_cuda, test/test_nn.py::TestNN::test_EmbeddingBag_sum_padding_idx, test/test_nn.py::TestNN::test_EmbeddingBag_sum_padding_idx_cuda, test/test_nn.py::TestNN::test_Embedding_cuda, test/test_nn.py::TestNN::test_Embedding_discontiguous, test/test_nn.py::TestNN::test_Embedding_discontiguous_cuda, test/test_nn.py::TestNN::test_Embedding_sparse, test/test_nn.py::TestNN::test_Embedding_sparse_cuda, test/test_nn.py::TestNN::test_Flatten, test/test_nn.py::TestNN::test_Flatten_cuda, test/test_nn.py::TestNN::test_Flatten_no_batch_dim, test/test_nn.py::TestNN::test_Flatten_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Fold, test/test_nn.py::TestNN::test_Fold_cuda, test/test_nn.py::TestNN::test_Fold_int_input, test/test_nn.py::TestNN::test_Fold_int_input_cuda, test/test_nn.py::TestNN::test_Fold_no_batch_dim_input, test/test_nn.py::TestNN::test_Fold_no_batch_dim_input_cuda, test/test_nn.py::TestNN::test_Fold_no_batch_dim_int_input, test/test_nn.py::TestNN::test_Fold_no_batch_dim_int_input_cuda, test/test_nn.py::TestNN::test_GELU_no_batch_dim, test/test_nn.py::TestNN::test_GELU_no_batch_dim_cuda, test/test_nn.py::TestNN::test_GLU_no_batch_dim, test/test_nn.py::TestNN::test_GLU_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Hardshrink_no_batch_dim, test/test_nn.py::TestNN::test_Hardshrink_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Hardsigmoid_no_batch_dim, test/test_nn.py::TestNN::test_Hardsigmoid_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Hardswish_no_batch_dim, test/test_nn.py::TestNN::test_Hardswish_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Hardtanh_no_batch_dim, test/test_nn.py::TestNN::test_Hardtanh_no_batch_dim_cuda, test/test_nn.py::TestNN::test_HingeEmbeddingLoss_margin_no_reduce, test/test_nn.py::TestNN::test_HingeEmbeddingLoss_margin_no_reduce_cuda, test/test_nn.py::TestNN::test_HingeEmbeddingLoss_no_batch_dim_mean, test/test_nn.py::TestNN::test_HingeEmbeddingLoss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_HingeEmbeddingLoss_no_batch_dim_mean_cuda_float, test/test_nn.py::TestNN::test_HingeEmbeddingLoss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_HingeEmbeddingLoss_no_batch_dim_none, test/test_nn.py::TestNN::test_HingeEmbeddingLoss_no_batch_dim_none_cuda_double, test/test_nn.py::TestNN::test_HingeEmbeddingLoss_no_batch_dim_none_cuda_float, test/test_nn.py::TestNN::test_HingeEmbeddingLoss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_HingeEmbeddingLoss_no_batch_dim_sum, test/test_nn.py::TestNN::test_HingeEmbeddingLoss_no_batch_dim_sum_cuda_double, test/test_nn.py::TestNN::test_HingeEmbeddingLoss_no_batch_dim_sum_cuda_float, test/test_nn.py::TestNN::test_HingeEmbeddingLoss_no_batch_dim_sum_cuda_half, test/test_nn.py::TestNN::test_HingeEmbeddingLoss_no_reduce, test/test_nn.py::TestNN::test_HingeEmbeddingLoss_no_reduce_cuda, test/test_nn.py::TestNN::test_HuberLoss_delta, test/test_nn.py::TestNN::test_HuberLoss_delta_cuda, test/test_nn.py::TestNN::test_HuberLoss_no_batch_dim_mean, test/test_nn.py::TestNN::test_HuberLoss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_HuberLoss_no_batch_dim_mean_cuda_float, test/test_nn.py::TestNN::test_HuberLoss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_HuberLoss_no_batch_dim_none, test/test_nn.py::TestNN::test_HuberLoss_no_batch_dim_none_cuda_double, test/test_nn.py::TestNN::test_HuberLoss_no_batch_dim_none_cuda_float, test/test_nn.py::TestNN::test_HuberLoss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_HuberLoss_no_batch_dim_sum, test/test_nn.py::TestNN::test_HuberLoss_no_batch_dim_sum_cuda_double, test/test_nn.py::TestNN::test_HuberLoss_no_batch_dim_sum_cuda_float, test/test_nn.py::TestNN::test_HuberLoss_no_batch_dim_sum_cuda_half, test/test_nn.py::TestNN::test_KLDivLoss_batch_mean, test/test_nn.py::TestNN::test_KLDivLoss_batch_mean_log_target, test/test_nn.py::TestNN::test_KLDivLoss_no_batch_dim_mean, test/test_nn.py::TestNN::test_KLDivLoss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_KLDivLoss_no_batch_dim_mean_cuda_float, test/test_nn.py::TestNN::test_KLDivLoss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_KLDivLoss_no_batch_dim_none, test/test_nn.py::TestNN::test_KLDivLoss_no_batch_dim_none_cuda_double, test/test_nn.py::TestNN::test_KLDivLoss_no_batch_dim_none_cuda_float, test/test_nn.py::TestNN::test_KLDivLoss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_KLDivLoss_no_batch_dim_sum, test/test_nn.py::TestNN::test_KLDivLoss_no_batch_dim_sum_cuda_double, test/test_nn.py::TestNN::test_KLDivLoss_no_batch_dim_sum_cuda_float, test/test_nn.py::TestNN::test_KLDivLoss_no_batch_dim_sum_cuda_half, test/test_nn.py::TestNN::test_KLDivLoss_no_reduce, test/test_nn.py::TestNN::test_KLDivLoss_no_reduce_cuda, test/test_nn.py::TestNN::test_KLDivLoss_no_reduce_log_target, test/test_nn.py::TestNN::test_KLDivLoss_no_reduce_log_target_cuda, test/test_nn.py::TestNN::test_KLDivLoss_no_reduce_scalar, test/test_nn.py::TestNN::test_KLDivLoss_no_reduce_scalar_cuda, test/test_nn.py::TestNN::test_KLDivLoss_no_reduce_scalar_log_target, test/test_nn.py::TestNN::test_KLDivLoss_no_reduce_scalar_log_target_cuda, test/test_nn.py::TestNN::test_KLDivLoss_with_log_target_no_reduce, test/test_nn.py::TestNN::test_KLDivLoss_with_log_target_no_reduce_cuda, test/test_nn.py::TestNN::test_KLDivLoss_with_target_no_reduce, test/test_nn.py::TestNN::test_KLDivLoss_with_target_no_reduce_cuda, test/test_nn.py::TestNN::test_L1Loss_no_batch_dim_mean, test/test_nn.py::TestNN::test_L1Loss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_L1Loss_no_batch_dim_mean_cuda_float, test/test_nn.py::TestNN::test_L1Loss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_L1Loss_no_batch_dim_none, test/test_nn.py::TestNN::test_L1Loss_no_batch_dim_none_cuda_double, test/test_nn.py::TestNN::test_L1Loss_no_batch_dim_none_cuda_float, test/test_nn.py::TestNN::test_L1Loss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_L1Loss_no_batch_dim_sum, test/test_nn.py::TestNN::test_L1Loss_no_batch_dim_sum_cuda_double, test/test_nn.py::TestNN::test_L1Loss_no_batch_dim_sum_cuda_float, test/test_nn.py::TestNN::test_L1Loss_no_batch_dim_sum_cuda_half, test/test_nn.py::TestNN::test_L1Loss_no_reduce, test/test_nn.py::TestNN::test_L1Loss_no_reduce_complex, test/test_nn.py::TestNN::test_L1Loss_no_reduce_complex_cuda, test/test_nn.py::TestNN::test_L1Loss_no_reduce_cuda, test/test_nn.py::TestNN::test_L1Loss_no_reduce_scalar, test/test_nn.py::TestNN::test_L1Loss_no_reduce_scalar_cuda, test/test_nn.py::TestNN::test_LSTM_cell, test/test_nn.py::TestNN::test_LSTM_cell_forward_hidden_size, test/test_nn.py::TestNN::test_LSTM_cell_forward_input_size, test/test_nn.py::TestNN::test_LayerNorm_3d_no_affine_large_feature, test/test_nn.py::TestNN::test_LayerNorm_3d_no_affine_large_feature_cuda, test/test_nn.py::TestNN::test_LayerNorm_3d_no_affine_large_feature_eval, test/test_nn.py::TestNN::test_LayerNorm_3d_no_affine_large_feature_eval_cuda, test/test_nn.py::TestNN::test_LeakyReLU_no_batch_dim, test/test_nn.py::TestNN::test_LeakyReLU_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Linear, test/test_nn.py::TestNN::test_Linear_cuda, test/test_nn.py::TestNN::test_Linear_no_batch_dim, test/test_nn.py::TestNN::test_Linear_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Linear_no_bias, test/test_nn.py::TestNN::test_Linear_no_bias_cuda, test/test_nn.py::TestNN::test_LogSigmoid_no_batch_dim, test/test_nn.py::TestNN::test_LogSigmoid_no_batch_dim_cuda, test/test_nn.py::TestNN::test_MSELoss_no_batch_dim_mean, test/test_nn.py::TestNN::test_MSELoss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_MSELoss_no_batch_dim_mean_cuda_float, test/test_nn.py::TestNN::test_MSELoss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_MSELoss_no_batch_dim_none, test/test_nn.py::TestNN::test_MSELoss_no_batch_dim_none_cuda_double, test/test_nn.py::TestNN::test_MSELoss_no_batch_dim_none_cuda_float, test/test_nn.py::TestNN::test_MSELoss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_MSELoss_no_batch_dim_sum, test/test_nn.py::TestNN::test_MSELoss_no_batch_dim_sum_cuda_double, test/test_nn.py::TestNN::test_MSELoss_no_batch_dim_sum_cuda_float, test/test_nn.py::TestNN::test_MSELoss_no_batch_dim_sum_cuda_half, test/test_nn.py::TestNN::test_MSELoss_no_reduce, test/test_nn.py::TestNN::test_MSELoss_no_reduce_cuda, test/test_nn.py::TestNN::test_MSELoss_no_reduce_scalar, test/test_nn.py::TestNN::test_MSELoss_no_reduce_scalar_cuda, test/test_nn.py::TestNN::test_MarginRankingLoss_no_batch_dim_mean, test/test_nn.py::TestNN::test_MarginRankingLoss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_MarginRankingLoss_no_batch_dim_mean_cuda_float, test/test_nn.py::TestNN::test_MarginRankingLoss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_MarginRankingLoss_no_batch_dim_none, test/test_nn.py::TestNN::test_MarginRankingLoss_no_batch_dim_none_cuda_double, test/test_nn.py::TestNN::test_MarginRankingLoss_no_batch_dim_none_cuda_float, test/test_nn.py::TestNN::test_MarginRankingLoss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_MarginRankingLoss_no_batch_dim_sum, test/test_nn.py::TestNN::test_MarginRankingLoss_no_batch_dim_sum_cuda_double, test/test_nn.py::TestNN::test_MarginRankingLoss_no_batch_dim_sum_cuda_float, test/test_nn.py::TestNN::test_MarginRankingLoss_no_batch_dim_sum_cuda_half, test/test_nn.py::TestNN::test_MaxUnpool1d_net, test/test_nn.py::TestNN::test_MaxUnpool1d_net_cuda, test/test_nn.py::TestNN::test_MaxUnpool1d_net_no_batch_dim, test/test_nn.py::TestNN::test_MaxUnpool1d_net_no_batch_dim_cuda, test/test_nn.py::TestNN::test_MaxUnpool2d_net, test/test_nn.py::TestNN::test_MaxUnpool2d_net_cuda, test/test_nn.py::TestNN::test_MaxUnpool2d_net_no_batch_dim, test/test_nn.py::TestNN::test_MaxUnpool2d_net_no_batch_dim_cuda, test/test_nn.py::TestNN::test_MaxUnpool3d_net, test/test_nn.py::TestNN::test_MaxUnpool3d_net_cuda, test/test_nn.py::TestNN::test_MaxUnpool3d_net_no_batch_dim, test/test_nn.py::TestNN::test_MaxUnpool3d_net_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Mish_no_batch_dim, test/test_nn.py::TestNN::test_Mish_no_batch_dim_cuda, test/test_nn.py::TestNN::test_ModuleDict, test/test_nn.py::TestNN::test_ModuleList, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_0d_no_reduce, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_0d_no_reduce_cuda, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_1d_no_reduce, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_1d_no_reduce_cuda, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_index_neg, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_index_neg_cuda, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_no_batch_dim_mean, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_no_batch_dim_mean_cuda_float, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_no_batch_dim_none, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_no_batch_dim_none_cuda_double, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_no_batch_dim_none_cuda_float, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_no_batch_dim_sum, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_no_batch_dim_sum_cuda_double, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_no_batch_dim_sum_cuda_float, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_no_batch_dim_sum_cuda_half, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_no_reduce, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_no_reduce_cuda, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_no_batch_dim_mean, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_no_batch_dim_mean_cuda_float, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_no_batch_dim_none, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_no_batch_dim_none_cuda_double, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_no_batch_dim_none_cuda_float, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_no_batch_dim_sum, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_no_batch_dim_sum_cuda_double, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_no_batch_dim_sum_cuda_float, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_no_batch_dim_sum_cuda_half, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_no_reduce, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_no_reduce_cuda, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_weights_no_reduce, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_weights_no_reduce_cuda, test/test_nn.py::TestNN::test_MultiMarginLoss_1d_no_reduce, test/test_nn.py::TestNN::test_MultiMarginLoss_1d_no_reduce_cuda, test/test_nn.py::TestNN::test_MultiMarginLoss_margin_no_reduce, test/test_nn.py::TestNN::test_MultiMarginLoss_margin_no_reduce_cuda, test/test_nn.py::TestNN::test_MultiMarginLoss_no_reduce, test/test_nn.py::TestNN::test_MultiMarginLoss_no_reduce_cuda, test/test_nn.py::TestNN::test_MultiMarginLoss_p_no_reduce, test/test_nn.py::TestNN::test_MultiMarginLoss_p_no_reduce_cuda, test/test_nn.py::TestNN::test_MultiMarginLoss_weights_no_reduce, test/test_nn.py::TestNN::test_MultiMarginLoss_weights_no_reduce_cuda, test/test_nn.py::TestNN::test_NLLLoss2d_no_reduce, test/test_nn.py::TestNN::test_NLLLoss2d_no_reduce_cuda, test/test_nn.py::TestNN::test_NLLLoss2d_no_reduce_ignore_index, test/test_nn.py::TestNN::test_NLLLoss2d_no_reduce_ignore_index_cuda, test/test_nn.py::TestNN::test_NLLLoss2d_no_reduce_weights, test/test_nn.py::TestNN::test_NLLLoss2d_no_reduce_weights_cuda, test/test_nn.py::TestNN::test_NLLLossNd_no_reduce, test/test_nn.py::TestNN::test_NLLLossNd_no_reduce_cuda, test/test_nn.py::TestNN::test_NLLLossNd_no_reduce_ignore_index, test/test_nn.py::TestNN::test_NLLLossNd_no_reduce_ignore_index_cuda, test/test_nn.py::TestNN::test_NLLLossNd_no_reduce_weights, test/test_nn.py::TestNN::test_NLLLossNd_no_reduce_weights_cuda, test/test_nn.py::TestNN::test_NLLLoss_no_batch_dim_mean, test/test_nn.py::TestNN::test_NLLLoss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_NLLLoss_no_batch_dim_mean_cuda_float, test/test_nn.py::TestNN::test_NLLLoss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_NLLLoss_no_batch_dim_none, test/test_nn.py::TestNN::test_NLLLoss_no_batch_dim_none_cuda_double, test/test_nn.py::TestNN::test_NLLLoss_no_batch_dim_none_cuda_float, test/test_nn.py::TestNN::test_NLLLoss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_NLLLoss_no_batch_dim_sum, test/test_nn.py::TestNN::test_NLLLoss_no_batch_dim_sum_cuda_double, test/test_nn.py::TestNN::test_NLLLoss_no_batch_dim_sum_cuda_float, test/test_nn.py::TestNN::test_NLLLoss_no_batch_dim_sum_cuda_half, test/test_nn.py::TestNN::test_NLLLoss_no_reduce, test/test_nn.py::TestNN::test_NLLLoss_no_reduce_cuda, test/test_nn.py::TestNN::test_NLLLoss_no_reduce_ignore_index, test/test_nn.py::TestNN::test_NLLLoss_no_reduce_ignore_index_cuda, test/test_nn.py::TestNN::test_NLLLoss_no_reduce_weights, test/test_nn.py::TestNN::test_NLLLoss_no_reduce_weights_cuda, test/test_nn.py::TestNN::test_NLLLoss_no_reduce_weights_ignore_index, test/test_nn.py::TestNN::test_NLLLoss_no_reduce_weights_ignore_index_cuda, test/test_nn.py::TestNN::test_NLLLoss_no_reduce_weights_ignore_index_neg, test/test_nn.py::TestNN::test_NLLLoss_no_reduce_weights_ignore_index_neg_cuda, test/test_nn.py::TestNN::test_PReLU_backward_requires_grad_false, test/test_nn.py::TestNN::test_PReLU_no_batch_dim, test/test_nn.py::TestNN::test_PReLU_no_batch_dim_cuda, test/test_nn.py::TestNN::test_PairwiseDistance, test/test_nn.py::TestNN::test_PairwiseDistance_broadcast_lhs, test/test_nn.py::TestNN::test_PairwiseDistance_broadcast_lhs_cuda, test/test_nn.py::TestNN::test_PairwiseDistance_broadcast_rhs, test/test_nn.py::TestNN::test_PairwiseDistance_broadcast_rhs_cuda, test/test_nn.py::TestNN::test_PairwiseDistance_cuda, test/test_nn.py::TestNN::test_PairwiseDistance_no_batch_dim, test/test_nn.py::TestNN::test_PairwiseDistance_no_batch_dim_cuda, test/test_nn.py::TestNN::test_PairwiseDistance_with_non_default_args, test/test_nn.py::TestNN::test_PairwiseDistance_with_non_default_args_cuda, test/test_nn.py::TestNN::test_ParameterDict, test/test_nn.py::TestNN::test_ParameterDict_replication, test/test_nn.py::TestNN::test_ParameterList, test/test_nn.py::TestNN::test_ParameterList_meta, test/test_nn.py::TestNN::test_ParameterList_replication, test/test_nn.py::TestNN::test_PixelShuffle, test/test_nn.py::TestNN::test_PixelShuffle_cuda, test/test_nn.py::TestNN::test_PixelUnshuffle, test/test_nn.py::TestNN::test_PixelUnshuffle_cuda, test/test_nn.py::TestNN::test_PoissonNLLLoss_no_batch_dim_mean, test/test_nn.py::TestNN::test_PoissonNLLLoss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_PoissonNLLLoss_no_batch_dim_mean_cuda_float, test/test_nn.py::TestNN::test_PoissonNLLLoss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_PoissonNLLLoss_no_batch_dim_none, test/test_nn.py::TestNN::test_PoissonNLLLoss_no_batch_dim_none_cuda_double, test/test_nn.py::TestNN::test_PoissonNLLLoss_no_batch_dim_none_cuda_float, test/test_nn.py::TestNN::test_PoissonNLLLoss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_PoissonNLLLoss_no_batch_dim_sum, test/test_nn.py::TestNN::test_PoissonNLLLoss_no_batch_dim_sum_cuda_double, test/test_nn.py::TestNN::test_PoissonNLLLoss_no_batch_dim_sum_cuda_float, test/test_nn.py::TestNN::test_PoissonNLLLoss_no_batch_dim_sum_cuda_half, test/test_nn.py::TestNN::test_PoissonNLLLoss_no_reduce, test/test_nn.py::TestNN::test_PoissonNLLLoss_no_reduce_cuda, test/test_nn.py::TestNN::test_RNN_cell, test/test_nn.py::TestNN::test_RNN_cell_forward_zero_hidden_size, test/test_nn.py::TestNN::test_RNN_cell_no_broadcasting, test/test_nn.py::TestNN::test_RNN_change_dropout, test/test_nn.py::TestNN::test_RNN_cpu_vs_cudnn_no_dropout, test/test_nn.py::TestNN::test_RNN_cpu_vs_cudnn_with_dropout, test/test_nn.py::TestNN::test_RNN_cudnn_weight_norm, test/test_nn.py::TestNN::test_RNN_dropout, test/test_nn.py::TestNN::test_RNN_dropout_state, test/test_nn.py::TestNN::test_RNN_input_size_zero, test/test_nn.py::TestNN::test_RNN_nonlinearity, test/test_nn.py::TestNN::test_RNN_nonlinearity_passed_as_arg, test/test_nn.py::TestNN::test_RReLU, test/test_nn.py::TestNN::test_RReLU_cuda, test/test_nn.py::TestNN::test_RReLU_no_batch_dim, test/test_nn.py::TestNN::test_RReLU_no_batch_dim_cuda, test/test_nn.py::TestNN::test_RReLU_with_up_down, test/test_nn.py::TestNN::test_RReLU_with_up_down_cuda, test/test_nn.py::TestNN::test_RReLU_with_up_down_scalar, test/test_nn.py::TestNN::test_RReLU_with_up_down_scalar_cuda, test/test_nn.py::TestNN::test_ReLU6_no_batch_dim, test/test_nn.py::TestNN::test_ReLU6_no_batch_dim_cuda, test/test_nn.py::TestNN::test_ReLU_no_batch_dim, test/test_nn.py::TestNN::test_ReLU_no_batch_dim_cuda, test/test_nn.py::TestNN::test_ReplicationPad3d, test/test_nn.py::TestNN::test_ReplicationPad3d_complex, test/test_nn.py::TestNN::test_ReplicationPad3d_complex_cuda, test/test_nn.py::TestNN::test_ReplicationPad3d_cuda, test/test_nn.py::TestNN::test_ReplicationPad3d_no_batch_dim, test/test_nn.py::TestNN::test_ReplicationPad3d_no_batch_dim_cuda, test/test_nn.py::TestNN::test_SELU_no_batch_dim, test/test_nn.py::TestNN::test_SELU_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Sequential_add, test/test_nn.py::TestNN::test_Sequential_append, test/test_nn.py::TestNN::test_Sequential_delitem, test/test_nn.py::TestNN::test_Sequential_extend, test/test_nn.py::TestNN::test_Sequential_getitem, test/test_nn.py::TestNN::test_Sequential_iadd, test/test_nn.py::TestNN::test_Sequential_imul, test/test_nn.py::TestNN::test_Sequential_insert, test/test_nn.py::TestNN::test_Sequential_insert_fail_case, test/test_nn.py::TestNN::test_Sequential_mul, test/test_nn.py::TestNN::test_Sequential_pop, test/test_nn.py::TestNN::test_Sequential_rmul, test/test_nn.py::TestNN::test_Sequential_setitem, test/test_nn.py::TestNN::test_Sequential_setitem_named, test/test_nn.py::TestNN::test_SiLU_no_batch_dim, test/test_nn.py::TestNN::test_SiLU_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Sigmoid_no_batch_dim, test/test_nn.py::TestNN::test_Sigmoid_no_batch_dim_cuda, test/test_nn.py::TestNN::test_SmoothL1Loss_beta, test/test_nn.py::TestNN::test_SmoothL1Loss_beta_cuda, test/test_nn.py::TestNN::test_SmoothL1Loss_no_batch_dim_mean, test/test_nn.py::TestNN::test_SmoothL1Loss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_SmoothL1Loss_no_batch_dim_mean_cuda_float, test/test_nn.py::TestNN::test_SmoothL1Loss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_SmoothL1Loss_no_batch_dim_none, test/test_nn.py::TestNN::test_SmoothL1Loss_no_batch_dim_none_cuda_double, test/test_nn.py::TestNN::test_SmoothL1Loss_no_batch_dim_none_cuda_float, test/test_nn.py::TestNN::test_SmoothL1Loss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_SmoothL1Loss_no_batch_dim_sum, test/test_nn.py::TestNN::test_SmoothL1Loss_no_batch_dim_sum_cuda_double, test/test_nn.py::TestNN::test_SmoothL1Loss_no_batch_dim_sum_cuda_float, test/test_nn.py::TestNN::test_SmoothL1Loss_no_batch_dim_sum_cuda_half, test/test_nn.py::TestNN::test_SmoothL1Loss_no_reduce, test/test_nn.py::TestNN::test_SmoothL1Loss_no_reduce_cuda, test/test_nn.py::TestNN::test_SmoothL1Loss_no_reduce_scalar, test/test_nn.py::TestNN::test_SmoothL1Loss_no_reduce_scalar_cuda, test/test_nn.py::TestNN::test_SmoothL1Loss_zero_beta, test/test_nn.py::TestNN::test_SmoothL1Loss_zero_beta_cuda, test/test_nn.py::TestNN::test_SoftMarginLoss_no_batch_dim_mean, test/test_nn.py::TestNN::test_SoftMarginLoss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_SoftMarginLoss_no_batch_dim_mean_cuda_float, test/test_nn.py::TestNN::test_SoftMarginLoss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_SoftMarginLoss_no_batch_dim_none, test/test_nn.py::TestNN::test_SoftMarginLoss_no_batch_dim_none_cuda_double, test/test_nn.py::TestNN::test_SoftMarginLoss_no_batch_dim_none_cuda_float, test/test_nn.py::TestNN::test_SoftMarginLoss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_SoftMarginLoss_no_batch_dim_sum, test/test_nn.py::TestNN::test_SoftMarginLoss_no_batch_dim_sum_cuda_double, test/test_nn.py::TestNN::test_SoftMarginLoss_no_batch_dim_sum_cuda_float, test/test_nn.py::TestNN::test_SoftMarginLoss_no_batch_dim_sum_cuda_half, test/test_nn.py::TestNN::test_SoftMarginLoss_no_reduce, test/test_nn.py::TestNN::test_SoftMarginLoss_no_reduce_cuda, test/test_nn.py::TestNN::test_Softplus_no_batch_dim, test/test_nn.py::TestNN::test_Softplus_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Softshrink_no_batch_dim, test/test_nn.py::TestNN::test_Softshrink_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Softsign_no_batch_dim, test/test_nn.py::TestNN::test_Softsign_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Tanh_no_batch_dim, test/test_nn.py::TestNN::test_Tanh_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Tanhshrink_no_batch_dim, test/test_nn.py::TestNN::test_Tanhshrink_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Threshold_no_batch_dim, test/test_nn.py::TestNN::test_Threshold_no_batch_dim_cuda, test/test_nn.py::TestNN::test_TransformerDecoderLayer_gelu_activation, test/test_nn.py::TestNN::test_TransformerDecoderLayer_gelu_activation_cuda, test/test_nn.py::TestNN::test_TransformerDecoderLayer_relu_activation, test/test_nn.py::TestNN::test_TransformerDecoderLayer_relu_activation_cuda, test/test_nn.py::TestNN::test_TransformerEncoderLayer_gelu_activation, test/test_nn.py::TestNN::test_TransformerEncoderLayer_gelu_activation_cuda, test/test_nn.py::TestNN::test_TransformerEncoderLayer_relu_activation, test/test_nn.py::TestNN::test_TransformerEncoderLayer_relu_activation_cuda, test/test_nn.py::TestNN::test_Transformer_cell, test/test_nn.py::TestNN::test_Transformer_multilayer_coder, test/test_nn.py::TestNN::test_Transformer_multilayer_coder_cuda, test/test_nn.py::TestNN::test_TripletMarginLoss_no_batch_dim_mean, test/test_nn.py::TestNN::test_TripletMarginLoss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_TripletMarginLoss_no_batch_dim_mean_cuda_float, test/test_nn.py::TestNN::test_TripletMarginLoss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_TripletMarginLoss_no_batch_dim_none, test/test_nn.py::TestNN::test_TripletMarginLoss_no_batch_dim_none_cuda_double, test/test_nn.py::TestNN::test_TripletMarginLoss_no_batch_dim_none_cuda_float, test/test_nn.py::TestNN::test_TripletMarginLoss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_TripletMarginLoss_no_batch_dim_sum, test/test_nn.py::TestNN::test_TripletMarginLoss_no_batch_dim_sum_cuda_double, test/test_nn.py::TestNN::test_TripletMarginLoss_no_batch_dim_sum_cuda_float, test/test_nn.py::TestNN::test_TripletMarginLoss_no_batch_dim_sum_cuda_half, test/test_nn.py::TestNN::test_Unflatten_no_batch_dim, test/test_nn.py::TestNN::test_Unflatten_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Unfold, test/test_nn.py::TestNN::test_Unfold_cuda, test/test_nn.py::TestNN::test_Unfold_int_input, test/test_nn.py::TestNN::test_Unfold_int_input_cuda, test/test_nn.py::TestNN::test_adaptive_log_softmax, test/test_nn.py::TestNN::test_add_module, test/test_nn.py::TestNN::test_add_module_raises_error_if_attr_exists, test/test_nn.py::TestNN::test_affine_grid, test/test_nn.py::TestNN::test_affine_grid_3d, test/test_nn.py::TestNN::test_affine_grid_backward_cl_cf_consistency_device_cpu_nd_2, test/test_nn.py::TestNN::test_affine_grid_backward_cl_cf_consistency_device_cpu_nd_3, test/test_nn.py::TestNN::test_affine_grid_error_checking, test/test_nn.py::TestNN::test_assignment, test/test_nn.py::TestNN::test_batch_norm_update_stats, test/test_nn.py::TestNN::test_batchnorm_2D_inference_NCHW_vs_cpu_float32, test/test_nn.py::TestNN::test_batchnorm_2D_inference_NCHW_vs_cpu_mixed_bfloat16, test/test_nn.py::TestNN::test_batchnorm_2D_inference_NCHW_vs_cpu_mixed_float16, test/test_nn.py::TestNN::test_batchnorm_2D_inference_NCHW_vs_native_float32, test/test_nn.py::TestNN::test_batchnorm_2D_inference_NCHW_vs_native_mixed_bfloat16, test/test_nn.py::TestNN::test_batchnorm_2D_inference_NCHW_vs_native_mixed_float16, test/test_nn.py::TestNN::test_batchnorm_2D_train_NCHW_vs_cpu_float32, test/test_nn.py::TestNN::test_batchnorm_2D_train_NCHW_vs_cpu_mixed_bfloat16, test/test_nn.py::TestNN::test_batchnorm_2D_train_NCHW_vs_cpu_mixed_float16, test/test_nn.py::TestNN::test_batchnorm_2D_train_NCHW_vs_native_float32, test/test_nn.py::TestNN::test_batchnorm_2D_train_NCHW_vs_native_mixed_bfloat16, test/test_nn.py::TestNN::test_batchnorm_2D_train_NCHW_vs_native_mixed_float16, test/test_nn.py::TestNN::test_batchnorm_3D_inference_NCHW_vs_cpu_float32, test/test_nn.py::TestNN::test_batchnorm_3D_inference_NCHW_vs_cpu_mixed_bfloat16, test/test_nn.py::TestNN::test_batchnorm_3D_inference_NCHW_vs_cpu_mixed_float16, test/test_nn.py::TestNN::test_batchnorm_3D_inference_NCHW_vs_native_float32, test/test_nn.py::TestNN::test_batchnorm_3D_inference_NCHW_vs_native_mixed_bfloat16, test/test_nn.py::TestNN::test_batchnorm_3D_inference_NCHW_vs_native_mixed_float16, test/test_nn.py::TestNN::test_batchnorm_3D_train_NCHW_vs_cpu_float32, test/test_nn.py::TestNN::test_batchnorm_3D_train_NCHW_vs_cpu_mixed_bfloat16, test/test_nn.py::TestNN::test_batchnorm_3D_train_NCHW_vs_cpu_mixed_float16, test/test_nn.py::TestNN::test_batchnorm_3D_train_NCHW_vs_native_float32, test/test_nn.py::TestNN::test_batchnorm_3D_train_NCHW_vs_native_mixed_bfloat16, test/test_nn.py::TestNN::test_batchnorm_3D_train_NCHW_vs_native_mixed_float16, test/test_nn.py::TestNN::test_batchnorm_buffer_update_when_stats_are_not_tracked, test/test_nn.py::TestNN::test_batchnorm_cudnn_half, test/test_nn.py::TestNN::test_batchnorm_cudnn_nhwc, test/test_nn.py::TestNN::test_batchnorm_half_overflow, test/test_nn.py::TestNN::test_batchnorm_load_state_dict, test/test_nn.py::TestNN::test_batchnorm_nhwc_cpu, test/test_nn.py::TestNN::test_batchnorm_nhwc_cuda, test/test_nn.py::TestNN::test_batchnorm_non_contig_cpu_BatchNorm2d, test/test_nn.py::TestNN::test_batchnorm_non_contig_cpu_SyncBatchNorm, test/test_nn.py::TestNN::test_batchnorm_nonaffine_cuda_half_input, test/test_nn.py::TestNN::test_batchnorm_raises_error_if_bias_is_not_same_size_as_input, test/test_nn.py::TestNN::test_batchnorm_raises_error_if_less_than_one_value_per_channel, test/test_nn.py::TestNN::test_batchnorm_raises_error_if_running_mean_is_not_same_size_as_input, test/test_nn.py::TestNN::test_batchnorm_raises_error_if_running_var_is_not_same_size_as_input, test/test_nn.py::TestNN::test_batchnorm_raises_error_if_running_var_or_running_mean_have_forward_grad, test/test_nn.py::TestNN::test_batchnorm_raises_error_if_weight_is_not_same_size_as_input, test/test_nn.py::TestNN::test_bce_loss_always_nonnegative, test/test_nn.py::TestNN::test_bce_loss_broadcasts_weights, test/test_nn.py::TestNN::test_bce_loss_input_range, test/test_nn.py::TestNN::test_bce_loss_size_mismatch, test/test_nn.py::TestNN::test_bce_with_logits_broadcasts_pos_weights, test/test_nn.py::TestNN::test_bce_with_logits_broadcasts_weights, test/test_nn.py::TestNN::test_bce_with_logits_gives_same_result_as_sigmoid_and_bce_loss, test/test_nn.py::TestNN::test_bce_with_logits_gives_same_result_as_sigmoid_and_bce_loss_large_tensors_with_grad, test/test_nn.py::TestNN::test_bce_with_logits_has_correct_forward_grad, test/test_nn.py::TestNN::test_bce_with_logits_has_correct_grad_at_zero, test/test_nn.py::TestNN::test_bce_with_logits_ones_in_pos_weights_are_the_same_as_none, test/test_nn.py::TestNN::test_bce_with_logits_raises_if_target_and_input_are_different_size, test/test_nn.py::TestNN::test_bce_with_logits_stability, test/test_nn.py::TestNN::test_bce_with_logits_with_pos_weight_has_correct_grad_at_zero, test/test_nn.py::TestNN::test_bilinear, test/test_nn.py::TestNN::test_bilinear_broadcasting, test/test_nn.py::TestNN::test_bilinear_no_bias, test/test_nn.py::TestNN::test_bilinear_non_contiguous, test/test_nn.py::TestNN::test_bilinear_value_error, test/test_nn.py::TestNN::test_broadcast_double_backwards_gpu, test/test_nn.py::TestNN::test_broadcast_no_grad, test/test_nn.py::TestNN::test_broadcast_not_requiring_grad, test/test_nn.py::TestNN::test_buffer_bad_module_subclass, test/test_nn.py::TestNN::test_buffer_not_persistent, test/test_nn.py::TestNN::test_buffer_not_persistent_assign, test/test_nn.py::TestNN::test_buffer_not_persistent_del, test/test_nn.py::TestNN::test_buffer_not_persistent_load, test/test_nn.py::TestNN::test_buffer_not_persistent_overwrite, test/test_nn.py::TestNN::test_buffers_and_named_buffers, test/test_nn.py::TestNN::test_call_supports_python_dict_output, test/test_nn.py::TestNN::test_channel_shuffle_input_checks, test/test_nn.py::TestNN::test_channel_shuffle_return_alias_of_self, test/test_nn.py::TestNN::test_children, test/test_nn.py::TestNN::test_container_copy, test/test_nn.py::TestNN::test_convert_sync_batchnorm, test/test_nn.py::TestNN::test_cosine_embedding_loss_error_on_diff_shapes, test/test_nn.py::TestNN::test_cosine_embedding_loss_error_on_nonexpandable_shapes, test/test_nn.py::TestNN::test_cosine_embedding_loss_invalid_shape, test/test_nn.py::TestNN::test_cosine_embedding_loss_margin_no_reduce, test/test_nn.py::TestNN::test_cosine_embedding_loss_no_reduce, test/test_nn.py::TestNN::test_cosine_embedding_loss_with_diff_type, test/test_nn.py::TestNN::test_cosine_similarity, test/test_nn.py::TestNN::test_cross_entropy_loss, test/test_nn.py::TestNN::test_cross_entropy_loss_precision, test/test_nn.py::TestNN::test_cross_entropy_loss_zero_div, test/test_nn.py::TestNN::test_cudnn_forward_exception, test/test_nn.py::TestNN::test_cudnn_rnn_dropout_states_device, test/test_nn.py::TestNN::test_cudnn_weight_format, test/test_nn.py::TestNN::test_cudnn_weight_tying, test/test_nn.py::TestNN::test_dir, test/test_nn.py::TestNN::test_dir_digit, test/test_nn.py::TestNN::test_elu_inplace_gradgrad, test/test_nn.py::TestNN::test_elu_inplace_on_view, test/test_nn.py::TestNN::test_error_RNN_seq_len_zero, test/test_nn.py::TestNN::test_extra_state, test/test_nn.py::TestNN::test_extra_state_missing_get_extra_state, test/test_nn.py::TestNN::test_extra_state_missing_set_extra_state, test/test_nn.py::TestNN::test_extra_state_non_dict, test/test_nn.py::TestNN::test_fb_fc_packed, test/test_nn.py::TestNN::test_flatten, test/test_nn.py::TestNN::test_fold_invalid_arg, test/test_nn.py::TestNN::test_fractional_max_pool2d_invalid_output_ratio, test/test_nn.py::TestNN::test_gaussian_nll_loss_args, test/test_nn.py::TestNN::test_gaussian_nll_loss_broadcasting, test/test_nn.py::TestNN::test_gaussian_nll_loss_scalar_var, test/test_nn.py::TestNN::test_get_buffer, test/test_nn.py::TestNN::test_get_buffer_from_submodules, test/test_nn.py::TestNN::test_getattr_with_property, test/test_nn.py::TestNN::test_grid_sample, test/test_nn.py::TestNN::test_grid_sample_3d, test/test_nn.py::TestNN::test_grid_sample_error_checking, test/test_nn.py::TestNN::test_grid_sample_nearest_neighbor_rounding_mode_consistency, test/test_nn.py::TestNN::test_hardtanh_backward, test/test_nn.py::TestNN::test_hardtanh_inplace_gradgrad, test/test_nn.py::TestNN::test_huber_loss_invalid_delta, test/test_nn.py::TestNN::test_inplace_thnn, test/test_nn.py::TestNN::test_interpolate, test/test_nn.py::TestNN::test_interpolate_bicubic_2d, test/test_nn.py::TestNN::test_interpolate_bicubic_2d_cuda, test/test_nn.py::TestNN::test_interpolate_bicubic_2d_zero_dim, test/test_nn.py::TestNN::test_interpolate_bicubic_2d_zero_dim_cuda, test/test_nn.py::TestNN::test_interpolate_bicubic_scale_2d, test/test_nn.py::TestNN::test_interpolate_bicubic_scale_2d_cuda, test/test_nn.py::TestNN::test_interpolate_bicubic_scale_tuple_shared_2d, test/test_nn.py::TestNN::test_interpolate_bicubic_scale_tuple_shared_2d_cuda, test/test_nn.py::TestNN::test_interpolate_bicubic_scale_tuple_skewed_2d, test/test_nn.py::TestNN::test_interpolate_bicubic_scale_tuple_skewed_2d_align_corners, test/test_nn.py::TestNN::test_interpolate_bicubic_scale_tuple_skewed_2d_align_corners_cuda, test/test_nn.py::TestNN::test_interpolate_bicubic_scale_tuple_skewed_2d_cuda, test/test_nn.py::TestNN::test_interpolate_bicubic_tuple_2d, test/test_nn.py::TestNN::test_interpolate_bicubic_tuple_2d_align_corners, test/test_nn.py::TestNN::test_interpolate_bicubic_tuple_2d_align_corners_cuda, test/test_nn.py::TestNN::test_interpolate_bicubic_tuple_2d_cuda, test/test_nn.py::TestNN::test_interpolate_bilinear_2d, test/test_nn.py::TestNN::test_interpolate_bilinear_2d_cuda, test/test_nn.py::TestNN::test_interpolate_bilinear_2d_zero_dim, test/test_nn.py::TestNN::test_interpolate_bilinear_2d_zero_dim_cuda, test/test_nn.py::TestNN::test_interpolate_bilinear_scale_2d, test/test_nn.py::TestNN::test_interpolate_bilinear_scale_2d_cuda, test/test_nn.py::TestNN::test_interpolate_bilinear_scale_tuple_shared_2d, test/test_nn.py::TestNN::test_interpolate_bilinear_scale_tuple_shared_2d_cuda, test/test_nn.py::TestNN::test_interpolate_bilinear_scale_tuple_skewed_2d, test/test_nn.py::TestNN::test_interpolate_bilinear_scale_tuple_skewed_2d_align_corners, test/test_nn.py::TestNN::test_interpolate_bilinear_scale_tuple_skewed_2d_align_corners_cuda, test/test_nn.py::TestNN::test_interpolate_bilinear_scale_tuple_skewed_2d_cuda, test/test_nn.py::TestNN::test_interpolate_bilinear_tuple_2d, test/test_nn.py::TestNN::test_interpolate_bilinear_tuple_2d_align_corners, test/test_nn.py::TestNN::test_interpolate_bilinear_tuple_2d_align_corners_cuda, test/test_nn.py::TestNN::test_interpolate_bilinear_tuple_2d_cuda, test/test_nn.py::TestNN::test_interpolate_buffer_overflow, test/test_nn.py::TestNN::test_interpolate_illegal_memory_access, test/test_nn.py::TestNN::test_interpolate_linear_1d, test/test_nn.py::TestNN::test_interpolate_linear_1d_align_corners, test/test_nn.py::TestNN::test_interpolate_linear_1d_align_corners_cuda, test/test_nn.py::TestNN::test_interpolate_linear_1d_cuda, test/test_nn.py::TestNN::test_interpolate_linear_1d_zero_dim, test/test_nn.py::TestNN::test_interpolate_linear_1d_zero_dim_cuda, test/test_nn.py::TestNN::test_interpolate_linear_scale_1d, test/test_nn.py::TestNN::test_interpolate_linear_scale_1d_align_corners, test/test_nn.py::TestNN::test_interpolate_linear_scale_1d_align_corners_cuda, test/test_nn.py::TestNN::test_interpolate_linear_scale_1d_cuda, test/test_nn.py::TestNN::test_interpolate_linear_tuple_1d, test/test_nn.py::TestNN::test_interpolate_linear_tuple_1d_cuda, test/test_nn.py::TestNN::test_interpolate_nearest_1d, test/test_nn.py::TestNN::test_interpolate_nearest_1d_cuda, test/test_nn.py::TestNN::test_interpolate_nearest_1d_zero_dim, test/test_nn.py::TestNN::test_interpolate_nearest_1d_zero_dim_cuda, test/test_nn.py::TestNN::test_interpolate_nearest_2d, test/test_nn.py::TestNN::test_interpolate_nearest_2d_cuda, test/test_nn.py::TestNN::test_interpolate_nearest_2d_launch_configs, test/test_nn.py::TestNN::test_interpolate_nearest_2d_launch_configs_cuda, test/test_nn.py::TestNN::test_interpolate_nearest_2d_zero_dim, test/test_nn.py::TestNN::test_interpolate_nearest_2d_zero_dim_cuda, test/test_nn.py::TestNN::test_interpolate_nearest_3d, test/test_nn.py::TestNN::test_interpolate_nearest_3d_cuda, test/test_nn.py::TestNN::test_interpolate_nearest_3d_zero_dim, test/test_nn.py::TestNN::test_interpolate_nearest_3d_zero_dim_cuda, test/test_nn.py::TestNN::test_interpolate_nearest_scale_1d, test/test_nn.py::TestNN::test_interpolate_nearest_scale_1d_cuda, test/test_nn.py::TestNN::test_interpolate_nearest_scale_2d, test/test_nn.py::TestNN::test_interpolate_nearest_scale_2d_cuda, test/test_nn.py::TestNN::test_interpolate_nearest_scale_3d, test/test_nn.py::TestNN::test_interpolate_nearest_scale_3d_cuda, test/test_nn.py::TestNN::test_interpolate_nearest_tuple_1d, test/test_nn.py::TestNN::test_interpolate_nearest_tuple_1d_cuda, test/test_nn.py::TestNN::test_interpolate_nearest_tuple_2d, test/test_nn.py::TestNN::test_interpolate_nearest_tuple_2d_cuda, test/test_nn.py::TestNN::test_interpolate_nearest_tuple_3d, test/test_nn.py::TestNN::test_interpolate_nearest_tuple_3d_cuda, test/test_nn.py::TestNN::test_interpolate_trilinear_3d, test/test_nn.py::TestNN::test_interpolate_trilinear_3d_cuda, test/test_nn.py::TestNN::test_interpolate_trilinear_3d_zero_dim, test/test_nn.py::TestNN::test_interpolate_trilinear_3d_zero_dim_cuda, test/test_nn.py::TestNN::test_interpolate_trilinear_scale_3d, test/test_nn.py::TestNN::test_interpolate_trilinear_scale_3d_align_corners, test/test_nn.py::TestNN::test_interpolate_trilinear_scale_3d_align_corners_cuda, test/test_nn.py::TestNN::test_interpolate_trilinear_scale_3d_cuda, test/test_nn.py::TestNN::test_interpolate_trilinear_tuple_3d, test/test_nn.py::TestNN::test_interpolate_trilinear_tuple_3d_align_corners, test/test_nn.py::TestNN::test_interpolate_trilinear_tuple_3d_align_corners_cuda, test/test_nn.py::TestNN::test_interpolate_trilinear_tuple_3d_cuda, test/test_nn.py::TestNN::test_interpolate_undefined_behavior_casting, test/test_nn.py::TestNN::test_kl_div_log_softmax_target, test/test_nn.py::TestNN::test_kl_div_with_diff_type, test/test_nn.py::TestNN::test_kl_div_with_diff_type_log_target, test/test_nn.py::TestNN::test_l1_loss_correct, test/test_nn.py::TestNN::test_layer_norm_backwards_eps, test/test_nn.py::TestNN::test_layer_norm_eps, test/test_nn.py::TestNN::test_layer_norm_grads_with_create_graph_flag, test/test_nn.py::TestNN::test_layer_norm_large_tensor, test/test_nn.py::TestNN::test_linear_autograd_device_cpu_bias_weightCOO, test/test_nn.py::TestNN::test_linear_autograd_device_cpu_bias_weightCSC, test/test_nn.py::TestNN::test_linear_autograd_device_cpu_bias_weightCSR, test/test_nn.py::TestNN::test_linear_autograd_device_cpu_bias_weightStrided, test/test_nn.py::TestNN::test_linear_autograd_device_cpu_nobias_weightCOO, test/test_nn.py::TestNN::test_linear_autograd_device_cpu_nobias_weightCSC, test/test_nn.py::TestNN::test_linear_autograd_device_cpu_nobias_weightCSR, test/test_nn.py::TestNN::test_linear_autograd_device_cpu_nobias_weightStrided, test/test_nn.py::TestNN::test_linear_broadcasting, test/test_nn.py::TestNN::test_linear_raise_on_scalar_input, test/test_nn.py::TestNN::test_log_softmax_dim0, test/test_nn.py::TestNN::test_log_softmax_dim0_cuda, test/test_nn.py::TestNN::test_log_softmax_dim3, test/test_nn.py::TestNN::test_log_softmax_dim3_cuda, test/test_nn.py::TestNN::test_log_softmax_lastdim, test/test_nn.py::TestNN::test_log_softmax_lastdim_cuda, test/test_nn.py::TestNN::test_log_softmax_scalar, test/test_nn.py::TestNN::test_log_softmax_scalar_cuda, test/test_nn.py::TestNN::test_log_softmax_spatial, test/test_nn.py::TestNN::test_log_softmax_spatial_cuda, test/test_nn.py::TestNN::test_log_softmax_spatial_special, test/test_nn.py::TestNN::test_log_softmax_spatial_special_cuda, test/test_nn.py::TestNN::test_loss_equal_input_target_shape, test/test_nn.py::TestNN::test_margin_ranking_loss_margin_no_reduce, test/test_nn.py::TestNN::test_margin_ranking_loss_no_reduce, test/test_nn.py::TestNN::test_max_pool1d_invalid_output_size, test/test_nn.py::TestNN::test_module_apply_inplace_op, test/test_nn.py::TestNN::test_module_backcompat, test/test_nn.py::TestNN::test_module_super_init, test/test_nn.py::TestNN::test_module_to_argparse, test/test_nn.py::TestNN::test_modules, test/test_nn.py::TestNN::test_mse_loss_size_warning, test/test_nn.py::TestNN::test_multimarginloss_1d_input_0d_target_no_reduce, test/test_nn.py::TestNN::test_multimarginloss_1d_input_0d_target_no_reduce_cuda, test/test_nn.py::TestNN::test_named_children, test/test_nn.py::TestNN::test_named_modules, test/test_nn.py::TestNN::test_named_parameters_remove_duplicate, test/test_nn.py::TestNN::test_native_channel_shuffle_return_alias_of_self, test/test_nn.py::TestNN::test_nested_tensor_from_mask, test/test_nn.py::TestNN::test_nested_tensor_from_mask_error, test/test_nn.py::TestNN::test_no_grad, test/test_nn.py::TestNN::test_non_leaf_parameters, test/test_nn.py::TestNN::test_normalize, test/test_nn.py::TestNN::test_overwrite_module_params_on_conversion, test/test_nn.py::TestNN::test_pack_sequence_batch_sizes_throw, test/test_nn.py::TestNN::test_pad_scalar_error, test/test_nn.py::TestNN::test_padding_list, test/test_nn.py::TestNN::test_pairwise_distance, test/test_nn.py::TestNN::test_parameter_assignment, test/test_nn.py::TestNN::test_parameterlistdict_pickle, test/test_nn.py::TestNN::test_parameterlistdict_setting_attributes, test/test_nn.py::TestNN::test_parameters_and_named_parameters, test/test_nn.py::TestNN::test_parameters_to_vector, test/test_nn.py::TestNN::test_parse_to, test/test_nn.py::TestNN::test_partial_flat_weights, test/test_nn.py::TestNN::test_pdist, test/test_nn.py::TestNN::test_pdist_cpu_gradgrad_unimplemented, test/test_nn.py::TestNN::test_pdist_cuda_gradgrad_unimplemented, test/test_nn.py::TestNN::test_pdist_empty_col, test/test_nn.py::TestNN::test_pdist_empty_row, test/test_nn.py::TestNN::test_pdist_large, test/test_nn.py::TestNN::test_pdist_zeros, test/test_nn.py::TestNN::test_pickle_module_no_weights_only_warning, test/test_nn.py::TestNN::test_pixel_shuffle_nhwc_cpu, test/test_nn.py::TestNN::test_pixel_shuffle_unshuffle, test/test_nn.py::TestNN::test_pointwise_loss_broadcast, test/test_nn.py::TestNN::test_pointwise_loss_target_grad_none_reduction, test/test_nn.py::TestNN::test_projections_errors_on_gru_and_rnn, test/test_nn.py::TestNN::test_projections_lstm_args_check, test/test_nn.py::TestNN::test_projections_lstm_check_device, test/test_nn.py::TestNN::test_projections_lstm_initial_hidden_state, test/test_nn.py::TestNN::test_register_buffer_allows_overwriting_with_same_name, test/test_nn.py::TestNN::test_register_buffer_allows_tensor_like_object, test/test_nn.py::TestNN::test_register_buffer_raises_error_if_attr_exists, test/test_nn.py::TestNN::test_register_buffer_raises_error_if_name_is_not_string, test/test_nn.py::TestNN::test_register_buffer_raises_error_if_not_tensor, test/test_nn.py::TestNN::test_register_parameter_allows_overwriting_with_same_name, test/test_nn.py::TestNN::test_register_parameter_raises_error_if_attr_exists, test/test_nn.py::TestNN::test_register_parameter_raises_error_if_name_is_not_string, test/test_nn.py::TestNN::test_relu_inplace_on_view, test/test_nn.py::TestNN::test_repr, test/test_nn.py::TestNN::test_requires_grad_, test/test_nn.py::TestNN::test_rnn_args_check, test/test_nn.py::TestNN::test_rnn_check_device, test/test_nn.py::TestNN::test_rnn_initial_hidden_state, test/test_nn.py::TestNN::test_rnn_weight_norm, test/test_nn.py::TestNN::test_set_submodule, test/test_nn.py::TestNN::test_share_memory, test/test_nn.py::TestNN::test_smoothl1loss_intergral_target, test/test_nn.py::TestNN::test_smoothl1loss_negative_beta_not_supported, test/test_nn.py::TestNN::test_softmax_functional_dim0, test/test_nn.py::TestNN::test_softmax_functional_dim0_cuda, test/test_nn.py::TestNN::test_softmax_functional_dim3, test/test_nn.py::TestNN::test_softmax_functional_dim3_cuda, test/test_nn.py::TestNN::test_softmax_functional_scalar, test/test_nn.py::TestNN::test_softmax_functional_scalar_cuda, test/test_nn.py::TestNN::test_softmax_lastdim, test/test_nn.py::TestNN::test_softmax_lastdim_cuda, test/test_nn.py::TestNN::test_softmax_lastdim_dtype, test/test_nn.py::TestNN::test_softmax_lastdim_dtype_cuda, test/test_nn.py::TestNN::test_softmax_spatial, test/test_nn.py::TestNN::test_softmax_spatial_cuda, test/test_nn.py::TestNN::test_softmax_spatial_dtype, test/test_nn.py::TestNN::test_softmax_spatial_dtype_cuda, test/test_nn.py::TestNN::test_softmax_spatial_special, test/test_nn.py::TestNN::test_softmax_spatial_special_cuda, test/test_nn.py::TestNN::test_softmin, test/test_nn.py::TestNN::test_spectral_norm, test/test_nn.py::TestNN::test_spectral_norm_dim, test/test_nn.py::TestNN::test_spectral_norm_forward, test/test_nn.py::TestNN::test_spectral_norm_load_state_dict, test/test_nn.py::TestNN::test_spectral_norm_pickle, test/test_nn.py::TestNN::test_state_dict, test/test_nn.py::TestNN::test_swap_module_params_poisons_acc_grad, test/test_nn.py::TestNN::test_sync_batchnorm_accuracy_cuda, test/test_nn.py::TestNN::test_sync_batchnorm_backward_elemt, test/test_nn.py::TestNN::test_threshold_bfloat16_half, test/test_nn.py::TestNN::test_threshold_int, test/test_nn.py::TestNN::test_to, test/test_nn.py::TestNN::test_train_errors_for_invalid_mode, test/test_nn.py::TestNN::test_transformer_args_check, test/test_nn.py::TestNN::test_transformer_layer_args_check, test/test_nn.py::TestNN::test_transformerdecoder, test/test_nn.py::TestNN::test_transformerdecoderlayer, test/test_nn.py::TestNN::test_transformerdecoderlayer_gelu, test/test_nn.py::TestNN::test_triplet_margin_loss, test/test_nn.py::TestNN::test_triplet_margin_loss_no_reduce, test/test_nn.py::TestNN::test_triplet_margin_loss_swap, test/test_nn.py::TestNN::test_triplet_margin_loss_swap_no_reduce, test/test_nn.py::TestNN::test_type, test/test_nn.py::TestNN::test_unflatten, test/test_nn.py::TestNN::test_unflatten_invalid_arg, test/test_nn.py::TestNN::test_unfold_invalid_arg, test/test_nn.py::TestNN::test_upsamplingBilinear2d_spatial_invariance, test/test_nn.py::TestNN::test_upsamplingLinear1d, test/test_nn.py::TestNN::test_upsamplingLinear1d_spatial_invariance, test/test_nn.py::TestNN::test_upsamplingTrilinear3d_spatial_invariance, test/test_nn.py::TestNN::test_upsampling_bfloat16, test/test_nn.py::TestNN::test_upsampling_not_recompute_scale_factor, test/test_nn.py::TestNN::test_upsampling_small_scale, test/test_nn.py::TestNN::test_vector_to_parameters, test/test_nn.py::TestNN::test_weight_norm, test/test_nn.py::TestNN::test_weight_norm_pickle, test/test_nn.py::TestNN::test_weighted_huber_loss, test/test_nn.py::TestNN::test_weighted_l1_loss_with_weights, test/test_nn.py::TestNN::test_weighted_mse_loss, test/test_nn.py::TestNN::test_zero_grad, test/test_nn.py::TestFusionEval::test_fuse_module_eval_numerics, test/test_nn.py::TestConstantPadNd::test_constant_pad_nd, test/test_nn.py::TestConstantPadNd::test_preserves_memory_format, test/test_nn.py::TestAddRelu::test_add_relu, test/test_nn.py::TestAddRelu::test_add_relu_broadcasting, test/test_nn.py::TestFunctionalPickle::test_pickle_softsign, test/test_nn.py::TestFusionUtils::test_fuse_conv_bn_requires_grad, test/test_nn.py::TestFusionUtils::test_fuse_linear_bn_requires_grad, test/test_nn.py::TestUtils::test_consume_prefix_in_state_dict_if_present, test/test_nn.py::TestNNDeviceTypeCPU::test_BatchNorm_empty_cpu_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_False_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_CTCLoss_no_batch_dim_reduction_mean_use_module_form_True_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_CTCLoss_no_batch_dim_reduction_none_use_module_form_False_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_CTCLoss_no_batch_dim_reduction_none_use_module_form_True_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_CTCLoss_no_batch_dim_reduction_sum_use_module_form_False_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_CTCLoss_no_batch_dim_reduction_sum_use_module_form_True_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_GRU_grad_and_gradgrad_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_GroupNorm_empty_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_GroupNorm_general_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_GroupNorm_memory_format_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_GroupNorm_numeric_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_GroupNorm_raises_error_if_one_value_per_group_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_InstanceNorm1d_general_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_InstanceNorm2d_general_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_InstanceNorm3d_general_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_LSTM_differentiable_backward_using_oneDNN_cpu_bfloat16, test/test_nn.py::TestNNDeviceTypeCPU::test_LSTM_differentiable_backward_using_oneDNN_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_LSTM_grad_and_gradgrad_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_LayerNorm_general_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_LayerNorm_numeric_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_LocalResponseNorm_empty_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_MarginLoss_empty_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_MarginLoss_empty_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_MarginLoss_race_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_MarginLoss_race_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_MarginLoss_warnings_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_ReflectionPad2d_large_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_ReflectionPad2d_large_deterministic_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_ReflectionPad3d_large_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_ReflectionPad_empty_cpu_complex64, test/test_nn.py::TestNNDeviceTypeCPU::test_ReflectionPad_empty_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_ReflectionPad_fails_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_ReplicationPad1d_large_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_ReplicationPad2d_large_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_ReplicationPad3d_large_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_ReplicationPad_empty_cpu_complex128, test/test_nn.py::TestNNDeviceTypeCPU::test_ReplicationPad_empty_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_TransformerDecoderLayer_empty_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_TransformerDecoder_empty_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_TransformerEncoderLayer_empty_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_TransformerEncoder_empty_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_Transformer_empty_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_Unfold_empty_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_activations_bfloat16_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_activations_bfloat16_half_cpu_cpu_bfloat16, test/test_nn.py::TestNNDeviceTypeCPU::test_activations_bfloat16_half_cpu_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_adaptiveavg_pool1d_shmem_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_affine_2d_rotate0_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_affine_2d_rotate45_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_affine_2d_rotate90_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_affine_2d_rotateRandom_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_affine_3d_rotateRandom_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_avg_pool_large_tensor2_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_avg_pool_large_tensor_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_batchnorm_affine_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_batchnorm_affine_mixed_cpu_bfloat16, test/test_nn.py::TestNNDeviceTypeCPU::test_batchnorm_affine_mixed_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_batchnorm_eval_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_batchnorm_eval_mixed_cpu_bfloat16, test/test_nn.py::TestNNDeviceTypeCPU::test_batchnorm_eval_mixed_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_batchnorm_grad_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_batchnorm_large_batch_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_batchnorm_large_batch_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_batchnorm_simple_average_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_batchnorm_simple_average_mixed_cpu_bfloat16, test/test_nn.py::TestNNDeviceTypeCPU::test_batchnorm_simple_average_mixed_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_batchnorm_update_stats_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_channel_shuffle_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_clip_grad_norm_error_if_nonfinite_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_clip_grad_norm_foreach_False_norm_type_0_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_clip_grad_norm_foreach_False_norm_type_1_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_clip_grad_norm_foreach_False_norm_type_2_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_clip_grad_norm_foreach_False_norm_type_4_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_clip_grad_norm_foreach_False_norm_type_inf_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_clip_grad_norm_foreach_True_norm_type_0_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_clip_grad_norm_foreach_True_norm_type_1_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_clip_grad_norm_foreach_True_norm_type_2_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_clip_grad_norm_foreach_True_norm_type_4_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_clip_grad_norm_foreach_True_norm_type_inf_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_clip_grad_norm_multi_device_foreach_False_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_clip_grad_norm_multi_device_foreach_True_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_clip_grad_value_foreach_False_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_clip_grad_value_foreach_True_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_conv_empty_input_cpu_bfloat16, test/test_nn.py::TestNNDeviceTypeCPU::test_conv_empty_input_cpu_complex128, test/test_nn.py::TestNNDeviceTypeCPU::test_conv_empty_input_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_conv_empty_input_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_64bit_reduction_mean_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_64bit_reduction_none_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_64bit_reduction_sum_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_label_smoothing_consistent_index_target_and_probs_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_label_smoothing_errors_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_label_smoothing_weight_ignore_indices_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_label_smoothing_with_probs_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_large_tensor_reduction_mean_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_large_tensor_reduction_none_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_large_tensor_reduction_sum_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_loss_2d_out_of_bounds_class_index_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_loss_2d_out_of_bounds_class_index_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_loss_index_target_unit_weights_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_loss_one_hot_target_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_loss_prob_target_all_reductions_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_loss_prob_target_no_batch_dim_reduction_mean_weighted_False_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_loss_prob_target_no_batch_dim_reduction_mean_weighted_True_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_loss_prob_target_no_batch_dim_reduction_none_weighted_False_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_loss_prob_target_no_batch_dim_reduction_none_weighted_True_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_loss_prob_target_no_batch_dim_reduction_sum_weighted_False_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_loss_prob_target_no_batch_dim_reduction_sum_weighted_True_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_loss_prob_target_unit_weights_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_ctc_loss_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_ctc_loss_cudnn_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_ctc_loss_cudnn_tensor_cpu_length_cuda_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_ctc_loss_cudnn_tensor_cuda_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_ctc_loss_error_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_device_mask_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_elu_inplace_overlap_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_elu_inplace_with_neg_alpha_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_fold_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_glu_bfloat16_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_grid_sample_bfloat16_precision_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_grid_sample_half_precision_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_grid_sample_large_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_grid_sample_large_index_2d_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_grid_sample_large_index_2d_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_grid_sample_large_index_3d_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_grid_sample_large_index_3d_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_grid_sample_nan_inf_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_grid_sample_nan_inf_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_groupnorm_nhwc_cpu_bfloat16, test/test_nn.py::TestNNDeviceTypeCPU::test_groupnorm_nhwc_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_groupnorm_nhwc_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_groupnorm_nhwc_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_gumbel_softmax_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_gumbel_softmax_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_hardsigmoid_grad_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_hardswish_grad_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_hardswish_inplace_overlap_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_instancenorm_raises_error_for_single_spatial_element_during_training_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_instancenorm_raises_error_if_input_channels_is_not_num_features_InstanceNorm1d_no_batch_dim_False_affine_False_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_instancenorm_raises_error_if_input_channels_is_not_num_features_InstanceNorm1d_no_batch_dim_False_affine_True_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_instancenorm_raises_error_if_input_channels_is_not_num_features_InstanceNorm1d_no_batch_dim_True_affine_False_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_instancenorm_raises_error_if_input_channels_is_not_num_features_InstanceNorm1d_no_batch_dim_True_affine_True_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_instancenorm_raises_error_if_input_channels_is_not_num_features_InstanceNorm2d_no_batch_dim_False_affine_False_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_instancenorm_raises_error_if_input_channels_is_not_num_features_InstanceNorm2d_no_batch_dim_False_affine_True_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_instancenorm_raises_error_if_input_channels_is_not_num_features_InstanceNorm2d_no_batch_dim_True_affine_False_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_instancenorm_raises_error_if_input_channels_is_not_num_features_InstanceNorm2d_no_batch_dim_True_affine_True_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_instancenorm_raises_error_if_input_channels_is_not_num_features_InstanceNorm3d_no_batch_dim_False_affine_False_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_instancenorm_raises_error_if_input_channels_is_not_num_features_InstanceNorm3d_no_batch_dim_False_affine_True_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_instancenorm_raises_error_if_input_channels_is_not_num_features_InstanceNorm3d_no_batch_dim_True_affine_False_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_instancenorm_raises_error_if_input_channels_is_not_num_features_InstanceNorm3d_no_batch_dim_True_affine_True_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_instancenorm_raises_error_if_less_than_one_value_per_channel_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_invalid_reduction_strings_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_layernorm_half_precision_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_layernorm_weight_bias_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_leaky_relu_inplace_overlap_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_leaky_relu_inplace_with_neg_slope_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_leaky_relu_inplace_with_zero_slope_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_linear_empty_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_log_softmax_big_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_log_softmax_cpu_cpu_bfloat16, test/test_nn.py::TestNNDeviceTypeCPU::test_log_softmax_cpu_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_logsigmoid_out_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_lstmcell_backward_only_one_output_grad_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_masked_softmax_TxT_layout_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_masked_softmax_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_masked_softmax_devices_parity_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_masked_softmax_forward_with_nans_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_masked_softmax_grad_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_masked_softmax_lowp_cpu_bfloat16, test/test_nn.py::TestNNDeviceTypeCPU::test_masked_softmax_lowp_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_masked_softmax_mask_types_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_masked_softmax_transformer_layout_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_mish_inplace_overlap_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_module_to_empty_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_module_to_empty_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_module_to_empty_non_recursive_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_mse_loss_error_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_nll_loss_1d_input_1d_target_invalid_size_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_nll_loss_all_ignored_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_nll_loss_byte_target_matches_long_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_nll_loss_empty_tensor_reduction_mean_cpu_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_invalid_weights_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_nll_loss_large_tensor_reduction_mean_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_nll_loss_large_tensor_reduction_none_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_nll_loss_large_tensor_reduction_sum_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_nll_loss_mismatched_batch_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_nll_loss_out_of_bounds_ignore_index_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_nll_loss_total_weight_is_zero_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_nn_empty_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_nn_scalars_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_nn_scalars_reductions_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_nonlinearity_propagate_nan_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_one_hot_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_overwrite_module_params_on_conversion_cpu_device_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_pad_cpu_complex128, test/test_nn.py::TestNNDeviceTypeCPU::test_pad_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_prelu_backward_32bit_indexing_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_replicatepad_64bit_indexing_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_rmsnorm_epsilon_cpu_bfloat16, test/test_nn.py::TestNNDeviceTypeCPU::test_rmsnorm_epsilon_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_rmsnorm_epsilon_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_rmsnorm_epsilon_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_rmsnorm_numeric_cpu_bfloat16, test/test_nn.py::TestNNDeviceTypeCPU::test_rmsnorm_numeric_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_rnn_fused_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_rnn_fused_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_rnn_retain_variables_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_save_lstm_compatibility_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_silu_inplace_overlap_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_skip_init_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_smooth_l1_loss_bfloat16_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_smooth_l1_loss_vs_huber_loss_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_smoothl1loss_backward_zero_beta_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_softmax_64bit_indexing_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_softmax_backward_64bit_indexing_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_softmax_backward_smem_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_softmax_backward_unaligned_grad_output_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_softmax_backward_unaligned_output_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_softmax_backward_without_fully_vectorized_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_softmax_bfloat16_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_softmax_cpu_cpu_bfloat16, test/test_nn.py::TestNNDeviceTypeCPU::test_softmax_cpu_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_softmax_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_softmax_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_softmax_double_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_softmax_forward_64bit_indexing_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_softmax_results_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_softplus_inplace_overlap_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_softplus_low_threshold_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_softshrink_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_softshrink_inplace_overlap_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_softshrink_negative_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_threshold_inplace_overlap_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_to_complex_cpu_complex128, test/test_nn.py::TestNNDeviceTypeCPU::test_to_complex_cpu_complex64, test/test_nn.py::TestNNDeviceTypeCPU::test_to_complex_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_transformerencoderlayer_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_transformerencoderlayer_fast_path_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_transformerencoderlayer_gelu_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_triplet_margin_with_distance_loss_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_triplet_margin_with_distance_loss_default_parity_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiLinear2d_consistency_interp_size_bug_memory_format0_align_corners_False_input_size_399_output_size_437_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiLinear2d_consistency_interp_size_bug_memory_format0_align_corners_False_input_size_403_output_size_377_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiLinear2d_consistency_interp_size_bug_memory_format0_align_corners_True_input_size_399_output_size_437_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiLinear2d_consistency_interp_size_bug_memory_format0_align_corners_True_input_size_403_output_size_377_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiLinear2d_consistency_interp_size_bug_memory_format1_align_corners_False_input_size_399_output_size_437_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiLinear2d_consistency_interp_size_bug_memory_format1_align_corners_False_input_size_403_output_size_377_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiLinear2d_consistency_interp_size_bug_memory_format1_align_corners_True_input_size_399_output_size_437_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiLinear2d_consistency_interp_size_bug_memory_format1_align_corners_True_input_size_403_output_size_377_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_antialias_False_align_corners_False_mode_bicubic_memory_format0_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_antialias_False_align_corners_False_mode_bicubic_memory_format1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_antialias_False_align_corners_False_mode_bilinear_memory_format0_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_antialias_False_align_corners_False_mode_bilinear_memory_format1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_antialias_False_align_corners_True_mode_bicubic_memory_format0_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_antialias_False_align_corners_True_mode_bicubic_memory_format1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_antialias_False_align_corners_True_mode_bilinear_memory_format0_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_antialias_False_align_corners_True_mode_bilinear_memory_format1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_antialias_True_align_corners_False_mode_bicubic_memory_format0_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_antialias_True_align_corners_False_mode_bicubic_memory_format1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_antialias_True_align_corners_False_mode_bilinear_memory_format0_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_antialias_True_align_corners_False_mode_bilinear_memory_format1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_antialias_True_align_corners_True_mode_bicubic_memory_format0_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_antialias_True_align_corners_True_mode_bicubic_memory_format1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_antialias_True_align_corners_True_mode_bilinear_memory_format0_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_antialias_True_align_corners_True_mode_bilinear_memory_format1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_bicubic_float32_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_bicubic_float64_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_bicubic_int16_cpu_int16, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_bicubic_int32_cpu_int32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_bicubic_int64_cpu_int64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_bicubic_int8_cpu_int8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_bicubic_uint8_cpu_uint8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_bilinear_float32_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_bilinear_float64_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_bilinear_int16_cpu_int16, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_bilinear_int32_cpu_int32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_bilinear_int64_cpu_int64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_bilinear_int8_cpu_int8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_bilinear_uint8_cpu_uint8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_nearest-exact_float32_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_nearest-exact_float64_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_nearest-exact_int16_cpu_int16, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_nearest-exact_int32_cpu_int32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_nearest-exact_int64_cpu_int64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_nearest-exact_int8_cpu_int8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_nearest-exact_uint8_cpu_uint8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_nearest_float32_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_nearest_float64_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_nearest_int16_cpu_int16, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_nearest_int32_cpu_int32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_nearest_int64_cpu_int64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_nearest_int8_cpu_int8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_nearest_uint8_cpu_uint8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_bicubic_float32_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_bicubic_float64_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_bicubic_int16_cpu_int16, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_bicubic_int32_cpu_int32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_bicubic_int64_cpu_int64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_bicubic_int8_cpu_int8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_bicubic_uint8_cpu_uint8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_bilinear_float32_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_bilinear_float64_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_bilinear_int16_cpu_int16, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_bilinear_int32_cpu_int32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_bilinear_int64_cpu_int64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_bilinear_int8_cpu_int8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_bilinear_uint8_cpu_uint8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_nearest-exact_float32_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_nearest-exact_float64_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_nearest-exact_int16_cpu_int16, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_nearest-exact_int32_cpu_int32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_nearest-exact_int64_cpu_int64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_nearest-exact_int8_cpu_int8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_nearest-exact_uint8_cpu_uint8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_nearest_float32_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_nearest_float64_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_nearest_int16_cpu_int16, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_nearest_int32_cpu_int32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_nearest_int64_cpu_int64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_nearest_int8_cpu_int8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_nearest_uint8_cpu_uint8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_bicubic_float32_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_bicubic_float64_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_bicubic_int16_cpu_int16, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_bicubic_int32_cpu_int32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_bicubic_int64_cpu_int64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_bicubic_int8_cpu_int8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_bicubic_uint8_cpu_uint8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_bilinear_float32_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_bilinear_float64_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_bilinear_int16_cpu_int16, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_bilinear_int32_cpu_int32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_bilinear_int64_cpu_int64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_bilinear_int8_cpu_int8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_bilinear_uint8_cpu_uint8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_nearest-exact_float32_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_nearest-exact_float64_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_nearest-exact_int16_cpu_int16, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_nearest-exact_int32_cpu_int32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_nearest-exact_int64_cpu_int64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_nearest-exact_int8_cpu_int8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_nearest-exact_uint8_cpu_uint8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_nearest_float32_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_nearest_float64_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_nearest_int16_cpu_int16, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_nearest_int32_cpu_int32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_nearest_int64_cpu_int64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_nearest_int8_cpu_int8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_nearest_uint8_cpu_uint8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_bicubic_float32_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_bicubic_float64_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_bicubic_int16_cpu_int16, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_bicubic_int32_cpu_int32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_bicubic_int64_cpu_int64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_bicubic_int8_cpu_int8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_bicubic_uint8_cpu_uint8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_bilinear_float32_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_bilinear_float64_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_bilinear_int16_cpu_int16, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_bilinear_int32_cpu_int32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_bilinear_int64_cpu_int64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_bilinear_int8_cpu_int8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_bilinear_uint8_cpu_uint8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_nearest-exact_float32_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_nearest-exact_float64_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_nearest-exact_int16_cpu_int16, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_nearest-exact_int32_cpu_int32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_nearest-exact_int64_cpu_int64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_nearest-exact_int8_cpu_int8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_nearest-exact_uint8_cpu_uint8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_nearest_float32_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_nearest_float64_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_nearest_int16_cpu_int16, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_nearest_int32_cpu_int32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_nearest_int64_cpu_int64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_nearest_int8_cpu_int8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_nearest_uint8_cpu_uint8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBicubic2d_aa_correctness_memory_format0_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBicubic2d_aa_correctness_memory_format1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBicubic2d_correctness_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBilinear2d_aa_correctness_memory_format0_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBilinear2d_aa_correctness_memory_format1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest1d_correctness_isize_10_osize_15_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest1d_correctness_isize_20_osize_11_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest1d_launch_config_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest1d_mode_nearest-exact_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest1d_mode_nearest_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest2d_correctness_memory_format0_isize_10_osize_15_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest2d_correctness_memory_format0_isize_20_osize_11_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest2d_correctness_memory_format1_isize_10_osize_15_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest2d_correctness_memory_format1_isize_20_osize_11_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest2d_launch_config_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest2d_launch_fail_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest2d_launch_rocm_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest2d_memory_format0_mode_nearest-exact_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest2d_memory_format0_mode_nearest_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest2d_memory_format1_mode_nearest-exact_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest2d_memory_format1_mode_nearest_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest3d_correctness_memory_format0_isize_10_osize_15_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest3d_correctness_memory_format0_isize_20_osize_11_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest3d_correctness_memory_format1_isize_10_osize_15_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest3d_correctness_memory_format1_isize_20_osize_11_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest3d_launch_config_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest3d_memory_format0_mode_nearest-exact_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest3d_memory_format0_mode_nearest_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest3d_memory_format1_mode_nearest-exact_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest3d_memory_format1_mode_nearest_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearestExact1d_correctness_isize_10_osize_15_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearestExact1d_correctness_isize_20_osize_11_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearestExact1d_rescale_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearestExact2d_correctness_memory_format0_isize_10_osize_15_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearestExact2d_correctness_memory_format0_isize_20_osize_11_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearestExact2d_correctness_memory_format1_isize_10_osize_15_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearestExact2d_correctness_memory_format1_isize_20_osize_11_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearestExact3d_correctness_memory_format0_isize_10_osize_15_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearestExact3d_correctness_memory_format0_isize_20_osize_11_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearestExact3d_correctness_memory_format1_isize_10_osize_15_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearestExact3d_correctness_memory_format1_isize_20_osize_11_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingTrilinear3d_align_corners_False_memory_format0_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingTrilinear3d_align_corners_False_memory_format1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingTrilinear3d_align_corners_True_memory_format0_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingTrilinear3d_align_corners_True_memory_format1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsampling_64bit_indexing_channels_last_cpu_bfloat16, test/test_nn.py::TestNNDeviceTypeCPU::test_upsampling_64bit_indexing_channels_last_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingnearest2d_backward_64bit_indexing_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_variable_sequence_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_warp_softmax_64bit_indexing_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_warp_softmax_64bit_indexing_cpu_float32 2025-09-07T06:38:21.7298108Z 2025-09-07T06:38:21.7298307Z Running test_overrides 1/1 ... [2025-09-07 06:38:21.495194] 2025-09-07T06:38:21.7298730Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T06:38:21.7299813Z 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'] ... [2025-09-07 06:38:21.495541] 2025-09-07T06:38:28.1687847Z 2025-09-07T06:38:28.1688826Z test_overrides 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_overrides_1.1_6a9d2214092ec262_.log 2025-09-07T06:38:28.2187524Z Running 1478 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_floor_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_floor_divide, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_floor_divide_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_fmax, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_fmin, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_fmod, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_fmod_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_frac, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_frac_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_frexp, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_gather, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_gcd, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_gcd_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_ge, 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test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_igammac, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_igammac_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_index_add, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_index_add_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_index_copy, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_index_copy_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_index_fill, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_index_fill_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_index_put, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_index_put_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_index_reduce, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_index_reduce_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_index_select, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_indices, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_inner, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_int, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_int_repr, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_inverse, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_ipu, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_is_coalesced, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_is_complex, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_is_conj, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_is_contiguous, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_is_distributed, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_is_floating_point, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_is_inference, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_is_neg, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_is_nonzero, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_is_pinned, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_is_same_size, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_is_set_to, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_is_shared, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_is_signed, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_isclose, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_isfinite, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_isinf, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_isnan, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_isneginf, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_isposinf, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_isreal, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_istft, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_item, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_kron, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_kthvalue, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_lcm, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_lcm_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_ldexp, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_ldexp_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_le, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_le_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_lerp, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_lerp_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_less, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_less_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_less_equal, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_less_equal_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_lgamma, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_lgamma_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_log, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_log10, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_log10_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_log1p, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_log1p_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_log2, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_log2_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_log_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_log_normal_, 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, 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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, 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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__fused_rms_norm, 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_function_empty_lists, test/test_overrides.py::TestTorchFunctionOverride::test_torch_function_in_float_lists, test/test_overrides.py::TestTorchFunctionOverride::test_torch_function_in_lists, test/test_overrides.py::TestTorchFunctionOverride::test_torch_function_in_scalar_lists, test/test_overrides.py::TestTorchFunctionOverride::test_torch_function_mixed_lists, test/test_overrides.py::TestTorchFunctionOverride::test_torch_function_nested_tuple_getitem, test/test_overrides.py::TestTorchFunctionOverride::test_torch_function_not_first_in_list, test/test_overrides.py::TestTorchFunctionOverride::test_torch_function_precedence_in_lists, 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_hash_tensor, test/test_overrides.py::TestTorchFunctionOverride::test_torch_heaviside, test/test_overrides.py::TestTorchFunctionOverride::test_torch_hinge_embedding_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_histc, test/test_overrides.py::TestTorchFunctionOverride::test_torch_histogram, test/test_overrides.py::TestTorchFunctionOverride::test_torch_histogramdd, test/test_overrides.py::TestTorchFunctionOverride::test_torch_hsmm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_hsplit, test/test_overrides.py::TestTorchFunctionOverride::test_torch_hstack, test/test_overrides.py::TestTorchFunctionOverride::test_torch_hypot, test/test_overrides.py::TestTorchFunctionOverride::test_torch_i0, test/test_overrides.py::TestTorchFunctionOverride::test_torch_igamma, test/test_overrides.py::TestTorchFunctionOverride::test_torch_igammac, test/test_overrides.py::TestTorchFunctionOverride::test_torch_imag, test/test_overrides.py::TestTorchFunctionOverride::test_torch_index_add, test/test_overrides.py::TestTorchFunctionOverride::test_torch_index_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_index_fill, test/test_overrides.py::TestTorchFunctionOverride::test_torch_index_put, test/test_overrides.py::TestTorchFunctionOverride::test_torch_index_reduce, test/test_overrides.py::TestTorchFunctionOverride::test_torch_index_select, test/test_overrides.py::TestTorchFunctionOverride::test_torch_indices_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_inner, test/test_overrides.py::TestTorchFunctionOverride::test_torch_instance_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_int_repr, test/test_overrides.py::TestTorchFunctionOverride::test_torch_inverse, test/test_overrides.py::TestTorchFunctionOverride::test_torch_is_complex, test/test_overrides.py::TestTorchFunctionOverride::test_torch_is_conj, test/test_overrides.py::TestTorchFunctionOverride::test_torch_is_distributed, test/test_overrides.py::TestTorchFunctionOverride::test_torch_is_floating_point, test/test_overrides.py::TestTorchFunctionOverride::test_torch_is_inference, test/test_overrides.py::TestTorchFunctionOverride::test_torch_is_neg, test/test_overrides.py::TestTorchFunctionOverride::test_torch_is_nonzero, test/test_overrides.py::TestTorchFunctionOverride::test_torch_is_same_size, test/test_overrides.py::TestTorchFunctionOverride::test_torch_is_signed, test/test_overrides.py::TestTorchFunctionOverride::test_torch_isclose, test/test_overrides.py::TestTorchFunctionOverride::test_torch_isfinite, test/test_overrides.py::TestTorchFunctionOverride::test_torch_isin, test/test_overrides.py::TestTorchFunctionOverride::test_torch_isinf, test/test_overrides.py::TestTorchFunctionOverride::test_torch_isnan, test/test_overrides.py::TestTorchFunctionOverride::test_torch_isneginf, test/test_overrides.py::TestTorchFunctionOverride::test_torch_isposinf, test/test_overrides.py::TestTorchFunctionOverride::test_torch_isreal, test/test_overrides.py::TestTorchFunctionOverride::test_torch_istft, test/test_overrides.py::TestTorchFunctionOverride::test_torch_kl_div, test/test_overrides.py::TestTorchFunctionOverride::test_torch_kron, test/test_overrides.py::TestTorchFunctionOverride::test_torch_kthvalue, test/test_overrides.py::TestTorchFunctionOverride::test_torch_layer_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_lcm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_ldexp, test/test_overrides.py::TestTorchFunctionOverride::test_torch_le, test/test_overrides.py::TestTorchFunctionOverride::test_torch_lerp, test/test_overrides.py::TestTorchFunctionOverride::test_torch_less, test/test_overrides.py::TestTorchFunctionOverride::test_torch_less_equal, test/test_overrides.py::TestTorchFunctionOverride::test_torch_lgamma, test/test_overrides.py::TestTorchFunctionOverride::test_torch_log, test/test_overrides.py::TestTorchFunctionOverride::test_torch_log10, test/test_overrides.py::TestTorchFunctionOverride::test_torch_log1p, test/test_overrides.py::TestTorchFunctionOverride::test_torch_log2, test/test_overrides.py::TestTorchFunctionOverride::test_torch_log_softmax, test/test_overrides.py::TestTorchFunctionOverride::test_torch_logaddexp, test/test_overrides.py::TestTorchFunctionOverride::test_torch_logaddexp2, test/test_overrides.py::TestTorchFunctionOverride::test_torch_logcumsumexp, test/test_overrides.py::TestTorchFunctionOverride::test_torch_logdet, test/test_overrides.py::TestTorchFunctionOverride::test_torch_logical_and, test/test_overrides.py::TestTorchFunctionOverride::test_torch_logical_not, test/test_overrides.py::TestTorchFunctionOverride::test_torch_logical_or, test/test_overrides.py::TestTorchFunctionOverride::test_torch_logical_xor, test/test_overrides.py::TestTorchFunctionOverride::test_torch_logit, test/test_overrides.py::TestTorchFunctionOverride::test_torch_logsumexp, test/test_overrides.py::TestTorchFunctionOverride::test_torch_lstm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_lstm_cell, test/test_overrides.py::TestTorchFunctionOverride::test_torch_lt, test/test_overrides.py::TestTorchFunctionOverride::test_torch_lu_solve, test/test_overrides.py::TestTorchFunctionOverride::test_torch_lu_unpack, test/test_overrides.py::TestTorchFunctionOverride::test_torch_margin_ranking_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_masked_fill, test/test_overrides.py::TestTorchFunctionOverride::test_torch_masked_scatter, test/test_overrides.py::TestTorchFunctionOverride::test_torch_masked_select, test/test_overrides.py::TestTorchFunctionOverride::test_torch_matmul, test/test_overrides.py::TestTorchFunctionOverride::test_torch_matrix_exp, test/test_overrides.py::TestTorchFunctionOverride::test_torch_matrix_power, test/test_overrides.py::TestTorchFunctionOverride::test_torch_max, test/test_overrides.py::TestTorchFunctionOverride::test_torch_max_pool1d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_max_pool1d_with_indices, test/test_overrides.py::TestTorchFunctionOverride::test_torch_max_pool2d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_max_pool3d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_maximum, test/test_overrides.py::TestTorchFunctionOverride::test_torch_mean, test/test_overrides.py::TestTorchFunctionOverride::test_torch_median, test/test_overrides.py::TestTorchFunctionOverride::test_torch_min, test/test_overrides.py::TestTorchFunctionOverride::test_torch_minimum, test/test_overrides.py::TestTorchFunctionOverride::test_torch_miopen_batch_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_miopen_convolution, test/test_overrides.py::TestTorchFunctionOverride::test_torch_miopen_convolution_add_relu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_miopen_convolution_relu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_miopen_convolution_transpose, test/test_overrides.py::TestTorchFunctionOverride::test_torch_miopen_depthwise_convolution, test/test_overrides.py::TestTorchFunctionOverride::test_torch_miopen_rnn, test/test_overrides.py::TestTorchFunctionOverride::test_torch_mode, test/test_overrides.py::TestTorchFunctionOverride::test_torch_moveaxis, test/test_overrides.py::TestTorchFunctionOverride::test_torch_movedim, test/test_overrides.py::TestTorchFunctionOverride::test_torch_msort, test/test_overrides.py::TestTorchFunctionOverride::test_torch_mul, test/test_overrides.py::TestTorchFunctionOverride::test_torch_multinomial, test/test_overrides.py::TestTorchFunctionOverride::test_torch_multiply, test/test_overrides.py::TestTorchFunctionOverride::test_torch_mv, test/test_overrides.py::TestTorchFunctionOverride::test_torch_mvlgamma, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nan_to_num, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nanmean, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nanmedian, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nanquantile, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nansum, test/test_overrides.py::TestTorchFunctionOverride::test_torch_narrow, test/test_overrides.py::TestTorchFunctionOverride::test_torch_narrow_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_native_batch_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_native_channel_shuffle, test/test_overrides.py::TestTorchFunctionOverride::test_torch_native_dropout, test/test_overrides.py::TestTorchFunctionOverride::test_torch_native_group_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_native_layer_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_native_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_ne, test/test_overrides.py::TestTorchFunctionOverride::test_torch_neg, test/test_overrides.py::TestTorchFunctionOverride::test_torch_negative, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nextafter, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional__threshold, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_adaptive_avg_pool2d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_adaptive_avg_pool3d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_adaptive_max_pool1d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_adaptive_max_pool1d_with_indices, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_adaptive_max_pool2d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_adaptive_max_pool2d_with_indices, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_adaptive_max_pool3d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_adaptive_max_pool3d_with_indices, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_affine_grid, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_alpha_dropout, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_batch_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_binary_cross_entropy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_binary_cross_entropy_with_logits, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_celu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_cosine_embedding_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_cross_entropy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_ctc_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_dropout, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_dropout1d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_dropout2d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_dropout3d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_elu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_embedding, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_embedding_bag, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_feature_alpha_dropout, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_fold, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_fractional_max_pool2d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_fractional_max_pool2d_with_indices, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_fractional_max_pool3d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_fractional_max_pool3d_with_indices, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_gaussian_nll_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_glu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_grid_sample, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_group_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_gumbel_softmax, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_hardtanh, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_hinge_embedding_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_huber_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_instance_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_interpolate, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_kl_div, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_l1_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_layer_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_leaky_relu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_local_response_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_log_softmax, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_lp_pool1d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_lp_pool2d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_lp_pool3d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_margin_ranking_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_max_pool1d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_max_pool1d_with_indices, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_max_pool2d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_max_pool2d_with_indices, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_max_pool3d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_max_pool3d_with_indices, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_max_unpool1d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_max_unpool2d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_max_unpool3d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_mish, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_mse_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_multi_head_attention_forward, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_multi_margin_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_multilabel_margin_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_multilabel_soft_margin_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_nll_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_normalize, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_pad, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_poisson_nll_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_relu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_relu6, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_rms_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_rrelu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_selu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_silu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_smooth_l1_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_soft_margin_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_softmax, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_softmin, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_softsign, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_tanhshrink, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_triplet_margin_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_triplet_margin_with_distance_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_unfold, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_init_constant_, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_init_kaiming_uniform_, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_init_normal_, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_init_uniform_, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nonzero, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nonzero_static, test/test_overrides.py::TestTorchFunctionOverride::test_torch_norm_except_dim, test/test_overrides.py::TestTorchFunctionOverride::test_torch_not_equal, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nuclear_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_numel, test/test_overrides.py::TestTorchFunctionOverride::test_torch_ones_like, test/test_overrides.py::TestTorchFunctionOverride::test_torch_orgqr, test/test_overrides.py::TestTorchFunctionOverride::test_torch_ormqr, test/test_overrides.py::TestTorchFunctionOverride::test_torch_outer, test/test_overrides.py::TestTorchFunctionOverride::test_torch_pairwise_distance, test/test_overrides.py::TestTorchFunctionOverride::test_torch_pdist, test/test_overrides.py::TestTorchFunctionOverride::test_torch_permute, test/test_overrides.py::TestTorchFunctionOverride::test_torch_permute_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_pinverse, test/test_overrides.py::TestTorchFunctionOverride::test_torch_pixel_shuffle, test/test_overrides.py::TestTorchFunctionOverride::test_torch_pixel_unshuffle, test/test_overrides.py::TestTorchFunctionOverride::test_torch_poisson, test/test_overrides.py::TestTorchFunctionOverride::test_torch_poisson_nll_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_polar, test/test_overrides.py::TestTorchFunctionOverride::test_torch_polygamma, test/test_overrides.py::TestTorchFunctionOverride::test_torch_positive, test/test_overrides.py::TestTorchFunctionOverride::test_torch_pow, test/test_overrides.py::TestTorchFunctionOverride::test_torch_prelu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_prod, test/test_overrides.py::TestTorchFunctionOverride::test_torch_put, test/test_overrides.py::TestTorchFunctionOverride::test_torch_q_per_channel_axis, test/test_overrides.py::TestTorchFunctionOverride::test_torch_q_per_channel_scales, test/test_overrides.py::TestTorchFunctionOverride::test_torch_q_per_channel_zero_points, test/test_overrides.py::TestTorchFunctionOverride::test_torch_q_scale, test/test_overrides.py::TestTorchFunctionOverride::test_torch_q_zero_point, test/test_overrides.py::TestTorchFunctionOverride::test_torch_qr, test/test_overrides.py::TestTorchFunctionOverride::test_torch_quantile, test/test_overrides.py::TestTorchFunctionOverride::test_torch_quantize_per_channel, test/test_overrides.py::TestTorchFunctionOverride::test_torch_quantize_per_tensor, test/test_overrides.py::TestTorchFunctionOverride::test_torch_quantize_per_tensor_dynamic, test/test_overrides.py::TestTorchFunctionOverride::test_torch_quantized_batch_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_quantized_gru_cell, test/test_overrides.py::TestTorchFunctionOverride::test_torch_quantized_lstm_cell, test/test_overrides.py::TestTorchFunctionOverride::test_torch_quantized_max_pool1d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_quantized_max_pool2d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_quantized_max_pool3d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_quantized_rnn_relu_cell, test/test_overrides.py::TestTorchFunctionOverride::test_torch_quantized_rnn_tanh_cell, test/test_overrides.py::TestTorchFunctionOverride::test_torch_rad2deg, test/test_overrides.py::TestTorchFunctionOverride::test_torch_rand_like, test/test_overrides.py::TestTorchFunctionOverride::test_torch_randint_like, test/test_overrides.py::TestTorchFunctionOverride::test_torch_randn_like, test/test_overrides.py::TestTorchFunctionOverride::test_torch_ravel, test/test_overrides.py::TestTorchFunctionOverride::test_torch_real, test/test_overrides.py::TestTorchFunctionOverride::test_torch_reciprocal, test/test_overrides.py::TestTorchFunctionOverride::test_torch_relu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_remainder, test/test_overrides.py::TestTorchFunctionOverride::test_torch_renorm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_repeat_interleave, test/test_overrides.py::TestTorchFunctionOverride::test_torch_reshape, test/test_overrides.py::TestTorchFunctionOverride::test_torch_resolve_conj, test/test_overrides.py::TestTorchFunctionOverride::test_torch_resolve_neg, test/test_overrides.py::TestTorchFunctionOverride::test_torch_rms_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_rnn_relu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_rnn_relu_cell, test/test_overrides.py::TestTorchFunctionOverride::test_torch_rnn_tanh, test/test_overrides.py::TestTorchFunctionOverride::test_torch_rnn_tanh_cell, test/test_overrides.py::TestTorchFunctionOverride::test_torch_roll, test/test_overrides.py::TestTorchFunctionOverride::test_torch_rot90, test/test_overrides.py::TestTorchFunctionOverride::test_torch_round, test/test_overrides.py::TestTorchFunctionOverride::test_torch_row_indices_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_row_stack, test/test_overrides.py::TestTorchFunctionOverride::test_torch_rrelu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_rsqrt, test/test_overrides.py::TestTorchFunctionOverride::test_torch_rsub, test/test_overrides.py::TestTorchFunctionOverride::test_torch_saddmm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_scatter, test/test_overrides.py::TestTorchFunctionOverride::test_torch_scatter_add, test/test_overrides.py::TestTorchFunctionOverride::test_torch_scatter_reduce, test/test_overrides.py::TestTorchFunctionOverride::test_torch_searchsorted, test/test_overrides.py::TestTorchFunctionOverride::test_torch_segment_reduce, test/test_overrides.py::TestTorchFunctionOverride::test_torch_select, test/test_overrides.py::TestTorchFunctionOverride::test_torch_select_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_select_scatter, test/test_overrides.py::TestTorchFunctionOverride::test_torch_selu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_sgn, test/test_overrides.py::TestTorchFunctionOverride::test_torch_sigmoid, test/test_overrides.py::TestTorchFunctionOverride::test_torch_sign, test/test_overrides.py::TestTorchFunctionOverride::test_torch_signbit, test/test_overrides.py::TestTorchFunctionOverride::test_torch_sin, test/test_overrides.py::TestTorchFunctionOverride::test_torch_sinc, test/test_overrides.py::TestTorchFunctionOverride::test_torch_sinh, test/test_overrides.py::TestTorchFunctionOverride::test_torch_slice_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_slice_inverse, test/test_overrides.py::TestTorchFunctionOverride::test_torch_slice_scatter, test/test_overrides.py::TestTorchFunctionOverride::test_torch_slogdet, test/test_overrides.py::TestTorchFunctionOverride::test_torch_smm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_softmax, test/test_overrides.py::TestTorchFunctionOverride::test_torch_sort, test/test_overrides.py::TestTorchFunctionOverride::test_torch_split_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_split_with_sizes, test/test_overrides.py::TestTorchFunctionOverride::test_torch_split_with_sizes_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_sqrt, test/test_overrides.py::TestTorchFunctionOverride::test_torch_square, test/test_overrides.py::TestTorchFunctionOverride::test_torch_squeeze, test/test_overrides.py::TestTorchFunctionOverride::test_torch_squeeze_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_stack, test/test_overrides.py::TestTorchFunctionOverride::test_torch_std, test/test_overrides.py::TestTorchFunctionOverride::test_torch_std_mean, test/test_overrides.py::TestTorchFunctionOverride::test_torch_sub, test/test_overrides.py::TestTorchFunctionOverride::test_torch_subtract, test/test_overrides.py::TestTorchFunctionOverride::test_torch_sum, test/test_overrides.py::TestTorchFunctionOverride::test_torch_svd, test/test_overrides.py::TestTorchFunctionOverride::test_torch_swapaxes, test/test_overrides.py::TestTorchFunctionOverride::test_torch_swapdims, test/test_overrides.py::TestTorchFunctionOverride::test_torch_sym_float, test/test_overrides.py::TestTorchFunctionOverride::test_torch_sym_int, test/test_overrides.py::TestTorchFunctionOverride::test_torch_sym_ite, test/test_overrides.py::TestTorchFunctionOverride::test_torch_sym_max, test/test_overrides.py::TestTorchFunctionOverride::test_torch_sym_min, test/test_overrides.py::TestTorchFunctionOverride::test_torch_sym_not, test/test_overrides.py::TestTorchFunctionOverride::test_torch_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_torch_function_standalone_class, test/test_overrides.py::TestTorchFunctionWarning::test_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 2025-09-07T06:38:28.2678617Z 2025-09-07T06:38:28.2678846Z Running test_extension_utils 1/1 ... [2025-09-07 06:38:28.170412] 2025-09-07T06:38:28.2679290Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T06:38:28.2680362Z 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'] ... [2025-09-07 06:38:28.170728] 2025-09-07T06:38:30.8385351Z 2025-09-07T06:38:30.8386284Z test_extension_utils 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_extension_utils_1.1_1e30fc992c08e15f_.log 2025-09-07T06:38:30.8387841Z 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 2025-09-07T06:38:30.8388869Z 2025-09-07T06:38:30.8389127Z Running inductor/test_flex_attention 1/5 ... [2025-09-07 06:38:30.838747] 2025-09-07T06:38:30.8389600Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T06:38:30.8392960Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_flex_attention.py', '--shard-id=1', '--num-shards=5', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 06:38:30.839038] 2025-09-07T06:48:02.4119576Z 2025-09-07T06:48:02.4120628Z inductor/test_flex_attention 1/5 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_flex_attention_1.5_013e1dafee2d2e38_.log 2025-09-07T06:48:02.4187874Z Running 123 items in this shard: test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_GQA_causal_mask_cpu, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_GQA_score_mod0_cpu_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_GQA_score_mod2_cpu_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_GQA_score_mod4_cpu_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_autograd_function_in_score_mod_cpu, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_builtin_score_mods_automatic_dynamic_score_mod0_cpu_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_builtin_score_mods_automatic_dynamic_score_mod1_cpu_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_builtin_score_mods_automatic_dynamic_score_mod2_cpu_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_builtin_score_mods_automatic_dynamic_score_mod3_cpu_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_builtin_score_mods_automatic_dynamic_score_mod6_cpu_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_builtin_score_mods_different_block_size_score_mod0_BLOCK_SIZE2_cpu_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_builtin_score_mods_different_block_size_score_mod0_BLOCK_SIZE_128_cpu_bfloat16, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_builtin_score_mods_different_block_size_score_mod0_BLOCK_SIZE_128_cpu_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_builtin_score_mods_different_block_size_score_mod1_BLOCK_SIZE3_cpu_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_builtin_score_mods_different_block_size_score_mod2_BLOCK_SIZE_256_cpu_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_builtin_score_mods_different_block_size_score_mod3_BLOCK_SIZE2_cpu_bfloat16, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_builtin_score_mods_different_block_size_score_mod3_BLOCK_SIZE_128_cpu_bfloat16, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_builtin_score_mods_different_block_size_score_mod3_BLOCK_SIZE_256_cpu_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_builtin_score_mods_different_block_size_score_mod4_BLOCK_SIZE2_cpu_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_builtin_score_mods_different_block_size_score_mod4_BLOCK_SIZE3_cpu_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_builtin_score_mods_different_block_size_score_mod4_BLOCK_SIZE_256_cpu_bfloat16, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_builtin_score_mods_different_block_size_score_mod4_BLOCK_SIZE_256_cpu_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_builtin_score_mods_different_block_size_score_mod5_BLOCK_SIZE2_cpu_bfloat16, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_builtin_score_mods_different_block_size_score_mod5_BLOCK_SIZE2_cpu_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_builtin_score_mods_different_block_size_score_mod5_BLOCK_SIZE3_cpu_bfloat16, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_builtin_score_mods_different_block_size_score_mod6_BLOCK_SIZE2_cpu_bfloat16, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_builtin_score_mods_different_block_size_score_mod6_BLOCK_SIZE3_cpu_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_builtin_score_mods_different_block_size_score_mod7_BLOCK_SIZE2_cpu_bfloat16, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_builtin_score_mods_different_block_size_score_mod7_BLOCK_SIZE3_cpu_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_builtin_score_mods_different_block_size_score_mod7_BLOCK_SIZE_128_cpu_bfloat16, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_builtin_score_mods_different_block_size_score_mod7_BLOCK_SIZE_256_cpu_bfloat16, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_builtin_score_mods_different_block_size_score_mod7_BLOCK_SIZE_256_cpu_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_builtin_score_mods_different_seqlen_score_mod4_cpu_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_builtin_score_mods_dynamic_score_mask_mod1_cpu_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_builtin_score_mods_dynamic_score_mask_mod5_cpu_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_builtin_score_mods_score_mod2_cpu_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_builtin_score_mods_score_mod5_cpu_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_builtin_score_mods_score_mod7_cpu_bfloat16, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_builtin_score_mods_seqlen_lt_default_sparse_block_size_score_mod5_cpu_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_captured_wrong_device_error_message_cpu, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_causal_block_non_divisible_cpu, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_cpu_error_message_return_lse_cpu, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_differentiable_logsumexp_gradcheck_cpu, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_document_masking_edge_case_mode_eager_cpu, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_dynamic_shapes_bug_dynamic_batch_cpu, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_flex_attention_backward_stride_ordering_mode_eager_permute_order4_shape1_cpu, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_flex_attention_backward_stride_ordering_mode_inductor_permute_order0_shape0_cpu, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_flex_attention_stride_ordering_mode_eager_permute_order0_shape1_cpu, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_flex_attention_stride_ordering_mode_eager_permute_order1_shape0_cpu, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_flex_attention_stride_ordering_mode_eager_permute_order2_shape0_cpu, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_flex_attention_stride_ordering_mode_inductor_permute_order4_shape0_cpu, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_flex_attention_stride_ordering_mode_paged_attention_permute_order1_shape1_cpu, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_flex_attention_stride_ordering_mode_paged_attention_permute_order2_shape0_cpu, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_flex_attention_stride_ordering_mode_paged_attention_permute_order4_shape1_cpu, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_float32_matmul_precision_cpu, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_kv_batch_broadcast_batch_dims0_head_dims0_score_mod4_cpu_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_kv_batch_broadcast_batch_dims0_head_dims1_score_mod0_cpu_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_kv_batch_broadcast_batch_dims0_head_dims1_score_mod1_cpu_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_kv_batch_broadcast_batch_dims1_head_dims0_score_mod2_cpu_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_kv_batch_broadcast_batch_dims1_head_dims1_score_mod2_cpu_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_kv_batch_broadcast_batch_dims1_head_dims1_score_mod4_cpu_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_kv_batch_broadcast_causal_mask_batch_dims0_head_dims0_score_mod0_cpu_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_kv_batch_broadcast_causal_mask_batch_dims0_head_dims0_score_mod3_cpu_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_kv_batch_broadcast_causal_mask_batch_dims0_head_dims0_score_mod4_cpu_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_kv_batch_broadcast_causal_mask_batch_dims0_head_dims1_score_mod1_cpu_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_kv_batch_broadcast_causal_mask_batch_dims1_head_dims0_score_mod2_cpu_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_kv_batch_broadcast_causal_mask_batch_dims1_head_dims1_score_mod3_cpu_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_kv_batch_broadcast_causal_mask_batch_dims2_head_dims0_score_mod1_cpu_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_kv_batch_broadcast_causal_mask_batch_dims2_head_dims0_score_mod7_cpu_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_kv_batch_broadcast_causal_mask_batch_dims2_head_dims1_score_mod1_cpu_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_load_from_bias_seq_batch_cpu_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_logsumexp_correctness_score_mod0_cpu_bfloat16, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_logsumexp_correctness_score_mod0_cpu_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_make_block_mask_cpu, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_max_autotune_cpu, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_mixed_device_error_message_cpu, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_mixed_dtypes_fails_cpu, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_multiple_score_mod_calls2_cpu, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_multiple_score_mod_calls_paged_attention_cpu, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_non_equal_head_dims_score_mod3_head_dims0_cpu_bfloat16, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_non_equal_head_dims_score_mod3_head_dims1_cpu_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_non_equal_head_dims_score_mod4_head_dims0_cpu_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_non_equal_head_dims_score_mod5_head_dims0_cpu_bfloat16, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_non_equal_head_dims_score_mod6_head_dims0_cpu_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_non_equal_head_dims_score_mod6_head_dims1_cpu_bfloat16, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_non_equal_head_dims_score_mod7_head_dims0_cpu_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_non_equal_head_dims_score_mod7_head_dims1_cpu_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_non_equal_head_dims_score_mod7_head_dims1_cpu_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_strided_inputs_q_s0_k_s0_v_s0_do_s0_cpu_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_strided_inputs_q_s0_k_s1_v_s1_do_s1_cpu_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_strided_inputs_q_s0_k_s2_v_s2_do_s0_cpu_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_strided_inputs_q_s1_k_s1_v_s1_do_s1_cpu_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_strided_inputs_q_s1_k_s1_v_s1_do_s2_cpu_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_strided_inputs_q_s1_k_s2_v_s2_do_s1_cpu_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_strided_inputs_q_s1_k_s2_v_s2_do_s2_cpu_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_strided_inputs_q_s1_k_s3_v_s3_do_s2_cpu_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_tma_with_customer_kernel_options_cpu, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_triton_template_warp_specialization_cpu, test/inductor/test_flex_attention.py::TestPagedAttentionCPU::test_paged_builtin_score_mods_score_mod1_cpu_bfloat16, test/inductor/test_flex_attention.py::TestPagedAttentionCPU::test_paged_builtin_score_mods_score_mod2_cpu_float32, test/inductor/test_flex_attention.py::TestPagedAttentionCPU::test_paged_builtin_score_mods_score_mod4_cpu_float16, test/inductor/test_flex_attention.py::TestPagedAttentionCPU::test_paged_builtin_score_mods_score_mod5_cpu_float16, test/inductor/test_flex_attention.py::TestPagedAttentionCPU::test_paged_builtin_score_mods_score_mod6_cpu_float32, test/inductor/test_flex_attention.py::TestPagedAttentionCPU::test_paged_builtin_score_mods_score_mod7_cpu_bfloat16, test/inductor/test_flex_attention.py::TestPagedAttentionCPU::test_paged_builtin_score_mods_score_mod7_cpu_float16, test/inductor/test_flex_attention.py::TestPagedAttentionCPU::test_paged_builtin_score_mods_score_mod7_cpu_float32, test/inductor/test_flex_attention.py::TestLearnableBiasesCPU::test_absolute_2d_bias_batch:2_head:4_seq_len:37_headdim:16_dtype:float16_cpu, test/inductor/test_flex_attention.py::TestLearnableBiasesCPU::test_distinct_biases_batch:2_head:4_seq_len:256_headdim:16_dtype:float16_cpu, test/inductor/test_flex_attention.py::TestLearnableBiasesCPU::test_flex_attention_with_dynamic_max_autotune_cpu, test/inductor/test_flex_attention.py::TestLearnableBiasesCPU::test_head_specific_bias_batch:2_head:4_seq_len:256_headdim:16_dtype:float32_cpu, test/inductor/test_flex_attention.py::TestLearnableBiasesCPU::test_head_specific_gate_batch:2_head:4_seq_len:256_headdim:16_dtype:float16_mode_default_cpu, 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test/inductor/test_flex_attention.py::TestLearnableBiasesCPU::test_local_window_bias_batch:2_head:4_seq_len:37_headdim:16_dtype:float16_cpu, test/inductor/test_flex_attention.py::TestLearnableBiasesCPU::test_relative_1d_bias_batch:2_head:4_seq_len:256_headdim:16_dtype:float16_mode_default_cpu, test/inductor/test_flex_attention.py::TestLearnableBiasesCPU::test_relative_1d_bias_batch:2_head:4_seq_len:277_headdim:16_dtype:float16_mode_default_cpu, test/inductor/test_flex_attention.py::TestLearnableBiasesCPU::test_symmetric_bias_batch:2_head:4_seq_len:256_headdim:16_dtype:float16_mode_default_cpu, test/inductor/test_flex_attention.py::TestLearnableBiasesCPU::test_symmetric_bias_batch:2_head:4_seq_len:277_headdim:16_dtype:float16_mode_max-autotune-no-cudagraphs_cpu, test/inductor/test_flex_attention.py::TestLearnableBiasesCPU::test_weird_bias_batch:2_head:4_seq_len:256_headdim:16_dtype:float16_cpu 2025-09-07T06:48:02.4253758Z 2025-09-07T06:48:02.4254006Z Running inductor/test_flex_attention 2/5 ... [2025-09-07 06:48:02.411900] 2025-09-07T06:48:02.4254475Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T06:48:02.4255586Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_flex_attention.py', '--shard-id=2', '--num-shards=5', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 06:48:02.412220] 2025-09-07T06:56:26.4761082Z 2025-09-07T06:56:26.4764009Z inductor/test_flex_attention 2/5 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_flex_attention_2.5_8028ace6964102a8_.log 2025-09-07T06:56:26.4834842Z Running 133 items in this shard: test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_GQA_score_mod3_cpu_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_block_mask_non_divisible_cpu, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_builtin_score_mods_different_block_size_score_mod0_BLOCK_SIZE2_cpu_bfloat16, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_builtin_score_mods_different_block_size_score_mod0_BLOCK_SIZE2_cpu_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_builtin_score_mods_different_block_size_score_mod0_BLOCK_SIZE3_cpu_bfloat16, 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test/inductor/test_flex_attention.py::TestLearnableBiasesCPU::test_relative_1d_bias_only_grad_batch:2_head:4_seq_len:37_headdim:16_dtype:float16_cpu, test/inductor/test_flex_attention.py::TestLearnableBiasesCPU::test_symmetric_bias_batch:2_head:4_seq_len:37_headdim:16_dtype:float32_mode_default_cpu, test/inductor/test_flex_attention.py::TestLearnableBiasesCPU::test_symmetric_bias_batch:2_head:4_seq_len:37_headdim:16_dtype:float32_mode_max-autotune-no-cudagraphs_cpu, test/inductor/test_flex_attention.py::TestLearnableBiasesCPU::test_weird_bias_batch:2_head:4_seq_len:277_headdim:16_dtype:float16_cpu, test/inductor/test_flex_attention.py::TestLearnableBiasesCPU::test_weird_bias_batch:2_head:4_seq_len:277_headdim:16_dtype:float32_cpu, test/inductor/test_flex_attention.py::TestLearnableBiasesCPU::test_weird_bias_batch:2_head:4_seq_len:37_headdim:16_dtype:float32_cpu 2025-09-07T06:56:26.4904939Z 2025-09-07T06:56:26.8015558Z Uploading artifacts took 0.33 seconds 2025-09-07T06:56:26.8018124Z Running inductor/test_flex_attention 3/5 ... [2025-09-07 06:56:26.801645] 2025-09-07T06:56:26.8018698Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T06:56:26.8022011Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_flex_attention.py', '--shard-id=3', '--num-shards=5', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 06:56:26.801972] 2025-09-07T07:05:01.3761963Z 2025-09-07T07:05:01.3763128Z inductor/test_flex_attention 3/5 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_flex_attention_3.5_b64ef2b1a0a608b6_.log 2025-09-07T07:05:01.3834498Z Running 129 items in this shard: test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_GQA_score_mod5_cpu_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_GQA_score_mod7_cpu_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_aot_eager_gradcheck_score_mod0_cpu, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_aot_eager_gradcheck_score_mod2_cpu, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_aot_eager_gradcheck_score_mod3_cpu, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_builtin_score_mods_automatic_dynamic_score_mod7_cpu_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_builtin_score_mods_different_block_size_score_mod0_BLOCK_SIZE_128_cpu_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_builtin_score_mods_different_block_size_score_mod0_BLOCK_SIZE_256_cpu_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_builtin_score_mods_different_block_size_score_mod1_BLOCK_SIZE_128_cpu_bfloat16, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_builtin_score_mods_different_block_size_score_mod1_BLOCK_SIZE_256_cpu_bfloat16, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_builtin_score_mods_different_block_size_score_mod2_BLOCK_SIZE3_cpu_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_builtin_score_mods_different_block_size_score_mod2_BLOCK_SIZE_128_cpu_float32, 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test/inductor/test_flex_attention.py::TestPagedAttentionCPU::test_allocate_cpu, test/inductor/test_flex_attention.py::TestPagedAttentionCPU::test_paged_builtin_score_mods_score_mod0_cpu_float16, test/inductor/test_flex_attention.py::TestPagedAttentionCPU::test_paged_builtin_score_mods_score_mod3_cpu_bfloat16, test/inductor/test_flex_attention.py::TestLearnableBiasesCPU::test_absolute_2d_bias_batch:2_head:4_seq_len:256_headdim:16_dtype:float16_cpu, test/inductor/test_flex_attention.py::TestLearnableBiasesCPU::test_absolute_2d_bias_batch:2_head:4_seq_len:256_headdim:16_dtype:float32_cpu, test/inductor/test_flex_attention.py::TestLearnableBiasesCPU::test_absolute_2d_bias_batch:2_head:4_seq_len:37_headdim:16_dtype:float32_cpu, test/inductor/test_flex_attention.py::TestLearnableBiasesCPU::test_batch_head_bias_batch:2_head:4_seq_len:256_headdim:16_dtype:float16_cpu, 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test/inductor/test_flex_attention.py::TestLearnableBiasesCPU::test_head_specific_gate_batch:2_head:4_seq_len:37_headdim:16_dtype:float16_mode_default_cpu, test/inductor/test_flex_attention.py::TestLearnableBiasesCPU::test_head_specific_gate_batch:2_head:4_seq_len:37_headdim:16_dtype:float32_mode_default_cpu, test/inductor/test_flex_attention.py::TestLearnableBiasesCPU::test_head_specific_gate_batch:2_head:4_seq_len:37_headdim:16_dtype:float32_mode_max-autotune-no-cudagraphs_cpu, test/inductor/test_flex_attention.py::TestLearnableBiasesCPU::test_indirect_bias_batch:2_head:4_seq_len:256_headdim:16_dtype:float32_cpu, test/inductor/test_flex_attention.py::TestLearnableBiasesCPU::test_indirect_bias_batch:2_head:4_seq_len:277_headdim:16_dtype:float32_cpu, test/inductor/test_flex_attention.py::TestLearnableBiasesCPU::test_indirect_bias_batch:2_head:4_seq_len:37_headdim:16_dtype:float16_cpu, test/inductor/test_flex_attention.py::TestLearnableBiasesCPU::test_local_window_bias_batch:2_head:4_seq_len:256_headdim:16_dtype:float32_cpu, test/inductor/test_flex_attention.py::TestLearnableBiasesCPU::test_multiplicative_bias_batch:2_head:4_seq_len:256_headdim:16_dtype:float32_cpu, test/inductor/test_flex_attention.py::TestLearnableBiasesCPU::test_relative_1d_bias_batch:2_head:4_seq_len:256_headdim:16_dtype:float32_mode_default_cpu, test/inductor/test_flex_attention.py::TestLearnableBiasesCPU::test_relative_1d_bias_only_grad_batch:2_head:4_seq_len:277_headdim:16_dtype:float32_cpu, test/inductor/test_flex_attention.py::TestLearnableBiasesCPU::test_symmetric_bias_batch:2_head:4_seq_len:277_headdim:16_dtype:float32_mode_max-autotune-no-cudagraphs_cpu, test/inductor/test_flex_attention.py::TestLearnableBiasesCPU::test_weird_bias_batch:2_head:4_seq_len:256_headdim:16_dtype:float32_cpu 2025-09-07T07:05:01.3903565Z 2025-09-07T07:05:01.3903803Z Running inductor/test_flex_attention 4/5 ... [2025-09-07 07:05:01.376561] 2025-09-07T07:05:01.3904279Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:05:01.3905408Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_flex_attention.py', '--shard-id=4', '--num-shards=5', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 07:05:01.376853] 2025-09-07T07:13:06.8180441Z 2025-09-07T07:13:06.8181832Z inductor/test_flex_attention 4/5 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_flex_attention_4.5_36172368931abc7b_.log 2025-09-07T07:13:06.8254981Z Running 129 items in this shard: test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_GQA_score_mod1_cpu_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_GQA_score_mod6_cpu_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_aot_eager_gradcheck_score_mod1_cpu, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_aot_eager_gradcheck_score_mod5_cpu, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_builtin_score_mods_different_block_size_score_mod0_BLOCK_SIZE3_cpu_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_builtin_score_mods_different_block_size_score_mod0_BLOCK_SIZE_256_cpu_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_builtin_score_mods_different_block_size_score_mod1_BLOCK_SIZE2_cpu_bfloat16, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_builtin_score_mods_different_block_size_score_mod1_BLOCK_SIZE3_cpu_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_builtin_score_mods_different_block_size_score_mod1_BLOCK_SIZE_128_cpu_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_builtin_score_mods_different_block_size_score_mod1_BLOCK_SIZE_256_cpu_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_builtin_score_mods_different_block_size_score_mod2_BLOCK_SIZE2_cpu_bfloat16, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_builtin_score_mods_different_block_size_score_mod2_BLOCK_SIZE2_cpu_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_builtin_score_mods_different_block_size_score_mod2_BLOCK_SIZE3_cpu_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_builtin_score_mods_different_block_size_score_mod2_BLOCK_SIZE_128_cpu_bfloat16, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_builtin_score_mods_different_block_size_score_mod2_BLOCK_SIZE_128_cpu_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_builtin_score_mods_different_block_size_score_mod3_BLOCK_SIZE3_cpu_bfloat16, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_builtin_score_mods_different_block_size_score_mod3_BLOCK_SIZE3_cpu_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_builtin_score_mods_different_block_size_score_mod3_BLOCK_SIZE_256_cpu_bfloat16, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_builtin_score_mods_different_block_size_score_mod4_BLOCK_SIZE2_cpu_bfloat16, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_builtin_score_mods_different_block_size_score_mod4_BLOCK_SIZE_256_cpu_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_builtin_score_mods_different_block_size_score_mod5_BLOCK_SIZE_128_cpu_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_builtin_score_mods_different_block_size_score_mod5_BLOCK_SIZE_256_cpu_bfloat16, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_builtin_score_mods_different_block_size_score_mod5_BLOCK_SIZE_256_cpu_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_builtin_score_mods_different_block_size_score_mod7_BLOCK_SIZE_128_cpu_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_builtin_score_mods_different_seqlen_score_mod2_cpu_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_builtin_score_mods_different_seqlen_score_mod3_cpu_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_builtin_score_mods_score_mod0_cpu_bfloat16, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_builtin_score_mods_score_mod3_cpu_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_builtin_score_mods_score_mod4_cpu_bfloat16, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_builtin_score_mods_score_mod4_cpu_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_builtin_score_mods_score_mod5_cpu_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_builtin_score_mods_score_mod7_cpu_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_builtin_score_mods_seqlen_lt_custom_sparse_block_size_score_mod0_cpu_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_builtin_score_mods_seqlen_lt_custom_sparse_block_size_score_mod3_cpu_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_builtin_score_mods_seqlen_lt_custom_sparse_block_size_score_mod4_cpu_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_builtin_score_mods_seqlen_lt_custom_sparse_block_size_score_mod7_cpu_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_builtin_score_mods_seqlen_lt_default_sparse_block_size_score_mod2_cpu_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_builtin_score_mods_seqlen_lt_default_sparse_block_size_score_mod4_cpu_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_captured_buffers_all_dims_cpu_bfloat16, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_captured_buffers_all_dims_cpu_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_captured_reduction_cpu_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_debug_flag_disables_internal_compilation_cpu, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_flex_attention_backward_stride_ordering_mode_eager_permute_order1_shape0_cpu, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_flex_attention_backward_stride_ordering_mode_eager_permute_order2_shape0_cpu, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_flex_attention_backward_stride_ordering_mode_eager_permute_order2_shape1_cpu, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_flex_attention_backward_stride_ordering_mode_inductor_permute_order1_shape1_cpu, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_flex_attention_backward_stride_ordering_mode_inductor_permute_order2_shape1_cpu, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_flex_attention_backward_stride_ordering_mode_inductor_permute_order3_shape0_cpu, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_flex_attention_backward_stride_ordering_mode_inductor_permute_order4_shape1_cpu, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_flex_attention_stride_ordering_mode_eager_permute_order4_shape1_cpu, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_flex_attention_stride_ordering_mode_inductor_permute_order1_shape1_cpu, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_flex_attention_stride_ordering_mode_inductor_permute_order2_shape1_cpu, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_flex_attention_stride_ordering_mode_inductor_permute_order3_shape1_cpu, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_flex_attention_stride_ordering_mode_inductor_permute_order4_shape1_cpu, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_flex_attention_stride_ordering_mode_paged_attention_permute_order4_shape0_cpu, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_free_symbol_dynamic_cpu, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_function_composition_cpu_bfloat16, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_function_composition_cpu_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_kv_batch_broadcast_batch_dims0_head_dims0_score_mod2_cpu_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_kv_batch_broadcast_batch_dims0_head_dims0_score_mod3_cpu_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_kv_batch_broadcast_batch_dims0_head_dims0_score_mod5_cpu_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_kv_batch_broadcast_batch_dims0_head_dims1_score_mod3_cpu_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_kv_batch_broadcast_batch_dims1_head_dims0_score_mod0_cpu_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_kv_batch_broadcast_batch_dims1_head_dims1_score_mod0_cpu_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_kv_batch_broadcast_batch_dims1_head_dims1_score_mod6_cpu_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_kv_batch_broadcast_batch_dims2_head_dims1_score_mod3_cpu_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_kv_batch_broadcast_causal_mask_batch_dims0_head_dims0_score_mod5_cpu_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_kv_batch_broadcast_causal_mask_batch_dims0_head_dims0_score_mod6_cpu_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_kv_batch_broadcast_causal_mask_batch_dims0_head_dims1_score_mod6_cpu_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_kv_batch_broadcast_causal_mask_batch_dims1_head_dims0_score_mod6_cpu_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_kv_batch_broadcast_causal_mask_batch_dims1_head_dims1_score_mod6_cpu_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_kv_batch_broadcast_causal_mask_batch_dims2_head_dims0_score_mod0_cpu_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_kv_batch_broadcast_causal_mask_batch_dims2_head_dims0_score_mod3_cpu_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_kv_batch_broadcast_causal_mask_batch_dims2_head_dims0_score_mod6_cpu_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_kv_batch_broadcast_causal_mask_batch_dims2_head_dims1_score_mod0_cpu_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_kv_batch_broadcast_causal_mask_batch_dims2_head_dims1_score_mod3_cpu_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_kv_batch_broadcast_causal_mask_batch_dims2_head_dims1_score_mod5_cpu_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_load_from_view_buffer_cpu, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_logsumexp_correctness_score_mod1_cpu_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_logsumexp_only_return_cpu, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_njt_causal_cpu_bfloat16, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_non_equal_head_dims_score_mod0_head_dims0_cpu_bfloat16, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_non_equal_head_dims_score_mod0_head_dims0_cpu_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_non_equal_head_dims_score_mod1_head_dims1_cpu_bfloat16, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_non_equal_head_dims_score_mod2_head_dims1_cpu_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_non_equal_head_dims_score_mod4_head_dims0_cpu_bfloat16, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_non_equal_head_dims_score_mod5_head_dims1_cpu_bfloat16, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_non_equal_head_dims_score_mod6_head_dims1_cpu_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_non_equal_head_dims_score_mod7_head_dims1_cpu_bfloat16, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_padded_dense_causal_cpu_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_recompile_changed_score_mod_cpu_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_selective_ac_ops_to_save1_cpu, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_selective_ac_ops_to_save2_cpu, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_strided_inputs_q_s0_k_s0_v_s0_do_s1_cpu_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_strided_inputs_q_s1_k_s0_v_s0_do_s0_cpu_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_strided_inputs_q_s1_k_s1_v_s1_do_s0_cpu_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_strided_inputs_q_s1_k_s3_v_s3_do_s0_cpu_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_zero_length_sequence_error_cpu, test/inductor/test_flex_attention.py::TestPagedAttentionCPU::test_page_allocation_cpu, test/inductor/test_flex_attention.py::TestPagedAttentionCPU::test_paged_builtin_score_mods_score_mod0_cpu_float32, test/inductor/test_flex_attention.py::TestPagedAttentionCPU::test_paged_builtin_score_mods_score_mod1_cpu_float16, test/inductor/test_flex_attention.py::TestPagedAttentionCPU::test_paged_builtin_score_mods_score_mod5_cpu_float32, test/inductor/test_flex_attention.py::TestPagedAttentionCPU::test_paged_builtin_score_mods_score_mod6_cpu_float16, test/inductor/test_flex_attention.py::TestLearnableBiasesCPU::test_absolute_2d_bias_batch:2_head:4_seq_len:277_headdim:16_dtype:float32_cpu, test/inductor/test_flex_attention.py::TestLearnableBiasesCPU::test_batch_head_bias_batch:2_head:4_seq_len:37_headdim:16_dtype:float16_cpu, test/inductor/test_flex_attention.py::TestLearnableBiasesCPU::test_comparison_vs_sdpa_with_learnable_bias_cpu, test/inductor/test_flex_attention.py::TestLearnableBiasesCPU::test_flipped_indexed_bias_batch:2_head:4_seq_len:256_headdim:16_dtype:float16_cpu, test/inductor/test_flex_attention.py::TestLearnableBiasesCPU::test_flipped_indexed_bias_batch:2_head:4_seq_len:37_headdim:16_dtype:float32_cpu, test/inductor/test_flex_attention.py::TestLearnableBiasesCPU::test_global_tokens_bias_batch:2_head:4_seq_len:277_headdim:16_dtype:float32_cpu, test/inductor/test_flex_attention.py::TestLearnableBiasesCPU::test_head_bias_req_grad_cpu, test/inductor/test_flex_attention.py::TestLearnableBiasesCPU::test_head_specific_bias_batch:2_head:4_seq_len:37_headdim:16_dtype:float32_cpu, test/inductor/test_flex_attention.py::TestLearnableBiasesCPU::test_indirect_bias_batch:2_head:4_seq_len:256_headdim:16_dtype:float16_cpu, test/inductor/test_flex_attention.py::TestLearnableBiasesCPU::test_indirect_bias_batch:2_head:4_seq_len:277_headdim:16_dtype:float16_cpu, test/inductor/test_flex_attention.py::TestLearnableBiasesCPU::test_indirect_bias_batch:2_head:4_seq_len:37_headdim:16_dtype:float32_cpu, test/inductor/test_flex_attention.py::TestLearnableBiasesCPU::test_learnable_bias_global_compiled_batch:2_head:4_seq_len:37_headdim:16_dtype:float16_cpu, test/inductor/test_flex_attention.py::TestLearnableBiasesCPU::test_learnable_bias_global_compiled_batch:2_head:4_seq_len:37_headdim:16_dtype:float32_cpu, test/inductor/test_flex_attention.py::TestLearnableBiasesCPU::test_local_window_bias_batch:2_head:4_seq_len:277_headdim:16_dtype:float32_cpu, test/inductor/test_flex_attention.py::TestLearnableBiasesCPU::test_multiplicative_bias_batch:2_head:4_seq_len:277_headdim:16_dtype:float16_cpu, test/inductor/test_flex_attention.py::TestLearnableBiasesCPU::test_multiplicative_bias_batch:2_head:4_seq_len:37_headdim:16_dtype:float16_cpu, test/inductor/test_flex_attention.py::TestLearnableBiasesCPU::test_multiplicative_bias_batch:2_head:4_seq_len:37_headdim:16_dtype:float32_cpu, test/inductor/test_flex_attention.py::TestLearnableBiasesCPU::test_relative_1d_bias_batch:2_head:4_seq_len:256_headdim:16_dtype:float16_mode_max-autotune-no-cudagraphs_cpu, test/inductor/test_flex_attention.py::TestLearnableBiasesCPU::test_relative_1d_bias_batch:2_head:4_seq_len:277_headdim:16_dtype:float32_mode_default_cpu, test/inductor/test_flex_attention.py::TestLearnableBiasesCPU::test_relative_1d_bias_batch:2_head:4_seq_len:37_headdim:16_dtype:float32_mode_default_cpu, test/inductor/test_flex_attention.py::TestLearnableBiasesCPU::test_relative_1d_bias_only_grad_batch:2_head:4_seq_len:256_headdim:16_dtype:float16_cpu, test/inductor/test_flex_attention.py::TestLearnableBiasesCPU::test_symmetric_bias_batch:2_head:4_seq_len:256_headdim:16_dtype:float16_mode_max-autotune-no-cudagraphs_cpu, test/inductor/test_flex_attention.py::TestLearnableBiasesCPU::test_symmetric_bias_batch:2_head:4_seq_len:256_headdim:16_dtype:float32_mode_default_cpu, test/inductor/test_flex_attention.py::TestLearnableBiasesCPU::test_symmetric_bias_batch:2_head:4_seq_len:277_headdim:16_dtype:float32_mode_default_cpu, test/inductor/test_flex_attention.py::TestLearnableBiasesCPU::test_symmetric_bias_batch:2_head:4_seq_len:37_headdim:16_dtype:float16_mode_max-autotune-no-cudagraphs_cpu, test/inductor/test_flex_attention.py::TestLearnableBiasesCPU::test_weird_bias_batch:2_head:4_seq_len:37_headdim:16_dtype:float16_cpu 2025-09-07T07:13:06.8325332Z 2025-09-07T07:13:06.8325579Z Running inductor/test_flex_attention 5/5 ... [2025-09-07 07:13:06.817807] 2025-09-07T07:13:06.8326051Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:13:06.8327167Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_flex_attention.py', '--shard-id=5', '--num-shards=5', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 07:13:06.818142] 2025-09-07T07:21:35.1996960Z 2025-09-07T07:21:35.1997951Z inductor/test_flex_attention 5/5 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_flex_attention_5.5_9520964aca9f693f_.log 2025-09-07T07:21:35.2069118Z Running 130 items in this shard: test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_aot_eager_gradcheck_score_mod4_cpu, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_builtin_score_mods_automatic_dynamic_score_mod4_cpu_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_builtin_score_mods_automatic_dynamic_score_mod5_cpu_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_builtin_score_mods_different_block_size_score_mod0_BLOCK_SIZE3_cpu_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_builtin_score_mods_different_block_size_score_mod0_BLOCK_SIZE_256_cpu_bfloat16, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_builtin_score_mods_different_block_size_score_mod1_BLOCK_SIZE3_cpu_bfloat16, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_builtin_score_mods_different_block_size_score_mod2_BLOCK_SIZE3_cpu_bfloat16, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_builtin_score_mods_different_block_size_score_mod2_BLOCK_SIZE_256_cpu_bfloat16, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_builtin_score_mods_different_block_size_score_mod3_BLOCK_SIZE_256_cpu_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_builtin_score_mods_different_block_size_score_mod4_BLOCK_SIZE3_cpu_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_builtin_score_mods_different_block_size_score_mod4_BLOCK_SIZE_128_cpu_bfloat16, 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test/inductor/test_flex_attention.py::TestLearnableBiasesCPU::test_symmetric_bias_batch:2_head:4_seq_len:277_headdim:16_dtype:float16_mode_default_cpu, test/inductor/test_flex_attention.py::TestLearnableBiasesCPU::test_symmetric_bias_batch:2_head:4_seq_len:37_headdim:16_dtype:float16_mode_default_cpu 2025-09-07T07:21:35.2139003Z 2025-09-07T07:21:35.4621143Z Uploading artifacts took 0.26 seconds 2025-09-07T07:21:35.4624006Z Running inductor/test_cutlass_backend 1/1 ... [2025-09-07 07:21:35.462214] 2025-09-07T07:21:35.4624491Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:21:35.4627823Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_cutlass_backend.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 07:21:35.462565] 2025-09-07T07:21:42.1115718Z 2025-09-07T07:21:42.1117349Z inductor/test_cutlass_backend 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_cutlass_backend_1.1_f93b974325d9361e_.log 2025-09-07T07:21:42.1118700Z 2025-09-07T07:21:42.1120956Z Running test_cpp_api_parity 1/1 ... [2025-09-07 07:21:42.111879] 2025-09-07T07:21:42.1121601Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:21:42.1126466Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_cpp_api_parity.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 07:21:42.112315] 2025-09-07T07:21:51.9415394Z 2025-09-07T07:21:51.9416272Z test_cpp_api_parity 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_cpp_api_parity_1.1_c6d70c05fda8d922_.log 2025-09-07T07:21:51.9609392Z Running 488 items in this shard: test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_BCELoss_no_batch_dim_mean, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_BCELoss_no_batch_dim_mean_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_BCELoss_no_batch_dim_none, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_BCELoss_no_batch_dim_none_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_BCELoss_no_batch_dim_sum, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_BCELoss_no_batch_dim_sum_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_BCEWithLogitsLoss_no_batch_dim_mean, 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test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_bicubic_tuple_2d_align_corners, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_bicubic_tuple_2d_align_corners_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_bicubic_tuple_2d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_bilinear_2d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_bilinear_2d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_bilinear_2d_zero_dim, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_bilinear_2d_zero_dim_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_bilinear_scale_2d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_bilinear_scale_2d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_bilinear_scale_tuple_shared_2d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_bilinear_scale_tuple_shared_2d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_bilinear_scale_tuple_skewed_2d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_bilinear_scale_tuple_skewed_2d_align_corners, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_bilinear_scale_tuple_skewed_2d_align_corners_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_bilinear_scale_tuple_skewed_2d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_bilinear_tuple_2d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_bilinear_tuple_2d_align_corners, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_bilinear_tuple_2d_align_corners_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_bilinear_tuple_2d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_linear_1d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_linear_1d_align_corners, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_linear_1d_align_corners_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_linear_1d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_linear_1d_zero_dim, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_linear_1d_zero_dim_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_linear_scale_1d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_linear_scale_1d_align_corners, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_linear_scale_1d_align_corners_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_linear_scale_1d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_linear_tuple_1d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_linear_tuple_1d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_1d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_1d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_1d_zero_dim, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_1d_zero_dim_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_2d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_2d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_2d_launch_configs, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_2d_launch_configs_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_2d_zero_dim, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_2d_zero_dim_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_3d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_3d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_3d_zero_dim, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_3d_zero_dim_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_scale_1d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_scale_1d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_scale_2d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_scale_2d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_scale_3d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_scale_3d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_tuple_1d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_tuple_1d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_tuple_2d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_tuple_2d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_tuple_3d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_tuple_3d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_trilinear_3d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_trilinear_3d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_trilinear_3d_zero_dim, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_trilinear_3d_zero_dim_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_trilinear_scale_3d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_trilinear_scale_3d_align_corners, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_trilinear_scale_3d_align_corners_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_trilinear_scale_3d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_trilinear_tuple_3d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_trilinear_tuple_3d_align_corners, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_trilinear_tuple_3d_align_corners_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_trilinear_tuple_3d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_log_softmax_dim0, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_log_softmax_dim0_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_log_softmax_dim3, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_log_softmax_dim3_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_log_softmax_lastdim, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_log_softmax_lastdim_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_log_softmax_scalar, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_log_softmax_scalar_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_log_softmax_spatial, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_log_softmax_spatial_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_log_softmax_spatial_special, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_log_softmax_spatial_special_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_multimarginloss_1d_input_0d_target_no_reduce, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_multimarginloss_1d_input_0d_target_no_reduce_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_sample_functional_has_parity, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_sample_functional_has_parity_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_sample_functional_no_parity, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_sample_functional_no_parity_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_functional_dim0, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_functional_dim0_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_functional_dim3, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_functional_dim3_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_functional_scalar, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_functional_scalar_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_lastdim, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_lastdim_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_lastdim_dtype, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_lastdim_dtype_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_spatial, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_spatial_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_spatial_dtype, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_spatial_dtype_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_spatial_special, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_spatial_special_cuda 2025-09-07T07:21:51.9802145Z 2025-09-07T07:21:51.9802309Z Running test_fx 1/1 ... [2025-09-07 07:21:51.942353] 2025-09-07T07:21:51.9802691Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:21:51.9803697Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_fx.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 07:21:51.942681] 2025-09-07T07:25:44.2476143Z 2025-09-07T07:25:44.2477153Z test_fx 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_fx_1.1_8c72ee581a8d1681_.log 2025-09-07T07:25:44.2965291Z Running 1261 items in this shard: test/test_fx.py::TestCommonPass::test_correctness_CSEPass_MutationInput_cpu, test/test_fx.py::TestCommonPass::test_correctness_CSEPass_MutationMetadata_cpu, test/test_fx.py::TestCommonPass::test_correctness_CSEPass_MutationTorchTensorCall_cpu, test/test_fx.py::TestCommonPass::test_correctness_CSEPass_Mutation_cpu, test/test_fx.py::TestCommonPass::test_correctness_CSEPass_ReturnList_cpu, test/test_fx.py::TestCommonPass::test_correctness_CSEPass_TakeList_cpu, test/test_fx.py::TestCommonPass::test_correctness_factory_CSEPass_FactoryFunctionCall_cpu, test/test_fx.py::TestCommonPass::test_correctness_factory_CSEPass_MutationFactory_cpu, test/test_fx.py::TestCSEPass::test_banned_list, test/test_fx.py::TestCSEPass::test_empty, test/test_fx.py::TestCSEPass::test_immutable_list_multiple_entries, test/test_fx.py::TestCSEPass::test_immutable_list_type, test/test_fx.py::TestCSEPass::test_kwarg, test/test_fx.py::TestCSEPass::test_nested_immutable_list_type, test/test_fx.py::TestCSEPass::test_nochange, test/test_fx.py::TestCSEPass::test_rand_like, test/test_fx.py::TestCSEPass::test_rand_n, test/test_fx.py::TestCSEPass::test_random, test/test_fx.py::TestCSEPass::test_simple, test/test_fx.py::TestCSEPass::test_simple_2, test/test_fx.py::TestCSEPass::test_simple_multiple_same_ops, test/test_fx.py::TestCSEPass::test_two_args, test/test_fx.py::TestCSEPass::test_two_args_default, test/test_fx.py::TestDCE::test_dead_chain, test/test_fx.py::TestDCE::test_dead_getattr, test/test_fx.py::TestDCE::test_dead_placeholder, test/test_fx.py::TestDCE::test_dead_placeholder_with_user, test/test_fx.py::TestDCE::test_impure_custom, test/test_fx.py::TestDCE::test_impure_kwargs, test/test_fx.py::TestDCE::test_impure_nodes_args, test/test_fx.py::TestDCE::test_impure_random, test/test_fx.py::TestDCE::test_keep_collectives, test/test_fx.py::TestDCE::test_keep_collectives_no_overload, test/test_fx.py::TestDCE::test_keep_module_with_side_effects, test/test_fx.py::TestDCE::test_keep_setitem, test/test_fx.py::TestDCE::test_keep_torch_assert, test/test_fx.py::TestDCE::test_simple, test/test_fx.py::TestConstFold::test_check_inline_non_const, test/test_fx.py::TestConstFold::test_check_inline_non_const_mult_return, test/test_fx.py::TestConstFold::test_check_skip_folding_quant_dequant_pattern, test/test_fx.py::TestConstFold::test_const_fold_basic_one_attr_name_collision, test/test_fx.py::TestConstFold::test_const_fold_basic_one_attr_no_name_collision, test/test_fx.py::TestConstFold::test_const_fold_basic_placeholder_reordered, test/test_fx.py::TestConstFold::test_const_fold_basic_two_attr, test/test_fx.py::TestConstFold::test_const_fold_basic_two_attr_three_input, test/test_fx.py::TestConstFold::test_const_fold_has_inlined_call_module_node, test/test_fx.py::TestConstFold::test_const_fold_module_attr, test/test_fx.py::TestConstFold::test_const_fold_multi_const_folded_attrs, test/test_fx.py::TestConstFold::test_const_fold_noop, test/test_fx.py::TestConstFold::test_const_fold_submod_hierarchy, test/test_fx.py::TestConstFold::test_const_fold_tensor_meta, test/test_fx.py::TestConstFold::test_const_fold_unused_placeholder, test/test_fx.py::TestConstFold::test_dict_output, test/test_fx.py::TestConstFold::test_fold_module, test/test_fx.py::TestConstFold::test_retain_node_meta, test/test_fx.py::TestConstFold::test_three_outputs, test/test_fx.py::TestConstFold::test_two_outputs, test/test_fx.py::TestConstParamShapeInControlFlow::test_param_dim_const, test/test_fx.py::TestConstParamShapeInControlFlow::test_param_ndim_const, test/test_fx.py::TestConstParamShapeInControlFlow::test_param_nelement_const, test/test_fx.py::TestConstParamShapeInControlFlow::test_param_numel_const, test/test_fx.py::TestConstParamShapeInControlFlow::test_param_shape_const, test/test_fx.py::TestConstParamShapeInControlFlow::test_param_size_const, test/test_fx.py::AnnotationsTest::test_annotate, test/test_fx.py::AnnotationsTest::test_annotations, test/test_fx.py::AnnotationsTest::test_broadcasting1, test/test_fx.py::AnnotationsTest::test_broadcasting2, test/test_fx.py::AnnotationsTest::test_broadcasting3, test/test_fx.py::AnnotationsTest::test_consistency, test/test_fx.py::AnnotationsTest::test_precision, test/test_fx.py::TypeCheckerTest::test_flatten_fully_static, test/test_fx.py::TypeCheckerTest::test_resnet50, test/test_fx.py::TypeCheckerTest::test_symbolic_add_with_broadcast, test/test_fx.py::TypeCheckerTest::test_symbolic_add_with_broadcast_2, test/test_fx.py::TypeCheckerTest::test_type_check_add_false, test/test_fx.py::TypeCheckerTest::test_type_check_add_true, test/test_fx.py::TypeCheckerTest::test_type_check_add_with_broadcast, test/test_fx.py::TypeCheckerTest::test_type_check_add_with_scalar, test/test_fx.py::TypeCheckerTest::test_type_check_batch_norm_2D, test/test_fx.py::TypeCheckerTest::test_type_check_batch_norm_2D_broadcast, test/test_fx.py::TypeCheckerTest::test_type_check_batch_norm_2D_false, test/test_fx.py::TypeCheckerTest::test_type_check_batch_norm_symbolic, test/test_fx.py::TypeCheckerTest::test_type_check_conv2D, test/test_fx.py::TypeCheckerTest::test_type_check_conv2D_2, test/test_fx.py::TypeCheckerTest::test_type_check_conv2D_2_fully_static, test/test_fx.py::TypeCheckerTest::test_type_check_conv2D_maxpool2d_flatten, test/test_fx.py::TypeCheckerTest::test_type_check_conv2D_types, test/test_fx.py::TypeCheckerTest::test_type_check_flatten, test/test_fx.py::TypeCheckerTest::test_type_check_flatten3, test/test_fx.py::TypeCheckerTest::test_type_check_flatten_2, test/test_fx.py::TypeCheckerTest::test_type_check_reshape_dyn_false, test/test_fx.py::TypeCheckerTest::test_type_check_reshape_dyn_true, test/test_fx.py::TypeCheckerTest::test_type_check_reshape_dyn_true_param_false, test/test_fx.py::TypeCheckerTest::test_type_check_reshape_false, test/test_fx.py::TypeCheckerTest::test_type_check_reshape_true, test/test_fx.py::TypeCheckerTest::test_type_check_symbolic_inferenceconv2D_maxpool2d_flatten, test/test_fx.py::TypeCheckerTest::test_type_check_transpose_False, test/test_fx.py::TypeCheckerTest::test_type_check_transpose_true, test/test_fx.py::TypeCheckerTest::test_type_maxpool2d_fully_static, test/test_fx.py::TypeCheckerTest::test_type_typechecl_maxpool2d_3dinput, test/test_fx.py::TypeCheckerTest::test_typecheck_basicblock, test/test_fx.py::TestMatcher::test_matcher_with_name_node_map_function, test/test_fx.py::TestMatcher::test_matcher_with_name_node_map_module, test/test_fx.py::TestMatcher::test_split_to_graph_and_name_node_map, test/test_fx.py::TestMatcher::test_subgraph_matcher_ignore_literals, test/test_fx.py::TestMatcher::test_subgraph_matcher_with_attributes, test/test_fx.py::TestMatcher::test_subgraph_matcher_with_list, test/test_fx.py::TestMatcher::test_subgraph_matcher_with_list_bad, test/test_fx.py::TestMatcher::test_variatic_arg_matching, test/test_fx.py::TestPassManager::test_pass_manager, test/test_fx.py::TestPassManager::test_pass_manager_bad_checks, test/test_fx.py::TestPassManager::test_pass_manager_checks, test/test_fx.py::TestPassManager::test_pass_manager_error, test/test_fx.py::TestPassManager::test_this_before_that_pass_constraint, test/test_fx.py::TestPassManager::test_topological_sort, test/test_fx.py::TestSourceMatcher::test_legalize_slice, test/test_fx.py::TestSourceMatcher::test_module_partitioner_conv_relu_maxpool, test/test_fx.py::TestSourceMatcher::test_module_partitioner_conv_relu_maxpool_torch_fn_export_strict_False, test/test_fx.py::TestSourceMatcher::test_module_partitioner_conv_relu_maxpool_torch_fn_export_strict_True, test/test_fx.py::TestSourceMatcher::test_module_partitioner_functional_conv_relu_conv, test/test_fx.py::TestSourceMatcher::test_module_partitioner_functional_conv_relu_conv_torch_fn_export_strict_False, test/test_fx.py::TestSourceMatcher::test_module_partitioner_functional_conv_relu_conv_torch_fn_export_strict_True, test/test_fx.py::TestSourceMatcher::test_module_partitioner_functional_linear_relu_linear, test/test_fx.py::TestSourceMatcher::test_module_partitioner_functional_linear_relu_linear_torch_fn_export_strict_False, test/test_fx.py::TestSourceMatcher::test_module_partitioner_functional_linear_relu_linear_torch_fn_export_strict_True, test/test_fx.py::TestSourceMatcher::test_module_partitioner_linear_relu_linear, test/test_fx.py::TestSourceMatcher::test_module_partitioner_linear_relu_linear_torch_fn_export_strict_False, test/test_fx.py::TestSourceMatcher::test_module_partitioner_linear_relu_linear_torch_fn_export_strict_True, test/test_fx.py::TestSourceMatcher::test_module_partitioner_weight_tied_strict_False, test/test_fx.py::TestSourceMatcher::test_module_partitioner_weight_tied_strict_True, test/test_fx.py::TestSubgraphRewriter::test_matching_pattern_with_list_type_arg, test/test_fx.py::TestSubgraphRewriter::test_matching_variable_arguments, test/test_fx.py::TestSubgraphRewriter::test_replace_pattern_with_callback, test/test_fx.py::TestSubgraphRewriter::test_replace_pattern_with_filters, test/test_fx.py::TestSubgraphRewriter::test_replaced_nodes, test/test_fx.py::TestSubgraphRewriter::test_replacement_with_attrs, test/test_fx.py::TestSubgraphRewriter::test_subgraph_rewriter_annotations_int, test/test_fx.py::TestSubgraphRewriter::test_subgraph_rewriter_call_method, test/test_fx.py::TestSubgraphRewriter::test_subgraph_rewriter_correct_output_replacement, test/test_fx.py::TestSubgraphRewriter::test_subgraph_rewriter_graph_argument_order, test/test_fx.py::TestSubgraphRewriter::test_subgraph_rewriter_internal_pattern_nodes_cannot_have_users_that_are_not_matched, test/test_fx.py::TestSubgraphRewriter::test_subgraph_rewriter_local_revert, test/test_fx.py::TestSubgraphRewriter::test_subgraph_rewriter_multiple_pattern_match, test/test_fx.py::TestSubgraphRewriter::test_subgraph_rewriter_nodes_with_kwargs, test/test_fx.py::TestSubgraphRewriter::test_subgraph_rewriter_pattern_is_entire_graph, test/test_fx.py::TestSubgraphRewriter::test_subgraph_rewriter_pattern_output_pattern_node_can_have_users_that_are_not_matched, test/test_fx.py::TestSubgraphRewriter::test_subgraph_rewriter_placeholder_matching, test/test_fx.py::TestSubgraphRewriter::test_subgraph_rewriter_preserves_logic, test/test_fx.py::TestSubgraphRewriter::test_subgraph_rewriter_replace_consecutive_submodules, test/test_fx.py::TestSubgraphRewriter::test_subgraph_rewriter_replace_with_duplicated_outputs, test/test_fx.py::TestSubgraphRewriter::test_subgraph_rewriter_replace_with_multiple_outputs, test/test_fx.py::TestSubgraphRewriter::test_subgraph_rewriter_replaces_referenced_submodules, test/test_fx.py::TestSubgraphRewriter::test_subgraph_rewriter_single_pattern_match, test/test_fx.py::TestSubgraphRewriter::test_subgraph_rewriter_traced_as_callable, test/test_fx.py::TestSubgraphRewriter::test_subgraph_rewriter_with_oneliner_pattern, test/test_fx.py::TestSubgraphRewriter::test_subgraph_rewriter_with_overlapping_matches, test/test_fx.py::TestSubgraphRewriter::test_subgraph_rewriter_with_trivial_replacement, test/test_fx.py::TestSubgraphRewriter::test_subgraph_rewriter_with_unused_args, test/test_fx.py::TestSubgraphRewriter::test_subgraph_rewriter_with_unused_results, test/test_fx.py::TestFX::test_all_input_nodes, test/test_fx.py::TestFX::test_annotation_with_future, test/test_fx.py::TestFX::test_annotations_empty_tuple, test/test_fx.py::TestFX::test_annotations_with_forward_references, test/test_fx.py::TestFX::test_annotations_with_no_forward_references, test/test_fx.py::TestFX::test_annotations_with_non_torch_reference_and_internal_forward_references, test/test_fx.py::TestFX::test_annotations_with_non_torch_reference_and_no_internal_forward_references, test/test_fx.py::TestFX::test_args_kwargs, test/test_fx.py::TestFX::test_args_kwargs_no_self, test/test_fx.py::TestFX::test_assert, test/test_fx.py::TestFX::test_ast_rewriter_reassigns_submodules, test/test_fx.py::TestFX::test_ast_rewriter_rewrites_assert, test/test_fx.py::TestFX::test_ast_rewriter_rewrites_assert_with_message, test/test_fx.py::TestFX::test_ast_rewriter_wrap, test/test_fx.py::TestFX::test_ast_rewriter_wrap_fn_directly, test/test_fx.py::TestFX::test_ast_rewriter_wrap_with_submodule, test/test_fx.py::TestFX::test_ast_rewriter_wrapped_via_decorator, test/test_fx.py::TestFX::test_ast_rewriter_wrapped_via_decorator_and_transformed, test/test_fx.py::TestFX::test_autowrap_functions, test/test_fx.py::TestFX::test_concrete_arg_none_assert, test/test_fx.py::TestFX::test_construct_root_dict, test/test_fx.py::TestFX::test_control_flow_tracing, test/test_fx.py::TestFX::test_copy_it, test/test_fx.py::TestFX::test_copy_no_remap, test/test_fx.py::TestFX::test_ctx_mgr, test/test_fx.py::TestFX::test_custom_codegen, test/test_fx.py::TestFX::test_custom_codegen_with_transformer, test/test_fx.py::TestFX::test_custom_import, test/test_fx.py::TestFX::test_custom_proxy_dynamic_value, test/test_fx.py::TestFX::test_custom_proxy_input_dependent_control_flow, test/test_fx.py::TestFX::test_custom_proxy_type, test/test_fx.py::TestFX::test_custom_proxy_type_literal, test/test_fx.py::TestFX::test_custom_traceback_not_raised_when_exception_source_is_submodule, test/test_fx.py::TestFX::test_custom_traceback_raised_when_exception_source_is_graphmodule, test/test_fx.py::TestFX::test_deepcopy_graph_with_tracer_cls, test/test_fx.py::TestFX::test_deepcopy_graphmodule, test/test_fx.py::TestFX::test_deepcopy_graphmodule_with_transform, test/test_fx.py::TestFX::test_deepcopy_no_recursion, test/test_fx.py::TestFX::test_deepcopy_recursion_depth, test/test_fx.py::TestFX::test_deepcopy_tracer, test/test_fx.py::TestFX::test_deepcopy_with_submods_params, test/test_fx.py::TestFX::test_delete_unused_submodules_leaf, test/test_fx.py::TestFX::test_delete_unused_values, test/test_fx.py::TestFX::test_dict, test/test_fx.py::TestFX::test_direct_param_use, test/test_fx.py::TestFX::test_disallow_override, test/test_fx.py::TestFX::test_ellipsis, test/test_fx.py::TestFX::test_empty_graph_codegen, test/test_fx.py::TestFX::test_enum, test/test_fx.py::TestFX::test_erase_node_error, test/test_fx.py::TestFX::test_example_shape_prop, test/test_fx.py::TestFX::test_find_uses, test/test_fx.py::TestFX::test_fn_type_annotation_empty, test/test_fx.py::TestFX::test_fn_type_annotations, test/test_fx.py::TestFX::test_fx_and_or, test/test_fx.py::TestFX::test_fx_create_arg, test/test_fx.py::TestFX::test_fx_shifts, test/test_fx.py::TestFX::test_fx_stateless, test/test_fx.py::TestFX::test_get_torch_func_signature, test/test_fx.py::TestFX::test_getitem, test/test_fx.py::TestFX::test_getitem_subproc, test/test_fx.py::TestFX::test_graph_edit_with_proxy, test/test_fx.py::TestFX::test_graph_fns, test/test_fx.py::TestFX::test_graph_module, test/test_fx.py::TestFX::test_graph_module_init_buffer_param_copied_dict_init, test/test_fx.py::TestFX::test_graph_module_init_buffer_param_copied_mod_init, test/test_fx.py::TestFX::test_graph_module_replicate_for_dp, test/test_fx.py::TestFX::test_graph_unique_names, test/test_fx.py::TestFX::test_graph_unique_names_manual, test/test_fx.py::TestFX::test_immutable_dict_pytree_ops, test/test_fx.py::TestFX::test_immutable_list_pytree_ops, test/test_fx.py::TestFX::test_imul_code_print, test/test_fx.py::TestFX::test_inf_nan, test/test_fx.py::TestFX::test_inf_nan_kwds, test/test_fx.py::TestFX::test_informative_co_filename, test/test_fx.py::TestFX::test_inline_graph, test/test_fx.py::TestFX::test_insert_arg, test/test_fx.py::TestFX::test_insertion_point, test/test_fx.py::TestFX::test_interpreter, test/test_fx.py::TestFX::test_interpreter_default_args, test/test_fx.py::TestFX::test_interpreter_gc_values, test/test_fx.py::TestFX::test_interpreter_noop_resnet18, test/test_fx.py::TestFX::test_interpreter_not_enough_args, test/test_fx.py::TestFX::test_interpreter_onthefly_swap, test/test_fx.py::TestFX::test_interpreter_other_graph, test/test_fx.py::TestFX::test_interpreter_partial_eval, test/test_fx.py::TestFX::test_interpreter_run_node_override, test/test_fx.py::TestFX::test_interpreter_star_args, test/test_fx.py::TestFX::test_interpreter_with_codegen, test/test_fx.py::TestFX::test_layout, test/test_fx.py::TestFX::test_leaf_module, test/test_fx.py::TestFX::test_lineno_map, test/test_fx.py::TestFX::test_matmul_tracing, test/test_fx.py::TestFX::test_metadata_on_ph, test/test_fx.py::TestFX::test_module_deepcopy_edit_nodes, test/test_fx.py::TestFX::test_move_before, test/test_fx.py::TestFX::test_multi_insert_point, test/test_fx.py::TestFX::test_multiple_default_args, test/test_fx.py::TestFX::test_named_tuple_inlined, test/test_fx.py::TestFX::test_namedtuple_return_qualname, test/test_fx.py::TestFX::test_namedtuple_return_trace, test/test_fx.py::TestFX::test_native_callable, test/test_fx.py::TestFX::test_nn_module_stack, test/test_fx.py::TestFX::test_no_mutation, test/test_fx.py::TestFX::test_node_tagging, test/test_fx.py::TestFX::test_nonetype_annotation, test/test_fx.py::TestFX::test_partial_trace, test/test_fx.py::TestFX::test_pickle_custom_import, test/test_fx.py::TestFX::test_pickle_graphmodule, test/test_fx.py::TestFX::test_pickle_nonetype_annotation, test/test_fx.py::TestFX::test_pickle_torch_custom_ops, test/test_fx.py::TestFX::test_prepend_self, test/test_fx.py::TestFX::test_pretty_print, test/test_fx.py::TestFX::test_pretty_print_graph, test/test_fx.py::TestFX::test_pretty_print_node, test/test_fx.py::TestFX::test_pretty_print_targets, test/test_fx.py::TestFX::test_print_graph, test/test_fx.py::TestFX::test_profiler_ranges_side_effect, test/test_fx.py::TestFX::test_proxy_deepcopy_with_tracer, test/test_fx.py::TestFX::test_proxy_deepcopy_without_tracer, test/test_fx.py::TestFX::test_pytree, test/test_fx.py::TestFX::test_pytree_concrete, test/test_fx.py::TestFX::test_reassign_args_kwargs_uses, test/test_fx.py::TestFX::test_regular_and_default_args, test/test_fx.py::TestFX::test_remove_uses, test/test_fx.py::TestFX::test_remove_uses_with_custom_filter, test/test_fx.py::TestFX::test_replace_input, test/test_fx.py::TestFX::test_replace_uses, test/test_fx.py::TestFX::test_reserved_getattr, test/test_fx.py::TestFX::test_return_tuple, test/test_fx.py::TestFX::test_return_type_exists, test/test_fx.py::TestFX::test_return_type_exists_pre_pep585, test/test_fx.py::TestFX::test_script_method_trace, test/test_fx.py::TestFX::test_script_tensor_constant, test/test_fx.py::TestFX::test_sequential, test/test_fx.py::TestFX::test_shape_prop_aggregate, test/test_fx.py::TestFX::test_shape_prop_layout, test/test_fx.py::TestFX::test_shape_prop_layout_3d, test/test_fx.py::TestFX::test_shape_prop_unbacked_sym, test/test_fx.py::TestFX::test_single_default_arg, test/test_fx.py::TestFX::test_snake_case, test/test_fx.py::TestFX::test_sqrt, test/test_fx.py::TestFX::test_stack_traces, test/test_fx.py::TestFX::test_stack_traces_with_transformer, test/test_fx.py::TestFX::test_string_literal_return, test/test_fx.py::TestFX::test_submodule_manipulation_API, test/test_fx.py::TestFX::test_symbolic_trace_assert, test/test_fx.py::TestFX::test_symbolic_trace_sequential, test/test_fx.py::TestFX::test_tensor_attribute, test/test_fx.py::TestFX::test_tensor_attribute_coalseced, test/test_fx.py::TestFX::test_tensor_constant, test/test_fx.py::TestFX::test_throw_out_variant, test/test_fx.py::TestFX::test_torch_custom_ops, test/test_fx.py::TestFX::test_torch_fx_getattr, test/test_fx.py::TestFX::test_torch_fx_len, test/test_fx.py::TestFX::test_torch_op_overloads, test/test_fx.py::TestFX::test_torchbind_class_attribute_in_fx, test/test_fx.py::TestFX::test_torchbind_class_attribute_in_fx_tensor_arg, test/test_fx.py::TestFX::test_trace_buffer_slice, test/test_fx.py::TestFX::test_trace_dict_int_keys, test/test_fx.py::TestFX::test_trace_dict_proxy_keys, test/test_fx.py::TestFX::test_trace_fn_constant, test/test_fx.py::TestFX::test_trace_function, test/test_fx.py::TestFX::test_trace_multiple_funcs, test/test_fx.py::TestFX::test_trace_return_dataclass, test/test_fx.py::TestFX::test_trace_return_dataclass_nested, test/test_fx.py::TestFX::test_trace_return_namedtuple, test/test_fx.py::TestFX::test_tracing_graphmodules_as_leaf_submodules, test/test_fx.py::TestFX::test_transformer_multi_outputs, test/test_fx.py::TestFX::test_transformer_noop, test/test_fx.py::TestFX::test_transformer_op_swap, test/test_fx.py::TestFX::test_transformer_preserves_nn_module_stack_for_get_attr, test/test_fx.py::TestFX::test_tuple_no_subscript, test/test_fx.py::TestFX::test_typename_print, test/test_fx.py::TestFX::test_typename_print_pre_pep585, test/test_fx.py::TestFX::test_unpack, test/test_fx.py::TestFX::test_unpack_dict_better_error, test/test_fx.py::TestFX::test_unpack_list_better_error, test/test_fx.py::TestFX::test_update_args_api, test/test_fx.py::TestFX::test_update_args_kwargs_yells_at_you, test/test_fx.py::TestFX::test_update_kwargs_api, test/test_fx.py::TestFX::test_user_friendly_call_provenance_with_function, test/test_fx.py::TestFX::test_user_friendly_call_provenance_with_module, test/test_fx.py::TestFX::test_varargs_concrete, test/test_fx.py::TestFX::test_wrap, test/test_fx.py::TestFX::test_wrap_decorated_function, test/test_fx.py::TestFX::test_wrap_fn_directly, test/test_fx.py::TestFX::test_wrap_with_submodule, test/test_fx.py::TestFX::test_wrapped_method, test/test_fx.py::TestFX::test_wrapped_retrace, test/test_fx.py::TestFX::test_wrapped_via_decorator, test/test_fx.py::TestFX::test_wrapped_via_decorator_and_transformed, test/test_fx.py::TestFX::test_wrong_target_type, test/test_fx.py::TestFX::test_wrong_topo, test/test_fx.py::TestFXAPIBackwardCompatibility::test_adding_side_effect_function, test/test_fx.py::TestFXAPIBackwardCompatibility::test_class_member_back_compat, test/test_fx.py::TestFXAPIBackwardCompatibility::test_function_back_compat, test/test_fx.py::TestFXAPIBackwardCompatibility::test_preserve_unused_attr_after_unpickle, test/test_fx.py::TestFXAPIBackwardCompatibility::test_public_api_surface, test/test_fx.py::TestFunctionalTracing::test_nn_functional_adaptive_avg_pool1d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_adaptive_avg_pool2d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_adaptive_avg_pool3d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_adaptive_max_pool1d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_adaptive_max_pool1d_with_indices, test/test_fx.py::TestFunctionalTracing::test_nn_functional_adaptive_max_pool2d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_adaptive_max_pool2d_with_indices, test/test_fx.py::TestFunctionalTracing::test_nn_functional_adaptive_max_pool3d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_adaptive_max_pool3d_with_indices, test/test_fx.py::TestFunctionalTracing::test_nn_functional_affine_grid, test/test_fx.py::TestFunctionalTracing::test_nn_functional_alpha_dropout, test/test_fx.py::TestFunctionalTracing::test_nn_functional_avg_pool1d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_avg_pool2d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_avg_pool3d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_batch_norm, test/test_fx.py::TestFunctionalTracing::test_nn_functional_bilinear, test/test_fx.py::TestFunctionalTracing::test_nn_functional_binary_cross_entropy, test/test_fx.py::TestFunctionalTracing::test_nn_functional_binary_cross_entropy_with_logits, test/test_fx.py::TestFunctionalTracing::test_nn_functional_celu, test/test_fx.py::TestFunctionalTracing::test_nn_functional_celu_, test/test_fx.py::TestFunctionalTracing::test_nn_functional_channel_shuffle, test/test_fx.py::TestFunctionalTracing::test_nn_functional_conv1d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_conv2d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_conv3d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_conv_tbc, test/test_fx.py::TestFunctionalTracing::test_nn_functional_conv_transpose1d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_conv_transpose2d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_conv_transpose3d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_cosine_embedding_loss, test/test_fx.py::TestFunctionalTracing::test_nn_functional_cosine_similarity, test/test_fx.py::TestFunctionalTracing::test_nn_functional_cross_entropy, test/test_fx.py::TestFunctionalTracing::test_nn_functional_ctc_loss, test/test_fx.py::TestFunctionalTracing::test_nn_functional_dropout, test/test_fx.py::TestFunctionalTracing::test_nn_functional_dropout1d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_dropout2d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_dropout3d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_elu, test/test_fx.py::TestFunctionalTracing::test_nn_functional_elu_, test/test_fx.py::TestFunctionalTracing::test_nn_functional_embedding, test/test_fx.py::TestFunctionalTracing::test_nn_functional_embedding_bag, test/test_fx.py::TestFunctionalTracing::test_nn_functional_feature_alpha_dropout, test/test_fx.py::TestFunctionalTracing::test_nn_functional_fold, test/test_fx.py::TestFunctionalTracing::test_nn_functional_fractional_max_pool2d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_fractional_max_pool2d_with_indices, test/test_fx.py::TestFunctionalTracing::test_nn_functional_fractional_max_pool3d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_fractional_max_pool3d_with_indices, test/test_fx.py::TestFunctionalTracing::test_nn_functional_gaussian_nll_loss, test/test_fx.py::TestFunctionalTracing::test_nn_functional_gelu, test/test_fx.py::TestFunctionalTracing::test_nn_functional_glu, test/test_fx.py::TestFunctionalTracing::test_nn_functional_grid_sample, test/test_fx.py::TestFunctionalTracing::test_nn_functional_group_norm, test/test_fx.py::TestFunctionalTracing::test_nn_functional_gumbel_softmax, test/test_fx.py::TestFunctionalTracing::test_nn_functional_hardshrink, test/test_fx.py::TestFunctionalTracing::test_nn_functional_hardsigmoid, test/test_fx.py::TestFunctionalTracing::test_nn_functional_hardswish, test/test_fx.py::TestFunctionalTracing::test_nn_functional_hardtanh, test/test_fx.py::TestFunctionalTracing::test_nn_functional_hardtanh_, test/test_fx.py::TestFunctionalTracing::test_nn_functional_hinge_embedding_loss, test/test_fx.py::TestFunctionalTracing::test_nn_functional_huber_loss, test/test_fx.py::TestFunctionalTracing::test_nn_functional_instance_norm, test/test_fx.py::TestFunctionalTracing::test_nn_functional_interpolate, test/test_fx.py::TestFunctionalTracing::test_nn_functional_kl_div, test/test_fx.py::TestFunctionalTracing::test_nn_functional_l1_loss, test/test_fx.py::TestFunctionalTracing::test_nn_functional_layer_norm, test/test_fx.py::TestFunctionalTracing::test_nn_functional_leaky_relu, test/test_fx.py::TestFunctionalTracing::test_nn_functional_leaky_relu_, test/test_fx.py::TestFunctionalTracing::test_nn_functional_linear, test/test_fx.py::TestFunctionalTracing::test_nn_functional_local_response_norm, test/test_fx.py::TestFunctionalTracing::test_nn_functional_log_softmax, test/test_fx.py::TestFunctionalTracing::test_nn_functional_logsigmoid, test/test_fx.py::TestFunctionalTracing::test_nn_functional_lp_pool1d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_lp_pool2d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_lp_pool3d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_margin_ranking_loss, test/test_fx.py::TestFunctionalTracing::test_nn_functional_max_pool1d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_max_pool1d_with_indices, test/test_fx.py::TestFunctionalTracing::test_nn_functional_max_pool2d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_max_pool2d_with_indices, test/test_fx.py::TestFunctionalTracing::test_nn_functional_max_pool3d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_max_pool3d_with_indices, test/test_fx.py::TestFunctionalTracing::test_nn_functional_max_unpool1d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_max_unpool2d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_max_unpool3d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_mish, test/test_fx.py::TestFunctionalTracing::test_nn_functional_mse_loss, test/test_fx.py::TestFunctionalTracing::test_nn_functional_multi_head_attention_forward, test/test_fx.py::TestFunctionalTracing::test_nn_functional_multi_margin_loss, test/test_fx.py::TestFunctionalTracing::test_nn_functional_multilabel_margin_loss, test/test_fx.py::TestFunctionalTracing::test_nn_functional_multilabel_soft_margin_loss, test/test_fx.py::TestFunctionalTracing::test_nn_functional_native_channel_shuffle, test/test_fx.py::TestFunctionalTracing::test_nn_functional_nll_loss, test/test_fx.py::TestFunctionalTracing::test_nn_functional_normalize, test/test_fx.py::TestFunctionalTracing::test_nn_functional_one_hot, test/test_fx.py::TestFunctionalTracing::test_nn_functional_pad, test/test_fx.py::TestFunctionalTracing::test_nn_functional_pairwise_distance, test/test_fx.py::TestFunctionalTracing::test_nn_functional_pdist, test/test_fx.py::TestFunctionalTracing::test_nn_functional_pixel_shuffle, test/test_fx.py::TestFunctionalTracing::test_nn_functional_pixel_unshuffle, test/test_fx.py::TestFunctionalTracing::test_nn_functional_poisson_nll_loss, test/test_fx.py::TestFunctionalTracing::test_nn_functional_prelu, test/test_fx.py::TestFunctionalTracing::test_nn_functional_relu, test/test_fx.py::TestFunctionalTracing::test_nn_functional_relu6, test/test_fx.py::TestFunctionalTracing::test_nn_functional_relu_, test/test_fx.py::TestFunctionalTracing::test_nn_functional_rms_norm, test/test_fx.py::TestFunctionalTracing::test_nn_functional_rrelu, test/test_fx.py::TestFunctionalTracing::test_nn_functional_rrelu_, test/test_fx.py::TestFunctionalTracing::test_nn_functional_scaled_dot_product_attention, test/test_fx.py::TestFunctionalTracing::test_nn_functional_selu, test/test_fx.py::TestFunctionalTracing::test_nn_functional_selu_, test/test_fx.py::TestFunctionalTracing::test_nn_functional_silu, test/test_fx.py::TestFunctionalTracing::test_nn_functional_smooth_l1_loss, test/test_fx.py::TestFunctionalTracing::test_nn_functional_soft_margin_loss, test/test_fx.py::TestFunctionalTracing::test_nn_functional_softmax, test/test_fx.py::TestFunctionalTracing::test_nn_functional_softmin, test/test_fx.py::TestFunctionalTracing::test_nn_functional_softplus, test/test_fx.py::TestFunctionalTracing::test_nn_functional_softshrink, test/test_fx.py::TestFunctionalTracing::test_nn_functional_threshold, test/test_fx.py::TestFunctionalTracing::test_nn_functional_threshold_, test/test_fx.py::TestFunctionalTracing::test_nn_functional_triplet_margin_loss, test/test_fx.py::TestFunctionalTracing::test_nn_functional_triplet_margin_with_distance_loss, test/test_fx.py::TestFunctionalTracing::test_nn_functional_unfold, test/test_fx.py::TestFunctionalTracing::test_nn_functional_upsample, test/test_fx.py::TestFunctionalTracing::test_nn_functional_upsample_bilinear, test/test_fx.py::TestFunctionalTracing::test_nn_functional_upsample_nearest, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_H_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_T_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive___getitem___cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive___radd___cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive___rdiv___cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive___rmatmul___cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive___rmod___cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive___rmul___cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive___rpow___cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive___rsub___cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive__batch_norm_with_update_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive__chunk_cat_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive__native_batch_norm_legit_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive__segment_reduce_lengths_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive__segment_reduce_offsets_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive__softmax_backward_data_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive__unsafe_masked_index_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive__unsafe_masked_index_put_accumulate_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive__upsample_bilinear2d_aa_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_abs_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_acos_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_acosh_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_add_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_addbmm_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_addcdiv_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_addcmul_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_addmm_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_addmm_decomposed_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_addmv_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_addr_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_alias_copy_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_all_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_allclose_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_amax_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_amin_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_aminmax_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_angle_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_any_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_arange_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_argmax_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_argmin_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_argsort_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_argwhere_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_as_strided_copy_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_as_strided_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_as_strided_partial_views_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_as_strided_scatter_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_asin_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_asinh_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_atan2_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_atan_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_atanh_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_atleast_1d_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_atleast_2d_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_atleast_3d_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_baddbmm_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_bernoulli_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_bfloat16_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_block_diag_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_bmm_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_bool_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_broadcast_shapes_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_broadcast_tensors_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_broadcast_to_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_bucketize_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_byte_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_cartesian_prod_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_cat_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_cauchy_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_cdist_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_cdouble_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_ceil_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_cfloat_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_chalf_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_char_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_cholesky_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_cholesky_inverse_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_cholesky_solve_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_chunk_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_clamp_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_clamp_max_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_clamp_min_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_clone_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_column_stack_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_combinations_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_complex_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_conj_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_conj_physical_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_constant_pad_nd_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_contiguous_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_copysign_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_corrcoef_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_cos_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_cosh_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_count_nonzero_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_cov_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_cross_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_cummax_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_cummin_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_cumprod_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_cumsum_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_cumulative_trapezoid_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_deg2rad_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_diag_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_diag_embed_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_diagflat_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_diagonal_copy_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_diagonal_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_diagonal_scatter_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_diff_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_digamma_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_dist_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_div_floor_rounding_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_div_no_rounding_mode_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_div_trunc_rounding_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_dot_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_double_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_dsplit_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_dstack_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_einsum_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_empty_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_empty_like_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_empty_permuted_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_empty_strided_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_eq_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_equal_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_erf_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_erfc_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_erfinv_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_exp2_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_exp_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_expand_as_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_expand_copy_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_expand_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_expm1_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_exponential_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_eye_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_fft_fft2_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_fft_fft_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_fft_fftn_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_fft_fftshift_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_fft_hfft2_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_fft_hfft_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_fft_hfftn_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_fft_ifft2_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_fft_ifft_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_fft_ifftn_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_fft_ifftshift_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_fft_ihfft2_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_fft_ihfft_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_fft_ihfftn_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_fft_irfft2_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_fft_irfft_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_fft_irfftn_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_fft_rfft2_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_fft_rfft_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_fft_rfftn_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_fill_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_flatten_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_flip_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_fliplr_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_flipud_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_float_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_float_power_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_floor_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_floor_divide_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_fmax_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_fmin_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_fmod_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_frac_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_frexp_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_full_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_full_like_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_gather_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_ge_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_geometric_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_geqrf_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_gradient_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_grid_sampler_2d_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_grid_sampler_3d_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_gt_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_half_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_hash_tensor_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_heaviside_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_histc_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_histogram_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_histogramdd_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_hsplit_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_hstack_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_hypot_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_i0_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_igamma_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_igammac_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_index_add_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_index_copy_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_index_fill_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_index_put_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_index_reduce_amax_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_index_reduce_amin_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_index_reduce_mean_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_index_reduce_prod_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_index_select_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_inner_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_int_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_isclose_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_isfinite_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_isin_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_isinf_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_isnan_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_isneginf_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_isposinf_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_isreal_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_item_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_jiterator_2inputs_2outputs_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_jiterator_4inputs_with_extra_args_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_jiterator_binary_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_jiterator_binary_return_by_ref_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_jiterator_unary_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_kron_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_kthvalue_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_ldexp_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_le_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_lerp_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_lgamma_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_cholesky_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_cholesky_ex_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_cond_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_cross_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_det_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_diagonal_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_eig_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_eigh_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_eigvals_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_eigvalsh_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_householder_product_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_inv_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_inv_ex_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_ldl_factor_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_ldl_factor_ex_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_ldl_solve_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_lstsq_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_lstsq_grad_oriented_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_lu_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_lu_factor_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_lu_factor_ex_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_lu_solve_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_matrix_norm_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_matrix_power_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_matrix_rank_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_matrix_rank_hermitian_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_multi_dot_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_norm_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_norm_subgradients_at_zero_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_pinv_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_pinv_hermitian_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_pinv_singular_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_qr_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_slogdet_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_solve_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_solve_ex_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_solve_triangular_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_svd_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_svdvals_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_tensorinv_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_tensorsolve_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_vander_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_vecdot_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_vector_norm_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linspace_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linspace_tensor_overload_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_log10_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_log1p_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_log2_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_log_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_log_normal_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_log_softmax_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_log_softmax_with_dtype_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_logaddexp2_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_logaddexp_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_logcumsumexp_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_logdet_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_logical_and_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_logical_not_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_logical_or_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_logical_xor_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_logit_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_logspace_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_logspace_tensor_overload_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_logsumexp_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_long_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_lt_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_lu_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_lu_solve_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_lu_unpack_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_mH_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_mT_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_masked_amax_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_masked_amin_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_masked_argmax_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_masked_argmin_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_masked_cumprod_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_masked_cumsum_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_masked_fill_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_masked_log_softmax_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_masked_logaddexp_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_masked_logsumexp_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_masked_mean_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_masked_median_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_masked_norm_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_masked_normalize_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_masked_prod_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_masked_scatter_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_masked_select_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_masked_softmax_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_masked_softmin_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_masked_std_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_masked_sum_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_masked_var_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_matmul_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_matrix_exp_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_max_binary_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_max_pool2d_with_indices_backward_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_max_reduction_no_dim_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_max_reduction_with_dim_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_maximum_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_mean_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_median_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_meshgrid_list_of_tensors_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_meshgrid_variadic_tensors_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_min_binary_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_min_reduction_no_dim_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_min_reduction_with_dim_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_minimum_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_mm_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_mode_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_movedim_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_msort_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_mul_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_multinomial_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_mv_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_mvlgamma_mvlgamma_p_1_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_mvlgamma_mvlgamma_p_3_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_mvlgamma_mvlgamma_p_5_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nan_to_num_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nanmean_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nanmedian_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nanquantile_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nansum_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_narrow_copy_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_narrow_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_native_batch_norm_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_native_dropout_backward_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_native_layer_norm_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_ne_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_neg_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_new_empty_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_new_empty_strided_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_new_full_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_new_ones_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_new_zeros_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nextafter_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_adaptive_avg_pool1d_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_adaptive_avg_pool2d_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_adaptive_avg_pool3d_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_adaptive_max_pool1d_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_adaptive_max_pool2d_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_adaptive_max_pool3d_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_alpha_dropout_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_avg_pool1d_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_avg_pool2d_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_avg_pool3d_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_batch_norm_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_bilinear_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_binary_cross_entropy_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_binary_cross_entropy_with_logits_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_celu_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_channel_shuffle_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_conv1d_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_conv2d_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_conv3d_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_conv_transpose1d_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_conv_transpose2d_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_conv_transpose3d_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_cosine_embedding_loss_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_cosine_similarity_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_cross_entropy_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_ctc_loss_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_dropout2d_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_dropout3d_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_dropout_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_elu_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_embedding_bag_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_embedding_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_feature_alpha_dropout_with_train_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_feature_alpha_dropout_without_train_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_fractional_max_pool2d_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_fractional_max_pool3d_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_gaussian_nll_loss_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_gelu_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_glu_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_grid_sample_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_group_norm_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_hardshrink_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_hardsigmoid_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_hardswish_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_hardtanh_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_hinge_embedding_loss_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_huber_loss_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_instance_norm_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_interpolate_area_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_interpolate_bicubic_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_interpolate_bilinear_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_interpolate_linear_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_interpolate_nearest-exact_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_interpolate_nearest_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_interpolate_trilinear_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_kl_div_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_l1_loss_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_layer_norm_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_leaky_relu_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_linear_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_local_response_norm_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_logsigmoid_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_margin_ranking_loss_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_max_pool1d_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_max_pool2d_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_max_pool3d_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_max_unpool1d_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_max_unpool1d_grad_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_max_unpool2d_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_max_unpool2d_grad_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_max_unpool3d_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_max_unpool3d_grad_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_mish_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_mse_loss_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_multi_head_attention_forward_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_multi_margin_loss_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_multilabel_margin_loss_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_multilabel_soft_margin_loss_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_nll_loss_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_normalize_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_pad_circular_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_pad_constant_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_pad_reflect_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_pad_replicate_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_pad_replicate_negative_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_pairwise_distance_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_pdist_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_pixel_shuffle_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_pixel_unshuffle_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_poisson_nll_loss_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_prelu_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_relu6_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_relu_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_rms_norm_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_rrelu_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_scaled_dot_product_attention_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_selu_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_silu_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_smooth_l1_loss_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_soft_margin_loss_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_softmin_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_softmin_with_dtype_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_softplus_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_softshrink_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_softsign_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_tanhshrink_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_threshold_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_triplet_margin_loss_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_triplet_margin_with_distance_loss_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_unfold_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_upsample_bilinear_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_upsample_nearest_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nonzero_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nonzero_static_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_norm_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_norm_fro_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_norm_inf_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_norm_nuc_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_normal_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_normal_in_place_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_normal_number_mean_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_ones_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_ones_like_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_ormqr_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_outer_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_pca_lowrank_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_permute_copy_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_permute_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_pinverse_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_polar_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_polygamma_polygamma_n_0_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_polygamma_polygamma_n_1_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_polygamma_polygamma_n_2_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_polygamma_polygamma_n_3_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_polygamma_polygamma_n_4_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_positive_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_pow_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_prod_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_put_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_qr_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_quantile_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_rad2deg_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_rand_like_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_randint_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_randint_like_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_randn_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_randn_like_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_ravel_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_real_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_reciprocal_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_remainder_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_renorm_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_repeat_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_repeat_interleave_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_reshape_as_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_reshape_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_resize__cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_resize_as__cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_resolve_conj_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_resolve_neg_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_roll_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_rot90_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_round_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_round_decimals_0_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_round_decimals_3_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_round_decimals_neg_3_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_rsqrt_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_rsub_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_scalar_tensor_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_scatter_add_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_scatter_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_scatter_reduce_amax_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_scatter_reduce_amin_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_scatter_reduce_mean_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_scatter_reduce_prod_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_scatter_reduce_sum_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_searchsorted_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_select_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_select_scatter_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_sgn_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_short_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_sigmoid_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_sign_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_signal_windows_bartlett_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_signal_windows_blackman_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_signal_windows_cosine_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_signal_windows_exponential_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_signal_windows_gaussian_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_signal_windows_general_cosine_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_signal_windows_general_hamming_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_signal_windows_hamming_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_signal_windows_hann_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_signal_windows_kaiser_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_signal_windows_nuttall_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_signbit_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_sin_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_sinc_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_sinh_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_slice_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_slice_scatter_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_softmax_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_softmax_with_dtype_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_sort_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_sparse_mm_reduce_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_sparse_sampled_addmm_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_airy_ai_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_bessel_j0_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_bessel_j1_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_bessel_y0_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_bessel_y1_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_chebyshev_polynomial_t_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_chebyshev_polynomial_u_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_chebyshev_polynomial_v_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_chebyshev_polynomial_w_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_entr_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_erfcx_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_hermite_polynomial_h_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_hermite_polynomial_he_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_i0e_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_i1_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_i1e_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_laguerre_polynomial_l_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_legendre_polynomial_p_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_log_ndtr_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_modified_bessel_i0_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_modified_bessel_i1_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_modified_bessel_k0_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_modified_bessel_k1_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_ndtr_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_ndtri_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_polygamma_special_polygamma_n_0_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_scaled_modified_bessel_k0_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_scaled_modified_bessel_k1_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_shifted_chebyshev_polynomial_t_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_shifted_chebyshev_polynomial_u_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_shifted_chebyshev_polynomial_v_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_shifted_chebyshev_polynomial_w_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_spherical_bessel_j0_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_xlog1py_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_zeta_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_split_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_split_list_args_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_split_with_sizes_copy_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_split_with_sizes_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_sqrt_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_square_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_squeeze_copy_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_squeeze_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_squeeze_multiple_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_stack_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_std_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_std_mean_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_std_mean_unbiased_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_std_unbiased_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_stft_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_sub_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_sum_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_sum_to_size_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_svd_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_svd_lowrank_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_t_copy_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_t_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_take_along_dim_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_take_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_tan_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_tanh_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_tensor_split_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_tensordot_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_tile_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_to_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_to_sparse_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_topk_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_torch_ops_aten__safe_softmax_default_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_trace_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_transpose_copy_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_transpose_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_trapezoid_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_trapz_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_triangular_solve_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_tril_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_triu_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_true_divide_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_trunc_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_unbind_copy_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_unbind_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_unflatten_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_unfold_copy_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_unfold_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_uniform_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_unique_consecutive_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_unique_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_unsafe_chunk_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_unsafe_split_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_unsqueeze_copy_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_unsqueeze_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_var_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_var_mean_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_var_mean_unbiased_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_var_unbiased_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_vdot_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_view_as_complex_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_view_as_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_view_copy_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_view_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_vsplit_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_vstack_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_where_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_xlogy_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_zero__cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_zeros_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_zeros_like_cpu_float32, test/test_fx.py::TestVisionTracing::test_torchvision_models_alexnet, test/test_fx.py::TestVisionTracing::test_torchvision_models_convnext_base, test/test_fx.py::TestVisionTracing::test_torchvision_models_convnext_large, test/test_fx.py::TestVisionTracing::test_torchvision_models_convnext_small, test/test_fx.py::TestVisionTracing::test_torchvision_models_convnext_tiny, test/test_fx.py::TestVisionTracing::test_torchvision_models_densenet121, test/test_fx.py::TestVisionTracing::test_torchvision_models_densenet161, test/test_fx.py::TestVisionTracing::test_torchvision_models_densenet169, test/test_fx.py::TestVisionTracing::test_torchvision_models_densenet201, test/test_fx.py::TestVisionTracing::test_torchvision_models_detection_fasterrcnn_mobilenet_v3_large_320_fpn, test/test_fx.py::TestVisionTracing::test_torchvision_models_detection_fasterrcnn_mobilenet_v3_large_fpn, test/test_fx.py::TestVisionTracing::test_torchvision_models_detection_fasterrcnn_resnet50_fpn, test/test_fx.py::TestVisionTracing::test_torchvision_models_detection_fasterrcnn_resnet50_fpn_v2, test/test_fx.py::TestVisionTracing::test_torchvision_models_detection_fcos_resnet50_fpn, test/test_fx.py::TestVisionTracing::test_torchvision_models_detection_keypointrcnn_resnet50_fpn, test/test_fx.py::TestVisionTracing::test_torchvision_models_detection_maskrcnn_resnet50_fpn, test/test_fx.py::TestVisionTracing::test_torchvision_models_detection_maskrcnn_resnet50_fpn_v2, test/test_fx.py::TestVisionTracing::test_torchvision_models_detection_retinanet_resnet50_fpn, test/test_fx.py::TestVisionTracing::test_torchvision_models_detection_retinanet_resnet50_fpn_v2, test/test_fx.py::TestVisionTracing::test_torchvision_models_detection_ssd300_vgg16, test/test_fx.py::TestVisionTracing::test_torchvision_models_detection_ssdlite320_mobilenet_v3_large, test/test_fx.py::TestVisionTracing::test_torchvision_models_efficientnet_b0, test/test_fx.py::TestVisionTracing::test_torchvision_models_efficientnet_b1, test/test_fx.py::TestVisionTracing::test_torchvision_models_efficientnet_b2, test/test_fx.py::TestVisionTracing::test_torchvision_models_efficientnet_b3, test/test_fx.py::TestVisionTracing::test_torchvision_models_efficientnet_b4, test/test_fx.py::TestVisionTracing::test_torchvision_models_efficientnet_b5, test/test_fx.py::TestVisionTracing::test_torchvision_models_efficientnet_b6, test/test_fx.py::TestVisionTracing::test_torchvision_models_efficientnet_b7, test/test_fx.py::TestVisionTracing::test_torchvision_models_efficientnet_v2_l, test/test_fx.py::TestVisionTracing::test_torchvision_models_efficientnet_v2_m, test/test_fx.py::TestVisionTracing::test_torchvision_models_efficientnet_v2_s, test/test_fx.py::TestVisionTracing::test_torchvision_models_googlenet, test/test_fx.py::TestVisionTracing::test_torchvision_models_inception_v3, test/test_fx.py::TestVisionTracing::test_torchvision_models_maxvit_t, test/test_fx.py::TestVisionTracing::test_torchvision_models_mnasnet0_5, test/test_fx.py::TestVisionTracing::test_torchvision_models_mnasnet0_75, test/test_fx.py::TestVisionTracing::test_torchvision_models_mnasnet1_0, test/test_fx.py::TestVisionTracing::test_torchvision_models_mnasnet1_3, test/test_fx.py::TestVisionTracing::test_torchvision_models_mobilenet_v2, test/test_fx.py::TestVisionTracing::test_torchvision_models_mobilenet_v3_large, test/test_fx.py::TestVisionTracing::test_torchvision_models_mobilenet_v3_small, test/test_fx.py::TestVisionTracing::test_torchvision_models_regnet_x_16gf, test/test_fx.py::TestVisionTracing::test_torchvision_models_regnet_x_1_6gf, test/test_fx.py::TestVisionTracing::test_torchvision_models_regnet_x_32gf, test/test_fx.py::TestVisionTracing::test_torchvision_models_regnet_x_3_2gf, test/test_fx.py::TestVisionTracing::test_torchvision_models_regnet_x_400mf, test/test_fx.py::TestVisionTracing::test_torchvision_models_regnet_x_800mf, test/test_fx.py::TestVisionTracing::test_torchvision_models_regnet_x_8gf, test/test_fx.py::TestVisionTracing::test_torchvision_models_regnet_y_128gf, test/test_fx.py::TestVisionTracing::test_torchvision_models_regnet_y_16gf, test/test_fx.py::TestVisionTracing::test_torchvision_models_regnet_y_1_6gf, test/test_fx.py::TestVisionTracing::test_torchvision_models_regnet_y_32gf, test/test_fx.py::TestVisionTracing::test_torchvision_models_regnet_y_3_2gf, test/test_fx.py::TestVisionTracing::test_torchvision_models_regnet_y_400mf, test/test_fx.py::TestVisionTracing::test_torchvision_models_regnet_y_800mf, test/test_fx.py::TestVisionTracing::test_torchvision_models_regnet_y_8gf, test/test_fx.py::TestVisionTracing::test_torchvision_models_resnet101, test/test_fx.py::TestVisionTracing::test_torchvision_models_resnet152, test/test_fx.py::TestVisionTracing::test_torchvision_models_resnet18, test/test_fx.py::TestVisionTracing::test_torchvision_models_resnet34, test/test_fx.py::TestVisionTracing::test_torchvision_models_resnet50, test/test_fx.py::TestVisionTracing::test_torchvision_models_resnext101_32x8d, test/test_fx.py::TestVisionTracing::test_torchvision_models_resnext101_64x4d, test/test_fx.py::TestVisionTracing::test_torchvision_models_resnext50_32x4d, test/test_fx.py::TestVisionTracing::test_torchvision_models_segmentation_deeplabv3_mobilenet_v3_large, test/test_fx.py::TestVisionTracing::test_torchvision_models_segmentation_deeplabv3_resnet101, test/test_fx.py::TestVisionTracing::test_torchvision_models_segmentation_deeplabv3_resnet50, test/test_fx.py::TestVisionTracing::test_torchvision_models_segmentation_fcn_resnet101, test/test_fx.py::TestVisionTracing::test_torchvision_models_segmentation_fcn_resnet50, test/test_fx.py::TestVisionTracing::test_torchvision_models_segmentation_lraspp_mobilenet_v3_large, test/test_fx.py::TestVisionTracing::test_torchvision_models_shufflenet_v2_x0_5, test/test_fx.py::TestVisionTracing::test_torchvision_models_shufflenet_v2_x1_0, test/test_fx.py::TestVisionTracing::test_torchvision_models_shufflenet_v2_x1_5, test/test_fx.py::TestVisionTracing::test_torchvision_models_shufflenet_v2_x2_0, test/test_fx.py::TestVisionTracing::test_torchvision_models_squeezenet1_0, test/test_fx.py::TestVisionTracing::test_torchvision_models_squeezenet1_1, test/test_fx.py::TestVisionTracing::test_torchvision_models_swin_b, test/test_fx.py::TestVisionTracing::test_torchvision_models_swin_s, test/test_fx.py::TestVisionTracing::test_torchvision_models_swin_t, test/test_fx.py::TestVisionTracing::test_torchvision_models_swin_v2_b, test/test_fx.py::TestVisionTracing::test_torchvision_models_swin_v2_s, test/test_fx.py::TestVisionTracing::test_torchvision_models_swin_v2_t, test/test_fx.py::TestVisionTracing::test_torchvision_models_vgg11, test/test_fx.py::TestVisionTracing::test_torchvision_models_vgg11_bn, test/test_fx.py::TestVisionTracing::test_torchvision_models_vgg13, test/test_fx.py::TestVisionTracing::test_torchvision_models_vgg13_bn, test/test_fx.py::TestVisionTracing::test_torchvision_models_vgg16, test/test_fx.py::TestVisionTracing::test_torchvision_models_vgg16_bn, test/test_fx.py::TestVisionTracing::test_torchvision_models_vgg19, test/test_fx.py::TestVisionTracing::test_torchvision_models_vgg19_bn, test/test_fx.py::TestVisionTracing::test_torchvision_models_video_mc3_18, test/test_fx.py::TestVisionTracing::test_torchvision_models_video_mvit_v1_b, test/test_fx.py::TestVisionTracing::test_torchvision_models_video_mvit_v2_s, test/test_fx.py::TestVisionTracing::test_torchvision_models_video_r2plus1d_18, test/test_fx.py::TestVisionTracing::test_torchvision_models_video_r3d_18, test/test_fx.py::TestVisionTracing::test_torchvision_models_video_s3d, test/test_fx.py::TestVisionTracing::test_torchvision_models_video_swin3d_b, test/test_fx.py::TestVisionTracing::test_torchvision_models_video_swin3d_s, test/test_fx.py::TestVisionTracing::test_torchvision_models_video_swin3d_t, test/test_fx.py::TestVisionTracing::test_torchvision_models_vit_b_16, test/test_fx.py::TestVisionTracing::test_torchvision_models_vit_b_32, test/test_fx.py::TestVisionTracing::test_torchvision_models_vit_h_14, test/test_fx.py::TestVisionTracing::test_torchvision_models_vit_l_16, test/test_fx.py::TestVisionTracing::test_torchvision_models_vit_l_32, test/test_fx.py::TestVisionTracing::test_torchvision_models_wide_resnet101_2, test/test_fx.py::TestVisionTracing::test_torchvision_models_wide_resnet50_2 2025-09-07T07:25:44.3448949Z 2025-09-07T07:25:44.3449211Z Running test_transformers_privateuse1 1/1 ... [2025-09-07 07:25:44.249199] 2025-09-07T07:25:44.5752631Z Processing /var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/torch_openreg 2025-09-07T07:25:44.8465317Z Preparing metadata (pyproject.toml) ... [?25l- done 2025-09-07T07:25:44.8494211Z [?25hRequirement already satisfied: torch in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch_openreg==0.0.1) (2.9.0a0+git93fb23d) 2025-09-07T07:25:44.8506086Z Requirement already satisfied: filelock in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch->torch_openreg==0.0.1) (3.19.1) 2025-09-07T07:25:44.8510688Z Requirement already satisfied: typing-extensions>=4.10.0 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch->torch_openreg==0.0.1) (4.15.0) 2025-09-07T07:25:44.8516984Z Requirement already satisfied: setuptools in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch->torch_openreg==0.0.1) (80.9.0) 2025-09-07T07:25:44.8521181Z Requirement already satisfied: sympy>=1.13.3 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch->torch_openreg==0.0.1) (1.13.3) 2025-09-07T07:25:44.8525563Z Requirement already satisfied: networkx>=2.5.1 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch->torch_openreg==0.0.1) (2.8.8) 2025-09-07T07:25:44.8530876Z Requirement already satisfied: jinja2 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch->torch_openreg==0.0.1) (3.1.6) 2025-09-07T07:25:44.8535372Z Requirement already satisfied: fsspec>=0.8.5 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch->torch_openreg==0.0.1) (2025.7.0) 2025-09-07T07:25:44.8644825Z Requirement already satisfied: mpmath<1.4,>=1.1.0 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from sympy>=1.13.3->torch->torch_openreg==0.0.1) (1.3.0) 2025-09-07T07:25:44.8673901Z Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from jinja2->torch->torch_openreg==0.0.1) (3.0.2) 2025-09-07T07:25:44.8737879Z Building wheels for collected packages: torch_openreg 2025-09-07T07:25:49.9483715Z Building wheel for torch_openreg (pyproject.toml) ... [?25l- \ | / - \ | / - \ | / - \ | / - done 2025-09-07T07:25:49.9499061Z [?25h Created wheel for torch_openreg: filename=torch_openreg-0.0.1-cp313-cp313-linux_x86_64.whl size=277882 sha256=619956c160f2e2684d10861ba7f7c966134b4e46c2e6bcc63f67a22f71222527 2025-09-07T07:25:49.9501137Z Stored in directory: /tmp/pip-ephem-wheel-cache-n53745oc/wheels/bc/4f/31/9af65770c0a69187e95f1d791df9c71156b2b3f469bce9d735 2025-09-07T07:25:49.9526015Z Successfully built torch_openreg 2025-09-07T07:25:50.0962099Z Installing collected packages: torch_openreg 2025-09-07T07:25:50.1155586Z Successfully installed torch_openreg-0.0.1 2025-09-07T07:25:50.1774332Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:25:50.1777453Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_transformers_privateuse1.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 07:25:50.177516] 2025-09-07T07:25:52.9460459Z 2025-09-07T07:25:52.9461270Z test_transformers_privateuse1 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_transformers_privateuse1_1.1_fd535687cc340b32_.log 2025-09-07T07:25:52.9463858Z Running 3 items in this shard: test/test_transformers_privateuse1.py::TestSDPAPrivateUse1Only::test_fused_sdp_choice_privateuseone, test/test_transformers_privateuse1.py::TestSDPAPrivateUse1Only::test_scaled_dot_product_fused_attention_overrideable, test/test_transformers_privateuse1.py::TestSDPAPrivateUse1Only::test_scaled_dot_product_fused_attention_overrideable_backward 2025-09-07T07:25:52.9465874Z 2025-09-07T07:25:52.9466184Z Running inductor/test_benchmark_fusion 1/1 ... [2025-09-07 07:25:52.946266] 2025-09-07T07:25:52.9466668Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:25:52.9467993Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_benchmark_fusion.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 07:25:52.946570] 2025-09-07T07:26:06.3792139Z 2025-09-07T07:26:06.3793478Z inductor/test_benchmark_fusion 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_benchmark_fusion_1.1_921aa96ed27120ba_.log 2025-09-07T07:26:06.3797502Z Running 6 items in this shard: test/inductor/test_benchmark_fusion.py::BenchmarkFusionCpuTest::test_avoid_register_spilling_cpu, test/inductor/test_benchmark_fusion.py::BenchmarkFusionCpuTest::test_foreach_kernel_cpu, test/inductor/test_benchmark_fusion.py::BenchmarkFusionCpuTest::test_register_spills_cpu, test/inductor/test_benchmark_fusion.py::BenchmarkFusionCpuTest::test_resnet18_cpu, test/inductor/test_benchmark_fusion.py::BenchmarkFusionCpuTest::test_softmax_cpu, test/inductor/test_benchmark_fusion.py::BenchmarkFusionCpuTest::test_tield_kernel_fusion_cpu 2025-09-07T07:26:06.3800496Z 2025-09-07T07:26:06.3800690Z Running test_show_pickle 1/1 ... [2025-09-07 07:26:06.379393] 2025-09-07T07:26:06.3801097Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:26:06.3802137Z 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'] ... [2025-09-07 07:26:06.379689] 2025-09-07T07:26:09.0975655Z 2025-09-07T07:26:09.0976766Z test_show_pickle 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_show_pickle_1.1_fb65404251318381_.log 2025-09-07T07:26:09.0977824Z Running 1 items in this shard: test/test_show_pickle.py::TestShowPickle::test_scripted_model 2025-09-07T07:26:09.0978252Z 2025-09-07T07:26:09.0979347Z Running test_utils 1/1 ... [2025-09-07 07:26:09.097780] 2025-09-07T07:26:09.0980016Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:26:09.0982931Z 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'] ... [2025-09-07 07:26:09.098073] 2025-09-07T07:29:22.3662138Z 2025-09-07T07:29:22.3662977Z test_utils 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_utils_1.1_1bbf5a10c4292131_.log 2025-09-07T07:29:22.6293004Z Running 6047 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::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, 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test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmm_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmm_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmm_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmm_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmm_decomposed_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmm_decomposed_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmm_decomposed_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmm_decomposed_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmm_decomposed_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmm_decomposed_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmm_decomposed_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmm_decomposed_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmm_decomposed_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmm_decomposed_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmm_decomposed_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmv_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmv_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmv_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmv_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmv_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmv_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmv_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmv_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmv_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmv_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmv_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addr_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addr_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addr_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addr_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addr_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addr_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addr_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addr_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addr_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addr_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addr_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addr_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_alias_copy_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_alias_copy_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_alias_copy_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_alias_copy_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_alias_copy_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_alias_copy_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_alias_copy_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_alias_copy_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_alias_copy_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_alias_copy_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_alias_copy_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_alias_copy_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_alias_copy_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_all_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_all_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_all_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_all_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_all_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_all_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_all_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_all_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_all_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_all_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_all_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_all_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_allclose_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_allclose_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_allclose_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_allclose_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_allclose_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_allclose_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amax_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amax_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amax_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amax_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amax_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amax_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amax_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amax_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amax_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amax_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amin_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amin_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amin_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amin_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amin_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amin_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amin_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amin_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amin_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amin_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_aminmax_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_aminmax_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_aminmax_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_aminmax_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_aminmax_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_aminmax_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_aminmax_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_aminmax_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_aminmax_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_aminmax_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_angle_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_angle_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_angle_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_angle_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_angle_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_angle_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_angle_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_angle_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_angle_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_angle_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_angle_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_angle_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_any_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_any_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_any_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_any_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_any_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_any_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_any_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_any_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_any_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_any_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_any_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_any_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_arange_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_arange_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_arange_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_arange_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_arange_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_arange_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_arange_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_arange_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_arange_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmax_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmax_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmax_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmax_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmax_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmax_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmax_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmax_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmax_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmin_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmin_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmin_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmin_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmin_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmin_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmin_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmin_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmin_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argsort_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argsort_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argsort_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argsort_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argsort_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argsort_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argsort_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argsort_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argsort_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argsort_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argwhere_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argwhere_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argwhere_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argwhere_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argwhere_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argwhere_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argwhere_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argwhere_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argwhere_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argwhere_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argwhere_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argwhere_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_copy_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_copy_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_copy_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_copy_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_copy_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_copy_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_copy_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_copy_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_copy_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_copy_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_copy_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_copy_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_copy_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_partial_views_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_partial_views_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_partial_views_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_partial_views_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_partial_views_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_partial_views_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_partial_views_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_partial_views_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_partial_views_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_partial_views_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_partial_views_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_partial_views_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_partial_views_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_scatter_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_scatter_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_scatter_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_scatter_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_scatter_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_scatter_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_scatter_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_scatter_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_scatter_cpu_int16, 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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, 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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, 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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, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftn_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftn_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftn_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftn_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftn_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftshift_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftshift_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftshift_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftshift_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftshift_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftshift_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftshift_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftshift_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftshift_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftshift_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftshift_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftshift_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftshift_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft2_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft2_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft2_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft2_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft2_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft2_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft2_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft2_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft2_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft2_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfftn_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfftn_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfftn_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfftn_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfftn_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfftn_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfftn_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfftn_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfftn_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfftn_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft2_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft2_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft2_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft2_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft2_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft2_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft2_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft2_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft2_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft2_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftn_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftn_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftn_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftn_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftn_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftn_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftn_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftn_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftn_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftn_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftshift_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftshift_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftshift_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftshift_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftshift_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftshift_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftshift_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftshift_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftshift_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftshift_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftshift_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftshift_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftshift_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft2_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft2_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft2_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft2_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft2_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft2_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft2_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft2_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfftn_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfftn_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfftn_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfftn_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfftn_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfftn_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfftn_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfftn_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft2_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft2_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft2_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft2_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft2_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft2_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft2_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft2_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft2_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft2_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfftn_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfftn_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfftn_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfftn_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfftn_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfftn_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfftn_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfftn_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfftn_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfftn_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft2_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft2_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft2_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft2_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft2_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft2_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft2_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft2_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfftn_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfftn_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfftn_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfftn_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfftn_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfftn_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfftn_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfftn_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fill_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fill_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fill_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fill_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fill_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fill_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fill_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fill_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fill_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fill_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fill_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fill_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fill_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flatten_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flatten_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flatten_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flatten_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flatten_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flatten_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flatten_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flatten_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flatten_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flatten_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flatten_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flatten_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flatten_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flip_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flip_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flip_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flip_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flip_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flip_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flip_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flip_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flip_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flip_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flip_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flip_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fliplr_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fliplr_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fliplr_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fliplr_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fliplr_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fliplr_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fliplr_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fliplr_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fliplr_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fliplr_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fliplr_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fliplr_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flipud_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flipud_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flipud_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flipud_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flipud_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flipud_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flipud_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flipud_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flipud_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flipud_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flipud_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flipud_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_power_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_power_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_power_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_power_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_power_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_power_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_power_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_power_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_power_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_power_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_power_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_power_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_divide_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_divide_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_divide_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_divide_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_divide_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_divide_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_divide_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_divide_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_divide_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmax_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmax_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmax_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmax_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmax_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmax_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmax_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmax_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmax_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmax_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmin_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmin_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmin_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmin_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmin_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmin_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmin_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmin_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmin_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmin_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmod_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmod_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmod_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmod_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmod_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmod_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmod_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmod_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmod_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_frac_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_frac_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_frac_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_frac_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_frexp_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_frexp_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_frexp_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_frexp_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_like_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_like_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_like_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_like_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_like_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_like_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_like_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_like_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_like_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_like_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_like_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_like_cpu_uint16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_like_cpu_uint32, 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_grid_sampler_3d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_grid_sampler_3d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_grid_sampler_3d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_grid_sampler_3d_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_hash_tensor_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hash_tensor_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hash_tensor_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hash_tensor_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hash_tensor_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hash_tensor_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hash_tensor_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hash_tensor_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hash_tensor_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hash_tensor_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_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, 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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, 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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, 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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, 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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, 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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, 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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, 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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__scaled_mm_cpu_float8_e4m3fn, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_torch__scaled_mm_cpu_float8_e4m3fnuz, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_torch__scaled_mm_cpu_float8_e5m2, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_torch__scaled_mm_cpu_float8_e5m2fnuz, 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, test/test_utils.py::TestTryImport::test_import_existing, test/test_utils.py::TestTryImport::test_import_imported, test/test_utils.py::TestTryImport::test_import_missing, test/test_utils.py::TestDeprecate::test_deprecated 2025-09-07T07:29:22.8399344Z 2025-09-07T07:29:22.8399560Z Running test_tensorexpr 1/1 ... [2025-09-07 07:29:22.373062] 2025-09-07T07:29:22.8399992Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:29:22.8401315Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_tensorexpr.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 07:29:22.373395] 2025-09-07T07:29:42.2134907Z 2025-09-07T07:29:42.2135851Z test_tensorexpr 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_tensorexpr_1.1_f83886f2e82c2199_.log 2025-09-07T07:29:42.2160223Z Running 74 items in this shard: test/test_tensorexpr.py::TestTensorExprFuser::test_add_const_rhs, test/test_tensorexpr.py::TestTensorExprFuser::test_add_sub, test/test_tensorexpr.py::TestTensorExprFuser::test_alias_analysis_input_and_module, test/test_tensorexpr.py::TestTensorExprFuser::test_alias_analysis_inputs, test/test_tensorexpr.py::TestTensorExprFuser::test_alias_analysis_module, test/test_tensorexpr.py::TestTensorExprFuser::test_all_combos, test/test_tensorexpr.py::TestTensorExprFuser::test_alpha, test/test_tensorexpr.py::TestTensorExprFuser::test_binary_ops, test/test_tensorexpr.py::TestTensorExprFuser::test_bitwise_ops, test/test_tensorexpr.py::TestTensorExprFuser::test_broadcast, test/test_tensorexpr.py::TestTensorExprFuser::test_broadcast3, test/test_tensorexpr.py::TestTensorExprFuser::test_broadcast_2, test/test_tensorexpr.py::TestTensorExprFuser::test_broadcast_big2, test/test_tensorexpr.py::TestTensorExprFuser::test_cat, test/test_tensorexpr.py::TestTensorExprFuser::test_cat_empty_tensors, test/test_tensorexpr.py::TestTensorExprFuser::test_cat_negative_dim, test/test_tensorexpr.py::TestTensorExprFuser::test_cat_only, test/test_tensorexpr.py::TestTensorExprFuser::test_cat_promote_inputs, test/test_tensorexpr.py::TestTensorExprFuser::test_cat_with_constant_dim, test/test_tensorexpr.py::TestTensorExprFuser::test_char, test/test_tensorexpr.py::TestTensorExprFuser::test_chunk, test/test_tensorexpr.py::TestTensorExprFuser::test_clamp, test/test_tensorexpr.py::TestTensorExprFuser::test_constant, test/test_tensorexpr.py::TestTensorExprFuser::test_double, test/test_tensorexpr.py::TestTensorExprFuser::test_double_intrinsics, test/test_tensorexpr.py::TestTensorExprFuser::test_dynamic_shape, test/test_tensorexpr.py::TestTensorExprFuser::test_easy, test/test_tensorexpr.py::TestTensorExprFuser::test_eq, test/test_tensorexpr.py::TestTensorExprFuser::test_exp_pow, test/test_tensorexpr.py::TestTensorExprFuser::test_four_arg, test/test_tensorexpr.py::TestTensorExprFuser::test_ge, test/test_tensorexpr.py::TestTensorExprFuser::test_gt, test/test_tensorexpr.py::TestTensorExprFuser::test_guard_fails, test/test_tensorexpr.py::TestTensorExprFuser::test_half_bn_relu, test/test_tensorexpr.py::TestTensorExprFuser::test_half_gelu, test/test_tensorexpr.py::TestTensorExprFuser::test_int64_promotion, test/test_tensorexpr.py::TestTensorExprFuser::test_int_output, test/test_tensorexpr.py::TestTensorExprFuser::test_le, test/test_tensorexpr.py::TestTensorExprFuser::test_loop, test/test_tensorexpr.py::TestTensorExprFuser::test_lt, test/test_tensorexpr.py::TestTensorExprFuser::test_mask, test/test_tensorexpr.py::TestTensorExprFuser::test_min_max, test/test_tensorexpr.py::TestTensorExprFuser::test_min_max_reduction, test/test_tensorexpr.py::TestTensorExprFuser::test_min_max_reduction2, test/test_tensorexpr.py::TestTensorExprFuser::test_min_max_reduction_dim1, test/test_tensorexpr.py::TestTensorExprFuser::test_min_max_reduction_dim1_2, test/test_tensorexpr.py::TestTensorExprFuser::test_multi_rand, test/test_tensorexpr.py::TestTensorExprFuser::test_multioutput, test/test_tensorexpr.py::TestTensorExprFuser::test_multiple_outputs, test/test_tensorexpr.py::TestTensorExprFuser::test_nans, test/test_tensorexpr.py::TestTensorExprFuser::test_ne, test/test_tensorexpr.py::TestTensorExprFuser::test_promotion, test/test_tensorexpr.py::TestTensorExprFuser::test_propagated_mem_layout, test/test_tensorexpr.py::TestTensorExprFuser::test_rand_like, test/test_tensorexpr.py::TestTensorExprFuser::test_rank_two, test/test_tensorexpr.py::TestTensorExprFuser::test_relu, test/test_tensorexpr.py::TestTensorExprFuser::test_remainder, test/test_tensorexpr.py::TestTensorExprFuser::test_reps, test/test_tensorexpr.py::TestTensorExprFuser::test_round_2, test/test_tensorexpr.py::TestTensorExprFuser::test_scalar, test/test_tensorexpr.py::TestTensorExprFuser::test_short, test/test_tensorexpr.py::TestTensorExprFuser::test_simple_add, test/test_tensorexpr.py::TestTensorExprFuser::test_sin_pow, test/test_tensorexpr.py::TestTensorExprFuser::test_slice, test/test_tensorexpr.py::TestTensorExprFuser::test_sliced_stride, test/test_tensorexpr.py::TestTensorExprFuser::test_softmax_cpu, test/test_tensorexpr.py::TestTensorExprFuser::test_softmax_cuda, test/test_tensorexpr.py::TestTensorExprFuser::test_strided_output_preserved, test/test_tensorexpr.py::TestTensorExprFuser::test_three_arg, test/test_tensorexpr.py::TestTensorExprFuser::test_three_arg2, test/test_tensorexpr.py::TestTensorExprFuser::test_transpose, test/test_tensorexpr.py::TestTensorExprFuser::test_unary_ops, test/test_tensorexpr.py::TestTensorExprFuser::test_unsqueeze, test/test_tensorexpr.py::TestTensorExprFuser::test_where 2025-09-07T07:29:42.2181635Z 2025-09-07T07:29:42.2181872Z Running inductor/test_max_autotune 1/1 ... [2025-09-07 07:29:42.213764] 2025-09-07T07:29:42.2182338Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:29:42.2183434Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_max_autotune.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 07:29:42.214130] 2025-09-07T07:29:48.8820022Z 2025-09-07T07:29:48.8821037Z inductor/test_max_autotune 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_max_autotune_1.1_880a9c6555c2f3f1_.log 2025-09-07T07:29:48.8821846Z 2025-09-07T07:29:48.8823450Z Running test_multiprocessing 1/1 ... [2025-09-07 07:29:48.882183] 2025-09-07T07:29:48.8824077Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:29:48.8827241Z 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'] ... [2025-09-07 07:29:48.882516] 2025-09-07T07:30:43.3708735Z 2025-09-07T07:30:43.3709624Z test_multiprocessing 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_multiprocessing_1.1_abed0983b86cd3e5_.log 2025-09-07T07:30:43.3725354Z Running 42 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_rebuild_cuda_tensor, 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 2025-09-07T07:30:43.3739650Z 2025-09-07T07:30:43.3739833Z Running test_dispatch 1/1 ... [2025-09-07 07:30:43.371055] 2025-09-07T07:30:43.3740216Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:30:43.3741246Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_dispatch.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 07:30:43.371402] 2025-09-07T07:31:09.8205006Z 2025-09-07T07:31:09.8205872Z test_dispatch 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_dispatch_1.1_9d911e6d5dae8932_.log 2025-09-07T07:31:09.8216528Z Running 32 items in this shard: test/test_dispatch.py::TestDispatch::test_all_invariants, test/test_dispatch.py::TestDispatch::test_computed_table, test/test_dispatch.py::TestDispatch::test_computed_table_with_ambiguous_autogradother, test/test_dispatch.py::TestDispatch::test_computed_table_with_autograd, test/test_dispatch.py::TestDispatch::test_computed_table_with_cpu_autograd_defaultbackend, test/test_dispatch.py::TestDispatch::test_computed_table_with_cpu_autograd_math, test/test_dispatch.py::TestDispatch::test_computed_table_with_cpu_autograd_math_defaultbackend, test/test_dispatch.py::TestDispatch::test_computed_table_with_cpu_defaultbackend, test/test_dispatch.py::TestDispatch::test_computed_table_with_cpu_math, test/test_dispatch.py::TestDispatch::test_computed_table_with_cpu_math_autogradcpu_fallthrough, test/test_dispatch.py::TestDispatch::test_computed_table_with_math, test/test_dispatch.py::TestDispatch::test_def, test/test_dispatch.py::TestDispatch::test_def_impl_schema_mismatch, test/test_dispatch.py::TestDispatch::test_def_only, test/test_dispatch.py::TestDispatch::test_def_with_explicit_alias, test/test_dispatch.py::TestDispatch::test_def_with_inference, test/test_dispatch.py::TestDispatch::test_dispatch_print_registrations_for_dispatch_key_invalid, test/test_dispatch.py::TestDispatch::test_find_dangling_impls, test/test_dispatch.py::TestDispatch::test_find_dangling_impls_ext, test/test_dispatch.py::TestDispatch::test_impl_only, test/test_dispatch.py::TestDispatch::test_multiple_def_alias_defaulting, test/test_dispatch.py::TestDispatch::test_multiple_def_alias_mismatch, test/test_dispatch.py::TestDispatch::test_multiple_def_error, test/test_dispatch.py::TestDispatch::test_multiple_fallback, test/test_dispatch.py::TestDispatch::test_overwrite_math, test/test_dispatch.py::TestPythonDispatcher::test_autogradother, test/test_dispatch.py::TestPythonDispatcher::test_basic, test/test_dispatch.py::TestPythonDispatcher::test_defaultbackend_autogradcpu, test/test_dispatch.py::TestPythonDispatcher::test_defaultbackend_math, test/test_dispatch.py::TestPythonDispatcher::test_duplicate_registrations, test/test_dispatch.py::TestPythonDispatcher::test_math_autogradcpu, test/test_dispatch.py::TestPythonDispatcher::test_quantized_structured_not_implemented 2025-09-07T07:31:09.8226607Z 2025-09-07T07:31:09.8226850Z Running test_namedtuple_return_api 1/1 ... [2025-09-07 07:31:09.820709] 2025-09-07T07:31:09.8227295Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:31:09.8228391Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_namedtuple_return_api.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 07:31:09.821045] 2025-09-07T07:31:13.5400950Z 2025-09-07T07:31:13.5402058Z test_namedtuple_return_api 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_namedtuple_return_api_1.1_fe5f8f7bb228dfb4_.log 2025-09-07T07:31:13.5403968Z Running 3 items in this shard: test/test_namedtuple_return_api.py::TestNamedTupleAPI::test_import_return_types, test/test_namedtuple_return_api.py::TestNamedTupleAPI::test_namedtuple_return, test/test_namedtuple_return_api.py::TestNamedTupleAPI::test_native_functions_yaml 2025-09-07T07:31:13.5405181Z 2025-09-07T07:31:13.5405385Z Running test_jit_disabled 1/1 ... [2025-09-07 07:31:13.540283] 2025-09-07T07:31:13.5405809Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:31:13.5407533Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_jit_disabled.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 07:31:13.540570] 2025-09-07T07:31:16.3585103Z 2025-09-07T07:31:16.3586315Z test_jit_disabled 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_jit_disabled_1.1_e47bc0bbe3bb1307_.log 2025-09-07T07:31:16.3587970Z Running 3 items in this shard: test/test_jit_disabled.py::TestJitDisabled::test_attribute, test/test_jit_disabled.py::TestJitDisabled::test_recursive_script, test/test_jit_disabled.py::TestJitDisabled::test_script_module_construction 2025-09-07T07:31:16.3589471Z 2025-09-07T07:31:16.3589669Z Running test_fake_tensor 1/1 ... [2025-09-07 07:31:16.358693] 2025-09-07T07:31:16.3590078Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:31:16.3591943Z 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'] ... [2025-09-07 07:31:16.358980] 2025-09-07T07:31:28.5908593Z 2025-09-07T07:31:28.5909486Z test_fake_tensor 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_fake_tensor_1.1_f9a05ebdc7bf9408_.log 2025-09-07T07:31:28.6027735Z Running 290 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_device, 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_fast_div, 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_nanmean_out, test/test_fake_tensor.py::FakeTensorTest::test_new, test/test_fake_tensor.py::FakeTensorTest::test_no_tag_func, 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_op_with_zero_dim_bypassed, 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_unbind_copy_out, 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_device_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_fast_div_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_nanmean_out_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_new_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_no_tag_func_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_op_with_zero_dim_bypassed_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_unbind_copy_out_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_module_to, 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_module_to_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_nan_to_num, test/test_fake_tensor.py::FakeTensorPropTest::test_nonzero_stride, 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_nan_to_num_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorPropTest::test_nonzero_stride_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_aten_index, 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_fft_hfft2_issue145522, test/test_fake_tensor.py::FakeTensorDispatchCache::test_from_buffer, test/test_fake_tensor.py::FakeTensorDispatchCache::test_inference_mode, test/test_fake_tensor.py::FakeTensorDispatchCache::test_invoke_subgraph, test/test_fake_tensor.py::FakeTensorDispatchCache::test_invoke_subgraph_cacheable_inplace, test/test_fake_tensor.py::FakeTensorDispatchCache::test_meta_tensor_to_fake_cpu, test/test_fake_tensor.py::FakeTensorDispatchCache::test_shape_env_settings, test/test_fake_tensor.py::FakeTensorDispatchCache::test_unbacked_output, test/test_fake_tensor.py::FakeTensorDispatchCache::test_wrapper_tensor_subclass_different_device, test/test_fake_tensor.py::FakeTensorPreferDeviceType::test_fake_tensor_prefer_device_type, test/test_fake_tensor.py::FakeTensorPreferDeviceType::test_fake_tensor_prefer_device_type_cpu_only 2025-09-07T07:31:28.6146165Z 2025-09-07T07:31:28.6146354Z Running test_cuda_trace 1/1 ... [2025-09-07 07:31:28.591409] 2025-09-07T07:31:28.6146760Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:31:28.6147867Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_cuda_trace.py', '--shard-id=1', '--num-shards=1', '-v', '--subprocess', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 07:31:28.591761] 2025-09-07T07:31:31.2348615Z 2025-09-07T07:31:31.2349434Z test_cuda_trace 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_cuda_trace_1.1_a2d8926a00e8db91_.log 2025-09-07T07:31:31.2350201Z Running 0 items in this shard: 2025-09-07T07:31:31.2350391Z 2025-09-07T07:31:31.2352123Z Running test_cuda_nvml_based_avail 1/1 ... [2025-09-07 07:31:31.235058] 2025-09-07T07:31:31.2352560Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:31:31.2356183Z 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'] ... [2025-09-07 07:31:31.235381] 2025-09-07T07:31:33.8582080Z 2025-09-07T07:31:33.8583033Z 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_4b40b70b8f76e7dd_.log 2025-09-07T07:31:33.8583856Z Running 0 items in this shard: 2025-09-07T07:31:33.8584061Z 2025-09-07T07:31:33.8585850Z Running test_autograd_fallback 1/1 ... [2025-09-07 07:31:33.858425] 2025-09-07T07:31:33.8586492Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:31:33.8589330Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_autograd_fallback.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 07:31:33.858747] 2025-09-07T07:31:36.6767571Z 2025-09-07T07:31:36.6768641Z test_autograd_fallback 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_autograd_fallback_1.1_d59fa3c9dc964ab6_.log 2025-09-07T07:31:36.6782696Z Running 28 items in this shard: test/test_autograd_fallback.py::TestAutogradFallback::test_autograd_function_registered_to_cpu_mode_nothing, test/test_autograd_fallback.py::TestAutogradFallback::test_autograd_function_registered_to_cpu_mode_warn, test/test_autograd_fallback.py::TestAutogradFallback::test_base_does_not_require_grad_mode_nothing, test/test_autograd_fallback.py::TestAutogradFallback::test_base_does_not_require_grad_mode_warn, test/test_autograd_fallback.py::TestAutogradFallback::test_composite_registered_to_cpu_mode_nothing, test/test_autograd_fallback.py::TestAutogradFallback::test_composite_registered_to_cpu_mode_warn, test/test_autograd_fallback.py::TestAutogradFallback::test_cpu_return_self_mode_nothing, test/test_autograd_fallback.py::TestAutogradFallback::test_cpu_return_self_mode_warn, test/test_autograd_fallback.py::TestAutogradFallback::test_inplace_autograd_function_registered_to_cpu_mode_nothing, test/test_autograd_fallback.py::TestAutogradFallback::test_inplace_autograd_function_registered_to_cpu_mode_warn, test/test_autograd_fallback.py::TestAutogradFallback::test_inplace_on_tensor_that_does_not_require_grad_mode_nothing, test/test_autograd_fallback.py::TestAutogradFallback::test_inplace_on_tensor_that_does_not_require_grad_mode_warn, test/test_autograd_fallback.py::TestAutogradFallback::test_no_autograd_kernel_inplace_mode_nothing, test/test_autograd_fallback.py::TestAutogradFallback::test_no_autograd_kernel_inplace_mode_warn, test/test_autograd_fallback.py::TestAutogradFallback::test_no_autograd_kernel_mode_nothing, test/test_autograd_fallback.py::TestAutogradFallback::test_no_autograd_kernel_mode_warn, test/test_autograd_fallback.py::TestAutogradFallback::test_no_grad_mode_nothing, test/test_autograd_fallback.py::TestAutogradFallback::test_no_grad_mode_warn, test/test_autograd_fallback.py::TestAutogradFallback::test_post_autograd_returns_leaf_mode_nothing, test/test_autograd_fallback.py::TestAutogradFallback::test_post_autograd_returns_leaf_mode_warn, test/test_autograd_fallback.py::TestAutogradFallback::test_post_autograd_returns_mix_of_requires_grad_tensors_mode_nothing, test/test_autograd_fallback.py::TestAutogradFallback::test_post_autograd_returns_mix_of_requires_grad_tensors_mode_warn, test/test_autograd_fallback.py::TestAutogradFallback::test_supports_tensor_lists_mode_nothing, test/test_autograd_fallback.py::TestAutogradFallback::test_supports_tensor_lists_mode_warn, test/test_autograd_fallback.py::TestAutogradFallback::test_undefined_grads_mode_nothing, test/test_autograd_fallback.py::TestAutogradFallback::test_undefined_grads_mode_warn, test/test_autograd_fallback.py::TestAutogradFallback::test_undefined_inputs_outputs_mode_nothing, test/test_autograd_fallback.py::TestAutogradFallback::test_undefined_inputs_outputs_mode_warn 2025-09-07T07:31:36.6794889Z 2025-09-07T07:31:36.6795156Z Running dynamo/test_fake_distributed 1/1 ... [2025-09-07 07:31:36.676997] 2025-09-07T07:31:36.6795626Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:31:36.6796726Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_fake_distributed.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 07:31:36.677314] 2025-09-07T07:31:46.0042596Z 2025-09-07T07:31:46.0043581Z dynamo/test_fake_distributed 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_fake_distributed_1.1_56ca6962c156123d_.log 2025-09-07T07:31:46.0045457Z Running 2 items in this shard: test/dynamo/test_fake_distributed.py::TestFakeDistributed::test_all_to_all_single_autograd, test/dynamo/test_fake_distributed.py::TestFakeDistributed::test_device_mesh_get_local_rank 2025-09-07T07:31:46.0046598Z 2025-09-07T07:31:46.0046870Z Running inductor/test_distributed_patterns 1/1 ... [2025-09-07 07:31:46.004442] 2025-09-07T07:31:46.0047366Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:31:46.0049724Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_distributed_patterns.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 07:31:46.004758] 2025-09-07T07:32:11.0534895Z 2025-09-07T07:32:11.0536066Z inductor/test_distributed_patterns 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_distributed_patterns_1.1_6a65f7d4acdd2f83_.log 2025-09-07T07:32:11.0548538Z Running 20 items in this shard: test/inductor/test_distributed_patterns.py::DistributedPatternTests::test_fake_distributed_aot_eager, test/inductor/test_distributed_patterns.py::DistributedPatternTests::test_fake_distributed_inductor, test/inductor/test_distributed_patterns.py::DistributedPatternTests::test_intermediate_hook_with_closure, test/inductor/test_distributed_patterns.py::DistributedPatternTests::test_intermediate_hook_with_nested_closure, test/inductor/test_distributed_patterns.py::DistributedPatternTests::test_module_backward_hooks_aot, test/inductor/test_distributed_patterns.py::DistributedPatternTests::test_module_backward_hooks_eager, test/inductor/test_distributed_patterns.py::DistributedPatternTests::test_module_backward_hooks_inductor, test/inductor/test_distributed_patterns.py::DistributedPatternTests::test_module_backward_hooks_multi_layers, test/inductor/test_distributed_patterns.py::DistributedPatternTests::test_nn_param_return1, test/inductor/test_distributed_patterns.py::DistributedPatternTests::test_nn_param_return2, test/inductor/test_distributed_patterns.py::DistributedPatternTests::test_nn_param_return3, test/inductor/test_distributed_patterns.py::DistributedPatternTests::test_nn_param_return4, test/inductor/test_distributed_patterns.py::DistributedPatternTests::test_storage_resize_nonzero_cpu, test/inductor/test_distributed_patterns.py::DistributedPatternTests::test_storage_resize_nonzero_gpu, test/inductor/test_distributed_patterns.py::DistributedPatternTests::test_storage_resize_zero_cpu, test/inductor/test_distributed_patterns.py::DistributedPatternTests::test_storage_resize_zero_gpu, test/inductor/test_distributed_patterns.py::DistributedPatternTests::test_unsafe_preserve_version_counter1, test/inductor/test_distributed_patterns.py::DistributedPatternTests::test_unsafe_preserve_version_counter2, test/inductor/test_distributed_patterns.py::DistributedPatternTests::test_unsafe_set_version_counter1, test/inductor/test_distributed_patterns.py::DistributedPatternTests::test_unsafe_set_version_counter2 2025-09-07T07:32:11.0558221Z 2025-09-07T07:32:11.0558409Z Running test_autocast 1/1 ... [2025-09-07 07:32:11.053713] 2025-09-07T07:32:11.0558796Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:32:11.0559831Z 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'] ... [2025-09-07 07:32:11.054019] 2025-09-07T07:32:14.5232883Z 2025-09-07T07:32:14.5233783Z test_autocast 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_autocast_1.1_b4c785076513b979_.log 2025-09-07T07:32:14.5240901Z 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 2025-09-07T07:32:14.5247263Z 2025-09-07T07:32:14.5247438Z Running test_torch 1/1 ... [2025-09-07 07:32:14.523501] 2025-09-07T07:32:14.5247814Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:32:14.5248842Z 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'] ... [2025-09-07 07:32:14.523791] 2025-09-07T07:32:27.8063457Z 2025-09-07T07:32:27.8064240Z test_torch 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_torch_1.1_308ffd4c77f2a30f_.log 2025-09-07T07:32:27.8365948Z Running 953 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_concat_non_tuple_sequence, test/test_torch.py::TestTorch::test_Size_concat_wildcard, 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_item_no_warning, 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, 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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, 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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, 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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_expected_failure_xla_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_exponential_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_exponential_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_exponential_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_exponential_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_exponential_kstest_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_exponential_kstest_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_exponential_kstest_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_exponential_kstest_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_exponential_no_zero_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_exponential_no_zero_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_gather_backward_deterministic_path_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_gather_backward_one_dim_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_geometric_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_geometric_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_geometric_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_geometric_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_geometric_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_geometric_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_geometric_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_geometric_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_geometric_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_geometric_kstest_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scale_will_not_overflow_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaler_deprecated_warning_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaler_pass_itself_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_accumulation_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_autocast_foreach0_fused0_AdamW_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_autocast_foreach0_fused0_Adam_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_autocast_foreach0_fused0_SGD_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_autocast_foreach2_fused_True_AdamW_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_autocast_foreach2_fused_True_Adam_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_autocast_foreach2_fused_True_SGD_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_autocast_foreach_True_fused1_AdamW_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_autocast_foreach_True_fused1_Adam_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_autocast_foreach_True_fused1_SGD_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_clipping_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_clipping_separate_unscale_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_multiple_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_penalty_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_state_dict_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_unscale_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_unscale_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_unscale_sparse_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_update_scale_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_gradient_all_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_gradient_all_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_gradient_all_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_gradient_extreme_cases_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_gradient_extreme_cases_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_gradient_extreme_cases_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_gradient_spacing_list_length_error_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_gradient_spacing_list_length_error_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_gradient_spacing_list_length_error_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_gradient_type_promotion_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_hook_remove_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_add_large_inputs_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_add_mem_overlap_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_copy_mem_overlap_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_fill_mem_overlap_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_put_mem_overlap_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_int64_upsample3d_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_invalid_shapes_grid_sampler_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_is_set_to_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_is_signed_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_item_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_item_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_item_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_item_cpu_complex32, test/test_torch.py::TestTorchDeviceTypeCPU::test_item_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_item_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_item_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_item_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_item_cpu_float8_e4m3fn, test/test_torch.py::TestTorchDeviceTypeCPU::test_item_cpu_float8_e4m3fnuz, test/test_torch.py::TestTorchDeviceTypeCPU::test_item_cpu_float8_e5m2, test/test_torch.py::TestTorchDeviceTypeCPU::test_item_cpu_float8_e5m2fnuz, test/test_torch.py::TestTorchDeviceTypeCPU::test_item_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_item_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_item_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_item_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_item_cpu_uint16, test/test_torch.py::TestTorchDeviceTypeCPU::test_item_cpu_uint32, test/test_torch.py::TestTorchDeviceTypeCPU::test_item_cpu_uint64, test/test_torch.py::TestTorchDeviceTypeCPU::test_item_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_large_cumprod_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_large_cumsum_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_binary_op_no_materialize_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_binary_op_no_materialize_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_binary_op_no_materialize_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_binary_op_no_materialize_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_binary_op_no_materialize_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_binary_op_no_materialize_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_binary_op_no_materialize_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_binary_op_no_materialize_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_binary_op_no_materialize_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_binary_op_no_materialize_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_binary_op_no_materialize_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_binary_op_no_materialize_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_view_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_view_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_view_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_view_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_view_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_view_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_view_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_view_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_view_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_view_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_view_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_view_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_view_materialize_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_view_materialize_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_view_materialize_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_view_materialize_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_view_materialize_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_view_materialize_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_view_materialize_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_view_materialize_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_view_materialize_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_view_materialize_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_view_materialize_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_view_materialize_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_log_normal_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_log_normal_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_log_normal_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_log_normal_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_logcumsumexp_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_lognormal_kstest_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_fill_bool_tensor_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_fill_cpu_bfloat16_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_fill_cpu_bfloat16_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_fill_cpu_bool_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_fill_cpu_bool_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_fill_cpu_complex128_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_fill_cpu_complex128_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_fill_cpu_complex64_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_fill_cpu_complex64_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_fill_cpu_float16_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_fill_cpu_float16_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_fill_cpu_float32_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_fill_cpu_float32_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_fill_cpu_float64_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_fill_cpu_float64_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_fill_cpu_int16_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_fill_cpu_int16_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_fill_cpu_int32_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_fill_cpu_int32_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_fill_cpu_int64_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_fill_cpu_int64_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_fill_cpu_int8_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_fill_cpu_int8_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_fill_cpu_uint8_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_fill_cpu_uint8_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_fill_mem_overlap_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_scatter_bool_tensor_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_scatter_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_scatter_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_scatter_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_scatter_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_scatter_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_scatter_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_scatter_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_scatter_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_scatter_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_scatter_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_scatter_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_scatter_inplace_noncontiguous_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_scatter_large_tensor_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_scatter_mem_overlap_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_select_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_select_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_select_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_select_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_select_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_select_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_select_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_select_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_select_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_select_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_select_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_select_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_select_discontiguous_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_memory_format_clone_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_memory_format_consistency_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_memory_format_cpu_and_cuda_ops_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_memory_format_empty_like_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_memory_format_factory_like_functions_preserve_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_memory_format_operators_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_memory_format_preserved_after_permute_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_memory_format_propagation_rules_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_memory_format_to_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_memory_format_type_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_memory_format_type_shortcuts_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_module_share_memory_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_multinomial_cpu_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_multinomial_cpu_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_multinomial_cpu_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_multinomial_cpu_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_multinomial_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_multinomial_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_multinomial_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_multinomial_deterministic_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_multinomial_deterministic_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_multinomial_deterministic_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_multinomial_device_constrain_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_multinomial_empty_w_replacement_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_multinomial_empty_wo_replacement_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_multinomial_gpu_device_constrain_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_multinomial_rng_state_advance_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_narrow_copy_non_contiguous_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_narrow_empty_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_no_nondeterministic_alert_interpolate_bilinear_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_no_nondeterministic_alert_interpolate_trilinear_cpu, 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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 2025-09-07T07:32:27.8907284Z 2025-09-07T07:32:27.8907676Z Running test_sort_and_select 1/1 ... [2025-09-07 07:32:27.807510] 2025-09-07T07:32:27.8908507Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:32:27.8910512Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_sort_and_select.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 07:32:27.807826] 2025-09-07T07:32:32.5283654Z 2025-09-07T07:32:32.5284540Z test_sort_and_select 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_sort_and_select_1.1_94a4691ef3529243_.log 2025-09-07T07:32:32.5324483Z Running 113 items in this shard: test/test_sort_and_select.py::TestSortAndSelectCPU::test_complex_unsupported_cpu_cpu, test/test_sort_and_select.py::TestSortAndSelectCPU::test_isin_cpu_float32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_isin_cpu_float64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_isin_cpu_int16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_isin_cpu_int32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_isin_cpu_int64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_isin_cpu_int8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_isin_cpu_uint8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_isin_different_devices_cpu_float32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_isin_different_devices_cpu_float64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_isin_different_devices_cpu_int16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_isin_different_devices_cpu_int32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_isin_different_devices_cpu_int64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_isin_different_devices_cpu_int8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_isin_different_devices_cpu_uint8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_isin_different_dtypes_cpu, test/test_sort_and_select.py::TestSortAndSelectCPU::test_kthvalue_cpu_float64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_kthvalue_scalar_cpu_float32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_msort_cpu_bfloat16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_msort_cpu_float16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_msort_cpu_float32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_msort_cpu_float64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_msort_cpu_int16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_msort_cpu_int32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_msort_cpu_int64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_msort_cpu_int8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_msort_cpu_uint8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_1d_output_discontiguous_cpu_float32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_1d_parallel_cpu_int16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_1d_parallel_cpu_int32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_1d_parallel_cpu_int64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_1d_parallel_cpu_int8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_1d_parallel_cpu_uint8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_cpu, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_discontiguous_cpu_float32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_discontiguous_slow_cpu_float32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_expanded_tensor_cpu_float32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_large_cpu_float16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_large_slice_cpu, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_overflow_cpu_int16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_overflow_cpu_int32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_overflow_cpu_int64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_overflow_cpu_int8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_overflow_cpu_uint8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_restride_cpu_float32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_stable_none_cpu, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_against_numpy_cpu_bfloat16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_against_numpy_cpu_bool, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_against_numpy_cpu_float16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_against_numpy_cpu_float32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_against_numpy_cpu_float64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_against_numpy_cpu_int16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_against_numpy_cpu_int32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_against_numpy_cpu_int64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_against_numpy_cpu_int8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_against_numpy_cpu_uint8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_cpu_bfloat16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_cpu_bool, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_cpu_float16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_cpu_float32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_cpu_float64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_cpu_int16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_cpu_int32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_cpu_int64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_cpu_int8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_cpu_uint8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_1d_output_discontiguous_cpu_float32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_4d_cpu, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_arguments_cpu, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_cpu, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_integral_cpu_int16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_integral_cpu_int32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_integral_cpu_int64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_integral_cpu_int8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_integral_cpu_uint8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_lower_precision_cpu_bfloat16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_lower_precision_cpu_float16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_noncontiguous_gpu_cpu, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_nonfinite_cpu_bfloat16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_nonfinite_cpu_float16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_nonfinite_cpu_float32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_nonfinite_cpu_float64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_quantized_scalar_input_cpu, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_zero_cpu_bfloat16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_zero_cpu_float16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_zero_cpu_float32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_zero_cpu_float64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_zero_cpu_int16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_zero_cpu_int32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_zero_cpu_int64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_zero_cpu_int8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_zero_cpu_uint8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_consecutive_cpu_bfloat16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_consecutive_cpu_bool, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_consecutive_cpu_float16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_consecutive_cpu_float32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_consecutive_cpu_float64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_consecutive_cpu_int16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_consecutive_cpu_int32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_consecutive_cpu_int64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_consecutive_cpu_int8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_consecutive_cpu_uint8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_cpu_bfloat16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_cpu_bool, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_cpu_float16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_cpu_float32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_cpu_float64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_cpu_int16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_cpu_int32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_cpu_int64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_cpu_int8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_cpu_uint8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_dim_cpu 2025-09-07T07:32:32.5363262Z 2025-09-07T07:32:32.5363448Z Running test_native_mha 1/1 ... [2025-09-07 07:32:32.528726] 2025-09-07T07:32:32.5363855Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:32:32.5364945Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_native_mha.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 07:32:32.529022] 2025-09-07T07:32:35.5472704Z 2025-09-07T07:32:35.5473558Z test_native_mha 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_native_mha_1.1_7c6594ff358ba2e4_.log 2025-09-07T07:32:35.5495188Z Running 28 items in this shard: test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_attention_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_encoder_decoder_attention_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_False_use_padding_False_pad_all_False_need_weights_False_average_attn_weights_False_fused_False_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_False_use_padding_False_pad_all_False_need_weights_False_average_attn_weights_False_fused_True_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_False_use_padding_False_pad_all_False_need_weights_False_average_attn_weights_True_fused_False_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_False_use_padding_False_pad_all_False_need_weights_False_average_attn_weights_True_fused_True_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_False_use_padding_True_pad_all_False_need_weights_False_average_attn_weights_False_fused_False_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_False_use_padding_True_pad_all_False_need_weights_False_average_attn_weights_False_fused_True_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_False_use_padding_True_pad_all_False_need_weights_False_average_attn_weights_True_fused_False_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_False_use_padding_True_pad_all_False_need_weights_False_average_attn_weights_True_fused_True_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_False_use_padding_True_pad_all_True_need_weights_False_average_attn_weights_False_fused_False_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_False_use_padding_True_pad_all_True_need_weights_False_average_attn_weights_False_fused_True_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_False_use_padding_True_pad_all_True_need_weights_False_average_attn_weights_True_fused_False_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_False_use_padding_True_pad_all_True_need_weights_False_average_attn_weights_True_fused_True_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_True_use_padding_False_pad_all_False_need_weights_False_average_attn_weights_False_fused_False_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_True_use_padding_False_pad_all_False_need_weights_False_average_attn_weights_False_fused_True_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_True_use_padding_False_pad_all_False_need_weights_False_average_attn_weights_True_fused_False_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_True_use_padding_False_pad_all_False_need_weights_False_average_attn_weights_True_fused_True_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_True_use_padding_True_pad_all_False_need_weights_False_average_attn_weights_False_fused_False_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_True_use_padding_True_pad_all_False_need_weights_False_average_attn_weights_False_fused_True_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_True_use_padding_True_pad_all_False_need_weights_False_average_attn_weights_True_fused_False_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_True_use_padding_True_pad_all_False_need_weights_False_average_attn_weights_True_fused_True_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_True_use_padding_True_pad_all_True_need_weights_False_average_attn_weights_False_fused_False_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_True_use_padding_True_pad_all_True_need_weights_False_average_attn_weights_False_fused_True_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_True_use_padding_True_pad_all_True_need_weights_False_average_attn_weights_True_fused_False_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_True_use_padding_True_pad_all_True_need_weights_False_average_attn_weights_True_fused_True_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_transform_bias_rescale_qkv_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_transform_bias_rescale_qkv_nested_cpu_float32 2025-09-07T07:32:35.5516322Z 2025-09-07T07:32:35.5516531Z Running test_cuda_primary_ctx 1/1 ... [2025-09-07 07:32:35.547494] 2025-09-07T07:32:35.5516960Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:32:35.5518072Z 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'] ... [2025-09-07 07:32:35.547791] 2025-09-07T07:32:38.1757482Z 2025-09-07T07:32:38.1758541Z 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_6005fa96c009bd8c_.log 2025-09-07T07:32:38.1759432Z Running 0 items in this shard: 2025-09-07T07:32:38.1759625Z 2025-09-07T07:32:38.1761193Z Running nn/test_pooling 1/1 ... [2025-09-07 07:32:38.175965] 2025-09-07T07:32:38.1761611Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:32:38.1764943Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'nn/test_pooling.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 07:32:38.176281] 2025-09-07T07:33:05.1331624Z 2025-09-07T07:33:05.1332920Z nn/test_pooling 1/1 was successful, full logs can be found in artifacts with path test/test-reports/nn.test_pooling_1.1_65093bc4ee64eb33_.log 2025-09-07T07:33:05.1378091Z Running 111 items in this shard: test/nn/test_pooling.py::TestAvgPool::test_avg_pool1d_ceil_mode, test/nn/test_pooling.py::TestAvgPool::test_avg_pool2d_ceil_mode, test/nn/test_pooling.py::TestAvgPool::test_avg_pool3d_ceil_mode, test/nn/test_pooling.py::TestAvgPool::test_doubletensor_avg_pool2d, test/nn/test_pooling.py::TestAvgPool::test_doubletensor_avg_pool2d_with_divisor, test/nn/test_pooling.py::TestAvgPool::test_doubletensor_avg_pool3d, test/nn/test_pooling.py::TestAvgPool::test_doubletensor_avg_pool3d_with_divisor, test/nn/test_pooling.py::TestPoolingNN::test_MaxUnpool2d_output_size, test/nn/test_pooling.py::TestPoolingNN::test_adaptive_avg_pooling_nhwc_overflow, test/nn/test_pooling.py::TestPoolingNN::test_adaptive_avg_pooling_overflow, test/nn/test_pooling.py::TestPoolingNN::test_adaptive_pooling_avg_nhwc, test/nn/test_pooling.py::TestPoolingNN::test_adaptive_pooling_avg_nhwc_launch_config_backward, test/nn/test_pooling.py::TestPoolingNN::test_adaptive_pooling_avg_nhwc_launch_config_forward, test/nn/test_pooling.py::TestPoolingNN::test_adaptive_pooling_avg_nhwc_non_contiguous, test/nn/test_pooling.py::TestPoolingNN::test_adaptive_pooling_lower_precision, test/nn/test_pooling.py::TestPoolingNN::test_adaptive_pooling_size_none, test/nn/test_pooling.py::TestPoolingNN::test_adaptive_pooling_size_overflow, test/nn/test_pooling.py::TestPoolingNN::test_max_unpool, test/nn/test_pooling.py::TestPoolingNN::test_max_unpool2d_nhwc_cpu, test/nn/test_pooling.py::TestPoolingNN::test_max_unpool3d_input_check, test/nn/test_pooling.py::TestPoolingNN::test_quantized_max_pool1d_empty_kernel, test/nn/test_pooling.py::TestPoolingNN::test_quantized_max_pool3d, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_AdaptiveMaxPool1d_indices_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_AdaptiveMaxPool2d_indices_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_AdaptiveMaxPool3d_indices_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_AdaptiveMaxPool_zero_batch_dim_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_AvgPool2d_empty_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_AvgPool3d_backward_after_cat_dim1_device_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_FractionalMaxPool2d_zero_batch_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_FractionalMaxPool2d_zero_out_size_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_FractionalMaxPool2d_zero_samples_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_FractionalMaxPool3d_errors_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_FractionalMaxPool3d_zero_batch_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_FractionalMaxPool3d_zero_out_size_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_FractionalMaxPool3d_zero_samples_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_MaxPool1d_indices_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_MaxPool2d_indices_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_MaxPool3d_indices_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_MaxPool_zero_batch_dim_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_MaxUnpool_index_errors_case10_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_MaxUnpool_index_errors_case1_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_MaxUnpool_index_errors_case2_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_MaxUnpool_index_errors_case3_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_MaxUnpool_index_errors_case4_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_MaxUnpool_index_errors_case5_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_MaxUnpool_index_errors_case6_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_MaxUnpool_index_errors_case7_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_MaxUnpool_index_errors_case8_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_MaxUnpool_index_errors_case9_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_MaxUnpool_zero_batch_dim_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_adaptive_avg_pool2d_output_size_one_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_adaptive_avg_pool3d_output_size_one_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_adaptive_avg_pooling_backward_fails_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_adaptive_max_pooling_backward_fails_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_adaptive_pool_odd_size_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_adaptive_pooling_empty_output_size_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_adaptive_pooling_empty_output_size_cpu_float64, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_adaptive_pooling_max_nhwc_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_adaptive_pooling_max_nhwc_cpu_float64, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_adaptive_pooling_no_suppot_input_cpu_int16, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_adaptive_pooling_no_suppot_input_cpu_int32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_adaptive_pooling_no_suppot_input_cpu_int64, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_adaptive_pooling_no_suppot_input_cpu_int8, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_adaptive_pooling_no_suppot_input_cpu_uint8, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_adaptive_pooling_zero_batch_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_adaptive_pooling_zero_batch_cpu_float64, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_avg_pool2d_nhwc_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_avg_pool2d_nhwc_cpu_float64, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_avg_pool2d_reduced_floating_cpu_bfloat16, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_avg_pool2d_reduced_floating_cpu_float16, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_fractional_max_pool2d_backward_fails_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_fractional_max_pool2d_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_fractional_max_pool3d_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_fractional_max_pool_nan_inf_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool1d_corner_cases_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool1d_corner_cases_cpu_float64, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool1d_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool1d_cpu_float64, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool2d_corner_cases_cpu_int32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool2d_corner_cases_cpu_int64, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool2d_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool2d_indices_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool2d_nhwc_cpu_bfloat16, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool2d_nhwc_cpu_float16, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool2d_nhwc_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool2d_nhwc_cpu_float64, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool2d_with_indices_backward_fails_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool3d_ndhwc_cpu_bfloat16, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool3d_ndhwc_cpu_float16, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool3d_ndhwc_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool3d_ndhwc_cpu_float64, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool_bfloat16_half_cpu_bfloat16, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool_bfloat16_half_cpu_float16, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool_nan_inf_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_maxpool3d_non_square_backward_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_maxpool_indices_no_batch_dim_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_pool3d_large_size_int64_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_pool3d_size_one_feature_dim_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_pool_invalid_size_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_pool_large_size_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_pooling_bfloat16_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_pooling_large_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_pooling_max_nhwc_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_pooling_max_nhwc_cpu_float64, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_pooling_shape_kernel_avg_pooling_dims_1_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_pooling_shape_kernel_avg_pooling_dims_2_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_pooling_shape_kernel_avg_pooling_dims_3_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_pooling_shape_kernel_max_pooling_dims_1_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_pooling_shape_kernel_max_pooling_dims_2_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_pooling_shape_kernel_max_pooling_dims_3_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_pooling_zero_stride_cpu 2025-09-07T07:33:05.1420630Z 2025-09-07T07:33:05.1420867Z Running test_multiprocessing_spawn 1/1 ... [2025-09-07 07:33:05.133512] 2025-09-07T07:33:05.1421327Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:33:05.1422435Z 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'] ... [2025-09-07 07:33:05.133852] 2025-09-07T07:35:17.7218978Z 2025-09-07T07:35:17.7220067Z test_multiprocessing_spawn 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_multiprocessing_spawn_1.1_a6fa9694d258e69f_.log 2025-09-07T07:35:17.7232773Z 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_20, 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_20, 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_20, 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 2025-09-07T07:35:17.7245237Z 2025-09-07T07:35:17.7245459Z Running nn/test_convolution 1/1 ... [2025-09-07 07:35:17.722121] 2025-09-07T07:35:17.7246074Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:35:17.7247259Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'nn/test_convolution.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 07:35:17.722486] 2025-09-07T07:35:39.4186073Z 2025-09-07T07:35:39.4186994Z nn/test_convolution 1/1 was successful, full logs can be found in artifacts with path test/test-reports/nn.test_convolution_1.1_75d55036615959ab_.log 2025-09-07T07:35:39.4539503Z Running 598 items in this shard: test/nn/test_convolution.py::TestConvolutionNN::test_Conv1d_module_same_padding, test/nn/test_convolution.py::TestConvolutionNN::test_Conv2d_1x1, test/nn/test_convolution.py::TestConvolutionNN::test_Conv2d_OneDNN, test/nn/test_convolution.py::TestConvolutionNN::test_Conv2d_backward_twice, test/nn/test_convolution.py::TestConvolutionNN::test_Conv2d_groups_nobias, test/nn/test_convolution.py::TestConvolutionNN::test_Conv2d_groups_nobias_v2, test/nn/test_convolution.py::TestConvolutionNN::test_Conv2d_inconsistent_types, test/nn/test_convolution.py::TestConvolutionNN::test_Conv2d_inconsistent_types_on_GPU_with_cudnn, test/nn/test_convolution.py::TestConvolutionNN::test_Conv2d_inconsistent_types_on_GPU_without_cudnn, test/nn/test_convolution.py::TestConvolutionNN::test_Conv2d_missing_argument, test/nn/test_convolution.py::TestConvolutionNN::test_Conv2d_module_same_padding, test/nn/test_convolution.py::TestConvolutionNN::test_Conv3d_groups_nobias, test/nn/test_convolution.py::TestConvolutionNN::test_Conv3d_groups_wbias, test/nn/test_convolution.py::TestConvolutionNN::test_Conv3d_module_same_padding, test/nn/test_convolution.py::TestConvolutionNN::test_ConvTranspose2d_half_cublas_gemm, test/nn/test_convolution.py::TestConvolutionNN::test_ConvTranspose2d_output_size, test/nn/test_convolution.py::TestConvolutionNN::test_ConvTranspose2d_output_size_downsample_upsample, test/nn/test_convolution.py::TestConvolutionNN::test_ConvTranspose3d_correct_output_size, test/nn/test_convolution.py::TestConvolutionNN::test_conv1d_issue_120547, test/nn/test_convolution.py::TestConvolutionNN::test_conv2d_discontiguous_weight, test/nn/test_convolution.py::TestConvolutionNN::test_conv3d_issue_120406, test/nn/test_convolution.py::TestConvolutionNN::test_conv_backcompat, test/nn/test_convolution.py::TestConvolutionNN::test_conv_cudnn_memory_layout_dominance, test/nn/test_convolution.py::TestConvolutionNN::test_conv_invalid_groups, test/nn/test_convolution.py::TestConvolutionNN::test_conv_modules_raise_error_on_incorrect_input_size, test/nn/test_convolution.py::TestConvolutionNN::test_conv_padding_mode, test/nn/test_convolution.py::TestConvolutionNN::test_conv_shapecheck, test/nn/test_convolution.py::TestConvolutionNN::test_conv_tbc, test/nn/test_convolution.py::TestConvolutionNN::test_cudnn_non_contiguous, test/nn/test_convolution.py::TestConvolutionNN::test_cudnn_noncontiguous_weight, test/nn/test_convolution.py::TestConvolutionNN::test_cudnn_not_mutate_stride, test/nn/test_convolution.py::TestConvolutionNN::test_functional_grad_conv, test/nn/test_convolution.py::TestConvolutionNN::test_functional_grad_conv2d, test/nn/test_convolution.py::TestConvolutionNN::test_grad_conv1d_input, test/nn/test_convolution.py::TestConvolutionNN::test_grad_conv1d_weight, test/nn/test_convolution.py::TestConvolutionNN::test_grad_conv2d_input, test/nn/test_convolution.py::TestConvolutionNN::test_grad_conv2d_weight, test/nn/test_convolution.py::TestConvolutionNN::test_grad_conv3d_input, test/nn/test_convolution.py::TestConvolutionNN::test_grad_conv3d_weight, test/nn/test_convolution.py::TestConvolutionNN::test_grouped_conv_cudnn_nhwc_support, test/nn/test_convolution.py::TestConvolutionNN::test_invalid_conv1d, test/nn/test_convolution.py::TestConvolutionNN::test_invalid_conv2d, test/nn/test_convolution.py::TestConvolutionNN::test_invalid_conv3d, test/nn/test_convolution.py::TestConvolutionNN::test_mismatch_shape_conv2d, test/nn/test_convolution.py::TestConvolutionNN::test_nnpack_conv, test/nn/test_convolution.py::TestConvolutionNN::test_permute_conv2d_issue_120211, test/nn/test_convolution.py::TestConvolutionNN::test_thnn_conv_strided_padded_dilated, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv2d_backward_depthwise_cpu_complex128, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv2d_backward_depthwise_cpu_float64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv2d_depthwise_naive_groups_cpu_float16, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv2d_depthwise_naive_groups_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv2d_depthwise_naive_groups_cpu_float64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv2d_deterministic_cudnn_dilation_1_cpu_complex128, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv2d_deterministic_cudnn_dilation_1_cpu_complex64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv2d_deterministic_cudnn_dilation_1_cpu_float16, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv2d_deterministic_cudnn_dilation_1_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv2d_deterministic_cudnn_dilation_1_cpu_float64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv2d_deterministic_cudnn_dilation_2_cpu_complex128, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv2d_deterministic_cudnn_dilation_2_cpu_complex64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv2d_deterministic_cudnn_dilation_2_cpu_float16, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv2d_deterministic_cudnn_dilation_2_cpu_float32, 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test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow1d_dilated_transposed_has_bias_False_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow1d_dilated_transposed_has_bias_False_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow1d_dilated_transposed_has_bias_False_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow1d_dilated_transposed_has_bias_False_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow1d_dilated_transposed_has_bias_True_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow1d_dilated_transposed_has_bias_True_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow1d_dilated_transposed_has_bias_True_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow1d_dilated_transposed_has_bias_True_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow1d_has_bias_False_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow1d_has_bias_False_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow1d_has_bias_False_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow1d_has_bias_False_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow1d_has_bias_True_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow1d_has_bias_True_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow1d_has_bias_True_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow1d_has_bias_True_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow1d_transposed_has_bias_False_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow1d_transposed_has_bias_False_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow1d_transposed_has_bias_False_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow1d_transposed_has_bias_False_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow1d_transposed_has_bias_True_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow1d_transposed_has_bias_True_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow1d_transposed_has_bias_True_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow1d_transposed_has_bias_True_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow2d_dilated_has_bias_False_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow2d_dilated_has_bias_False_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow2d_dilated_has_bias_False_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow2d_dilated_has_bias_False_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow2d_dilated_has_bias_True_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow2d_dilated_has_bias_True_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow2d_dilated_has_bias_True_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow2d_dilated_has_bias_True_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow2d_dilated_transposed_has_bias_False_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow2d_dilated_transposed_has_bias_False_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow2d_dilated_transposed_has_bias_False_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow2d_dilated_transposed_has_bias_False_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow2d_dilated_transposed_has_bias_True_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow2d_dilated_transposed_has_bias_True_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow2d_dilated_transposed_has_bias_True_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow2d_dilated_transposed_has_bias_True_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow2d_has_bias_False_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow2d_has_bias_False_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow2d_has_bias_False_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow2d_has_bias_False_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow2d_has_bias_True_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow2d_has_bias_True_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow2d_has_bias_True_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow2d_has_bias_True_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow2d_transposed_has_bias_False_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow2d_transposed_has_bias_False_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow2d_transposed_has_bias_False_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow2d_transposed_has_bias_False_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow2d_transposed_has_bias_True_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow2d_transposed_has_bias_True_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow2d_transposed_has_bias_True_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow2d_transposed_has_bias_True_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow3d_cpu_has_bias_False_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow3d_cpu_has_bias_False_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow3d_cpu_has_bias_False_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow3d_cpu_has_bias_False_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow3d_cpu_has_bias_True_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow3d_cpu_has_bias_True_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow3d_cpu_has_bias_True_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow3d_cpu_has_bias_True_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow3d_cuda_has_bias_False_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow3d_cuda_has_bias_False_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow3d_cuda_has_bias_False_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow3d_cuda_has_bias_False_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow3d_cuda_has_bias_True_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow3d_cuda_has_bias_True_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow3d_cuda_has_bias_True_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow3d_cuda_has_bias_True_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow3d_dilated_has_bias_False_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow3d_dilated_has_bias_False_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow3d_dilated_has_bias_False_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow3d_dilated_has_bias_False_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow3d_dilated_has_bias_True_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow3d_dilated_has_bias_True_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow3d_dilated_has_bias_True_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow3d_dilated_has_bias_True_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_contiguous_for_oneDNN_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_cudnn_mismatch_memory_format_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_cudnn_ndhwc_cpu_float16, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_cudnn_ndhwc_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_cudnn_nhwc_cpu_complex64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_cudnn_nhwc_cpu_float16, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_cudnn_nhwc_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_cudnn_nhwc_support_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_cudnn_nhwc_support_cpu_float64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_double_backward_cpu_float64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_double_backward_groups_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_double_backward_no_bias_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_double_backward_stride_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_double_backward_strided_with_3D_input_and_weight_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_empty_channel_cpu_complex64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_empty_channel_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_ic1_channels_last_for_oneDNN_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_large_batch_1_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_large_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_large_nosplit_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_noncontig_weights_and_bias_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_noncontig_weights_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_thnn_nhwc_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_thnn_nhwc_cpu_float64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_transpose_with_output_size_and_no_batch_dim_ConvTranspose2d_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_transpose_with_output_size_and_no_batch_dim_ConvTranspose3d_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_transposed_large_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_convert_conv2d_weight_memory_format_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_convert_conv3d_weight_memory_format_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_cudnn_convolution_add_relu_cpu_float16, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_cudnn_convolution_add_relu_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_cudnn_convolution_relu_cpu_float16, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_cudnn_convolution_relu_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_depthwise_conv_64bit_indexing_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_group_convTranspose_empty_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_group_conv_empty_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_noncontig_conv_grad_cpu_float16, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_noncontig_conv_grad_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_noncontig_conv_grad_cpu_float64 2025-09-07T07:35:39.4881408Z 2025-09-07T07:35:39.4881630Z Running test_mobile_optimizer 1/1 ... [2025-09-07 07:35:39.419751] 2025-09-07T07:35:39.4882077Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:35:39.4883156Z 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'] ... [2025-09-07 07:35:39.420084] 2025-09-07T07:35:44.4417783Z 2025-09-07T07:35:44.4418716Z test_mobile_optimizer 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_mobile_optimizer_1.1_10998b0c409b41f6_.log 2025-09-07T07:35:44.4422283Z 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 2025-09-07T07:35:44.4425108Z 2025-09-07T07:35:44.4425316Z Running test_spectral_ops 1/1 ... [2025-09-07 07:35:44.441957] 2025-09-07T07:35:44.4425732Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:35:44.4426784Z 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'] ... [2025-09-07 07:35:44.442261] 2025-09-07T07:35:54.0700238Z 2025-09-07T07:35:54.0701487Z test_spectral_ops 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_spectral_ops_1.1_46e68a9c57f73c3f_.log 2025-09-07T07:35:54.0795016Z Running 281 items in this shard: test/test_spectral_ops.py::TestFFTCPU::test_batch_istft_cpu, test/test_spectral_ops.py::TestFFTCPU::test_complex_istft_real_equiv_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_complex_stft_definition_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_complex_stft_onesided_cpu, test/test_spectral_ops.py::TestFFTCPU::test_complex_stft_real_equiv_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_complex_stft_roundtrip_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_complex_stft_roundtrip_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_cufft_context_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_cufft_context_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_cufft_plan_cache_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_fft2_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_fft2_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_fft_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_fft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_fftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_fftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_hfft2_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_hfft2_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_hfft_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_hfft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_hfftn_cpu_complex64, 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test/test_spectral_ops.py::TestFFTCPU::test_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_align_to_window_only_requires_non_center_cpu, 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 2025-09-07T07:35:54.0887017Z 2025-09-07T07:35:54.0887290Z Running distributions/test_distributions 1/1 ... [2025-09-07 07:35:54.070459] 2025-09-07T07:35:54.0887891Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:35:54.0889027Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'distributions/test_distributions.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 07:35:54.070790] 2025-09-07T07:36:37.4436917Z 2025-09-07T07:36:37.4438053Z distributions/test_distributions 1/1 was successful, full logs can be found in artifacts with path test/test-reports/distributions.test_distributions_1.1_af2542d4438f6583_.log 2025-09-07T07:36:37.4534411Z Running 230 items in this shard: test/distributions/test_distributions.py::TestDistributions::test_argmax_relaxed_categorical, test/distributions/test_distributions.py::TestDistributions::test_bernoulli, test/distributions/test_distributions.py::TestDistributions::test_bernoulli_3d, test/distributions/test_distributions.py::TestDistributions::test_bernoulli_enumerate_support, test/distributions/test_distributions.py::TestDistributions::test_beta_log_prob, test/distributions/test_distributions.py::TestDistributions::test_beta_sample, test/distributions/test_distributions.py::TestDistributions::test_beta_shape, test/distributions/test_distributions.py::TestDistributions::test_beta_underflow, test/distributions/test_distributions.py::TestDistributions::test_beta_underflow_gpu, test/distributions/test_distributions.py::TestDistributions::test_binomial, 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_half, test/distributions/test_distributions.py::TestDistributions::test_binomial_log_prob_and_entropy, test/distributions/test_distributions.py::TestDistributions::test_binomial_log_prob_vectorized_count, 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_binomial_vectorized_count, test/distributions/test_distributions.py::TestDistributions::test_categorical_1d, test/distributions/test_distributions.py::TestDistributions::test_categorical_2d, test/distributions/test_distributions.py::TestDistributions::test_categorical_enumerate_support, test/distributions/test_distributions.py::TestDistributions::test_cauchy, test/distributions/test_distributions.py::TestDistributions::test_cdf_icdf_inverse, test/distributions/test_distributions.py::TestDistributions::test_cdf_log_prob, test/distributions/test_distributions.py::TestDistributions::test_chi2_sample, test/distributions/test_distributions.py::TestDistributions::test_chi2_shape, test/distributions/test_distributions.py::TestDistributions::test_continuous_bernoulli, test/distributions/test_distributions.py::TestDistributions::test_continuous_bernoulli_3d, test/distributions/test_distributions.py::TestDistributions::test_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_expand, test/distributions/test_distributions.py::TestDistributions::test_distribution_subclass_expand, test/distributions/test_distributions.py::TestDistributions::test_enumerate_support_type, test/distributions/test_distributions.py::TestDistributions::test_exponential, test/distributions/test_distributions.py::TestDistributions::test_exponential_sample, test/distributions/test_distributions.py::TestDistributions::test_fishersnedecor, test/distributions/test_distributions.py::TestDistributions::test_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_gamma_sample, test/distributions/test_distributions.py::TestDistributions::test_gamma_shape, test/distributions/test_distributions.py::TestDistributions::test_generalized_pareto, test/distributions/test_distributions.py::TestDistributions::test_generalized_pareto_sample, test/distributions/test_distributions.py::TestDistributions::test_geometric, test/distributions/test_distributions.py::TestDistributions::test_geometric_log_prob_and_entropy, test/distributions/test_distributions.py::TestDistributions::test_geometric_sample, test/distributions/test_distributions.py::TestDistributions::test_gumbel, test/distributions/test_distributions.py::TestDistributions::test_gumbel_sample, test/distributions/test_distributions.py::TestDistributions::test_halfcauchy, test/distributions/test_distributions.py::TestDistributions::test_halfnormal, test/distributions/test_distributions.py::TestDistributions::test_halfnormal_logprob, test/distributions/test_distributions.py::TestDistributions::test_halfnormal_sample, test/distributions/test_distributions.py::TestDistributions::test_has_examples, test/distributions/test_distributions.py::TestDistributions::test_independent_expand, test/distributions/test_distributions.py::TestDistributions::test_independent_shape, test/distributions/test_distributions.py::TestDistributions::test_invalid_parameter_broadcasting, test/distributions/test_distributions.py::TestDistributions::test_inversegamma, test/distributions/test_distributions.py::TestDistributions::test_inversegamma_sample, test/distributions/test_distributions.py::TestDistributions::test_kumaraswamy_mean_variance, test/distributions/test_distributions.py::TestDistributions::test_kumaraswamy_shape, test/distributions/test_distributions.py::TestDistributions::test_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_lkj_cholesky_log_prob, test/distributions/test_distributions.py::TestDistributions::test_logisticnormal, test/distributions/test_distributions.py::TestDistributions::test_logisticnormal_logprob, test/distributions/test_distributions.py::TestDistributions::test_logisticnormal_sample, test/distributions/test_distributions.py::TestDistributions::test_lognormal, test/distributions/test_distributions.py::TestDistributions::test_lognormal_logprob, test/distributions/test_distributions.py::TestDistributions::test_lognormal_sample, test/distributions/test_distributions.py::TestDistributions::test_lowrank_multivariate_normal_log_prob, test/distributions/test_distributions.py::TestDistributions::test_lowrank_multivariate_normal_moments, test/distributions/test_distributions.py::TestDistributions::test_lowrank_multivariate_normal_properties, test/distributions/test_distributions.py::TestDistributions::test_lowrank_multivariate_normal_sample, test/distributions/test_distributions.py::TestDistributions::test_lowrank_multivariate_normal_shape, test/distributions/test_distributions.py::TestDistributions::test_mixture_same_family_binomial_log_prob, test/distributions/test_distributions.py::TestDistributions::test_mixture_same_family_normal_log_prob, 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_multinomial_1d_log_prob_and_entropy, test/distributions/test_distributions.py::TestDistributions::test_multinomial_2d, test/distributions/test_distributions.py::TestDistributions::test_multinomial_sequential_draw, test/distributions/test_distributions.py::TestDistributions::test_multivariate_normal_log_prob, test/distributions/test_distributions.py::TestDistributions::test_multivariate_normal_moments, test/distributions/test_distributions.py::TestDistributions::test_multivariate_normal_properties, test/distributions/test_distributions.py::TestDistributions::test_multivariate_normal_sample, test/distributions/test_distributions.py::TestDistributions::test_multivariate_normal_shape, test/distributions/test_distributions.py::TestDistributions::test_multivariate_normal_stable_with_precision_matrix, test/distributions/test_distributions.py::TestDistributions::test_negative_binomial, 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_normal, test/distributions/test_distributions.py::TestDistributions::test_normal_sample, test/distributions/test_distributions.py::TestDistributions::test_one_hot_categorical_1d, test/distributions/test_distributions.py::TestDistributions::test_one_hot_categorical_2d, test/distributions/test_distributions.py::TestDistributions::test_one_hot_categorical_enumerate_support, test/distributions/test_distributions.py::TestDistributions::test_pareto, test/distributions/test_distributions.py::TestDistributions::test_pareto_sample, test/distributions/test_distributions.py::TestDistributions::test_poisson_forward_ad, test/distributions/test_distributions.py::TestDistributions::test_poisson_gpu_sample, test/distributions/test_distributions.py::TestDistributions::test_poisson_log_prob, 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_repr, test/distributions/test_distributions.py::TestDistributions::test_rounded_relaxed_bernoulli, 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_studentT, test/distributions/test_distributions.py::TestDistributions::test_studentT_log_prob, test/distributions/test_distributions.py::TestDistributions::test_studentT_sample, test/distributions/test_distributions.py::TestDistributions::test_support_attributes, test/distributions/test_distributions.py::TestDistributions::test_torch_binomial_dtype_errors, 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_vonmises_logprob, test/distributions/test_distributions.py::TestDistributions::test_vonmises_sample, test/distributions/test_distributions.py::TestDistributions::test_wishart_log_prob, 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test/distributions/test_distributions.py::TestDistributionShapes::test_chi2_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_chi2_shape_tensor_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_exponential_shape_scalar_param, test/distributions/test_distributions.py::TestDistributionShapes::test_exponential_shape_tensor_param, test/distributions/test_distributions.py::TestDistributionShapes::test_gamma_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_gamma_shape_tensor_params, test/distributions/test_distributions.py::TestDistributionShapes::test_geometric_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_geometric_shape_tensor_params, test/distributions/test_distributions.py::TestDistributionShapes::test_gumbel_shape_scalar_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_laplace_shape_tensor_params, test/distributions/test_distributions.py::TestDistributionShapes::test_mixture_same_family_mean_shape, test/distributions/test_distributions.py::TestDistributionShapes::test_mixture_same_family_shape, test/distributions/test_distributions.py::TestDistributionShapes::test_multinomial_shape, test/distributions/test_distributions.py::TestDistributionShapes::test_normal_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_normal_shape_tensor_params, test/distributions/test_distributions.py::TestDistributionShapes::test_one_hot_categorical_shape, test/distributions/test_distributions.py::TestDistributionShapes::test_pareto_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_studentT_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_studentT_shape_tensor_params, test/distributions/test_distributions.py::TestDistributionShapes::test_uniform_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_uniform_shape_tensor_params, test/distributions/test_distributions.py::TestDistributionShapes::test_vonmises_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_vonmises_shape_tensor_params, test/distributions/test_distributions.py::TestDistributionShapes::test_weibull_scale_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_wishart_shape_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_entropy_monte_carlo, test/distributions/test_distributions.py::TestKL::test_kl_edgecases, test/distributions/test_distributions.py::TestKL::test_kl_exponential_family, test/distributions/test_distributions.py::TestKL::test_kl_infinite, test/distributions/test_distributions.py::TestKL::test_kl_lowrank_multivariate_normal, test/distributions/test_distributions.py::TestKL::test_kl_lowrank_multivariate_normal_batched, test/distributions/test_distributions.py::TestKL::test_kl_monte_carlo, test/distributions/test_distributions.py::TestKL::test_kl_multivariate_normal, test/distributions/test_distributions.py::TestKL::test_kl_multivariate_normal_batched, test/distributions/test_distributions.py::TestKL::test_kl_multivariate_normal_batched_broadcasted, test/distributions/test_distributions.py::TestKL::test_kl_shape, test/distributions/test_distributions.py::TestKL::test_kl_transformed, test/distributions/test_distributions.py::TestConstraints::test_params_constraints, test/distributions/test_distributions.py::TestConstraints::test_support_constraints, test/distributions/test_distributions.py::TestNumericalStability::test_bernoulli_gradient, test/distributions/test_distributions.py::TestNumericalStability::test_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_categorical_log_prob_with_logits, test/distributions/test_distributions.py::TestNumericalStability::test_continuous_bernoulli_gradient, test/distributions/test_distributions.py::TestNumericalStability::test_continuous_bernoulli_with_logits_overflow, test/distributions/test_distributions.py::TestNumericalStability::test_continuous_bernoulli_with_logits_underflow, test/distributions/test_distributions.py::TestNumericalStability::test_multinomial_log_prob, test/distributions/test_distributions.py::TestNumericalStability::test_multinomial_log_prob_with_logits, test/distributions/test_distributions.py::TestLazyLogitsInitialization::test_lazy_logits_initialization, 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_icdf, test/distributions/test_distributions.py::TestAgainstScipy::test_mean, test/distributions/test_distributions.py::TestAgainstScipy::test_variance_stddev, test/distributions/test_distributions.py::TestFunctors::test_cat_event_dim, 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::TestFunctors::test_stack_transform, test/distributions/test_distributions.py::TestValidation::test_invalid, test/distributions/test_distributions.py::TestValidation::test_invalid_log_probs_arg, test/distributions/test_distributions.py::TestValidation::test_valid, test/distributions/test_distributions.py::TestValidation::test_warning_unimplemented_constraints, test/distributions/test_distributions.py::TestJit::test_cdf, test/distributions/test_distributions.py::TestJit::test_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_mean, test/distributions/test_distributions.py::TestJit::test_rsample, test/distributions/test_distributions.py::TestJit::test_sample, test/distributions/test_distributions.py::TestJit::test_variance 2025-09-07T07:36:37.4626084Z 2025-09-07T07:36:37.4626278Z Running doctests 1/1 ... [2025-09-07 07:36:37.444188] 2025-09-07T07:36:37.5312410Z Start doctest_module('/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch') 2025-09-07T07:36:37.5312944Z Listing tests 2025-09-07T07:36:37.7479892Z msg = Cannot scrape callname=Tensor.dim_order in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py line=1493. 2025-09-07T07:36:37.7480835Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:37.7481233Z 2025-09-07T07:36:37.7481366Z dim_order(ambiguity_check=False) -> tuple 2025-09-07T07:36:37.7481597Z 2025-09-07T07:36:37.7481847Z Returns the uniquely determined tuple of int describing the dim order or 2025-09-07T07:36:37.7482314Z physical layout of :attr:`self`. 2025-09-07T07:36:37.7482511Z 2025-09-07T07:36:37.7482767Z The dim order represents how dimensions are laid out in memory of dense tensors, 2025-09-07T07:36:37.7483320Z starting from the outermost to the innermost dimension. 2025-09-07T07:36:37.7483609Z 2025-09-07T07:36:37.7483793Z Note that the dim order may not always be uniquely determined. 2025-09-07T07:36:37.7484502Z If `ambiguity_check` is True, this function raises a RuntimeError when the dim order cannot be uniquely determined; 2025-09-07T07:36:37.7485412Z If `ambiguity_check` is a list of memory formats, this function raises a RuntimeError when tensor can not be interpreted 2025-09-07T07:36:37.7486185Z into exactly one of the given memory formats, or it cannot be uniquely determined. 2025-09-07T07:36:37.7486890Z If `ambiguity_check` is False, it will return one of legal dim order(s) without checking its uniqueness. 2025-09-07T07:36:37.7487451Z Otherwise, it will raise TypeError. 2025-09-07T07:36:37.7487661Z 2025-09-07T07:36:37.7487755Z Args: 2025-09-07T07:36:37.7488198Z ambiguity_check (bool or List[torch.memory_format]): The check method for ambiguity of dim order. 2025-09-07T07:36:37.7488635Z 2025-09-07T07:36:37.7488758Z Examples:: 2025-09-07T07:36:37.7488894Z 2025-09-07T07:36:37.7489008Z >>> torch.empty((2, 3, 5, 7)).dim_order() 2025-09-07T07:36:37.7489318Z (0, 1, 2, 3) 2025-09-07T07:36:37.7489614Z >>> torch.empty((2, 3, 5, 7)).transpose(1, 2).dim_order() 2025-09-07T07:36:37.7489962Z (0, 2, 1, 3) 2025-09-07T07:36:37.7490309Z >>> torch.empty((2, 3, 5, 7), memory_format=torch.channels_last).dim_order() 2025-09-07T07:36:37.7490725Z (0, 2, 3, 1) 2025-09-07T07:36:37.7490975Z >>> torch.empty((1, 2, 3, 4)).dim_order() 2025-09-07T07:36:37.7491267Z (0, 1, 2, 3) 2025-09-07T07:36:37.7491538Z >>> try: 2025-09-07T07:36:37.7491838Z ... torch.empty((1, 2, 3, 4)).dim_order(ambiguity_check=True) 2025-09-07T07:36:37.7492244Z ... except RuntimeError as e: 2025-09-07T07:36:37.7492521Z ... print(e) 2025-09-07T07:36:37.7492986Z The tensor does not have unique dim order, or cannot map to exact one of the given memory formats. 2025-09-07T07:36:37.7493539Z >>> torch.empty((1, 2, 3, 4)).dim_order( 2025-09-07T07:36:37.7493967Z ... ambiguity_check=[torch.contiguous_format, torch.channels_last] 2025-09-07T07:36:37.7494483Z ... ) # It can be mapped to contiguous format 2025-09-07T07:36:37.7494813Z (0, 1, 2, 3) 2025-09-07T07:36:37.7495041Z >>> try: 2025-09-07T07:36:37.7495357Z ... torch.empty((1, 2, 3, 4)).dim_order(ambiguity_check="ILLEGAL") 2025-09-07T07:36:37.7495771Z ... except TypeError as e: 2025-09-07T07:36:37.7496094Z ... print(e) 2025-09-07T07:36:37.7496477Z The ambiguity_check argument must be a bool or a list of memory formats. 2025-09-07T07:36:37.7496821Z 2025-09-07T07:36:37.7496920Z .. warning:: 2025-09-07T07:36:37.7497242Z The dim_order tensor API is experimental and subject to change. 2025-09-07T07:36:37.7497547Z 2025-09-07T07:36:37.7497793Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:37.7498171Z 2025-09-07T07:36:37.8030572Z msg = Cannot scrape callname=meshgrid in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py line=397. 2025-09-07T07:36:37.8031477Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:37.8032328Z Creates grids of coordinates specified by the 1D inputs in `attr`:tensors. 2025-09-07T07:36:37.8032684Z 2025-09-07T07:36:37.8032875Z This is helpful when you want to visualize data over some 2025-09-07T07:36:37.8033326Z range of inputs. See below for a plotting example. 2025-09-07T07:36:37.8033595Z 2025-09-07T07:36:37.8033762Z Given :math:`N` 1D tensors :math:`T_0 \ldots T_{N-1}` as 2025-09-07T07:36:37.8034235Z inputs with corresponding sizes :math:`S_0 \ldots S_{N-1}`, 2025-09-07T07:36:37.8034781Z this creates :math:`N` N-dimensional tensors :math:`G_0 \ldots 2025-09-07T07:36:37.8035251Z G_{N-1}`, each with shape :math:`(S_0, ..., S_{N-1})` where 2025-09-07T07:36:37.8035715Z the output :math:`G_i` is constructed by expanding :math:`T_i` 2025-09-07T07:36:37.8036116Z to the result shape. 2025-09-07T07:36:37.8036311Z 2025-09-07T07:36:37.8036423Z .. note:: 2025-09-07T07:36:37.8036726Z 0D inputs are treated equivalently to 1D inputs of a 2025-09-07T07:36:37.8037098Z single element. 2025-09-07T07:36:37.8037275Z 2025-09-07T07:36:37.8037363Z .. warning:: 2025-09-07T07:36:37.8037704Z `torch.meshgrid(*tensors)` currently has the same behavior 2025-09-07T07:36:37.8038169Z as calling `numpy.meshgrid(*arrays, indexing='ij')`. 2025-09-07T07:36:37.8038442Z 2025-09-07T07:36:37.8038604Z In the future `torch.meshgrid` will transition to 2025-09-07T07:36:37.8038973Z `indexing='xy'` as the default. 2025-09-07T07:36:37.8039200Z 2025-09-07T07:36:37.8039394Z https://github.com/pytorch/pytorch/issues/50276 tracks 2025-09-07T07:36:37.8039869Z this issue with the goal of migrating to NumPy's behavior. 2025-09-07T07:36:37.8040163Z 2025-09-07T07:36:37.8040265Z .. seealso:: 2025-09-07T07:36:37.8040411Z 2025-09-07T07:36:37.8040591Z :func:`torch.cartesian_prod` has the same effect but it 2025-09-07T07:36:37.8041001Z collects the data in a tensor of vectors. 2025-09-07T07:36:37.8041254Z 2025-09-07T07:36:37.8041341Z Args: 2025-09-07T07:36:37.8041733Z tensors (list of Tensor): list of scalars or 1 dimensional tensors. Scalars will be 2025-09-07T07:36:37.8042286Z treated as tensors of size :math:`(1,)` automatically 2025-09-07T07:36:37.8042559Z 2025-09-07T07:36:37.8042743Z indexing: (str, optional): the indexing mode, either "xy" 2025-09-07T07:36:37.8043189Z or "ij", defaults to "ij". See warning for future changes. 2025-09-07T07:36:37.8043500Z 2025-09-07T07:36:37.8043651Z If "xy" is selected, the first dimension corresponds 2025-09-07T07:36:37.8044080Z to the cardinality of the second input and the second 2025-09-07T07:36:37.8044528Z dimension corresponds to the cardinality of the first 2025-09-07T07:36:37.8044904Z input. 2025-09-07T07:36:37.8045118Z 2025-09-07T07:36:37.8045264Z If "ij" is selected, the dimensions are in the same 2025-09-07T07:36:37.8045668Z order as the cardinality of the inputs. 2025-09-07T07:36:37.8045919Z 2025-09-07T07:36:37.8046004Z Returns: 2025-09-07T07:36:37.8046311Z seq (sequence of Tensors): If the input has :math:`N` 2025-09-07T07:36:37.8046802Z tensors of size :math:`S_0 \ldots S_{N-1}``, then the 2025-09-07T07:36:37.8047239Z output will also have :math:`N` tensors, where each tensor 2025-09-07T07:36:37.8047655Z is of shape :math:`(S_0, ..., S_{N-1})`. 2025-09-07T07:36:37.8047898Z 2025-09-07T07:36:37.8047989Z Example:: 2025-09-07T07:36:37.8048124Z 2025-09-07T07:36:37.8048246Z >>> x = torch.tensor([1, 2, 3]) 2025-09-07T07:36:37.8048605Z >>> y = torch.tensor([4, 5, 6]) 2025-09-07T07:36:37.8048965Z 2025-09-07T07:36:37.8049161Z Observe the element-wise pairings across the grid, (1, 4), 2025-09-07T07:36:37.8049678Z (1, 5), ..., (3, 6). This is the same thing as the 2025-09-07T07:36:37.8050146Z cartesian product. 2025-09-07T07:36:37.8050502Z >>> grid_x, grid_y = torch.meshgrid(x, y, indexing='ij') 2025-09-07T07:36:37.8050855Z >>> grid_x 2025-09-07T07:36:37.8051108Z tensor([[1, 1, 1], 2025-09-07T07:36:37.8051387Z [2, 2, 2], 2025-09-07T07:36:37.8051656Z [3, 3, 3]]) 2025-09-07T07:36:37.8051908Z >>> grid_y 2025-09-07T07:36:37.8052152Z tensor([[4, 5, 6], 2025-09-07T07:36:37.8052417Z [4, 5, 6], 2025-09-07T07:36:37.8052681Z [4, 5, 6]]) 2025-09-07T07:36:37.8052852Z 2025-09-07T07:36:37.8053016Z This correspondence can be seen when these grids are 2025-09-07T07:36:37.8053403Z stacked properly. 2025-09-07T07:36:37.8053784Z >>> torch.equal(torch.cat(tuple(torch.dstack([grid_x, grid_y]))), 2025-09-07T07:36:37.8054235Z ... torch.cartesian_prod(x, y)) 2025-09-07T07:36:37.8054548Z True 2025-09-07T07:36:37.8054700Z 2025-09-07T07:36:37.8054872Z `torch.meshgrid` is commonly used to produce a grid for 2025-09-07T07:36:37.8055255Z plotting. 2025-09-07T07:36:37.8055545Z >>> # xdoctest: +REQUIRES(module:matplotlib) 2025-09-07T07:36:37.8055922Z >>> # xdoctest: +REQUIRES(env:DOCTEST_SHOW) 2025-09-07T07:36:37.8056276Z >>> import matplotlib.pyplot as plt 2025-09-07T07:36:37.8056642Z >>> xs = torch.linspace(-5, 5, steps=100) 2025-09-07T07:36:37.8057002Z >>> ys = torch.linspace(-5, 5, steps=100) 2025-09-07T07:36:37.8057375Z >>> x, y = torch.meshgrid(xs, ys, indexing='xy') 2025-09-07T07:36:37.8057739Z >>> z = torch.sin(torch.sqrt(x * x + y * y)) 2025-09-07T07:36:37.8058099Z >>> ax = plt.axes(projection='3d') 2025-09-07T07:36:37.8058495Z >>> ax.plot_surface(x.numpy(), y.numpy(), z.numpy()) 2025-09-07T07:36:37.8058863Z >>> plt.show() 2025-09-07T07:36:37.8059042Z 2025-09-07T07:36:37.8059210Z .. image:: ../_static/img/meshgrid.png 2025-09-07T07:36:37.8059536Z :width: 512 2025-09-07T07:36:37.8059689Z 2025-09-07T07:36:37.8059789Z 2025-09-07T07:36:37.8060151Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:37.8060539Z 2025-09-07T07:36:37.8061090Z msg = Cannot scrape callname=_unique_impl in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py line=793. 2025-09-07T07:36:37.8061959Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:37.8062712Z unique(input, sorted=True, return_inverse=False, return_counts=False, dim=None) -> tuple[Tensor, Tensor, Tensor] 2025-09-07T07:36:37.8063227Z 2025-09-07T07:36:37.8063378Z Returns the unique elements of the input tensor. 2025-09-07T07:36:37.8063694Z 2025-09-07T07:36:37.8063989Z .. note:: This function is different from :func:`torch.unique_consecutive` in the sense that 2025-09-07T07:36:37.8064618Z this function also eliminates non-consecutive duplicate values. 2025-09-07T07:36:37.8064943Z 2025-09-07T07:36:37.8065182Z .. note:: Currently in the CUDA implementation and the CPU implementation, 2025-09-07T07:36:37.8065842Z `torch.unique` always sort the tensor at the beginning regardless of the `sort` argument. 2025-09-07T07:36:37.8066543Z Sorting could be slow, so if your input tensor is already sorted, it is recommended to use 2025-09-07T07:36:37.8067135Z :func:`torch.unique_consecutive` which avoids the sorting. 2025-09-07T07:36:37.8067426Z 2025-09-07T07:36:37.8067523Z Args: 2025-09-07T07:36:37.8067761Z input (Tensor): the input tensor 2025-09-07T07:36:37.8068188Z sorted (bool): Whether to sort the unique elements in ascending order 2025-09-07T07:36:37.8068635Z before returning as output. 2025-09-07T07:36:37.8069069Z return_inverse (bool): Whether to also return the indices for where 2025-09-07T07:36:37.8069671Z elements in the original input ended up in the returned unique list. 2025-09-07T07:36:37.8070217Z return_counts (bool): Whether to also return the counts for each unique 2025-09-07T07:36:37.8070647Z element. 2025-09-07T07:36:37.8071000Z dim (int, optional): the dimension to operate upon. If ``None``, the 2025-09-07T07:36:37.8071532Z unique of the flattened input is returned. Otherwise, each of the 2025-09-07T07:36:37.8072045Z tensors indexed by the given dimension is treated as one of the 2025-09-07T07:36:37.8072578Z elements to apply the unique operation upon. See examples for more 2025-09-07T07:36:37.8073017Z details. Default: ``None`` 2025-09-07T07:36:37.8073224Z 2025-09-07T07:36:37.8073320Z Returns: 2025-09-07T07:36:37.8073735Z (Tensor, Tensor (optional), Tensor (optional)): A tensor or a tuple of tensors containing 2025-09-07T07:36:37.8074144Z 2025-09-07T07:36:37.8074419Z - **output** (*Tensor*): the output list of unique scalar elements. 2025-09-07T07:36:37.8074888Z - **inverse_indices** (*Tensor*): (optional) if 2025-09-07T07:36:37.8075327Z :attr:`return_inverse` is True, there will be an additional 2025-09-07T07:36:37.8075839Z returned tensor (same shape as input) representing the indices 2025-09-07T07:36:37.8076355Z for where elements in the original input map to in the output; 2025-09-07T07:36:37.8076852Z otherwise, this function will only return a single tensor. 2025-09-07T07:36:37.8077281Z - **counts** (*Tensor*): (optional) if 2025-09-07T07:36:37.8077693Z :attr:`return_counts` is True, there will be an additional 2025-09-07T07:36:37.8078174Z returned tensor (same shape as output or output.size(dim), 2025-09-07T07:36:37.8078653Z if dim was specified) representing the number of occurrences 2025-09-07T07:36:37.8079077Z for each unique value or tensor. 2025-09-07T07:36:37.8079314Z 2025-09-07T07:36:37.8079408Z Example:: 2025-09-07T07:36:37.8079535Z 2025-09-07T07:36:37.8079756Z >>> output = torch.unique(torch.tensor([1, 3, 2, 3], dtype=torch.long)) 2025-09-07T07:36:37.8080178Z >>> output 2025-09-07T07:36:37.8080404Z tensor([1, 2, 3]) 2025-09-07T07:36:37.8080580Z 2025-09-07T07:36:37.8080707Z >>> output, inverse_indices = torch.unique( 2025-09-07T07:36:37.8081190Z ... torch.tensor([1, 3, 2, 3], dtype=torch.long), sorted=True, return_inverse=True) 2025-09-07T07:36:37.8081634Z >>> output 2025-09-07T07:36:37.8081859Z tensor([1, 2, 3]) 2025-09-07T07:36:37.8082122Z >>> inverse_indices 2025-09-07T07:36:37.8082392Z tensor([0, 2, 1, 2]) 2025-09-07T07:36:37.8082563Z 2025-09-07T07:36:37.8082702Z >>> output, inverse_indices = torch.unique( 2025-09-07T07:36:37.8083214Z ... torch.tensor([[1, 3], [2, 3]], dtype=torch.long), sorted=True, return_inverse=True) 2025-09-07T07:36:37.8083666Z >>> output 2025-09-07T07:36:37.8083906Z tensor([1, 2, 3]) 2025-09-07T07:36:37.8084177Z >>> inverse_indices 2025-09-07T07:36:37.8084433Z tensor([[0, 2], 2025-09-07T07:36:37.8084709Z [1, 2]]) 2025-09-07T07:36:37.8084875Z 2025-09-07T07:36:37.8084969Z >>> a = torch.tensor([ 2025-09-07T07:36:37.8085234Z ... [ 2025-09-07T07:36:37.8085451Z ... [1, 1, 0, 0], 2025-09-07T07:36:37.8085724Z ... [1, 1, 0, 0], 2025-09-07T07:36:37.8086001Z ... [0, 0, 1, 1], 2025-09-07T07:36:37.8086269Z ... ], 2025-09-07T07:36:37.8086482Z ... [ 2025-09-07T07:36:37.8086708Z ... [0, 0, 1, 1], 2025-09-07T07:36:37.8086978Z ... [0, 0, 1, 1], 2025-09-07T07:36:37.8087246Z ... [1, 1, 1, 1], 2025-09-07T07:36:37.8087502Z ... ], 2025-09-07T07:36:37.8087721Z ... [ 2025-09-07T07:36:37.8087943Z ... [1, 1, 0, 0], 2025-09-07T07:36:37.8088276Z ... [1, 1, 0, 0], 2025-09-07T07:36:37.8088538Z ... [0, 0, 1, 1], 2025-09-07T07:36:37.8088803Z ... ], 2025-09-07T07:36:37.8089029Z ... ]) 2025-09-07T07:36:37.8089154Z 2025-09-07T07:36:37.8089377Z >>> # If we call `torch.unique(a, dim=0)`, each of the tensors `a[idx, :, :]` 2025-09-07T07:36:37.8089909Z >>> # will be compared. We can see that `a[0, :, :]` and `a[2, :, :]` match 2025-09-07T07:36:37.8090369Z >>> # each other, so one of them will be removed. 2025-09-07T07:36:37.8090722Z >>> (a[0, :, :] == a[2, :, :]).all() 2025-09-07T07:36:37.8091029Z tensor(True) 2025-09-07T07:36:37.8091306Z >>> a_unique_dim0 = torch.unique(a, dim=0) 2025-09-07T07:36:37.8091625Z >>> a_unique_dim0 2025-09-07T07:36:37.8091887Z tensor([[[0, 0, 1, 1], 2025-09-07T07:36:37.8092156Z [0, 0, 1, 1], 2025-09-07T07:36:37.8092428Z [1, 1, 1, 1]], 2025-09-07T07:36:37.8092683Z [[1, 1, 0, 0], 2025-09-07T07:36:37.8092946Z [1, 1, 0, 0], 2025-09-07T07:36:37.8093206Z [0, 0, 1, 1]]]) 2025-09-07T07:36:37.8093383Z 2025-09-07T07:36:37.8093607Z >>> # Notice which sub-tensors from `a` match with the sub-tensors from 2025-09-07T07:36:37.8094024Z >>> # `a_unique_dim0`: 2025-09-07T07:36:37.8094327Z >>> (a_unique_dim0[0, :, :] == a[1, :, :]).all() 2025-09-07T07:36:37.8094654Z tensor(True) 2025-09-07T07:36:37.8094929Z >>> (a_unique_dim0[1, :, :] == a[0, :, :]).all() 2025-09-07T07:36:37.8095244Z tensor(True) 2025-09-07T07:36:37.8095405Z 2025-09-07T07:36:37.8095608Z >>> # For `torch.unique(a, dim=1)`, each of the tensors `a[:, idx, :]` are 2025-09-07T07:36:37.8096122Z >>> # compared. `a[:, 0, :]` and `a[:, 1, :]` match each other, so one of 2025-09-07T07:36:37.8096533Z >>> # them will be removed. 2025-09-07T07:36:37.8096827Z >>> (a[:, 0, :] == a[:, 1, :]).all() 2025-09-07T07:36:37.8097143Z tensor(True) 2025-09-07T07:36:37.8097401Z >>> torch.unique(a, dim=1) 2025-09-07T07:36:37.8097705Z tensor([[[0, 0, 1, 1], 2025-09-07T07:36:37.8097969Z [1, 1, 0, 0]], 2025-09-07T07:36:37.8098240Z [[1, 1, 1, 1], 2025-09-07T07:36:37.8098513Z [0, 0, 1, 1]], 2025-09-07T07:36:37.8098780Z [[0, 0, 1, 1], 2025-09-07T07:36:37.8099038Z [1, 1, 0, 0]]]) 2025-09-07T07:36:37.8099224Z 2025-09-07T07:36:37.8099431Z >>> # For `torch.unique(a, dim=2)`, the tensors `a[:, :, idx]` are compared. 2025-09-07T07:36:37.8099925Z >>> # `a[:, :, 0]` and `a[:, :, 1]` match each other. Also, `a[:, :, 2]` and 2025-09-07T07:36:37.8100378Z >>> # `a[:, :, 3]` match each other as well. So in this case, two of the 2025-09-07T07:36:37.8100783Z >>> # sub-tensors will be removed. 2025-09-07T07:36:37.8101134Z >>> (a[:, :, 0] == a[:, :, 1]).all() 2025-09-07T07:36:37.8101440Z tensor(True) 2025-09-07T07:36:37.8101696Z >>> (a[:, :, 2] == a[:, :, 3]).all() 2025-09-07T07:36:37.8101994Z tensor(True) 2025-09-07T07:36:37.8102239Z >>> torch.unique(a, dim=2) 2025-09-07T07:36:37.8102563Z tensor([[[0, 1], 2025-09-07T07:36:37.8102811Z [0, 1], 2025-09-07T07:36:37.8103057Z [1, 0]], 2025-09-07T07:36:37.8103298Z [[1, 0], 2025-09-07T07:36:37.8103542Z [1, 0], 2025-09-07T07:36:37.8103786Z [1, 1]], 2025-09-07T07:36:37.8104035Z [[0, 1], 2025-09-07T07:36:37.8104263Z [0, 1], 2025-09-07T07:36:37.8104508Z [1, 0]]]) 2025-09-07T07:36:37.8104755Z 2025-09-07T07:36:37.8105127Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:37.8105494Z 2025-09-07T07:36:37.8219289Z msg = Cannot scrape callname=load in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/hub.py line=565. 2025-09-07T07:36:37.8220280Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:37.8220673Z 2025-09-07T07:36:37.8220830Z Load a model from a github repo or a local directory. 2025-09-07T07:36:37.8221101Z 2025-09-07T07:36:37.8221364Z Note: Loading a model is the typical use case, but this can also be used to 2025-09-07T07:36:37.8222009Z for loading other objects such as tokenizers, loss functions, etc. 2025-09-07T07:36:37.8222336Z 2025-09-07T07:36:37.8222516Z If ``source`` is 'github', ``repo_or_dir`` is expected to be 2025-09-07T07:36:37.8222955Z of the form ``repo_owner/repo_name[:ref]`` with an optional 2025-09-07T07:36:37.8223339Z ref (a tag or a branch). 2025-09-07T07:36:37.8223517Z 2025-09-07T07:36:37.8223849Z If ``source`` is 'local', ``repo_or_dir`` is expected to be a 2025-09-07T07:36:37.8224245Z path to a local directory. 2025-09-07T07:36:37.8224425Z 2025-09-07T07:36:37.8224522Z Args: 2025-09-07T07:36:37.8224763Z repo_or_dir (str): If ``source`` is 'github', 2025-09-07T07:36:37.8225303Z this should correspond to a github repo with format ``repo_owner/repo_name[:ref]`` with 2025-09-07T07:36:37.8226096Z an optional ref (tag or branch), for example 'pytorch/vision:0.10'. If ``ref`` is not specified, 2025-09-07T07:36:37.8226782Z the default branch is assumed to be ``main`` if it exists, and otherwise ``master``. 2025-09-07T07:36:37.8227351Z If ``source`` is 'local' then it should be a path to a local directory. 2025-09-07T07:36:37.8227866Z model (str): the name of a callable (entrypoint) defined in the 2025-09-07T07:36:37.8228281Z repo/dir's ``hubconf.py``. 2025-09-07T07:36:37.8228682Z *args (optional): the corresponding args for callable ``model``. 2025-09-07T07:36:37.8229172Z source (str, optional): 'github' or 'local'. Specifies how 2025-09-07T07:36:37.8229620Z ``repo_or_dir`` is to be interpreted. Default is 'github'. 2025-09-07T07:36:37.8230125Z trust_repo (bool, str or None): ``"check"``, ``True``, ``False`` or ``None``. 2025-09-07T07:36:37.8230786Z This parameter was introduced in v1.12 and helps ensuring that users 2025-09-07T07:36:37.8231284Z only run code from repos that they trust. 2025-09-07T07:36:37.8231522Z 2025-09-07T07:36:37.8231713Z - If ``False``, a prompt will ask the user whether the repo should 2025-09-07T07:36:37.8232115Z be trusted. 2025-09-07T07:36:37.8232457Z - If ``True``, the repo will be added to the trusted list and loaded 2025-09-07T07:36:37.8232897Z without requiring explicit confirmation. 2025-09-07T07:36:37.8233315Z - If ``"check"``, the repo will be checked against the list of 2025-09-07T07:36:37.8233795Z trusted repos in the cache. If it is not present in that list, the 2025-09-07T07:36:37.8234319Z behaviour will fall back onto the ``trust_repo=False`` option. 2025-09-07T07:36:37.8234979Z - If ``None``: this will raise a warning, inviting the user to set 2025-09-07T07:36:37.8235478Z ``trust_repo`` to either ``False``, ``True`` or ``"check"``. This 2025-09-07T07:36:37.8235976Z is only present for backward compatibility and will be removed in 2025-09-07T07:36:37.8236441Z v2.0. 2025-09-07T07:36:37.8236583Z 2025-09-07T07:36:37.8236787Z Default is ``None`` and will eventually change to ``"check"`` in v2.0. 2025-09-07T07:36:37.8237323Z force_reload (bool, optional): whether to force a fresh download of 2025-09-07T07:36:37.8237842Z the github repo unconditionally. Does not have any effect if 2025-09-07T07:36:37.8238263Z ``source = 'local'``. Default is ``False``. 2025-09-07T07:36:37.8238704Z verbose (bool, optional): If ``False``, mute messages about hitting 2025-09-07T07:36:37.8239235Z local caches. Note that the message about first download cannot be 2025-09-07T07:36:37.8239731Z muted. Does not have any effect if ``source = 'local'``. 2025-09-07T07:36:37.8240106Z Default is ``True``. 2025-09-07T07:36:37.8240689Z skip_validation (bool, optional): if ``False``, torchhub will check that the branch or commit 2025-09-07T07:36:37.8241395Z specified by the ``github`` argument properly belongs to the repo owner. This will make 2025-09-07T07:36:37.8242080Z requests to the GitHub API; you can specify a non-default GitHub token by setting the 2025-09-07T07:36:37.8242653Z ``GITHUB_TOKEN`` environment variable. Default is ``False``. 2025-09-07T07:36:37.8243148Z **kwargs (optional): the corresponding kwargs for callable ``model``. 2025-09-07T07:36:37.8243488Z 2025-09-07T07:36:37.8243572Z Returns: 2025-09-07T07:36:37.8243879Z The output of the ``model`` callable when called with the given 2025-09-07T07:36:37.8244283Z ``*args`` and ``**kwargs``. 2025-09-07T07:36:37.8244462Z 2025-09-07T07:36:37.8244549Z Example: 2025-09-07T07:36:37.8244805Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_HUB) 2025-09-07T07:36:37.8245157Z >>> # from a github repo 2025-09-07T07:36:37.8245438Z >>> repo = "pytorch/vision" 2025-09-07T07:36:37.8245731Z >>> model = torch.hub.load( 2025-09-07T07:36:37.8246106Z ... repo, "resnet50", weights="ResNet50_Weights.IMAGENET1K_V1" 2025-09-07T07:36:37.8246495Z ... ) 2025-09-07T07:36:37.8246723Z >>> # from a local directory 2025-09-07T07:36:37.8247031Z >>> path = "/some/local/path/pytorch/vision" 2025-09-07T07:36:37.8247373Z >>> # xdoctest: +SKIP 2025-09-07T07:36:37.8247788Z >>> model = torch.hub.load(path, "resnet50", weights="ResNet50_Weights.DEFAULT") 2025-09-07T07:36:37.8248152Z 2025-09-07T07:36:37.8248418Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:37.8248787Z 2025-09-07T07:36:37.8249277Z msg = Cannot scrape callname=_load_local in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/hub.py line=657. 2025-09-07T07:36:37.8250108Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:37.8250484Z 2025-09-07T07:36:37.8250677Z Load a model from a local directory with a ``hubconf.py``. 2025-09-07T07:36:37.8250960Z 2025-09-07T07:36:37.8251042Z Args: 2025-09-07T07:36:37.8251352Z hubconf_dir (str): path to a local directory that contains a 2025-09-07T07:36:37.8251757Z ``hubconf.py``. 2025-09-07T07:36:37.8252108Z model (str): name of an entrypoint defined in the directory's 2025-09-07T07:36:37.8252494Z ``hubconf.py``. 2025-09-07T07:36:37.8252859Z *args (optional): the corresponding args for callable ``model``. 2025-09-07T07:36:37.8253391Z **kwargs (optional): the corresponding kwargs for callable ``model``. 2025-09-07T07:36:37.8253722Z 2025-09-07T07:36:37.8253822Z Returns: 2025-09-07T07:36:37.8254101Z a single model with corresponding pretrained weights. 2025-09-07T07:36:37.8254389Z 2025-09-07T07:36:37.8254476Z Example: 2025-09-07T07:36:37.8254724Z >>> # xdoctest: +SKIP("stub local path") 2025-09-07T07:36:37.8255117Z >>> path = "/some/local/path/pytorch/vision" 2025-09-07T07:36:37.8255444Z >>> model = _load_local( 2025-09-07T07:36:37.8255708Z ... path, 2025-09-07T07:36:37.8255936Z ... "resnet50", 2025-09-07T07:36:37.8256231Z ... weights="ResNet50_Weights.IMAGENET1K_V1", 2025-09-07T07:36:37.8256583Z ... ) 2025-09-07T07:36:37.8256712Z 2025-09-07T07:36:37.8256960Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:37.8257336Z 2025-09-07T07:36:37.8257820Z 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=696. 2025-09-07T07:36:37.8258677Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:37.8259203Z Download object at the given URL to a local path. 2025-09-07T07:36:37.8259456Z 2025-09-07T07:36:37.8259541Z Args: 2025-09-07T07:36:37.8259789Z url (str): URL of the object to download 2025-09-07T07:36:37.8260265Z dst (str): Full path where object will be saved, e.g. ``/tmp/temporary_file`` 2025-09-07T07:36:37.8261020Z hash_prefix (str, optional): If not None, the SHA256 downloaded file should start with ``hash_prefix``. 2025-09-07T07:36:37.8261574Z Default: None 2025-09-07T07:36:37.8261986Z progress (bool, optional): whether or not to display a progress bar to stderr 2025-09-07T07:36:37.8262455Z Default: True 2025-09-07T07:36:37.8262627Z 2025-09-07T07:36:37.8262711Z Example: 2025-09-07T07:36:37.8262978Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_HUB) 2025-09-07T07:36:37.8263326Z >>> # xdoctest: +REQUIRES(POSIX) 2025-09-07T07:36:37.8263659Z >>> torch.hub.download_url_to_file( 2025-09-07T07:36:37.8264119Z ... "https://s3.amazonaws.com/pytorch/models/resnet18-5c106cde.pth", 2025-09-07T07:36:37.8264564Z ... "/tmp/temporary_file", 2025-09-07T07:36:37.8264849Z ... ) 2025-09-07T07:36:37.8264988Z 2025-09-07T07:36:37.8265067Z 2025-09-07T07:36:37.8265432Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:37.8265799Z 2025-09-07T07:36:37.8266309Z 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=821. 2025-09-07T07:36:37.8267186Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:37.8267704Z Loads the Torch serialized object at the given URL. 2025-09-07T07:36:37.8267975Z 2025-09-07T07:36:37.8268155Z If downloaded file is a zip file, it will be automatically 2025-09-07T07:36:37.8268539Z decompressed. 2025-09-07T07:36:37.8268681Z 2025-09-07T07:36:37.8268905Z If the object is already present in `model_dir`, it's deserialized and 2025-09-07T07:36:37.8269327Z returned. 2025-09-07T07:36:37.8269662Z The default value of ``model_dir`` is ``/checkpoints`` where 2025-09-07T07:36:37.8270190Z ``hub_dir`` is the directory returned by :func:`~torch.hub.get_dir`. 2025-09-07T07:36:37.8270496Z 2025-09-07T07:36:37.8270594Z Args: 2025-09-07T07:36:37.8270839Z url (str): URL of the object to download 2025-09-07T07:36:37.8271260Z model_dir (str, optional): directory in which to save the object 2025-09-07T07:36:37.8271928Z map_location (optional): a function or a dict specifying how to remap storage locations (see torch.load) 2025-09-07T07:36:37.8272650Z progress (bool, optional): whether or not to display a progress bar to stderr. 2025-09-07T07:36:37.8273124Z Default: True 2025-09-07T07:36:37.8273609Z check_hash(bool, optional): If True, the filename part of the URL should follow the naming convention 2025-09-07T07:36:37.8274270Z ``filename-.ext`` where ```` is the first eight or more 2025-09-07T07:36:37.8274936Z digits of the SHA256 hash of the contents of the file. The hash is used to 2025-09-07T07:36:37.8275526Z ensure unique names and to verify the contents of the file. 2025-09-07T07:36:37.8275934Z Default: False 2025-09-07T07:36:37.8276438Z file_name (str, optional): name for the downloaded file. Filename from ``url`` will be used if not set. 2025-09-07T07:36:37.8277252Z weights_only(bool, optional): If True, only weights will be loaded and no complex pickled objects. 2025-09-07T07:36:37.8277959Z Recommended for untrusted sources. See :func:`~torch.load` for more details. 2025-09-07T07:36:37.8278337Z 2025-09-07T07:36:37.8278434Z Example: 2025-09-07T07:36:37.8278701Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_HUB) 2025-09-07T07:36:37.8279084Z >>> state_dict = torch.hub.load_state_dict_from_url( 2025-09-07T07:36:37.8279572Z ... "https://s3.amazonaws.com/pytorch/models/resnet18-5c106cde.pth" 2025-09-07T07:36:37.8279994Z ... ) 2025-09-07T07:36:37.8280119Z 2025-09-07T07:36:37.8280211Z 2025-09-07T07:36:37.8280564Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:37.8280942Z 2025-09-07T07:36:37.8314916Z msg = Cannot scrape callname=Library.fallback in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py line=375. 2025-09-07T07:36:37.8315788Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-09-07T07:36:37.8316407Z Registers the function implementation as the fallback for the given key. 2025-09-07T07:36:37.8316764Z 2025-09-07T07:36:37.8316995Z This function only works for a library with global namespace ("_"). 2025-09-07T07:36:37.8317327Z 2025-09-07T07:36:37.8317425Z Args: 2025-09-07T07:36:37.8317826Z fn: function used as fallback for the given dispatch key or :func:`~fallthrough_kernel` 2025-09-07T07:36:37.8318340Z to register a fallthrough. 2025-09-07T07:36:37.8318884Z dispatch_key: dispatch key that the input function should be registered for. By default, it uses 2025-09-07T07:36:37.8332959Z the dispatch key that the library was created with. 2025-09-07T07:36:37.8333852Z with_keyset: flag controlling if the current dispatcher call keyset should be passed as the first argument 2025-09-07T07:36:37.8334685Z to :attr:`fn` when calling. This should be used to create the appropriate keyset for redispatch calls. 2025-09-07T07:36:37.8335138Z 2025-09-07T07:36:37.8335302Z Example:: 2025-09-07T07:36:37.8335445Z 2025-09-07T07:36:37.8335600Z >>> my_lib = Library("_", "IMPL") 2025-09-07T07:36:37.8335954Z >>> def fallback_kernel(op, *args, **kwargs): 2025-09-07T07:36:37.8336334Z >>> # Handle all autocast ops generically 2025-09-07T07:36:37.8336671Z >>> # ... 2025-09-07T07:36:37.8336984Z >>> my_lib.fallback(fallback_kernel, "Autocast") 2025-09-07T07:36:37.8337331Z 2025-09-07T07:36:37.8338074Z Original Error: IndentationError('expected an indented block after function definition on line 2', ('', 5, 1, 'my_lib.fallback(fallback_kernel, "Autocast")\n', 5, 7)) 2025-09-07T07:36:37.8338836Z 2025-09-07T07:36:37.8338969Z my_lib.fallback(fallback_kernel, "Autocast") 2025-09-07T07:36:37.8339293Z ^ 2025-09-07T07:36:37.8397902Z msg = Cannot scrape callname=register_fake in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py line=948. 2025-09-07T07:36:37.8398753Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-09-07T07:36:37.8399341Z Register a FakeTensor implementation ("fake impl") for this operator. 2025-09-07T07:36:37.8399697Z 2025-09-07T07:36:37.8399875Z Also sometimes known as a "meta kernel", "abstract impl". 2025-09-07T07:36:37.8400179Z 2025-09-07T07:36:37.8400422Z An "FakeTensor implementation" specifies the behavior of this operator on 2025-09-07T07:36:37.8401010Z Tensors that carry no data ("FakeTensor"). Given some input Tensors with 2025-09-07T07:36:37.8401767Z certain properties (sizes/strides/storage_offset/device), it specifies 2025-09-07T07:36:37.8402263Z what the properties of the output Tensors are. 2025-09-07T07:36:37.8402529Z 2025-09-07T07:36:37.8402757Z The FakeTensor implementation has the same signature as the operator. 2025-09-07T07:36:37.8403362Z It is run for both FakeTensors and meta tensors. To write a FakeTensor 2025-09-07T07:36:37.8403907Z implementation, assume that all Tensor inputs to the operator are 2025-09-07T07:36:37.8404432Z regular CPU/CUDA/Meta tensors, but they do not have storage, and 2025-09-07T07:36:37.8404958Z you are trying to return regular CPU/CUDA/Meta tensor(s) as output. 2025-09-07T07:36:37.8405512Z The FakeTensor implementation must consist of only PyTorch operations 2025-09-07T07:36:37.8406053Z (and may not directly access the storage or data of any input or 2025-09-07T07:36:37.8406467Z intermediate Tensors). 2025-09-07T07:36:37.8406643Z 2025-09-07T07:36:37.8406794Z This API may be used as a decorator (see examples). 2025-09-07T07:36:37.8407069Z 2025-09-07T07:36:37.8407293Z For a detailed guide on custom ops, please see 2025-09-07T07:36:37.8407799Z https://pytorch.org/tutorials/advanced/custom_ops_landing_page.html 2025-09-07T07:36:37.8408144Z 2025-09-07T07:36:37.8408297Z Args: 2025-09-07T07:36:37.8408753Z op_name: Operator name (along with the overload) or OpOverload object. 2025-09-07T07:36:37.8409225Z func: Fake tensor implementation. 2025-09-07T07:36:37.8409651Z lib (Optional[Library]): Library to register the fake tensor to. 2025-09-07T07:36:37.8410182Z allow_override: Flag controlling if we want to override an 2025-09-07T07:36:37.8410664Z existing registered fake impl. This is by default off, 2025-09-07T07:36:37.8411125Z and will error you're trying to register a fake impl to 2025-09-07T07:36:37.8411644Z an operator that already has a fake impl. This also only 2025-09-07T07:36:37.8412092Z applies if the custom operator was not created via 2025-09-07T07:36:37.8412564Z torch.library.custom_op, as overriding and existing fake 2025-09-07T07:36:37.8412991Z impl is already allowed. 2025-09-07T07:36:37.8413220Z 2025-09-07T07:36:37.8413322Z Examples: 2025-09-07T07:36:37.8413555Z >>> import torch 2025-09-07T07:36:37.8413817Z >>> import numpy as np 2025-09-07T07:36:37.8414110Z >>> from torch import Tensor 2025-09-07T07:36:37.8414403Z >>> 2025-09-07T07:36:37.8414715Z >>> # Example 1: an operator without data-dependent output shape 2025-09-07T07:36:37.8419391Z >>> @torch.library.custom_op("mylib::custom_linear", mutates_args=()) 2025-09-07T07:36:37.8419986Z >>> def custom_linear(x: Tensor, weight: Tensor, bias: Tensor) -> Tensor: 2025-09-07T07:36:37.8420511Z >>> raise NotImplementedError("Implementation goes here") 2025-09-07T07:36:37.8420902Z >>> 2025-09-07T07:36:37.8421197Z >>> @torch.library.register_fake("mylib::custom_linear") 2025-09-07T07:36:37.8421596Z >>> def _(x, weight, bias): 2025-09-07T07:36:37.8421902Z >>> assert x.dim() == 2 2025-09-07T07:36:37.8422213Z >>> assert weight.dim() == 2 2025-09-07T07:36:37.8422529Z >>> assert bias.dim() == 1 2025-09-07T07:36:37.8422867Z >>> assert x.shape[1] == weight.shape[1] 2025-09-07T07:36:37.8423241Z >>> assert weight.shape[0] == bias.shape[0] 2025-09-07T07:36:37.8423790Z >>> assert x.device == weight.device 2025-09-07T07:36:37.8424098Z >>> 2025-09-07T07:36:37.8424337Z >>> return (x @ weight.t()) + bias 2025-09-07T07:36:37.8424651Z >>> 2025-09-07T07:36:37.8424968Z >>> with torch._subclasses.fake_tensor.FakeTensorMode(): 2025-09-07T07:36:37.8425367Z >>> x = torch.randn(2, 3) 2025-09-07T07:36:37.8425682Z >>> w = torch.randn(3, 3) 2025-09-07T07:36:37.8426070Z >>> b = torch.randn(3) 2025-09-07T07:36:37.8426396Z >>> y = torch.ops.mylib.custom_linear(x, w, b) 2025-09-07T07:36:37.8426736Z >>> 2025-09-07T07:36:37.8426963Z >>> assert y.shape == (2, 3) 2025-09-07T07:36:37.8427273Z >>> 2025-09-07T07:36:37.8427604Z >>> # Example 2: an operator with data-dependent output shape 2025-09-07T07:36:37.8428122Z >>> @torch.library.custom_op("mylib::custom_nonzero", mutates_args=()) 2025-09-07T07:36:37.8428592Z >>> def custom_nonzero(x: Tensor) -> Tensor: 2025-09-07T07:36:37.8428949Z >>> x_np = x.numpy(force=True) 2025-09-07T07:36:37.8429310Z >>> res = np.stack(np.nonzero(x_np), axis=1) 2025-09-07T07:36:37.8429684Z >>> return torch.tensor(res, device=x.device) 2025-09-07T07:36:37.8430024Z >>> 2025-09-07T07:36:37.8430331Z >>> @torch.library.register_fake("mylib::custom_nonzero") 2025-09-07T07:36:37.8430715Z >>> def _(x): 2025-09-07T07:36:37.8431008Z >>> # Number of nonzero-elements is data-dependent. 2025-09-07T07:36:37.8431477Z >>> # Since we cannot peek at the data in an fake impl, 2025-09-07T07:36:37.8431911Z >>> # we use the ctx object to construct a new symint that 2025-09-07T07:36:37.8432318Z >>> # represents the data-dependent size. 2025-09-07T07:36:37.8432672Z >>> ctx = torch.library.get_ctx() 2025-09-07T07:36:37.8433018Z >>> nnz = ctx.new_dynamic_size() 2025-09-07T07:36:37.8433348Z >>> shape = [nnz, x.dim()] 2025-09-07T07:36:37.8433716Z >>> result = x.new_empty(shape, dtype=torch.int64) 2025-09-07T07:36:37.8434069Z >>> return result 2025-09-07T07:36:37.8434427Z >>> 2025-09-07T07:36:37.8434744Z >>> from torch.fx.experimental.proxy_tensor import make_fx 2025-09-07T07:36:37.8435132Z >>> 2025-09-07T07:36:37.8435360Z >>> x = torch.tensor([0, 1, 2, 3, 4, 0]) 2025-09-07T07:36:37.8435838Z >>> trace = make_fx(torch.ops.mylib.custom_nonzero, tracing_mode="symbolic")(x) 2025-09-07T07:36:37.8436324Z >>> trace.print_readable() 2025-09-07T07:36:37.8436618Z >>> 2025-09-07T07:36:37.8436965Z >>> assert torch.allclose(trace(x), torch.ops.mylib.custom_nonzero(x)) 2025-09-07T07:36:37.8437310Z 2025-09-07T07:36:37.8437390Z 2025-09-07T07:36:37.8438039Z Original Error: IndentationError('expected an indented block after function definition on line 37', ('', 38, 1, '_._ = None\n', 38, 2)) 2025-09-07T07:36:37.8438697Z 2025-09-07T07:36:37.8438778Z _._ = None 2025-09-07T07:36:37.8438985Z ^ 2025-09-07T07:36:37.8439590Z msg = Cannot scrape callname=register_autograd in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py line=1083. 2025-09-07T07:36:37.8440579Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:37.8441114Z Register a backward formula for this custom op. 2025-09-07T07:36:37.8441368Z 2025-09-07T07:36:37.8441586Z In order for an operator to work with autograd, you need to register 2025-09-07T07:36:37.8442013Z a backward formula: 2025-09-07T07:36:37.8442375Z 1. You must tell us how to compute gradients during the backward pass 2025-09-07T07:36:37.8442823Z by providing us a "backward" function. 2025-09-07T07:36:37.8443266Z 2. If you need any values from the forward to compute gradients, you can 2025-09-07T07:36:37.8443745Z use `setup_context` to save values for backward. 2025-09-07T07:36:37.8443996Z 2025-09-07T07:36:37.8444220Z ``backward`` runs during the backward pass. It accepts ``(ctx, *grads)``: 2025-09-07T07:36:37.8444762Z - ``grads`` is one or more gradients. The number of gradients matches 2025-09-07T07:36:37.8445201Z the number of outputs of the operator. 2025-09-07T07:36:37.8445644Z The ``ctx`` object is `the same ctx object `_ used by 2025-09-07T07:36:37.8446220Z :class:`torch.autograd.Function`. The semantics of ``backward_fn`` are the 2025-09-07T07:36:37.8446762Z same as :meth:`torch.autograd.Function.backward`. 2025-09-07T07:36:37.8447040Z 2025-09-07T07:36:37.8447252Z ``setup_context(ctx, inputs, output)`` runs during the forward pass. 2025-09-07T07:36:37.8447813Z Please save quantities needed for backward onto the ``ctx`` object via 2025-09-07T07:36:37.8448424Z either :meth:`torch.autograd.function.FunctionCtx.save_for_backward` 2025-09-07T07:36:37.8448966Z or assigning them as attributes of ``ctx``. If your custom op has 2025-09-07T07:36:37.8449496Z kwarg-only arguments, we expect the signature of ``setup_context`` 2025-09-07T07:36:37.8450025Z to be ``setup_context(ctx, inputs, keyword_only_inputs, output)``. 2025-09-07T07:36:37.8450333Z 2025-09-07T07:36:37.8450565Z Both ``setup_context_fn`` and ``backward_fn`` must be traceable. That is, 2025-09-07T07:36:37.8451132Z they may not directly access :meth:`torch.Tensor.data_ptr` and they must 2025-09-07T07:36:37.8451700Z not depend on or mutate global state. If you need a non-traceable backward, 2025-09-07T07:36:37.8452310Z you can make it a separate custom_op that you call inside ``backward_fn``. 2025-09-07T07:36:37.8452663Z 2025-09-07T07:36:37.8452880Z If you need different autograd behavior on different devices, then we 2025-09-07T07:36:37.8453461Z recommend creating two different custom operators, one for each device 2025-09-07T07:36:37.8454038Z that needs different behavior, and switching between them at runtime. 2025-09-07T07:36:37.8454375Z 2025-09-07T07:36:37.8454463Z Examples: 2025-09-07T07:36:37.8454695Z >>> import torch 2025-09-07T07:36:37.8454975Z >>> import numpy as np 2025-09-07T07:36:37.8455275Z >>> from torch import Tensor 2025-09-07T07:36:37.8455555Z >>> 2025-09-07T07:36:37.8455908Z >>> @torch.library.custom_op("mylib::numpy_sin", mutates_args=()) 2025-09-07T07:36:37.8456351Z >>> def numpy_sin(x: Tensor) -> Tensor: 2025-09-07T07:36:37.8456698Z >>> x_np = x.cpu().numpy() 2025-09-07T07:36:37.8456995Z >>> y_np = np.sin(x_np) 2025-09-07T07:36:37.8457358Z >>> return torch.from_numpy(y_np).to(device=x.device) 2025-09-07T07:36:37.8457723Z >>> 2025-09-07T07:36:37.8458002Z >>> def setup_context(ctx, inputs, output) -> Tensor: 2025-09-07T07:36:37.8458353Z >>> x, = inputs 2025-09-07T07:36:37.8458638Z >>> ctx.save_for_backward(x) 2025-09-07T07:36:37.8458936Z >>> 2025-09-07T07:36:37.8459165Z >>> def backward(ctx, grad): 2025-09-07T07:36:37.8459464Z >>> x, = ctx.saved_tensors 2025-09-07T07:36:37.8459775Z >>> return grad * x.cos() 2025-09-07T07:36:37.8460062Z >>> 2025-09-07T07:36:37.8460312Z >>> torch.library.register_autograd( 2025-09-07T07:36:37.8460761Z ... "mylib::numpy_sin", backward, setup_context=setup_context 2025-09-07T07:36:37.8461156Z ... ) 2025-09-07T07:36:37.8461369Z >>> 2025-09-07T07:36:37.8461626Z >>> x = torch.randn(3, requires_grad=True) 2025-09-07T07:36:37.8461970Z >>> y = numpy_sin(x) 2025-09-07T07:36:37.8462314Z >>> (grad_x,) = torch.autograd.grad(y, x, torch.ones_like(y)) 2025-09-07T07:36:37.8462805Z >>> assert torch.allclose(grad_x, x.cos()) 2025-09-07T07:36:37.8463314Z >>> 2025-09-07T07:36:37.8463564Z >>> # Example with a keyword-only arg 2025-09-07T07:36:37.8463991Z >>> @torch.library.custom_op("mylib::numpy_mul", mutates_args=()) 2025-09-07T07:36:37.8464475Z >>> def numpy_mul(x: Tensor, *, val: float) -> Tensor: 2025-09-07T07:36:37.8464847Z >>> x_np = x.cpu().numpy() 2025-09-07T07:36:37.8465151Z >>> y_np = x_np * val 2025-09-07T07:36:37.8465494Z >>> return torch.from_numpy(y_np).to(device=x.device) 2025-09-07T07:36:37.8465849Z >>> 2025-09-07T07:36:37.8466197Z >>> def setup_context(ctx, inputs, keyword_only_inputs, output) -> Tensor: 2025-09-07T07:36:37.8466715Z >>> ctx.val = keyword_only_inputs["val"] 2025-09-07T07:36:37.8467027Z >>> 2025-09-07T07:36:37.8467258Z >>> def backward(ctx, grad): 2025-09-07T07:36:37.8467575Z >>> return grad * ctx.val 2025-09-07T07:36:37.8467863Z >>> 2025-09-07T07:36:37.8468094Z >>> torch.library.register_autograd( 2025-09-07T07:36:37.8468557Z ... "mylib::numpy_mul", backward, setup_context=setup_context 2025-09-07T07:36:37.8468943Z ... ) 2025-09-07T07:36:37.8469162Z >>> 2025-09-07T07:36:37.8469396Z >>> x = torch.randn(3, requires_grad=True) 2025-09-07T07:36:37.8469745Z >>> y = numpy_mul(x, val=3.14) 2025-09-07T07:36:37.8470130Z >>> (grad_x,) = torch.autograd.grad(y, x, torch.ones_like(y)) 2025-09-07T07:36:37.8470594Z >>> assert torch.allclose(grad_x, torch.full_like(x, 3.14)) 2025-09-07T07:36:37.8470882Z 2025-09-07T07:36:37.8470974Z 2025-09-07T07:36:37.8471325Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:37.8471706Z 2025-09-07T07:36:37.8472228Z msg = Cannot scrape callname=get_kernel in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py line=1482. 2025-09-07T07:36:37.8473056Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-09-07T07:36:37.8473639Z Returns the computed kernel for a given operator and dispatch key. 2025-09-07T07:36:37.8473963Z 2025-09-07T07:36:37.8474195Z This function retrieves the kernel that would be executed for a given 2025-09-07T07:36:37.8474839Z operator and dispatch key combination. The returned SafeKernelFunction 2025-09-07T07:36:37.8475388Z can be used to call the kernel in a boxed fashion. The intended use 2025-09-07T07:36:37.8475971Z case for this function is to retrieve the original kernel for a given 2025-09-07T07:36:37.8476900Z dispatch key and then register another kernel to the same dispatch key 2025-09-07T07:36:37.8477491Z that calls into the original kernel for certain cases. 2025-09-07T07:36:37.8477772Z 2025-09-07T07:36:37.8477856Z Args: 2025-09-07T07:36:37.8478188Z op: Operator name (along with the overload) or OpOverload object 2025-09-07T07:36:37.8478733Z Can be a string (e.g., "aten::add.Tensor"), an OpOverload, or a CustomOpDef. 2025-09-07T07:36:37.8479343Z dispatch_key (str | torch.DispatchKey): The dispatch key to get the kernel for. 2025-09-07T07:36:37.8479913Z Can be a string (e.g., "CPU", "CUDA") or a DispatchKey enum value. 2025-09-07T07:36:37.8480219Z 2025-09-07T07:36:37.8480303Z Returns: 2025-09-07T07:36:37.8480672Z torch._C._SafeKernelFunction: A safe kernel function that can be used to 2025-09-07T07:36:37.8481129Z call the kernel. 2025-09-07T07:36:37.8481303Z 2025-09-07T07:36:37.8481452Z Raises: 2025-09-07T07:36:37.8481714Z RuntimeError: If the operator does not exist. 2025-09-07T07:36:37.8481985Z 2025-09-07T07:36:37.8482072Z Example: 2025-09-07T07:36:37.8482325Z >>> # Get the CPU kernel for torch.add 2025-09-07T07:36:37.8482755Z >>> kernel = torch.library.get_kernel("aten::add.Tensor", "CPU") 2025-09-07T07:36:37.8483142Z >>> 2025-09-07T07:36:37.8483393Z >>> # You can also use DispatchKey enum 2025-09-07T07:36:37.8483888Z >>> kernel = torch.library.get_kernel("aten::add.Tensor", torch.DispatchKey.CPU) 2025-09-07T07:36:37.8484355Z >>> 2025-09-07T07:36:37.8484590Z >>> # Or use an OpOverload directly 2025-09-07T07:36:37.8485023Z >>> kernel = torch.library.get_kernel(torch.ops.aten.add.Tensor, "CPU") 2025-09-07T07:36:37.8485454Z >>> 2025-09-07T07:36:37.8485788Z >>> # Example: Using get_kernel in a custom op with conditional dispatch 2025-09-07T07:36:37.8486244Z >>> # Get the original kernel for torch.sin 2025-09-07T07:36:37.8486687Z >>> original_sin_kernel = torch.library.get_kernel("aten::sin", "CPU") 2025-09-07T07:36:37.8487102Z >>> 2025-09-07T07:36:37.8487486Z >>> # If input has negative values, use original sin, otherwise return zeros 2025-09-07T07:36:37.8487977Z >>> def conditional_sin_impl(dispatch_keys, x): 2025-09-07T07:36:37.8488318Z >>> if (x < 0).any(): 2025-09-07T07:36:37.8488689Z >>> return original_sin_kernel.call_boxed(dispatch_keys, x) 2025-09-07T07:36:37.8489105Z >>> else: 2025-09-07T07:36:37.8489372Z >>> return torch.zeros_like(x) 2025-09-07T07:36:37.8489670Z >>> 2025-09-07T07:36:37.8489929Z >>> lib = torch.library.Library("aten", "IMPL") 2025-09-07T07:36:37.8490434Z >>> # with_keyset=True so the first argument to the impl is the current DispatchKeySet 2025-09-07T07:36:37.8491001Z >>> which needs to be the first argument to ``kernel.call_boxed`` 2025-09-07T07:36:37.8491501Z >>> lib.impl("sin", conditional_sin_impl, "CPU", with_keyset=True) 2025-09-07T07:36:37.8491882Z >>> 2025-09-07T07:36:37.8492115Z >>> # Test the conditional behavior 2025-09-07T07:36:37.8492463Z >>> x_positive = torch.tensor([1.0, 2.0]) 2025-09-07T07:36:37.8492813Z >>> x_mixed = torch.tensor([-1.0, 2.0]) 2025-09-07T07:36:37.8493161Z >>> torch.sin(x_positive) 2025-09-07T07:36:37.8493454Z tensor([0., 0.]) 2025-09-07T07:36:37.8493719Z >>> torch.sin(x_mixed) 2025-09-07T07:36:37.8494002Z tensor([-0.8415, 0.9093]) 2025-09-07T07:36:37.8494264Z 2025-09-07T07:36:37.8494859Z Original Error: SyntaxError('invalid syntax', ('', 23, 7, 'which needs to be the first argument to ``kernel.call_boxed``\n', 23, 12)) 2025-09-07T07:36:37.8495462Z 2025-09-07T07:36:37.8495641Z which needs to be the first argument to ``kernel.call_boxed`` 2025-09-07T07:36:37.8496029Z ^ 2025-09-07T07:36:37.8496648Z msg = Cannot scrape callname=opcheck in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py line=1571. 2025-09-07T07:36:37.8497487Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:37.8498078Z Given an operator and some sample arguments, tests if the operator is 2025-09-07T07:36:37.8498515Z registered correctly. 2025-09-07T07:36:37.8498697Z 2025-09-07T07:36:37.8498907Z That is, when you use the torch.library/TORCH_LIBRARY APIs to create a 2025-09-07T07:36:37.8499487Z custom op, you specified metadata (e.g. mutability info) about the custom op 2025-09-07T07:36:37.8500064Z and these APIs require that the functions you pass them satisfy certain 2025-09-07T07:36:37.8500643Z properties (e.g. no data pointer access in the fake/meta/abstract kernel) 2025-09-07T07:36:37.8501142Z ``opcheck`` tests these metadata and properties. 2025-09-07T07:36:37.8501398Z 2025-09-07T07:36:37.8501525Z Concretely, we test the following: 2025-09-07T07:36:37.8501775Z 2025-09-07T07:36:37.8501965Z - test_schema: If the schema matches the implementation of 2025-09-07T07:36:37.8502469Z the operator. For example: if the schema specifies a Tensor is mutated, 2025-09-07T07:36:37.8503027Z then we check the implementation mutates the Tensor. If the schema 2025-09-07T07:36:37.8503548Z specifies that we return a new Tensor, then we check that the 2025-09-07T07:36:37.8504075Z implementation returns a new Tensor (instead of an existing one or 2025-09-07T07:36:37.8504526Z a view of an existing one). 2025-09-07T07:36:37.8504916Z - test_autograd_registration: If the operator supports training 2025-09-07T07:36:37.8505437Z (autograd): we check that its autograd formula is registered via 2025-09-07T07:36:37.8505959Z torch.library.register_autograd or a manual registration to one 2025-09-07T07:36:37.8506493Z or more DispatchKey::Autograd keys. Any other DispatchKey-based 2025-09-07T07:36:37.8506941Z registrations may lead to undefined behavior. 2025-09-07T07:36:37.8507376Z - test_faketensor: If the operator has a FakeTensor kernel 2025-09-07T07:36:37.8507845Z (and if it is correct). The FakeTensor kernel is necessary ( 2025-09-07T07:36:37.8508385Z but not sufficient) for the operator to work with PyTorch compilation 2025-09-07T07:36:37.8508925Z APIs (torch.compile/export/FX). We check that a FakeTensor kernel 2025-09-07T07:36:37.8509450Z (also sometimes known as a meta kernel) was registered for the 2025-09-07T07:36:37.8509986Z operator and that it is correct. This test takes the result of 2025-09-07T07:36:37.8510491Z running the operator on real tensors and the result of running 2025-09-07T07:36:37.8510996Z the operator on FakeTensors and checks that they have the same 2025-09-07T07:36:37.8511444Z Tensor metadata (sizes/strides/dtype/device/etc). 2025-09-07T07:36:37.8511912Z - test_aot_dispatch_dynamic: If the operator has correct behavior 2025-09-07T07:36:37.8512408Z with PyTorch compilation APIs (torch.compile/export/FX). 2025-09-07T07:36:37.8512913Z This checks that the outputs (and gradients, if applicable) are the 2025-09-07T07:36:37.8513381Z same under eager-mode PyTorch and torch.compile. 2025-09-07T07:36:37.8513836Z This test is a superset of ``test_faketensor`` and is an e2e test; 2025-09-07T07:36:37.8514425Z other things it tests are that the operator supports 2025-09-07T07:36:37.8514930Z functionalization and that the backward pass (if it exists) also 2025-09-07T07:36:37.8515403Z supports FakeTensor and functionalization. 2025-09-07T07:36:37.8515646Z 2025-09-07T07:36:37.8515843Z For best results, please call ``opcheck`` multiple times with a 2025-09-07T07:36:37.8516338Z representative set of inputs. If your operator supports 2025-09-07T07:36:37.8516868Z autograd, please use ``opcheck`` with inputs with ``requires_grad = True``; 2025-09-07T07:36:37.8517458Z if your operator supports multiple devices (e.g. CPU and CUDA), please 2025-09-07T07:36:37.8517951Z use ``opcheck`` with inputs on all supported devices. 2025-09-07T07:36:37.8518229Z 2025-09-07T07:36:37.8518314Z Args: 2025-09-07T07:36:37.8518622Z op: The operator. Must either be a function decorated with 2025-09-07T07:36:37.8519133Z :func:`torch.library.custom_op` or an OpOverload/OpOverloadPacket 2025-09-07T07:36:37.8519687Z found in torch.ops.* (e.g. torch.ops.aten.sin, torch.ops.mylib.foo) 2025-09-07T07:36:37.8520124Z args: The args to the operator 2025-09-07T07:36:37.8520466Z kwargs: The kwargs to the operator 2025-09-07T07:36:37.8520865Z test_utils: Tests that we should run. Default: all of them. 2025-09-07T07:36:37.8521297Z Example: ("test_schema", "test_faketensor") 2025-09-07T07:36:37.8521728Z raise_exception: If we should raise an exception on the first 2025-09-07T07:36:37.8522202Z error. If False, we will return a dict with information 2025-09-07T07:36:37.8522636Z on if each test passed or not. 2025-09-07T07:36:37.8523097Z rtol (Optional[float]): Relative tolerance for floating point comparisons. 2025-09-07T07:36:37.8523588Z If specified ``atol`` must also be specified. 2025-09-07T07:36:37.8524198Z If omitted, default values based on the ``dtype`` are selected 2025-09-07T07:36:37.8524687Z (see the table in :func:`torch.testing.assert_close`). 2025-09-07T07:36:37.8525215Z atol (Optional[float]): Absolute tolerance for floating point comparisons. 2025-09-07T07:36:37.8525725Z If specified ``rtol`` must also be specified. 2025-09-07T07:36:37.8526156Z If omitted, default values based on the ``dtype`` are selected 2025-09-07T07:36:37.8526639Z (see the table in :func:`torch.testing.assert_close`). 2025-09-07T07:36:37.8526928Z 2025-09-07T07:36:37.8527020Z .. warning:: 2025-09-07T07:36:37.8527159Z 2025-09-07T07:36:37.8527391Z opcheck and :func:`torch.autograd.gradcheck` test different things; 2025-09-07T07:36:37.8527937Z opcheck tests if your usage of torch.library APIs is correct while 2025-09-07T07:36:37.8528464Z :func:`torch.autograd.gradcheck` tests if your autograd formula is 2025-09-07T07:36:37.8529100Z mathematically correct. Use both to test custom ops that support 2025-09-07T07:36:37.8529550Z gradient computation. 2025-09-07T07:36:37.8529734Z 2025-09-07T07:36:37.8529833Z Example: 2025-09-07T07:36:37.8529957Z 2025-09-07T07:36:37.8530133Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2025-09-07T07:36:37.8530583Z >>> @torch.library.custom_op("mylib::numpy_mul", mutates_args=()) 2025-09-07T07:36:37.8531049Z >>> def numpy_mul(x: Tensor, y: float) -> Tensor: 2025-09-07T07:36:37.8531416Z >>> x_np = x.numpy(force=True) 2025-09-07T07:36:37.8531737Z >>> z_np = x_np * y 2025-09-07T07:36:37.8532042Z >>> return torch.from_numpy(z_np).to(x.device) 2025-09-07T07:36:37.8532381Z >>> 2025-09-07T07:36:37.8532608Z >>> @numpy_mul.register_fake 2025-09-07T07:36:37.8532904Z >>> def _(x, y): 2025-09-07T07:36:37.8533226Z >>> return torch.empty_like(x) 2025-09-07T07:36:37.8533529Z >>> 2025-09-07T07:36:37.8533776Z >>> def setup_context(ctx, inputs, output): 2025-09-07T07:36:37.8534131Z >>> y, = inputs 2025-09-07T07:36:37.8534393Z >>> ctx.y = y 2025-09-07T07:36:37.8534638Z >>> 2025-09-07T07:36:37.8534863Z >>> def backward(ctx, grad): 2025-09-07T07:36:37.8535164Z >>> return grad * ctx.y, None 2025-09-07T07:36:37.8535462Z >>> 2025-09-07T07:36:37.8535803Z >>> numpy_mul.register_autograd(backward, setup_context=setup_context) 2025-09-07T07:36:37.8536238Z >>> 2025-09-07T07:36:37.8536449Z >>> sample_inputs = [ 2025-09-07T07:36:37.8536736Z >>> (torch.randn(3), 3.14), 2025-09-07T07:36:37.8537076Z >>> (torch.randn(2, 3, device='cuda'), 2.718), 2025-09-07T07:36:37.8537465Z >>> (torch.randn(1, 10, requires_grad=True), 1.234), 2025-09-07T07:36:37.8537906Z >>> (torch.randn(64, 64, device='cuda', requires_grad=True), 90.18), 2025-09-07T07:36:37.8538302Z >>> ] 2025-09-07T07:36:37.8538516Z >>> 2025-09-07T07:36:37.8538747Z >>> for args in sample_inputs: 2025-09-07T07:36:37.8539085Z >>> torch.library.opcheck(numpy_mul, args) 2025-09-07T07:36:37.8539338Z 2025-09-07T07:36:37.8539415Z 2025-09-07T07:36:37.8539785Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:37.8540152Z 2025-09-07T07:36:37.8927384Z msg = Cannot scrape callname=load in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/serialization.py line=1285. 2025-09-07T07:36:37.8928252Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:37.8929113Z load(f, map_location=None, pickle_module=pickle, *, weights_only=True, mmap=None, **pickle_load_args) 2025-09-07T07:36:37.8929581Z 2025-09-07T07:36:37.8929757Z Loads an object saved with :func:`torch.save` from a file. 2025-09-07T07:36:37.8930093Z 2025-09-07T07:36:37.8930330Z :func:`torch.load` uses Python's unpickling facilities but treats storages, 2025-09-07T07:36:37.8930916Z which underlie tensors, specially. They are first deserialized on the 2025-09-07T07:36:37.8931481Z CPU and are then moved to the device they were saved from. If this fails 2025-09-07T07:36:37.8932052Z (e.g. because the run time system doesn't have certain devices), an exception 2025-09-07T07:36:37.8932629Z is raised. However, storages can be dynamically remapped to an alternative 2025-09-07T07:36:37.8933152Z set of devices using the :attr:`map_location` argument. 2025-09-07T07:36:37.8933443Z 2025-09-07T07:36:37.8933682Z If :attr:`map_location` is a callable, it will be called once for each serialized 2025-09-07T07:36:37.8934279Z storage with two arguments: storage and location. The storage argument 2025-09-07T07:36:37.8934859Z will be the initial deserialization of the storage, residing on the CPU. 2025-09-07T07:36:37.8935417Z Each serialized storage has a location tag associated with it which 2025-09-07T07:36:37.8936059Z identifies the device it was saved from, and this tag is the second 2025-09-07T07:36:37.8936643Z argument passed to :attr:`map_location`. The builtin location tags are ``'cpu'`` 2025-09-07T07:36:37.8937263Z for CPU tensors and ``'cuda:device_id'`` (e.g. ``'cuda:2'``) for CUDA tensors. 2025-09-07T07:36:37.8937870Z :attr:`map_location` should return either ``None`` or a storage. If 2025-09-07T07:36:37.8938436Z :attr:`map_location` returns a storage, it will be used as the final deserialized 2025-09-07T07:36:37.8939064Z object, already moved to the right device. Otherwise, :func:`torch.load` will 2025-09-07T07:36:37.8939682Z fall back to the default behavior, as if :attr:`map_location` wasn't specified. 2025-09-07T07:36:37.8940047Z 2025-09-07T07:36:37.8940294Z If :attr:`map_location` is a :class:`torch.device` object or a string containing 2025-09-07T07:36:37.8940881Z a device tag, it indicates the location where all tensors should be loaded. 2025-09-07T07:36:37.8941246Z 2025-09-07T07:36:37.8941549Z Otherwise, if :attr:`map_location` is a dict, it will be used to remap location tags 2025-09-07T07:36:37.8942147Z appearing in the file (keys), to ones that specify where to put the 2025-09-07T07:36:37.8942576Z storages (values). 2025-09-07T07:36:37.8942736Z 2025-09-07T07:36:37.8942973Z User extensions can register their own location tags and tagging and 2025-09-07T07:36:37.8943572Z deserialization methods using :func:`torch.serialization.register_package`. 2025-09-07T07:36:37.8943967Z 2025-09-07T07:36:37.8944211Z See :ref:`layout-control` for more advanced tools to manipulate a checkpoint. 2025-09-07T07:36:37.8944588Z 2025-09-07T07:36:37.8944674Z Args: 2025-09-07T07:36:37.8945125Z f: a file-like object (has to implement :meth:`read`, :meth:`readline`, :meth:`tell`, and :meth:`seek`), 2025-09-07T07:36:37.8945748Z or a string or os.PathLike object containing a file name 2025-09-07T07:36:37.8946352Z map_location: a function, :class:`torch.device`, string or a dict specifying how to remap storage 2025-09-07T07:36:37.8946893Z locations 2025-09-07T07:36:37.8947279Z pickle_module: module used for unpickling metadata and objects (has to 2025-09-07T07:36:37.8947801Z match the :attr:`pickle_module` used to serialize file) 2025-09-07T07:36:37.8948297Z weights_only: Indicates whether unpickler should be restricted to 2025-09-07T07:36:37.8948799Z loading only tensors, primitive types, dictionaries 2025-09-07T07:36:37.8949305Z and any types added via :func:`torch.serialization.add_safe_globals`. 2025-09-07T07:36:37.8949777Z See :ref:`weights-only` for more details. 2025-09-07T07:36:37.8950400Z mmap: Indicates whether the file should be mapped rather than loading all the storages into memory. 2025-09-07T07:36:37.8951181Z Typically, tensor storages in the file will first be moved from disk to CPU memory, after which they 2025-09-07T07:36:37.8951988Z are moved to the location that they were tagged with when saving, or specified by ``map_location``. This 2025-09-07T07:36:37.8952787Z second step is a no-op if the final location is CPU. When the ``mmap`` flag is set, instead of copying the 2025-09-07T07:36:37.8953576Z tensor storages from disk to CPU memory in the first step, ``f`` is mapped, which means tensor storages 2025-09-07T07:36:37.8954187Z will be lazily loaded when their data is accessed. 2025-09-07T07:36:37.8954775Z pickle_load_args: (Python 3 only) optional keyword arguments passed over to 2025-09-07T07:36:37.8955365Z :func:`pickle_module.load` and :func:`pickle_module.Unpickler`, e.g., 2025-09-07T07:36:37.8955819Z :attr:`errors=...`. 2025-09-07T07:36:37.8956001Z 2025-09-07T07:36:37.8956121Z .. warning:: 2025-09-07T07:36:37.8956478Z :func:`torch.load()` unless `weights_only` parameter is set to `True`, 2025-09-07T07:36:37.8957040Z uses ``pickle`` module implicitly, which is known to be insecure. 2025-09-07T07:36:37.8957637Z It is possible to construct malicious pickle data which will execute arbitrary code 2025-09-07T07:36:37.8958278Z during unpickling. Never load data that could have come from an untrusted 2025-09-07T07:36:37.8958965Z source in an unsafe mode, or that could have been tampered with. **Only load data you trust**. 2025-09-07T07:36:37.8959375Z 2025-09-07T07:36:37.8959461Z .. note:: 2025-09-07T07:36:37.8959855Z When you call :func:`torch.load()` on a file which contains GPU tensors, those tensors 2025-09-07T07:36:37.8960497Z will be loaded to GPU by default. You can call ``torch.load(.., map_location='cpu')`` 2025-09-07T07:36:37.8961151Z and then :meth:`load_state_dict` to avoid GPU RAM surge when loading a model checkpoint. 2025-09-07T07:36:37.8961542Z 2025-09-07T07:36:37.8961640Z .. note:: 2025-09-07T07:36:37.8962010Z By default, we decode byte strings as ``utf-8``. This is to avoid a common error 2025-09-07T07:36:37.8962629Z case ``UnicodeDecodeError: 'ascii' codec can't decode byte 0x...`` 2025-09-07T07:36:37.8963175Z when loading files saved by Python 2 in Python 3. If this default 2025-09-07T07:36:37.8963763Z is incorrect, you may use an extra :attr:`encoding` keyword argument to specify how 2025-09-07T07:36:37.8964386Z these objects should be loaded, e.g., :attr:`encoding='latin1'` decodes them 2025-09-07T07:36:37.8964975Z to strings using ``latin1`` encoding, and :attr:`encoding='bytes'` keeps them 2025-09-07T07:36:37.8965569Z as byte arrays which can be decoded later with ``byte_array.decode(...)``. 2025-09-07T07:36:37.8965932Z 2025-09-07T07:36:37.8966020Z Example: 2025-09-07T07:36:37.8966284Z >>> # xdoctest: +SKIP("undefined filepaths") 2025-09-07T07:36:37.8966658Z >>> torch.load("tensors.pt", weights_only=True) 2025-09-07T07:36:37.8967024Z # Load all tensors onto the CPU 2025-09-07T07:36:37.8967333Z >>> torch.load( 2025-09-07T07:36:37.8967591Z ... "tensors.pt", 2025-09-07T07:36:37.8967890Z ... map_location=torch.device("cpu"), 2025-09-07T07:36:37.8968235Z ... weights_only=True, 2025-09-07T07:36:37.8968517Z ... ) 2025-09-07T07:36:37.8968792Z # Load all tensors onto the CPU, using a function 2025-09-07T07:36:37.8969133Z >>> torch.load( 2025-09-07T07:36:37.8969386Z ... "tensors.pt", 2025-09-07T07:36:37.8969705Z ... map_location=lambda storage, loc: storage, 2025-09-07T07:36:37.8970061Z ... weights_only=True, 2025-09-07T07:36:37.8970327Z ... ) 2025-09-07T07:36:37.8970566Z # Load all tensors onto GPU 1 2025-09-07T07:36:37.8970912Z >>> torch.load( 2025-09-07T07:36:37.8971168Z ... "tensors.pt", 2025-09-07T07:36:37.8971496Z ... map_location=lambda storage, loc: storage.cuda(1), 2025-09-07T07:36:37.8971877Z ... weights_only=True, 2025-09-07T07:36:37.8972185Z ... ) # type: ignore[attr-defined] 2025-09-07T07:36:37.8972523Z # Map tensors from GPU 1 to GPU 0 2025-09-07T07:36:37.8972823Z >>> torch.load( 2025-09-07T07:36:37.8973079Z ... "tensors.pt", 2025-09-07T07:36:37.8973383Z ... map_location={"cuda:1": "cuda:0"}, 2025-09-07T07:36:37.8973721Z ... weights_only=True, 2025-09-07T07:36:37.8973989Z ... ) 2025-09-07T07:36:37.8974237Z # Load tensor from io.BytesIO object 2025-09-07T07:36:37.8974721Z # Loading from a buffer setting weights_only=False, warning this can be unsafe 2025-09-07T07:36:37.8975215Z >>> with open("tensor.pt", "rb") as f: 2025-09-07T07:36:37.8975567Z ... buffer = io.BytesIO(f.read()) 2025-09-07T07:36:37.8975924Z >>> torch.load(buffer, weights_only=False) 2025-09-07T07:36:37.8976321Z # Load a module with 'ascii' encoding for unpickling 2025-09-07T07:36:37.8976869Z # Loading from a module setting weights_only=False, warning this can be unsafe 2025-09-07T07:36:37.8977435Z >>> torch.load("module.pt", encoding="ascii", weights_only=False) 2025-09-07T07:36:37.8977820Z 2025-09-07T07:36:37.8978186Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:37.8978601Z 2025-09-07T07:36:37.9457236Z msg = Cannot scrape callname=compute_required_storage_length in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/__init__.py line=1877. 2025-09-07T07:36:37.9458247Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:37.9458845Z Computes the minimum storage size to hold the given tensor geometry. 2025-09-07T07:36:37.9459175Z 2025-09-07T07:36:37.9459277Z Example 2025-09-07T07:36:37.9459476Z ======= 2025-09-07T07:36:37.9459607Z 2025-09-07T07:36:37.9459838Z This is the size of a newly allocated tensor's storage, in units of elements 2025-09-07T07:36:37.9460205Z 2025-09-07T07:36:37.9460308Z >>> t = torch.empty((10, 20)) 2025-09-07T07:36:37.9460909Z >>> compute_required_storage_length(t.shape, t.stride(), t.storage_offset()) 2025-09-07T07:36:37.9461351Z 200 2025-09-07T07:36:37.9461480Z 2025-09-07T07:36:37.9461583Z >>> # xdoctest: +SKIP(failing) 2025-09-07T07:36:37.9461924Z >>> t2 = torch.empty_strided((1, 2, 3), (5, 7, 11)) 2025-09-07T07:36:37.9462298Z >>> size = compute_required_storage_length( 2025-09-07T07:36:37.9462661Z ... t2.shape, t2.stride(), t2.storage_offset() 2025-09-07T07:36:37.9462999Z ... ) 2025-09-07T07:36:37.9463226Z >>> size == t.storage().size() 2025-09-07T07:36:37.9463517Z True 2025-09-07T07:36:37.9463638Z 2025-09-07T07:36:37.9463847Z A valid tensor may have a larger storage size, but never smaller 2025-09-07T07:36:37.9464159Z 2025-09-07T07:36:37.9464266Z >>> slice = torch.empty(100)[20:40] 2025-09-07T07:36:37.9464587Z >>> slice.storage().size() 2025-09-07T07:36:37.9464859Z 100 2025-09-07T07:36:37.9464970Z 2025-09-07T07:36:37.9465095Z >>> compute_required_storage_length( 2025-09-07T07:36:37.9465469Z ... slice.shape, slice.stride(), slice.storage_offset() 2025-09-07T07:36:37.9465833Z ... ) 2025-09-07T07:36:37.9466039Z 40 2025-09-07T07:36:37.9466154Z 2025-09-07T07:36:37.9466247Z 2025-09-07T07:36:37.9466606Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:37.9466984Z 2025-09-07T07:36:38.0041593Z msg = Cannot scrape callname=is_available in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/accelerator/__init__.py line=66. 2025-09-07T07:36:38.0042498Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-09-07T07:36:38.0043265Z Check if the current accelerator is available at runtime: it was build, all the 2025-09-07T07:36:38.0043862Z required drivers are available and at least one device is visible. 2025-09-07T07:36:38.0044342Z See :ref:`accelerator` for details. 2025-09-07T07:36:38.0044606Z 2025-09-07T07:36:38.0044689Z Returns: 2025-09-07T07:36:38.0045101Z bool: A boolean indicating if there is an available :ref:`accelerator`. 2025-09-07T07:36:38.0045506Z 2025-09-07T07:36:38.0045765Z .. note:: This API delegates to the device-specific version of `is_available`. 2025-09-07T07:36:38.0046394Z On CUDA, when the environment variable ``PYTORCH_NVML_BASED_CUDA_CHECK=1`` is set, 2025-09-07T07:36:38.0047040Z this function will NOT poison fork. Otherwise, it will. For more details, see 2025-09-07T07:36:38.0047549Z :ref:`multiprocessing-poison-fork-note`. 2025-09-07T07:36:38.0047811Z 2025-09-07T07:36:38.0047899Z Example:: 2025-09-07T07:36:38.0048027Z 2025-09-07T07:36:38.0048309Z >>> assert torch.accelerator.is_available() "No available accelerators detected." 2025-09-07T07:36:38.0048778Z 2025-09-07T07:36:38.0049436Z Original Error: SyntaxError('invalid syntax', ('', 1, 41, 'assert torch.accelerator.is_available() "No available accelerators detected."\n', 1, 78)) 2025-09-07T07:36:38.0050180Z 2025-09-07T07:36:38.0050445Z assert torch.accelerator.is_available() "No available accelerators detected." 2025-09-07T07:36:38.0050926Z ^ 2025-09-07T07:36:38.0058063Z msg = Cannot scrape callname=synchronize in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/accelerator/__init__.py line=212. 2025-09-07T07:36:38.0058959Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-09-07T07:36:38.0059531Z Wait for all kernels in all streams on the given device to complete. 2025-09-07T07:36:38.0059848Z 2025-09-07T07:36:38.0059931Z Args: 2025-09-07T07:36:38.0060372Z device (:class:`torch.device`, str, int, optional): device for which to synchronize. It must match 2025-09-07T07:36:38.0061034Z the current :ref:`accelerator` device type. If not given, 2025-09-07T07:36:38.0061584Z use :func:`torch.accelerator.current_device_index` by default. 2025-09-07T07:36:38.0061897Z 2025-09-07T07:36:38.0062286Z .. note:: This function is a no-op if the current :ref:`accelerator` is not initialized. 2025-09-07T07:36:38.0062713Z 2025-09-07T07:36:38.0062805Z Example:: 2025-09-07T07:36:38.0062952Z 2025-09-07T07:36:38.0063095Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2025-09-07T07:36:38.0063623Z >>> assert torch.accelerator.is_available() "No available accelerators detected." 2025-09-07T07:36:38.0064160Z >>> start_event = torch.Event(enable_timing=True) 2025-09-07T07:36:38.0064561Z >>> end_event = torch.Event(enable_timing=True) 2025-09-07T07:36:38.0064899Z >>> start_event.record() 2025-09-07T07:36:38.0065334Z >>> tensor = torch.randn(100, device=torch.accelerator.current_accelerator()) 2025-09-07T07:36:38.0065803Z >>> sum = torch.sum(tensor) 2025-09-07T07:36:38.0066109Z >>> end_event.record() 2025-09-07T07:36:38.0066413Z >>> torch.accelerator.synchronize() 2025-09-07T07:36:38.0066816Z >>> elapsed_time_ms = start_event.elapsed_time(end_event) 2025-09-07T07:36:38.0067181Z 2025-09-07T07:36:38.0067850Z Original Error: SyntaxError('invalid syntax', ('', 2, 41, 'assert torch.accelerator.is_available() "No available accelerators detected."\n', 2, 78)) 2025-09-07T07:36:38.0068519Z 2025-09-07T07:36:38.0068774Z assert torch.accelerator.is_available() "No available accelerators detected." 2025-09-07T07:36:38.0069252Z ^ 2025-09-07T07:36:38.0307181Z msg = Cannot scrape callname=cudart in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/__init__.py line=434. 2025-09-07T07:36:38.0308206Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-09-07T07:36:38.0308694Z Retrieves the CUDA runtime API module. 2025-09-07T07:36:38.0308910Z 2025-09-07T07:36:38.0308919Z 2025-09-07T07:36:38.0309182Z This function initializes the CUDA runtime environment if it is not already 2025-09-07T07:36:38.0309774Z initialized and returns the CUDA runtime API module (_cudart). The CUDA 2025-09-07T07:36:38.0310346Z runtime API module provides access to various CUDA runtime functions. 2025-09-07T07:36:38.0310694Z 2025-09-07T07:36:38.0310777Z Args: 2025-09-07T07:36:38.0310982Z ``None`` 2025-09-07T07:36:38.0311116Z 2025-09-07T07:36:38.0311211Z Returns: 2025-09-07T07:36:38.0311471Z module: The CUDA runtime API module (_cudart). 2025-09-07T07:36:38.0311732Z 2025-09-07T07:36:38.0311815Z Raises: 2025-09-07T07:36:38.0312169Z RuntimeError: If CUDA cannot be re-initialized in a forked subprocess. 2025-09-07T07:36:38.0312883Z AssertionError: If PyTorch is not compiled with CUDA support or if libcudart functions are unavailable. 2025-09-07T07:36:38.0313358Z 2025-09-07T07:36:38.0313488Z Example of CUDA operations with profiling: 2025-09-07T07:36:38.0313886Z >>> import torch 2025-09-07T07:36:38.0314190Z >>> from torch.cuda import cudart, check_error 2025-09-07T07:36:38.0314588Z >>> import os 2025-09-07T07:36:38.0314815Z >>> 2025-09-07T07:36:38.0315060Z >>> os.environ["CUDA_PROFILE"] = "1" 2025-09-07T07:36:38.0315395Z >>> 2025-09-07T07:36:38.0315712Z >>> def perform_cuda_operations_with_streams(): 2025-09-07T07:36:38.0316068Z >>> stream = torch.cuda.Stream() 2025-09-07T07:36:38.0316416Z >>> with torch.cuda.stream(stream): 2025-09-07T07:36:38.0316775Z >>> x = torch.randn(100, 100, device='cuda') 2025-09-07T07:36:38.0317146Z >>> y = torch.randn(100, 100, device='cuda') 2025-09-07T07:36:38.0317479Z >>> z = torch.mul(x, y) 2025-09-07T07:36:38.0317781Z >>> return z 2025-09-07T07:36:38.0318024Z >>> 2025-09-07T07:36:38.0318268Z >>> torch.cuda.synchronize() 2025-09-07T07:36:38.0318600Z >>> print("====== Start nsys profiling ======") 2025-09-07T07:36:38.0318991Z >>> check_error(cudart().cudaProfilerStart()) 2025-09-07T07:36:38.0319431Z >>> with torch.autograd.profiler.emit_nvtx(): 2025-09-07T07:36:38.0319844Z >>> result = perform_cuda_operations_with_streams() 2025-09-07T07:36:38.0320247Z >>> print("CUDA operations completed.") 2025-09-07T07:36:38.0320642Z >>> check_error(torch.cuda.cudart().cudaProfilerStop()) 2025-09-07T07:36:38.0321057Z >>> print("====== End nsys profiling ======") 2025-09-07T07:36:38.0321310Z 2025-09-07T07:36:38.0321508Z To run this example and save the profiling information, execute: 2025-09-07T07:36:38.0322201Z >>> $ nvprof --profile-from-start off --csv --print-summary -o trace_name.prof -f -- python cudart_test.py 2025-09-07T07:36:38.0322679Z 2025-09-07T07:36:38.0322942Z This command profiles the CUDA operations in the provided script and saves 2025-09-07T07:36:38.0323494Z the profiling information to a file named `trace_name.prof`. 2025-09-07T07:36:38.0324224Z The `--profile-from-start off` option ensures that profiling starts only 2025-09-07T07:36:38.0324732Z after the `cudaProfilerStart` call in the script. 2025-09-07T07:36:38.0325228Z The `--csv` and `--print-summary` options format the profiling output as a 2025-09-07T07:36:38.0325697Z CSV file and print a summary, respectively. 2025-09-07T07:36:38.0326190Z The `-o` option specifies the output file name, and the `-f` option forces the 2025-09-07T07:36:38.0326713Z overwrite of the output file if it already exists. 2025-09-07T07:36:38.0327067Z 2025-09-07T07:36:38.0327876Z 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)) 2025-09-07T07:36:38.0328648Z 2025-09-07T07:36:38.0328994Z $ nvprof --profile-from-start off --csv --print-summary -o trace_name.prof -f -- python cudart_test.py 2025-09-07T07:36:38.0329549Z ^ 2025-09-07T07:36:38.0439590Z msg = Cannot scrape callname=Future.then in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/futures/__init__.py line=101. 2025-09-07T07:36:38.0440499Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:38.0440881Z 2025-09-07T07:36:38.0441116Z Append the given callback function to this ``Future``, which will be run 2025-09-07T07:36:38.0441666Z when the ``Future`` is completed. Multiple callbacks can be added to 2025-09-07T07:36:38.0442186Z the same ``Future``, but the order in which they will be executed cannot 2025-09-07T07:36:38.0442701Z be guaranteed (to enforce a certain order consider chaining: 2025-09-07T07:36:38.0443201Z ``fut.then(cb1).then(cb2)``). The callback must take one argument, which 2025-09-07T07:36:38.0443733Z is the reference to this ``Future``. The callback function can use the 2025-09-07T07:36:38.0444267Z :meth:`value` method to get the value. Note that if this ``Future`` is 2025-09-07T07:36:38.0444882Z already completed, the given callback will be run immediately inline. 2025-09-07T07:36:38.0445231Z 2025-09-07T07:36:38.0445423Z If the ``Future``'s value contains tensors that reside on GPUs, the 2025-09-07T07:36:38.0445961Z callback might be invoked while the async kernels that are populating 2025-09-07T07:36:38.0446575Z those tensors haven't yet finished executing on the device. However, the 2025-09-07T07:36:38.0447122Z callback will be invoked with some dedicated streams set as current 2025-09-07T07:36:38.0447655Z (fetched from a global pool) which will be synchronized with those 2025-09-07T07:36:38.0448200Z kernels. Hence any operation performed by the callback on these tensors 2025-09-07T07:36:38.0448757Z will be scheduled on the device after the kernels complete. In other 2025-09-07T07:36:38.0449318Z words, as long as the callback doesn't switch streams, it can safely 2025-09-07T07:36:38.0449984Z manipulate the result without any additional synchronization. This is 2025-09-07T07:36:38.0450496Z similar to the non-blocking behavior of :meth:`wait`. 2025-09-07T07:36:38.0450773Z 2025-09-07T07:36:38.0451035Z Similarly, if the callback returns a value that contains tensors that 2025-09-07T07:36:38.0451559Z reside on a GPU, it can do so even if the kernels that are producing 2025-09-07T07:36:38.0452074Z these tensors are still running on the device, as long as the callback 2025-09-07T07:36:38.0452621Z didn't change streams during its execution. If one wants to change 2025-09-07T07:36:38.0453158Z streams, one must be careful to re-synchronize them with the original 2025-09-07T07:36:38.0453705Z streams, that is, those that were current when the callback was invoked. 2025-09-07T07:36:38.0454039Z 2025-09-07T07:36:38.0454138Z Args: 2025-09-07T07:36:38.0454459Z callback(``Callable``): a ``Callable`` that takes this ``Future`` as 2025-09-07T07:36:38.0454893Z the only argument. 2025-09-07T07:36:38.0455121Z 2025-09-07T07:36:38.0455204Z Returns: 2025-09-07T07:36:38.0455494Z A new ``Future`` object that holds the return value of the 2025-09-07T07:36:38.0455947Z ``callback`` and will be marked as completed when the given 2025-09-07T07:36:38.0456342Z ``callback`` finishes. 2025-09-07T07:36:38.0456521Z 2025-09-07T07:36:38.0456698Z .. note:: Note that if the callback function throws, either 2025-09-07T07:36:38.0457197Z through the original future being completed with an exception and 2025-09-07T07:36:38.0457717Z calling ``fut.wait()``, or through other code in the callback, the 2025-09-07T07:36:38.0458218Z future returned by ``then`` will be marked appropriately with the 2025-09-07T07:36:38.0458737Z encountered error. However, if this callback later completes 2025-09-07T07:36:38.0459318Z additional futures, those futures are not marked as completed with 2025-09-07T07:36:38.0459856Z an error and the user is responsible for handling completion/waiting 2025-09-07T07:36:38.0460279Z on those futures independently. 2025-09-07T07:36:38.0460500Z 2025-09-07T07:36:38.0460588Z Example:: 2025-09-07T07:36:38.0460721Z 2025-09-07T07:36:38.0460867Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_FUTURES) 2025-09-07T07:36:38.0461226Z >>> def callback(fut): 2025-09-07T07:36:38.0461528Z ... print(f"RPC return value is {fut.wait()}.") 2025-09-07T07:36:38.0461889Z >>> fut = torch.futures.Future() 2025-09-07T07:36:38.0462268Z >>> # The inserted callback will print the return value when 2025-09-07T07:36:38.0462673Z >>> # receiving the response from "worker1" 2025-09-07T07:36:38.0463014Z >>> cb_fut = fut.then(callback) 2025-09-07T07:36:38.0463309Z >>> chain_cb_fut = cb_fut.then( 2025-09-07T07:36:38.0463665Z ... lambda x : print(f"Chained cb done. {x.wait()}") 2025-09-07T07:36:38.0464013Z ... ) 2025-09-07T07:36:38.0464234Z >>> fut.set_result(5) 2025-09-07T07:36:38.0464493Z RPC return value is 5. 2025-09-07T07:36:38.0464765Z Chained cb done. None 2025-09-07T07:36:38.0464929Z 2025-09-07T07:36:38.0465226Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:38.0465594Z 2025-09-07T07:36:38.0466163Z 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=211. 2025-09-07T07:36:38.0467071Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:38.0467484Z 2025-09-07T07:36:38.0467693Z Set the result for this ``Future``, which will mark this ``Future`` as 2025-09-07T07:36:38.0468230Z completed and trigger all attached callbacks. Note that a ``Future`` 2025-09-07T07:36:38.0468676Z cannot be marked completed twice. 2025-09-07T07:36:38.0468878Z 2025-09-07T07:36:38.0469107Z If the result contains tensors that reside on GPUs, this method can be 2025-09-07T07:36:38.0469649Z called even if the asynchronous kernels that are populating those 2025-09-07T07:36:38.0470184Z tensors haven't yet completed running on the device, provided that the 2025-09-07T07:36:38.0470744Z streams on which those kernels were enqueued are set as the current ones 2025-09-07T07:36:38.0471335Z when this method is called. Put simply, it's safe to call this method 2025-09-07T07:36:38.0471881Z immediately after launching those kernels, without any additional 2025-09-07T07:36:38.0472426Z synchronization, as long as one doesn't change streams in between. This 2025-09-07T07:36:38.0472998Z method will record events on all the relevant current streams and will 2025-09-07T07:36:38.0473539Z use them to ensure proper scheduling for all the consumers of this 2025-09-07T07:36:38.0473949Z ``Future``. 2025-09-07T07:36:38.0474070Z 2025-09-07T07:36:38.0474164Z Args: 2025-09-07T07:36:38.0474526Z result (object): the result object of this ``Future``. 2025-09-07T07:36:38.0474817Z 2025-09-07T07:36:38.0474908Z Example:: 2025-09-07T07:36:38.0475027Z 2025-09-07T07:36:38.0475187Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_FUTURES) 2025-09-07T07:36:38.0475548Z >>> import threading 2025-09-07T07:36:38.0475800Z >>> import time 2025-09-07T07:36:38.0476063Z >>> def slow_set_future(fut, value): 2025-09-07T07:36:38.0476391Z ... time.sleep(0.5) 2025-09-07T07:36:38.0476676Z ... fut.set_result(value) 2025-09-07T07:36:38.0476973Z >>> fut = torch.futures.Future() 2025-09-07T07:36:38.0477291Z >>> t = threading.Thread( 2025-09-07T07:36:38.0477585Z ... target=slow_set_future, 2025-09-07T07:36:38.0477898Z ... args=(fut, torch.ones(2) * 3) 2025-09-07T07:36:38.0478185Z ... ) 2025-09-07T07:36:38.0478392Z >>> t.start() 2025-09-07T07:36:38.0478630Z >>> print(fut.wait()) 2025-09-07T07:36:38.0478891Z tensor([3., 3.]) 2025-09-07T07:36:38.0479120Z >>> t.join() 2025-09-07T07:36:38.0479124Z 2025-09-07T07:36:38.0479426Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:38.0479431Z 2025-09-07T07:36:38.0545495Z msg = Cannot scrape callname=compile_shader in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/mps/__init__.py line=145. 2025-09-07T07:36:38.0545775Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:38.0545994Z Compiles compute shader from source and allows one to invoke kernels 2025-09-07T07:36:38.0546150Z defined there from the comfort of Python runtime 2025-09-07T07:36:38.0546246Z Example:: 2025-09-07T07:36:38.0546251Z 2025-09-07T07:36:38.0546387Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_MPS) 2025-09-07T07:36:38.0546512Z >>> lib = torch.mps.compile_shader( 2025-09-07T07:36:38.0546918Z ... "kernel void full(device float* out, constant float& val, uint idx [[thread_position_in_grid]]) { out[idx] = val; }" 2025-09-07T07:36:38.0547015Z ... ) 2025-09-07T07:36:38.0547139Z >>> x = torch.zeros(16, device="mps") 2025-09-07T07:36:38.0547236Z >>> lib.full(x, 3.14) 2025-09-07T07:36:38.0547325Z 2025-09-07T07:36:38.0547574Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:38.0547665Z 2025-09-07T07:36:38.0723088Z msg = Cannot scrape callname=sum in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/sparse/__init__.py line=202. 2025-09-07T07:36:38.0723349Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:38.0723599Z Return the sum of each row of the given sparse tensor. 2025-09-07T07:36:38.0723605Z 2025-09-07T07:36:38.0723949Z Returns the sum of each row of the sparse tensor :attr:`input` in the given 2025-09-07T07:36:38.0724145Z dimensions :attr:`dim`. If :attr:`dim` is a list of dimensions, 2025-09-07T07:36:38.0724364Z reduce over all of them. When sum over all ``sparse_dim``, this method 2025-09-07T07:36:38.0724512Z returns a dense tensor instead of a sparse tensor. 2025-09-07T07:36:38.0724519Z 2025-09-07T07:36:38.0724788Z All summed :attr:`dim` are squeezed (see :func:`torch.squeeze`), resulting an output 2025-09-07T07:36:38.0724978Z tensor having :attr:`dim` fewer dimensions than :attr:`input`. 2025-09-07T07:36:38.0724986Z 2025-09-07T07:36:38.0725290Z During backward, only gradients at ``nnz`` locations of :attr:`input` 2025-09-07T07:36:38.0725529Z will propagate back. Note that the gradients of :attr:`input` is coalesced. 2025-09-07T07:36:38.0725533Z 2025-09-07T07:36:38.0725618Z Args: 2025-09-07T07:36:38.0725754Z input (Tensor): the input sparse tensor 2025-09-07T07:36:38.0726028Z dim (int or tuple of ints): a dimension or a list of dimensions to reduce. Default: reduce 2025-09-07T07:36:38.0726139Z over all dims. 2025-09-07T07:36:38.0726407Z dtype (:class:`torch.dtype`, optional): the desired data type of returned Tensor. 2025-09-07T07:36:38.0726535Z Default: dtype of :attr:`input`. 2025-09-07T07:36:38.0726542Z 2025-09-07T07:36:38.0726635Z Example:: 2025-09-07T07:36:38.0726640Z 2025-09-07T07:36:38.0726726Z >>> nnz = 3 2025-09-07T07:36:38.0726833Z >>> dims = [5, 5, 2, 3] 2025-09-07T07:36:38.0726993Z >>> I = torch.cat([torch.randint(0, dims[0], size=(nnz,)), 2025-09-07T07:36:38.0727189Z torch.randint(0, dims[1], size=(nnz,))], 0).reshape(2, nnz) 2025-09-07T07:36:38.0727309Z >>> V = torch.randn(nnz, dims[2], dims[3]) 2025-09-07T07:36:38.0727408Z >>> size = torch.Size(dims) 2025-09-07T07:36:38.0727560Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2025-09-07T07:36:38.0727685Z >>> S = torch.sparse_coo_tensor(I, V, size) 2025-09-07T07:36:38.0727776Z >>> S 2025-09-07T07:36:38.0727884Z tensor(indices=tensor([[2, 0, 3], 2025-09-07T07:36:38.0727979Z [2, 4, 1]]), 2025-09-07T07:36:38.0728143Z values=tensor([[[-0.6438, -1.6467, 1.4004], 2025-09-07T07:36:38.0728305Z [ 0.3411, 0.0918, -0.2312]], 2025-09-07T07:36:38.0728310Z 2025-09-07T07:36:38.0728433Z [[ 0.5348, 0.0634, -2.0494], 2025-09-07T07:36:38.0728543Z [-0.7125, -1.0646, 2.1844]], 2025-09-07T07:36:38.0728548Z 2025-09-07T07:36:38.0728655Z [[ 0.1276, 0.1874, -0.6334], 2025-09-07T07:36:38.0728776Z [-1.9682, -0.5340, 0.7483]]]), 2025-09-07T07:36:38.0728918Z size=(5, 5, 2, 3), nnz=3, layout=torch.sparse_coo) 2025-09-07T07:36:38.0728925Z 2025-09-07T07:36:38.0729128Z # when sum over only part of sparse_dims, return a sparse tensor 2025-09-07T07:36:38.0729237Z >>> torch.sparse.sum(S, [1, 3]) 2025-09-07T07:36:38.0729363Z tensor(indices=tensor([[0, 2, 3]]), 2025-09-07T07:36:38.0729475Z values=tensor([[-1.4512, 0.4073], 2025-09-07T07:36:38.0729575Z [-0.8901, 0.2017], 2025-09-07T07:36:38.0729693Z [-0.3183, -1.7539]]), 2025-09-07T07:36:38.0729829Z size=(5, 2), nnz=3, layout=torch.sparse_coo) 2025-09-07T07:36:38.0729833Z 2025-09-07T07:36:38.0730036Z # when sum over all sparse dim, return a dense tensor 2025-09-07T07:36:38.0730139Z # with summed dims squeezed 2025-09-07T07:36:38.0730253Z >>> torch.sparse.sum(S, [0, 1, 3]) 2025-09-07T07:36:38.0730364Z tensor([-2.6596, -1.1450]) 2025-09-07T07:36:38.0730444Z 2025-09-07T07:36:38.0730745Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:38.0730750Z 2025-09-07T07:36:38.0733577Z msg = Cannot scrape callname=as_sparse_gradcheck in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/sparse/__init__.py line=550. 2025-09-07T07:36:38.0733838Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:38.0734037Z Decorate function, to extend gradcheck for sparse tensors. 2025-09-07T07:36:38.0734045Z 2025-09-07T07:36:38.0734253Z Decorator for torch.autograd.gradcheck or its functools.partial 2025-09-07T07:36:38.0734485Z variants that extends the gradcheck function with support to input 2025-09-07T07:36:38.0734660Z functions that operate on or/and return sparse tensors. 2025-09-07T07:36:38.0734665Z 2025-09-07T07:36:38.0734932Z The specified gradcheck function itself is guaranteed to operate 2025-09-07T07:36:38.0735035Z on strided tensors only. 2025-09-07T07:36:38.0735040Z 2025-09-07T07:36:38.0735128Z For example: 2025-09-07T07:36:38.0735145Z 2025-09-07T07:36:38.0735377Z >>> gradcheck = torch.sparse.as_sparse_gradcheck(torch.autograd.gradcheck) 2025-09-07T07:36:38.0735459Z >>> x = ( 2025-09-07T07:36:38.0735629Z ... torch.tensor([[0, 1], [2, 3]], dtype=torch.float64) 2025-09-07T07:36:38.0735727Z ... .to_sparse_coo() 2025-09-07T07:36:38.0735829Z ... .requires_grad_(True) 2025-09-07T07:36:38.0735925Z ... ) 2025-09-07T07:36:38.0736052Z >>> gradcheck(lambda x: x.to_sparse_csr(), x) 2025-09-07T07:36:38.0736149Z True 2025-09-07T07:36:38.0736227Z 2025-09-07T07:36:38.0736479Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:38.0736486Z 2025-09-07T07:36:38.7697541Z msg = Cannot scrape callname=vmap in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/apis.py line=39. 2025-09-07T07:36:38.7698503Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:38.7698904Z 2025-09-07T07:36:38.7699122Z vmap is the vectorizing map; ``vmap(func)`` returns a new function that 2025-09-07T07:36:38.7699660Z maps ``func`` over some dimension of the inputs. Semantically, vmap 2025-09-07T07:36:38.7700196Z pushes the map into PyTorch operations called by ``func``, effectively 2025-09-07T07:36:38.7700645Z vectorizing those operations. 2025-09-07T07:36:38.7700838Z 2025-09-07T07:36:38.7701214Z vmap is useful for handling batch dimensions: one can write a function 2025-09-07T07:36:38.7701757Z ``func`` that runs on examples and then lift it to a function that can 2025-09-07T07:36:38.7702300Z take batches of examples with ``vmap(func)``. vmap can also be used to 2025-09-07T07:36:38.7702814Z compute batched gradients when composed with autograd. 2025-09-07T07:36:38.7703092Z 2025-09-07T07:36:38.7703216Z .. note:: 2025-09-07T07:36:38.7703514Z :func:`torch.vmap` is aliased to :func:`torch.func.vmap` for 2025-09-07T07:36:38.7703945Z convenience. Use whichever one you'd like. 2025-09-07T07:36:38.7704184Z 2025-09-07T07:36:38.7704277Z Args: 2025-09-07T07:36:38.7704606Z func (function): A Python function that takes one or more arguments. 2025-09-07T07:36:38.7705034Z Must return one or more Tensors. 2025-09-07T07:36:38.7705452Z in_dims (int or nested structure): Specifies which dimension of the 2025-09-07T07:36:38.7705947Z inputs should be mapped over. ``in_dims`` should have a 2025-09-07T07:36:38.7706455Z structure like the inputs. If the ``in_dim`` for a particular 2025-09-07T07:36:38.7706936Z input is None, then that indicates there is no map dimension. 2025-09-07T07:36:38.7707334Z Default: 0. 2025-09-07T07:36:38.7707747Z out_dims (int or Tuple[int]): Specifies where the mapped dimension 2025-09-07T07:36:38.7708659Z should appear in the outputs. If ``out_dims`` is a Tuple, then 2025-09-07T07:36:38.7709231Z it should have one element per output. Default: 0. 2025-09-07T07:36:38.7709673Z randomness (str): Specifies whether the randomness in this 2025-09-07T07:36:38.7710309Z vmap should be the same or different across batches. If 'different', 2025-09-07T07:36:38.7710831Z the randomness for each batch will be different. If 'same', the 2025-09-07T07:36:38.7711365Z randomness will be the same across batches. If 'error', any calls to 2025-09-07T07:36:38.7711915Z random functions will error. Default: 'error'. WARNING: this flag 2025-09-07T07:36:38.7712451Z only applies to random PyTorch operations and does not apply to 2025-09-07T07:36:38.7712907Z Python's random module or numpy randomness. 2025-09-07T07:36:38.7713380Z chunk_size (None or int): If None (default), apply a single vmap over inputs. 2025-09-07T07:36:38.7726155Z If not None, then compute the vmap :attr:`chunk_size` samples at a time. 2025-09-07T07:36:38.7726798Z Note that :attr:`chunk_size=1` is equivalent to computing the vmap with a for-loop. 2025-09-07T07:36:38.7727453Z If you run into memory issues computing the vmap, please try a non-None chunk_size. 2025-09-07T07:36:38.7727857Z 2025-09-07T07:36:38.7727944Z Returns: 2025-09-07T07:36:38.7728267Z Returns a new "batched" function. It takes the same inputs as 2025-09-07T07:36:38.7728761Z ``func``, except each input has an extra dimension at the index 2025-09-07T07:36:38.7729259Z specified by ``in_dims``. It takes returns the same outputs as 2025-09-07T07:36:38.7729750Z ``func``, except each output has an extra dimension at the index 2025-09-07T07:36:38.7730166Z specified by ``out_dims``. 2025-09-07T07:36:38.7730351Z 2025-09-07T07:36:38.7730449Z .. warning: 2025-09-07T07:36:38.7730785Z :func:`vmap` works best with functional-style code. Please do not 2025-09-07T07:36:38.7731281Z perform any side-effects in ``func``, with the exception of 2025-09-07T07:36:38.7731823Z in-place PyTorch operations. Examples of side-effects include mutating 2025-09-07T07:36:38.7732408Z Python data structures and assigning values to variables not captured 2025-09-07T07:36:38.7732846Z in ``func``. 2025-09-07T07:36:38.7732984Z 2025-09-07T07:36:38.7733217Z One example of using :func:`vmap` is to compute batched dot products. PyTorch 2025-09-07T07:36:38.7733792Z doesn't provide a batched ``torch.dot`` API; instead of unsuccessfully 2025-09-07T07:36:38.7734351Z rummaging through docs, use :func:`vmap` to construct a new function. 2025-09-07T07:36:38.7734684Z 2025-09-07T07:36:38.7734891Z >>> torch.dot # [D], [D] -> [] 2025-09-07T07:36:38.7735307Z >>> batched_dot = torch.func.vmap(torch.dot) # [N, D], [N, D] -> [N] 2025-09-07T07:36:38.7735751Z >>> x, y = torch.randn(2, 5), torch.randn(2, 5) 2025-09-07T07:36:38.7736101Z >>> batched_dot(x, y) 2025-09-07T07:36:38.7736284Z 2025-09-07T07:36:38.7736512Z :func:`vmap` can be helpful in hiding batch dimensions, leading to a simpler 2025-09-07T07:36:38.7736975Z model authoring experience. 2025-09-07T07:36:38.7737154Z 2025-09-07T07:36:38.7737268Z >>> batch_size, feature_size = 3, 5 2025-09-07T07:36:38.7737660Z >>> weights = torch.randn(feature_size, requires_grad=True) 2025-09-07T07:36:38.7738033Z >>> 2025-09-07T07:36:38.7738255Z >>> def model(feature_vec): 2025-09-07T07:36:38.7738561Z >>> # Very simple linear model with activation 2025-09-07T07:36:38.7738932Z >>> return feature_vec.dot(weights).relu() 2025-09-07T07:36:38.7739253Z >>> 2025-09-07T07:36:38.7739525Z >>> examples = torch.randn(batch_size, feature_size) 2025-09-07T07:36:38.7739909Z >>> result = torch.vmap(model)(examples) 2025-09-07T07:36:38.7740135Z 2025-09-07T07:36:38.7740386Z :func:`vmap` can also help vectorize computations that were previously difficult 2025-09-07T07:36:38.7741059Z or impossible to batch. One example is higher-order gradient computation. 2025-09-07T07:36:38.7741644Z The PyTorch autograd engine computes vjps (vector-Jacobian products). 2025-09-07T07:36:38.7742212Z Computing a full Jacobian matrix for some function f: R^N -> R^N usually 2025-09-07T07:36:38.7742848Z requires N calls to ``autograd.grad``, one per Jacobian row. Using :func:`vmap`, 2025-09-07T07:36:38.7743460Z we can vectorize the whole computation, computing the Jacobian in a single 2025-09-07T07:36:38.7743923Z call to ``autograd.grad``. 2025-09-07T07:36:38.7744097Z 2025-09-07T07:36:38.7744199Z >>> # Setup 2025-09-07T07:36:38.7744428Z >>> N = 5 2025-09-07T07:36:38.7744646Z >>> f = lambda x: x**2 2025-09-07T07:36:38.7744948Z >>> x = torch.randn(N, requires_grad=True) 2025-09-07T07:36:38.7745272Z >>> y = f(x) 2025-09-07T07:36:38.7745497Z >>> I_N = torch.eye(N) 2025-09-07T07:36:38.7745752Z >>> 2025-09-07T07:36:38.7745980Z >>> # Sequential approach 2025-09-07T07:36:38.7746382Z >>> jacobian_rows = [torch.autograd.grad(y, x, v, retain_graph=True)[0] 2025-09-07T07:36:38.7746878Z >>> for v in I_N.unbind()] 2025-09-07T07:36:38.7747219Z >>> jacobian = torch.stack(jacobian_rows) 2025-09-07T07:36:38.7747539Z >>> 2025-09-07T07:36:38.7747781Z >>> # vectorized gradient computation 2025-09-07T07:36:38.7748103Z >>> def get_vjp(v): 2025-09-07T07:36:38.7748378Z >>> return torch.autograd.grad(y, x, v) 2025-09-07T07:36:38.7748734Z >>> jacobian = torch.vmap(get_vjp)(I_N) 2025-09-07T07:36:38.7748962Z 2025-09-07T07:36:38.7749241Z :func:`vmap` can also be nested, producing an output with multiple batched dimensions 2025-09-07T07:36:38.7749624Z 2025-09-07T07:36:38.7749742Z >>> torch.dot # [D], [D] -> [] 2025-09-07T07:36:38.7750041Z >>> batched_dot = torch.vmap( 2025-09-07T07:36:38.7750346Z ... torch.vmap(torch.dot) 2025-09-07T07:36:38.7750662Z ... ) # [N1, N0, D], [N1, N0, D] -> [N1, N0] 2025-09-07T07:36:38.7751031Z >>> x, y = torch.randn(2, 3, 5), torch.randn(2, 3, 5) 2025-09-07T07:36:38.7751400Z >>> batched_dot(x, y) # tensor of size [2, 3] 2025-09-07T07:36:38.7751652Z 2025-09-07T07:36:38.7751890Z If the inputs are not batched along the first dimension, ``in_dims`` specifies 2025-09-07T07:36:38.7752408Z the dimension that each inputs are batched along as 2025-09-07T07:36:38.7752671Z 2025-09-07T07:36:38.7752788Z >>> torch.dot # [N], [N] -> [] 2025-09-07T07:36:38.7753214Z >>> batched_dot = torch.vmap(torch.dot, in_dims=1) # [N, D], [N, D] -> [D] 2025-09-07T07:36:38.7753668Z >>> x, y = torch.randn(2, 5), torch.randn(2, 5) 2025-09-07T07:36:38.7754001Z >>> batched_dot( 2025-09-07T07:36:38.7754242Z ... x, y 2025-09-07T07:36:38.7754729Z ... ) # output is [5] instead of [2] if batched along the 0th dimension 2025-09-07T07:36:38.7755036Z 2025-09-07T07:36:38.7755295Z If there are multiple inputs each of which is batched along different dimensions, 2025-09-07T07:36:38.7755883Z ``in_dims`` must be a tuple with the batch dimension for each input as 2025-09-07T07:36:38.7756208Z 2025-09-07T07:36:38.7756313Z >>> torch.dot # [D], [D] -> [] 2025-09-07T07:36:38.7756751Z >>> batched_dot = torch.vmap(torch.dot, in_dims=(0, None)) # [N, D], [D] -> [N] 2025-09-07T07:36:38.7757238Z >>> x, y = torch.randn(2, 5), torch.randn(5) 2025-09-07T07:36:38.7757553Z >>> batched_dot( 2025-09-07T07:36:38.7757791Z ... x, y 2025-09-07T07:36:38.7758124Z ... ) # second arg doesn't have a batch dim because in_dim[1] was None 2025-09-07T07:36:38.7758435Z 2025-09-07T07:36:38.7758686Z If the input is a Python struct, ``in_dims`` must be a tuple containing a struct 2025-09-07T07:36:38.7759147Z matching the shape of the input: 2025-09-07T07:36:38.7759359Z 2025-09-07T07:36:38.7759498Z >>> f = lambda dict: torch.dot(dict["x"], dict["y"]) 2025-09-07T07:36:38.7759876Z >>> x, y = torch.randn(2, 5), torch.randn(5) 2025-09-07T07:36:38.7760241Z >>> input = {"x": x, "y": y} 2025-09-07T07:36:38.7760597Z >>> batched_dot = torch.vmap(f, in_dims=({"x": 0, "y": None},)) 2025-09-07T07:36:38.7760994Z >>> batched_dot(input) 2025-09-07T07:36:38.7761177Z 2025-09-07T07:36:38.7761451Z By default, the output is batched along the first dimension. However, it can be batched 2025-09-07T07:36:38.7762083Z along any dimension by using ``out_dims`` 2025-09-07T07:36:38.7762309Z 2025-09-07T07:36:38.7762419Z >>> f = lambda x: x**2 2025-09-07T07:36:38.7762682Z >>> x = torch.randn(2, 5) 2025-09-07T07:36:38.7762998Z >>> batched_pow = torch.vmap(f, out_dims=1) 2025-09-07T07:36:38.7763347Z >>> batched_pow(x) # [5, 2] 2025-09-07T07:36:38.7763536Z 2025-09-07T07:36:38.7763887Z For any function that uses kwargs, the returned function will not batch the kwargs but will 2025-09-07T07:36:38.7764384Z accept kwargs 2025-09-07T07:36:38.7764531Z 2025-09-07T07:36:38.7764632Z >>> x = torch.randn([2, 5]) 2025-09-07T07:36:38.7764924Z >>> def fn(x, scale=4.): 2025-09-07T07:36:38.7765208Z >>> return x * scale 2025-09-07T07:36:38.7765451Z >>> 2025-09-07T07:36:38.7765681Z >>> batched_pow = torch.vmap(fn) 2025-09-07T07:36:38.7766070Z >>> assert torch.allclose(batched_pow(x), x * 4) 2025-09-07T07:36:38.7766551Z >>> batched_pow(x, scale=x) # scale is not batched, output has shape [2, 2, 5] 2025-09-07T07:36:38.7766899Z 2025-09-07T07:36:38.7767012Z .. note:: 2025-09-07T07:36:38.7767348Z vmap does not provide general autobatching or handle variable-length 2025-09-07T07:36:38.7767791Z sequences out of the box. 2025-09-07T07:36:38.7767977Z 2025-09-07T07:36:38.7768241Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:38.7768606Z 2025-09-07T07:36:38.7769096Z msg = Cannot scrape callname=grad in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/apis.py line=306. 2025-09-07T07:36:38.7769944Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:38.7770565Z ``grad`` operator helps computing gradients of ``func`` with respect to the 2025-09-07T07:36:38.7771124Z input(s) specified by ``argnums``. This operator can be nested to 2025-09-07T07:36:38.7771557Z compute higher-order gradients. 2025-09-07T07:36:38.7771765Z 2025-09-07T07:36:38.7771864Z Args: 2025-09-07T07:36:38.7772189Z func (Callable): A Python function that takes one or more arguments. 2025-09-07T07:36:38.7772789Z Must return a single-element Tensor. If specified ``has_aux`` equals ``True``, 2025-09-07T07:36:38.7773441Z function can return a tuple of single-element Tensor and other auxiliary objects: 2025-09-07T07:36:38.7773934Z ``(output, aux)``. 2025-09-07T07:36:38.7774416Z argnums (int or Tuple[int]): Specifies arguments to compute gradients with respect to. 2025-09-07T07:36:38.7775017Z ``argnums`` can be single integer or tuple of integers. Default: 0. 2025-09-07T07:36:38.7775558Z has_aux (bool): Flag indicating that ``func`` returns a tensor and other 2025-09-07T07:36:38.7776064Z auxiliary objects: ``(output, aux)``. Default: False. 2025-09-07T07:36:38.7776341Z 2025-09-07T07:36:38.7776440Z Returns: 2025-09-07T07:36:38.7776841Z Function to compute gradients with respect to its inputs. By default, the output of 2025-09-07T07:36:38.7777479Z the function is the gradient tensor(s) with respect to the first argument. 2025-09-07T07:36:38.7778115Z If specified ``has_aux`` equals ``True``, tuple of gradients and output auxiliary objects 2025-09-07T07:36:38.7778758Z is returned. If ``argnums`` is a tuple of integers, a tuple of output gradients with 2025-09-07T07:36:38.7779273Z respect to each ``argnums`` value is returned. 2025-09-07T07:36:38.7779524Z 2025-09-07T07:36:38.7779627Z Example of using ``grad``: 2025-09-07T07:36:38.7779822Z 2025-09-07T07:36:38.7779924Z >>> # xdoctest: +SKIP 2025-09-07T07:36:38.7780219Z >>> from torch.func import grad 2025-09-07T07:36:38.7780577Z >>> x = torch.randn([]) 2025-09-07T07:36:38.7780880Z >>> cos_x = grad(lambda x: torch.sin(x))(x) 2025-09-07T07:36:38.7781249Z >>> assert torch.allclose(cos_x, x.cos()) 2025-09-07T07:36:38.7781575Z >>> 2025-09-07T07:36:38.7781811Z >>> # Second-order gradients 2025-09-07T07:36:38.7782189Z >>> neg_sin_x = grad(grad(lambda x: torch.sin(x)))(x) 2025-09-07T07:36:38.7782598Z >>> assert torch.allclose(neg_sin_x, -x.sin()) 2025-09-07T07:36:38.7782855Z 2025-09-07T07:36:38.7783107Z When composed with ``vmap``, ``grad`` can be used to compute per-sample-gradients: 2025-09-07T07:36:38.7783479Z 2025-09-07T07:36:38.7783590Z >>> # xdoctest: +SKIP 2025-09-07T07:36:38.7783899Z >>> from torch.func import grad, vmap 2025-09-07T07:36:38.7784232Z >>> batch_size, feature_size = 3, 5 2025-09-07T07:36:38.7784539Z >>> 2025-09-07T07:36:38.7784786Z >>> def model(weights, feature_vec): 2025-09-07T07:36:38.7785146Z >>> # Very simple linear model with activation 2025-09-07T07:36:38.7785491Z >>> assert feature_vec.dim() == 1 2025-09-07T07:36:38.7785877Z >>> return feature_vec.dot(weights).relu() 2025-09-07T07:36:38.7786200Z >>> 2025-09-07T07:36:38.7786465Z >>> def compute_loss(weights, example, target): 2025-09-07T07:36:38.7786816Z >>> y = model(weights, example) 2025-09-07T07:36:38.7787170Z >>> return ((y - target) ** 2).mean() # MSELoss 2025-09-07T07:36:38.7787505Z >>> 2025-09-07T07:36:38.7787806Z >>> weights = torch.randn(feature_size, requires_grad=True) 2025-09-07T07:36:38.7788238Z >>> examples = torch.randn(batch_size, feature_size) 2025-09-07T07:36:38.7788625Z >>> targets = torch.randn(batch_size) 2025-09-07T07:36:38.7788980Z >>> inputs = (weights, examples, targets) 2025-09-07T07:36:38.7789449Z >>> grad_weight_per_example = vmap(grad(compute_loss), in_dims=(None, 0, 0))( 2025-09-07T07:36:38.7789901Z ... *inputs 2025-09-07T07:36:38.7790129Z ... ) 2025-09-07T07:36:38.7790266Z 2025-09-07T07:36:38.7790442Z Example of using ``grad`` with ``has_aux`` and ``argnums``: 2025-09-07T07:36:38.7790728Z 2025-09-07T07:36:38.7790834Z >>> # xdoctest: +SKIP 2025-09-07T07:36:38.7791129Z >>> from torch.func import grad 2025-09-07T07:36:38.7791443Z >>> def my_loss_func(y, y_pred): 2025-09-07T07:36:38.7791792Z >>> loss_per_sample = (0.5 * y_pred - y) ** 2 2025-09-07T07:36:38.7792149Z >>> loss = loss_per_sample.mean() 2025-09-07T07:36:38.7792506Z >>> return loss, (y_pred, loss_per_sample) 2025-09-07T07:36:38.7792818Z >>> 2025-09-07T07:36:38.7793124Z >>> fn = grad(my_loss_func, argnums=(0, 1), has_aux=True) 2025-09-07T07:36:38.7793497Z >>> y_true = torch.rand(4) 2025-09-07T07:36:38.7793835Z >>> y_preds = torch.rand(4, requires_grad=True) 2025-09-07T07:36:38.7794178Z >>> out = fn(y_true, y_preds) 2025-09-07T07:36:38.7794713Z >>> # > output is ((grads w.r.t y_true, grads w.r.t y_preds), (y_pred, loss_per_sample)) 2025-09-07T07:36:38.7795090Z 2025-09-07T07:36:38.7795184Z .. note:: 2025-09-07T07:36:38.7795483Z Using PyTorch ``torch.no_grad`` together with ``grad``. 2025-09-07T07:36:38.7795767Z 2025-09-07T07:36:38.7795923Z Case 1: Using ``torch.no_grad`` inside a function: 2025-09-07T07:36:38.7796177Z 2025-09-07T07:36:38.7796274Z >>> # xdoctest: +SKIP 2025-09-07T07:36:38.7796562Z >>> def f(x): 2025-09-07T07:36:38.7796830Z >>> with torch.no_grad(): 2025-09-07T07:36:38.7797138Z >>> c = x ** 2 2025-09-07T07:36:38.7797414Z >>> return x - c 2025-09-07T07:36:38.7797605Z 2025-09-07T07:36:38.7797803Z In this case, ``grad(f)(x)`` will respect the inner ``torch.no_grad``. 2025-09-07T07:36:38.7798128Z 2025-09-07T07:36:38.7798310Z Case 2: Using ``grad`` inside ``torch.no_grad`` context manager: 2025-09-07T07:36:38.7798647Z 2025-09-07T07:36:38.7798759Z >>> # xdoctest: +SKIP 2025-09-07T07:36:38.7799058Z >>> with torch.no_grad(): 2025-09-07T07:36:38.7799349Z >>> grad(f)(x) 2025-09-07T07:36:38.7799532Z 2025-09-07T07:36:38.7799752Z In this case, ``grad`` will respect the inner ``torch.no_grad``, but not the 2025-09-07T07:36:38.7800358Z outer one. This is because ``grad`` is a "function transform": its result 2025-09-07T07:36:38.7800924Z should not depend on the result of a context manager outside of ``f``. 2025-09-07T07:36:38.7801258Z 2025-09-07T07:36:38.7801349Z 2025-09-07T07:36:38.7801709Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:38.7802086Z 2025-09-07T07:36:40.4610402Z msg = Cannot scrape callname=CustomOpDef.register_fake in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/custom_ops.py line=397. 2025-09-07T07:36:40.4611393Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-09-07T07:36:40.4611946Z Register a FakeTensor implementation for this custom op. 2025-09-07T07:36:40.4612451Z 2025-09-07T07:36:40.4612700Z This is necessary to get the operator to work efficiently with torch.compile. 2025-09-07T07:36:40.4613078Z 2025-09-07T07:36:40.4613312Z The Fake impl (sometimes also known as a meta kernel or abstract impl) 2025-09-07T07:36:40.4613884Z specifies the behavior of this operator on Tensors that carry no data. 2025-09-07T07:36:40.4614369Z Given some input Tensors with certain properties 2025-09-07T07:36:40.4614894Z (sizes/strides/storage_offset/device), it specifies what the properties of 2025-09-07T07:36:40.4615380Z the output Tensors are. 2025-09-07T07:36:40.4615570Z 2025-09-07T07:36:40.4615785Z Please see :func:`torch.library.register_fake` for more details. 2025-09-07T07:36:40.4616106Z 2025-09-07T07:36:40.4616202Z Args: 2025-09-07T07:36:40.4616498Z fn (Callable): The function to register as the FakeTensor 2025-09-07T07:36:40.4616891Z implementation. 2025-09-07T07:36:40.4617076Z 2025-09-07T07:36:40.4617178Z Examples: 2025-09-07T07:36:40.4617418Z >>> import torch 2025-09-07T07:36:40.4617686Z >>> import numpy as np 2025-09-07T07:36:40.4617997Z >>> from torch import Tensor 2025-09-07T07:36:40.4618296Z >>> 2025-09-07T07:36:40.4618621Z >>> # Example 1: an operator without data-dependent output shape 2025-09-07T07:36:40.4619126Z >>> @torch.library.custom_op("mylib::linear", mutates_args=()) 2025-09-07T07:36:40.4619628Z >>> def linear(x: Tensor, weight: Tensor, bias: Tensor) -> Tensor: 2025-09-07T07:36:40.4620128Z >>> return (x @ weight.t()) + bias 2025-09-07T07:36:40.4620453Z >>> 2025-09-07T07:36:40.4620686Z >>> @linear.register_fake 2025-09-07T07:36:40.4621005Z >>> def _(x, weight, bias): 2025-09-07T07:36:40.4621321Z >>> assert x.dim() == 2 2025-09-07T07:36:40.4621644Z >>> assert weight.dim() == 2 2025-09-07T07:36:40.4621961Z >>> assert bias.dim() == 1 2025-09-07T07:36:40.4622307Z >>> assert x.shape[1] == weight.shape[1] 2025-09-07T07:36:40.4622684Z >>> assert weight.shape[0] == bias.shape[0] 2025-09-07T07:36:40.4623055Z >>> assert x.device == weight.device 2025-09-07T07:36:40.4623462Z >>> return x.new_empty(x.size(0), weight.size(0)) 2025-09-07T07:36:40.4623993Z >>> 2025-09-07T07:36:40.4624239Z >>> x = torch.randn(2, 2) 2025-09-07T07:36:40.4624564Z >>> weight = torch.randn(2, 2) 2025-09-07T07:36:40.4624893Z >>> bias = torch.randn(2) 2025-09-07T07:36:40.4625226Z >>> # xdoctest: +SKIP("Requires Python <= 3.11") 2025-09-07T07:36:40.4625672Z >>> out = torch.compile(linear, fullgraph=True)(x, weight, bias) 2025-09-07T07:36:40.4626243Z >>> # xdoctest: +SKIP("Requires Python <= 3.11") 2025-09-07T07:36:40.4626743Z >>> assert torch.allclose(out, torch.nn.functional.linear(x, weight, bias)) 2025-09-07T07:36:40.4627199Z >>> 2025-09-07T07:36:40.4627506Z >>> # Example 2: an operator with data-dependent output shape 2025-09-07T07:36:40.4628058Z >>> @torch.library.custom_op("mylib::nonzero", mutates_args=()) 2025-09-07T07:36:40.4628495Z >>> def nonzero(x: Tensor) -> Tensor: 2025-09-07T07:36:40.4628841Z >>> x_np = x.cpu().numpy() 2025-09-07T07:36:40.4629180Z >>> res = np.stack(np.nonzero(x_np), axis=1) 2025-09-07T07:36:40.4629570Z >>> return torch.tensor(res, device=x.device) 2025-09-07T07:36:40.4629914Z >>> 2025-09-07T07:36:40.4630155Z >>> @nonzero.register_fake 2025-09-07T07:36:40.4630447Z >>> def _(x): 2025-09-07T07:36:40.4630761Z >>> # Number of nonzero-elements is data-dependent. 2025-09-07T07:36:40.4631196Z >>> # Since we cannot peek at the data in an abstract impl, 2025-09-07T07:36:40.4631688Z >>> # we use the ctx object to construct a new symint that 2025-09-07T07:36:40.4632101Z >>> # represents the data-dependent size. 2025-09-07T07:36:40.4632457Z >>> ctx = torch.library.get_ctx() 2025-09-07T07:36:40.4632808Z >>> nnz = ctx.new_dynamic_size() 2025-09-07T07:36:40.4633150Z >>> shape = [nnz, x.dim()] 2025-09-07T07:36:40.4633523Z >>> result = x.new_empty(shape, dtype=torch.int64) 2025-09-07T07:36:40.4633881Z >>> return result 2025-09-07T07:36:40.4634160Z >>> 2025-09-07T07:36:40.4634502Z >>> x = torch.tensor([0, 1, 2, 0, 0, 1]) 2025-09-07T07:36:40.4634876Z >>> # xdoctest: +SKIP("Requires Python <= 3.11") 2025-09-07T07:36:40.4635269Z >>> out = torch.compile(nonzero, fullgraph=True)(x) 2025-09-07T07:36:40.4635677Z >>> # xdoctest: +SKIP("Requires Python <= 3.11") 2025-09-07T07:36:40.4636067Z >>> assert torch.allclose(out, x.nonzero()) 2025-09-07T07:36:40.4636305Z 2025-09-07T07:36:40.4636402Z 2025-09-07T07:36:40.4637047Z Original Error: IndentationError('expected an indented block after function definition on line 36', ('', 37, 1, '_._ = None\n', 37, 2)) 2025-09-07T07:36:40.4637703Z 2025-09-07T07:36:40.4637790Z _._ = None 2025-09-07T07:36:40.4637998Z ^ 2025-09-07T07:36:40.4736281Z msg = Cannot scrape callname=unsafe_generate_fake_kernels in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/fake_profile.py line=94. 2025-09-07T07:36:40.4737274Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:40.4737763Z 2025-09-07T07:36:40.4737992Z Registers a fake kernel based on the given operator profiles. This fake 2025-09-07T07:36:40.4738589Z kernel registration will override any existing fake kernel registrations. 2025-09-07T07:36:40.4738971Z 2025-09-07T07:36:40.4739181Z The input is a dictionary mapping operator names to a set of operator 2025-09-07T07:36:40.4739750Z profiles, which we will use to generate fake kernels. The operator profiles 2025-09-07T07:36:40.4740323Z are a record of the input and output tensor metadata. Based on this 2025-09-07T07:36:40.4740885Z information we will match a given input to the recorded profile, and return 2025-09-07T07:36:40.4741460Z an output with the same metadata as in the recorded profile. If a profile 2025-09-07T07:36:40.4741941Z doesn't exist then an exception will be thrown. 2025-09-07T07:36:40.4742199Z 2025-09-07T07:36:40.4742424Z The fake kernel generation is considered unsafe because it relies on the 2025-09-07T07:36:40.4742998Z rigid, pre-defined operator profiles that do not account for potential 2025-09-07T07:36:40.4743596Z variations in output behavior. Specifically, the generated kernels assume a 2025-09-07T07:36:40.4744205Z fixed relationship between input and output ranks. However, in reality, it's 2025-09-07T07:36:40.4744880Z possible that data-dependent operations may produce outputs of different 2025-09-07T07:36:40.4745461Z ranks even when given inputs of the same rank. The generated fake kernels 2025-09-07T07:36:40.4746024Z are inflexible and unable to accommodate these nuances, making them 2025-09-07T07:36:40.4746484Z potentially unsafe. 2025-09-07T07:36:40.4746646Z 2025-09-07T07:36:40.4746726Z Args: 2025-09-07T07:36:40.4747063Z op_profiles (dict[str, set[OpProfile]]): A dictionary mapping operator 2025-09-07T07:36:40.4747603Z name to a set of operator profiles from which we will generate fake 2025-09-07T07:36:40.4747997Z kernels. 2025-09-07T07:36:40.4748146Z 2025-09-07T07:36:40.4748232Z Examples: 2025-09-07T07:36:40.4748365Z 2025-09-07T07:36:40.4748532Z >>> # Example: Registering an op-profile from draft-export 2025-09-07T07:36:40.4748910Z >>> import torch 2025-09-07T07:36:40.4749225Z >>> from torch.export._draft_export import draft_export 2025-09-07T07:36:40.4749575Z >>> 2025-09-07T07:36:40.4749931Z >>> @torch.library.custom_op("mylib::foo", mutates_args=()) 2025-09-07T07:36:40.4750352Z >>> def foo(x: Tensor, y: Tensor) -> Tensor: 2025-09-07T07:36:40.4750685Z >>> return x + y 2025-09-07T07:36:40.4750924Z >>> 2025-09-07T07:36:40.4751152Z >>> class M(torch.nn.Module): 2025-09-07T07:36:40.4751464Z >>> def forward(self, a, b): 2025-09-07T07:36:40.4751825Z >>> res = torch.ops.mylib.foo(a, b) # no fake impl 2025-09-07T07:36:40.4752173Z >>> return res 2025-09-07T07:36:40.4752428Z >>> 2025-09-07T07:36:40.4752716Z >>> ep = draft_export(M(), (torch.ones(3, 4), torch.ones(3, 4)) 2025-09-07T07:36:40.4753083Z >>> 2025-09-07T07:36:40.4753487Z >>> with torch._library.fake_profile.unsafe_generate_fake_kernels(ep._report.op_profiles): 2025-09-07T07:36:40.4754032Z >>> decomp = ep.run_decompositions() 2025-09-07T07:36:40.4754267Z 2025-09-07T07:36:40.4754273Z 2025-09-07T07:36:40.4754608Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:40.4754976Z 2025-09-07T07:36:40.4795644Z msg = Cannot scrape callname=triton_op in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/triton.py line=96. 2025-09-07T07:36:40.4796700Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:40.4797341Z Create a custom operator whose implementation is backed by 1+ triton kernels. 2025-09-07T07:36:40.4797712Z 2025-09-07T07:36:40.4797935Z This is a more structured way of using triton kernels with PyTorch. 2025-09-07T07:36:40.4798508Z Prefer using triton kernels with no ``torch.library`` custom operator wrappers 2025-09-07T07:36:40.4799209Z (like :func:`torch.library.custom_op`, :func:`torch.library.triton_op`) because 2025-09-07T07:36:40.4799686Z that is simpler; 2025-09-07T07:36:40.4800103Z only use :func:`torch.library.custom_op`/:func:`torch.library.triton_op` if you 2025-09-07T07:36:40.4800704Z want to create an operator that behaves like PyTorch built-in operators. 2025-09-07T07:36:40.4801266Z For example, you may use a ``torch.library`` wrapper API to define the 2025-09-07T07:36:40.4801815Z behavior of the triton kernel when passed a tensor subclass or under 2025-09-07T07:36:40.4802254Z a TorchDispatchMode. 2025-09-07T07:36:40.4802421Z 2025-09-07T07:36:40.4802680Z Use :func:`torch.library.triton_op` instead of :func:`torch.library.custom_op` 2025-09-07T07:36:40.4803143Z when the implementation 2025-09-07T07:36:40.4803705Z consists of 1+ triton kernels. :func:`torch.library.custom_op` treats 2025-09-07T07:36:40.4804498Z custom operators as opaque (:func:`torch.compile` and 2025-09-07T07:36:40.4805015Z :func:`torch.export.export` will never trace into them), but ``triton_op`` 2025-09-07T07:36:40.4805581Z makes the implementation visible to these subsystems, allowing them 2025-09-07T07:36:40.4806093Z to optimize the triton kernel(s). 2025-09-07T07:36:40.4806314Z 2025-09-07T07:36:40.4806503Z Note that ``fn`` must only consist of calls to PyTorch-understood 2025-09-07T07:36:40.4807041Z operators and triton kernels. Any triton kernels called inside ``fn`` 2025-09-07T07:36:40.4807579Z must be wrapped in a call to :func:`torch.library.wrap_triton`. 2025-09-07T07:36:40.4808453Z 2025-09-07T07:36:40.4808536Z Args: 2025-09-07T07:36:40.4808894Z name (str): A name for the custom op that looks like "{namespace}::{name}", 2025-09-07T07:36:40.4809460Z e.g. "mylib::my_linear". The name is used as the op's stable identifier 2025-09-07T07:36:40.4809992Z in PyTorch subsystems (e.g. torch.export, FX graphs). 2025-09-07T07:36:40.4810503Z To avoid name collisions, please use your project name as the namespace; 2025-09-07T07:36:40.4811073Z e.g. all custom ops in pytorch/fbgemm use "fbgemm" as the namespace. 2025-09-07T07:36:40.4811674Z mutates_args (Iterable[str] or "unknown"): The names of args that the function mutates. 2025-09-07T07:36:40.4812347Z This MUST be accurate, otherwise, the behavior is undefined. If "unknown", 2025-09-07T07:36:40.4812970Z it pessimistically assumes that all inputs to the operator are being mutated. 2025-09-07T07:36:40.4813534Z schema (None | str): A schema string for the operator. If None 2025-09-07T07:36:40.4814046Z (recommended) we'll infer a schema for the operator from its type 2025-09-07T07:36:40.4814577Z annotations. We recommend letting us infer a schema unless you 2025-09-07T07:36:40.4815021Z have a specific reason not to. 2025-09-07T07:36:40.4815390Z Example: "(Tensor x, int y) -> (Tensor, Tensor)". 2025-09-07T07:36:40.4815650Z 2025-09-07T07:36:40.4815755Z Example:: 2025-09-07T07:36:40.4815884Z 2025-09-07T07:36:40.4816042Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2025-09-07T07:36:40.4816374Z >>> import torch 2025-09-07T07:36:40.4816699Z >>> from torch.library import triton_op, wrap_triton 2025-09-07T07:36:40.4817049Z >>> 2025-09-07T07:36:40.4817271Z >>> import triton 2025-09-07T07:36:40.4817544Z >>> from triton import language as tl 2025-09-07T07:36:40.4817903Z >>> 2025-09-07T07:36:40.4818136Z >>> @triton.jit 2025-09-07T07:36:40.4818389Z >>> def add_kernel( 2025-09-07T07:36:40.4818639Z >>> in_ptr0, 2025-09-07T07:36:40.4818885Z >>> in_ptr1, 2025-09-07T07:36:40.4819133Z >>> out_ptr, 2025-09-07T07:36:40.4819385Z >>> n_elements, 2025-09-07T07:36:40.4819657Z >>> BLOCK_SIZE: "tl.constexpr", 2025-09-07T07:36:40.4819967Z >>> ): 2025-09-07T07:36:40.4820244Z >>> pid = tl.program_id(axis=0) 2025-09-07T07:36:40.4820581Z >>> block_start = pid * BLOCK_SIZE 2025-09-07T07:36:40.4820945Z >>> offsets = block_start + tl.arange(0, BLOCK_SIZE) 2025-09-07T07:36:40.4821321Z >>> mask = offsets < n_elements 2025-09-07T07:36:40.4821673Z >>> x = tl.load(in_ptr0 + offsets, mask=mask) 2025-09-07T07:36:40.4822050Z >>> y = tl.load(in_ptr1 + offsets, mask=mask) 2025-09-07T07:36:40.4822384Z >>> output = x + y 2025-09-07T07:36:40.4822712Z >>> tl.store(out_ptr + offsets, output, mask=mask) 2025-09-07T07:36:40.4823061Z >>> 2025-09-07T07:36:40.4823316Z >>> @triton_op("mylib::add", mutates_args={}) 2025-09-07T07:36:40.4823942Z >>> def add(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor: 2025-09-07T07:36:40.4824365Z >>> output = torch.empty_like(x) 2025-09-07T07:36:40.4824709Z >>> n_elements = output.numel() 2025-09-07T07:36:40.4825021Z >>> 2025-09-07T07:36:40.4825236Z >>> def grid(meta): 2025-09-07T07:36:40.4825598Z >>> return (triton.cdiv(n_elements, meta["BLOCK_SIZE"]),) 2025-09-07T07:36:40.4825979Z >>> 2025-09-07T07:36:40.4826362Z >>> # NB: we need to wrap the triton kernel in a call to wrap_triton 2025-09-07T07:36:40.4826844Z >>> wrap_triton(add_kernel)[grid](x, y, output, n_elements, 16) 2025-09-07T07:36:40.4827256Z >>> return output 2025-09-07T07:36:40.4827531Z >>> 2025-09-07T07:36:40.4827753Z >>> @torch.compile 2025-09-07T07:36:40.4828047Z >>> def f(x, y): 2025-09-07T07:36:40.4828316Z >>> return add(x, y) 2025-09-07T07:36:40.4828592Z >>> 2025-09-07T07:36:40.4828837Z >>> x = torch.randn(3, device="cuda") 2025-09-07T07:36:40.4829172Z >>> y = torch.randn(3, device="cuda") 2025-09-07T07:36:40.4829483Z >>> 2025-09-07T07:36:40.4829703Z >>> z = f(x, y) 2025-09-07T07:36:40.4829980Z >>> assert torch.allclose(z, x + y) 2025-09-07T07:36:40.4830202Z 2025-09-07T07:36:40.4830281Z 2025-09-07T07:36:40.4830645Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:40.4831021Z 2025-09-07T07:36:40.4831555Z msg = Cannot scrape callname=wrap_triton in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/triton.py line=296. 2025-09-07T07:36:40.4832474Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:40.4833050Z Allows capture of a triton kernel into a graph via make_fx or 2025-09-07T07:36:40.4833468Z non-strict ``torch.export``. 2025-09-07T07:36:40.4833661Z 2025-09-07T07:36:40.4833842Z These technologies perform Dispatcher-based tracing (via 2025-09-07T07:36:40.4834419Z ``__torch_dispatch__``) and cannot see calls to raw triton kernels. 2025-09-07T07:36:40.4834942Z The ``wrap_triton`` API wraps a triton kernel into a callable that 2025-09-07T07:36:40.4835372Z can actually be traced into a graph. 2025-09-07T07:36:40.4835592Z 2025-09-07T07:36:40.4835813Z Please use this API together with :func:`torch.library.triton_op`. 2025-09-07T07:36:40.4836137Z 2025-09-07T07:36:40.4836223Z Examples: 2025-09-07T07:36:40.4836365Z 2025-09-07T07:36:40.4836462Z >>> # xdoctest: +SKIP 2025-09-07T07:36:40.4836745Z >>> import torch 2025-09-07T07:36:40.4837006Z >>> import triton 2025-09-07T07:36:40.4837283Z >>> from triton import language as tl 2025-09-07T07:36:40.4837695Z >>> from torch.fx.experimental.proxy_tensor import make_fx 2025-09-07T07:36:40.4838121Z >>> from torch.library import wrap_triton 2025-09-07T07:36:40.4838451Z >>> 2025-09-07T07:36:40.4838652Z >>> @triton.jit 2025-09-07T07:36:40.4838934Z >>> def add_kernel( 2025-09-07T07:36:40.4839193Z >>> in_ptr0, 2025-09-07T07:36:40.4839439Z >>> in_ptr1, 2025-09-07T07:36:40.4839673Z >>> out_ptr, 2025-09-07T07:36:40.4839961Z >>> n_elements, 2025-09-07T07:36:40.4840314Z >>> BLOCK_SIZE: "tl.constexpr", 2025-09-07T07:36:40.4840628Z >>> ): 2025-09-07T07:36:40.4840862Z >>> pid = tl.program_id(axis=0) 2025-09-07T07:36:40.4841208Z >>> block_start = pid * BLOCK_SIZE 2025-09-07T07:36:40.4841588Z >>> offsets = block_start + tl.arange(0, BLOCK_SIZE) 2025-09-07T07:36:40.4841971Z >>> mask = offsets < n_elements 2025-09-07T07:36:40.4842310Z >>> x = tl.load(in_ptr0 + offsets, mask=mask) 2025-09-07T07:36:40.4842685Z >>> y = tl.load(in_ptr1 + offsets, mask=mask) 2025-09-07T07:36:40.4843030Z >>> output = x + y 2025-09-07T07:36:40.4843355Z >>> tl.store(out_ptr + offsets, output, mask=mask) 2025-09-07T07:36:40.4843687Z >>> 2025-09-07T07:36:40.4843908Z >>> def add(x, y): 2025-09-07T07:36:40.4844193Z >>> output = torch.empty_like(x) 2025-09-07T07:36:40.4844531Z >>> n_elements = output.numel() 2025-09-07T07:36:40.4844826Z >>> 2025-09-07T07:36:40.4845050Z >>> def grid_fn(meta): 2025-09-07T07:36:40.4845417Z >>> return (triton.cdiv(n_elements, meta["BLOCK_SIZE"]),) 2025-09-07T07:36:40.4845830Z >>> 2025-09-07T07:36:40.4846136Z >>> wrap_triton(add_kernel)[grid_fn](x, y, output, n_elements, 16) 2025-09-07T07:36:40.4846553Z >>> return output 2025-09-07T07:36:40.4846819Z >>> 2025-09-07T07:36:40.4847057Z >>> x = torch.randn(3, device="cuda") 2025-09-07T07:36:40.4847412Z >>> y = torch.randn(3, device="cuda") 2025-09-07T07:36:40.4847765Z >>> gm = make_fx(add)(x, y) 2025-09-07T07:36:40.4848061Z >>> print(gm.code) 2025-09-07T07:36:40.4848341Z >>> # def forward(self, x_1, y_1): 2025-09-07T07:36:40.4848789Z >>> # empty_like = torch.ops.aten.empty_like.default(x_1, pin_memory = False) 2025-09-07T07:36:40.4849376Z >>> # triton_kernel_wrapper_mutation_proxy = triton_kernel_wrapper_mutation( 2025-09-07T07:36:40.4849869Z >>> # kernel_idx = 0, constant_args_idx = 0, 2025-09-07T07:36:40.4850228Z >>> # grid = [(1, 1, 1)], kwargs = { 2025-09-07T07:36:40.4850608Z >>> # 'in_ptr0': x_1, 'in_ptr1': y_1, 'out_ptr': empty_like, 2025-09-07T07:36:40.4851001Z >>> # 'n_elements': 3, 'BLOCK_SIZE': 16 2025-09-07T07:36:40.4851361Z >>> # }) 2025-09-07T07:36:40.4851625Z >>> # return empty_like 2025-09-07T07:36:40.4851821Z 2025-09-07T07:36:40.4851920Z 2025-09-07T07:36:40.4852303Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:40.4852685Z 2025-09-07T07:36:40.5555836Z msg = Cannot scrape callname=print_assert_equal in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py line=286. 2025-09-07T07:36:40.5556786Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:40.5557194Z 2025-09-07T07:36:40.5557426Z Test if two objects are equal, and print an error message if test fails. 2025-09-07T07:36:40.5557776Z 2025-09-07T07:36:40.5557924Z The test is performed with ``actual == desired``. 2025-09-07T07:36:40.5558178Z 2025-09-07T07:36:40.5558277Z Parameters 2025-09-07T07:36:40.5558478Z ---------- 2025-09-07T07:36:40.5558700Z test_string : str 2025-09-07T07:36:40.5559059Z The message supplied to AssertionError. 2025-09-07T07:36:40.5559685Z actual : object 2025-09-07T07:36:40.5560042Z The object to test for equality against `desired`. 2025-09-07T07:36:40.5560403Z desired : object 2025-09-07T07:36:40.5560646Z The expected result. 2025-09-07T07:36:40.5560809Z 2025-09-07T07:36:40.5560906Z Examples 2025-09-07T07:36:40.5561103Z -------- 2025-09-07T07:36:40.5561339Z >>> np.testing.print_assert_equal( 2025-09-07T07:36:40.5561672Z ... "Test XYZ of func xyz", [0, 1], [0, 1] 2025-09-07T07:36:40.5562000Z ... ) # doctest: +SKIP 2025-09-07T07:36:40.5562262Z >>> np.testing.print_assert_equal( 2025-09-07T07:36:40.5562749Z ... "Test XYZ of func xyz", [0, 1], [0, 2] 2025-09-07T07:36:40.5563080Z ... ) # doctest: +SKIP 2025-09-07T07:36:40.5563355Z Traceback (most recent call last): 2025-09-07T07:36:40.5563639Z ... 2025-09-07T07:36:40.5563885Z AssertionError: Test XYZ of func xyz failed 2025-09-07T07:36:40.5564211Z ACTUAL: 2025-09-07T07:36:40.5564432Z [0, 1] 2025-09-07T07:36:40.5564627Z DESIRED: 2025-09-07T07:36:40.5564826Z [0, 2] 2025-09-07T07:36:40.5564947Z 2025-09-07T07:36:40.5564951Z 2025-09-07T07:36:40.5565201Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:40.5565569Z 2025-09-07T07:36:40.5566171Z 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=331. 2025-09-07T07:36:40.5567106Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:40.5567596Z 2025-09-07T07:36:40.5567947Z Raises an AssertionError if two items are not equal up to desired 2025-09-07T07:36:40.5568652Z precision. 2025-09-07T07:36:40.5568773Z 2025-09-07T07:36:40.5568966Z .. note:: It is recommended to use one of `assert_allclose`, 2025-09-07T07:36:40.5569502Z `assert_array_almost_equal_nulp` or `assert_array_max_ulp` 2025-09-07T07:36:40.5569997Z instead of this function for more consistent floating point 2025-09-07T07:36:40.5570395Z comparisons. 2025-09-07T07:36:40.5570548Z 2025-09-07T07:36:40.5570773Z The test verifies that the elements of `actual` and `desired` satisfy. 2025-09-07T07:36:40.5571156Z 2025-09-07T07:36:40.5571315Z ``abs(desired-actual) < float64(1.5 * 10**(-decimal))`` 2025-09-07T07:36:40.5571599Z 2025-09-07T07:36:40.5571823Z That is a looser test than originally documented, but agrees with what the 2025-09-07T07:36:40.5572904Z actual implementation in `assert_array_almost_equal` did up to rounding 2025-09-07T07:36:40.5573501Z vagaries. An exception is raised at conflicting values. For ndarrays this 2025-09-07T07:36:40.5573981Z delegates to assert_array_almost_equal 2025-09-07T07:36:40.5574197Z 2025-09-07T07:36:40.5574288Z Parameters 2025-09-07T07:36:40.5574508Z ---------- 2025-09-07T07:36:40.5574729Z actual : array_like 2025-09-07T07:36:40.5574980Z The object to check. 2025-09-07T07:36:40.5575231Z desired : array_like 2025-09-07T07:36:40.5575531Z The expected object. 2025-09-07T07:36:40.5575799Z decimal : int, optional 2025-09-07T07:36:40.5576074Z Desired precision, default is 7. 2025-09-07T07:36:40.5576372Z err_msg : str, optional 2025-09-07T07:36:40.5576683Z The error message to be printed in case of failure. 2025-09-07T07:36:40.5577050Z verbose : bool, optional 2025-09-07T07:36:40.5577425Z If True, the conflicting values are appended to the error message. 2025-09-07T07:36:40.5577740Z 2025-09-07T07:36:40.5577820Z Raises 2025-09-07T07:36:40.5578028Z ------ 2025-09-07T07:36:40.5578238Z AssertionError 2025-09-07T07:36:40.5578569Z If actual and desired are not equal up to specified precision. 2025-09-07T07:36:40.5578877Z 2025-09-07T07:36:40.5578959Z See Also 2025-09-07T07:36:40.5579165Z -------- 2025-09-07T07:36:40.5579519Z assert_allclose: Compare two array_like objects for equality with desired 2025-09-07T07:36:40.5580003Z relative and/or absolute precision. 2025-09-07T07:36:40.5580443Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2025-09-07T07:36:40.5580774Z 2025-09-07T07:36:40.5580857Z Examples 2025-09-07T07:36:40.5581064Z -------- 2025-09-07T07:36:40.5581345Z >>> from torch._numpy.testing import assert_almost_equal 2025-09-07T07:36:40.5581741Z >>> assert_almost_equal(2.3333333333333, 2.33333334) 2025-09-07T07:36:40.5582159Z >>> assert_almost_equal(2.3333333333333, 2.33333334, decimal=10) 2025-09-07T07:36:40.5582559Z Traceback (most recent call last): 2025-09-07T07:36:40.5582855Z ... 2025-09-07T07:36:40.5583053Z AssertionError: 2025-09-07T07:36:40.5583320Z Arrays are not almost equal to 10 decimals 2025-09-07T07:36:40.5583644Z ACTUAL: 2.3333333333333 2025-09-07T07:36:40.5583933Z DESIRED: 2.33333334 2025-09-07T07:36:40.5584079Z 2025-09-07T07:36:40.5584172Z >>> assert_almost_equal( 2025-09-07T07:36:40.5584544Z ... np.array([1.0, 2.3333333333333]), np.array([1.0, 2.33333334]), decimal=9 2025-09-07T07:36:40.5584945Z ... ) 2025-09-07T07:36:40.5585169Z Traceback (most recent call last): 2025-09-07T07:36:40.5585448Z ... 2025-09-07T07:36:40.5585657Z AssertionError: 2025-09-07T07:36:40.5585916Z Arrays are not almost equal to 9 decimals 2025-09-07T07:36:40.5586230Z 2025-09-07T07:36:40.5586452Z Mismatched elements: 1 / 2 (50%) 2025-09-07T07:36:40.5586777Z Max absolute difference: 6.666699636781459e-09 2025-09-07T07:36:40.5587142Z Max relative difference: 2.8571569790287484e-09 2025-09-07T07:36:40.5587519Z x: torch.ndarray([1.0000, 2.3333], dtype=float64) 2025-09-07T07:36:40.5587894Z y: torch.ndarray([1.0000, 2.3333], dtype=float64) 2025-09-07T07:36:40.5588149Z 2025-09-07T07:36:40.5588152Z 2025-09-07T07:36:40.5588399Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:40.5588779Z 2025-09-07T07:36:40.5589344Z 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=457. 2025-09-07T07:36:40.5590328Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:40.5590706Z 2025-09-07T07:36:40.5590942Z Raises an AssertionError if two items are not equal up to significant 2025-09-07T07:36:40.5591401Z digits. 2025-09-07T07:36:40.5591514Z 2025-09-07T07:36:40.5591693Z .. note:: It is recommended to use one of `assert_allclose`, 2025-09-07T07:36:40.5592160Z `assert_array_almost_equal_nulp` or `assert_array_max_ulp` 2025-09-07T07:36:40.5592644Z instead of this function for more consistent floating point 2025-09-07T07:36:40.5593041Z comparisons. 2025-09-07T07:36:40.5593194Z 2025-09-07T07:36:40.5593376Z Given two numbers, check that they are approximately equal. 2025-09-07T07:36:40.5593896Z Approximately equal is defined as the number of significant digits 2025-09-07T07:36:40.5594318Z that agree. 2025-09-07T07:36:40.5594533Z 2025-09-07T07:36:40.5594636Z Parameters 2025-09-07T07:36:40.5594862Z ---------- 2025-09-07T07:36:40.5595068Z actual : scalar 2025-09-07T07:36:40.5595310Z The object to check. 2025-09-07T07:36:40.5595613Z desired : scalar 2025-09-07T07:36:40.5595845Z The expected object. 2025-09-07T07:36:40.5596125Z significant : int, optional 2025-09-07T07:36:40.5596425Z Desired precision, default is 7. 2025-09-07T07:36:40.5596740Z err_msg : str, optional 2025-09-07T07:36:40.5597045Z The error message to be printed in case of failure. 2025-09-07T07:36:40.5597416Z verbose : bool, optional 2025-09-07T07:36:40.5597794Z If True, the conflicting values are appended to the error message. 2025-09-07T07:36:40.5598114Z 2025-09-07T07:36:40.5598214Z Raises 2025-09-07T07:36:40.5598422Z ------ 2025-09-07T07:36:40.5598622Z AssertionError 2025-09-07T07:36:40.5598961Z If actual and desired are not equal up to specified precision. 2025-09-07T07:36:40.5599265Z 2025-09-07T07:36:40.5599362Z See Also 2025-09-07T07:36:40.5599571Z -------- 2025-09-07T07:36:40.5599917Z assert_allclose: Compare two array_like objects for equality with desired 2025-09-07T07:36:40.5600403Z relative and/or absolute precision. 2025-09-07T07:36:40.5600858Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2025-09-07T07:36:40.5601180Z 2025-09-07T07:36:40.5601276Z Examples 2025-09-07T07:36:40.5601468Z -------- 2025-09-07T07:36:40.5601699Z >>> np.testing.assert_approx_equal( 2025-09-07T07:36:40.5602018Z ... 0.12345677777777e-20, 0.1234567e-20 2025-09-07T07:36:40.5602323Z ... ) # doctest: +SKIP 2025-09-07T07:36:40.5602580Z >>> np.testing.assert_approx_equal( 2025-09-07T07:36:40.5602877Z ... 0.12345670e-20, 2025-09-07T07:36:40.5603135Z ... 0.12345671e-20, # doctest: +SKIP 2025-09-07T07:36:40.5603444Z ... significant=8, 2025-09-07T07:36:40.5603703Z ... ) 2025-09-07T07:36:40.5603933Z >>> np.testing.assert_approx_equal( 2025-09-07T07:36:40.5604236Z ... 0.12345670e-20, 2025-09-07T07:36:40.5604541Z ... 0.12345672e-20, # doctest: +SKIP 2025-09-07T07:36:40.5604838Z ... significant=8, 2025-09-07T07:36:40.5605079Z ... ) 2025-09-07T07:36:40.5605306Z Traceback (most recent call last): 2025-09-07T07:36:40.5605603Z ... 2025-09-07T07:36:40.5605803Z AssertionError: 2025-09-07T07:36:40.5606078Z Items are not equal to 8 significant digits: 2025-09-07T07:36:40.5606409Z ACTUAL: 1.234567e-21 2025-09-07T07:36:40.5606646Z DESIRED: 1.2345672e-21 2025-09-07T07:36:40.5606812Z 2025-09-07T07:36:40.5606968Z the evaluated condition that raises the exception is 2025-09-07T07:36:40.5607246Z 2025-09-07T07:36:40.5607435Z >>> abs(0.12345670e-20 / 1e-21 - 0.12345672e-20 / 1e-21) >= 10 ** -(8 - 1) 2025-09-07T07:36:40.5607825Z True 2025-09-07T07:36:40.5607935Z 2025-09-07T07:36:40.5607939Z 2025-09-07T07:36:40.5608202Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:40.5608565Z 2025-09-07T07:36:40.5609191Z 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=744. 2025-09-07T07:36:40.5610163Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:40.5610545Z 2025-09-07T07:36:40.5610768Z Raises an AssertionError if two array_like objects are not equal. 2025-09-07T07:36:40.5611089Z 2025-09-07T07:36:40.5611334Z Given two array_like objects, check that the shape is equal and all 2025-09-07T07:36:40.5611865Z elements of these objects are equal (but see the Notes for the special 2025-09-07T07:36:40.5612404Z handling of a scalar). An exception is raised at shape mismatch or 2025-09-07T07:36:40.5612948Z conflicting values. In contrast to the standard usage in numpy, NaNs 2025-09-07T07:36:40.5613510Z are compared like numbers, no assertion is raised if both objects have 2025-09-07T07:36:40.5613941Z NaNs in the same positions. 2025-09-07T07:36:40.5614132Z 2025-09-07T07:36:40.5614354Z The usual caution for verifying equality with floating point numbers is 2025-09-07T07:36:40.5614781Z advised. 2025-09-07T07:36:40.5614897Z 2025-09-07T07:36:40.5614993Z Parameters 2025-09-07T07:36:40.5615190Z ---------- 2025-09-07T07:36:40.5615402Z x : array_like 2025-09-07T07:36:40.5615689Z The actual object to check. 2025-09-07T07:36:40.5615967Z y : array_like 2025-09-07T07:36:40.5616197Z The desired, expected object. 2025-09-07T07:36:40.5616497Z err_msg : str, optional 2025-09-07T07:36:40.5616815Z The error message to be printed in case of failure. 2025-09-07T07:36:40.5617183Z verbose : bool, optional 2025-09-07T07:36:40.5617553Z If True, the conflicting values are appended to the error message. 2025-09-07T07:36:40.5617971Z strict : bool, optional 2025-09-07T07:36:40.5618334Z If True, raise an AssertionError when either the shape or the data 2025-09-07T07:36:40.5618827Z type of the array_like objects does not match. The special 2025-09-07T07:36:40.5619322Z handling for scalars mentioned in the Notes section is disabled. 2025-09-07T07:36:40.5619634Z 2025-09-07T07:36:40.5619713Z Raises 2025-09-07T07:36:40.5619910Z ------ 2025-09-07T07:36:40.5620114Z AssertionError 2025-09-07T07:36:40.5620383Z If actual and desired objects are not equal. 2025-09-07T07:36:40.5620625Z 2025-09-07T07:36:40.5620706Z See Also 2025-09-07T07:36:40.5620911Z -------- 2025-09-07T07:36:40.5621265Z assert_allclose: Compare two array_like objects for equality with desired 2025-09-07T07:36:40.5621752Z relative and/or absolute precision. 2025-09-07T07:36:40.5622193Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2025-09-07T07:36:40.5622526Z 2025-09-07T07:36:40.5622606Z Notes 2025-09-07T07:36:40.5622803Z ----- 2025-09-07T07:36:40.5623178Z When one of `x` and `y` is a scalar and the other is array_like, the 2025-09-07T07:36:40.5624376Z function checks that each element of the array_like object is equal to 2025-09-07T07:36:40.5625059Z the scalar. This behaviour can be disabled with the `strict` parameter. 2025-09-07T07:36:40.5625415Z 2025-09-07T07:36:40.5625498Z Examples 2025-09-07T07:36:40.5625712Z -------- 2025-09-07T07:36:40.5625951Z The first assert does not raise an exception: 2025-09-07T07:36:40.5626205Z 2025-09-07T07:36:40.5626318Z >>> np.testing.assert_array_equal( 2025-09-07T07:36:40.5626668Z ... [1.0, 2.33333, np.nan], [np.exp(0), 2.33333, np.nan] 2025-09-07T07:36:40.5627004Z ... ) 2025-09-07T07:36:40.5627119Z 2025-09-07T07:36:40.5627354Z Use `assert_allclose` or one of the nulp (number of floating point values) 2025-09-07T07:36:40.5627804Z functions for these cases instead: 2025-09-07T07:36:40.5628018Z 2025-09-07T07:36:40.5628126Z >>> np.testing.assert_allclose( 2025-09-07T07:36:40.5628525Z ... [1.0, np.pi, np.nan], [1, np.sqrt(np.pi) ** 2, np.nan], rtol=1e-10, atol=0 2025-09-07T07:36:40.5628924Z ... ) 2025-09-07T07:36:40.5629036Z 2025-09-07T07:36:40.5629241Z As mentioned in the Notes section, `assert_array_equal` has special 2025-09-07T07:36:40.5629795Z handling for scalars. Here the test checks that each value in `x` is 3: 2025-09-07T07:36:40.5630151Z 2025-09-07T07:36:40.5630307Z >>> x = np.full((2, 5), fill_value=3) 2025-09-07T07:36:40.5630634Z >>> np.testing.assert_array_equal(x, 3) 2025-09-07T07:36:40.5630853Z 2025-09-07T07:36:40.5631080Z Use `strict` to raise an AssertionError when comparing a scalar with an 2025-09-07T07:36:40.5631490Z array: 2025-09-07T07:36:40.5631620Z 2025-09-07T07:36:40.5631812Z >>> np.testing.assert_array_equal(x, 3, strict=True) 2025-09-07T07:36:40.5632193Z Traceback (most recent call last): 2025-09-07T07:36:40.5632494Z ... 2025-09-07T07:36:40.5632693Z AssertionError: 2025-09-07T07:36:40.5632940Z Arrays are not equal 2025-09-07T07:36:40.5633190Z 2025-09-07T07:36:40.5633418Z (shapes (2, 5), () mismatch) 2025-09-07T07:36:40.5633694Z x: torch.ndarray([[3, 3, 3, 3, 3], 2025-09-07T07:36:40.5633989Z [3, 3, 3, 3, 3]]) 2025-09-07T07:36:40.5634248Z y: torch.ndarray(3) 2025-09-07T07:36:40.5634476Z 2025-09-07T07:36:40.5634699Z The `strict` parameter also ensures that the array data types match: 2025-09-07T07:36:40.5635027Z 2025-09-07T07:36:40.5635126Z >>> x = np.array([2, 2, 2]) 2025-09-07T07:36:40.5635433Z >>> y = np.array([2.0, 2.0, 2.0], dtype=np.float32) 2025-09-07T07:36:40.5635872Z >>> np.testing.assert_array_equal(x, y, strict=True) 2025-09-07T07:36:40.5636242Z Traceback (most recent call last): 2025-09-07T07:36:40.5636518Z ... 2025-09-07T07:36:40.5636731Z AssertionError: 2025-09-07T07:36:40.5636968Z Arrays are not equal 2025-09-07T07:36:40.5637211Z 2025-09-07T07:36:40.5637470Z (dtypes dtype("int64"), dtype("float32") mismatch) 2025-09-07T07:36:40.5637833Z x: torch.ndarray([2, 2, 2]) 2025-09-07T07:36:40.5638118Z y: torch.ndarray([2., 2., 2.]) 2025-09-07T07:36:40.5638301Z 2025-09-07T07:36:40.5638561Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:40.5638931Z 2025-09-07T07:36:40.5639527Z 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=851. 2025-09-07T07:36:40.5640475Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:40.5640868Z 2025-09-07T07:36:40.5641086Z Raises an AssertionError if two objects are not equal up to desired 2025-09-07T07:36:40.5641512Z precision. 2025-09-07T07:36:40.5641633Z 2025-09-07T07:36:40.5641825Z .. note:: It is recommended to use one of `assert_allclose`, 2025-09-07T07:36:40.5642284Z `assert_array_almost_equal_nulp` or `assert_array_max_ulp` 2025-09-07T07:36:40.5642766Z instead of this function for more consistent floating point 2025-09-07T07:36:40.5643165Z comparisons. 2025-09-07T07:36:40.5643317Z 2025-09-07T07:36:40.5643563Z The test verifies identical shapes and that the elements of ``actual`` and 2025-09-07T07:36:40.5644011Z ``desired`` satisfy. 2025-09-07T07:36:40.5644192Z 2025-09-07T07:36:40.5644324Z ``abs(desired-actual) < 1.5 * 10**(-decimal)`` 2025-09-07T07:36:40.5644574Z 2025-09-07T07:36:40.5644799Z That is a looser test than originally documented, but agrees with what the 2025-09-07T07:36:40.5645386Z actual implementation did up to rounding vagaries. An exception is raised 2025-09-07T07:36:40.5645981Z at shape mismatch or conflicting values. In contrast to the standard usage 2025-09-07T07:36:40.5646558Z in numpy, NaNs are compared like numbers, no assertion is raised if both 2025-09-07T07:36:40.5647015Z objects have NaNs in the same positions. 2025-09-07T07:36:40.5647251Z 2025-09-07T07:36:40.5647339Z Parameters 2025-09-07T07:36:40.5647553Z ---------- 2025-09-07T07:36:40.5647768Z x : array_like 2025-09-07T07:36:40.5647997Z The actual object to check. 2025-09-07T07:36:40.5648278Z y : array_like 2025-09-07T07:36:40.5648521Z The desired, expected object. 2025-09-07T07:36:40.5648820Z decimal : int, optional 2025-09-07T07:36:40.5649084Z Desired precision, default is 6. 2025-09-07T07:36:40.5649391Z err_msg : str, optional 2025-09-07T07:36:40.5649704Z The error message to be printed in case of failure. 2025-09-07T07:36:40.5650105Z verbose : bool, optional 2025-09-07T07:36:40.5650466Z If True, the conflicting values are appended to the error message. 2025-09-07T07:36:40.5650792Z 2025-09-07T07:36:40.5650875Z Raises 2025-09-07T07:36:40.5651075Z ------ 2025-09-07T07:36:40.5651286Z AssertionError 2025-09-07T07:36:40.5651608Z If actual and desired are not equal up to specified precision. 2025-09-07T07:36:40.5651957Z 2025-09-07T07:36:40.5652042Z See Also 2025-09-07T07:36:40.5652249Z -------- 2025-09-07T07:36:40.5652603Z assert_allclose: Compare two array_like objects for equality with desired 2025-09-07T07:36:40.5653076Z relative and/or absolute precision. 2025-09-07T07:36:40.5653522Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2025-09-07T07:36:40.5653851Z 2025-09-07T07:36:40.5653933Z Examples 2025-09-07T07:36:40.5654134Z -------- 2025-09-07T07:36:40.5654373Z the first assert does not raise an exception 2025-09-07T07:36:40.5654616Z 2025-09-07T07:36:40.5654862Z >>> np.testing.assert_array_almost_equal([1.0, 2.333, np.nan], [1.0, 2.333, np.nan]) 2025-09-07T07:36:40.5655243Z 2025-09-07T07:36:40.5655361Z >>> np.testing.assert_array_almost_equal( 2025-09-07T07:36:40.5655770Z ... [1.0, 2.33333, np.nan], [1.0, 2.33339, np.nan], decimal=5 2025-09-07T07:36:40.5656122Z ... ) 2025-09-07T07:36:40.5656337Z Traceback (most recent call last): 2025-09-07T07:36:40.5656631Z ... 2025-09-07T07:36:40.5656841Z AssertionError: 2025-09-07T07:36:40.5657090Z Arrays are not almost equal to 5 decimals 2025-09-07T07:36:40.5657409Z 2025-09-07T07:36:40.5657646Z Mismatched elements: 1 / 3 (33.3%) 2025-09-07T07:36:40.5657981Z Max absolute difference: 5.999999999994898e-05 2025-09-07T07:36:40.5658342Z Max relative difference: 2.5713661239633743e-05 2025-09-07T07:36:40.5658752Z x: torch.ndarray([1.0000, 2.3333, nan], dtype=float64) 2025-09-07T07:36:40.5659189Z y: torch.ndarray([1.0000, 2.3334, nan], dtype=float64) 2025-09-07T07:36:40.5659456Z 2025-09-07T07:36:40.5659587Z >>> np.testing.assert_array_almost_equal( 2025-09-07T07:36:40.5659955Z ... [1.0, 2.33333, np.nan], [1.0, 2.33333, 5], decimal=5 2025-09-07T07:36:40.5660275Z ... ) 2025-09-07T07:36:40.5660498Z Traceback (most recent call last): 2025-09-07T07:36:40.5660788Z ... 2025-09-07T07:36:40.5660995Z AssertionError: 2025-09-07T07:36:40.5661244Z Arrays are not almost equal to 5 decimals 2025-09-07T07:36:40.5661572Z 2025-09-07T07:36:40.5661807Z x and y nan location mismatch: 2025-09-07T07:36:40.5662166Z x: torch.ndarray([1.0000, 2.3333, nan], dtype=float64) 2025-09-07T07:36:40.5662588Z y: torch.ndarray([1.0000, 2.3333, 5.0000], dtype=float64) 2025-09-07T07:36:40.5662867Z 2025-09-07T07:36:40.5662870Z 2025-09-07T07:36:40.5663122Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:40.5663537Z 2025-09-07T07:36:40.5664151Z 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=1848. 2025-09-07T07:36:40.5665113Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:40.5665715Z Context manager that resets warning registry for catching warnings 2025-09-07T07:36:40.5666042Z 2025-09-07T07:36:40.5666296Z Warnings can be slippery, because, whenever a warning is triggered, Python 2025-09-07T07:36:40.5666874Z adds a ``__warningregistry__`` member to the *calling* module. This makes 2025-09-07T07:36:40.5667465Z it impossible to retrigger the warning in this module, whatever you put in 2025-09-07T07:36:40.5668072Z the warnings filters. This context manager accepts a sequence of `modules` 2025-09-07T07:36:40.5668579Z as a keyword argument to its constructor and: 2025-09-07T07:36:40.5668827Z 2025-09-07T07:36:40.5669069Z * stores and removes any ``__warningregistry__`` entries in given `modules` 2025-09-07T07:36:40.5669500Z on entry; 2025-09-07T07:36:40.5669826Z * resets ``__warningregistry__`` to its previous state on exit. 2025-09-07T07:36:40.5670160Z 2025-09-07T07:36:40.5670393Z This makes it possible to trigger any warning afresh inside the context 2025-09-07T07:36:40.5670928Z manager without disturbing the state of warnings outside. 2025-09-07T07:36:40.5671224Z 2025-09-07T07:36:40.5671458Z For compatibility with Python 3.0, please consider all arguments to be 2025-09-07T07:36:40.5671939Z keyword-only. 2025-09-07T07:36:40.5672099Z 2025-09-07T07:36:40.5672192Z Parameters 2025-09-07T07:36:40.5672420Z ---------- 2025-09-07T07:36:40.5672643Z record : bool, optional 2025-09-07T07:36:40.5673007Z Specifies whether warnings should be captured by a custom 2025-09-07T07:36:40.5673543Z implementation of ``warnings.showwarning()`` and be appended to a list 2025-09-07T07:36:40.5674107Z returned by the context manager. Otherwise None is returned by the 2025-09-07T07:36:40.5674741Z context manager. The objects appended to the list are arguments whose 2025-09-07T07:36:40.5675248Z attributes mirror the arguments to ``showwarning()``. 2025-09-07T07:36:40.5675644Z modules : sequence, optional 2025-09-07T07:36:40.5676097Z Sequence of modules for which to reset warnings registry on entry and 2025-09-07T07:36:40.5676634Z restore on exit. To work correctly, all 'ignore' filters should 2025-09-07T07:36:40.5677045Z filter by one of these modules. 2025-09-07T07:36:40.5677269Z 2025-09-07T07:36:40.5677358Z Examples 2025-09-07T07:36:40.5677575Z -------- 2025-09-07T07:36:40.5677803Z >>> import warnings 2025-09-07T07:36:40.5678139Z >>> with np.testing.clear_and_catch_warnings( # doctest: +SKIP 2025-09-07T07:36:40.5678556Z ... modules=[np.core.fromnumeric] 2025-09-07T07:36:40.5678865Z ... ): 2025-09-07T07:36:40.5679110Z ... warnings.simplefilter("always") 2025-09-07T07:36:40.5679547Z ... warnings.filterwarnings("ignore", module="np.core.fromnumeric") 2025-09-07T07:36:40.5680066Z ... # do something that raises a warning but ignore those in 2025-09-07T07:36:40.5680461Z ... # np.core.fromnumeric 2025-09-07T07:36:40.5680740Z 2025-09-07T07:36:40.5681096Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:40.5681474Z 2025-09-07T07:36:40.7305191Z 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. 2025-09-07T07:36:40.7306180Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:40.7306780Z Applies a 1D convolution over a quantized input signal composed of 2025-09-07T07:36:40.7307217Z several quantized input planes. 2025-09-07T07:36:40.7307425Z 2025-09-07T07:36:40.7307796Z For details on input arguments, parameters, and implementation see 2025-09-07T07:36:40.7308221Z :class:`~torch.nn.Conv1d`. 2025-09-07T07:36:40.7308423Z 2025-09-07T07:36:40.7308522Z .. note:: 2025-09-07T07:36:40.7308853Z Only `zeros` is supported for the :attr:`padding_mode` argument. 2025-09-07T07:36:40.7309168Z 2025-09-07T07:36:40.7309263Z .. note:: 2025-09-07T07:36:40.7309574Z Only `torch.quint8` is supported for the input data type. 2025-09-07T07:36:40.7309862Z 2025-09-07T07:36:40.7309867Z 2025-09-07T07:36:40.7309953Z Attributes: 2025-09-07T07:36:40.7310305Z weight (Tensor): packed tensor derived from the learnable weight 2025-09-07T07:36:40.7310737Z parameter. 2025-09-07T07:36:40.7311091Z scale (Tensor): scalar for the output scale 2025-09-07T07:36:40.7311500Z zero_point (Tensor): scalar for the output zero point 2025-09-07T07:36:40.7311787Z 2025-09-07T07:36:40.7311939Z See :class:`~torch.nn.Conv1d` for other attributes. 2025-09-07T07:36:40.7312210Z 2025-09-07T07:36:40.7312305Z Examples:: 2025-09-07T07:36:40.7312442Z 2025-09-07T07:36:40.7312608Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_QENGINE) 2025-09-07T07:36:40.7313003Z >>> m = nn.quantized.Conv1d(16, 33, 3, stride=2) 2025-09-07T07:36:40.7313416Z >>> input = torch.randn(20, 16, 100) 2025-09-07T07:36:40.7313753Z >>> # quantize input to quint8 2025-09-07T07:36:40.7314067Z >>> # xdoctest: +SKIP 2025-09-07T07:36:40.7314514Z >>> q_input = torch.quantize_per_tensor(input, scale=1.0, zero_point=0, 2025-09-07T07:36:40.7315062Z ... dtype=torch.quint8) 2025-09-07T07:36:40.7315412Z >>> output = m(q_input) 2025-09-07T07:36:40.7315609Z 2025-09-07T07:36:40.7315688Z 2025-09-07T07:36:40.7316055Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:40.7316424Z 2025-09-07T07:36:40.7493546Z 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=12. 2025-09-07T07:36:40.7494535Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:40.7495050Z A quantized long short-term memory (LSTM). 2025-09-07T07:36:40.7495301Z 2025-09-07T07:36:40.7495658Z For the description and the argument types, please, refer to :class:`~torch.nn.LSTM` 2025-09-07T07:36:40.7496060Z 2025-09-07T07:36:40.7505007Z Attributes: 2025-09-07T07:36:40.7505340Z layers : instances of the `_LSTMLayer` 2025-09-07T07:36:40.7505596Z 2025-09-07T07:36:40.7505698Z .. note:: 2025-09-07T07:36:40.7506056Z To access the weights and biases, you need to access them per layer. 2025-09-07T07:36:40.7506573Z See examples in :class:`~torch.ao.nn.quantizable.LSTM` 2025-09-07T07:36:40.7506858Z 2025-09-07T07:36:40.7506963Z Examples:: 2025-09-07T07:36:40.7507192Z >>> # xdoctest: +SKIP 2025-09-07T07:36:40.7507487Z >>> custom_module_config = { 2025-09-07T07:36:40.7507837Z ... 'float_to_observed_custom_module_class': { 2025-09-07T07:36:40.7508215Z ... nn.LSTM: nn.quantizable.LSTM, 2025-09-07T07:36:40.7508528Z ... }, 2025-09-07T07:36:40.7508826Z ... 'observed_to_quantized_custom_module_class': { 2025-09-07T07:36:40.7509239Z ... nn.quantizable.LSTM: nn.quantized.LSTM, 2025-09-07T07:36:40.7509601Z ... } 2025-09-07T07:36:40.7509817Z ... } 2025-09-07T07:36:40.7510170Z >>> tq.prepare(model, prepare_custom_module_class=custom_module_config) 2025-09-07T07:36:40.7510730Z >>> tq.convert(model, convert_custom_module_class=custom_module_config) 2025-09-07T07:36:40.7511151Z 2025-09-07T07:36:40.7511528Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:40.7511895Z 2025-09-07T07:36:40.8075815Z msg = Cannot scrape callname=ActivationSparsifier in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/pruning/_experimental/activation_sparsifier/activation_sparsifier.py line=16. 2025-09-07T07:36:40.8077064Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:40.8077445Z 2025-09-07T07:36:40.8077705Z The Activation sparsifier class aims to sparsify/prune activations in a neural 2025-09-07T07:36:40.8078314Z network. The idea is to attach the sparsifier to a layer (or layers) and it 2025-09-07T07:36:40.8078905Z zeroes out the activations based on the mask_fn (or sparsification function) 2025-09-07T07:36:40.8079369Z input by the user. 2025-09-07T07:36:40.8079728Z The mask_fn is applied once all the inputs are aggregated and reduced i.e. 2025-09-07T07:36:40.8080230Z mask = mask_fn(reduce_fn(aggregate_fn(activations))) 2025-09-07T07:36:40.8080505Z 2025-09-07T07:36:40.8080597Z Note:: 2025-09-07T07:36:40.8081037Z The sparsification mask is computed on the input **before it goes through the attached layer**. 2025-09-07T07:36:40.8081476Z 2025-09-07T07:36:40.8081570Z Args: 2025-09-07T07:36:40.8081772Z model (nn.Module): 2025-09-07T07:36:40.8082159Z The model whose layers will be sparsified. The layers that needs to be 2025-09-07T07:36:40.8082750Z sparsified should be added separately using the register_layer() function 2025-09-07T07:36:40.8083313Z aggregate_fn (Optional, Callable): 2025-09-07T07:36:40.8083784Z default aggregate_fn that is used if not specified while registering the layer. 2025-09-07T07:36:40.8084339Z specifies how inputs should be aggregated over time. 2025-09-07T07:36:40.8084964Z The aggregate_fn should usually take 2 torch tensors and return the aggregated tensor. 2025-09-07T07:36:40.8085461Z Example 2025-09-07T07:36:40.8085779Z def add_agg_fn(tensor1, tensor2): return tensor1 + tensor2 2025-09-07T07:36:40.8086178Z reduce_fn (Optional, Callable): 2025-09-07T07:36:40.8086654Z default reduce_fn that is used if not specified while registering the layer. 2025-09-07T07:36:40.8087286Z reduce_fn will be called on the aggregated tensor i.e. the tensor obtained after 2025-09-07T07:36:40.8087786Z calling agg_fn() on all inputs. 2025-09-07T07:36:40.8088097Z Example 2025-09-07T07:36:40.8088452Z def mean_reduce_fn(agg_tensor): return agg_tensor.mean(dim=0) 2025-09-07T07:36:40.8088934Z mask_fn (Optional, Callable): 2025-09-07T07:36:40.8089469Z default mask_fn that is used to create the sparsification mask using the tensor obtained after 2025-09-07T07:36:40.8090144Z calling the reduce_fn(). This is used by default if a custom one is passed in the 2025-09-07T07:36:40.8090622Z register_layer(). 2025-09-07T07:36:40.8091171Z Note that the mask_fn() definition should contain the sparse arguments that is passed in sparse_config 2025-09-07T07:36:40.8091737Z arguments. 2025-09-07T07:36:40.8092024Z features (Optional, list): 2025-09-07T07:36:40.8092362Z default selected features to sparsify. 2025-09-07T07:36:40.8092873Z If this is non-empty, then the mask_fn will be applied for each feature of the input. 2025-09-07T07:36:40.8093366Z For example, 2025-09-07T07:36:40.8093800Z mask = [mask_fn(reduce_fn(aggregated_fn(input[feature])) for feature in features] 2025-09-07T07:36:40.8094282Z feature_dim (Optional, int): 2025-09-07T07:36:40.8094782Z default dimension of input features. Again, features along this dim will be chosen 2025-09-07T07:36:40.8095288Z for sparsification. 2025-09-07T07:36:40.8095601Z sparse_config (Dict): 2025-09-07T07:36:40.8096004Z Default configuration for the mask_fn. This config will be passed 2025-09-07T07:36:40.8096447Z with the mask_fn() 2025-09-07T07:36:40.8096655Z 2025-09-07T07:36:40.8096742Z Example: 2025-09-07T07:36:40.8097049Z >>> # xdoctest: +SKIP 2025-09-07T07:36:40.8097664Z >>> model = SomeModel() 2025-09-07T07:36:40.8098335Z >>> act_sparsifier = ActivationSparsifier(...) # init activation sparsifier 2025-09-07T07:36:40.8098813Z >>> # Initialize aggregate_fn 2025-09-07T07:36:40.8099117Z >>> def agg_fn(x, y): 2025-09-07T07:36:40.8099381Z >>> return x + y 2025-09-07T07:36:40.8099618Z >>> 2025-09-07T07:36:40.8099842Z >>> # Initialize reduce_fn 2025-09-07T07:36:40.8100131Z >>> def reduce_fn(x): 2025-09-07T07:36:40.8100410Z >>> return torch.mean(x, dim=0) 2025-09-07T07:36:40.8100706Z >>> 2025-09-07T07:36:40.8100923Z >>> # Initialize mask_fn 2025-09-07T07:36:40.8101205Z >>> def mask_fn(data): 2025-09-07T07:36:40.8101519Z >>> return torch.eye(data.shape).to(data.device) 2025-09-07T07:36:40.8101846Z >>> 2025-09-07T07:36:40.8102047Z >>> 2025-09-07T07:36:40.8102280Z >>> act_sparsifier.register_layer( 2025-09-07T07:36:40.8102588Z ... model.some_layer, 2025-09-07T07:36:40.8102880Z ... aggregate_fn=agg_fn, 2025-09-07T07:36:40.8103176Z ... reduce_fn=reduce_fn, 2025-09-07T07:36:40.8103466Z ... mask_fn=mask_fn, 2025-09-07T07:36:40.8103716Z ... ) 2025-09-07T07:36:40.8103975Z >>> 2025-09-07T07:36:40.8104195Z >>> # start training process 2025-09-07T07:36:40.8104485Z >>> for _ in [...]: 2025-09-07T07:36:40.8104727Z >>> # epoch starts 2025-09-07T07:36:40.8105060Z >>> # model.forward(), compute_loss() and model.backwards() 2025-09-07T07:36:40.8105445Z >>> # epoch ends 2025-09-07T07:36:40.8105748Z >>> act_sparsifier.step() 2025-09-07T07:36:40.8106040Z >>> # end training process 2025-09-07T07:36:40.8106337Z >>> sparsifier.squash_mask() 2025-09-07T07:36:40.8106543Z 2025-09-07T07:36:40.8106795Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:40.8107164Z 2025-09-07T07:36:40.8108039Z msg = Cannot scrape callname=BaseDataScheduler.get_schedule_param in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/pruning/_experimental/data_scheduler/base_data_scheduler.py line=91. 2025-09-07T07:36:40.8109253Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:40.8109631Z 2025-09-07T07:36:40.8109846Z Abstract method that needs to be implemented by the child class. 2025-09-07T07:36:40.8110449Z The expected return type should is a dictionary of name to schedule_param value 2025-09-07T07:36:40.8111100Z The returned values will be updated in sparsifier when the scheduler step() function 2025-09-07T07:36:40.8111588Z is called. 2025-09-07T07:36:40.8111708Z 2025-09-07T07:36:40.8111806Z Example: 2025-09-07T07:36:40.8112027Z >>> def get_schedule_param(self): 2025-09-07T07:36:40.8112342Z ... new_param = {} 2025-09-07T07:36:40.8112671Z ... for name in self.sparsifier.data_groups.keys(): 2025-09-07T07:36:40.8113047Z ... new_param[name] = ( 2025-09-07T07:36:40.8113450Z ... self.sparsifier.data_groups[name][self.schedule_param] * 0.5 2025-09-07T07:36:40.8113867Z ... ) 2025-09-07T07:36:40.8114110Z ... return new_param 2025-09-07T07:36:40.8114284Z 2025-09-07T07:36:40.8114724Z When the step() function is called, the value in self.sparsifier.data_groups[name][self.schedule_param] 2025-09-07T07:36:40.8115291Z would be halved 2025-09-07T07:36:40.8115429Z 2025-09-07T07:36:40.8115680Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:40.8116060Z 2025-09-07T07:36:40.8321555Z 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=229. 2025-09-07T07:36:40.8322657Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:40.8323209Z Squashes the sparse masks into the appropriate tensors. 2025-09-07T07:36:40.8323489Z 2025-09-07T07:36:40.8323963Z If either the `params_to_keep` or `params_to_keep_per_layer` is set, 2025-09-07T07:36:40.8324483Z the module will have a `sparse_params` dict attached to it. 2025-09-07T07:36:40.8324778Z 2025-09-07T07:36:40.8324862Z Args: 2025-09-07T07:36:40.8325188Z params_to_keep: List of keys to save in the module or a dict 2025-09-07T07:36:40.8325657Z representing the modules and keys that will have 2025-09-07T07:36:40.8326062Z sparsity parameters saved 2025-09-07T07:36:40.8326504Z params_to_keep_per_layer: Dict to specify the params that should be 2025-09-07T07:36:40.8326998Z saved for specific layers. The keys in the dict 2025-09-07T07:36:40.8327431Z should be the module fqn, while the values should 2025-09-07T07:36:40.8327874Z be a list of strings with the names of the variables 2025-09-07T07:36:40.8328286Z to save in the `sparse_params` 2025-09-07T07:36:40.8328522Z 2025-09-07T07:36:40.8328615Z Examples: 2025-09-07T07:36:40.8328897Z >>> # xdoctest: +SKIP("locals are undefined") 2025-09-07T07:36:40.8329263Z >>> # Don't save any sparse params 2025-09-07T07:36:40.8329665Z >>> sparsifier.squash_mask() 2025-09-07T07:36:40.8330016Z >>> hasattr(model.submodule1, "sparse_params") 2025-09-07T07:36:40.8330361Z False 2025-09-07T07:36:40.8330498Z 2025-09-07T07:36:40.8330630Z >>> # Keep sparse params per layer 2025-09-07T07:36:40.8331037Z >>> sparsifier.squash_mask( 2025-09-07T07:36:40.8331357Z ... params_to_keep_per_layer={ 2025-09-07T07:36:40.8331721Z ... "submodule1.linear1": ("foo", "bar"), 2025-09-07T07:36:40.8332101Z ... "submodule2.linear42": ("baz",), 2025-09-07T07:36:40.8332427Z ... } 2025-09-07T07:36:40.8332663Z ... ) 2025-09-07T07:36:40.8332948Z >>> print(model.submodule1.linear1.sparse_params) 2025-09-07T07:36:40.8333321Z {'foo': 42, 'bar': 24} 2025-09-07T07:36:40.8333697Z >>> print(model.submodule2.linear42.sparse_params) 2025-09-07T07:36:40.8334061Z {'baz': 0.1} 2025-09-07T07:36:40.8334220Z 2025-09-07T07:36:40.8334344Z >>> # Keep sparse params for all layers 2025-09-07T07:36:40.8334803Z >>> sparsifier.squash_mask(params_to_keep=("foo", "bar")) 2025-09-07T07:36:40.8335253Z >>> print(model.submodule1.linear1.sparse_params) 2025-09-07T07:36:40.8335623Z {'foo': 42, 'bar': 24} 2025-09-07T07:36:40.8335962Z >>> print(model.submodule2.linear42.sparse_params) 2025-09-07T07:36:40.8336337Z {'foo': 42, 'bar': 24} 2025-09-07T07:36:40.8336536Z 2025-09-07T07:36:40.8336735Z >>> # Keep some sparse params for all layers, and specific ones for 2025-09-07T07:36:40.8337160Z >>> # some other layers 2025-09-07T07:36:40.8337474Z >>> sparsifier.squash_mask( 2025-09-07T07:36:40.8337805Z ... params_to_keep=("foo", "bar"), 2025-09-07T07:36:40.8338239Z ... params_to_keep_per_layer={"submodule2.linear42": ("baz",)}, 2025-09-07T07:36:40.8338643Z ... ) 2025-09-07T07:36:40.8338941Z >>> print(model.submodule1.linear1.sparse_params) 2025-09-07T07:36:40.8339301Z {'foo': 42, 'bar': 24} 2025-09-07T07:36:40.8339650Z >>> print(model.submodule2.linear42.sparse_params) 2025-09-07T07:36:40.8340034Z {'foo': 42, 'bar': 24, 'baz': 0.1} 2025-09-07T07:36:40.8340341Z 2025-09-07T07:36:40.8340701Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:40.8341082Z 2025-09-07T07:36:40.9108737Z 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. 2025-09-07T07:36:40.9109822Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:40.9110391Z 2025-09-07T07:36:40.9110660Z Config object that specifies the supported data types passed as arguments to 2025-09-07T07:36:40.9111265Z quantize ops in the reference model spec, for input and output activations, 2025-09-07T07:36:40.9111737Z weights, and biases. 2025-09-07T07:36:40.9111910Z 2025-09-07T07:36:40.9112071Z For example, consider the following reference model: 2025-09-07T07:36:40.9112341Z 2025-09-07T07:36:40.9112549Z quant1 - [dequant1 - fp32_linear - quant2] - dequant2 2025-09-07T07:36:40.9112819Z 2025-09-07T07:36:40.9113044Z The pattern in the square brackets refers to the reference pattern of 2025-09-07T07:36:40.9113596Z statically quantized linear. Setting the input dtype as `torch.quint8` 2025-09-07T07:36:40.9114179Z in the DTypeConfig means we pass in `torch.quint8` as the dtype argument 2025-09-07T07:36:40.9114805Z to the first quantize op (quant1). Similarly, setting the output dtype as 2025-09-07T07:36:40.9115367Z `torch.quint8` means we pass in `torch.quint8` as the dtype argument to 2025-09-07T07:36:40.9115820Z the second quantize op (quant2). 2025-09-07T07:36:40.9116015Z 2025-09-07T07:36:40.9116230Z Note that the dtype here does not refer to the interface dtypes of the 2025-09-07T07:36:40.9116842Z op. For example, the "input dtype" here is not the dtype of the input 2025-09-07T07:36:40.9117389Z tensor passed to the quantized linear op. Though it can still be the 2025-09-07T07:36:40.9117920Z same as the interface dtype, this is not always the case, e.g. the 2025-09-07T07:36:40.9118499Z interface dtype is fp32 in dynamic quantization but the "input dtype" 2025-09-07T07:36:40.9119051Z specified in the DTypeConfig would still be quint8. The semantics of 2025-09-07T07:36:40.9119592Z dtypes here are the same as the semantics of the dtypes specified in 2025-09-07T07:36:40.9120019Z the observers. 2025-09-07T07:36:40.9120153Z 2025-09-07T07:36:40.9120374Z These dtypes are matched against the ones specified in the user's 2025-09-07T07:36:40.9120906Z QConfig. If there is a match, and the QConfig satisfies the constraints 2025-09-07T07:36:40.9121461Z specified in the DTypeConfig (if any), then we will quantize the given 2025-09-07T07:36:40.9122023Z pattern using this DTypeConfig. Otherwise, the QConfig is ignored and 2025-09-07T07:36:40.9122474Z the pattern will not be quantized. 2025-09-07T07:36:40.9122677Z 2025-09-07T07:36:40.9122843Z Example usage:: 2025-09-07T07:36:40.9122993Z 2025-09-07T07:36:40.9123096Z >>> # xdoctest: +SKIP(failing) 2025-09-07T07:36:40.9123405Z >>> dtype_config1 = DTypeConfig( 2025-09-07T07:36:40.9123989Z ... input_dtype=torch.quint8, 2025-09-07T07:36:40.9124346Z ... output_dtype=torch.quint8, 2025-09-07T07:36:40.9124672Z ... weight_dtype=torch.qint8, 2025-09-07T07:36:40.9124993Z ... bias_dtype=torch.float) 2025-09-07T07:36:40.9125225Z 2025-09-07T07:36:40.9125350Z >>> dtype_config2 = DTypeConfig( 2025-09-07T07:36:40.9125690Z ... input_dtype=DTypeWithConstraints( 2025-09-07T07:36:40.9126021Z ... dtype=torch.quint8, 2025-09-07T07:36:40.9126323Z ... quant_min_lower_bound=0, 2025-09-07T07:36:40.9126649Z ... quant_max_upper_bound=255, 2025-09-07T07:36:40.9126955Z ... ), 2025-09-07T07:36:40.9127202Z ... output_dtype=DTypeWithConstraints( 2025-09-07T07:36:40.9127542Z ... dtype=torch.quint8, 2025-09-07T07:36:40.9127845Z ... quant_min_lower_bound=0, 2025-09-07T07:36:40.9128167Z ... quant_max_upper_bound=255, 2025-09-07T07:36:40.9128456Z ... ), 2025-09-07T07:36:40.9128735Z ... weight_dtype=DTypeWithConstraints( 2025-09-07T07:36:40.9129074Z ... dtype=torch.qint8, 2025-09-07T07:36:40.9129381Z ... quant_min_lower_bound=-128, 2025-09-07T07:36:40.9129704Z ... quant_max_upper_bound=127, 2025-09-07T07:36:40.9130004Z ... ), 2025-09-07T07:36:40.9130240Z ... bias_dtype=torch.float) 2025-09-07T07:36:40.9130438Z 2025-09-07T07:36:40.9130619Z >>> dtype_config1.input_dtype 2025-09-07T07:36:40.9130896Z torch.quint8 2025-09-07T07:36:40.9131047Z 2025-09-07T07:36:40.9131149Z >>> dtype_config2.input_dtype 2025-09-07T07:36:40.9131440Z torch.quint8 2025-09-07T07:36:40.9131580Z 2025-09-07T07:36:40.9131727Z >>> dtype_config2.input_dtype_with_constraints 2025-09-07T07:36:40.9132520Z DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=0, quant_max_upper_bound=255, scale_min_lower_bound=None, scale_max_upper_bound=None) 2025-09-07T07:36:40.9133180Z 2025-09-07T07:36:40.9133437Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:40.9133816Z 2025-09-07T07:36:41.1313013Z msg = Cannot scrape callname=ModelReport in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fx/_model_report/model_report.py line=24. 2025-09-07T07:36:41.1314154Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:41.1314601Z 2025-09-07T07:36:41.1314925Z The ModelReport class aims to provide users an easy way to diagnose issues that they run into 2025-09-07T07:36:41.1315654Z with their models. The class works with all traceable GraphModules to help diagnose issues, 2025-09-07T07:36:41.1316562Z though the requirements on the type of model more-so depends on the specific report the user 2025-09-07T07:36:41.1317279Z is trying to generate. With respect to the reports, the ModelReport class is initialized with 2025-09-07T07:36:41.1317982Z a set of Detector classes, each of which generate reports on quantization configuration 2025-09-07T07:36:41.1318558Z issues a use might have. 2025-09-07T07:36:41.1318729Z 2025-09-07T07:36:41.1318870Z Currently supports generating reports on: 2025-09-07T07:36:41.1319339Z - Suggestions for per-channel vs. per-tensor quantization (nn.Module) 2025-09-07T07:36:41.1319941Z - Suggestions for dynamic vs static quantization for linear layers (Graph Modules) 2025-09-07T07:36:41.1320623Z - Suggestions for input-weight equalization for linear and conv layers (Graph Modules) 2025-09-07T07:36:41.1321247Z - Suggestions for outlier detection for all layers (Graph Modules) 2025-09-07T07:36:41.1321571Z 2025-09-07T07:36:41.1321978Z The ModelReport class has the primary functionality of inserting observers (primarily the ModelReportObserver) 2025-09-07T07:36:41.1322911Z where needed for each detector to gather the information it needs, and then after calibration, the ModelReport 2025-09-07T07:36:41.1324050Z class compiles the report generated by each Detector class into a single report to return to the user. It also 2025-09-07T07:36:41.1324776Z has the capability to remove all the observers it inserted as well. 2025-09-07T07:36:41.1325114Z 2025-09-07T07:36:41.1325395Z * :attr:`_model` The model we wish to generate the report for. Must be a traceable GraphModule 2025-09-07T07:36:41.1325799Z 2025-09-07T07:36:41.1326187Z * :attr:`_desired_report_detectors` The set of Detectors representing desired reports from the ModelReport class 2025-09-07T07:36:41.1327005Z Make sure that these are all unique types of detectors [do not have more than 1 of the same class] 2025-09-07T07:36:41.1327438Z 2025-09-07T07:36:41.1327725Z * :attr:`_desired_detector_names` The set of detector names of the _desired_report_detectors. 2025-09-07T07:36:41.1328407Z This set is generated by calling the get_detector_name() of each detector 2025-09-07T07:36:41.1328767Z 2025-09-07T07:36:41.1329100Z * :attr:`_detector_name_to_observer_fqns` The mapping from each detector to fqns of observers of interest 2025-09-07T07:36:41.1329880Z The purpose of this is to keep track of what observers were inserted for each detector, so that they 2025-09-07T07:36:41.1330437Z can be removed at the end if desired 2025-09-07T07:36:41.1330655Z 2025-09-07T07:36:41.1331040Z * :attr:`_prepared_flag` A boolean flag that keeps track of whether we have prepared the model or not 2025-09-07T07:36:41.1331717Z This is to ensure we only insert observers once with the ModelReport instance 2025-09-07T07:36:41.1332130Z 2025-09-07T07:36:41.1332386Z * :attr:`_removed_observers` A boolean to track if we have removed observers already 2025-09-07T07:36:41.1333061Z The purpose is to ensure we don't attempt to remove observers twice with the same ModelReport 2025-09-07T07:36:41.1333799Z instance. This also allows the functionality where we can generate the report multiple times 2025-09-07T07:36:41.1334391Z as long as we haven't removed the observers yet. 2025-09-07T07:36:41.1334641Z 2025-09-07T07:36:41.1334724Z Note: 2025-09-07T07:36:41.1335150Z This class was initially designed to work with the Fx Graph Mode workflow in mind. However, 2025-09-07T07:36:41.1335886Z full functionality is available as long as there is a traceable GraphModule that is being used. 2025-09-07T07:36:41.1336623Z One method to get a traceable GraphModule without going through the Fx workflow is to use 2025-09-07T07:36:41.1337156Z the QuantizationTracer class. 2025-09-07T07:36:41.1337357Z 2025-09-07T07:36:41.1337464Z General Flow for Fx workflow: 2025-09-07T07:36:41.1338044Z 1.) Initialize ModelReport object with reports of interest by passing in initialized detector objects and model 2025-09-07T07:36:41.1338681Z 2.) Prepare your model with prepare_fx 2025-09-07T07:36:41.1339194Z 3.) Call model_report.prepare_detailed_calibration to add relevant observers 2025-09-07T07:36:41.1339673Z 4.) Calibrate your model with data 2025-09-07T07:36:41.1340229Z 5.) Call model_report.generate_report on your model to generate report and optionally remove added observers 2025-09-07T07:36:41.1340832Z Optional 2025-09-07T07:36:41.1341222Z 6.) Call model_report.generate_visualizer to get a ModelReportVisualizer instance 2025-09-07T07:36:41.1341859Z 7.) To help in parsing report information and debugging, view report info as a: 2025-09-07T07:36:41.1342306Z - Table 2025-09-07T07:36:41.1342532Z - Histogram 2025-09-07T07:36:41.1342768Z - Line plot 2025-09-07T07:36:41.1343231Z 8.) Call model_report.generate_qconfigs to generate the qconfigs based on the report suggestions 2025-09-07T07:36:41.1343667Z 2025-09-07T07:36:41.1343780Z Example (with QuantizationTracer): 2025-09-07T07:36:41.1344095Z >>> # xdoctest: +SKIP 2025-09-07T07:36:41.1344376Z >>> # get the necessary qconfig 2025-09-07T07:36:41.1344702Z >>> config = PrepareCustomConfig() 2025-09-07T07:36:41.1345114Z >>> skipped_module_names, skipped_module_classes = ( 2025-09-07T07:36:41.1345546Z ... get_skipped_module_name_and_classes(config, False) 2025-09-07T07:36:41.1345918Z ... ) 2025-09-07T07:36:41.1346036Z 2025-09-07T07:36:41.1346182Z >>> # initialize our model and get GraphModule 2025-09-07T07:36:41.1346518Z >>> model = SomeModel() 2025-09-07T07:36:41.1346949Z >>> tracer = QuantizationTracer(skipped_module_names, skipped_module_classes) 2025-09-07T07:36:41.1347495Z >>> graph_module = GraphModule(model, tracer.trace(model)) 2025-09-07T07:36:41.1347774Z 2025-09-07T07:36:41.1347961Z >>> # get our set of detectors and ModelReport instance 2025-09-07T07:36:41.1348329Z >>> detector_set = set( 2025-09-07T07:36:41.1348576Z ... [ 2025-09-07T07:36:41.1348841Z ... DynamicStaticDetector(tolerance=0.5), 2025-09-07T07:36:41.1349285Z ... InputWeightEqualizationDetector(ratio_threshold=0.7), 2025-09-07T07:36:41.1349683Z ... ] 2025-09-07T07:36:41.1349888Z ... ) 2025-09-07T07:36:41.1350220Z >>> tracer_reporter = ModelReport(graph_module, tracer_detector_set) 2025-09-07T07:36:41.1350559Z 2025-09-07T07:36:41.1350715Z >>> # now we insert the observers and calibrate the model 2025-09-07T07:36:41.1351248Z >>> tracer_model_with_observers = tracer_reporter.prepare_detailed_calibration() 2025-09-07T07:36:41.1351753Z >>> for i in range(num_callibration_batches): 2025-09-07T07:36:41.1352136Z >>> example_input = get_callibration_input() 2025-09-07T07:36:41.1352520Z >>> tracer_model_with_observers(example_input) 2025-09-07T07:36:41.1352762Z 2025-09-07T07:36:41.1353062Z >>> # finally we generate the reports and optionally remove the observers we inserted 2025-09-07T07:36:41.1353594Z >>> reports = tracer_reporter.generate_model_report( 2025-09-07T07:36:41.1353961Z ... remove_inserted_observers=True 2025-09-07T07:36:41.1354266Z ... ) 2025-09-07T07:36:41.1354481Z 2025-09-07T07:36:41.1354708Z >>> # Optional: we can generate the qconfig mapping based on the suggestions 2025-09-07T07:36:41.1355217Z >>> qconfigs = model_report.generate_qconfig_mapping() 2025-09-07T07:36:41.1355487Z 2025-09-07T07:36:41.1355739Z >>> # Optional: we can generate the equalization mapping based on the suggestions 2025-09-07T07:36:41.1356272Z >>> qconfigs = model_report.generate_equalization_mapping() 2025-09-07T07:36:41.1356578Z 2025-09-07T07:36:41.1356856Z >>> # Optional: we get a ModelReportVisualizer instance to do any visualizations desired 2025-09-07T07:36:41.1357474Z >>> model_report_visualizer = tracer_reporter.generate_visualizer() 2025-09-07T07:36:41.1357798Z 2025-09-07T07:36:41.1357802Z 2025-09-07T07:36:41.1358063Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:41.1358427Z 2025-09-07T07:36:41.1383742Z 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. 2025-09-07T07:36:41.1385026Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:41.1385488Z 2025-09-07T07:36:41.1385980Z Takes in optional filter values and generates two tables with desired information. 2025-09-07T07:36:41.1386627Z 2025-09-07T07:36:41.1386835Z The generated tables are presented in both a list-of-lists format 2025-09-07T07:36:41.1387168Z 2025-09-07T07:36:41.1387376Z The reason for the two tables are that they handle different things: 2025-09-07T07:36:41.1387861Z 1.) the first table handles all tensor level information 2025-09-07T07:36:41.1388355Z 2.) the second table handles and displays all channel based information 2025-09-07T07:36:41.1388686Z 2025-09-07T07:36:41.1389005Z The reasoning for this is that having all the info in one table can make it ambiguous which collected 2025-09-07T07:36:41.1389828Z statistics are global, and which are actually per-channel, so it's better to split it up into two 2025-09-07T07:36:41.1391125Z tables. This also makes the information much easier to digest given the plethora of statistics collected 2025-09-07T07:36:41.1391613Z 2025-09-07T07:36:41.1391709Z Tensor table columns: 2025-09-07T07:36:41.1392066Z idx layer_fqn feature_1 feature_2 feature_3 .... feature_n 2025-09-07T07:36:41.1392535Z ---- --------- --------- --------- --------- --------- 2025-09-07T07:36:41.1392805Z 2025-09-07T07:36:41.1392914Z Per-Channel table columns: 2025-09-07T07:36:41.1393320Z idx layer_fqn channel feature_1 feature_2 feature_3 .... feature_n 2025-09-07T07:36:41.1394259Z ---- --------- ------- --------- --------- --------- --------- 2025-09-07T07:36:41.1394675Z 2025-09-07T07:36:41.1394771Z Args: 2025-09-07T07:36:41.1395158Z feature_filter (str, optional): Filters the features presented to only those that 2025-09-07T07:36:41.1395654Z contain this filter substring 2025-09-07T07:36:41.1396032Z Default = "", results in all the features being printed 2025-09-07T07:36:41.1396570Z module_fqn_filter (str, optional): Only includes modules that contains this string 2025-09-07T07:36:41.1397201Z Default = "", results in all the modules in the reports to be visible in the table 2025-09-07T07:36:41.1397563Z 2025-09-07T07:36:41.1397674Z Returns a dictionary with two keys: 2025-09-07T07:36:41.1398062Z (Dict[str, Tuple[List, List]]) A dict containing two keys: 2025-09-07T07:36:41.1398473Z "tensor_level_info", "channel_level_info" 2025-09-07T07:36:41.1398819Z Each key maps to a tuple with: 2025-09-07T07:36:41.1399191Z A list of the headers of each table 2025-09-07T07:36:41.1399608Z A list of lists containing the table information row by row 2025-09-07T07:36:41.1400090Z The 0th index row will contain the headers of the columns 2025-09-07T07:36:41.1400514Z The rest of the rows will contain data 2025-09-07T07:36:41.1400750Z 2025-09-07T07:36:41.1400856Z Example Use: 2025-09-07T07:36:41.1401108Z >>> # xdoctest: +SKIP("undefined variables") 2025-09-07T07:36:41.1401496Z >>> mod_report_visualizer.generate_filtered_tables( 2025-09-07T07:36:41.1401952Z ... feature_filter="per_channel_min", module_fqn_filter="block1" 2025-09-07T07:36:41.1402536Z ... ) # generates table with per_channel_min info for all modules in block 1 of the model 2025-09-07T07:36:41.1402922Z 2025-09-07T07:36:41.1403183Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:41.1403552Z 2025-09-07T07:36:41.1404464Z 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=399. 2025-09-07T07:36:41.1405777Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:41.1406167Z 2025-09-07T07:36:41.1406439Z Takes in optional filter values and prints out formatted tables of the information. 2025-09-07T07:36:41.1406841Z 2025-09-07T07:36:41.1407179Z The reason for the two tables printed out instead of one large one are that they handle different things: 2025-09-07T07:36:41.1407843Z 1.) the first table handles all tensor level information 2025-09-07T07:36:41.1408319Z 2.) the second table handles and displays all channel based information 2025-09-07T07:36:41.1408658Z 2025-09-07T07:36:41.1408974Z The reasoning for this is that having all the info in one table can make it ambiguous which collected 2025-09-07T07:36:41.1409744Z statistics are global, and which are actually per-channel, so it's better to split it up into two 2025-09-07T07:36:41.1410546Z tables. This also makes the information much easier to digest given the plethora of statistics collected 2025-09-07T07:36:41.1411018Z 2025-09-07T07:36:41.1411125Z Tensor table columns: 2025-09-07T07:36:41.1411479Z idx layer_fqn feature_1 feature_2 feature_3 .... feature_n 2025-09-07T07:36:41.1411958Z ---- --------- --------- --------- --------- --------- 2025-09-07T07:36:41.1412236Z 2025-09-07T07:36:41.1412341Z Per-Channel table columns: 2025-09-07T07:36:41.1412520Z 2025-09-07T07:36:41.1412747Z idx layer_fqn channel feature_1 feature_2 feature_3 .... feature_n 2025-09-07T07:36:41.1413247Z ---- --------- ------- --------- --------- --------- --------- 2025-09-07T07:36:41.1413524Z 2025-09-07T07:36:41.1413604Z Args: 2025-09-07T07:36:41.1413988Z feature_filter (str, optional): Filters the features presented to only those that 2025-09-07T07:36:41.1414488Z contain this filter substring 2025-09-07T07:36:41.1414865Z Default = "", results in all the features being printed 2025-09-07T07:36:41.1415409Z module_fqn_filter (str, optional): Only includes modules that contains this string 2025-09-07T07:36:41.1416030Z Default = "", results in all the modules in the reports to be visible in the table 2025-09-07T07:36:41.1416409Z 2025-09-07T07:36:41.1416500Z Example Use: 2025-09-07T07:36:41.1416763Z >>> # xdoctest: +SKIP("undefined variables") 2025-09-07T07:36:41.1417170Z >>> mod_report_visualizer.generate_table_visualization( 2025-09-07T07:36:41.1417650Z ... feature_filter="per_channel_min", module_fqn_filter="block1" 2025-09-07T07:36:41.1418049Z ... ) 2025-09-07T07:36:41.1418364Z >>> # prints out neatly formatted table with per_channel_min info 2025-09-07T07:36:41.1418801Z >>> # for all modules in block 1 of the model 2025-09-07T07:36:41.1419035Z 2025-09-07T07:36:41.1419314Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:41.1419695Z 2025-09-07T07:36:41.1420602Z 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=564. 2025-09-07T07:36:41.1421884Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:41.1422278Z 2025-09-07T07:36:41.1422515Z Takes in a feature and optional module_filter and plots of the desired data. 2025-09-07T07:36:41.1422870Z 2025-09-07T07:36:41.1423156Z For per channel features, it averages the value across the channels and plots a point 2025-09-07T07:36:41.1424120Z per module. The reason for this is that for models with hundreds of channels, it can 2025-09-07T07:36:41.1424775Z be hard to differentiate one channel line from another, and so the point of generating 2025-09-07T07:36:41.1425455Z a single average point per module is to give a sense of general trends that encourage 2025-09-07T07:36:41.1425951Z further deep dives. 2025-09-07T07:36:41.1426100Z 2025-09-07T07:36:41.1426197Z Note: 2025-09-07T07:36:41.1426573Z Only features in the report that have tensor value data are plottable by this class 2025-09-07T07:36:41.1427221Z When the tensor information is plotted, it will plot: 2025-09-07T07:36:41.1427644Z idx as the x val, feature value as the y_val 2025-09-07T07:36:41.1428065Z When the channel information is plotted, it will plot: 2025-09-07T07:36:41.1428620Z the first idx of each module as the x val, feature value as the y_val [for each channel] 2025-09-07T07:36:41.1429272Z The reason for this is that we want to be able to compare values across the 2025-09-07T07:36:41.1429861Z channels for same layer, and it will be hard if values are staggered by idx 2025-09-07T07:36:41.1430394Z This means each module is represented by only 1 x value 2025-09-07T07:36:41.1430763Z Args: 2025-09-07T07:36:41.1431096Z feature_filter (str): Filters the features presented to only those that 2025-09-07T07:36:41.1431550Z contain this filter substring 2025-09-07T07:36:41.1432025Z module_fqn_filter (str, optional): Only includes modules that contains this string 2025-09-07T07:36:41.1432654Z Default = "", results in all the modules in the reports to be visible in the table 2025-09-07T07:36:41.1433057Z 2025-09-07T07:36:41.1433159Z Example Use: 2025-09-07T07:36:41.1433447Z >>> # xdoctest: +SKIP("undefined variables") 2025-09-07T07:36:41.1433848Z >>> mod_report_visualizer.generate_plot_visualization( 2025-09-07T07:36:41.1434319Z ... feature_filter="per_channel_min", module_fqn_filter="block1" 2025-09-07T07:36:41.1434778Z ... ) 2025-09-07T07:36:41.1435071Z >>> # outputs line plot of per_channel_min information for all 2025-09-07T07:36:41.1435557Z >>> # modules in block1 of model each channel gets it's own line, 2025-09-07T07:36:41.1436037Z >>> # and it's plotted across the in-order modules on the x-axis 2025-09-07T07:36:41.1436327Z 2025-09-07T07:36:41.1436592Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:41.1436957Z 2025-09-07T07:36:41.1437927Z 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=643. 2025-09-07T07:36:41.1439234Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:41.1439614Z 2025-09-07T07:36:41.1439889Z Takes in a feature and optional module_filter and plots the histogram of desired data. 2025-09-07T07:36:41.1440352Z 2025-09-07T07:36:41.1440473Z Note: 2025-09-07T07:36:41.1441022Z Only features in the report that have tensor value data can be viewed as a histogram 2025-09-07T07:36:41.1441992Z If you want to plot a histogram from all the channel values of a specific feature for 2025-09-07T07:36:41.1443086Z a specific model, make sure to specify both the model and the feature properly 2025-09-07T07:36:41.1443966Z in the filters and you should be able to see a distribution of the channel data 2025-09-07T07:36:41.1444629Z 2025-09-07T07:36:41.1444790Z Args: 2025-09-07T07:36:41.1445457Z feature_filter (str, optional): Filters the features presented to only those that 2025-09-07T07:36:41.1446375Z contain this filter substring 2025-09-07T07:36:41.1447069Z Default = "", results in all the features being printed 2025-09-07T07:36:41.1448076Z module_fqn_filter (str, optional): Only includes modules that contains this string 2025-09-07T07:36:41.1449239Z Default = "", results in all the modules in the reports to be visible in the table 2025-09-07T07:36:41.1450291Z num_bins (int, optional): The number of bins to create the histogram with 2025-09-07T07:36:41.1451239Z Default = 10, the values will be split into 10 equal sized bins 2025-09-07T07:36:41.1451546Z 2025-09-07T07:36:41.1451641Z Example Use: 2025-09-07T07:36:41.1451877Z >>> # xdoctest: +SKIP 2025-09-07T07:36:41.1452351Z >>> mod_report_visualizer.generategenerate_histogram_visualization_plot_visualization( 2025-09-07T07:36:41.1453046Z ... feature_filter="per_channel_min", module_fqn_filter="block1" 2025-09-07T07:36:41.1453429Z ... ) 2025-09-07T07:36:41.1453834Z # outputs histogram of per_channel_min information for all modules in block1 of model 2025-09-07T07:36:41.1454491Z information is gathered across all channels for all modules in block 1 for the 2025-09-07T07:36:41.1455123Z per_channel_min and is displayed in a histogram of equally sized bins 2025-09-07T07:36:41.1455459Z 2025-09-07T07:36:41.1455720Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:41.1456083Z 2025-09-07T07:36:41.2645433Z msg = Cannot scrape callname=record_function in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/profiler.py line=734. 2025-09-07T07:36:41.2646374Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:41.2647140Z Context manager/function decorator that adds a label to a code block/function when running autograd profiler. 2025-09-07T07:36:41.2647812Z Label will only appear if CPU activity tracing is enabled. 2025-09-07T07:36:41.2648123Z 2025-09-07T07:36:41.2648416Z It is useful when tracing the code profile. 2025-09-07T07:36:41.2648673Z 2025-09-07T07:36:41.2648758Z Args: 2025-09-07T07:36:41.2649027Z name (str): Label assigned to the block of code. 2025-09-07T07:36:41.2649468Z node_id (int): ID of node, for distributed profiling. Unset in 2025-09-07T07:36:41.2649862Z non-distributed cases. 2025-09-07T07:36:41.2650058Z 2025-09-07T07:36:41.2650144Z Example: 2025-09-07T07:36:41.2650461Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_AUTOGRAD_PROFILER) 2025-09-07T07:36:41.2650899Z >>> x = torch.randn((1, 1), requires_grad=True) 2025-09-07T07:36:41.2651299Z >>> with torch.autograd.profiler.profile() as prof: 2025-09-07T07:36:41.2651668Z ... y = x**2 2025-09-07T07:36:41.2651993Z ... with torch.autograd.profiler.record_function( 2025-09-07T07:36:41.2652363Z ... "label-z" 2025-09-07T07:36:41.2652631Z ... ): # label the block 2025-09-07T07:36:41.2652932Z ... z = y**3 2025-09-07T07:36:41.2653201Z ... y.backward() 2025-09-07T07:36:41.2653481Z >>> # xdoctest: +IGNORE_WANT 2025-09-07T07:36:41.2653810Z >>> # NOTE: some columns were removed for brevity 2025-09-07T07:36:41.2654265Z >>> print(prof.key_averages().table(sort_by="self_cpu_time_total")) 2025-09-07T07:36:41.2654793Z ----------------------------------- --------------- --------------- --------------- 2025-09-07T07:36:41.2655298Z Name Self CPU total % CPU time avg Number of Calls 2025-09-07T07:36:41.2655889Z ----------------------------------- --------------- --------------- --------------- 2025-09-07T07:36:41.2656329Z pow 60.77% 47.470us 3 2025-09-07T07:36:41.2656724Z mul 21.73% 25.465us 2 2025-09-07T07:36:41.2657153Z PowBackward0 12.03% 121.891us 1 2025-09-07T07:36:41.2657703Z torch::autograd::AccumulateGrad 2.70% 6.324us 1 2025-09-07T07:36:41.2658186Z label-z 2.13% 12.421us 1 2025-09-07T07:36:41.2658643Z torch::autograd::GraphRoot 0.64% 1.503us 1 2025-09-07T07:36:41.2659160Z ----------------------------------- --------------- --------------- --------------- 2025-09-07T07:36:41.2659591Z Self CPU time total: 234.344us 2025-09-07T07:36:41.2659911Z CUDA time total: 0.000us 2025-09-07T07:36:41.2660100Z 2025-09-07T07:36:41.2660196Z 2025-09-07T07:36:41.2660549Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:41.2660925Z 2025-09-07T07:36:41.4013677Z 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=701. 2025-09-07T07:36:41.4014810Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-09-07T07:36:41.4015188Z 2025-09-07T07:36:41.4015455Z Slice the current DeviceMesh based on the mesh_dim_names given to create a submesh. 2025-09-07T07:36:41.4016171Z The submesh created consists of the dimensions and the communicators indicated by 2025-09-07T07:36:41.4016651Z ``mesh_dim_names`` 2025-09-07T07:36:41.4016792Z 2025-09-07T07:36:41.4016906Z Args: 2025-09-07T07:36:41.4017247Z mesh_dim_names (Union[str, Tuple[str]]): the name or the tuple of names of the 2025-09-07T07:36:41.4017788Z mesh dimension of the DeviceMesh to create the submesh for. 2025-09-07T07:36:41.4018177Z Returns: 2025-09-07T07:36:41.4018407Z A :class:`DeviceMesh` object 2025-09-07T07:36:41.4018596Z 2025-09-07T07:36:41.4018879Z The following program runs on each process/rank in an SPMD manner in a world size of 8. 2025-09-07T07:36:41.4019381Z In the first example: 2025-09-07T07:36:41.4019873Z Calling mesh_2d["tp"] on rank 0, 1, 2, 3 returns a 1D submesh of DeviceMesh:([0, 1, 2, 3]). 2025-09-07T07:36:41.4020487Z Calling mesh_2d["tp"] on rank 4, 5, 6, 7 returns a 1D submesh of DeviceMesh:([4, 5, 6, 7]). 2025-09-07T07:36:41.4021079Z Calling mesh_2d["dp"] on rank 0, 4 returns a 1D submesh of DeviceMesh:([0, 4]). 2025-09-07T07:36:41.4021638Z Calling mesh_2d["dp"] on rank 1, 5 returns a 1D submesh of DeviceMesh:([1, 5]). 2025-09-07T07:36:41.4022207Z Calling mesh_2d["dp"] on rank 2, 6 returns a 1D submesh of DeviceMesh:([2, 6]). 2025-09-07T07:36:41.4022772Z Calling mesh_2d["dp"] on rank 3, 7 returns a 1D submesh of DeviceMesh:([3, 7]). 2025-09-07T07:36:41.4023112Z 2025-09-07T07:36:41.4023226Z In the second example: 2025-09-07T07:36:41.4024074Z Calling mesh_3d["dp", "cp"] on rank 0, 1, 4, 5 returns a 2D submesh of DeviceMesh:([[0, 1], [4, 5]]). 2025-09-07T07:36:41.4024731Z Calling mesh_3d["dp", "cp"] on rank 2, 3, 6, 7 returns a 2D submesh of DeviceMesh:([[2, 3], [6, 7]]). 2025-09-07T07:36:41.4025387Z Calling mesh_3d["cp", "dp"] on rank 0, 1, 4, 5 returns a 2D submesh of DeviceMesh:([[0, 4], [1, 5]]). 2025-09-07T07:36:41.4026037Z Calling mesh_3d["cp", "dp"] on rank 2, 3, 6, 7 returns a 2D submesh of DeviceMesh:([[2, 6], [3, 7]]). 2025-09-07T07:36:41.4026418Z 2025-09-07T07:36:41.4026534Z Example:: 2025-09-07T07:36:41.4026655Z 2025-09-07T07:36:41.4026772Z >>> # xdoctest: +SKIP("no rank") 2025-09-07T07:36:41.4027145Z >>> from torch.distributed.device_mesh import DeviceMesh 2025-09-07T07:36:41.4027515Z >>> 2025-09-07T07:36:41.4027834Z >>> # Initialize a 2D device mesh as (2, 4) to represent the topology 2025-09-07T07:36:41.4028363Z >>> # of cross-host(dim 0), and within-host (dim 1). 2025-09-07T07:36:41.4028857Z >>> mesh_2d = init_device_mesh(device_type="cuda", (2,4), mesh_dim_names=("dp", "tp")) 2025-09-07T07:36:41.4029342Z >>> tp_mesh = mesh_2d["tp"] 2025-09-07T07:36:41.4029635Z >>> dp_mesh = mesh_2d["dp"] 2025-09-07T07:36:41.4029909Z >>> 2025-09-07T07:36:41.4030178Z >>> # Initialize a 3D mesh. 2025-09-07T07:36:41.4030655Z >>> mesh_3d = init_device_mesh(device_type="cuda", (2,2,2), mesh_dim_names=("dp", "pp", "cp")) 2025-09-07T07:36:41.4031367Z >>> # The order of the mesh_dim_names provided deteremines the order of dimensions in the submesh. 2025-09-07T07:36:41.4031906Z >>> dp_cp_mesh = mesh_3d["dp", "cp"] 2025-09-07T07:36:41.4032223Z >>> cp_dp_mesh = mesh_3d["cp", "dp"] 2025-09-07T07:36:41.4032451Z 2025-09-07T07:36:41.4033125Z 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)) 2025-09-07T07:36:41.4033929Z 2025-09-07T07:36:41.4034172Z mesh_2d = init_device_mesh(device_type="cuda", (2,4), mesh_dim_names=("dp", "tp")) 2025-09-07T07:36:41.4034730Z ^ 2025-09-07T07:36:41.4355276Z msg = Cannot scrape callname=batch_isend_irecv in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py line=2705. 2025-09-07T07:36:41.4356266Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:41.4357423Z 2025-09-07T07:36:41.4357673Z Send or Receive a batch of tensors asynchronously and return a list of requests. 2025-09-07T07:36:41.4358075Z 2025-09-07T07:36:41.4358319Z Process each of the operations in ``p2p_op_list`` and return the corresponding 2025-09-07T07:36:41.4358874Z requests. NCCL, Gloo, and UCC backend are currently supported. 2025-09-07T07:36:41.4359194Z 2025-09-07T07:36:41.4359275Z Args: 2025-09-07T07:36:41.4359623Z p2p_op_list: A list of point-to-point operations(type of each operator is 2025-09-07T07:36:41.4360203Z ``torch.distributed.P2POp``). The order of the isend/irecv in the list 2025-09-07T07:36:41.4360749Z matters and it needs to match with corresponding isend/irecv on the 2025-09-07T07:36:41.4361167Z remote end. 2025-09-07T07:36:41.4361401Z 2025-09-07T07:36:41.4361498Z Returns: 2025-09-07T07:36:41.4361870Z A list of distributed request objects returned by calling the corresponding 2025-09-07T07:36:41.4362320Z op in the op_list. 2025-09-07T07:36:41.4362491Z 2025-09-07T07:36:41.4362574Z Examples: 2025-09-07T07:36:41.4362864Z >>> # xdoctest: +SKIP("no rank") 2025-09-07T07:36:41.4363581Z >>> send_tensor = torch.arange(2, dtype=torch.float32) + 2 * rank 2025-09-07T07:36:41.4364173Z >>> recv_tensor = torch.randn(2, dtype=torch.float32) 2025-09-07T07:36:41.4364646Z >>> send_op = dist.P2POp(dist.isend, send_tensor, (rank + 1) % world_size) 2025-09-07T07:36:41.4365084Z >>> recv_op = dist.P2POp( 2025-09-07T07:36:41.4365471Z ... dist.irecv, recv_tensor, (rank - 1 + world_size) % world_size 2025-09-07T07:36:41.4365853Z ... ) 2025-09-07T07:36:41.4366111Z >>> reqs = batch_isend_irecv([send_op, recv_op]) 2025-09-07T07:36:41.4366458Z >>> for req in reqs: 2025-09-07T07:36:41.4366722Z >>> req.wait() 2025-09-07T07:36:41.4366958Z >>> recv_tensor 2025-09-07T07:36:41.4367201Z tensor([2, 3]) # Rank 0 2025-09-07T07:36:41.4367486Z tensor([0, 1]) # Rank 1 2025-09-07T07:36:41.4367666Z 2025-09-07T07:36:41.4367971Z .. note:: Note that when this API is used with the NCCL PG backend, users must set 2025-09-07T07:36:41.4368559Z the current GPU device with `torch.cuda.set_device`, otherwise it will 2025-09-07T07:36:41.4368996Z lead to unexpected hang issues. 2025-09-07T07:36:41.4369205Z 2025-09-07T07:36:41.4369440Z In addition, if this API is the first collective call in the ``group`` 2025-09-07T07:36:41.4370050Z passed to ``dist.P2POp``, all ranks of the ``group`` must participate in 2025-09-07T07:36:41.4370627Z this API call; otherwise, the behavior is undefined. If this API call is 2025-09-07T07:36:41.4371198Z not the first collective call in the ``group``, batched P2P operations 2025-09-07T07:36:41.4371743Z involving only a subset of ranks of the ``group`` are allowed. 2025-09-07T07:36:41.4372051Z 2025-09-07T07:36:41.4372314Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:41.4372683Z 2025-09-07T07:36:41.4373294Z msg = Cannot scrape callname=all_reduce in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py line=2837. 2025-09-07T07:36:41.4374230Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:41.4374620Z 2025-09-07T07:36:41.4374887Z Reduces the tensor data across all machines in a way that all get the final result. 2025-09-07T07:36:41.4375289Z 2025-09-07T07:36:41.4375517Z After the call ``tensor`` is going to be bitwise identical in all processes. 2025-09-07T07:36:41.4375866Z 2025-09-07T07:36:41.4375990Z Complex tensors are supported. 2025-09-07T07:36:41.4376240Z 2025-09-07T07:36:41.4376336Z Args: 2025-09-07T07:36:41.4376652Z tensor (Tensor): Input and output of the collective. The function 2025-09-07T07:36:41.4377085Z operates in-place. 2025-09-07T07:36:41.4377384Z op (optional): One of the values from 2025-09-07T07:36:41.4377729Z ``torch.distributed.ReduceOp`` 2025-09-07T07:36:41.4378176Z enum. Specifies an operation used for element-wise reductions. 2025-09-07T07:36:41.4378729Z group (ProcessGroup, optional): The process group to work on. If None, 2025-09-07T07:36:41.4379205Z the default process group will be used. 2025-09-07T07:36:41.4379642Z async_op (bool, optional): Whether this op should be an async op 2025-09-07T07:36:41.4379947Z 2025-09-07T07:36:41.4380033Z Returns: 2025-09-07T07:36:41.4380295Z Async work handle, if async_op is set to True. 2025-09-07T07:36:41.4380690Z None, if not async_op or if not part of the group 2025-09-07T07:36:41.4380940Z 2025-09-07T07:36:41.4381061Z Examples: 2025-09-07T07:36:41.4381413Z >>> # xdoctest: +SKIP("no rank") 2025-09-07T07:36:41.4381997Z >>> # All tensors below are of torch.int64 type. 2025-09-07T07:36:41.4382602Z >>> # We have 2 process groups, 2 ranks. 2025-09-07T07:36:41.4382948Z >>> device = torch.device(f"cuda:{rank}") 2025-09-07T07:36:41.4383399Z >>> tensor = torch.arange(2, dtype=torch.int64, device=device) + 1 + 2 * rank 2025-09-07T07:36:41.4383821Z >>> tensor 2025-09-07T07:36:41.4384071Z tensor([1, 2], device='cuda:0') # Rank 0 2025-09-07T07:36:41.4384413Z tensor([3, 4], device='cuda:1') # Rank 1 2025-09-07T07:36:41.4384766Z >>> dist.all_reduce(tensor, op=ReduceOp.SUM) 2025-09-07T07:36:41.4385075Z >>> tensor 2025-09-07T07:36:41.4385321Z tensor([4, 6], device='cuda:0') # Rank 0 2025-09-07T07:36:41.4385656Z tensor([4, 6], device='cuda:1') # Rank 1 2025-09-07T07:36:41.4385872Z 2025-09-07T07:36:41.4386020Z >>> # All tensors below are of torch.cfloat type. 2025-09-07T07:36:41.4386369Z >>> # We have 2 process groups, 2 ranks. 2025-09-07T07:36:41.4386698Z >>> tensor = torch.tensor( 2025-09-07T07:36:41.4387029Z ... [1 + 1j, 2 + 2j], dtype=torch.cfloat, device=device 2025-09-07T07:36:41.4387379Z ... ) + 2 * rank * (1 + 1j) 2025-09-07T07:36:41.4387632Z >>> tensor 2025-09-07T07:36:41.4387904Z tensor([1.+1.j, 2.+2.j], device='cuda:0') # Rank 0 2025-09-07T07:36:41.4388289Z tensor([3.+3.j, 4.+4.j], device='cuda:1') # Rank 1 2025-09-07T07:36:41.4388665Z >>> dist.all_reduce(tensor, op=ReduceOp.SUM) 2025-09-07T07:36:41.4388980Z >>> tensor 2025-09-07T07:36:41.4389251Z tensor([4.+4.j, 6.+6.j], device='cuda:0') # Rank 0 2025-09-07T07:36:41.4389637Z tensor([4.+4.j, 6.+6.j], device='cuda:1') # Rank 1 2025-09-07T07:36:41.4389879Z 2025-09-07T07:36:41.4389883Z 2025-09-07T07:36:41.4390180Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:41.4390548Z 2025-09-07T07:36:41.4391175Z 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=3201. 2025-09-07T07:36:41.4392149Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:41.4392528Z 2025-09-07T07:36:41.4392757Z Gathers picklable objects from the whole group in a single process. 2025-09-07T07:36:41.4393092Z 2025-09-07T07:36:41.4393321Z Similar to :func:`gather`, but Python objects can be passed in. Note that the 2025-09-07T07:36:41.4393828Z object must be picklable in order to be gathered. 2025-09-07T07:36:41.4394092Z 2025-09-07T07:36:41.4394173Z Args: 2025-09-07T07:36:41.4394496Z obj (Any): Input object. Must be picklable. 2025-09-07T07:36:41.4394951Z object_gather_list (list[Any]): Output list. On the ``dst`` rank, it 2025-09-07T07:36:41.4395456Z should be correctly sized as the size of the group for this 2025-09-07T07:36:41.4395982Z collective and will contain the output. Must be ``None`` on non-dst 2025-09-07T07:36:41.4396464Z ranks. (default is ``None``) 2025-09-07T07:36:41.4396991Z dst (int, optional): Destination rank on global process group (regardless of ``group`` argument). 2025-09-07T07:36:41.4397617Z (If both ``dst`` and ``group_dst`` are None, default is global rank 0) 2025-09-07T07:36:41.4398164Z group: (ProcessGroup, optional): The process group to work on. If None, 2025-09-07T07:36:41.4398738Z the default process group will be used. Default is ``None``. 2025-09-07T07:36:41.4399384Z group_dst (int, optional): Destination rank on ``group``. Invalid to specify both ``dst`` and ``group_dst`` 2025-09-07T07:36:41.4399842Z 2025-09-07T07:36:41.4399938Z Returns: 2025-09-07T07:36:41.4400231Z None. On the ``dst`` rank, ``object_gather_list`` will contain the 2025-09-07T07:36:41.4400634Z output of the collective. 2025-09-07T07:36:41.4400817Z 2025-09-07T07:36:41.4401045Z .. note:: Note that this API differs slightly from the gather collective 2025-09-07T07:36:41.4401592Z since it does not provide an async_op handle and thus will be a blocking 2025-09-07T07:36:41.4402004Z call. 2025-09-07T07:36:41.4402132Z 2025-09-07T07:36:41.4402388Z .. note:: For NCCL-based processed groups, internal tensor representations 2025-09-07T07:36:41.4402946Z of objects must be moved to the GPU device before communication takes 2025-09-07T07:36:41.4403423Z place. In this case, the device used is given by 2025-09-07T07:36:41.4403900Z ``torch.cuda.current_device()`` and it is the user's responsibility to 2025-09-07T07:36:41.4404437Z ensure that this is set so that each rank has an individual GPU, via 2025-09-07T07:36:41.4404869Z ``torch.cuda.set_device()``. 2025-09-07T07:36:41.4405062Z 2025-09-07T07:36:41.4405161Z .. warning:: 2025-09-07T07:36:41.4405530Z Object collectives have a number of serious performance and scalability 2025-09-07T07:36:41.4406055Z limitations. See :ref:`object_collectives` for details. 2025-09-07T07:36:41.4406357Z 2025-09-07T07:36:41.4406444Z .. warning:: 2025-09-07T07:36:41.4406779Z :func:`gather_object` uses ``pickle`` module implicitly, which is 2025-09-07T07:36:41.4407321Z known to be insecure. It is possible to construct malicious pickle data 2025-09-07T07:36:41.4407871Z which will execute arbitrary code during unpickling. Only call this 2025-09-07T07:36:41.4408317Z function with data you trust. 2025-09-07T07:36:41.4408524Z 2025-09-07T07:36:41.4408607Z .. warning:: 2025-09-07T07:36:41.4408948Z Calling :func:`gather_object` with GPU tensors is not well supported 2025-09-07T07:36:41.4409504Z and inefficient as it incurs GPU -> CPU transfer since tensors would be 2025-09-07T07:36:41.4410008Z pickled. Please consider using :func:`gather` instead. 2025-09-07T07:36:41.4410296Z 2025-09-07T07:36:41.4410383Z Example:: 2025-09-07T07:36:41.4410676Z >>> # xdoctest: +SKIP("need process group init") 2025-09-07T07:36:41.4411111Z >>> # Note: Process group initialization omitted on each rank. 2025-09-07T07:36:41.4411515Z >>> import torch.distributed as dist 2025-09-07T07:36:41.4411852Z >>> # Assumes world_size of 3. 2025-09-07T07:36:41.4412232Z >>> gather_objects = ["foo", 12, {1: 2}] # any picklable object 2025-09-07T07:36:41.4412649Z >>> output = [None for _ in gather_objects] 2025-09-07T07:36:41.4412972Z >>> dist.gather_object( 2025-09-07T07:36:41.4413277Z ... gather_objects[dist.get_rank()], 2025-09-07T07:36:41.4413641Z ... output if dist.get_rank() == 0 else None, 2025-09-07T07:36:41.4413978Z ... dst=0 2025-09-07T07:36:41.4414191Z ... ) 2025-09-07T07:36:41.4414405Z >>> # On rank 0 2025-09-07T07:36:41.4414639Z >>> output 2025-09-07T07:36:41.4414868Z ['foo', 12, {1: 2}] 2025-09-07T07:36:41.4415020Z 2025-09-07T07:36:41.4415276Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:41.4415661Z 2025-09-07T07:36:41.4424440Z msg = Cannot scrape callname=all_gather in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py line=3849. 2025-09-07T07:36:41.4425527Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:41.4425924Z 2025-09-07T07:36:41.4426063Z Gathers tensors from the whole group in a list. 2025-09-07T07:36:41.4426324Z 2025-09-07T07:36:41.4426464Z Complex and uneven sized tensors are supported. 2025-09-07T07:36:41.4426775Z 2025-09-07T07:36:41.4426869Z Args: 2025-09-07T07:36:41.4427154Z tensor_list (list[Tensor]): Output list. It should contain 2025-09-07T07:36:41.4427666Z correctly-sized tensors to be used for output of the collective. 2025-09-07T07:36:41.4428121Z Uneven sized tensors are supported. 2025-09-07T07:36:41.4428548Z tensor (Tensor): Tensor to be broadcast from current process. 2025-09-07T07:36:41.4429081Z group (ProcessGroup, optional): The process group to work on. If None, 2025-09-07T07:36:41.4429543Z the default process group will be used. 2025-09-07T07:36:41.4429974Z async_op (bool, optional): Whether this op should be an async op 2025-09-07T07:36:41.4430293Z 2025-09-07T07:36:41.4430376Z Returns: 2025-09-07T07:36:41.4430677Z Async work handle, if async_op is set to True. 2025-09-07T07:36:41.4431064Z None, if not async_op or if not part of the group 2025-09-07T07:36:41.4431328Z 2025-09-07T07:36:41.4431415Z Examples: 2025-09-07T07:36:41.4431674Z >>> # xdoctest: +SKIP("need process group init") 2025-09-07T07:36:41.4432057Z >>> # All tensors below are of torch.int64 dtype. 2025-09-07T07:36:41.4432412Z >>> # We have 2 process groups, 2 ranks. 2025-09-07T07:36:41.4432755Z >>> device = torch.device(f"cuda:{rank}") 2025-09-07T07:36:41.4433081Z >>> tensor_list = [ 2025-09-07T07:36:41.4433446Z ... torch.zeros(2, dtype=torch.int64, device=device) for _ in range(2) 2025-09-07T07:36:41.4433864Z ... ] 2025-09-07T07:36:41.4434062Z >>> tensor_list 2025-09-07T07:36:41.4434473Z [tensor([0, 0], device='cuda:0'), tensor([0, 0], device='cuda:0')] # Rank 0 2025-09-07T07:36:41.4434992Z [tensor([0, 0], device='cuda:1'), tensor([0, 0], device='cuda:1')] # Rank 1 2025-09-07T07:36:41.4435535Z >>> tensor = torch.arange(2, dtype=torch.int64, device=device) + 1 + 2 * rank 2025-09-07T07:36:41.4435952Z >>> tensor 2025-09-07T07:36:41.4436199Z tensor([1, 2], device='cuda:0') # Rank 0 2025-09-07T07:36:41.4436540Z tensor([3, 4], device='cuda:1') # Rank 1 2025-09-07T07:36:41.4436875Z >>> dist.all_gather(tensor_list, tensor) 2025-09-07T07:36:41.4437180Z >>> tensor_list 2025-09-07T07:36:41.4437523Z [tensor([1, 2], device='cuda:0'), tensor([3, 4], device='cuda:0')] # Rank 0 2025-09-07T07:36:41.4438035Z [tensor([1, 2], device='cuda:1'), tensor([3, 4], device='cuda:1')] # Rank 1 2025-09-07T07:36:41.4438349Z 2025-09-07T07:36:41.4438551Z >>> # All tensors below are of torch.cfloat dtype. 2025-09-07T07:36:41.4438914Z >>> # We have 2 process groups, 2 ranks. 2025-09-07T07:36:41.4439233Z >>> tensor_list = [ 2025-09-07T07:36:41.4439614Z ... torch.zeros(2, dtype=torch.cfloat, device=device) for _ in range(2) 2025-09-07T07:36:41.4440037Z ... ] 2025-09-07T07:36:41.4440236Z >>> tensor_list 2025-09-07T07:36:41.4440642Z [tensor([0.+0.j, 0.+0.j], device='cuda:0'), tensor([0.+0.j, 0.+0.j], device='cuda:0')] # Rank 0 2025-09-07T07:36:41.4441251Z [tensor([0.+0.j, 0.+0.j], device='cuda:1'), tensor([0.+0.j, 0.+0.j], device='cuda:1')] # Rank 1 2025-09-07T07:36:41.4441720Z >>> tensor = torch.tensor( 2025-09-07T07:36:41.4442051Z ... [1 + 1j, 2 + 2j], dtype=torch.cfloat, device=device 2025-09-07T07:36:41.4442423Z ... ) + 2 * rank * (1 + 1j) 2025-09-07T07:36:41.4442687Z >>> tensor 2025-09-07T07:36:41.4442954Z tensor([1.+1.j, 2.+2.j], device='cuda:0') # Rank 0 2025-09-07T07:36:41.4443344Z tensor([3.+3.j, 4.+4.j], device='cuda:1') # Rank 1 2025-09-07T07:36:41.4443700Z >>> dist.all_gather(tensor_list, tensor) 2025-09-07T07:36:41.4444015Z >>> tensor_list 2025-09-07T07:36:41.4444400Z [tensor([1.+1.j, 2.+2.j], device='cuda:0'), tensor([3.+3.j, 4.+4.j], device='cuda:0')] # Rank 0 2025-09-07T07:36:41.4445042Z [tensor([1.+1.j, 2.+2.j], device='cuda:1'), tensor([3.+3.j, 4.+4.j], device='cuda:1')] # Rank 1 2025-09-07T07:36:41.4445401Z 2025-09-07T07:36:41.4445406Z 2025-09-07T07:36:41.4445670Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:41.4446067Z 2025-09-07T07:36:41.4483188Z msg = Cannot scrape callname=all_to_all_single in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py line=4555. 2025-09-07T07:36:41.4484276Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:41.4484650Z 2025-09-07T07:36:41.4484908Z Split input tensor and then scatter the split list to all processes in a group. 2025-09-07T07:36:41.4485287Z 2025-09-07T07:36:41.4485544Z Later the received tensors are concatenated from all the processes in the group 2025-09-07T07:36:41.4486042Z and returned as a single output tensor. 2025-09-07T07:36:41.4486261Z 2025-09-07T07:36:41.4486380Z Complex tensors are supported. 2025-09-07T07:36:41.4486569Z 2025-09-07T07:36:41.4486711Z Args: 2025-09-07T07:36:41.4486983Z output (Tensor): Gathered concatenated output tensor. 2025-09-07T07:36:41.4487385Z input (Tensor): Input tensor to scatter. 2025-09-07T07:36:41.4487842Z output_split_sizes: (list[Int], optional): Output split sizes for dim 0 2025-09-07T07:36:41.4488390Z if specified None or empty, dim 0 of ``output`` tensor must divide 2025-09-07T07:36:41.4488806Z equally by ``world_size``. 2025-09-07T07:36:41.4489223Z input_split_sizes: (list[Int], optional): Input split sizes for dim 0 2025-09-07T07:36:41.4489763Z if specified None or empty, dim 0 of ``input`` tensor must divide 2025-09-07T07:36:41.4490194Z equally by ``world_size``. 2025-09-07T07:36:41.4490615Z group (ProcessGroup, optional): The process group to work on. If None, 2025-09-07T07:36:41.4491101Z the default process group will be used. 2025-09-07T07:36:41.4491541Z async_op (bool, optional): Whether this op should be an async op. 2025-09-07T07:36:41.4491855Z 2025-09-07T07:36:41.4491949Z Returns: 2025-09-07T07:36:41.4492206Z Async work handle, if async_op is set to True. 2025-09-07T07:36:41.4492595Z None, if not async_op or if not part of the group. 2025-09-07T07:36:41.4492862Z 2025-09-07T07:36:41.4492954Z .. warning:: 2025-09-07T07:36:41.4493252Z `all_to_all_single` is experimental and subject to change. 2025-09-07T07:36:41.4493535Z 2025-09-07T07:36:41.4493643Z Examples: 2025-09-07T07:36:41.4493874Z >>> # xdoctest: +SKIP("Undefined rank") 2025-09-07T07:36:41.4494216Z >>> input = torch.arange(4) + rank * 4 2025-09-07T07:36:41.4494525Z >>> input 2025-09-07T07:36:41.4494798Z tensor([0, 1, 2, 3]) # Rank 0 2025-09-07T07:36:41.4495090Z tensor([4, 5, 6, 7]) # Rank 1 2025-09-07T07:36:41.4495474Z tensor([8, 9, 10, 11]) # Rank 2 2025-09-07T07:36:41.4495969Z tensor([12, 13, 14, 15]) # Rank 3 2025-09-07T07:36:41.4496624Z >>> output = torch.empty([4], dtype=torch.int64) 2025-09-07T07:36:41.4497006Z >>> dist.all_to_all_single(output, input) 2025-09-07T07:36:41.4497330Z >>> output 2025-09-07T07:36:41.4497568Z tensor([0, 4, 8, 12]) # Rank 0 2025-09-07T07:36:41.4497873Z tensor([1, 5, 9, 13]) # Rank 1 2025-09-07T07:36:41.4498158Z tensor([2, 6, 10, 14]) # Rank 2 2025-09-07T07:36:41.4498461Z tensor([3, 7, 11, 15]) # Rank 3 2025-09-07T07:36:41.4498672Z 2025-09-07T07:36:41.4498835Z >>> # Essentially, it is similar to following operation: 2025-09-07T07:36:41.4499250Z >>> scatter_list = list(input.chunk(world_size)) 2025-09-07T07:36:41.4499630Z >>> gather_list = list(output.chunk(world_size)) 2025-09-07T07:36:41.4499992Z >>> for i in range(world_size): 2025-09-07T07:36:41.4500428Z >>> dist.scatter(gather_list[i], scatter_list if i == rank else [], src = i) 2025-09-07T07:36:41.4500773Z 2025-09-07T07:36:41.4500938Z >>> # Another example with uneven split 2025-09-07T07:36:41.4501249Z >>> input 2025-09-07T07:36:41.4501521Z tensor([0, 1, 2, 3, 4, 5]) # Rank 0 2025-09-07T07:36:41.4501953Z tensor([10, 11, 12, 13, 14, 15, 16, 17, 18]) # Rank 1 2025-09-07T07:36:41.4502419Z tensor([20, 21, 22, 23, 24]) # Rank 2 2025-09-07T07:36:41.4502855Z tensor([30, 31, 32, 33, 34, 35, 36]) # Rank 3 2025-09-07T07:36:41.4503217Z >>> input_splits 2025-09-07T07:36:41.4503493Z [2, 2, 1, 1] # Rank 0 2025-09-07T07:36:41.4503863Z [3, 2, 2, 2] # Rank 1 2025-09-07T07:36:41.4504229Z [2, 1, 1, 1] # Rank 2 2025-09-07T07:36:41.4504594Z [2, 2, 2, 1] # Rank 3 2025-09-07T07:36:41.4504925Z >>> output_splits 2025-09-07T07:36:41.4505204Z [2, 3, 2, 2] # Rank 0 2025-09-07T07:36:41.4505596Z [2, 2, 1, 2] # Rank 1 2025-09-07T07:36:41.4505962Z [1, 2, 1, 2] # Rank 2 2025-09-07T07:36:41.4506315Z [1, 2, 1, 1] # Rank 3 2025-09-07T07:36:41.4506653Z >>> output = ... 2025-09-07T07:36:41.4507008Z >>> dist.all_to_all_single(output, input, output_splits, input_splits) 2025-09-07T07:36:41.4507419Z >>> output 2025-09-07T07:36:41.4507698Z tensor([ 0, 1, 10, 11, 12, 20, 21, 30, 31]) # Rank 0 2025-09-07T07:36:41.4508137Z tensor([ 2, 3, 13, 14, 22, 32, 33]) # Rank 1 2025-09-07T07:36:41.4508567Z tensor([ 4, 15, 16, 23, 34, 35]) # Rank 2 2025-09-07T07:36:41.4508996Z tensor([ 5, 17, 18, 24, 36]) # Rank 3 2025-09-07T07:36:41.4509267Z 2025-09-07T07:36:41.4509271Z 2025-09-07T07:36:41.4509442Z >>> # Another example with tensors of torch.cfloat type. 2025-09-07T07:36:41.4509810Z >>> input = torch.tensor( 2025-09-07T07:36:41.4510136Z ... [1 + 1j, 2 + 2j, 3 + 3j, 4 + 4j], dtype=torch.cfloat 2025-09-07T07:36:41.4510487Z ... ) + 4 * rank * (1 + 1j) 2025-09-07T07:36:41.4510758Z >>> input 2025-09-07T07:36:41.4511054Z tensor([1+1j, 2+2j, 3+3j, 4+4j]) # Rank 0 2025-09-07T07:36:41.4511518Z tensor([5+5j, 6+6j, 7+7j, 8+8j]) # Rank 1 2025-09-07T07:36:41.4511990Z tensor([9+9j, 10+10j, 11+11j, 12+12j]) # Rank 2 2025-09-07T07:36:41.4512520Z tensor([13+13j, 14+14j, 15+15j, 16+16j]) # Rank 3 2025-09-07T07:36:41.4512968Z >>> output = torch.empty([4], dtype=torch.int64) 2025-09-07T07:36:41.4513329Z >>> dist.all_to_all_single(output, input) 2025-09-07T07:36:41.4513644Z >>> output 2025-09-07T07:36:41.4513951Z tensor([1+1j, 5+5j, 9+9j, 13+13j]) # Rank 0 2025-09-07T07:36:41.4514500Z tensor([2+2j, 6+6j, 10+10j, 14+14j]) # Rank 1 2025-09-07T07:36:41.4514964Z tensor([3+3j, 7+7j, 11+11j, 15+15j]) # Rank 2 2025-09-07T07:36:41.4515465Z tensor([4+4j, 8+8j, 12+12j, 16+16j]) # Rank 3 2025-09-07T07:36:41.4515770Z 2025-09-07T07:36:41.4516018Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:41.4516383Z 2025-09-07T07:36:41.4516998Z msg = Cannot scrape callname=all_to_all in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py line=4697. 2025-09-07T07:36:41.4517939Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:41.4518314Z 2025-09-07T07:36:41.4518696Z Scatters list of input tensors to all processes in a group and return gathered list of tensors in output list. 2025-09-07T07:36:41.4519213Z 2025-09-07T07:36:41.4519334Z Complex tensors are supported. 2025-09-07T07:36:41.4519521Z 2025-09-07T07:36:41.4519601Z Args: 2025-09-07T07:36:41.4519937Z output_tensor_list (list[Tensor]): List of tensors to be gathered one 2025-09-07T07:36:41.4520425Z per rank. 2025-09-07T07:36:41.4520797Z input_tensor_list (list[Tensor]): List of tensors to scatter one per rank. 2025-09-07T07:36:41.4521367Z group (ProcessGroup, optional): The process group to work on. If None, 2025-09-07T07:36:41.4521844Z the default process group will be used. 2025-09-07T07:36:41.4522277Z async_op (bool, optional): Whether this op should be an async op. 2025-09-07T07:36:41.4522587Z 2025-09-07T07:36:41.4522686Z Returns: 2025-09-07T07:36:41.4522942Z Async work handle, if async_op is set to True. 2025-09-07T07:36:41.4523328Z None, if not async_op or if not part of the group. 2025-09-07T07:36:41.4523596Z 2025-09-07T07:36:41.4523891Z .. warning:: 2025-09-07T07:36:41.4524242Z `all_to_all` is experimental and subject to change. 2025-09-07T07:36:41.4524572Z 2025-09-07T07:36:41.4524672Z Examples: 2025-09-07T07:36:41.4524899Z >>> # xdoctest: +SKIP("Undefined rank") 2025-09-07T07:36:41.4525238Z >>> input = torch.arange(4) + rank * 4 2025-09-07T07:36:41.4525575Z >>> input = list(input.chunk(4)) 2025-09-07T07:36:41.4525874Z >>> input 2025-09-07T07:36:41.4526168Z [tensor([0]), tensor([1]), tensor([2]), tensor([3])] # Rank 0 2025-09-07T07:36:41.4526627Z [tensor([4]), tensor([5]), tensor([6]), tensor([7])] # Rank 1 2025-09-07T07:36:41.4527078Z [tensor([8]), tensor([9]), tensor([10]), tensor([11])] # Rank 2 2025-09-07T07:36:41.4527532Z [tensor([12]), tensor([13]), tensor([14]), tensor([15])] # Rank 3 2025-09-07T07:36:41.4527989Z >>> output = list(torch.empty([4], dtype=torch.int64).chunk(4)) 2025-09-07T07:36:41.4538168Z >>> dist.all_to_all(output, input) 2025-09-07T07:36:41.4538599Z >>> output 2025-09-07T07:36:41.4538914Z [tensor([0]), tensor([4]), tensor([8]), tensor([12])] # Rank 0 2025-09-07T07:36:41.4539369Z [tensor([1]), tensor([5]), tensor([9]), tensor([13])] # Rank 1 2025-09-07T07:36:41.4539827Z [tensor([2]), tensor([6]), tensor([10]), tensor([14])] # Rank 2 2025-09-07T07:36:41.4540283Z [tensor([3]), tensor([7]), tensor([11]), tensor([15])] # Rank 3 2025-09-07T07:36:41.4540563Z 2025-09-07T07:36:41.4540740Z >>> # Essentially, it is similar to following operation: 2025-09-07T07:36:41.4541124Z >>> scatter_list = input 2025-09-07T07:36:41.4541392Z >>> gather_list = output 2025-09-07T07:36:41.4541680Z >>> for i in range(world_size): 2025-09-07T07:36:41.4542244Z >>> dist.scatter(gather_list[i], scatter_list if i == rank else [], src=i) 2025-09-07T07:36:41.4542588Z 2025-09-07T07:36:41.4542685Z >>> input 2025-09-07T07:36:41.4542961Z tensor([0, 1, 2, 3, 4, 5]) # Rank 0 2025-09-07T07:36:41.4543402Z tensor([10, 11, 12, 13, 14, 15, 16, 17, 18]) # Rank 1 2025-09-07T07:36:41.4543846Z tensor([20, 21, 22, 23, 24]) # Rank 2 2025-09-07T07:36:41.4544400Z tensor([30, 31, 32, 33, 34, 35, 36]) # Rank 3 2025-09-07T07:36:41.4544987Z >>> input_splits 2025-09-07T07:36:41.4545427Z [2, 2, 1, 1] # Rank 0 2025-09-07T07:36:41.4546078Z [3, 2, 2, 2] # Rank 1 2025-09-07T07:36:41.4546592Z [2, 1, 1, 1] # Rank 2 2025-09-07T07:36:41.4546956Z [2, 2, 2, 1] # Rank 3 2025-09-07T07:36:41.4547283Z >>> output_splits 2025-09-07T07:36:41.4547560Z [2, 3, 2, 2] # Rank 0 2025-09-07T07:36:41.4547920Z [2, 2, 1, 2] # Rank 1 2025-09-07T07:36:41.4548387Z [1, 2, 1, 2] # Rank 2 2025-09-07T07:36:41.4548740Z [1, 2, 1, 1] # Rank 3 2025-09-07T07:36:41.4549116Z >>> input = list(input.split(input_splits)) 2025-09-07T07:36:41.4549493Z >>> input 2025-09-07T07:36:41.4549829Z [tensor([0, 1]), tensor([2, 3]), tensor([4]), tensor([5])] # Rank 0 2025-09-07T07:36:41.4550361Z [tensor([10, 11, 12]), tensor([13, 14]), tensor([15, 16]), tensor([17, 18])] # Rank 1 2025-09-07T07:36:41.4550877Z [tensor([20, 21]), tensor([22]), tensor([23]), tensor([24])] # Rank 2 2025-09-07T07:36:41.4551407Z [tensor([30, 31]), tensor([32, 33]), tensor([34, 35]), tensor([36])] # Rank 3 2025-09-07T07:36:41.4551826Z >>> output = ... 2025-09-07T07:36:41.4552095Z >>> dist.all_to_all(output, input) 2025-09-07T07:36:41.4552387Z >>> output 2025-09-07T07:36:41.4552723Z [tensor([0, 1]), tensor([10, 11, 12]), tensor([20, 21]), tensor([30, 31])] # Rank 0 2025-09-07T07:36:41.4553281Z [tensor([2, 3]), tensor([13, 14]), tensor([22]), tensor([32, 33])] # Rank 1 2025-09-07T07:36:41.4553815Z [tensor([4]), tensor([15, 16]), tensor([23]), tensor([34, 35])] # Rank 2 2025-09-07T07:36:41.4554418Z [tensor([5]), tensor([17, 18]), tensor([24]), tensor([36])] # Rank 3 2025-09-07T07:36:41.4554762Z 2025-09-07T07:36:41.4554922Z >>> # Another example with tensors of torch.cfloat type. 2025-09-07T07:36:41.4555307Z >>> input = torch.tensor( 2025-09-07T07:36:41.4555637Z ... [1 + 1j, 2 + 2j, 3 + 3j, 4 + 4j], dtype=torch.cfloat 2025-09-07T07:36:41.4555993Z ... ) + 4 * rank * (1 + 1j) 2025-09-07T07:36:41.4556280Z >>> input = list(input.chunk(4)) 2025-09-07T07:36:41.4556583Z >>> input 2025-09-07T07:36:41.4556934Z [tensor([1+1j]), tensor([2+2j]), tensor([3+3j]), tensor([4+4j])] # Rank 0 2025-09-07T07:36:41.4557481Z [tensor([5+5j]), tensor([6+6j]), tensor([7+7j]), tensor([8+8j])] # Rank 1 2025-09-07T07:36:41.4558023Z [tensor([9+9j]), tensor([10+10j]), tensor([11+11j]), tensor([12+12j])] # Rank 2 2025-09-07T07:36:41.4558586Z [tensor([13+13j]), tensor([14+14j]), tensor([15+15j]), tensor([16+16j])] # Rank 3 2025-09-07T07:36:41.4559117Z >>> output = list(torch.empty([4], dtype=torch.int64).chunk(4)) 2025-09-07T07:36:41.4559531Z >>> dist.all_to_all(output, input) 2025-09-07T07:36:41.4559834Z >>> output 2025-09-07T07:36:41.4560166Z [tensor([1+1j]), tensor([5+5j]), tensor([9+9j]), tensor([13+13j])] # Rank 0 2025-09-07T07:36:41.4560710Z [tensor([2+2j]), tensor([6+6j]), tensor([10+10j]), tensor([14+14j])] # Rank 1 2025-09-07T07:36:41.4561283Z [tensor([3+3j]), tensor([7+7j]), tensor([11+11j]), tensor([15+15j])] # Rank 2 2025-09-07T07:36:41.4561831Z [tensor([4+4j]), tensor([8+8j]), tensor([12+12j]), tensor([16+16j])] # Rank 3 2025-09-07T07:36:41.4562161Z 2025-09-07T07:36:41.4562165Z 2025-09-07T07:36:41.4562421Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:41.4562805Z 2025-09-07T07:36:41.4563354Z msg = Cannot scrape callname=__doc__ in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/launch.py line=2. 2025-09-07T07:36:41.4564219Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:41.4564600Z 2025-09-07T07:36:41.4564729Z Module ``torch.distributed.launch``. 2025-09-07T07:36:41.4564942Z 2025-09-07T07:36:41.4565190Z ``torch.distributed.launch`` is a module that spawns up multiple distributed 2025-09-07T07:36:41.4565713Z training processes on each of the training nodes. 2025-09-07T07:36:41.4565977Z 2025-09-07T07:36:41.4566087Z .. warning:: 2025-09-07T07:36:41.4566211Z 2025-09-07T07:36:41.4566473Z This module is going to be deprecated in favor of :ref:`torchrun `. 2025-09-07T07:36:41.4566878Z 2025-09-07T07:36:41.4567114Z The utility can be used for single-node distributed training, in which one or 2025-09-07T07:36:41.4567719Z more processes per node will be spawned. The utility can be used for either 2025-09-07T07:36:41.4568298Z CPU training or GPU training. If the utility is used for GPU training, 2025-09-07T07:36:41.4568913Z each distributed process will be operating on a single GPU. This can achieve 2025-09-07T07:36:41.4569511Z well-improved single-node training performance. It can also be used in 2025-09-07T07:36:41.4570115Z multi-node distributed training, by spawning up multiple processes on each node 2025-09-07T07:36:41.4570737Z for well-improved multi-node distributed training performance as well. 2025-09-07T07:36:41.4571321Z This will especially be beneficial for systems with multiple Infiniband 2025-09-07T07:36:41.4571969Z interfaces that have direct-GPU support, since all of them can be utilized for 2025-09-07T07:36:41.4572461Z aggregated communication bandwidth. 2025-09-07T07:36:41.4572672Z 2025-09-07T07:36:41.4572903Z In both cases of single-node distributed training or multi-node distributed 2025-09-07T07:36:41.4573525Z training, this utility will launch the given number of processes per node 2025-09-07T07:36:41.4574112Z (``--nproc-per-node``). If used for GPU training, this number needs to be less 2025-09-07T07:36:41.4574685Z or equal to the number of GPUs on the current system (``nproc_per_node``), 2025-09-07T07:36:41.4575218Z and each process will be operating on a single GPU from *GPU 0 to 2025-09-07T07:36:41.4575648Z GPU (nproc_per_node - 1)*. 2025-09-07T07:36:41.4575839Z 2025-09-07T07:36:41.4575942Z **How to use this module:** 2025-09-07T07:36:41.4576116Z 2025-09-07T07:36:41.4576281Z 1. Single-Node multi-process distributed training 2025-09-07T07:36:41.4576543Z 2025-09-07T07:36:41.4576641Z :: 2025-09-07T07:36:41.4576749Z 2025-09-07T07:36:41.4576986Z python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE 2025-09-07T07:36:41.4577536Z YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3 and all other 2025-09-07T07:36:41.4577971Z arguments of your training script) 2025-09-07T07:36:41.4578204Z 2025-09-07T07:36:41.4578429Z 2. Multi-Node multi-process distributed training: (e.g. two nodes) 2025-09-07T07:36:41.4578751Z 2025-09-07T07:36:41.4578756Z 2025-09-07T07:36:41.4578912Z Node 1: *(IP: 192.168.1.1, and has a free port: 1234)* 2025-09-07T07:36:41.4579166Z 2025-09-07T07:36:41.4579249Z :: 2025-09-07T07:36:41.4579368Z 2025-09-07T07:36:41.4579601Z python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE 2025-09-07T07:36:41.4580118Z --nnodes=2 --node-rank=0 --master-addr="192.168.1.1" 2025-09-07T07:36:41.4580605Z --master-port=1234 YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3 2025-09-07T07:36:41.4581104Z and all other arguments of your training script) 2025-09-07T07:36:41.4581385Z 2025-09-07T07:36:41.4581470Z Node 2: 2025-09-07T07:36:41.4581596Z 2025-09-07T07:36:41.4581680Z :: 2025-09-07T07:36:41.4581786Z 2025-09-07T07:36:41.4582033Z python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE 2025-09-07T07:36:41.4582551Z --nnodes=2 --node-rank=1 --master-addr="192.168.1.1" 2025-09-07T07:36:41.4583021Z --master-port=1234 YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3 2025-09-07T07:36:41.4583504Z and all other arguments of your training script) 2025-09-07T07:36:41.4583779Z 2025-09-07T07:36:41.4583936Z 3. To look up what optional arguments this module offers: 2025-09-07T07:36:41.4584206Z 2025-09-07T07:36:41.4584300Z :: 2025-09-07T07:36:41.4584405Z 2025-09-07T07:36:41.4584552Z python -m torch.distributed.launch --help 2025-09-07T07:36:41.4584794Z 2025-09-07T07:36:41.4584798Z 2025-09-07T07:36:41.4584894Z **Important Notices:** 2025-09-07T07:36:41.4585067Z 2025-09-07T07:36:41.4585253Z 1. This utility and multi-process distributed (single-node or 2025-09-07T07:36:41.4585805Z multi-node) GPU training currently only achieves the best performance using 2025-09-07T07:36:41.4586459Z the NCCL distributed backend. Thus NCCL backend is the recommended backend to 2025-09-07T07:36:41.4586940Z use for GPU training. 2025-09-07T07:36:41.4587097Z 2025-09-07T07:36:41.4587310Z 2. In your training program, you must parse the command-line argument: 2025-09-07T07:36:41.4587914Z ``--local-rank=LOCAL_PROCESS_RANK``, which will be provided by this module. 2025-09-07T07:36:41.4588497Z If your training program uses GPUs, you should ensure that your code only 2025-09-07T07:36:41.4589040Z runs on the GPU device of LOCAL_PROCESS_RANK. This can be done by: 2025-09-07T07:36:41.4589346Z 2025-09-07T07:36:41.4589454Z Parsing the local_rank argument 2025-09-07T07:36:41.4589652Z 2025-09-07T07:36:41.4589732Z :: 2025-09-07T07:36:41.4589855Z 2025-09-07T07:36:41.4589951Z >>> # xdoctest: +SKIP 2025-09-07T07:36:41.4590220Z >>> import argparse 2025-09-07T07:36:41.4590505Z >>> parser = argparse.ArgumentParser() 2025-09-07T07:36:41.4590923Z >>> parser.add_argument("--local-rank", "--local_rank", type=int) 2025-09-07T07:36:41.4591341Z >>> args = parser.parse_args() 2025-09-07T07:36:41.4591569Z 2025-09-07T07:36:41.4591705Z Set your device to local rank using either 2025-09-07T07:36:41.4591928Z 2025-09-07T07:36:41.4592020Z :: 2025-09-07T07:36:41.4592126Z 2025-09-07T07:36:41.4592325Z >>> torch.cuda.set_device(args.local_rank) # before your code runs 2025-09-07T07:36:41.4592652Z 2025-09-07T07:36:41.4592730Z or 2025-09-07T07:36:41.4592847Z 2025-09-07T07:36:41.4592926Z :: 2025-09-07T07:36:41.4593030Z 2025-09-07T07:36:41.4593171Z >>> with torch.cuda.device(args.local_rank): 2025-09-07T07:36:41.4593516Z >>> # your code to run 2025-09-07T07:36:41.4593775Z >>> ... 2025-09-07T07:36:41.4593914Z 2025-09-07T07:36:41.4594018Z .. versionchanged:: 2.0.0 2025-09-07T07:36:41.4594186Z 2025-09-07T07:36:41.4594530Z The launcher will passes the ``--local-rank=`` argument to your script. 2025-09-07T07:36:41.4595150Z From PyTorch 2.0.0 onwards, the dashed ``--local-rank`` is preferred over the 2025-09-07T07:36:41.4595641Z previously used underscored ``--local_rank``. 2025-09-07T07:36:41.4595903Z 2025-09-07T07:36:41.4596139Z For backward compatibility, it may be necessary for users to handle both 2025-09-07T07:36:41.4596770Z cases in their argument parsing code. This means including both ``"--local-rank"`` 2025-09-07T07:36:41.4597380Z and ``"--local_rank"`` in the argument parser. If only ``"--local_rank"`` is 2025-09-07T07:36:41.4597973Z provided, the launcher will trigger an error: "error: unrecognized arguments: 2025-09-07T07:36:41.4598570Z --local-rank=". For training code that only supports PyTorch 2.0.0+, 2025-09-07T07:36:41.4599110Z including ``"--local-rank"`` should be sufficient. 2025-09-07T07:36:41.4599382Z 2025-09-07T07:36:41.4599615Z 3. In your training program, you are supposed to call the following function 2025-09-07T07:36:41.4600207Z at the beginning to start the distributed backend. It is strongly recommended 2025-09-07T07:36:41.4600805Z that ``init_method=env://``. Other init methods (e.g. ``tcp://``) may work, 2025-09-07T07:36:41.4601326Z but ``env://`` is the one that is officially supported by this module. 2025-09-07T07:36:41.4601641Z 2025-09-07T07:36:41.4601724Z :: 2025-09-07T07:36:41.4601848Z 2025-09-07T07:36:41.4602063Z >>> torch.distributed.init_process_group(backend='YOUR BACKEND', 2025-09-07T07:36:41.4602524Z >>> init_method='env://') 2025-09-07T07:36:41.4602763Z 2025-09-07T07:36:41.4602999Z 4. In your training program, you can either use regular distributed functions 2025-09-07T07:36:41.4603598Z or use :func:`torch.nn.parallel.DistributedDataParallel` module. If your 2025-09-07T07:36:41.4604164Z training program uses GPUs for training and you would like to use 2025-09-07T07:36:41.4604685Z :func:`torch.nn.parallel.DistributedDataParallel` module, 2025-09-07T07:36:41.4605095Z here is how to configure it. 2025-09-07T07:36:41.4605312Z 2025-09-07T07:36:41.4605391Z :: 2025-09-07T07:36:41.4605511Z 2025-09-07T07:36:41.4605703Z >>> model = torch.nn.parallel.DistributedDataParallel(model, 2025-09-07T07:36:41.4606153Z >>> device_ids=[args.local_rank], 2025-09-07T07:36:41.4606584Z >>> output_device=args.local_rank) 2025-09-07T07:36:41.4606842Z 2025-09-07T07:36:41.4607092Z Please ensure that ``device_ids`` argument is set to be the only GPU device id 2025-09-07T07:36:41.4607678Z that your code will be operating on. This is generally the local rank of the 2025-09-07T07:36:41.4608261Z process. In other words, the ``device_ids`` needs to be ``[args.local_rank]``, 2025-09-07T07:36:41.4608839Z and ``output_device`` needs to be ``args.local_rank`` in order to use this 2025-09-07T07:36:41.4609261Z utility 2025-09-07T07:36:41.4609375Z 2025-09-07T07:36:41.4609631Z 5. Another way to pass ``local_rank`` to the subprocesses via environment variable 2025-09-07T07:36:41.4610205Z ``LOCAL_RANK``. This behavior is enabled when you launch the script with 2025-09-07T07:36:41.4610790Z ``--use-env=True``. You must adjust the subprocess example above to replace 2025-09-07T07:36:41.4611328Z ``args.local_rank`` with ``os.environ['LOCAL_RANK']``; the launcher 2025-09-07T07:36:41.4611815Z will not pass ``--local-rank`` when you specify this flag. 2025-09-07T07:36:41.4612099Z 2025-09-07T07:36:41.4612185Z .. warning:: 2025-09-07T07:36:41.4612322Z 2025-09-07T07:36:41.4612521Z ``local_rank`` is NOT globally unique: it is only unique per process 2025-09-07T07:36:41.4613027Z on a machine. Thus, don't use it to decide if you should, e.g., 2025-09-07T07:36:41.4613444Z write to a networked filesystem. See 2025-09-07T07:36:41.4613891Z https://github.com/pytorch/pytorch/issues/12042 for an example of 2025-09-07T07:36:41.4614376Z how things can go wrong if you don't do this correctly. 2025-09-07T07:36:41.4614658Z 2025-09-07T07:36:41.4614664Z 2025-09-07T07:36:41.4614668Z 2025-09-07T07:36:41.4614672Z 2025-09-07T07:36:41.4614921Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:41.4615298Z 2025-09-07T07:36:41.5028219Z 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. 2025-09-07T07:36:41.5029289Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:41.5029670Z 2025-09-07T07:36:41.5029921Z Creates an :class:`ShardedTensor` from local shards and the global metadata. 2025-09-07T07:36:41.5030425Z Needs to be called on all ranks in an SPMD fashion. 2025-09-07T07:36:41.5030680Z 2025-09-07T07:36:41.5030764Z Args: 2025-09-07T07:36:41.5031311Z local_shards (List[:class `torch.distributed._shard.sharded_tensor.Shard`]): A list 2025-09-07T07:36:41.5031885Z of shards that represent the local shards on this rank. 2025-09-07T07:36:41.5032398Z global_size (int...): a list, tuple, or `torch.Size` of integers defining the 2025-09-07T07:36:41.5032878Z shape of the overall sharded tensor. 2025-09-07T07:36:41.5033103Z 2025-09-07T07:36:41.5033191Z Keyword args: 2025-09-07T07:36:41.5033593Z process_group (ProcessGroup, optional): The process group to work on. If None, 2025-09-07T07:36:41.5034107Z the default process group will be used. 2025-09-07T07:36:41.5034564Z init_rrefs (bool, optional): Whether or not to initialize 2025-09-07T07:36:41.5035046Z :class:`torch.distributed.rpc.RRef`s pointing to remote shards. 2025-09-07T07:36:41.5035572Z Need to initialize the RPC Framework if specified as ``True``. 2025-09-07T07:36:41.5035980Z Default: ``False``. 2025-09-07T07:36:41.5036156Z 2025-09-07T07:36:41.5036252Z Returns: 2025-09-07T07:36:41.5036513Z A :class:`ShardedTensor` object handle on this rank 2025-09-07T07:36:41.5036784Z 2025-09-07T07:36:41.5036788Z 2025-09-07T07:36:41.5036933Z Examples: 2025-09-07T07:36:41.5037309Z Suppose we want construct a sharded tensor on two ranks, global size = (10, 5), 2025-09-07T07:36:41.5037870Z each shard have a (5, 5) local tensor, we can do it like below: 2025-09-07T07:36:41.5038166Z 2025-09-07T07:36:41.5038265Z on rank 0: 2025-09-07T07:36:41.5038569Z >>> # xdoctest: +SKIP("not distributed") 2025-09-07T07:36:41.5038926Z >>> local_shard_metadata = ShardMetadata( 2025-09-07T07:36:41.5039267Z >>> shard_offsets=[0, 0], 2025-09-07T07:36:41.5039563Z >>> shard_lengths=[5, 5], 2025-09-07T07:36:41.5039851Z >>> placement="rank:0/cuda:0" 2025-09-07T07:36:41.5040144Z >>> ) 2025-09-07T07:36:41.5040456Z >>> local_shards = [Shard(torch.randn(5, 5), local_shard_metadata)] 2025-09-07T07:36:41.5040966Z >>> sharded_tensor = init_from_local_shards(local_shards, [10, 5]) 2025-09-07T07:36:41.5041273Z 2025-09-07T07:36:41.5041369Z on rank 1: 2025-09-07T07:36:41.5041610Z >>> # xdoctest: +SKIP("not distributed") 2025-09-07T07:36:41.5041961Z >>> local_shard_metadata = ShardMetadata( 2025-09-07T07:36:41.5042292Z >>> shard_offsets=[5, 0], 2025-09-07T07:36:41.5042625Z >>> shard_lengths=[5, 5], 2025-09-07T07:36:41.5042912Z >>> placement="rank:1/cuda:1" 2025-09-07T07:36:41.5043203Z >>> ) 2025-09-07T07:36:41.5043516Z >>> local_shards = [Shard(torch.randn(5, 5), local_shard_metadata)] 2025-09-07T07:36:41.5044024Z >>> sharded_tensor = init_from_local_shards(local_shards, [10, 5]) 2025-09-07T07:36:41.5044328Z 2025-09-07T07:36:41.5044576Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:41.5044952Z 2025-09-07T07:36:41.5131297Z 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=835. 2025-09-07T07:36:41.5132403Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:41.5132800Z 2025-09-07T07:36:41.5133058Z Initialize a ShardedTensor given only one local tensor, global sharded tensor 2025-09-07T07:36:41.5133554Z size and sharding spec on each rank. 2025-09-07T07:36:41.5133763Z 2025-09-07T07:36:41.5133857Z Args: 2025-09-07T07:36:41.5134182Z local_tensor (Tensor): Single tensor of local shard stored in each rank. 2025-09-07T07:36:41.5134789Z sharding_spec (:class:`torch.distributed._shard.sharding_spec.ShardingSpec`): 2025-09-07T07:36:41.5135335Z The specification describing how to shard the Tensor. 2025-09-07T07:36:41.5135787Z global_size (Sequence[int]): Size of the sharded tensor. 2025-09-07T07:36:41.5136312Z process_group (ProcessGroup, optional): The process group to aggregate on. 2025-09-07T07:36:41.5136775Z Default: None 2025-09-07T07:36:41.5137190Z init_rrefs (bool, optional): Whether or not to initialize 2025-09-07T07:36:41.5137691Z :class:`torch.distributed.rpc.RRef`s pointing to remote shards. 2025-09-07T07:36:41.5138203Z Need to initialize the RPC Framework if specified as ``True``. 2025-09-07T07:36:41.5138910Z Default: ``False``. 2025-09-07T07:36:41.5139256Z 2025-09-07T07:36:41.5139357Z Returns: 2025-09-07T07:36:41.5139724Z A :class:`ShardedTensor` sharded based on the given sharding_spec with local 2025-09-07T07:36:41.5140209Z tensor stored in the current rank. 2025-09-07T07:36:41.5140428Z 2025-09-07T07:36:41.5140512Z Examples: 2025-09-07T07:36:41.5140730Z >>> # xdoctest: +SKIP 2025-09-07T07:36:41.5141062Z >>> # All tensors below are of torch.int64 type. 2025-09-07T07:36:41.5141431Z >>> # We have 2 process groups, 2 ranks. 2025-09-07T07:36:41.5141835Z >>> tensor = torch.arange(2, dtype=torch.int64) + 1 + 2 * rank 2025-09-07T07:36:41.5142318Z >>> local_tensor = torch.unsqueeze(torch.cat([tensor, tensor + 2])) 2025-09-07T07:36:41.5142734Z >>> local_tensor 2025-09-07T07:36:41.5142985Z tensor([[1, 2, 3, 4]]) # Rank 0 2025-09-07T07:36:41.5143285Z tensor([[3, 4, 5, 6]]) # Rank 1 2025-09-07T07:36:41.5143631Z >>> sharding_dim = 0 2025-09-07T07:36:41.5143928Z >>> sharding_spec = ChunkShardingSpec( 2025-09-07T07:36:41.5144266Z dim=sharding_dim, 2025-09-07T07:36:41.5144547Z placements=[ 2025-09-07T07:36:41.5144802Z "rank:0/cuda:0", 2025-09-07T07:36:41.5145132Z "rank:1/cuda:1", 2025-09-07T07:36:41.5145400Z ], 2025-09-07T07:36:41.5145615Z ) 2025-09-07T07:36:41.5145856Z >>> st = ShardedTensor._init_from_local_tensor( 2025-09-07T07:36:41.5146232Z ... local_tensor, sharding_spec, [2, 4] 2025-09-07T07:36:41.5146557Z ... ) 2025-09-07T07:36:41.5146763Z >>> st 2025-09-07T07:36:41.5146965Z ShardedTensor( 2025-09-07T07:36:41.5147223Z ShardedTensorMetadata( 2025-09-07T07:36:41.5147517Z shards_metadata=[ 2025-09-07T07:36:41.5147969Z ShardMetadata(shard_offsets=[0, 0], shard_sizes=[1, 4], placement=rank:0/cuda:0), 2025-09-07T07:36:41.5148614Z ShardMetadata(shard_offsets=[1, 0], shard_sizes=[1, 4], placement=rank:1/cuda:1), 2025-09-07T07:36:41.5149083Z ], 2025-09-07T07:36:41.5149368Z size=torch.Size([2, 4]) 2025-09-07T07:36:41.5149655Z ) 2025-09-07T07:36:41.5149852Z >>> st.local_tensor() 2025-09-07T07:36:41.5150117Z tensor([1, 2, 3, 4]) # Rank 0 2025-09-07T07:36:41.5150402Z tensor([3, 4, 5, 6]) # Rank 1 2025-09-07T07:36:41.5150585Z 2025-09-07T07:36:41.5150862Z Warning: This API is experimental and subject to change. It lacks of a fully across 2025-09-07T07:36:41.5151496Z rank validations, and we only validate the local shard on the current rank. 2025-09-07T07:36:41.5152062Z We fully rely on the user to ensure local tensor is sharded based on the 2025-09-07T07:36:41.5152492Z sharding spec. 2025-09-07T07:36:41.5152647Z 2025-09-07T07:36:41.5152909Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:41.5153279Z 2025-09-07T07:36:41.5153971Z 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=1076. 2025-09-07T07:36:41.5155124Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:41.5155505Z 2025-09-07T07:36:41.5155752Z Reshard a sharded tensor given the ``resharding_spec``. For now, we only support 2025-09-07T07:36:41.5156220Z single local shard. 2025-09-07T07:36:41.5156379Z 2025-09-07T07:36:41.5156598Z If ``resharding_spec`` is same as the original one, this becomes a no-op. 2025-09-07T07:36:41.5157180Z If only ``resharding_spec`` shares the same sharding dim with the original one, 2025-09-07T07:36:41.5157641Z we swap local shards directly. 2025-09-07T07:36:41.5158130Z For more generic cases, we merge different shards across different ranks and split 2025-09-07T07:36:41.5158774Z the local shards based on the ``resharding_spec`` via `all_to_all` collective API. 2025-09-07T07:36:41.5159144Z 2025-09-07T07:36:41.5159235Z Args: 2025-09-07T07:36:41.5159640Z resharding_spec (:class:`torch.distributed._shard.sharding_spec.ShardingSpec`): The 2025-09-07T07:36:41.5160202Z specification describing how the tensor is sharded. 2025-09-07T07:36:41.5160486Z 2025-09-07T07:36:41.5160570Z Returns: 2025-09-07T07:36:41.5160925Z A :class:`ShardedTensor` object whose local shards are resharded. 2025-09-07T07:36:41.5161242Z 2025-09-07T07:36:41.5161338Z Examples: 2025-09-07T07:36:41.5161545Z >>> # xdoctest: +SKIP 2025-09-07T07:36:41.5161833Z >>> # We have 2 process groups, 2 ranks. 2025-09-07T07:36:41.5162237Z >>> tensor = torch.arange(4, dtype=torch.int64) + 1 + 2 * rank 2025-09-07T07:36:41.5162655Z >>> tensor = torch.stack([tensor, tensor]) 2025-09-07T07:36:41.5162962Z >>> tensor 2025-09-07T07:36:41.5163213Z tensor([[1, 2, 3, 4], [1, 2, 3, 4]]) # Rank 0 2025-09-07T07:36:41.5163561Z tensor([[3, 4, 5, 6], [3, 4, 5, 6]]) # Rank 1 2025-09-07T07:36:41.5163932Z tensor([[5, 6, 7, 8], [5, 6, 7, 8]]) # Rank 2 2025-09-07T07:36:41.5164284Z tensor([[7, 8, 9, 10], [7, 8, 9, 10]]) # Rank 3 2025-09-07T07:36:41.5164603Z >>> sharding_dim = 0 2025-09-07T07:36:41.5164880Z >>> spec = ChunkShardingSpec( 2025-09-07T07:36:41.5165181Z dim=sharding_dim, 2025-09-07T07:36:41.5165492Z placements=[ 2025-09-07T07:36:41.5165741Z "rank:0/cuda:0", 2025-09-07T07:36:41.5166017Z "rank:1/cuda:1", 2025-09-07T07:36:41.5166292Z "rank:2/cuda:2", 2025-09-07T07:36:41.5166568Z "rank:3/cuda:3", 2025-09-07T07:36:41.5166823Z ], 2025-09-07T07:36:41.5167040Z ) 2025-09-07T07:36:41.5167261Z >>> current_offsets = [0] * 2 2025-09-07T07:36:41.5167551Z >>> current_offsets[0] = rank * 2 2025-09-07T07:36:41.5167874Z >>> shard_metadata = ShardMetadata( 2025-09-07T07:36:41.5168237Z shard_offsets=copy.deepcopy(current_offsets), 2025-09-07T07:36:41.5168607Z shard_sizes=tensor.size(), 2025-09-07T07:36:41.5168943Z placement=spec.placements[rank], 2025-09-07T07:36:41.5169247Z ) 2025-09-07T07:36:41.5169488Z >>> local_shards = [ 2025-09-07T07:36:41.5169739Z Shard( 2025-09-07T07:36:41.5169963Z tensor=tensor, 2025-09-07T07:36:41.5170252Z metadata=shard_metadata, 2025-09-07T07:36:41.5170551Z ) 2025-09-07T07:36:41.5170762Z ] 2025-09-07T07:36:41.5171095Z >>> st = ShardedTensor._init_from_local_shards(local_shards, tensor.size()) 2025-09-07T07:36:41.5171542Z >>> sharding_dim = 1 2025-09-07T07:36:41.5171834Z >>> resharding_spec = ChunkShardingSpec( 2025-09-07T07:36:41.5172167Z dim=sharding_dim, 2025-09-07T07:36:41.5172433Z placements=[ 2025-09-07T07:36:41.5172699Z "rank:0/cuda:0", 2025-09-07T07:36:41.5172985Z "rank:1/cuda:1", 2025-09-07T07:36:41.5173262Z "rank:2/cuda:2", 2025-09-07T07:36:41.5173530Z "rank:3/cuda:3", 2025-09-07T07:36:41.5173793Z ], 2025-09-07T07:36:41.5174004Z ) 2025-09-07T07:36:41.5174233Z >>> st.reshard(resharding_spec) 2025-09-07T07:36:41.5174541Z >>> tensor = st.local_shards()[0].tensor 2025-09-07T07:36:41.5174848Z >>> tensor 2025-09-07T07:36:41.5175124Z tensor([[1], [1], [3], [3], [5], [5], [7], [7]]) # Rank 0 2025-09-07T07:36:41.5175521Z tensor([[2], [2], [4], [4], [6], [6], [8], [8]]) # Rank 1 2025-09-07T07:36:41.5175901Z tensor([[3], [3], [5], [5], [7], [7], [9], [9]]) # Rank 2 2025-09-07T07:36:41.5176295Z tensor([[4], [4], [6], [6], [8], [8], [10], [10]]) # Rank 3 2025-09-07T07:36:41.5176556Z 2025-09-07T07:36:41.5176806Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:41.5177214Z 2025-09-07T07:36:41.5268993Z 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. 2025-09-07T07:36:41.5270015Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:41.5270398Z 2025-09-07T07:36:41.5270635Z Representation of a sharding plan, describes how to shard a module 2025-09-07T07:36:41.5271224Z across hosts. `plan` is used to shard module parameters according to the spec provided, 2025-09-07T07:36:41.5271911Z `output_plan` and `return_local_tensor` are optional, they are used to specify the output 2025-09-07T07:36:41.5272569Z layout of a module with a spec, and when to convert back to data parallel fashion. 2025-09-07T07:36:41.5272931Z 2025-09-07T07:36:41.5273023Z Args: 2025-09-07T07:36:41.5273410Z plan (Dict[str, Union[:class:`torch.distributed._shard.sharding_spec.ShardingSpec`, 2025-09-07T07:36:41.5273961Z :class:`torch.distributed._shard.sharder.Sharder`]): 2025-09-07T07:36:41.5274563Z a dict describes how to shard a module, there're currently two ways to shard a module: 2025-09-07T07:36:41.5275211Z 1. directly shard a module parameter by a `ShardingSpec`, keyed by the name of 2025-09-07T07:36:41.5275834Z a parameter to a `ShardingSpec`. 2025-09-07T07:36:41.5276330Z 2. shard a submodule by applying a `Sharder` on it, keyed by the name of a module 2025-09-07T07:36:41.5276800Z to a `Sharder` object. 2025-09-07T07:36:41.5277387Z output_plan (Dict[str, :class:`torch.distributed._shard.sharding_spec.ShardingSpec`), optional): 2025-09-07T07:36:41.5278101Z a dict specifies the layout of a module's output which produces a ShardedTensor, 2025-09-07T07:36:41.5278746Z keyed by the name of module to ShardingSpec("" in key means the root module). 2025-09-07T07:36:41.5279189Z Default: `None` 2025-09-07T07:36:41.5279609Z return_local_tensor (List[str], optional): a list of string, each element enables 2025-09-07T07:36:41.5280226Z a module's sharded output to be returned as a Tensor from its local shards to 2025-09-07T07:36:41.5280848Z ensure further processing in a data parallel fashion. ("" in list means the 2025-09-07T07:36:41.5281319Z root module). 2025-09-07T07:36:41.5281609Z Default: None 2025-09-07T07:36:41.5281854Z Example: 2025-09-07T07:36:41.5282272Z Suppose we want to shard a module with two linear layers and then run it with DDP, we also 2025-09-07T07:36:41.5282965Z want to convert the output of the second linear layer back to DDP, we can do it as follows: 2025-09-07T07:36:41.5283381Z 2025-09-07T07:36:41.5283550Z >>> # xdoctest: +REQUIRES(module:torch._C._distributed_c10d) 2025-09-07T07:36:41.5283943Z >>> class MyModule(nn.Module): 2025-09-07T07:36:41.5284261Z >>> def __init__(self) -> None: 2025-09-07T07:36:41.5284576Z >>> super().__init__() 2025-09-07T07:36:41.5284861Z >>> self.fc1 = nn.Linear() 2025-09-07T07:36:41.5285174Z >>> self.gelu = nn.GELU() 2025-09-07T07:36:41.5285483Z >>> self.fc2 = nn.Linear() 2025-09-07T07:36:41.5285796Z >>> self.relu = nn.Linear() 2025-09-07T07:36:41.5286096Z >>> 2025-09-07T07:36:41.5286325Z >>> def forward(self, input): 2025-09-07T07:36:41.5286719Z >>> return self.relu(self.fc2(self.gelu(self.fc1(input)))) 2025-09-07T07:36:41.5287005Z 2025-09-07T07:36:41.5287009Z 2025-09-07T07:36:41.5287153Z >>> # xdoctest: +SKIP("Undefined spec1, spec2) 2025-09-07T07:36:41.5287503Z >>> sharding_plan = ShardingPlan( 2025-09-07T07:36:41.5287809Z >>> plan={ 2025-09-07T07:36:41.5288056Z >>> "fc1.weight": spec1, 2025-09-07T07:36:41.5288363Z >>> "fc2.weight": spec2 2025-09-07T07:36:41.5288632Z >>> }, 2025-09-07T07:36:41.5288861Z >>> output_plan={ 2025-09-07T07:36:41.5289130Z >>> "fc2": output_spec 2025-09-07T07:36:41.5289409Z >>> }, 2025-09-07T07:36:41.5289672Z >>> return_local_tensor=["fc2"] 2025-09-07T07:36:41.5289965Z >>> ) 2025-09-07T07:36:41.5290090Z 2025-09-07T07:36:41.5290339Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:41.5290709Z 2025-09-07T07:36:41.6029689Z 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. 2025-09-07T07:36:41.6030837Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:41.6031216Z 2025-09-07T07:36:41.6031336Z Run post-localSGD algorithm. 2025-09-07T07:36:41.6031523Z 2025-09-07T07:36:41.6031756Z This DDP communication hook is used for running post-localSGD algorithm, 2025-09-07T07:36:41.6032272Z by combining with a model averaging component (e.g., 2025-09-07T07:36:41.6032869Z :class:`~torch.distributed.algorithms.model_averaging.averagers.PeriodicModelAverager`) 2025-09-07T07:36:41.6033434Z that runs after the optimizer step. 2025-09-07T07:36:41.6033640Z 2025-09-07T07:36:41.6033735Z Args: 2025-09-07T07:36:41.6034064Z state (PostLocalSGDState): State information to run post-localSGD. 2025-09-07T07:36:41.6034858Z Users mainly need to tune ``start_localSGD_iter`` to determine when to start local SGD. 2025-09-07T07:36:41.6035682Z bucket (dist.GradBucket): Bucket that stores a 1D flattened gradient tensor that batches multiple per-variable tensors. 2025-09-07T07:36:41.6036476Z Note that since DDP comm hook only supports single process single device mode, 2025-09-07T07:36:41.6037045Z only exactly one tensor is stored in this bucket. 2025-09-07T07:36:41.6037320Z 2025-09-07T07:36:41.6037404Z Returns: 2025-09-07T07:36:41.6037770Z Future handler of the communication, which updates the gradients in place. 2025-09-07T07:36:41.6038125Z 2025-09-07T07:36:41.6038250Z Example:: 2025-09-07T07:36:41.6038473Z >>> # xdoctest: +SKIP 2025-09-07T07:36:41.6038890Z >>> state = PostLocalSGDState(process_group=process_group, subgroup=subgroup, 2025-09-07T07:36:41.6039384Z start_localSGD_iter=10) 2025-09-07T07:36:41.6039790Z >>> ddp_model.register_comm_hook(state, post_localSGD_hook) 2025-09-07T07:36:41.6040466Z >>> # Also need to establish a model averaging module and run model averaging after ``optimizer.step()``. 2025-09-07T07:36:41.6041262Z >>> # Please refer to the examples in ``torch.distributed.algorithms.model_averaging.averagers`` module. 2025-09-07T07:36:41.6041733Z 2025-09-07T07:36:41.6041989Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:41.6042366Z 2025-09-07T07:36:41.6076955Z 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. 2025-09-07T07:36:41.6078053Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:41.6078439Z 2025-09-07T07:36:41.6078606Z Implement PowerSGD algorithm. 2025-09-07T07:36:41.6078798Z 2025-09-07T07:36:41.6079034Z This DDP communication hook implements PowerSGD gradient compression 2025-09-07T07:36:41.6079625Z algorithm described in the `paper `_. 2025-09-07T07:36:41.6080220Z Once gradient tensors are aggregated across all workers, this hook applies 2025-09-07T07:36:41.6080686Z compression as follows: 2025-09-07T07:36:41.6080848Z 2025-09-07T07:36:41.6081300Z 1. Views the input flattened 1D gradient tensor as a list of per-parameter tensors, and divides all the tensors into two groups: 2025-09-07T07:36:41.6081853Z 2025-09-07T07:36:41.6082286Z 1.1 The tensors that should be compressed before allreduce, because the compression can give enough saving in bandwidth. 2025-09-07T07:36:41.6082818Z 2025-09-07T07:36:41.6083234Z 1.2 Rest of the tensors will be directly allreduced without compression, including all the vector tensors (for biases). 2025-09-07T07:36:41.6083846Z 2025-09-07T07:36:41.6083955Z 2. Handles uncompressed tensors: 2025-09-07T07:36:41.6084168Z 2025-09-07T07:36:41.6084683Z 2.1. Allocate contiguous memory for those uncompressed tensors, and allreduces all the uncompressed tensors as a batch, without compression; 2025-09-07T07:36:41.6085329Z 2025-09-07T07:36:41.6085671Z 2.2. Copies the individual uncompressed tensors from the contiguous memory back to the input tensor. 2025-09-07T07:36:41.6086129Z 2025-09-07T07:36:41.6086376Z 3. Handles the tensors that should be compressed by PowerSGD compression: 2025-09-07T07:36:41.6086725Z 2025-09-07T07:36:41.6086979Z 3.1. For each tensor M, creates two low-rank tensors P and Q for decomposing M, 2025-09-07T07:36:41.6087655Z such that M = PQ^T, where Q is initialized from a standard normal distribution and orthogonalized; 2025-09-07T07:36:41.6088111Z 2025-09-07T07:36:41.6088255Z 3.2. Computes each P in Ps, which is equal to MQ; 2025-09-07T07:36:41.6088521Z 2025-09-07T07:36:41.6088647Z 3.3. Allreduces Ps as a batch; 2025-09-07T07:36:41.6088857Z 2025-09-07T07:36:41.6088971Z 3.4. Orthogonalizes each P in Ps; 2025-09-07T07:36:41.6089181Z 2025-09-07T07:36:41.6089419Z 3.5. Computes each Q in Qs, which is approximately equal to M^TP; 2025-09-07T07:36:41.6089726Z 2025-09-07T07:36:41.6089841Z 3.6. Allreduces Qs as a batch; 2025-09-07T07:36:41.6090038Z 2025-09-07T07:36:41.6090333Z 3.7. Computes each M among all the compressed tensors, which is approximately equal to PQ^T. 2025-09-07T07:36:41.6090790Z 2025-09-07T07:36:41.6091203Z Note that this communication hook enforces vanilla allreduce for the first ``state.start_powerSGD_iter`` iterations. 2025-09-07T07:36:41.6092019Z This not only gives the user more control over the tradeoff between speedup and accuracy, 2025-09-07T07:36:41.6092868Z but also helps abstract away some complexity of the internal optimization of DDP for future communication hook developers. 2025-09-07T07:36:41.6093418Z 2025-09-07T07:36:41.6093514Z Args: 2025-09-07T07:36:41.6094059Z state (PowerSGDState): State information to configure the compression rate and support error feedback, warm start, etc. 2025-09-07T07:36:41.6094978Z To tune the compression configs, mainly need to tune ``matrix_approximation_rank``, ``start_powerSGD_iter`` 2025-09-07T07:36:41.6095599Z and ``min_compression_rate``. 2025-09-07T07:36:41.6096235Z bucket (dist.GradBucket): Bucket that stores a 1D flattened gradient tensor that batches multiple per-variable tensors. 2025-09-07T07:36:41.6097029Z Note that since DDP comm hook only supports single process single device mode, 2025-09-07T07:36:41.6097547Z only exactly one tensor is stored in this bucket. 2025-09-07T07:36:41.6097821Z 2025-09-07T07:36:41.6097910Z Returns: 2025-09-07T07:36:41.6098277Z Future handler of the communication, which updates the gradients in place. 2025-09-07T07:36:41.6098638Z 2025-09-07T07:36:41.6098753Z Example:: 2025-09-07T07:36:41.6098984Z >>> # xdoctest: +SKIP 2025-09-07T07:36:41.6099418Z >>> state = PowerSGDState(process_group=process_group, matrix_approximation_rank=1, 2025-09-07T07:36:41.6099976Z start_powerSGD_iter=10, min_compression_rate=0.5) 2025-09-07T07:36:41.6100416Z >>> ddp_model.register_comm_hook(state, powerSGD_hook) 2025-09-07T07:36:41.6100689Z 2025-09-07T07:36:41.6100954Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:41.6101321Z 2025-09-07T07:36:41.6114877Z 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. 2025-09-07T07:36:41.6115999Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:41.6116392Z 2025-09-07T07:36:41.6116580Z Averages parameters periodically after the warm-up stage. 2025-09-07T07:36:41.6116929Z 2025-09-07T07:36:41.6117240Z This can be used for running `post-local SGD `_, 2025-09-07T07:36:41.6117811Z by running :class:`~torch.nn.DistributedDataParallel` (DDP) 2025-09-07T07:36:41.6118356Z using the subgroups created by :meth:`~torch.distributed.new_subgroups`. 2025-09-07T07:36:41.6118710Z 2025-09-07T07:36:41.6118792Z Args: 2025-09-07T07:36:41.6119101Z period (int): The number of steps per model averaging. 2025-09-07T07:36:41.6119652Z Usually the period should be greater than ``1`` to reduce the communication cost. 2025-09-07T07:36:41.6120183Z Otherwise, only DDP needs to be used. 2025-09-07T07:36:41.6120637Z warmup_steps (int): The number of warm-up steps. During this stage, 2025-09-07T07:36:41.6121073Z model averaging is skipped. 2025-09-07T07:36:41.6121501Z process_group: The process group to be used for all-reduce. 2025-09-07T07:36:41.6121949Z If ``None``, the default process group, which 2025-09-07T07:36:41.6122403Z is created by :func:`torch.distributed.init_process_group`, 2025-09-07T07:36:41.6122849Z will be used. (default: ``None``) 2025-09-07T07:36:41.6123082Z 2025-09-07T07:36:41.6123178Z Example:: 2025-09-07T07:36:41.6123346Z 2025-09-07T07:36:41.6123472Z >>> # xdoctest: +SKIP("undefined variables") 2025-09-07T07:36:41.6123973Z >>> import torch 2025-09-07T07:36:41.6124246Z >>> import torch.distributed as dist 2025-09-07T07:36:41.6124776Z >>> import torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook as post_localSGD 2025-09-07T07:36:41.6125555Z >>> import torch.distributed.algorithms.model_averaging.averagers as averagers 2025-09-07T07:36:41.6126051Z >>> import torch.nn as nn 2025-09-07T07:36:41.6126324Z >>> 2025-09-07T07:36:41.6126611Z >>> dist.init_process_group("nccl", rank=rank, world_size=16) 2025-09-07T07:36:41.6127012Z >>> torch.cuda.set_device(rank) 2025-09-07T07:36:41.6127349Z >>> module = nn.Linear(1, 1, bias=False).cuda() 2025-09-07T07:36:41.6127749Z >>> model = nn.parallel.DistributedDataParallel( 2025-09-07T07:36:41.6128139Z >>> module, device_ids=[rank], output_device=rank 2025-09-07T07:36:41.6128473Z >>> ) 2025-09-07T07:36:41.6128741Z >>> # Register a post-localSGD communication hook. 2025-09-07T07:36:41.6129364Z >>> state = PostLocalSGDState(process_group=None, subgroup=None, start_localSGD_iter=100) 2025-09-07T07:36:41.6129947Z >>> model.register_comm_hook(state, post_localSGD_hook) 2025-09-07T07:36:41.6130460Z >>> 2025-09-07T07:36:41.6131119Z >>> # In the first 100 steps, run global gradient averaging like normal DDP at every step. 2025-09-07T07:36:41.6131914Z >>> # After 100 steps, run model averaging every 4 steps. 2025-09-07T07:36:41.6132510Z >>> # Note that ``warmup_steps`` must be the same as ``start_localSGD_iter`` used in ``PostLocalSGDState``. 2025-09-07T07:36:41.6133192Z >>> averager = averagers.PeriodicModelAverager(period=4, warmup_steps=100) 2025-09-07T07:36:41.6133672Z >>> for step in range(0, 200): 2025-09-07T07:36:41.6133981Z >>> optimizer.zero_grad() 2025-09-07T07:36:41.6134289Z >>> loss = loss_fn(output, labels) 2025-09-07T07:36:41.6134598Z >>> loss.backward() 2025-09-07T07:36:41.6134874Z >>> optimizer.step() 2025-09-07T07:36:41.6135247Z >>> # Will average model parameters globally every 4 steps. Thus, 2025-09-07T07:36:41.6135758Z >>> # inter-node communication only occurs every 4 iterations after 2025-09-07T07:36:41.6136204Z >>> # the initial ``warmup_steps`` period. 2025-09-07T07:36:41.6136590Z >>> averager.average_parameters(model.parameters()) 2025-09-07T07:36:41.6136869Z 2025-09-07T07:36:41.6137117Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:41.6137495Z 2025-09-07T07:36:41.6138438Z 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. 2025-09-07T07:36:41.6139675Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:41.6140066Z 2025-09-07T07:36:41.6140414Z Runs hierarchical model averaging (`hierarchical SGD `_). 2025-09-07T07:36:41.6140867Z 2025-09-07T07:36:41.6141188Z Process groups of different sizes are organized in a hierarchy, and they average parameters 2025-09-07T07:36:41.6141822Z by using different periods concurrently after the warm-up stage. 2025-09-07T07:36:41.6142539Z This is an extension of :class:`~torch.distributed.algorithms.model_averaging.averagers.PeriodicModelAverager` 2025-09-07T07:36:41.6143411Z that supports `post-local SGD `_, which essentially only supports 2025-09-07T07:36:41.6144176Z a two-level hierarchy: the intra-machine level and the global level, where the intra-machine 2025-09-07T07:36:41.6144961Z level is usually embedded in :meth:`~torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook`. 2025-09-07T07:36:41.6145736Z Similarly, the process groups within this class do not have such an intra-machine process 2025-09-07T07:36:41.6146413Z subgroup, which should be embedded by the post-local SGD communication hook instead. 2025-09-07T07:36:41.6146864Z 2025-09-07T07:36:41.6146944Z Args: 2025-09-07T07:36:41.6147324Z period_group_size_dict: An ordered dict mapping keys of model averaging period to 2025-09-07T07:36:41.6147910Z process group size, used for initializing process groups of 2025-09-07T07:36:41.6148474Z different sizes in a hierarchy to average parameters concurrently. 2025-09-07T07:36:41.6149033Z Particularly, at each iteration, there will be at most a single 2025-09-07T07:36:41.6149596Z process group that runs averaging -- the period of such group should 2025-09-07T07:36:41.6150162Z have the largest period which the current step can be divided by. 2025-09-07T07:36:41.6150666Z For example, if the dict has three keys: 2, 4, and 8, 2025-09-07T07:36:41.6151148Z then this means totally three process groups will be created to 2025-09-07T07:36:41.6151721Z average parameters every 2, 4, and 8 iterations, respectively. 2025-09-07T07:36:41.6152251Z At the 4th iteration, only the second process group will run 2025-09-07T07:36:41.6152735Z averaging, because the first process group should be a 2025-09-07T07:36:41.6153255Z subset of the second process group, and no need to execute the first 2025-09-07T07:36:41.6153708Z process group redundantly. 2025-09-07T07:36:41.6154138Z On the other hand, the third process group can only be triggered 2025-09-07T07:36:41.6154769Z every 8 iterations, so it will not be triggered at the 4th iteration. 2025-09-07T07:36:41.6155421Z warmup_steps (int): The number of warm-up steps. During this stage, model averaging is skipped. 2025-09-07T07:36:41.6156298Z process_group (ProcessGroup, optional): The overall process group containing all the processes that runs model averaging. 2025-09-07T07:36:41.6157033Z If ``None``, the default process group, which is created 2025-09-07T07:36:41.6157541Z by :func:`torch.distributed.init_process_group`, will be used. 2025-09-07T07:36:41.6157994Z (default: ``None``) 2025-09-07T07:36:41.6158227Z 2025-09-07T07:36:41.6158334Z Example:: 2025-09-07T07:36:41.6158565Z >>> # xdoctest: +SKIP('undefined rank') 2025-09-07T07:36:41.6158915Z >>> from collections import OrderedDict 2025-09-07T07:36:41.6159243Z >>> import torch 2025-09-07T07:36:41.6159516Z >>> import torch.distributed as dist 2025-09-07T07:36:41.6160035Z >>> from torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook import ( 2025-09-07T07:36:41.6160542Z >>> PostLocalSGDState, 2025-09-07T07:36:41.6160833Z >>> post_localSGD_hook, 2025-09-07T07:36:41.6161101Z >>> ) 2025-09-07T07:36:41.6161592Z >>> import torch.distributed.algorithms.model_averaging.hierarchical_model_averager as hierarchicalSGD 2025-09-07T07:36:41.6162191Z >>> import torch.nn as nn 2025-09-07T07:36:41.6162458Z >>> 2025-09-07T07:36:41.6162756Z >>> dist.init_process_group("nccl", rank=rank, world_size=16) 2025-09-07T07:36:41.6163157Z >>> torch.cuda.set_device(rank) 2025-09-07T07:36:41.6163486Z >>> module = nn.Linear(1, 1, bias=False).to(rank) 2025-09-07T07:36:41.6163900Z >>> model = nn.parallel.DistributedDataParallel( 2025-09-07T07:36:41.6164308Z >>> module, device_ids=[rank], output_device=rank 2025-09-07T07:36:41.6164651Z >>> ) 2025-09-07T07:36:41.6164910Z >>> # Register a post-localSGD communication hook. 2025-09-07T07:36:41.6165445Z >>> # Assume that each machine has 4 GPUs, then each intra-machine subgroup has a size of 4. 2025-09-07T07:36:41.6165969Z >>> subgroup, _ = dist.new_subgroups() 2025-09-07T07:36:41.6166545Z >>> state = PostLocalSGDState(process_group=None, subgroup=subgroup, start_localSGD_iter=100) 2025-09-07T07:36:41.6167134Z >>> model.register_comm_hook(state, post_localSGD_hook) 2025-09-07T07:36:41.6167501Z >>> 2025-09-07T07:36:41.6167911Z >>> # Average parameters among each group of 8 processes every 4 iterations, and among all 2025-09-07T07:36:41.6168467Z >>> # the 16 processes every 16 iterations. 2025-09-07T07:36:41.6168881Z >>> averager = hierarchicalSGD.HierarchicalModelAverager( 2025-09-07T07:36:41.6169412Z >>> period_group_size_dict=OrderedDict([(4, 8), (16, 16)]), warmup_steps=100) 2025-09-07T07:36:41.6170088Z >>> # Note that ``warmup_steps`` must be the same as ``start_localSGD_iter`` used in ``PostLocalSGDState``. 2025-09-07T07:36:41.6170802Z >>> # In the first 100 steps, run global gradient averaging like normal DDP at every step. 2025-09-07T07:36:41.6171349Z >>> # After 100 steps, run model averaging at two levels. 2025-09-07T07:36:41.6171714Z >>> for step in range(0, 200): 2025-09-07T07:36:41.6172023Z >>> optimizer.zero_grad() 2025-09-07T07:36:41.6172362Z >>> loss = loss_fn(output, labels) 2025-09-07T07:36:41.6172684Z >>> loss.backward() 2025-09-07T07:36:41.6172944Z >>> optimizer.step() 2025-09-07T07:36:41.6173275Z >>> # Average parameters after ``optimizer.step()``. 2025-09-07T07:36:41.6173827Z >>> # Thus, the inter-node communication only occurs periodically after ``warmup_steps``. 2025-09-07T07:36:41.6174392Z >>> averager.average_parameters(model.parameters()) 2025-09-07T07:36:41.6174662Z 2025-09-07T07:36:41.6174761Z .. warning :: 2025-09-07T07:36:41.6175131Z The last group size in the dict must be the size of the provided ``process_group``, 2025-09-07T07:36:41.6175743Z which indicates model averaging at the highest level of the hierarchy. 2025-09-07T07:36:41.6176396Z If ``process_group`` is not provided, then the last group size should be equal to the world size. 2025-09-07T07:36:41.6176818Z 2025-09-07T07:36:41.6176915Z .. warning :: 2025-09-07T07:36:41.6177270Z `HierarchicalModelAverager` is experimental and subject to change. 2025-09-07T07:36:41.6177631Z 2025-09-07T07:36:41.6177883Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:41.6178264Z 2025-09-07T07:36:41.6517825Z 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. 2025-09-07T07:36:41.6518930Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:41.6519311Z 2025-09-07T07:36:41.6519604Z StorageReader for reading a Torch Save file. This reader will read the entire checkpoint 2025-09-07T07:36:41.6520696Z on the coordinator rank, and then broadcast and shard each tensor to all ranks. 2025-09-07T07:36:41.6521255Z 2025-09-07T07:36:41.6521422Z . N.B. Intended to be used with DynamicMetaLoadPlanner 2025-09-07T07:36:41.6521699Z 2025-09-07T07:36:41.6521813Z .. warning:: 2025-09-07T07:36:41.6522127Z Current implementation only supports loading Tensors. 2025-09-07T07:36:41.6522406Z 2025-09-07T07:36:41.6522520Z >>> # xdoctest: +SKIP("undefined vars") 2025-09-07T07:36:41.6522837Z >>> sd = {"mode": model} 2025-09-07T07:36:41.6523096Z >>> dcp.load( 2025-09-07T07:36:41.6523311Z >>> sd, 2025-09-07T07:36:41.6523569Z >>> storage_reader=BroadcastingTorchSaveReader(), 2025-09-07T07:36:41.6524111Z >>> planner=DynamicMetaLoadPlanner(), 2025-09-07T07:36:41.6524462Z >>> checkpoint_id="path_to_model.pt" 2025-09-07T07:36:41.6524767Z >>> ) 2025-09-07T07:36:41.6524878Z 2025-09-07T07:36:41.6525129Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:41.6525514Z 2025-09-07T07:36:41.6526215Z 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. 2025-09-07T07:36:41.6527349Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:41.6527738Z 2025-09-07T07:36:41.6528104Z Extension of DefaultLoadPlanner, which creates a new Metadata object based on the passed in state dict, 2025-09-07T07:36:41.6528922Z avoiding the need to read metadata from disk. This is useful when reading formats which don't have a 2025-09-07T07:36:41.6529534Z metadata file, like Torch Save files. 2025-09-07T07:36:41.6529743Z 2025-09-07T07:36:41.6529922Z . N.B. Intended to be used with BroadcastingTorchSaveReader 2025-09-07T07:36:41.6530219Z 2025-09-07T07:36:41.6530308Z .. warning:: 2025-09-07T07:36:41.6530611Z Current implementation only supports loading Tensors. 2025-09-07T07:36:41.6530888Z 2025-09-07T07:36:41.6531017Z >>> # xdoctest: +SKIP("undefined vars") 2025-09-07T07:36:41.6531320Z >>> sd = {"mode": model} 2025-09-07T07:36:41.6531576Z >>> dcp.load( 2025-09-07T07:36:41.6531790Z >>> sd, 2025-09-07T07:36:41.6532061Z >>> storage_reader=BroadcastingTorchSaveReader(), 2025-09-07T07:36:41.6532437Z >>> planner=DynamicMetaLoadPlanner(), 2025-09-07T07:36:41.6532823Z >>> checkpoint_id="path_to_model.pt" 2025-09-07T07:36:41.6533126Z >>> ) 2025-09-07T07:36:41.6533236Z 2025-09-07T07:36:41.6533496Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:41.6533862Z 2025-09-07T07:36:41.6601422Z 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. 2025-09-07T07:36:41.6602615Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:41.6603029Z 2025-09-07T07:36:41.6603250Z Load a state_dict in conjunction with FSDP sharded optimizer state. 2025-09-07T07:36:41.6603587Z 2025-09-07T07:36:41.6603749Z This is the current recommended way to checkpoint FSDP. 2025-09-07T07:36:41.6604122Z >>> # xdoctest: +SKIP 2025-09-07T07:36:41.6604437Z >>> import torch.distributed.checkpoint as dist_cp 2025-09-07T07:36:41.6604775Z >>> # Save 2025-09-07T07:36:41.6605003Z >>> model: torch.nn.Model 2025-09-07T07:36:41.6605295Z >>> optim_params = model.parameters() 2025-09-07T07:36:41.6605658Z >>> optim = torch.optim.SGD(optim_params, lr=0.01) 2025-09-07T07:36:41.6605984Z >>> # Save 2025-09-07T07:36:41.6606327Z >>> with FSDP.state_dict_type(model, StateDictType.SHARDED_STATE_DICT): 2025-09-07T07:36:41.6606756Z >>> state_dict = { 2025-09-07T07:36:41.6607072Z >>> "optimizer": FSDP.optim_state_dict(model, optim), 2025-09-07T07:36:41.6607453Z >>> "model": model.state_dict() 2025-09-07T07:36:41.6607738Z >>> } 2025-09-07T07:36:41.6607967Z >>> dist_cp.save_state_dict( 2025-09-07T07:36:41.6608266Z >>> state_dict=optim_state, 2025-09-07T07:36:41.6608734Z >>> storage_writer=dist_cp.FileSystemWriter("checkpoint"), 2025-09-07T07:36:41.6609152Z >>> planner=dist_cp.DefaultSavePlanner(), 2025-09-07T07:36:41.6609485Z >>> ) 2025-09-07T07:36:41.6609696Z >>> 2025-09-07T07:36:41.6609884Z >>> # Load 2025-09-07T07:36:41.6610240Z >>> with FSDP.state_dict_type(model_tp, StateDictType.SHARDED_STATE_DICT): 2025-09-07T07:36:41.6610726Z >>> model_state_dict = model_tp.state_dict() 2025-09-07T07:36:41.6611061Z >>> checkpoint = { 2025-09-07T07:36:41.6611331Z >>> "model": model_state_dict 2025-09-07T07:36:41.6611614Z >>> } 2025-09-07T07:36:41.6611836Z >>> dist_cp.load_state_dict( 2025-09-07T07:36:41.6612135Z >>> state_dict=checkpoint, 2025-09-07T07:36:41.6612524Z >>> storage_reader=dist_cp.FileSystemReader(checkpoint_file), 2025-09-07T07:36:41.6612979Z >>> planner=dist_cp.DefaultLoadPlanner(), 2025-09-07T07:36:41.6613302Z >>> ) 2025-09-07T07:36:41.6613573Z >>> model.load_state_dict(checkpoint["model_state"]) 2025-09-07T07:36:41.6613919Z >>> 2025-09-07T07:36:41.6614189Z >>> optim_state = dist_cp.load_sharded_optimizer_state_dict( 2025-09-07T07:36:41.6614573Z >>> model_state_dict, 2025-09-07T07:36:41.6614904Z >>> optimizer_key="optimizer", 2025-09-07T07:36:41.6615296Z >>> storage_reader=dist_cp.FileSystemReader("checkpoint"), 2025-09-07T07:36:41.6615664Z >>> ) 2025-09-07T07:36:41.6615866Z >>> 2025-09-07T07:36:41.6616185Z >>> flattened_osd = FSDP.optim_state_dict_to_load( 2025-09-07T07:36:41.6616639Z >>> model, optim, optim_state["optimizer"] 2025-09-07T07:36:41.6616950Z >>> ) 2025-09-07T07:36:41.6617153Z >>> 2025-09-07T07:36:41.6617385Z >>> optim.load_state_dict(flattened_osd) 2025-09-07T07:36:41.6617612Z 2025-09-07T07:36:41.6617875Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:41.6618243Z 2025-09-07T07:36:41.6626506Z msg = Cannot scrape callname=SavePlanner in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/checkpoint/planner.py line=122. 2025-09-07T07:36:41.6627612Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:41.6628011Z 2025-09-07T07:36:41.6628294Z Abstract class defining the protocol used by save_state_dict to plan the save process. 2025-09-07T07:36:41.6628714Z 2025-09-07T07:36:41.6629096Z SavePlanners are stateful objects that can be used to customize the whole save process. 2025-09-07T07:36:41.6629509Z 2025-09-07T07:36:41.6629805Z SavePlanner acts as an access proxy to the state_dict, so any transformation done to it 2025-09-07T07:36:41.6630320Z will be visible to the whole process. 2025-09-07T07:36:41.6630533Z 2025-09-07T07:36:41.6630808Z A planner subclass can expect the following sequence of calls during save_state_dict: 2025-09-07T07:36:41.6631214Z 2025-09-07T07:36:41.6631333Z 1) set_up_planner - called on all ranks. 2025-09-07T07:36:41.6631685Z Signals the start of a checkpoint save. 2025-09-07T07:36:41.6631916Z 2025-09-07T07:36:41.6632050Z 2) create_local_plan - called on all ranks. 2025-09-07T07:36:41.6632567Z Process the state_dict and produces a `SavePlan` that will be sent for global planning. 2025-09-07T07:36:41.6632983Z 2025-09-07T07:36:41.6633295Z 3) create_global_plan - called on the coordinator rank only. 2025-09-07T07:36:41.6634137Z Takes the SavePlan from all ranks and make any global decision. 2025-09-07T07:36:41.6634518Z 2025-09-07T07:36:41.6634634Z 4) finish_plan - called on all ranks. 2025-09-07T07:36:41.6635075Z This gives each rank a chance to adjust to global planning decisions. 2025-09-07T07:36:41.6635408Z 2025-09-07T07:36:41.6635576Z 5) resolve_data - called multiple times on each rank 2025-09-07T07:36:41.6636032Z Lookups a value on the `state_dict` for the storage layer to write. 2025-09-07T07:36:41.6636363Z 2025-09-07T07:36:41.6636723Z Users are recommended to extend DefaultSavePlanner instead of this interface directly as 2025-09-07T07:36:41.6637407Z most changes can be expressed by changes in a single method. 2025-09-07T07:36:41.6637717Z 2025-09-07T07:36:41.6637838Z There are 3 usual patterns of extension: 2025-09-07T07:36:41.6638078Z 2025-09-07T07:36:41.6638371Z Rewriting state_dict. This is the simplest way to extend the save process as it 2025-09-07T07:36:41.6638978Z doesn't requite understanding the intrincacies of how SavePlan works: 2025-09-07T07:36:41.6639321Z 2025-09-07T07:36:41.6639446Z >>> # xdoctest: +SKIP("undefined vars") 2025-09-07T07:36:41.6639785Z >>> class RenamePlanner(DefaultSavePlanner): 2025-09-07T07:36:41.6640125Z >>> def set_up_planner( 2025-09-07T07:36:41.6640388Z >>> self, 2025-09-07T07:36:41.6640640Z >>> state_dict: STATE_DICT_TYPE, 2025-09-07T07:36:41.6640969Z >>> storage_meta: Optional[StorageMeta], 2025-09-07T07:36:41.6641327Z >>> is_coordinator: bool, 2025-09-07T07:36:41.6641612Z >>> ) -> None: 2025-09-07T07:36:41.6641861Z >>> # prefix all keys with `foo_`` 2025-09-07T07:36:41.6642344Z >>> super().set_up_planner({"foo_" + k: v for k, v in state_dict.items()}, storage_meta, is_coordinator) 2025-09-07T07:36:41.6642763Z 2025-09-07T07:36:41.6643089Z Modifying local plan and lookup in tandem. This is useful when fine control of how data is persisted 2025-09-07T07:36:41.6643599Z 2025-09-07T07:36:41.6643713Z >>> # xdoctest: +SKIP("undefined vars") 2025-09-07T07:36:41.6644056Z >>> class FP16Planner(DefaultSavePlanner): 2025-09-07T07:36:41.6644395Z >>> def create_local_plan(self): 2025-09-07T07:36:41.6644751Z >>> plan = super().create_local_plan() 2025-09-07T07:36:41.6645078Z >>> for p in plan: 2025-09-07T07:36:41.6645369Z >>> if p.tensor_data is not None: 2025-09-07T07:36:41.6645753Z >>> p.tensor_data.properties.dtype = torch.float16 2025-09-07T07:36:41.6646105Z >>> return plan 2025-09-07T07:36:41.6646348Z >>> 2025-09-07T07:36:41.6646574Z >>> def resolve_data(self, write_item): 2025-09-07T07:36:41.6646926Z >>> item = super().resolve_data(write_item) 2025-09-07T07:36:41.6647425Z >>> return item if write_item.type == WriteItemType.BYTE_IO else item.to(torch.float16) 2025-09-07T07:36:41.6647832Z 2025-09-07T07:36:41.6648177Z Using the global planning step to make central decisions that can't be made individually by each rank 2025-09-07T07:36:41.6648651Z 2025-09-07T07:36:41.6648792Z >>> # xdoctest: +SKIP("undefined vars") 2025-09-07T07:36:41.6649172Z >>> from itertools import zip_longest 2025-09-07T07:36:41.6649500Z >>> from dataclasses import replace 2025-09-07T07:36:41.6649871Z >>> class DDPLoadBalancingPlanner(DefaultSavePlanner): 2025-09-07T07:36:41.6650428Z >>> # This uses the default local plan behavior of having all non-sharded writes in rank 0 2025-09-07T07:36:41.6650954Z >>> # This sample doesn't handle ShardedTensors 2025-09-07T07:36:41.6651321Z >>> def create_global_plan(self, all_plans): 2025-09-07T07:36:41.6651698Z >>> iters = [iter(all_plans[0].items)] * len(all_plans) 2025-09-07T07:36:41.6652068Z >>> items_per_rank = [ 2025-09-07T07:36:41.6652385Z >>> [item for item in items if item is not None] 2025-09-07T07:36:41.6652793Z >>> for items in zip(*zip_longest(*iters), strict=True) 2025-09-07T07:36:41.6653144Z >>> ] 2025-09-07T07:36:41.6653372Z >>> all_plans = [ 2025-09-07T07:36:41.6653677Z >>> replace(plan, items=items) 2025-09-07T07:36:41.6654090Z >>> for plan, items in zip(all_plans, items_per_rank, strict=True) 2025-09-07T07:36:41.6654490Z >>> ] 2025-09-07T07:36:41.6654752Z >>> return super().create_global_plan(all_plans) 2025-09-07T07:36:41.6655017Z 2025-09-07T07:36:41.6655282Z Finally, some planners need to save additional metadata in the checkpoint, this is 2025-09-07T07:36:41.6655948Z accomplished by having each rank contribute their data items in the local plan and 2025-09-07T07:36:41.6656457Z the global planner aggregate them: 2025-09-07T07:36:41.6656665Z 2025-09-07T07:36:41.6656807Z >>> # xdoctest: +SKIP("undefined vars") 2025-09-07T07:36:41.6657188Z >>> class SaveExtraDataPlanner(DefaultSavePlanner): 2025-09-07T07:36:41.6657591Z >>> def create_local_plan(self) -> SavePlan: 2025-09-07T07:36:41.6657958Z >>> plan = super().create_local_plan() 2025-09-07T07:36:41.6658343Z >>> return replace(plan, planner_data="per-rank-data") 2025-09-07T07:36:41.6658712Z >>> 2025-09-07T07:36:41.6659127Z >>> def create_global_plan(self, all_plans: List[SavePlan]) -> Tuple[List[SavePlan], Metadata]: 2025-09-07T07:36:41.6659750Z >>> global_plan, metadata = super().create_global_plan(all_plans) 2025-09-07T07:36:41.6660226Z >>> merged_data = [p.planner_data for p in global_plan] 2025-09-07T07:36:41.6660724Z >>> metadata = replace(metadata, planner_data=merged_data) 2025-09-07T07:36:41.6661126Z >>> return global_plan, metadata 2025-09-07T07:36:41.6661355Z 2025-09-07T07:36:41.6661605Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:41.6661976Z 2025-09-07T07:36:41.6662586Z msg = Cannot scrape callname=LoadPlanner in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/checkpoint/planner.py line=305. 2025-09-07T07:36:41.6663590Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:41.6663964Z 2025-09-07T07:36:41.6664253Z Abstract class defining the protocol used by load_state_dict to plan the load process. 2025-09-07T07:36:41.6664666Z 2025-09-07T07:36:41.6664952Z LoadPlanner are stateful objects that can be used to customize the whole load process. 2025-09-07T07:36:41.6665395Z 2025-09-07T07:36:41.6665667Z LoadPlanner acts as an access proxy to the state_dict, so any transformation done to it 2025-09-07T07:36:41.6666185Z will be visible to the whole process. 2025-09-07T07:36:41.6666400Z 2025-09-07T07:36:41.6666692Z A planner subclass can expect the following sequence of calls during load_state_dict: 2025-09-07T07:36:41.6667087Z 2025-09-07T07:36:41.6667205Z 1) set_up_planner - called on all ranks. 2025-09-07T07:36:41.6667560Z Signals the start of loading a checkpoint. 2025-09-07T07:36:41.6667810Z 2025-09-07T07:36:41.6667931Z 2) create_local_plan - called on all ranks. 2025-09-07T07:36:41.6668448Z Process the state_dict and produces a `LoadPlan` that will be sent for global planning. 2025-09-07T07:36:41.6668848Z 2025-09-07T07:36:41.6669068Z 3) create_global_plan - called on the coordinator rank only. 2025-09-07T07:36:41.6669550Z Takes the LoadPlan from all ranks and make any global decision. 2025-09-07T07:36:41.6669874Z 2025-09-07T07:36:41.6670017Z 4) load_bytes - called multiple times on each rank 2025-09-07T07:36:41.6670447Z This is called once per non-tensor value in state_dict. 2025-09-07T07:36:41.6670721Z 2025-09-07T07:36:41.6670957Z 5) resolve_tensor and commit_tensor - called multiple times on each rank 2025-09-07T07:36:41.6671487Z They are called in pair for each Tensor value in state_dict. 2025-09-07T07:36:41.6671778Z 2025-09-07T07:36:41.6672077Z Users are recommended to extend DefaultLoadPlanner instead of this interface directly as 2025-09-07T07:36:41.6672687Z most changes can be expressed by changes in a single method. 2025-09-07T07:36:41.6672993Z 2025-09-07T07:36:41.6673120Z There are two usual patterns of extension: 2025-09-07T07:36:41.6673348Z 2025-09-07T07:36:41.6673611Z Rewriting state_dict. This is the simplest way to extend the load process as it 2025-09-07T07:36:41.6674239Z doesn't requite understanding the intrincacies of how LoadPlan works. We need 2025-09-07T07:36:41.6674928Z to keep a reference to the original state_dict as load happens in place so 2025-09-07T07:36:41.6675398Z we need to be able to perform it in place 2025-09-07T07:36:41.6675634Z 2025-09-07T07:36:41.6675747Z >>> # xdoctest: +SKIP("undefined vars") 2025-09-07T07:36:41.6676106Z >>> class RenamePlanner(DefaultLoadPlanner): 2025-09-07T07:36:41.6676436Z >>> def set_up_planner( 2025-09-07T07:36:41.6676700Z >>> self, 2025-09-07T07:36:41.6676990Z >>> state_dict: STATE_DICT_TYPE, 2025-09-07T07:36:41.6677309Z >>> metadata: Metadata, 2025-09-07T07:36:41.6677591Z >>> is_coordinator: bool, 2025-09-07T07:36:41.6677879Z >>> ) -> None: 2025-09-07T07:36:41.6678149Z >>> self.original_state_dict = state_dict 2025-09-07T07:36:41.6678568Z >>> state_dict = {"foo_" + k: v for k, v in state_dict.items()} 2025-09-07T07:36:41.6678935Z >>> 2025-09-07T07:36:41.6679173Z >>> if self.flatten_sharded_tensors: 2025-09-07T07:36:41.6679556Z >>> state_dict = _flatten_sharded_tensors(state_dict) 2025-09-07T07:36:41.6679911Z >>> 2025-09-07T07:36:41.6680123Z >>> if self.flatten_state_dict: 2025-09-07T07:36:41.6680520Z >>> state_dict, self.mappings = flatten_state_dict(state_dict) 2025-09-07T07:36:41.6680938Z >>> 2025-09-07T07:36:41.6681157Z >>> self.state_dict = state_dict 2025-09-07T07:36:41.6681520Z >>> self.metadata = metadata 2025-09-07T07:36:41.6681850Z >>> self.is_coordinator = is_coordinator 2025-09-07T07:36:41.6682171Z >>> 2025-09-07T07:36:41.6682406Z >>> def load_bytes(self, read_item, value): 2025-09-07T07:36:41.6682732Z >>> # Remove the "foo_" prefix 2025-09-07T07:36:41.6683273Z >>> self.original_state_dict[read_item.dest_index.fqn[4:]] = torch.load(value, weights_only=False) 2025-09-07T07:36:41.6683718Z 2025-09-07T07:36:41.6683724Z 2025-09-07T07:36:41.6683980Z Modifying resolve_tensor and commit_tensor to handle load time transformation. 2025-09-07T07:36:41.6684356Z 2025-09-07T07:36:41.6684509Z >>> # xdoctest: +SKIP("undefined vars") 2025-09-07T07:36:41.6684884Z >>> class MetaModelMaterialize(DefaultSavePlanner): 2025-09-07T07:36:41.6685259Z >>> def resolve_tensor(self, read_item): 2025-09-07T07:36:41.6685619Z >>> tensor = super().resolve_tensor(read_item) 2025-09-07T07:36:41.6686007Z >>> return torch.empty_like(tensor, device="cpu") 2025-09-07T07:36:41.6686344Z >>> 2025-09-07T07:36:41.6686578Z >>> def commit_tensor(self, read_item, tensor): 2025-09-07T07:36:41.6686979Z >>> self.state_dict[read_item.dest_index.fqn] = tensor 2025-09-07T07:36:41.6687260Z 2025-09-07T07:36:41.6687510Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:41.6687877Z 2025-09-07T07:36:41.6832333Z msg = Cannot scrape callname=get_state_dict in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/checkpoint/state_dict.py line=1118. 2025-09-07T07:36:41.6833337Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:41.6833717Z 2025-09-07T07:36:41.6833891Z Return the model state_dict and optimizers state_dict. 2025-09-07T07:36:41.6834159Z 2025-09-07T07:36:41.6834421Z ``get_state_dict`` can process any module that is parallelized by PyTorch 2025-09-07T07:36:41.6835023Z FSDP/fully_shard, DDP/replicate, tensor_parallel/parallelize_module, and any 2025-09-07T07:36:41.6835644Z combination of these parallelisms. The main functions of ``get_state_dict`` 2025-09-07T07:36:41.6836239Z are: 1.) returning a model and optimizer state_dict that can be resharded 2025-09-07T07:36:41.6836782Z with a different number of trainers and/or different parallelisms. 2025-09-07T07:36:41.6837350Z 2.) hiding the parallelism-specific state_dict APIs. Users don't have to call 2025-09-07T07:36:41.6837808Z these APIs. 2025-09-07T07:36:41.6838064Z 3.) sanity checking the result state_dict. 2025-09-07T07:36:41.6838290Z 2025-09-07T07:36:41.6838507Z The keys of the result state dictionary are the canonical FQNs (Fully 2025-09-07T07:36:41.6839057Z Qualified Names). A canonical FQN refers to the FQN based on a parameter's 2025-09-07T07:36:41.6839648Z position in an nn.Module hierarchy. More specifically, a canonical FQN to a 2025-09-07T07:36:41.6840214Z parameter is the FQN returned by ``module.named_parameters()`` or 2025-09-07T07:36:41.6840743Z ``module.named_buffers()`` when the module is not distributed by any 2025-09-07T07:36:41.6852485Z parallelisms. Since the optimizer internally uses parameter IDs to represent 2025-09-07T07:36:41.6853201Z a parameter, there will be a conversion from the parameter IDs to the 2025-09-07T07:36:41.6853668Z canonical FQNs when calling this API. 2025-09-07T07:36:41.6853891Z 2025-09-07T07:36:41.6854126Z ``get_state_dict`` can also process a module that is not parallelized. In 2025-09-07T07:36:41.6854697Z such a case, ``get_state_dict`` only performs one function -- converting the 2025-09-07T07:36:41.6855180Z optimizer parameter IDs to the canonical FQNs. 2025-09-07T07:36:41.6855448Z 2025-09-07T07:36:41.6855537Z Example: 2025-09-07T07:36:41.6855760Z >>> # xdoctest: +SKIP 2025-09-07T07:36:41.6856025Z >>> import torch 2025-09-07T07:36:41.6856405Z >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP 2025-09-07T07:36:41.6856962Z >>> from torch.nn.parallel import DistributedDataParallel as DDP 2025-09-07T07:36:41.6857508Z >>> from torch.distributed.checkpoint.state_dict import get_state_dict 2025-09-07T07:36:41.6857847Z 2025-09-07T07:36:41.6857990Z >>> fsdp_model = FSDP(copy.deepcopy(model)) 2025-09-07T07:36:41.6858404Z >>> fsdp_optim = torch.optim.Adam(model.parameters(), lr=1e-3) 2025-09-07T07:36:41.6858890Z >>> ddp_model = DDP(copy.deepcopy(model)) 2025-09-07T07:36:41.6859383Z >>> ddp_optim = torch.optim.Adam(model.parameters(), lr=1e-3) 2025-09-07T07:36:41.6859677Z 2025-09-07T07:36:41.6859718Z 2025-09-07T07:36:41.6859951Z >>> ddp_state_dict, ddp_optim_state_dict = get_state_dict(ddp_model, ddp_optim) 2025-09-07T07:36:41.6860521Z >>> fsdp_state_dict, fsdp_optim_state_dict = get_state_dict( 2025-09-07T07:36:41.6860911Z ... fsdp_model, fsdp_optim 2025-09-07T07:36:41.6861188Z ... ) 2025-09-07T07:36:41.6861318Z 2025-09-07T07:36:41.6861532Z >>> # if we simply call ddp_model.state_dict() and fsdp_model.state_dict(), 2025-09-07T07:36:41.6861968Z >>> # the asserts will fail. 2025-09-07T07:36:41.6862293Z >>> assert ddp_state_dict == fsdp_state_dict 2025-09-07T07:36:41.6862670Z >>> assert ddp_optim_state == fsdp_optim_state_dict 2025-09-07T07:36:41.6862935Z 2025-09-07T07:36:41.6862940Z 2025-09-07T07:36:41.6863021Z Args: 2025-09-07T07:36:41.6863280Z model (nn.Module): the nn.Module to the model. 2025-09-07T07:36:41.6863717Z optimizers (Union[None, Optimizer, Iterable[Optimizer]]): 2025-09-07T07:36:41.6864192Z The optimizers that are used to optimize ``model``. 2025-09-07T07:36:41.6864987Z submodules (deprecated): Optional[set[nn.Module]]: only return the model parameters 2025-09-07T07:36:41.6865916Z that belong to the submodules. 2025-09-07T07:36:41.6866412Z options (StateDictOptions): the options to control how 2025-09-07T07:36:41.6866907Z model state_dict and optimizer state_dict should be returned. See 2025-09-07T07:36:41.6867344Z `StateDictOptions` for the details. 2025-09-07T07:36:41.6867581Z 2025-09-07T07:36:41.6867667Z Returns: 2025-09-07T07:36:41.6867987Z ``Tuple`` that contain model state_dict and optimizer state_dict. 2025-09-07T07:36:41.6868301Z 2025-09-07T07:36:41.6868546Z :rtype: typing.Tuple[typing.Dict[str, ValueType], OptimizerStateType] 2025-09-07T07:36:41.6868889Z 2025-09-07T07:36:41.6869154Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:41.6869522Z 2025-09-07T07:36:41.6870189Z 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=69. 2025-09-07T07:36:41.6871156Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:41.6871547Z 2025-09-07T07:36:41.6871741Z Load a checkpoint into a distributed state dict in SPMD style. 2025-09-07T07:36:41.6872059Z 2025-09-07T07:36:41.6872277Z Each rank must have the same keys in their ``state_dict`` provided to this 2025-09-07T07:36:41.6872857Z API. Mismatched keys may result in hangs or errors. If unsure, you can use 2025-09-07T07:36:41.6873472Z the ``utils._assert_same_keys`` API to check (but may incur communication 2025-09-07T07:36:41.6873890Z costs). 2025-09-07T07:36:41.6874020Z 2025-09-07T07:36:41.6874200Z Each rank will try to read the least amount of data necessary 2025-09-07T07:36:41.6874824Z to fulfill the requested `state_dict`. When loading :class:`ShardedTensor` 2025-09-07T07:36:41.6875431Z or :class:`DTensor` instances, each rank only reads data for their local shards. 2025-09-07T07:36:41.6875792Z 2025-09-07T07:36:41.6876064Z For each ``Stateful`` object (having both a ``state_dict`` and a ``load_state_dict``), 2025-09-07T07:36:41.6876700Z load will first call ``state_dict`` before attempting deserialization, followed by 2025-09-07T07:36:41.6877251Z ``load_state_dict`` once the deserialization is complete. 2025-09-07T07:36:41.6877793Z For each non-``Stateful`` object, load will deserialize the object, and then replace 2025-09-07T07:36:41.6878325Z it in the ``state_dict`` with the deserialized object. 2025-09-07T07:36:41.6878581Z 2025-09-07T07:36:41.6878709Z .. warning:: 2025-09-07T07:36:41.6878998Z All tensors in ``state_dict`` must be allocated on their 2025-09-07T07:36:41.6879444Z destination device *prior to* calling this function. 2025-09-07T07:36:41.6879711Z 2025-09-07T07:36:41.6879983Z All non-tensor data is loaded using `torch.load()` and modified in place 2025-09-07T07:36:41.6880423Z on state_dict. 2025-09-07T07:36:41.6880570Z 2025-09-07T07:36:41.6880658Z .. warning:: 2025-09-07T07:36:41.6880995Z Users must call `load_state_dict` on the root module to ensure load 2025-09-07T07:36:41.6881539Z pos-processing and non-tensor data properly propagates. 2025-09-07T07:36:41.6881829Z 2025-09-07T07:36:41.6881927Z .. note: 2025-09-07T07:36:41.6882260Z If no process group is initialized, this function will assume the intent 2025-09-07T07:36:41.6882832Z is to load a checkpoint into the local process. This can be useful in the 2025-09-07T07:36:41.6883426Z case of local inference, and when using regular Tensors (as opposed to DTensor 2025-09-07T07:36:41.6883894Z or ShardedTensor) 2025-09-07T07:36:41.6884051Z 2025-09-07T07:36:41.6884148Z .. note: 2025-09-07T07:36:41.6884396Z Rank 0 is assumed to be the coordinator rank. 2025-09-07T07:36:41.6884657Z 2025-09-07T07:36:41.6884737Z Args: 2025-09-07T07:36:41.6885066Z state_dict (Dict[str, Any]): The state_dict to load the checkpoint into. 2025-09-07T07:36:41.6885577Z checkpoint_id (Union[str, os.PathLike, None]): 2025-09-07T07:36:41.6886035Z The ID of this checkpoint instance. The meaning of the checkpoint_id 2025-09-07T07:36:41.6886569Z depends on the storage. It can be a path to a folder or to a file. 2025-09-07T07:36:41.6887059Z It can also be a key if the storage is a key-value store. 2025-09-07T07:36:41.6887444Z (Default: ``None``) 2025-09-07T07:36:41.6887765Z storage_reader (Optional[StorageReader]): 2025-09-07T07:36:41.6888213Z Instance of StorageWriter used to perform reads. If this is not 2025-09-07T07:36:41.6888741Z specified, DCP will automatically infer the reader based on the 2025-09-07T07:36:41.6889267Z checkpoint_id. If checkpoint_id is also None, an exception will 2025-09-07T07:36:41.6889697Z be raised. (Default: ``None``) 2025-09-07T07:36:41.6890055Z planner (Optional[LoadPlanner]): 2025-09-07T07:36:41.6890474Z Instance of LoadPlanner. If this is not specified, the default 2025-09-07T07:36:41.6890926Z planner will be used. (Default: ``None``) 2025-09-07T07:36:41.6891304Z process_group (Optional[ProcessGroup]): 2025-09-07T07:36:41.6891711Z ProcessGroup to be used for cross-rank synchronization. 2025-09-07T07:36:41.6892106Z (Default: ``None``) 2025-09-07T07:36:41.6892498Z no_dist (bool): If ``True``, this function will assume the intent is to load 2025-09-07T07:36:41.6893092Z a checkpoint without using cross-rank synchronization. (Default: ``False``) 2025-09-07T07:36:41.6893547Z Returns: 2025-09-07T07:36:41.6893758Z None. 2025-09-07T07:36:41.6893888Z 2025-09-07T07:36:41.6894002Z Examples 2025-09-07T07:36:41.6894222Z >>> # xdoctest: +SKIP 2025-09-07T07:36:41.6894484Z >>> my_model = MyModule() 2025-09-07T07:36:41.6894809Z >>> optimizer = Adagrad(my_model.parameters()) 2025-09-07T07:36:41.6895191Z >>> model_state_dict = my_model.state_dict() 2025-09-07T07:36:41.6895667Z >>> fs_storage_reader = torch.distributed.checkpoint.FileSystemReader( 2025-09-07T07:36:41.6896243Z ... "/checkpoint/1" 2025-09-07T07:36:41.6896519Z ... ) 2025-09-07T07:36:41.6896650Z 2025-09-07T07:36:41.6896806Z >>> torch.distributed.checkpoint.load_state_dict( 2025-09-07T07:36:41.6897194Z >>> state_dict=model_state_dict, 2025-09-07T07:36:41.6897535Z >>> storage_reader=fs_storage_reader, 2025-09-07T07:36:41.6897837Z >>> ) 2025-09-07T07:36:41.6897966Z 2025-09-07T07:36:41.6898161Z >>> # module.load_state_dict() function might have customized steps 2025-09-07T07:36:41.6898607Z >>> # to flush the state_dict, must call it to 2025-09-07T07:36:41.6898958Z >>> # ensure correct behavior. 2025-09-07T07:36:41.6899277Z >>> my_model.load_state_dict(model_state_dict) 2025-09-07T07:36:41.6899530Z 2025-09-07T07:36:41.6899617Z .. note:: 2025-09-07T07:36:41.6899989Z load_state_dict uses collectives to coordinate reads across ranks. 2025-09-07T07:36:41.6900532Z For NCCL-based process groups, internal tensor representations of 2025-09-07T07:36:41.6901085Z objects must be moved to the GPU device before communication takes place. 2025-09-07T07:36:41.6901660Z In this case, the device used is given by ``torch.cuda.current_device()`` 2025-09-07T07:36:41.6902340Z and it is the user's responsibility to ensure that this is set so that each 2025-09-07T07:36:41.6902877Z rank has an individual GPU, via ``torch.cuda.set_device()``. 2025-09-07T07:36:41.6903171Z 2025-09-07T07:36:41.6903435Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:41.6903803Z 2025-09-07T07:36:41.6904462Z 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=97. 2025-09-07T07:36:41.6905514Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:41.6905893Z 2025-09-07T07:36:41.6906010Z Save a distributed model in SPMD style. 2025-09-07T07:36:41.6906283Z 2025-09-07T07:36:41.6906473Z This function is different from ``torch.save()`` as it handles 2025-09-07T07:36:41.6907034Z ``ShardedTensor`` , and ``DTensor`` by having each rank only save their local shards. 2025-09-07T07:36:41.6907410Z 2025-09-07T07:36:41.6907678Z For each ``Stateful`` object (having both a ``state_dict`` and a ``load_state_dict``), 2025-09-07T07:36:41.6908207Z save will call ``state_dict`` before serialization. 2025-09-07T07:36:41.6908466Z 2025-09-07T07:36:41.6908554Z .. warning:: 2025-09-07T07:36:41.6908923Z There is no guarantees of Backwards Compatibility across PyTorch versions 2025-09-07T07:36:41.6909382Z for saved state_dicts. 2025-09-07T07:36:41.6909554Z 2025-09-07T07:36:41.6909655Z .. warning:: 2025-09-07T07:36:41.6909984Z If using the `process_group` argument, make sure that only its ranks 2025-09-07T07:36:41.6910524Z call `save_state_dict` and that all data in state_dict belong to it. 2025-09-07T07:36:41.6910859Z 2025-09-07T07:36:41.6910985Z .. note:: 2025-09-07T07:36:41.6911373Z When saving checkpoint for FSDP's `ShardingStrategy.HYBRID_SHARD`, only one of 2025-09-07T07:36:41.6912016Z the shard_group should be calling `save_state_dict` and the corresponding process 2025-09-07T07:36:41.6912500Z group needs to be passed in. 2025-09-07T07:36:41.6912712Z 2025-09-07T07:36:41.6912796Z .. note:: 2025-09-07T07:36:41.6913192Z If no process group is available, this function assumes the intention is to save the 2025-09-07T07:36:41.6913696Z state_dict in the local process. 2025-09-07T07:36:41.6913907Z 2025-09-07T07:36:41.6913989Z .. note: 2025-09-07T07:36:41.6914276Z Rank 0 is assumed to be the coordinator rank. 2025-09-07T07:36:41.6914621Z 2025-09-07T07:36:41.6914625Z 2025-09-07T07:36:41.6914708Z Args: 2025-09-07T07:36:41.6914983Z state_dict (Dict[str, Any]): The state_dict to save. 2025-09-07T07:36:41.6915401Z checkpoint_id (Union[str, os.PathLike, None]): 2025-09-07T07:36:41.6915862Z The ID of this checkpoint instance. The meaning of the checkpoint_id 2025-09-07T07:36:41.6916398Z depends on the storage. It can be a path to a folder or to a file. 2025-09-07T07:36:41.6916887Z It can also be a key if the storage is a key-value store. 2025-09-07T07:36:41.6917271Z (Default: ``None``) 2025-09-07T07:36:41.6917622Z storage_writer (Optional[StorageWriter]): 2025-09-07T07:36:41.6918070Z Instance of StorageWriter used to perform writes. If this is not 2025-09-07T07:36:41.6918603Z specified, DCP will automatically infer the writer based on the 2025-09-07T07:36:41.6919129Z checkpoint_id. If checkpoint_id is also None, an exception will 2025-09-07T07:36:41.6919545Z be raised. (Default: ``None``) 2025-09-07T07:36:41.6919874Z planner (Optional[SavePlanner]): 2025-09-07T07:36:41.6920287Z Instance of SavePlanner. If this is not specified, the default 2025-09-07T07:36:41.6920766Z planner will be used. (Default: ``None``) 2025-09-07T07:36:41.6921158Z process_group (Optional[ProcessGroup]): 2025-09-07T07:36:41.6921577Z ProcessGroup to be used for cross-rank synchronization. 2025-09-07T07:36:41.6921973Z (Default: ``None``) 2025-09-07T07:36:41.6922266Z no_dist (bool): 2025-09-07T07:36:41.6922581Z If ``True``, this function will assume the intent is to load 2025-09-07T07:36:41.6922981Z a checkpoint on a single rank/process. 2025-09-07T07:36:41.6923339Z (Default: ``False``) 2025-09-07T07:36:41.6923954Z use_collectives (bool): If ``False``, this function will assume the intent is to save 2025-09-07T07:36:41.6924514Z a checkpoint without using cross-rank synchronization. 2025-09-07T07:36:41.6924888Z (Default: ``True``) 2025-09-07T07:36:41.6925288Z This configuration is experimental and should be used with caution. 2025-09-07T07:36:41.6925909Z It will change the format of the saved checkpoint and may not be backward compatible. 2025-09-07T07:36:41.6926303Z 2025-09-07T07:36:41.6926400Z Returns: 2025-09-07T07:36:41.6926762Z Metadata: Metadata object for the saved checkpoint. 2025-09-07T07:36:41.6927029Z 2025-09-07T07:36:41.6927115Z Example: 2025-09-07T07:36:41.6927340Z >>> # xdoctest: +SKIP 2025-09-07T07:36:41.6927670Z >>> my_model = MyModule() 2025-09-07T07:36:41.6927851Z 2025-09-07T07:36:41.6927971Z >>> state_dict = {"model": my_model} 2025-09-07T07:36:41.6928183Z 2025-09-07T07:36:41.6928411Z >>> fs_storage_writer = torch.distributed.checkpoint.FileSystemWriter( 2025-09-07T07:36:41.6928860Z ... "/checkpoint/1" 2025-09-07T07:36:41.6929121Z ... ) 2025-09-07T07:36:41.6929372Z >>> torch.distributed.checkpoint.save( 2025-09-07T07:36:41.6929697Z >>> state_dict=state_dict, 2025-09-07T07:36:41.6930037Z >>> storage_writer=fs_storage_writer, 2025-09-07T07:36:41.6930348Z >>> ) 2025-09-07T07:36:41.6930463Z 2025-09-07T07:36:41.6930562Z .. note:: 2025-09-07T07:36:41.6930885Z save_state_dict uses collectives to coordinate writes across ranks. 2025-09-07T07:36:41.6931435Z For NCCL-based process groups, internal tensor representations of 2025-09-07T07:36:41.6932000Z objects must be moved to the GPU device before communication takes place. 2025-09-07T07:36:41.6932575Z In this case, the device used is given by ``torch.cuda.current_device()`` 2025-09-07T07:36:41.6933127Z and it is the user's responsibility to ensure that this is set so that 2025-09-07T07:36:41.6933643Z each rank has an individual GPU, via ``torch.cuda.set_device()``. 2025-09-07T07:36:41.6933967Z 2025-09-07T07:36:41.6934213Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:41.6934654Z 2025-09-07T07:36:41.6935311Z 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=230. 2025-09-07T07:36:41.6936324Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:41.6936978Z Asynchronous version of ``save``. This code first de-stages the state_dict on to the 2025-09-07T07:36:41.6937661Z staging storage (defaults to CPU memory), and then calls the `save` in a separate thread. 2025-09-07T07:36:41.6938069Z 2025-09-07T07:36:41.6938160Z .. warning:: 2025-09-07T07:36:41.6938469Z This feature is experimental and subject to change. 2025-09-07T07:36:41.6938893Z MUST CALL CLOSE AFTER LAST CHECKPOINT IS SAVED 2025-09-07T07:36:41.6939145Z 2025-09-07T07:36:41.6939238Z Args: 2025-09-07T07:36:41.6939515Z state_dict (Dict[str, Any]): The state_dict to save. 2025-09-07T07:36:41.6939918Z checkpoint_id (Union[str, os.PathLike, None]): 2025-09-07T07:36:41.6940387Z The ID of this checkpoint instance. The meaning of the checkpoint_id 2025-09-07T07:36:41.6940916Z depends on the storage. It can be a path to a folder or to a file. 2025-09-07T07:36:41.6941454Z It can also be a key if the storage is a key-value store. 2025-09-07T07:36:41.6941825Z (Default: ``None``) 2025-09-07T07:36:41.6942141Z storage_writer (Optional[StorageWriter]): 2025-09-07T07:36:41.6942598Z Instance of StorageWriter used to perform 'stage' and 'save'. If 2025-09-07T07:36:41.6943206Z this is not specified, DCP will automatically infer the writer based on the 2025-09-07T07:36:41.6943772Z checkpoint_id. If checkpoint_id is also None, an exception will 2025-09-07T07:36:41.6944193Z be raised. (Default: ``None``) 2025-09-07T07:36:41.6944525Z planner (Optional[SavePlanner]): 2025-09-07T07:36:41.6944950Z Instance of SavePlanner. If this is not specified, the default 2025-09-07T07:36:41.6945404Z planner will be used. (Default: ``None``) 2025-09-07T07:36:41.6945766Z process_group (Optional[ProcessGroup]): 2025-09-07T07:36:41.6946193Z ProcessGroup to be used for cross-rank synchronization. 2025-09-07T07:36:41.6946591Z (Default: ``None``) 2025-09-07T07:36:41.6946965Z async_checkpointer_type (AsyncCheckpointerType): 2025-09-07T07:36:41.6947401Z whether to do checkpoint in separate thread or process 2025-09-07T07:36:41.6947838Z (Default: ``AsyncCheckpointerType.THREAD``) 2025-09-07T07:36:41.6948208Z async_stager (AsyncStager): 2025-09-07T07:36:41.6948673Z provides staging implementation. If storage_writer implements AsyncStager 2025-09-07T07:36:41.6949253Z and async_stager is provided, async_stager will be used for staging 2025-09-07T07:36:41.6949677Z no_dist (bool): 2025-09-07T07:36:41.6950008Z If ``True``, this function will assume the intent is to save 2025-09-07T07:36:41.6950422Z a checkpoint on a single rank/process. 2025-09-07T07:36:41.6950763Z (Default: ``False``) 2025-09-07T07:36:41.6951254Z use_collectives: If False, Save the checkpoint without rank coordination. (Default: ``True``) 2025-09-07T07:36:41.6951950Z This configuration is experimental and should be used with caution. 2025-09-07T07:36:41.6952564Z It will change the format of the saved checkpoint and may not be backward compatible. 2025-09-07T07:36:41.6952952Z 2025-09-07T07:36:41.6953049Z Returns: 2025-09-07T07:36:41.6953381Z Future: A future holding the resultant Metadata object from `save`. 2025-09-07T07:36:41.6953720Z 2025-09-07T07:36:41.6953804Z Example: 2025-09-07T07:36:41.6954036Z >>> # xdoctest: +SKIP 2025-09-07T07:36:41.6954320Z >>> my_model = MyModule() 2025-09-07T07:36:41.6954586Z 2025-09-07T07:36:41.6954712Z >>> state_dict = {"model": my_model} 2025-09-07T07:36:41.6954968Z 2025-09-07T07:36:41.6955197Z >>> fs_storage_writer = torch.distributed.checkpoint.FileSystemWriter( 2025-09-07T07:36:41.6955648Z ... "/checkpoint/1" 2025-09-07T07:36:41.6955916Z ... ) 2025-09-07T07:36:41.6956254Z >>> checkpoint_future = torch.distributed.checkpoint.async_save( 2025-09-07T07:36:41.6956678Z >>> state_dict=state_dict, 2025-09-07T07:36:41.6957013Z >>> storage_writer=fs_storage_writer, 2025-09-07T07:36:41.6957332Z >>> ) 2025-09-07T07:36:41.6957549Z >>> 2025-09-07T07:36:41.6957760Z >>> # ... do some work ... 2025-09-07T07:36:41.6958048Z >>> 2025-09-07T07:36:41.6958282Z >>> checkpoint_future.result() 2025-09-07T07:36:41.6958493Z 2025-09-07T07:36:41.6958584Z 2025-09-07T07:36:41.6958939Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:41.6959319Z 2025-09-07T07:36:41.7152769Z 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=94. 2025-09-07T07:36:41.7153837Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:41.7154404Z 2025-09-07T07:36:41.7154608Z Initialize rendezvous event object and record its operations. 2025-09-07T07:36:41.7154940Z 2025-09-07T07:36:41.7155019Z Args: 2025-09-07T07:36:41.7155270Z run_id (str): The run id of the rendezvous. 2025-09-07T07:36:41.7155641Z message (str): The message describing the event. 2025-09-07T07:36:41.7156208Z node_state (NodeState): The state of the node (INIT, RUNNING, SUCCEEDED, FAILED). 2025-09-07T07:36:41.7156770Z name (str): Event name. (E.g. Current action being performed). 2025-09-07T07:36:41.7157185Z hostname (str): Hostname of the node. 2025-09-07T07:36:41.7157539Z pid (Optional[int]): The process id of the node. 2025-09-07T07:36:41.7158039Z master_endpoint (str): The master endpoint for the rendezvous store, if known. 2025-09-07T07:36:41.7158674Z local_id (Optional[int]): The local_id of the node, if defined in dynamic_rendezvous.py 2025-09-07T07:36:41.7159221Z rank (Optional[int]): The rank of the node, if known. 2025-09-07T07:36:41.7159582Z Returns: 2025-09-07T07:36:41.7159773Z None 2025-09-07T07:36:41.7159976Z Example: 2025-09-07T07:36:41.7160272Z >>> # See DynamicRendezvousHandler class 2025-09-07T07:36:41.7160604Z >>> def _record( 2025-09-07T07:36:41.7160828Z ... self, 2025-09-07T07:36:41.7161063Z ... message: str, 2025-09-07T07:36:41.7161368Z ... node_state: NodeState = NodeState.RUNNING, 2025-09-07T07:36:41.7161724Z ... rank: Optional[int] = None, 2025-09-07T07:36:41.7162022Z ... ) -> None: 2025-09-07T07:36:41.7162288Z ... construct_and_record_rdzv_event( 2025-09-07T07:36:41.7162681Z ... name=f"{self.__class__.__name__}.{get_method_name()}", 2025-09-07T07:36:41.7163084Z ... run_id=self._settings.run_id, 2025-09-07T07:36:41.7163400Z ... message=message, 2025-09-07T07:36:41.7163692Z ... node_state=node_state, 2025-09-07T07:36:41.7164019Z ... hostname=self._this_node.addr, 2025-09-07T07:36:41.7164362Z ... pid=self._this_node.pid, 2025-09-07T07:36:41.7164689Z ... local_id=self._this_node.local_id, 2025-09-07T07:36:41.7165019Z ... rank=rank, 2025-09-07T07:36:41.7165271Z ... ) 2025-09-07T07:36:41.7165394Z 2025-09-07T07:36:41.7165660Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:41.7166078Z 2025-09-07T07:36:41.8721685Z 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. 2025-09-07T07:36:41.8722624Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:41.8723017Z 2025-09-07T07:36:41.8723197Z This configures FSDP-native mixed precision training. 2025-09-07T07:36:41.8723618Z 2025-09-07T07:36:41.8723978Z Attributes: 2025-09-07T07:36:41.8724347Z param_dtype (Optional[torch.dtype]): This specifies the dtype for model 2025-09-07T07:36:41.8724909Z parameters during forward and backward and thus the dtype for 2025-09-07T07:36:41.8725438Z forward and backward computation. Outside forward and backward, the 2025-09-07T07:36:41.8725974Z *sharded* parameters are kept in full precision (e.g. for the 2025-09-07T07:36:41.8726496Z optimizer step), and for model checkpointing, the parameters are 2025-09-07T07:36:41.8726978Z always saved in full precision. (Default: ``None``) 2025-09-07T07:36:41.8727453Z reduce_dtype (Optional[torch.dtype]): This specifies the dtype for 2025-09-07T07:36:41.8728002Z gradient reduction (i.e. reduce-scatter or all-reduce). If this is 2025-09-07T07:36:41.8728513Z ``None`` but ``param_dtype`` is not ``None``, then this takes on 2025-09-07T07:36:41.8729004Z the ``param_dtype`` value, still running gradient reduction in low 2025-09-07T07:36:41.8729536Z precision. This is permitted to differ from ``param_dtype``, e.g. 2025-09-07T07:36:41.8730048Z to force gradient reduction to run in full precision. (Default: 2025-09-07T07:36:41.8730522Z ``None``) 2025-09-07T07:36:41.8730880Z buffer_dtype (Optional[torch.dtype]): This specifies the dtype for 2025-09-07T07:36:41.8731411Z buffers. FSDP does not shard buffers. Rather, FSDP casts them to 2025-09-07T07:36:41.8731962Z ``buffer_dtype`` in the first forward pass and keeps them in that 2025-09-07T07:36:41.8732485Z dtype thereafter. For model checkpointing, the buffers are saved 2025-09-07T07:36:41.8733025Z in full precision except for ``LOCAL_STATE_DICT``. (Default: 2025-09-07T07:36:41.8733411Z ``None``) 2025-09-07T07:36:41.8733736Z keep_low_precision_grads (bool): If ``False``, then FSDP upcasts 2025-09-07T07:36:41.8734277Z gradients to full precision after the backward pass in preparation 2025-09-07T07:36:41.8734854Z for the optimizer step. If ``True``, then FSDP keeps the gradients 2025-09-07T07:36:41.8735379Z in the dtype used for gradient reduction, which can save memory if 2025-09-07T07:36:41.8735951Z using a custom optimizer that supports running in low precision. 2025-09-07T07:36:41.8736428Z (Default: ``False``) 2025-09-07T07:36:41.8736814Z cast_forward_inputs (bool): If ``True``, then this FSDP module casts 2025-09-07T07:36:41.8737349Z its forward args and kwargs to ``param_dtype``. This is to ensure 2025-09-07T07:36:41.8737879Z that parameter and input dtypes match for forward computation, as 2025-09-07T07:36:41.8738416Z required by many ops. This may need to be set to ``True`` when only 2025-09-07T07:36:41.8738954Z applying mixed precision to some but not all FSDP modules, in which 2025-09-07T07:36:41.8739484Z case a mixed-precision FSDP submodule needs to recast its inputs. 2025-09-07T07:36:41.8739906Z (Default: ``False``) 2025-09-07T07:36:41.8740307Z cast_root_forward_inputs (bool): If ``True``, then the root FSDP module 2025-09-07T07:36:41.8740843Z casts its forward args and kwargs to ``param_dtype``, overriding 2025-09-07T07:36:41.8741339Z the value of ``cast_forward_inputs``. For non-root FSDP modules, 2025-09-07T07:36:41.8741830Z this does not do anything. (Default: ``True``) 2025-09-07T07:36:41.8742302Z _module_classes_to_ignore: (Sequence[Type[nn.Module]]): This specifies 2025-09-07T07:36:41.8742829Z module classes to ignore for mixed precision when using an 2025-09-07T07:36:41.8743299Z ``auto_wrap_policy``: Modules of these classes will have FSDP 2025-09-07T07:36:41.8743806Z applied to them separately with mixed precision disabled (meaning 2025-09-07T07:36:41.8744020Z that the final FSDP construction would deviate from the specified 2025-09-07T07:36:41.8744256Z policy). If ``auto_wrap_policy`` is not specified, then this does 2025-09-07T07:36:41.8744452Z not do anything. This API is experimental and subject to change. 2025-09-07T07:36:41.8744569Z (Default: ``(_BatchNorm,)``) 2025-09-07T07:36:41.8744577Z 2025-09-07T07:36:41.8744767Z .. note:: This API is experimental and subject to change. 2025-09-07T07:36:41.8744772Z 2025-09-07T07:36:41.8745052Z .. note:: Only floating point tensors are cast to their specified dtypes. 2025-09-07T07:36:41.8745057Z 2025-09-07T07:36:41.8745237Z .. note:: In ``summon_full_params``, parameters are forced to full 2025-09-07T07:36:41.8745364Z precision, but buffers are not. 2025-09-07T07:36:41.8745368Z 2025-09-07T07:36:41.8745571Z .. note:: Layer norm and batch norm accumulate in ``float32`` even when 2025-09-07T07:36:41.8745777Z their inputs are in a low precision like ``float16`` or ``bfloat16``. 2025-09-07T07:36:41.8746016Z Disabling FSDP's mixed precision for those norm modules only means that 2025-09-07T07:36:41.8746223Z the affine parameters are kept in ``float32``. However, this incurs 2025-09-07T07:36:41.8746461Z separate all-gathers and reduce-scatters for those norm modules, which 2025-09-07T07:36:41.8746686Z may be inefficient, so if the workload permits, the user should prefer 2025-09-07T07:36:41.8746862Z to still apply mixed precision to those modules. 2025-09-07T07:36:41.8746868Z 2025-09-07T07:36:41.8747083Z .. note:: By default, if the user passes a model with any ``_BatchNorm`` 2025-09-07T07:36:41.8747285Z modules and specifies an ``auto_wrap_policy``, then the batch norm 2025-09-07T07:36:41.8747537Z modules will have FSDP applied to them separately with mixed precision 2025-09-07T07:36:41.8747720Z disabled. See the ``_module_classes_to_ignore`` argument. 2025-09-07T07:36:41.8747725Z 2025-09-07T07:36:41.8747929Z .. note:: ``MixedPrecision`` has ``cast_root_forward_inputs=True`` and 2025-09-07T07:36:41.8748152Z ``cast_forward_inputs=False`` by default. For the root FSDP instance, 2025-09-07T07:36:41.8748322Z its ``cast_root_forward_inputs`` takes precedence over its 2025-09-07T07:36:41.8748496Z ``cast_forward_inputs``. For non-root FSDP instances, their 2025-09-07T07:36:41.8748722Z ``cast_root_forward_inputs`` values are ignored. The default setting is 2025-09-07T07:36:41.8748994Z sufficient for the typical case where each FSDP instance has the same 2025-09-07T07:36:41.8749228Z ``MixedPrecision`` configuration and only needs to cast inputs to the 2025-09-07T07:36:41.8749407Z ``param_dtype`` at the beginning of the model's forward pass. 2025-09-07T07:36:41.8749414Z 2025-09-07T07:36:41.8749629Z .. note:: For nested FSDP instances with different ``MixedPrecision`` 2025-09-07T07:36:41.8749861Z configurations, we recommend setting individual ``cast_forward_inputs`` 2025-09-07T07:36:41.8750059Z values to configure casting inputs or not before each instance's 2025-09-07T07:36:41.8750260Z forward. In such a case, since the casts happen before each FSDP 2025-09-07T07:36:41.8750474Z instance's forward, a parent FSDP instance should have its non-FSDP 2025-09-07T07:36:41.8750715Z submodules run before its FSDP submodules to avoid the activation dtype 2025-09-07T07:36:41.8750920Z being changed due to a different ``MixedPrecision`` configuration. 2025-09-07T07:36:41.8750925Z 2025-09-07T07:36:41.8751014Z Example:: 2025-09-07T07:36:41.8751021Z 2025-09-07T07:36:41.8751161Z >>> # xdoctest: +SKIP("undefined variables") 2025-09-07T07:36:41.8751326Z >>> model = nn.Sequential(nn.Linear(3, 3), nn.Linear(3, 3)) 2025-09-07T07:36:41.8751435Z >>> model[1] = FSDP( 2025-09-07T07:36:41.8751577Z >>> model[1], 2025-09-07T07:36:41.8751880Z >>> mixed_precision=MixedPrecision(param_dtype=torch.float16, cast_forward_inputs=True), 2025-09-07T07:36:41.8751973Z >>> ) 2025-09-07T07:36:41.8752113Z >>> model = FSDP( 2025-09-07T07:36:41.8752209Z >>> model, 2025-09-07T07:36:41.8752536Z >>> mixed_precision=MixedPrecision(param_dtype=torch.bfloat16, cast_forward_inputs=True), 2025-09-07T07:36:41.8752617Z >>> ) 2025-09-07T07:36:41.8752621Z 2025-09-07T07:36:41.8752841Z The above shows a working example. On the other hand, if ``model[1]`` 2025-09-07T07:36:41.8753040Z were replaced with ``model[0]``, meaning that the submodule using 2025-09-07T07:36:41.8753273Z different ``MixedPrecision`` ran its forward first, then ``model[1]`` 2025-09-07T07:36:41.8753489Z would incorrectly see ``float16`` activations instead of ``bfloat16`` 2025-09-07T07:36:41.8753584Z ones. 2025-09-07T07:36:41.8753588Z 2025-09-07T07:36:41.8753593Z 2025-09-07T07:36:41.8753843Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:41.8753848Z 2025-09-07T07:36:41.8754522Z msg = Cannot scrape callname=FullStateDictConfig in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/fsdp/api.py line=295. 2025-09-07T07:36:41.8754802Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:41.8754806Z 2025-09-07T07:36:41.8755013Z ``FullStateDictConfig`` is a config class meant to be used with 2025-09-07T07:36:41.8755254Z ``StateDictType.FULL_STATE_DICT``. We recommend enabling both 2025-09-07T07:36:41.8755458Z ``offload_to_cpu=True`` and ``rank0_only=True`` when saving full state 2025-09-07T07:36:41.8755685Z dicts to save GPU memory and CPU memory, respectively. This config class 2025-09-07T07:36:41.8755883Z is meant to be used via the :func:`state_dict_type` context manager as 2025-09-07T07:36:41.8755994Z follows: 2025-09-07T07:36:41.8756000Z 2025-09-07T07:36:41.8756139Z >>> # xdoctest: +SKIP("undefined variables") 2025-09-07T07:36:41.8756376Z >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP 2025-09-07T07:36:41.8756515Z >>> fsdp = FSDP(model, auto_wrap_policy=...) 2025-09-07T07:36:41.8756716Z >>> cfg = FullStateDictConfig(offload_to_cpu=True, rank0_only=True) 2025-09-07T07:36:41.8756939Z >>> with FSDP.state_dict_type(fsdp, StateDictType.FULL_STATE_DICT, cfg): 2025-09-07T07:36:41.8757061Z >>> state = fsdp.state_dict() 2025-09-07T07:36:41.8757274Z >>> # `state` will be empty on non rank 0 and contain CPU tensors on rank 0. 2025-09-07T07:36:41.8757553Z >>> # To reload checkpoint for inference, finetuning, transfer learning, etc: 2025-09-07T07:36:41.8757783Z >>> model = model_fn() # Initialize model in preparation for wrapping with FSDP 2025-09-07T07:36:41.8757884Z >>> if dist.get_rank() == 0: 2025-09-07T07:36:41.8758093Z >>> # Load checkpoint only on rank 0 to avoid memory redundancy 2025-09-07T07:36:41.8758234Z >>> state_dict = torch.load("my_checkpoint.pt") 2025-09-07T07:36:41.8758364Z >>> model.load_state_dict(state_dict) 2025-09-07T07:36:41.8758600Z >>> # All ranks initialize FSDP module as usual. `sync_module_states` argument 2025-09-07T07:36:41.8758840Z >>> # communicates loaded checkpoint states from rank 0 to rest of the world. 2025-09-07T07:36:41.8758945Z >>> fsdp = FSDP( 2025-09-07T07:36:41.8759029Z ... model, 2025-09-07T07:36:41.8759173Z ... device_id=torch.cuda.current_device(), 2025-09-07T07:36:41.8759278Z ... auto_wrap_policy=..., 2025-09-07T07:36:41.8759380Z ... sync_module_states=True, 2025-09-07T07:36:41.8759480Z ... ) 2025-09-07T07:36:41.8759693Z >>> # After this point, all ranks have FSDP model with loaded checkpoint. 2025-09-07T07:36:41.8759698Z 2025-09-07T07:36:41.8759799Z Attributes: 2025-09-07T07:36:41.8759998Z rank0_only (bool): If ``True``, then only rank 0 saves the full state 2025-09-07T07:36:41.8760196Z dict, and nonzero ranks save an empty dict. If ``False``, then all 2025-09-07T07:36:41.8760367Z ranks save the full state dict. (Default: ``False``) 2025-09-07T07:36:41.8760372Z 2025-09-07T07:36:41.8760622Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:41.8760626Z 2025-09-07T07:36:41.8855089Z 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=634. 2025-09-07T07:36:41.8855357Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:41.8855619Z Set the ``state_dict_type`` of all the descendant FSDP modules of the target module. 2025-09-07T07:36:41.8855624Z 2025-09-07T07:36:41.8855884Z Also takes (optional) configuration for the model's and optimizer's state dict. 2025-09-07T07:36:41.8856091Z The target module does not have to be a FSDP module. If the target 2025-09-07T07:36:41.8856314Z module is a FSDP module, its ``state_dict_type`` will also be changed. 2025-09-07T07:36:41.8856319Z 2025-09-07T07:36:41.8856517Z .. note:: This API should be called for only the top-level (root) 2025-09-07T07:36:41.8856618Z module. 2025-09-07T07:36:41.8856622Z 2025-09-07T07:36:41.8856843Z .. note:: This API enables users to transparently use the conventional 2025-09-07T07:36:41.8857046Z ``state_dict`` API to take model checkpoints in cases where the 2025-09-07T07:36:41.8857293Z root FSDP module is wrapped by another ``nn.Module``. For example, 2025-09-07T07:36:41.8857515Z the following will ensure ``state_dict`` is called on all non-FSDP 2025-09-07T07:36:41.8857749Z instances, while dispatching into `sharded_state_dict` implementation 2025-09-07T07:36:41.8857879Z for FSDP: 2025-09-07T07:36:41.8857884Z 2025-09-07T07:36:41.8857989Z Example:: 2025-09-07T07:36:41.8857994Z 2025-09-07T07:36:41.8858128Z >>> # xdoctest: +SKIP("undefined variables") 2025-09-07T07:36:41.8858246Z >>> model = DDP(FSDP(...)) 2025-09-07T07:36:41.8858356Z >>> FSDP.set_state_dict_type( 2025-09-07T07:36:41.8858449Z >>> model, 2025-09-07T07:36:41.8858622Z >>> StateDictType.SHARDED_STATE_DICT, 2025-09-07T07:36:41.8858837Z >>> state_dict_config = ShardedStateDictConfig(offload_to_cpu=True), 2025-09-07T07:36:41.8859072Z >>> optim_state_dict_config = OptimStateDictConfig(offload_to_cpu=True), 2025-09-07T07:36:41.8859159Z >>> ) 2025-09-07T07:36:41.8859285Z >>> param_state_dict = model.state_dict() 2025-09-07T07:36:41.8859506Z >>> optim_state_dict = FSDP.optim_state_dict(model, optim) 2025-09-07T07:36:41.8859511Z 2025-09-07T07:36:41.8859595Z Args: 2025-09-07T07:36:41.8859733Z module (torch.nn.Module): Root module. 2025-09-07T07:36:41.8859961Z state_dict_type (StateDictType): the desired ``state_dict_type`` to set. 2025-09-07T07:36:41.8860205Z state_dict_config (Optional[StateDictConfig]): the configuration for the 2025-09-07T07:36:41.8860315Z target ``state_dict_type``. 2025-09-07T07:36:41.8860563Z optim_state_dict_config (Optional[OptimStateDictConfig]): the configuration 2025-09-07T07:36:41.8860688Z for the optimizer state dict. 2025-09-07T07:36:41.8860693Z 2025-09-07T07:36:41.8860775Z Returns: 2025-09-07T07:36:41.8861008Z A StateDictSettings that include the previous state_dict type and 2025-09-07T07:36:41.8861122Z configuration for the module. 2025-09-07T07:36:41.8861205Z 2025-09-07T07:36:41.8861470Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:41.8861475Z 2025-09-07T07:36:41.8862290Z 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=792. 2025-09-07T07:36:41.8862558Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:41.8862802Z Set the ``state_dict_type`` of all the descendant FSDP modules of the target module. 2025-09-07T07:36:41.8862806Z 2025-09-07T07:36:41.8863161Z This context manager has the same functions as :meth:`set_state_dict_type`. Read the document of 2025-09-07T07:36:41.8863291Z :meth:`set_state_dict_type` for the detail. 2025-09-07T07:36:41.8863298Z 2025-09-07T07:36:41.8863385Z Example:: 2025-09-07T07:36:41.8863402Z 2025-09-07T07:36:41.8863532Z >>> # xdoctest: +SKIP("undefined variables") 2025-09-07T07:36:41.8863638Z >>> model = DDP(FSDP(...)) 2025-09-07T07:36:41.8863758Z >>> with FSDP.state_dict_type( 2025-09-07T07:36:41.8863850Z >>> model, 2025-09-07T07:36:41.8863991Z >>> StateDictType.SHARDED_STATE_DICT, 2025-09-07T07:36:41.8864076Z >>> ): 2025-09-07T07:36:41.8864195Z >>> checkpoint = model.state_dict() 2025-09-07T07:36:41.8864199Z 2025-09-07T07:36:41.8864295Z Args: 2025-09-07T07:36:41.8864418Z module (torch.nn.Module): Root module. 2025-09-07T07:36:41.8864667Z state_dict_type (StateDictType): the desired ``state_dict_type`` to set. 2025-09-07T07:36:41.8864895Z state_dict_config (Optional[StateDictConfig]): the model ``state_dict`` 2025-09-07T07:36:41.8865050Z configuration for the target ``state_dict_type``. 2025-09-07T07:36:41.8865323Z optim_state_dict_config (Optional[OptimStateDictConfig]): the optimizer 2025-09-07T07:36:41.8865521Z ``state_dict`` configuration for the target ``state_dict_type``. 2025-09-07T07:36:41.8865618Z 2025-09-07T07:36:41.8865867Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:41.8865902Z 2025-09-07T07:36:41.8913706Z 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=1805. 2025-09-07T07:36:41.8913978Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:41.8913982Z 2025-09-07T07:36:41.8914220Z Transform the state-dict of an optimizer corresponding to a sharded model. 2025-09-07T07:36:41.8914224Z 2025-09-07T07:36:41.8914467Z The given state-dict can be transformed to one of three types: 2025-09-07T07:36:41.8914769Z 1) full optimizer state_dict, 2) sharded optimizer state_dict, 3) local optimizer state_dict. 2025-09-07T07:36:41.8914774Z 2025-09-07T07:36:41.8915057Z For full optimizer state_dict, all states are unflattened and not sharded. 2025-09-07T07:36:41.8915270Z Rank0 only and CPU only can be specified via :meth:`state_dict_type` to 2025-09-07T07:36:41.8915369Z avoid OOM. 2025-09-07T07:36:41.8915374Z 2025-09-07T07:36:41.8915620Z For sharded optimizer state_dict, all states are unflattened but sharded. 2025-09-07T07:36:41.8915833Z CPU only can be specified via :meth:`state_dict_type` to further save 2025-09-07T07:36:41.8915918Z memory. 2025-09-07T07:36:41.8915923Z 2025-09-07T07:36:41.8916137Z For local state_dict, no transformation will be performed. But a state 2025-09-07T07:36:41.8916386Z will be converted from nn.Tensor to ShardedTensor to represent its sharding 2025-09-07T07:36:41.8916497Z nature (this is not supported yet). 2025-09-07T07:36:41.8916501Z 2025-09-07T07:36:41.8916606Z Example:: 2025-09-07T07:36:41.8916610Z 2025-09-07T07:36:41.8916735Z >>> # xdoctest: +SKIP("undefined variables") 2025-09-07T07:36:41.8916969Z >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP 2025-09-07T07:36:41.8917137Z >>> from torch.distributed.fsdp import StateDictType 2025-09-07T07:36:41.8917315Z >>> from torch.distributed.fsdp import FullStateDictConfig 2025-09-07T07:36:41.8917524Z >>> from torch.distributed.fsdp import FullOptimStateDictConfig 2025-09-07T07:36:41.8917621Z >>> # Save a checkpoint 2025-09-07T07:36:41.8917716Z >>> model, optim = ... 2025-09-07T07:36:41.8917835Z >>> FSDP.set_state_dict_type( 2025-09-07T07:36:41.8917920Z >>> model, 2025-09-07T07:36:41.8918051Z >>> StateDictType.FULL_STATE_DICT, 2025-09-07T07:36:41.8918206Z >>> FullStateDictConfig(rank0_only=False), 2025-09-07T07:36:41.8918346Z >>> FullOptimStateDictConfig(rank0_only=False), 2025-09-07T07:36:41.8918439Z >>> ) 2025-09-07T07:36:41.8918549Z >>> state_dict = model.state_dict() 2025-09-07T07:36:41.8918724Z >>> optim_state_dict = FSDP.optim_state_dict(model, optim) 2025-09-07T07:36:41.8918867Z >>> save_a_checkpoint(state_dict, optim_state_dict) 2025-09-07T07:36:41.8918961Z >>> # Load a checkpoint 2025-09-07T07:36:41.8919068Z >>> model, optim = ... 2025-09-07T07:36:41.8919217Z >>> state_dict, optim_state_dict = load_a_checkpoint() 2025-09-07T07:36:41.8919331Z >>> FSDP.set_state_dict_type( 2025-09-07T07:36:41.8919414Z >>> model, 2025-09-07T07:36:41.8919526Z >>> StateDictType.FULL_STATE_DICT, 2025-09-07T07:36:41.8919662Z >>> FullStateDictConfig(rank0_only=False), 2025-09-07T07:36:41.8919803Z >>> FullOptimStateDictConfig(rank0_only=False), 2025-09-07T07:36:41.8919884Z >>> ) 2025-09-07T07:36:41.8920008Z >>> model.load_state_dict(state_dict) 2025-09-07T07:36:41.8920155Z >>> optim_state_dict = FSDP.optim_state_dict_to_load( 2025-09-07T07:36:41.8920278Z >>> model, optim, optim_state_dict 2025-09-07T07:36:41.8920388Z >>> ) 2025-09-07T07:36:41.8920510Z >>> optim.load_state_dict(optim_state_dict) 2025-09-07T07:36:41.8920515Z 2025-09-07T07:36:41.8920606Z Args: 2025-09-07T07:36:41.8920798Z model (torch.nn.Module): Root module (which may or may not be a 2025-09-07T07:36:41.8921007Z :class:`FullyShardedDataParallel` instance) whose parameters 2025-09-07T07:36:41.8921187Z were passed into the optimizer ``optim``. 2025-09-07T07:36:41.8921428Z optim (torch.optim.Optimizer): Optimizer for ``model`` 's 2025-09-07T07:36:41.8921531Z parameters. 2025-09-07T07:36:41.8921742Z optim_state_dict (Dict[str, Any]): the target optimizer state_dict to 2025-09-07T07:36:41.8921964Z transform. If the value is None, optim.state_dict() will be used. ( 2025-09-07T07:36:41.8922059Z Default: ``None``) 2025-09-07T07:36:41.8922293Z group (dist.ProcessGroup): Model's process group across which parameters 2025-09-07T07:36:41.8922487Z are sharded or ``None`` if using the default process group. ( 2025-09-07T07:36:41.8922581Z Default: ``None``) 2025-09-07T07:36:41.8922586Z 2025-09-07T07:36:41.8922709Z Returns: 2025-09-07T07:36:41.8922899Z Dict[str, Any]: A :class:`dict` containing the optimizer state for 2025-09-07T07:36:41.8923061Z ``model``. The sharding of the optimizer state is based on 2025-09-07T07:36:41.8923168Z ``state_dict_type``. 2025-09-07T07:36:41.8923172Z 2025-09-07T07:36:41.8923421Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:41.8923425Z 2025-09-07T07:36:41.8924475Z 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=1903. 2025-09-07T07:36:41.8924744Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:41.8924749Z 2025-09-07T07:36:41.8925103Z Convert an optimizer state-dict so that it can be loaded into the optimizer associated with the FSDP model. 2025-09-07T07:36:41.8925111Z 2025-09-07T07:36:41.8925278Z Given a ``optim_state_dict`` that is transformed through 2025-09-07T07:36:41.8925500Z :meth:`optim_state_dict`, it gets converted to the flattened optimizer 2025-09-07T07:36:41.8925707Z state_dict that can be loaded to ``optim`` which is the optimizer for 2025-09-07T07:36:41.8925906Z ``model``. ``model`` must be sharded by FullyShardedDataParallel. 2025-09-07T07:36:41.8925910Z 2025-09-07T07:36:41.8926034Z >>> # xdoctest: +SKIP("undefined variables") 2025-09-07T07:36:41.8926278Z >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP 2025-09-07T07:36:41.8926430Z >>> from torch.distributed.fsdp import StateDictType 2025-09-07T07:36:41.8926662Z >>> from torch.distributed.fsdp import FullStateDictConfig 2025-09-07T07:36:41.8926871Z >>> from torch.distributed.fsdp import FullOptimStateDictConfig 2025-09-07T07:36:41.8926967Z >>> # Save a checkpoint 2025-09-07T07:36:41.8927067Z >>> model, optim = ... 2025-09-07T07:36:41.8927183Z >>> FSDP.set_state_dict_type( 2025-09-07T07:36:41.8927267Z >>> model, 2025-09-07T07:36:41.8927399Z >>> StateDictType.FULL_STATE_DICT, 2025-09-07T07:36:41.8927528Z >>> FullStateDictConfig(rank0_only=False), 2025-09-07T07:36:41.8927669Z >>> FullOptimStateDictConfig(rank0_only=False), 2025-09-07T07:36:41.8927766Z >>> ) 2025-09-07T07:36:41.8927873Z >>> state_dict = model.state_dict() 2025-09-07T07:36:41.8927997Z >>> original_osd = optim.state_dict() 2025-09-07T07:36:41.8928124Z >>> optim_state_dict = FSDP.optim_state_dict( 2025-09-07T07:36:41.8928205Z >>> model, 2025-09-07T07:36:41.8928302Z >>> optim, 2025-09-07T07:36:41.8928413Z >>> optim_state_dict=original_osd 2025-09-07T07:36:41.8928504Z >>> ) 2025-09-07T07:36:41.8928646Z >>> save_a_checkpoint(state_dict, optim_state_dict) 2025-09-07T07:36:41.8928739Z >>> # Load a checkpoint 2025-09-07T07:36:41.8928888Z >>> model, optim = ... 2025-09-07T07:36:41.8929040Z >>> state_dict, optim_state_dict = load_a_checkpoint() 2025-09-07T07:36:41.8929156Z >>> FSDP.set_state_dict_type( 2025-09-07T07:36:41.8929237Z >>> model, 2025-09-07T07:36:41.8929350Z >>> StateDictType.FULL_STATE_DICT, 2025-09-07T07:36:41.8929526Z >>> FullStateDictConfig(rank0_only=False), 2025-09-07T07:36:41.8929664Z >>> FullOptimStateDictConfig(rank0_only=False), 2025-09-07T07:36:41.8929745Z >>> ) 2025-09-07T07:36:41.8929866Z >>> model.load_state_dict(state_dict) 2025-09-07T07:36:41.8930011Z >>> optim_state_dict = FSDP.optim_state_dict_to_load( 2025-09-07T07:36:41.8930134Z >>> model, optim, optim_state_dict 2025-09-07T07:36:41.8930217Z >>> ) 2025-09-07T07:36:41.8930341Z >>> optim.load_state_dict(optim_state_dict) 2025-09-07T07:36:41.8930346Z 2025-09-07T07:36:41.8930440Z Args: 2025-09-07T07:36:41.8930634Z model (torch.nn.Module): Root module (which may or may not be a 2025-09-07T07:36:41.8930848Z :class:`FullyShardedDataParallel` instance) whose parameters 2025-09-07T07:36:41.8931013Z were passed into the optimizer ``optim``. 2025-09-07T07:36:41.8931192Z optim (torch.optim.Optimizer): Optimizer for ``model`` 's 2025-09-07T07:36:41.8931294Z parameters. 2025-09-07T07:36:41.8931506Z optim_state_dict (Dict[str, Any]): The optimizer states to be loaded. 2025-09-07T07:36:41.8931719Z is_named_optimizer (bool): Is this optimizer a NamedOptimizer or 2025-09-07T07:36:41.8931910Z KeyedOptimizer. Only set to True if ``optim`` is TorchRec's 2025-09-07T07:36:41.8932084Z KeyedOptimizer or torch.distributed's NamedOptimizer. 2025-09-07T07:36:41.8932291Z load_directly (bool): If this is set to True, this API will also 2025-09-07T07:36:41.8932491Z call optim.load_state_dict(result) before returning the result. 2025-09-07T07:36:41.8932726Z Otherwise, users are responsible to call ``optim.load_state_dict()`` 2025-09-07T07:36:41.8932828Z (Default: ``False``) 2025-09-07T07:36:41.8933065Z group (dist.ProcessGroup): Model's process group across which parameters 2025-09-07T07:36:41.8933262Z are sharded or ``None`` if using the default process group. ( 2025-09-07T07:36:41.8933357Z Default: ``None``) 2025-09-07T07:36:41.8933363Z 2025-09-07T07:36:41.8933627Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:41.8933632Z 2025-09-07T07:36:41.9400711Z 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=129. 2025-09-07T07:36:41.9401044Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:41.9401051Z 2025-09-07T07:36:41.9401482Z RemoteModule instance can only be created after RPC initialization. 2025-09-07T07:36:41.9401488Z 2025-09-07T07:36:41.9401702Z It creates a user-specified module on a specified remote node. 2025-09-07T07:36:41.9401943Z It behaves like a regular ``nn.Module`` except that the ``forward`` method is 2025-09-07T07:36:41.9402075Z executed on the remote node. 2025-09-07T07:36:41.9402310Z It takes care of autograd recording to ensure the backward pass propagates 2025-09-07T07:36:41.9402464Z gradients back to the corresponding remote module. 2025-09-07T07:36:41.9402847Z It can be shared across processors using `RPC framework `__, 2025-09-07T07:36:41.9403038Z without incurring any overheads of copying the actual module, 2025-09-07T07:36:41.9403260Z which is equivalent to an :class:`~torch.distributed.rpc.RRef` 2025-09-07T07:36:41.9403367Z pointing to the remote module. 2025-09-07T07:36:41.9403372Z 2025-09-07T07:36:41.9403593Z The arguments of ``forward_async`` and ``forward`` are the same as 2025-09-07T07:36:41.9403792Z the ``forward`` method of the module returned by the ``module_cls``. 2025-09-07T07:36:41.9403797Z 2025-09-07T07:36:41.9404162Z Apart from ``forward_async`` and ``forward``, no other methods are supported from nn.Module for now. 2025-09-07T07:36:41.9404167Z 2025-09-07T07:36:41.9404426Z Particularly, to create a hybrid model, typically the local modules should be 2025-09-07T07:36:41.9404797Z created outside of remote modules, rather than as submodules of any remote module (by calling ``add_module``). 2025-09-07T07:36:41.9404953Z Hybrid Example: 2025-09-07T07:36:41.9405067Z >>> class HybridModel(nn.Module): 2025-09-07T07:36:41.9405176Z >>> def __init__(self) -> None: 2025-09-07T07:36:41.9405298Z >>> nn.Module.__init__(self) 2025-09-07T07:36:41.9405436Z >>> self.remote_embedding = RemoteModule(...) 2025-09-07T07:36:41.9405570Z >>> self.local_linear = nn.Linear(...) 2025-09-07T07:36:41.9405577Z 2025-09-07T07:36:41.9405773Z For example, if ``module_cls`` returns an instance of ``nn.Linear``, 2025-09-07T07:36:41.9406031Z that has ``forward`` method signature, ``def forward(input: Tensor) -> Tensor:``, 2025-09-07T07:36:41.9406237Z the generated ``RemoteModule`` will have 2 methods in signature of 2025-09-07T07:36:41.9406414Z ``def forward(input: Tensor) -> Tensor:`` and 2025-09-07T07:36:41.9406588Z ``def forward_async(input: Tensor) -> Future[Tensor]:``. 2025-09-07T07:36:41.9406593Z 2025-09-07T07:36:41.9406702Z .. note:: 2025-09-07T07:36:41.9406854Z If the remote module is placed on a cuda device, 2025-09-07T07:36:41.9407088Z any input CPU tensors will be automatically moved to the same cuda device, 2025-09-07T07:36:41.9407487Z and GPU tensors are returned over the wire according to the device map of the remote worker on TensorPipe RPC backend. 2025-09-07T07:36:41.9407493Z 2025-09-07T07:36:41.9407586Z Args: 2025-09-07T07:36:41.9407884Z remote_device (str): Device on the destination worker where we'd like to place this module. 2025-09-07T07:36:41.9408181Z The device can be a local device or a remote device specified by one of the following remote 2025-09-07T07:36:41.9408267Z formats: 2025-09-07T07:36:41.9408272Z 2025-09-07T07:36:41.9408409Z 1. "rank:/" (ex: "rank:0/cuda:0"). 2025-09-07T07:36:41.9408572Z 2. "/" (ex: "trainer0/cuda:0"). 2025-09-07T07:36:41.9408577Z 2025-09-07T07:36:41.9408822Z In addition, the device field can be optional and the default value is "cpu". 2025-09-07T07:36:41.9408950Z module_cls (nn.Module): For example, 2025-09-07T07:36:41.9409061Z >>> class MyModule(nn.Module): 2025-09-07T07:36:41.9409178Z >>> def forward(input): 2025-09-07T07:36:41.9409276Z >>> return input + 1 2025-09-07T07:36:41.9409357Z >>> 2025-09-07T07:36:41.9409469Z >>> module_cls = MyModule 2025-09-07T07:36:41.9409698Z args (Sequence, optional): args to be passed to ``module_cls``. 2025-09-07T07:36:41.9409888Z kwargs (Dict, optional): kwargs to be passed to ``module_cls``. 2025-09-07T07:36:41.9410176Z _module_interface_cls (type, optional): The TorchScript interface type for the module 2025-09-07T07:36:41.9410415Z to be created. The type object should be decorated by @torch.jit.interface. 2025-09-07T07:36:41.9410642Z If not provided, the generated RemoteModule is not torchscript-able. 2025-09-07T07:36:41.9410873Z Warning, this is an experimental API and susceptible to frequent changes. 2025-09-07T07:36:41.9410880Z 2025-09-07T07:36:41.9410977Z Returns: 2025-09-07T07:36:41.9411217Z A remote module instance which wraps the :class:`~nn.Module` created by the 2025-09-07T07:36:41.9411445Z user-provided ``module_cls``, it has a blocking ``forward`` method and an 2025-09-07T07:36:41.9411722Z asynchronous ``forward_async`` method that returns a future of the ``forward`` call 2025-09-07T07:36:41.9411864Z on the user-provided module on the remote side. 2025-09-07T07:36:41.9411870Z 2025-09-07T07:36:41.9411999Z Example:: 2025-09-07T07:36:41.9412148Z Run the following code in two different processes: 2025-09-07T07:36:41.9412181Z 2025-09-07T07:36:41.9412292Z >>> # xdoctest: +SKIP("distributed") 2025-09-07T07:36:41.9412394Z >>> # On worker 0: 2025-09-07T07:36:41.9412541Z >>> import torch 2025-09-07T07:36:41.9412676Z >>> import torch.distributed.rpc as rpc 2025-09-07T07:36:41.9412779Z >>> from torch import nn, Tensor 2025-09-07T07:36:41.9413028Z >>> from torch.distributed.nn.api.remote_module import RemoteModule 2025-09-07T07:36:41.9413123Z >>> 2025-09-07T07:36:41.9413258Z >>> rpc.init_rpc("worker0", rank=0, world_size=2) 2025-09-07T07:36:41.9413386Z >>> remote_linear_module = RemoteModule( 2025-09-07T07:36:41.9413510Z >>> "worker1/cpu", nn.Linear, args=(20, 30), 2025-09-07T07:36:41.9413591Z >>> ) 2025-09-07T07:36:41.9413707Z >>> input = torch.randn(128, 20) 2025-09-07T07:36:41.9413860Z >>> ret_fut = remote_linear_module.forward_async(input) 2025-09-07T07:36:41.9413970Z >>> ret = ret_fut.wait() 2025-09-07T07:36:41.9414063Z >>> rpc.shutdown() 2025-09-07T07:36:41.9414069Z 2025-09-07T07:36:41.9414156Z >>> # On worker 1: 2025-09-07T07:36:41.9414258Z >>> import torch 2025-09-07T07:36:41.9414407Z >>> import torch.distributed.rpc as rpc 2025-09-07T07:36:41.9414500Z >>> 2025-09-07T07:36:41.9414636Z >>> rpc.init_rpc("worker1", rank=1, world_size=2) 2025-09-07T07:36:41.9414728Z >>> rpc.shutdown() 2025-09-07T07:36:41.9414732Z 2025-09-07T07:36:41.9414997Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:41.9415001Z 2025-09-07T07:36:41.9415745Z 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=506. 2025-09-07T07:36:41.9416006Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:41.9416010Z 2025-09-07T07:36:41.9416327Z Besides the constructor, a RemoteModule instance can also be initialized given a module RRef. 2025-09-07T07:36:41.9416334Z 2025-09-07T07:36:41.9416667Z This alternate initialization method can be particularly useful if we want to create multiple 2025-09-07T07:36:41.9416982Z RemoteModule instances that share the same underlying module and reduce memory consumption. 2025-09-07T07:36:41.9416986Z 2025-09-07T07:36:41.9417273Z Moreover, this also provides a workaround for passing script RemoteModule over RPC, 2025-09-07T07:36:41.9417454Z which is not supported. The recommended way is as follows: 2025-09-07T07:36:41.9417459Z 2025-09-07T07:36:41.9417591Z 1. the sender creates a RemoteModule; 2025-09-07T07:36:41.9417778Z 2. the sender sends its ``module_rref`` over RPC; 2025-09-07T07:36:41.9418146Z 3. the receiver calls this method to initialize another RemoteModule using the same ``module_rref``. 2025-09-07T07:36:41.9418184Z 2025-09-07T07:36:41.9418285Z Example:: 2025-09-07T07:36:41.9418433Z Run the following code in two different processes: 2025-09-07T07:36:41.9418438Z 2025-09-07T07:36:41.9418565Z >>> # xdoctest: +SKIP("distributed") 2025-09-07T07:36:41.9418653Z >>> # On worker 0: 2025-09-07T07:36:41.9418739Z >>> import torch 2025-09-07T07:36:41.9418877Z >>> import torch.distributed.rpc as rpc 2025-09-07T07:36:41.9418979Z >>> from torch import nn, Tensor 2025-09-07T07:36:41.9419212Z >>> from torch.distributed.nn.api.remote_module import RemoteModule 2025-09-07T07:36:41.9419294Z >>> 2025-09-07T07:36:41.9419431Z >>> rpc.init_rpc("worker0", rank=0, world_size=2) 2025-09-07T07:36:41.9419553Z >>> remote_module = RemoteModule( 2025-09-07T07:36:41.9419677Z >>> "worker1/cpu", nn.Linear, args=(20, 30), 2025-09-07T07:36:41.9419770Z >>> ) 2025-09-07T07:36:41.9419849Z >>> 2025-09-07T07:36:41.9419957Z >>> remote_module1 = rpc.rpc_sync( 2025-09-07T07:36:41.9420063Z >>> "worker1/cpu", 2025-09-07T07:36:41.9420183Z >>> RemoteModule.init_from_module_rref, 2025-09-07T07:36:41.9420345Z >>> ("worker1/cpu", remote_module1.get_module_rref()), 2025-09-07T07:36:41.9420479Z >>> ) 2025-09-07T07:36:41.9420569Z >>> rpc.shutdown() 2025-09-07T07:36:41.9420574Z 2025-09-07T07:36:41.9420672Z >>> # On worker 1: 2025-09-07T07:36:41.9420758Z >>> import torch 2025-09-07T07:36:41.9420877Z >>> import torch.distributed.rpc as rpc 2025-09-07T07:36:41.9420967Z >>> 2025-09-07T07:36:41.9421130Z >>> rpc.init_rpc("worker1", rank=1, world_size=2) 2025-09-07T07:36:41.9421234Z >>> rpc.shutdown() 2025-09-07T07:36:41.9421239Z 2025-09-07T07:36:41.9421318Z Args: 2025-09-07T07:36:41.9421612Z remote_device (str): Device on the destination worker where we'd like to place this module. 2025-09-07T07:36:41.9421909Z The device can be a local device or a remote device specified by one of the following remote 2025-09-07T07:36:41.9421996Z formats: 2025-09-07T07:36:41.9422001Z 2025-09-07T07:36:41.9422152Z 1. "rank:/" (ex: "rank:0/cuda:0"). 2025-09-07T07:36:41.9422299Z 2. "/" (ex: "trainer0/cuda:0"). 2025-09-07T07:36:41.9422305Z 2025-09-07T07:36:41.9422558Z In addition, the device field can be optional and the default value is "cpu". 2025-09-07T07:36:41.9422830Z module_rref (RRef[nn.Module]): The module reference shared by both the caller and 2025-09-07T07:36:41.9422936Z the created remote module. 2025-09-07T07:36:41.9423225Z _module_interface_cls (type, optional): The TorchScript interface type for the module 2025-09-07T07:36:41.9423455Z to be created. The type object should be decorated by @torch.jit.interface. 2025-09-07T07:36:41.9423888Z If not provided, the generated RemoteModule is not torchscript-able. 2025-09-07T07:36:41.9424127Z Warning, this is an experimental API and susceptible to frequent changes. 2025-09-07T07:36:41.9424131Z 2025-09-07T07:36:41.9424217Z Returns: 2025-09-07T07:36:41.9424472Z A remote module instance which wraps the :class:`~nn.Module` created by the 2025-09-07T07:36:41.9424701Z user-provided ``module_rref``, it has a blocking ``forward`` method and an 2025-09-07T07:36:41.9424985Z asynchronous ``forward_async`` method that returns a future of the ``forward`` call 2025-09-07T07:36:41.9425126Z on the user-provided module on the remote side. 2025-09-07T07:36:41.9425130Z 2025-09-07T07:36:41.9425392Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:41.9425399Z 2025-09-07T07:36:41.9426036Z 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=598. 2025-09-07T07:36:41.9426308Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:41.9426312Z 2025-09-07T07:36:41.9426620Z A RemoteModule instance can only be created after RPC initialization. 2025-09-07T07:36:41.9426625Z 2025-09-07T07:36:41.9426835Z It creates a user-specified module on a specified remote node. 2025-09-07T07:36:41.9427067Z It behaves like a regular ``nn.Module`` except that the ``forward`` method is 2025-09-07T07:36:41.9427175Z executed on the remote node. 2025-09-07T07:36:41.9427422Z It takes care of autograd recording to ensure the backward pass propagates 2025-09-07T07:36:41.9427576Z gradients back to the corresponding remote module. 2025-09-07T07:36:41.9427583Z 2025-09-07T07:36:41.9427811Z It generates two methods ``forward_async`` and ``forward`` based on the 2025-09-07T07:36:41.9428021Z signature of the ``forward`` method of ``module_cls``. ``forward_async`` 2025-09-07T07:36:41.9428280Z runs asynchronously and returns a Future. The arguments of ``forward_async`` 2025-09-07T07:36:41.9428473Z and ``forward`` are the same as the ``forward`` method of the module 2025-09-07T07:36:41.9428583Z returned by the ``module_cls``. 2025-09-07T07:36:41.9428588Z 2025-09-07T07:36:41.9428793Z For example, if ``module_cls`` returns an instance of ``nn.Linear``, 2025-09-07T07:36:41.9429041Z that has ``forward`` method signature: ``def forward(input: Tensor) -> Tensor:``, 2025-09-07T07:36:41.9429316Z the generated ``RemoteModule`` will have 2 methods with the signatures: 2025-09-07T07:36:41.9429323Z 2025-09-07T07:36:41.9429444Z | ``def forward(input: Tensor) -> Tensor:`` 2025-09-07T07:36:41.9429607Z | ``def forward_async(input: Tensor) -> Future[Tensor]:`` 2025-09-07T07:36:41.9429663Z 2025-09-07T07:36:41.9429743Z Args: 2025-09-07T07:36:41.9430037Z remote_device (str): Device on the destination worker where we'd like to place this module. 2025-09-07T07:36:41.9430401Z The format should be "/", where the device field can be parsed as torch.device type. 2025-09-07T07:36:41.9430543Z E.g., "trainer0/cpu", "trainer0", "ps0/cuda:0". 2025-09-07T07:36:41.9430800Z In addition, the device field can be optional and the default value is "cpu". 2025-09-07T07:36:41.9431047Z module_cls (nn.Module): Class for the module to be created remotely. For example, 2025-09-07T07:36:41.9431053Z 2025-09-07T07:36:41.9431161Z >>> class MyModule(nn.Module): 2025-09-07T07:36:41.9431275Z >>> def forward(input): 2025-09-07T07:36:41.9431450Z >>> return input + 1 2025-09-07T07:36:41.9431545Z >>> 2025-09-07T07:36:41.9431644Z >>> module_cls = MyModule 2025-09-07T07:36:41.9431651Z 2025-09-07T07:36:41.9431850Z args (Sequence, optional): args to be passed to ``module_cls``. 2025-09-07T07:36:41.9432057Z kwargs (Dict, optional): kwargs to be passed to ``module_cls``. 2025-09-07T07:36:41.9432061Z 2025-09-07T07:36:41.9432146Z Returns: 2025-09-07T07:36:41.9432400Z A remote module instance which wraps the :class:`~nn.Module` created by the 2025-09-07T07:36:41.9432631Z user-provided ``module_cls``, it has a blocking ``forward`` method and an 2025-09-07T07:36:41.9432900Z asynchronous ``forward_async`` method that returns a future of the ``forward`` call 2025-09-07T07:36:41.9433055Z on the user-provided module on the remote side. 2025-09-07T07:36:41.9433062Z 2025-09-07T07:36:41.9433154Z Example:: 2025-09-07T07:36:41.9433319Z Run the following code in two different processes: 2025-09-07T07:36:41.9433325Z 2025-09-07T07:36:41.9433438Z >>> # xdoctest: +SKIP("distributed") 2025-09-07T07:36:41.9433540Z >>> # On worker 0: 2025-09-07T07:36:41.9433633Z >>> import torch 2025-09-07T07:36:41.9433756Z >>> import torch.distributed.rpc as rpc 2025-09-07T07:36:41.9433879Z >>> from torch import nn, Tensor 2025-09-07T07:36:41.9434097Z >>> from torch.distributed.nn.api.remote_module import RemoteModule 2025-09-07T07:36:41.9434182Z >>> 2025-09-07T07:36:41.9434392Z >>> rpc.init_rpc("worker0", rank=0, world_size=2) 2025-09-07T07:36:41.9434514Z >>> remote_linear_module = RemoteModule( 2025-09-07T07:36:41.9434686Z >>> "worker1/cpu", nn.Linear, args=(20, 30), 2025-09-07T07:36:41.9434770Z >>> ) 2025-09-07T07:36:41.9434871Z >>> input = torch.randn(128, 20) 2025-09-07T07:36:41.9435037Z >>> ret_fut = remote_linear_module.forward_async(input) 2025-09-07T07:36:41.9435137Z >>> ret = ret_fut.wait() 2025-09-07T07:36:41.9435241Z >>> rpc.shutdown() 2025-09-07T07:36:41.9435248Z 2025-09-07T07:36:41.9435334Z >>> # On worker 1: 2025-09-07T07:36:41.9435425Z >>> import torch 2025-09-07T07:36:41.9435560Z >>> import torch.distributed.rpc as rpc 2025-09-07T07:36:41.9435645Z >>> 2025-09-07T07:36:41.9435796Z >>> rpc.init_rpc("worker1", rank=1, world_size=2) 2025-09-07T07:36:41.9435885Z >>> rpc.shutdown() 2025-09-07T07:36:41.9435889Z 2025-09-07T07:36:41.9436078Z Furthermore, a more practical example that is combined with 2025-09-07T07:36:41.9436575Z `DistributedDataParallel `__ (DDP) 2025-09-07T07:36:41.9436905Z can be found in this `tutorial `__. 2025-09-07T07:36:41.9436910Z 2025-09-07T07:36:41.9437171Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:41.9437205Z 2025-09-07T07:36:41.9563905Z msg = Cannot scrape callname=DistributedOptimizer in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/optim/optimizer.py line=129. 2025-09-07T07:36:41.9564179Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:41.9564276Z 2025-09-07T07:36:41.9564515Z DistributedOptimizer takes remote references to parameters scattered 2025-09-07T07:36:41.9564762Z across workers and applies the given optimizer locally for each parameter. 2025-09-07T07:36:41.9564766Z 2025-09-07T07:36:41.9564998Z This class uses :meth:`~torch.distributed.autograd.get_gradients` in order 2025-09-07T07:36:41.9565159Z to retrieve the gradients for specific parameters. 2025-09-07T07:36:41.9565167Z 2025-09-07T07:36:41.9565261Z Concurrent calls to 2025-09-07T07:36:41.9565462Z :meth:`~torch.distributed.optim.DistributedOptimizer.step`, 2025-09-07T07:36:41.9565615Z either from the same or different clients, will 2025-09-07T07:36:41.9565838Z be serialized on each worker -- as each worker's optimizer can only work 2025-09-07T07:36:41.9566093Z on one set of gradients at a time. However, there is no guarantee that 2025-09-07T07:36:41.9566335Z the full forward-backward-optimizer sequence will execute for one client 2025-09-07T07:36:41.9566550Z at a time. This means that the gradients being applied may not correspond 2025-09-07T07:36:41.9566779Z to the latest forward pass executed on a given worker. Also, there is no 2025-09-07T07:36:41.9566890Z guaranteed ordering across workers. 2025-09-07T07:36:41.9566895Z 2025-09-07T07:36:41.9567165Z `DistributedOptimizer` creates the local optimizer with TorchScript enabled 2025-09-07T07:36:41.9567390Z by default, so that optimizer updates are not blocked by the Python Global 2025-09-07T07:36:41.9567628Z Interpreter Lock (GIL) in the case of multithreaded training (e.g. Distributed 2025-09-07T07:36:41.9567878Z Model Parallel). This feature is currently enabled for most optimizers. You 2025-09-07T07:36:41.9568127Z can also follow `the recipe`__ in PyTorch tutorials to enable TorchScript support 2025-09-07T07:36:41.9568250Z for your own custom optimizers. 2025-09-07T07:36:41.9568255Z 2025-09-07T07:36:41.9568336Z Args: 2025-09-07T07:36:41.9568528Z optimizer_class (optim.Optimizer): the class of optimizer to 2025-09-07T07:36:41.9568650Z instantiate on each worker. 2025-09-07T07:36:41.9568857Z params_rref (list[RRef]): list of RRefs to local or remote parameters 2025-09-07T07:36:41.9568961Z to optimize. 2025-09-07T07:36:41.9569173Z args: arguments to pass to the optimizer constructor on each worker. 2025-09-07T07:36:41.9569392Z kwargs: arguments to pass to the optimizer constructor on each worker. 2025-09-07T07:36:41.9569408Z 2025-09-07T07:36:41.9569542Z Example:: 2025-09-07T07:36:41.9569656Z >>> # xdoctest: +SKIP("distributed") 2025-09-07T07:36:41.9569828Z >>> import torch.distributed.autograd as dist_autograd 2025-09-07T07:36:41.9569950Z >>> import torch.distributed.rpc as rpc 2025-09-07T07:36:41.9570049Z >>> from torch import optim 2025-09-07T07:36:41.9570246Z >>> from torch.distributed.optim import DistributedOptimizer 2025-09-07T07:36:41.9570328Z >>> 2025-09-07T07:36:41.9570469Z >>> with dist_autograd.context() as context_id: 2025-09-07T07:36:41.9570565Z >>> # Forward pass. 2025-09-07T07:36:41.9570762Z >>> rref1 = rpc.remote("worker1", torch.add, args=(torch.ones(2), 3)) 2025-09-07T07:36:41.9570965Z >>> rref2 = rpc.remote("worker1", torch.add, args=(torch.ones(2), 1)) 2025-09-07T07:36:41.9571087Z >>> loss = rref1.to_here() + rref2.to_here() 2025-09-07T07:36:41.9571180Z >>> 2025-09-07T07:36:41.9571276Z >>> # Backward pass. 2025-09-07T07:36:41.9571425Z >>> dist_autograd.backward(context_id, [loss.sum()]) 2025-09-07T07:36:41.9571517Z >>> 2025-09-07T07:36:41.9571605Z >>> # Optimizer. 2025-09-07T07:36:41.9571737Z >>> dist_optim = DistributedOptimizer( 2025-09-07T07:36:41.9571930Z >>> optim.SGD, 2025-09-07T07:36:41.9572024Z >>> [rref1, rref2], 2025-09-07T07:36:41.9572127Z >>> lr=0.05, 2025-09-07T07:36:41.9572210Z >>> ) 2025-09-07T07:36:41.9572333Z >>> dist_optim.step(context_id) 2025-09-07T07:36:41.9572338Z 2025-09-07T07:36:41.9572519Z __ https://github.com/pytorch/tutorials/pull/1465 2025-09-07T07:36:41.9572524Z 2025-09-07T07:36:41.9572780Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:41.9572785Z 2025-09-07T07:36:41.9576595Z 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. 2025-09-07T07:36:41.9576858Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:41.9576863Z 2025-09-07T07:36:41.9577267Z Wraps an arbitrary :class:`torch.optim.Optimizer` and runs `post-local SGD `_, 2025-09-07T07:36:41.9577419Z This optimizer runs local optimizer at every step. 2025-09-07T07:36:41.9577802Z After the warm-up stage, it averages parameters periodically after the local optimizer is applied. 2025-09-07T07:36:41.9577808Z 2025-09-07T07:36:41.9577890Z Args: 2025-09-07T07:36:41.9578007Z optim: The local optimizer. 2025-09-07T07:36:41.9578223Z averager: A model averager instance to run post-localSGD algorithm. 2025-09-07T07:36:41.9578228Z 2025-09-07T07:36:41.9578317Z Example:: 2025-09-07T07:36:41.9578322Z 2025-09-07T07:36:41.9578456Z >>> # xdoctest: +SKIP("undefined variables") 2025-09-07T07:36:41.9578543Z >>> import torch 2025-09-07T07:36:41.9578669Z >>> import torch.distributed as dist 2025-09-07T07:36:41.9578939Z >>> import torch.distributed.algorithms.model_averaging.averagers as averagers 2025-09-07T07:36:41.9579062Z >>> import torch.nn as nn 2025-09-07T07:36:41.9579253Z >>> from torch.distributed.optim import PostLocalSGDOptimizer 2025-09-07T07:36:41.9579524Z >>> from torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook import ( 2025-09-07T07:36:41.9579634Z >>> PostLocalSGDState, 2025-09-07T07:36:41.9579732Z >>> post_localSGD_hook, 2025-09-07T07:36:41.9579813Z >>> ) 2025-09-07T07:36:41.9579904Z >>> 2025-09-07T07:36:41.9580054Z >>> model = nn.parallel.DistributedDataParallel( 2025-09-07T07:36:41.9580205Z >>> module, device_ids=[rank], output_device=rank 2025-09-07T07:36:41.9580284Z >>> ) 2025-09-07T07:36:41.9580363Z >>> 2025-09-07T07:36:41.9580518Z >>> # Register a post-localSGD communication hook. 2025-09-07T07:36:41.9580810Z >>> state = PostLocalSGDState(process_group=None, subgroup=None, start_localSGD_iter=100) 2025-09-07T07:36:41.9581029Z >>> model.register_comm_hook(state, post_localSGD_hook) 2025-09-07T07:36:41.9581108Z >>> 2025-09-07T07:36:41.9581307Z >>> # Create a post-localSGD optimizer that wraps a local optimizer. 2025-09-07T07:36:41.9581565Z >>> # Note that ``warmup_steps`` used in ``PostLocalSGDOptimizer`` must be the same as 2025-09-07T07:36:41.9581729Z >>> # ``start_localSGD_iter`` used in ``PostLocalSGDState``. 2025-09-07T07:36:41.9581954Z >>> local_optim = torch.optim.SGD(params=model.parameters(), lr=0.01) 2025-09-07T07:36:41.9582066Z >>> opt = PostLocalSGDOptimizer( 2025-09-07T07:36:41.9582167Z >>> optim=local_optim, 2025-09-07T07:36:41.9582422Z >>> averager=averagers.PeriodicModelAverager(period=4, warmup_steps=100) 2025-09-07T07:36:41.9582503Z >>> ) 2025-09-07T07:36:41.9582597Z >>> 2025-09-07T07:36:41.9582816Z >>> # In the first 100 steps, DDP runs global gradient averaging at every step. 2025-09-07T07:36:41.9583115Z >>> # After 100 steps, DDP runs gradient averaging within each subgroup (intra-node by default), 2025-09-07T07:36:41.9583507Z >>> # and post-localSGD optimizer runs global model averaging every 4 steps after applying the local optimizer. 2025-09-07T07:36:41.9583608Z >>> for step in range(0, 200): 2025-09-07T07:36:41.9583743Z >>> opt.zero_grad() 2025-09-07T07:36:41.9583851Z >>> loss = loss_fn(output, labels) 2025-09-07T07:36:41.9583948Z >>> loss.backward() 2025-09-07T07:36:41.9584046Z >>> opt.step() 2025-09-07T07:36:41.9584051Z 2025-09-07T07:36:41.9584301Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:41.9584335Z 2025-09-07T07:36:41.9678545Z 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=284. 2025-09-07T07:36:41.9678829Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:41.9678848Z 2025-09-07T07:36:41.9679262Z Wrap an arbitrary :class:`optim.Optimizer ` and shards its states across ranks in the group. 2025-09-07T07:36:41.9679267Z 2025-09-07T07:36:41.9679511Z The sharing is done as described by `ZeRO `_. 2025-09-07T07:36:41.9679518Z 2025-09-07T07:36:41.9679673Z The local optimizer instance in each rank is only 2025-09-07T07:36:41.9679989Z responsible for updating approximately ``1 / world_size`` parameters and 2025-09-07T07:36:41.9680204Z hence only needs to keep ``1 / world_size`` optimizer states. After 2025-09-07T07:36:41.9680447Z parameters are updated locally, each rank will broadcast its parameters to 2025-09-07T07:36:41.9680627Z all other peers to keep all model replicas in the same state. 2025-09-07T07:36:41.9680832Z ``ZeroRedundancyOptimizer`` can be used in conjunction with 2025-09-07T07:36:41.9681086Z :class:`torch.nn.parallel.DistributedDataParallel` to reduce per-rank peak 2025-09-07T07:36:41.9681196Z memory consumption. 2025-09-07T07:36:41.9681200Z 2025-09-07T07:36:41.9681459Z ``ZeroRedundancyOptimizer`` uses a sorted-greedy algorithm to pack a number 2025-09-07T07:36:41.9681706Z of parameters at each rank. Each parameter belongs to a single rank and is 2025-09-07T07:36:41.9681947Z not divided among ranks. The partition is arbitrary and might not match the 2025-09-07T07:36:41.9682075Z the parameter registration or usage order. 2025-09-07T07:36:41.9682082Z 2025-09-07T07:36:41.9682183Z Arguments: 2025-09-07T07:36:41.9682372Z params (``Iterable``): an ``Iterable`` of :class:`torch.Tensor` s 2025-09-07T07:36:41.9682571Z or :class:`dict` s giving all parameters, which will be sharded 2025-09-07T07:36:41.9682662Z across ranks. 2025-09-07T07:36:41.9682666Z 2025-09-07T07:36:41.9682753Z Keyword Args: 2025-09-07T07:36:41.9682985Z optimizer_class (:class:`torch.nn.Optimizer`): the class of the local 2025-09-07T07:36:41.9683071Z optimizer. 2025-09-07T07:36:41.9683289Z process_group (``ProcessGroup``, optional): ``torch.distributed`` 2025-09-07T07:36:41.9683529Z ``ProcessGroup`` (default: ``dist.group.WORLD`` initialized by 2025-09-07T07:36:41.9683675Z :meth:`torch.distributed.init_process_group`). 2025-09-07T07:36:41.9683909Z parameters_as_bucket_view (bool, optional): if ``True``, parameters are 2025-09-07T07:36:41.9684120Z packed into buckets to speed up communication, and ``param.data`` 2025-09-07T07:36:41.9684331Z fields point to bucket views at different offsets; if ``False``, 2025-09-07T07:36:41.9684536Z each individual parameter is communicated separately, and each 2025-09-07T07:36:41.9684687Z ``params.data`` stays intact (default: ``False``). 2025-09-07T07:36:41.9684892Z overlap_with_ddp (bool, optional): if ``True``, :meth:`step` is 2025-09-07T07:36:41.9685088Z overlapped with :class:`DistributedDataParallel` 's gradient 2025-09-07T07:36:41.9685312Z synchronization; this requires (1) either a functional optimizer 2025-09-07T07:36:41.9685493Z for the ``optimizer_class`` argument or one with a functional 2025-09-07T07:36:41.9685669Z equivalent and (2) registering a DDP communication hook 2025-09-07T07:36:41.9685883Z constructed from one of the functions in ``ddp_zero_hook.py``; 2025-09-07T07:36:41.9686085Z parameters are packed into buckets matching those in 2025-09-07T07:36:41.9686251Z :class:`DistributedDataParallel`, meaning that the 2025-09-07T07:36:41.9686401Z ``parameters_as_bucket_view`` argument is ignored. 2025-09-07T07:36:41.9686584Z If ``False``, :meth:`step` runs disjointly after the backward pass 2025-09-07T07:36:41.9686724Z (per normal). 2025-09-07T07:36:41.9686822Z (default: ``False``) 2025-09-07T07:36:41.9687048Z **defaults: any trailing arguments, which are forwarded to the local 2025-09-07T07:36:41.9687136Z optimizer. 2025-09-07T07:36:41.9687141Z 2025-09-07T07:36:41.9687234Z Example:: 2025-09-07T07:36:41.9687252Z 2025-09-07T07:36:41.9687348Z >>> # xdoctest: +SKIP 2025-09-07T07:36:41.9687449Z >>> import torch.nn as nn 2025-09-07T07:36:41.9687663Z >>> from torch.distributed.optim import ZeroRedundancyOptimizer 2025-09-07T07:36:41.9687860Z >>> from torch.nn.parallel import DistributedDataParallel as DDP 2025-09-07T07:36:41.9688105Z >>> model = nn.Sequential(*[nn.Linear(2000, 2000).to(rank) for _ in range(20)]) 2025-09-07T07:36:41.9688250Z >>> ddp = DDP(model, device_ids=[rank]) 2025-09-07T07:36:41.9688367Z >>> opt = ZeroRedundancyOptimizer( 2025-09-07T07:36:41.9688485Z >>> ddp.parameters(), 2025-09-07T07:36:41.9688608Z >>> optimizer_class=torch.optim.Adam, 2025-09-07T07:36:41.9688693Z >>> lr=0.01 2025-09-07T07:36:41.9688792Z >>> ) 2025-09-07T07:36:41.9688898Z >>> ddp(inputs).sum().backward() 2025-09-07T07:36:41.9688999Z >>> opt.step() 2025-09-07T07:36:41.9689003Z 2025-09-07T07:36:41.9689092Z .. warning:: 2025-09-07T07:36:41.9689300Z Currently, ``ZeroRedundancyOptimizer`` requires that all of the 2025-09-07T07:36:41.9689458Z passed-in parameters are the same dense type. 2025-09-07T07:36:41.9689463Z 2025-09-07T07:36:41.9689550Z .. warning:: 2025-09-07T07:36:41.9689776Z If you pass ``overlap_with_ddp=True``, be wary of the following: Given 2025-09-07T07:36:41.9690016Z the way that overlapping :class:`DistributedDataParallel` with 2025-09-07T07:36:41.9690250Z :class:`ZeroRedundancyOptimizer` is currently implemented, the first 2025-09-07T07:36:41.9690471Z two or three training iterations do not perform parameter updates in 2025-09-07T07:36:41.9690665Z the optimizer step, depending on if ``static_graph=False`` or 2025-09-07T07:36:41.9690859Z ``static_graph=True``, respectively. This is because it needs 2025-09-07T07:36:41.9691040Z information about the gradient bucketing strategy used by 2025-09-07T07:36:41.9691269Z :class:`DistributedDataParallel`, which is not finalized until the 2025-09-07T07:36:41.9691464Z second forward pass if ``static_graph=False`` or until the third 2025-09-07T07:36:41.9691697Z forward pass if ``static_graph=True``. To adjust for this, one option 2025-09-07T07:36:41.9691813Z is to prepend dummy inputs. 2025-09-07T07:36:41.9691818Z 2025-09-07T07:36:41.9692070Z .. warning:: ZeroRedundancyOptimizer is experimental and subject to change. 2025-09-07T07:36:41.9692076Z 2025-09-07T07:36:41.9692342Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:41.9692346Z 2025-09-07T07:36:41.9876593Z msg = Cannot scrape callname=_CustomReducer in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/pipelining/microbatch.py line=29. 2025-09-07T07:36:41.9876882Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:41.9876886Z 2025-09-07T07:36:41.9877123Z Custom reducer class that can be used to specify a custom operation that 2025-09-07T07:36:41.9877291Z reduces losses of multiple microbatches into one value. 2025-09-07T07:36:41.9877307Z 2025-09-07T07:36:41.9877390Z Example: 2025-09-07T07:36:41.9877485Z >>> # xdoctest: +SKIP 2025-09-07T07:36:41.9877600Z >>> sum_reducer = _CustomReducer( 2025-09-07T07:36:41.9877699Z >>> torch.tensor(0.0), 2025-09-07T07:36:41.9877791Z >>> lambda a, b: a + b 2025-09-07T07:36:41.9877970Z >>> ) 2025-09-07T07:36:41.9877975Z 2025-09-07T07:36:41.9878225Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:41.9878230Z 2025-09-07T07:36:42.0374054Z 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. 2025-09-07T07:36:42.0374705Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:42.0374715Z 2025-09-07T07:36:42.0375095Z A decorator for a function indicating that the return value of the function 2025-09-07T07:36:42.0375299Z is guaranteed to be a :class:`~torch.futures.Future` object and this 2025-09-07T07:36:42.0375554Z function can run asynchronously on the RPC callee. More specifically, the 2025-09-07T07:36:42.0375789Z callee extracts the :class:`~torch.futures.Future` returned by the wrapped 2025-09-07T07:36:42.0376019Z function and installs subsequent processing steps as a callback to that 2025-09-07T07:36:42.0376265Z :class:`~torch.futures.Future`. The installed callback will read the value 2025-09-07T07:36:42.0376521Z from the :class:`~torch.futures.Future` when completed and send the 2025-09-07T07:36:42.0376713Z value back as the RPC response. That also means the returned 2025-09-07T07:36:42.0376940Z :class:`~torch.futures.Future` only exists on the callee side and is never 2025-09-07T07:36:42.0377161Z sent through RPC. This decorator is useful when the wrapped function's 2025-09-07T07:36:42.0377369Z (``fn``) execution needs to pause and resume due to, e.g., containing 2025-09-07T07:36:42.0377588Z :meth:`~torch.distributed.rpc.rpc_async` or waiting for other signals. 2025-09-07T07:36:42.0377593Z 2025-09-07T07:36:42.0377827Z .. note:: To enable asynchronous execution, applications must pass the 2025-09-07T07:36:42.0378056Z function object returned by this decorator to RPC APIs. If RPC detected 2025-09-07T07:36:42.0378271Z attributes installed by this decorator, it knows that this function 2025-09-07T07:36:42.0378472Z returns a ``Future`` object and will handle that accordingly. 2025-09-07T07:36:42.0378687Z However, this does not mean this decorator has to be outmost one when 2025-09-07T07:36:42.0378920Z defining a function. For example, when combined with ``@staticmethod`` 2025-09-07T07:36:42.0379129Z or ``@classmethod``, ``@rpc.functions.async_execution`` needs to be the 2025-09-07T07:36:42.0379359Z inner decorator to allow the target function be recognized as a static 2025-09-07T07:36:42.0379586Z or class function. This target function can still execute asynchronously 2025-09-07T07:36:42.0379806Z because, when accessed, the static or class method preserves attributes 2025-09-07T07:36:42.0380010Z installed by ``@rpc.functions.async_execution``. 2025-09-07T07:36:42.0380015Z 2025-09-07T07:36:42.0380050Z 2025-09-07T07:36:42.0380151Z Example:: 2025-09-07T07:36:42.0380346Z The returned :class:`~torch.futures.Future` object can come from 2025-09-07T07:36:42.0380489Z :meth:`~torch.distributed.rpc.rpc_async`, 2025-09-07T07:36:42.0380711Z :meth:`~torch.futures.Future.then`, or :class:`~torch.futures.Future` 2025-09-07T07:36:42.0380883Z constructor. The example below shows directly using the 2025-09-07T07:36:42.0381024Z :class:`~torch.futures.Future` returned by 2025-09-07T07:36:42.0381142Z :meth:`~torch.futures.Future.then`. 2025-09-07T07:36:42.0381146Z 2025-09-07T07:36:42.0381272Z >>> from torch.distributed import rpc 2025-09-07T07:36:42.0381355Z >>> 2025-09-07T07:36:42.0381465Z >>> # omitting setup and shutdown RPC 2025-09-07T07:36:42.0381556Z >>> 2025-09-07T07:36:42.0381647Z >>> # On all workers 2025-09-07T07:36:42.0381772Z >>> @rpc.functions.async_execution 2025-09-07T07:36:42.0381885Z >>> def async_add_chained(to, x, y, z): 2025-09-07T07:36:42.0382072Z >>> # This function runs on "worker1" and returns immediately when 2025-09-07T07:36:42.0382270Z >>> # the callback is installed through the `then(cb)` API. In the 2025-09-07T07:36:42.0382496Z >>> # mean time, the `rpc_async` to "worker2" can run concurrently. 2025-09-07T07:36:42.0382668Z >>> # When the return value of that `rpc_async` arrives at 2025-09-07T07:36:42.0382851Z >>> # "worker1", "worker1" will run the lambda function accordingly 2025-09-07T07:36:42.0383061Z >>> # and set the value for the previously returned `Future`, which 2025-09-07T07:36:42.0383256Z >>> # will then trigger RPC to send the result back to "worker0". 2025-09-07T07:36:42.0383423Z >>> return rpc.rpc_async(to, torch.add, args=(x, y)).then( 2025-09-07T07:36:42.0383546Z >>> lambda fut: fut.wait() + z 2025-09-07T07:36:42.0383629Z >>> ) 2025-09-07T07:36:42.0383710Z >>> 2025-09-07T07:36:42.0383816Z >>> # On worker0 2025-09-07T07:36:42.0383911Z >>> # xdoctest: +SKIP 2025-09-07T07:36:42.0384018Z >>> ret = rpc.rpc_sync( 2025-09-07T07:36:42.0384102Z >>> "worker1", 2025-09-07T07:36:42.0384198Z >>> async_add_chained, 2025-09-07T07:36:42.0384329Z >>> args=("worker2", torch.ones(2), 1, 1) 2025-09-07T07:36:42.0384465Z >>> ) 2025-09-07T07:36:42.0384579Z >>> print(ret) # prints tensor([3., 3.]) 2025-09-07T07:36:42.0384584Z 2025-09-07T07:36:42.0384821Z When combined with TorchScript decorators, this decorator must be the 2025-09-07T07:36:42.0384910Z outmost one. 2025-09-07T07:36:42.0384915Z 2025-09-07T07:36:42.0385029Z >>> from torch import Tensor 2025-09-07T07:36:42.0385140Z >>> from torch.futures import Future 2025-09-07T07:36:42.0385253Z >>> from torch.distributed import rpc 2025-09-07T07:36:42.0385345Z >>> 2025-09-07T07:36:42.0385454Z >>> # omitting setup and shutdown RPC 2025-09-07T07:36:42.0385546Z >>> 2025-09-07T07:36:42.0385639Z >>> # On all workers 2025-09-07T07:36:42.0385734Z >>> @torch.jit.script 2025-09-07T07:36:42.0385893Z >>> def script_add(x: Tensor, y: Tensor) -> Tensor: 2025-09-07T07:36:42.0385986Z >>> return x + y 2025-09-07T07:36:42.0386079Z >>> 2025-09-07T07:36:42.0386190Z >>> @rpc.functions.async_execution 2025-09-07T07:36:42.0386285Z >>> @torch.jit.script 2025-09-07T07:36:42.0386481Z >>> def async_add(to: str, x: Tensor, y: Tensor) -> Future[Tensor]: 2025-09-07T07:36:42.0386618Z >>> return rpc.rpc_async(to, script_add, (x, y)) 2025-09-07T07:36:42.0386712Z >>> 2025-09-07T07:36:42.0386797Z >>> # On worker0 2025-09-07T07:36:42.0386891Z >>> ret = rpc.rpc_sync( 2025-09-07T07:36:42.0386988Z >>> "worker1", 2025-09-07T07:36:42.0387073Z >>> async_add, 2025-09-07T07:36:42.0387185Z >>> args=("worker2", torch.ones(2), 1) 2025-09-07T07:36:42.0387277Z >>> ) 2025-09-07T07:36:42.0387391Z >>> print(ret) # prints tensor([2., 2.]) 2025-09-07T07:36:42.0387422Z 2025-09-07T07:36:42.0387654Z When combined with static or class method, this decorator must be the 2025-09-07T07:36:42.0387738Z inner one. 2025-09-07T07:36:42.0387744Z 2025-09-07T07:36:42.0387868Z >>> from torch.distributed import rpc 2025-09-07T07:36:42.0387947Z >>> 2025-09-07T07:36:42.0388060Z >>> # omitting setup and shutdown RPC 2025-09-07T07:36:42.0388149Z >>> 2025-09-07T07:36:42.0388237Z >>> # On all workers 2025-09-07T07:36:42.0388345Z >>> class AsyncExecutionClass: 2025-09-07T07:36:42.0388437Z >>> 2025-09-07T07:36:42.0388532Z >>> @staticmethod 2025-09-07T07:36:42.0388659Z >>> @rpc.functions.async_execution 2025-09-07T07:36:42.0388771Z >>> def static_async_add(to, x, y, z): 2025-09-07T07:36:42.0388937Z >>> return rpc.rpc_async(to, torch.add, args=(x, y)).then( 2025-09-07T07:36:42.0389059Z >>> lambda fut: fut.wait() + z 2025-09-07T07:36:42.0389141Z >>> ) 2025-09-07T07:36:42.0389235Z >>> 2025-09-07T07:36:42.0389323Z >>> @classmethod 2025-09-07T07:36:42.0389436Z >>> @rpc.functions.async_execution 2025-09-07T07:36:42.0389569Z >>> def class_async_add(cls, to, x, y, z): 2025-09-07T07:36:42.0389715Z >>> ret_fut = torch.futures.Future() 2025-09-07T07:36:42.0389874Z >>> rpc.rpc_async(to, torch.add, args=(x, y)).then( 2025-09-07T07:36:42.0390022Z >>> lambda fut: ret_fut.set_result(fut.wait() + z) 2025-09-07T07:36:42.0390108Z >>> ) 2025-09-07T07:36:42.0390247Z >>> return ret_fut 2025-09-07T07:36:42.0390328Z >>> 2025-09-07T07:36:42.0390442Z >>> @rpc.functions.async_execution 2025-09-07T07:36:42.0390579Z >>> def bound_async_add(self, to, x, y, z): 2025-09-07T07:36:42.0390746Z >>> return rpc.rpc_async(to, torch.add, args=(x, y)).then( 2025-09-07T07:36:42.0390870Z >>> lambda fut: fut.wait() + z 2025-09-07T07:36:42.0390961Z >>> ) 2025-09-07T07:36:42.0391044Z >>> 2025-09-07T07:36:42.0391138Z >>> # On worker0 2025-09-07T07:36:42.0391235Z >>> ret = rpc.rpc_sync( 2025-09-07T07:36:42.0391334Z >>> "worker1", 2025-09-07T07:36:42.0391463Z >>> AsyncExecutionClass.static_async_add, 2025-09-07T07:36:42.0391578Z >>> args=("worker2", torch.ones(2), 1, 2) 2025-09-07T07:36:42.0391698Z >>> ) 2025-09-07T07:36:42.0391813Z >>> print(ret) # prints tensor([4., 4.]) 2025-09-07T07:36:42.0391905Z >>> 2025-09-07T07:36:42.0391999Z >>> ret = rpc.rpc_sync( 2025-09-07T07:36:42.0392086Z >>> "worker1", 2025-09-07T07:36:42.0392223Z >>> AsyncExecutionClass.class_async_add, 2025-09-07T07:36:42.0392336Z >>> args=("worker2", torch.ones(2), 1, 2) 2025-09-07T07:36:42.0392416Z >>> ) 2025-09-07T07:36:42.0392552Z >>> print(ret) # prints tensor([4., 4.]) 2025-09-07T07:36:42.0392556Z 2025-09-07T07:36:42.0392716Z This decorator also works with RRef helpers, i.e., . 2025-09-07T07:36:42.0392865Z :meth:`torch.distributed.rpc.RRef.rpc_sync`, 2025-09-07T07:36:42.0393018Z :meth:`torch.distributed.rpc.RRef.rpc_async`, and 2025-09-07T07:36:42.0393165Z :meth:`torch.distributed.rpc.RRef.remote`. 2025-09-07T07:36:42.0393172Z 2025-09-07T07:36:42.0393288Z >>> from torch.distributed import rpc 2025-09-07T07:36:42.0393369Z >>> 2025-09-07T07:36:42.0393517Z >>> # reuse the AsyncExecutionClass class above 2025-09-07T07:36:42.0393668Z >>> rref = rpc.remote("worker1", AsyncExecutionClass) 2025-09-07T07:36:42.0393891Z >>> ret = rref.rpc_sync().static_async_add("worker2", torch.ones(2), 1, 2) 2025-09-07T07:36:42.0394006Z >>> print(ret) # prints tensor([4., 4.]) 2025-09-07T07:36:42.0394086Z >>> 2025-09-07T07:36:42.0394249Z >>> rref = rpc.remote("worker1", AsyncExecutionClass) 2025-09-07T07:36:42.0394570Z >>> ret = rref.rpc_async().static_async_add("worker2", torch.ones(2), 1, 2).wait() 2025-09-07T07:36:42.0394703Z >>> print(ret) # prints tensor([4., 4.]) 2025-09-07T07:36:42.0394822Z >>> 2025-09-07T07:36:42.0394970Z >>> rref = rpc.remote("worker1", AsyncExecutionClass) 2025-09-07T07:36:42.0395214Z >>> ret = rref.remote().static_async_add("worker2", torch.ones(2), 1, 2).to_here() 2025-09-07T07:36:42.0395333Z >>> print(ret) # prints tensor([4., 4.]) 2025-09-07T07:36:42.0395337Z 2025-09-07T07:36:42.0395606Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:42.0395611Z 2025-09-07T07:36:42.0411805Z 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=113. 2025-09-07T07:36:42.0412110Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:42.0412116Z 2025-09-07T07:36:42.0412317Z Set device mapping between each RPC caller and callee pair. This 2025-09-07T07:36:42.0412512Z function can be called multiple times to incrementally add 2025-09-07T07:36:42.0412630Z device placement configurations. 2025-09-07T07:36:42.0412634Z 2025-09-07T07:36:42.0412715Z Args: 2025-09-07T07:36:42.0412829Z to (str): Callee name. 2025-09-07T07:36:42.0413023Z device_map (Dict of int, str, or torch.device): Device placement 2025-09-07T07:36:42.0413262Z mappings from this worker to the callee. This map must be 2025-09-07T07:36:42.0413353Z invertible. 2025-09-07T07:36:42.0413357Z 2025-09-07T07:36:42.0413442Z Example: 2025-09-07T07:36:42.0413571Z >>> # xdoctest: +SKIP("distributed") 2025-09-07T07:36:42.0413687Z >>> # both workers 2025-09-07T07:36:42.0413788Z >>> def add(x, y): 2025-09-07T07:36:42.0413924Z >>> print(x) # tensor([1., 1.], device='cuda:1') 2025-09-07T07:36:42.0414024Z >>> return x + y, (x + y).to(2) 2025-09-07T07:36:42.0414120Z >>> 2025-09-07T07:36:42.0414209Z >>> # on worker 0 2025-09-07T07:36:42.0414342Z >>> options = TensorPipeRpcBackendOptions( 2025-09-07T07:36:42.0414463Z >>> num_worker_threads=8, 2025-09-07T07:36:42.0414571Z >>> device_maps={"worker1": {0: 1}} 2025-09-07T07:36:42.0414705Z >>> # maps worker0's cuda:0 to worker1's cuda:1 2025-09-07T07:36:42.0414784Z >>> ) 2025-09-07T07:36:42.0414910Z >>> options.set_device_map("worker1", {1: 2}) 2025-09-07T07:36:42.0415042Z >>> # maps worker0's cuda:1 to worker1's cuda:2 2025-09-07T07:36:42.0415150Z >>> 2025-09-07T07:36:42.0415251Z >>> rpc.init_rpc( 2025-09-07T07:36:42.0415336Z >>> "worker0", 2025-09-07T07:36:42.0415420Z >>> rank=0, 2025-09-07T07:36:42.0415524Z >>> world_size=2, 2025-09-07T07:36:42.0415653Z >>> backend=rpc.BackendType.TENSORPIPE, 2025-09-07T07:36:42.0415770Z >>> rpc_backend_options=options 2025-09-07T07:36:42.0415851Z >>> ) 2025-09-07T07:36:42.0415930Z >>> 2025-09-07T07:36:42.0416034Z >>> x = torch.ones(2) 2025-09-07T07:36:42.0416190Z >>> rets = rpc.rpc_sync("worker1", add, args=(x.to(0), 1)) 2025-09-07T07:36:42.0416376Z >>> # The first argument will be moved to cuda:1 on worker1. When 2025-09-07T07:36:42.0416569Z >>> # sending the return value back, it will follow the invert of 2025-09-07T07:36:42.0416739Z >>> # the device map, and hence will be moved back to cuda:0 and 2025-09-07T07:36:42.0416844Z >>> # cuda:1 on worker0 2025-09-07T07:36:42.0416991Z >>> print(rets[0]) # tensor([2., 2.], device='cuda:0') 2025-09-07T07:36:42.0417134Z >>> print(rets[1]) # tensor([2., 2.], device='cuda:1') 2025-09-07T07:36:42.0417151Z 2025-09-07T07:36:42.0417400Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:42.0417407Z 2025-09-07T07:36:42.0434022Z msg = Cannot scrape callname=_server_process_global_profile in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/rpc/server_process_global_profiler.py line=19. 2025-09-07T07:36:42.0434378Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:42.0434386Z 2025-09-07T07:36:42.0434682Z It has the same API as ``torch.autograd.profiler.profile`` class, 2025-09-07T07:36:42.0434975Z except that it enables profiling on all threads running RPC server request callbacks. 2025-09-07T07:36:42.0434983Z 2025-09-07T07:36:42.0435261Z Context manager that manages autograd profiler state and holds a summary of results. 2025-09-07T07:36:42.0435509Z Under the hood it just records events of functions being executed in C++ and 2025-09-07T07:36:42.0435738Z exposes those events to Python. You can wrap any code into it and it will 2025-09-07T07:36:42.0435864Z only report runtime of PyTorch functions. 2025-09-07T07:36:42.0436141Z Note: profiler is thread local and is automatically propagated into the async tasks 2025-09-07T07:36:42.0436146Z 2025-09-07T07:36:42.0436266Z Args: 2025-09-07T07:36:42.0436530Z enabled (bool, optional): Setting this to False makes this context manager a no-op. 2025-09-07T07:36:42.0436627Z Default: ``True``. 2025-09-07T07:36:42.0436631Z 2025-09-07T07:36:42.0436925Z use_cuda (bool, optional): Enables timing of CUDA events as well using the cudaEvent API. 2025-09-07T07:36:42.0437122Z Adds approximately 4us of overhead to each tensor operation. 2025-09-07T07:36:42.0437276Z Default: ``False`` 2025-09-07T07:36:42.0437281Z 2025-09-07T07:36:42.0437503Z record_shapes (bool, optional): If shapes recording is set, information 2025-09-07T07:36:42.0437730Z about input dimensions will be collected. This allows one to see which 2025-09-07T07:36:42.0437953Z dimensions have been used under the hood and further group by them 2025-09-07T07:36:42.0438207Z using prof.key_averages(group_by_input_shape=True). Please note that 2025-09-07T07:36:42.0438444Z shape recording might skew your profiling data. It is recommended to 2025-09-07T07:36:42.0438680Z use separate runs with and without shape recording to validate the timing. 2025-09-07T07:36:42.0438916Z Most likely the skew will be negligible for bottom most events (in a case 2025-09-07T07:36:42.0439130Z of nested function calls). But for higher level functions the total 2025-09-07T07:36:42.0439329Z self cpu time might be artificially increased because of the shape 2025-09-07T07:36:42.0439431Z collection. 2025-09-07T07:36:42.0439435Z 2025-09-07T07:36:42.0439741Z profile_memory (bool, optional): Whether to report memory usage, default: ``False`` 2025-09-07T07:36:42.0439746Z 2025-09-07T07:36:42.0439856Z .. warning:: 2025-09-07T07:36:42.0440058Z Enabling memory profiling incurs additional profiler overhead 2025-09-07T07:36:42.0440065Z 2025-09-07T07:36:42.0440150Z .. warning:: 2025-09-07T07:36:42.0440448Z Due to some CUDA multiprocessing limitations (see :ref:`multiprocessing-cuda-note`), 2025-09-07T07:36:42.0440643Z one cannot use the profiler with ``use_cuda = True`` to benchmark 2025-09-07T07:36:42.0440890Z DataLoaders with ``num_workers > 0``. If you wish to benchmark data loading, 2025-09-07T07:36:42.0441057Z please use ``use_cuda = False`` or ``num_workers = 0``. 2025-09-07T07:36:42.0441062Z 2025-09-07T07:36:42.0441162Z Example: 2025-09-07T07:36:42.0441260Z >>> # xdoctest: +SKIP 2025-09-07T07:36:42.0441349Z >>> # On worker 0: 2025-09-07T07:36:42.0441450Z >>> import torch 2025-09-07T07:36:42.0441571Z >>> import torch.distributed.rpc as rpc 2025-09-07T07:36:42.0441709Z >>> rpc.init_rpc("worker0", rank=0, world_size=2) 2025-09-07T07:36:42.0441841Z >>> x, y = torch.tensor(1), torch.tensor(2) 2025-09-07T07:36:42.0441952Z >>> outer_profile_rref = rpc.remote( 2025-09-07T07:36:42.0442125Z ... dst_worker_name, rpc._server_process_global_profile 2025-09-07T07:36:42.0442205Z ... ) 2025-09-07T07:36:42.0442329Z >>> outer_profile_rref.rpc_sync().__enter__() 2025-09-07T07:36:42.0442486Z >>> rpc.rpc_sync(dst_worker_name, torch.add, (x, y)) 2025-09-07T07:36:42.0442595Z >>> inner_profile_rref = rpc.remote( 2025-09-07T07:36:42.0442763Z ... dst_worker_name, rpc._server_process_global_profile 2025-09-07T07:36:42.0442875Z ... ) 2025-09-07T07:36:42.0442999Z >>> inner_profile_rref.rpc_sync().__enter__() 2025-09-07T07:36:42.0443153Z >>> rpc.rpc_sync(dst_worker_name, torch.sub, (x, y)) 2025-09-07T07:36:42.0443325Z >>> inner_profile_rref.rpc_sync().__exit__(None, None, None) 2025-09-07T07:36:42.0443510Z >>> outer_profile_rref.rpc_sync().__exit__(None, None, None) 2025-09-07T07:36:42.0443666Z >>> print(inner_profile_rref.rpc_sync().key_averages()) 2025-09-07T07:36:42.0443905Z --------- --------------- --------------- --------------- --------------- --------------- --------------- 2025-09-07T07:36:42.0444232Z Name Self CPU total % Self CPU total CPU total % CPU total CPU time avg Number of Calls 2025-09-07T07:36:42.0444458Z --------- --------------- --------------- --------------- --------------- --------------- --------------- 2025-09-07T07:36:42.0444663Z sub 85.06% 76.275us 100.00% 89.667us 89.667us 1 2025-09-07T07:36:42.0444850Z empty 14.94% 13.392us 14.94% 13.392us 13.392us 1 2025-09-07T07:36:42.0445087Z --------- --------------- --------------- --------------- --------------- --------------- --------------- 2025-09-07T07:36:42.0445223Z Self CPU time total: 89.667us 2025-09-07T07:36:42.0445383Z >>> print(outer_profile_rref.rpc_sync().key_averages()) 2025-09-07T07:36:42.0445615Z --------- --------------- --------------- --------------- --------------- --------------- --------------- 2025-09-07T07:36:42.0445968Z Name Self CPU total % Self CPU total CPU total % CPU total CPU time avg Number of Calls 2025-09-07T07:36:42.0446197Z --------- --------------- --------------- --------------- --------------- --------------- --------------- 2025-09-07T07:36:42.0446376Z sub 35.65% 76.275us 41.91% 89.667us 89.667us 1 2025-09-07T07:36:42.0446560Z empty 12.67% 27.101us 12.67% 27.101us 13.551us 2 2025-09-07T07:36:42.0446748Z add 51.68% 110.550us 58.09% 124.259us 124.259us 1 2025-09-07T07:36:42.0446996Z --------- --------------- --------------- --------------- --------------- --------------- --------------- 2025-09-07T07:36:42.0447112Z Self CPU time total: 213.926us 2025-09-07T07:36:42.0447203Z >>> rpc.shutdown() 2025-09-07T07:36:42.0447210Z 2025-09-07T07:36:42.0447311Z >>> # On worker 1: 2025-09-07T07:36:42.0447432Z >>> import torch.distributed.rpc as rpc 2025-09-07T07:36:42.0447567Z >>> rpc.init_rpc("worker1", rank=1, world_size=2) 2025-09-07T07:36:42.0447740Z >>> # wait for worker 0 to finish work, and then shutdown. 2025-09-07T07:36:42.0447832Z >>> rpc.shutdown() 2025-09-07T07:36:42.0447836Z 2025-09-07T07:36:42.0448100Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:42.0448107Z 2025-09-07T07:36:42.1504513Z 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=35. 2025-09-07T07:36:42.1505561Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:42.1505957Z 2025-09-07T07:36:42.1506262Z :meth:`local_map` is an experimental API that allows users to pass :class:`DTensor` s 2025-09-07T07:36:42.1506913Z to a function that is written to be applied on ``torch.Tensor`` s. It is done by extracting 2025-09-07T07:36:42.1507582Z the local components of :class:`DTensor`, call the function, and wrap the outputs to 2025-09-07T07:36:42.1508126Z :class:`DTensor` according to the ``out_placements``. 2025-09-07T07:36:42.1508389Z 2025-09-07T07:36:42.1508481Z Args: 2025-09-07T07:36:42.1508807Z func (Callable): the function to be applied on each local shard of 2025-09-07T07:36:42.1509223Z :class:`DTensor` s. 2025-09-07T07:36:42.1509740Z out_placements (Union[`PlacementType`, Tuple[`PlacementType`, ...]]): 2025-09-07T07:36:42.1510349Z the desired placements of the :class:`DTensor` s in ``func``'s flattened output. 2025-09-07T07:36:42.1510972Z If the flattened ``output`` is a single value, the ``out_placements`` should be 2025-09-07T07:36:42.1511571Z of type `PlacementType`. Otherwise if the flattened ``output`` has multiple 2025-09-07T07:36:42.1521357Z values, the ``out_placements`` should be a tuple of `PlacementType` values 1:1 2025-09-07T07:36:42.1522108Z mapping to the flattened ``output``. 2025-09-07T07:36:42.1522558Z Besides, for :class:`Tensor` output, we use `PlacementType` as its 2025-09-07T07:36:42.1523153Z placements (a `Tuple[Placement]` value). For non-Tensor output, the `PlacementType` 2025-09-07T07:36:42.1523828Z should be `None`. 2025-09-07T07:36:42.1524250Z Note that the only exception is when no :class:`DTensor` argument is passed 2025-09-07T07:36:42.1524845Z in. In this case, even if `out_placements` is not `None`, the result function 2025-09-07T07:36:42.1525449Z should ignore the desired placements because the function is not running with 2025-09-07T07:36:42.1526054Z :class:`DTensor` s. 2025-09-07T07:36:42.1526410Z in_placements (Tuple[`PlacementType`, ...], optional): 2025-09-07T07:36:42.1526975Z the required placements of the :class:`DTensor` s in the flattened inputs of ``func``. 2025-09-07T07:36:42.1527611Z If ``in_placements`` is specified, :meth:`local_map` would examine whether the 2025-09-07T07:36:42.1528255Z placements of each :class:`DTensor` argument is the same as the required 2025-09-07T07:36:42.1528789Z placements or not. If the placements are not the same and 2025-09-07T07:36:42.1529334Z ``redistribute_inputs`` is ``False``, an exception will be raised. Otherwise if 2025-09-07T07:36:42.1529953Z ``redistribute_inputs`` is ``True``, the argument will be first redistributed to 2025-09-07T07:36:42.1530581Z the required sharding placements before passing its local tensor to ``func``. 2025-09-07T07:36:42.1531178Z The only exception is when required placements are not ``None`` and the 2025-09-07T07:36:42.1531862Z argument is a :class:`torch.Tensor`. In this case, the placements examination 2025-09-07T07:36:42.1532445Z will be skipped and the argument will be directly passed to ``func``. 2025-09-07T07:36:42.1533008Z If ``in_placements`` is ``None``, no placements examination will be performed. 2025-09-07T07:36:42.1533441Z Default: None 2025-09-07T07:36:42.1533780Z in_grad_placements (Tuple[`PlacementType`, ...], optional): 2025-09-07T07:36:42.1534285Z the placements hint of the :class:`DTensor` s gradient corresponds 2025-09-07T07:36:42.1534823Z to the flattened input DTensor. This argument is the hint that user 2025-09-07T07:36:42.1535343Z can give to :meth:`to_local` in case the gradient layout of the 2025-09-07T07:36:42.1535847Z local tensor input does not match its :class:`DTensor` input layout. 2025-09-07T07:36:42.1536378Z If not specified, we will assume the gradient layout of the local 2025-09-07T07:36:42.1536907Z tensor input remains the same as the original :class:`DTensor` input 2025-09-07T07:36:42.1537410Z and use that for gradient computation. Default: None. 2025-09-07T07:36:42.1537813Z device_mesh (:class:`DeviceMesh`, optional): 2025-09-07T07:36:42.1538285Z the device mesh that the output :class:`DTensor` s are placed on. If not 2025-09-07T07:36:42.1538889Z specified, this will be inferred from the first input :class:`DTensor`'s device 2025-09-07T07:36:42.1539369Z mesh. Default: None. 2025-09-07T07:36:42.1539550Z 2025-09-07T07:36:42.1539652Z Keyword Args: 2025-09-07T07:36:42.1539897Z redistribute_inputs (bool, optional): 2025-09-07T07:36:42.1540427Z the bool value indicating whether to reshard the input :class:`DTensor` s when 2025-09-07T07:36:42.1541055Z their placements are different from the required input placements. If this 2025-09-07T07:36:42.1541656Z value is ``False`` and some :class:`DTensor` input has a different placement, 2025-09-07T07:36:42.1542142Z an exception will be raised. Default: False. 2025-09-07T07:36:42.1542402Z 2025-09-07T07:36:42.1542490Z Returns: 2025-09-07T07:36:42.1542867Z A ``Callable`` that applies ``func`` to each local shard of the input :class:`DTensor` 2025-09-07T07:36:42.1543494Z and returns a :class:`DTensor` constructed from the return value of ``func``. 2025-09-07T07:36:42.1543850Z 2025-09-07T07:36:42.1543947Z Raises: 2025-09-07T07:36:42.1544294Z AssertionError: For any non-DTensor output, we require its corresponding 2025-09-07T07:36:42.1544920Z output placement in ``out_placements`` be None. An AssertionError will be raised 2025-09-07T07:36:42.1545401Z if this is not the case. 2025-09-07T07:36:42.1545591Z 2025-09-07T07:36:42.1545868Z ValueError: If ``redistribute_inputs=False`` but the input :class:`DTensor` needs 2025-09-07T07:36:42.1546404Z a redistribution according to ``in_placements``. 2025-09-07T07:36:42.1546724Z 2025-09-07T07:36:42.1546808Z Example: 2025-09-07T07:36:42.1547042Z >>> # xdoctest: +SKIP("distributed") 2025-09-07T07:36:42.1547405Z >>> def mm_allreduce_forward(device_mesh, W, X): 2025-09-07T07:36:42.1547765Z >>> partial_sum_tensor = torch.mm(W, X) 2025-09-07T07:36:42.1548244Z >>> reduced_tensor = funcol.all_reduce(partial_sum_tensor, "sum", device_mesh) 2025-09-07T07:36:42.1548748Z >>> return reduced_tensor 2025-09-07T07:36:42.1549029Z >>> 2025-09-07T07:36:42.1549272Z >>> W = torch.randn(12, 8, requires_grad=False) 2025-09-07T07:36:42.1549629Z >>> X = torch.randn(8, 16, requires_grad=False) 2025-09-07T07:36:42.1549966Z >>> Y = torch.mm(W, X) 2025-09-07T07:36:42.1550326Z >>> row_wise = [Shard(0)] # row-wise sharding placements on 1-d mesh 2025-09-07T07:36:42.1550821Z >>> col_wise = [Shard(1)] # col-wise sharding placements on 1-d mesh 2025-09-07T07:36:42.1551200Z >>> 2025-09-07T07:36:42.1551591Z >>> # local_mm_allreduce_forward is the function wrapped with DTensor/Tensor conversion 2025-09-07T07:36:42.1552112Z >>> local_mm_allreduce_forward = local_map( 2025-09-07T07:36:42.1552486Z >>> mm_allreduce_forward, 2025-09-07T07:36:42.1552787Z >>> out_placements=[Replicate()], 2025-09-07T07:36:42.1553136Z >>> in_placements=[col_wise, row_wise], 2025-09-07T07:36:42.1553478Z >>> device_mesh=device_mesh, 2025-09-07T07:36:42.1553771Z >>> ) 2025-09-07T07:36:42.1553959Z >>> 2025-09-07T07:36:42.1554177Z >>> W_dt = distribute_tensor( 2025-09-07T07:36:42.1554563Z ... W, device_mesh, (col_wise) 2025-09-07T07:36:42.1554885Z ... ) # col-wisely sharded W tensor 2025-09-07T07:36:42.1555191Z >>> X_dt = distribute_tensor( 2025-09-07T07:36:42.1555492Z ... X, device_mesh, (row_wise) 2025-09-07T07:36:42.1555815Z ... ) # row-wisely sharded X tensor 2025-09-07T07:36:42.1556147Z >>> Y_dt = local_mm_allreduce_forward( 2025-09-07T07:36:42.1556461Z ... device_mesh, W_dt, X_dt 2025-09-07T07:36:42.1556812Z ... ) # apply local_mm_allreduce_forward to DTensors 2025-09-07T07:36:42.1557080Z 2025-09-07T07:36:42.1557289Z .. note:: This API is currently experimental and subject to change 2025-09-07T07:36:42.1557595Z 2025-09-07T07:36:42.1557862Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:42.1558228Z 2025-09-07T07:36:42.1558996Z 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=25. 2025-09-07T07:36:42.1560099Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:42.1560475Z 2025-09-07T07:36:42.1560807Z :meth:`register_sharding` is an experimental API that allows users to register sharding 2025-09-07T07:36:42.1561453Z strategies for an operator when the tensor inputs and outputs are DTensor. 2025-09-07T07:36:42.1562059Z It can be useful when: (1) there doesn't exist a default sharding strategy for ``op``, 2025-09-07T07:36:42.1562681Z e.g. when ``op`` is a custom operator that is not supported by :class:`DTensor`; (2) 2025-09-07T07:36:42.1563324Z when users would like to overwrite default sharding strategies of existing operators. 2025-09-07T07:36:42.1563720Z 2025-09-07T07:36:42.1563805Z Args: 2025-09-07T07:36:42.1564055Z op (Union[OpOverload, List[OpOverload]]): 2025-09-07T07:36:42.1564487Z An op or a list of ops to register the customized sharding function. 2025-09-07T07:36:42.1564794Z 2025-09-07T07:36:42.1564891Z Returns: 2025-09-07T07:36:42.1565265Z A function decorator which can be used to wrap a function that defines the sharding 2025-09-07T07:36:42.1565933Z strategy for the operator specified in ``op``. The defined sharding strategy will be 2025-09-07T07:36:42.1566606Z registered to DTensor and will override the default sharding strategy if DTensor has 2025-09-07T07:36:42.1567304Z already implemented the operator. The customized sharding function takes the same inputs 2025-09-07T07:36:42.1568003Z as the original op (except that if an arg is a :class:`torch.Tensor`, it will be 2025-09-07T07:36:42.1568628Z replaced by a tensor-like object that DTensor uses internally). The function should 2025-09-07T07:36:42.1569300Z return a sequence of 2-tuples, each specifying acceptable output placements and its 2025-09-07T07:36:42.1569847Z corresponding input placements. 2025-09-07T07:36:42.1570054Z 2025-09-07T07:36:42.1570152Z Example: 2025-09-07T07:36:42.1570379Z >>> # xdoctest: +SKIP("distributed") 2025-09-07T07:36:42.1570733Z >>> @register_sharding(aten._softmax.default) 2025-09-07T07:36:42.1571132Z >>> def custom_softmax_sharding(x, dim, half_to_float): 2025-09-07T07:36:42.1571551Z >>> softmax_dim = dim if dim >= 0 else dim + x.ndim 2025-09-07T07:36:42.1571924Z >>> acceptable_shardings = [] 2025-09-07T07:36:42.1572213Z >>> 2025-09-07T07:36:42.1572508Z >>> all_replicate = ([Replicate()], [Replicate(), None, None]) 2025-09-07T07:36:42.1572950Z >>> acceptable_shardings.append(all_replicate) 2025-09-07T07:36:42.1573292Z >>> 2025-09-07T07:36:42.1573545Z >>> for sharding_dim in range(x.ndim): 2025-09-07T07:36:42.1573895Z >>> if sharding_dim != softmax_dim: 2025-09-07T07:36:42.1574227Z >>> all_sharded = ( 2025-09-07T07:36:42.1574535Z >>> [Shard(sharding_dim)], 2025-09-07T07:36:42.1574863Z >>> [Shard(sharding_dim), None, None], 2025-09-07T07:36:42.1575184Z >>> ) 2025-09-07T07:36:42.1575477Z >>> acceptable_shardings.append(all_sharded) 2025-09-07T07:36:42.1575815Z >>> 2025-09-07T07:36:42.1576027Z >>> return acceptable_shardings 2025-09-07T07:36:42.1576253Z 2025-09-07T07:36:42.1576448Z .. note:: This API is currently experimental and subject to change 2025-09-07T07:36:42.1576766Z 2025-09-07T07:36:42.1577014Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:42.1577382Z 2025-09-07T07:36:42.1725301Z 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=428. 2025-09-07T07:36:42.1726339Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:42.1726720Z 2025-09-07T07:36:42.1727113Z Configure the nn.Module's inputs to convert the input tensors of the nn.Module to DTensors at runtime according to 2025-09-07T07:36:42.1727926Z ``input_layouts``, and perform layout redistribution according to the ``desired_input_layouts``. 2025-09-07T07:36:42.1728376Z 2025-09-07T07:36:42.1728465Z Keyword Args: 2025-09-07T07:36:42.1728806Z input_layouts (Union[Placement, Tuple[Optional[Placement]]]): 2025-09-07T07:36:42.1729560Z The DTensor layouts of input tensors for the nn.Module, this is used to convert the input tensors to 2025-09-07T07:36:42.1730376Z DTensors. If some inputs are not torch.Tensor or no need to convert to DTensors, ``None`` need to be specified 2025-09-07T07:36:42.1731041Z as a placeholder. default: None. 2025-09-07T07:36:42.1731501Z desired_input_layouts (Union[Placement, Tuple[Optional[Placement]]]): 2025-09-07T07:36:42.1732229Z The desired DTensor layout of input tensors for the nn.Module, this is used to ensure the inputs of the nn.Module 2025-09-07T07:36:42.1733129Z have the desired DTensor layouts. This argument needs to have the same length with ``input_layouts``. default: None. 2025-09-07T07:36:42.1733781Z input_kwarg_layouts (Dict[str, Placement]): 2025-09-07T07:36:42.1734397Z The DTensor layouts of input kwargs for the nn.Module, this is used to convert the input kwarg tensors to DTensors. 2025-09-07T07:36:42.1734990Z default: None 2025-09-07T07:36:42.1735310Z desired_input_kwarg_layouts: (Dict[str, Placement]): 2025-09-07T07:36:42.1735951Z The desired DTensor layout of input kwargs for the nn.Module, this is used to ensure the inputs of the nn.Module 2025-09-07T07:36:42.1736669Z have the desired DTensor layouts. default: None. 2025-09-07T07:36:42.1737053Z use_local_output (bool, optional): 2025-09-07T07:36:42.1737638Z Whether to use local :class:`torch.Tensor` instead of :class:`DTensor` for the module inputs, default: False. 2025-09-07T07:36:42.1738251Z Returns: 2025-09-07T07:36:42.1738741Z A :class:`ParallelStyle` object that prepares the sharding layouts of the nn.Module's inputs. 2025-09-07T07:36:42.1739193Z 2025-09-07T07:36:42.1739285Z Example:: 2025-09-07T07:36:42.1739514Z >>> # xdoctest: +SKIP(failing) 2025-09-07T07:36:42.1740030Z >>> from torch.distributed.tensor.parallel import parallelize_module, PrepareModuleInput 2025-09-07T07:36:42.1740657Z >>> from torch.distributed.device_mesh import init_device_mesh 2025-09-07T07:36:42.1741038Z >>> ... 2025-09-07T07:36:42.1741471Z >>> block = TransformerBlock(...) # block is a nn.Module that contains an "attn" Attention submodule 2025-09-07T07:36:42.1742027Z >>> tp_mesh = init_device_mesh("cuda", (8,)) 2025-09-07T07:36:42.1742350Z >>> 2025-09-07T07:36:42.1742845Z >>> # According to the style specified below, the first input of attn will be annotated to Sharded DTensor 2025-09-07T07:36:42.1743452Z >>> # and then redistributed to Replicated DTensor. 2025-09-07T07:36:42.1743819Z >>> parallelize_module( 2025-09-07T07:36:42.1744134Z >>> block, # this can be a submodule or module 2025-09-07T07:36:42.1744466Z >>> tp_mesh, 2025-09-07T07:36:42.1744714Z >>> parallelize_plan={ 2025-09-07T07:36:42.1745019Z >>> "attn": PrepareModuleInput( 2025-09-07T07:36:42.1745381Z >>> input_layouts=(Shard(0), None, None, ...), 2025-09-07T07:36:42.1745799Z >>> desired_input_layouts=(Replicate(), None, None, ...) 2025-09-07T07:36:42.1746204Z >>> ), 2025-09-07T07:36:42.1746417Z >>> } 2025-09-07T07:36:42.1746635Z >>> ) 2025-09-07T07:36:42.1746764Z 2025-09-07T07:36:42.1747016Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:42.1747384Z 2025-09-07T07:36:42.1748059Z 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=597. 2025-09-07T07:36:42.1749098Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:42.1749473Z 2025-09-07T07:36:42.1749861Z Configure the nn.Module's outputs to convert the output tensors of the nn.Module to DTensors at runtime according to 2025-09-07T07:36:42.1750713Z ``output_layouts``, and perform layout redistribution according to the ``desired_output_layouts``. 2025-09-07T07:36:42.1751172Z 2025-09-07T07:36:42.1751295Z Keyword Args: 2025-09-07T07:36:42.1751601Z output_layouts (Union[Placement, Tuple[Placement]]): 2025-09-07T07:36:42.1752216Z The DTensor layouts of output tensors for the nn.Module, this is used to convert the output tensors to 2025-09-07T07:36:42.1753052Z DTensors if they are :class:`torch.Tensor`. If some outputs are not torch.Tensor or no need to convert to DTensors, 2025-09-07T07:36:42.1753710Z ``None`` need to be specified as a placeholder. 2025-09-07T07:36:42.1754169Z desired_output_layouts (Union[Placement, Tuple[Placement]]): 2025-09-07T07:36:42.1754963Z The desired DTensor layouts of output tensors for the nn.Module, this is used to ensure the outputs of the nn.Module 2025-09-07T07:36:42.1755589Z have the desired DTensor layouts. 2025-09-07T07:36:42.1755919Z use_local_output (bool, optional): 2025-09-07T07:36:42.1756502Z Whether to use local :class:`torch.Tensor` instead of :class:`DTensor` for the module outputs, default: True. 2025-09-07T07:36:42.1757085Z Returns: 2025-09-07T07:36:42.1757501Z A ParallelStyle object that prepares the sharding layouts of the nn.Module's outputs. 2025-09-07T07:36:42.1757915Z 2025-09-07T07:36:42.1758003Z Example:: 2025-09-07T07:36:42.1758276Z >>> # xdoctest: +SKIP(failing) 2025-09-07T07:36:42.1758797Z >>> from torch.distributed.tensor.parallel import parallelize_module, PrepareModuleOutput 2025-09-07T07:36:42.1759433Z >>> from torch.distributed.device_mesh import init_device_mesh 2025-09-07T07:36:42.1759829Z >>> ... 2025-09-07T07:36:42.1760279Z >>> block = TransformerBlock(...) # block is a nn.Module that contains an "attn" Attention submodule 2025-09-07T07:36:42.1760829Z >>> tp_mesh = init_device_mesh("cuda", (8,)) 2025-09-07T07:36:42.1761150Z >>> 2025-09-07T07:36:42.1761674Z >>> # According to the style specified below, the output of the TransformerBlock will be converted to Replicated DTensor 2025-09-07T07:36:42.1762320Z >>> # and then redistributed to Sharded DTensor. 2025-09-07T07:36:42.1762674Z >>> parallelize_module( 2025-09-07T07:36:42.1762987Z >>> block, # this can be a submodule or module 2025-09-07T07:36:42.1763320Z >>> tp_mesh, 2025-09-07T07:36:42.1763595Z >>> parallelize_plan = PrepareModuleOutput( 2025-09-07T07:36:42.1763957Z >>> output_layouts=Replicate(), 2025-09-07T07:36:42.1764330Z >>> desired_output_layouts=Shard(0) 2025-09-07T07:36:42.1764644Z >>> ) 2025-09-07T07:36:42.1764842Z >>> ) 2025-09-07T07:36:42.1764968Z 2025-09-07T07:36:42.1765220Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:42.1765600Z 2025-09-07T07:36:42.1766285Z msg = Cannot scrape callname=PrepareModuleInputOutput in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/parallel/style.py line=705. 2025-09-07T07:36:42.1767349Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:42.1767727Z 2025-09-07T07:36:42.1768170Z Configure the nn.Module's inputs (and outputs) to convert the input tensors (and output tensors, respectively) of the nn.Module 2025-09-07T07:36:42.1769149Z to DTensors at runtime according to ``input_layouts`` (and output_layouts, respectively), and perform layout redistribution 2025-09-07T07:36:42.1770052Z according to the ``desired_input_layouts`` (and ``desired_output_layouts``, respectively). This is a combination of 2025-09-07T07:36:42.1770761Z :class:`PrepareModuleInput` and :class:`PrepareModuleOutput`. 2025-09-07T07:36:42.1771093Z 2025-09-07T07:36:42.1771180Z Keyword Args: 2025-09-07T07:36:42.1771517Z input_layouts (Union[Placement, Tuple[Optional[Placement]]]): 2025-09-07T07:36:42.1772150Z The DTensor layouts of input tensors for the nn.Module, this is used to convert the input tensors to 2025-09-07T07:36:42.1772965Z DTensors. If some inputs are not torch.Tensor or no need to convert to DTensors, ``None`` need to be specified 2025-09-07T07:36:42.1773588Z as a placeholder. default: None. 2025-09-07T07:36:42.1774042Z desired_input_layouts (Union[Placement, Tuple[Optional[Placement]]]): 2025-09-07T07:36:42.1774770Z The desired DTensor layout of input tensors for the nn.Module, this is used to ensure the inputs of the nn.Module 2025-09-07T07:36:42.1775656Z have the desired DTensor layouts. This argument needs to have the same length with ``input_layouts``. default: None. 2025-09-07T07:36:42.1776302Z input_kwarg_layouts (Dict[str, Placement]): 2025-09-07T07:36:42.1776926Z The DTensor layouts of input kwargs for the nn.Module, this is used to convert the input kwarg tensors to DTensors. 2025-09-07T07:36:42.1777519Z default: None 2025-09-07T07:36:42.1777838Z desired_input_kwarg_layouts: (Dict[str, Placement]): 2025-09-07T07:36:42.1778472Z The desired DTensor layout of input kwargs for the nn.Module, this is used to ensure the inputs of the nn.Module 2025-09-07T07:36:42.1779111Z have the desired DTensor layouts. default: None. 2025-09-07T07:36:42.1779486Z use_local_input (bool, optional): 2025-09-07T07:36:42.1780070Z Whether to use local :class:`torch.Tensor` instead of :class:`DTensor` for the module inputs, default: False. 2025-09-07T07:36:42.1780738Z output_layouts (Union[Placement, Tuple[Placement]]): 2025-09-07T07:36:42.1781359Z The DTensor layouts of output tensors for the nn.Module, this is used to convert the output tensors to 2025-09-07T07:36:42.1782198Z DTensors if they are :class:`torch.Tensor`. If some outputs are not torch.Tensor or no need to convert to DTensors, 2025-09-07T07:36:42.1782870Z ``None`` need to be specified as a placeholder. 2025-09-07T07:36:42.1783320Z desired_output_layouts (Union[Placement, Tuple[Placement]]): 2025-09-07T07:36:42.1784006Z The desired DTensor layouts of output tensors for the nn.Module, this is used to ensure the outputs of the nn.Module 2025-09-07T07:36:42.1784629Z have the desired DTensor layouts. 2025-09-07T07:36:42.1784970Z use_local_output (bool, optional): 2025-09-07T07:36:42.1785546Z Whether to use local :class:`torch.Tensor` instead of :class:`DTensor` for the module outputs, default: True. 2025-09-07T07:36:42.1786101Z Returns: 2025-09-07T07:36:42.1786621Z A :class:`ParallelStyle` object that prepares the sharding layouts of the nn.Module's inputs and outputs. 2025-09-07T07:36:42.1787117Z 2025-09-07T07:36:42.1787205Z Example:: 2025-09-07T07:36:42.1787434Z >>> # xdoctest: +SKIP(failing) 2025-09-07T07:36:42.1787979Z >>> from torch.distributed.tensor.parallel import parallelize_module, PrepareModuleInputOutput 2025-09-07T07:36:42.1788620Z >>> from torch.distributed.device_mesh import init_device_mesh 2025-09-07T07:36:42.1789014Z >>> ... 2025-09-07T07:36:42.1789447Z >>> block = TransformerBlock(...) # block is a nn.Module that contains an "attn" Attention submodule 2025-09-07T07:36:42.1789998Z >>> tp_mesh = init_device_mesh("cuda", (8,)) 2025-09-07T07:36:42.1790308Z >>> 2025-09-07T07:36:42.1790762Z >>> # According to the style specified below, the first input of attn will be annotated as Sharded DTensor 2025-09-07T07:36:42.1791575Z >>> # and then redistributed to Replicated DTensor, and the output of the TransformerBlock will be annotated 2025-09-07T07:36:42.1792281Z >>> # as Replicated DTensor and then redistributed to Sharded DTensor. 2025-09-07T07:36:42.1792711Z >>> parallelize_module( 2025-09-07T07:36:42.1793012Z >>> block, # this can be a submodule or module 2025-09-07T07:36:42.1793351Z >>> tp_mesh, 2025-09-07T07:36:42.1793603Z >>> parallelize_plan={ 2025-09-07T07:36:42.1793922Z >>> "attn": PrepareModuleInputOutput( 2025-09-07T07:36:42.1794288Z >>> input_layouts=(Shard(0), None, None, ...), 2025-09-07T07:36:42.1794800Z >>> desired_input_layouts=(Replicate(), None, None, ...), 2025-09-07T07:36:42.1795206Z >>> output_layouts=Replicate(), 2025-09-07T07:36:42.1795602Z >>> desired_output_layouts=Shard(0), 2025-09-07T07:36:42.1795919Z >>> ), 2025-09-07T07:36:42.1796146Z >>> } 2025-09-07T07:36:42.1796360Z >>> ) 2025-09-07T07:36:42.1796477Z 2025-09-07T07:36:42.1796743Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:42.1797111Z 2025-09-07T07:36:42.2298318Z msg = Cannot scrape callname=LowRankMultivariateNormal in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/lowrank_multivariate_normal.py line=56. 2025-09-07T07:36:42.2299438Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:42.2299813Z 2025-09-07T07:36:42.2300113Z Creates a multivariate normal distribution with covariance matrix having a low-rank form 2025-09-07T07:36:42.2300741Z parameterized by :attr:`cov_factor` and :attr:`cov_diag`:: 2025-09-07T07:36:42.2301043Z 2025-09-07T07:36:42.2301218Z covariance_matrix = cov_factor @ cov_factor.T + cov_diag 2025-09-07T07:36:42.2301509Z 2025-09-07T07:36:42.2301607Z Example: 2025-09-07T07:36:42.2301860Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_LAPACK) 2025-09-07T07:36:42.2302256Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2025-09-07T07:36:42.2302773Z >>> m = LowRankMultivariateNormal( 2025-09-07T07:36:42.2303181Z ... torch.zeros(2), torch.tensor([[1.0], [0.0]]), torch.ones(2) 2025-09-07T07:36:42.2303573Z ... ) 2025-09-07T07:36:42.2303972Z >>> m.sample() # normally distributed with mean=`[0,0]`, cov_factor=`[[1],[0]]`, cov_diag=`[1,1]` 2025-09-07T07:36:42.2304538Z tensor([-0.2102, -0.5429]) 2025-09-07T07:36:42.2304717Z 2025-09-07T07:36:42.2304809Z Args: 2025-09-07T07:36:42.2305163Z loc (Tensor): mean of the distribution with shape `batch_shape + event_shape` 2025-09-07T07:36:42.2305761Z cov_factor (Tensor): factor part of low-rank form of covariance matrix with shape 2025-09-07T07:36:42.2306262Z `batch_shape + event_shape + (rank,)` 2025-09-07T07:36:42.2306750Z cov_diag (Tensor): diagonal part of low-rank form of covariance matrix with shape 2025-09-07T07:36:42.2307237Z `batch_shape + event_shape` 2025-09-07T07:36:42.2307436Z 2025-09-07T07:36:42.2307517Z Note: 2025-09-07T07:36:42.2307905Z The computation for determinant and inverse of covariance matrix is avoided when 2025-09-07T07:36:42.2308574Z `cov_factor.shape[1] << cov_factor.shape[0]` thanks to `Woodbury matrix identity 2025-09-07T07:36:42.2309151Z `_ and 2025-09-07T07:36:42.2309767Z `matrix determinant lemma `_. 2025-09-07T07:36:42.2310417Z Thanks to these formulas, we just need to compute the determinant and inverse of 2025-09-07T07:36:42.2310916Z the small size "capacitance" matrix:: 2025-09-07T07:36:42.2311153Z 2025-09-07T07:36:42.2311332Z capacitance = I + cov_factor.T @ inv(cov_diag) @ cov_factor 2025-09-07T07:36:42.2311629Z 2025-09-07T07:36:42.2311892Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:42.2312255Z 2025-09-07T07:36:42.2316930Z 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=15. 2025-09-07T07:36:42.2317949Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:42.2318339Z 2025-09-07T07:36:42.2318559Z The `MixtureSameFamily` distribution implements a (batch of) mixture 2025-09-07T07:36:42.2319161Z distribution where all component are from different parameterizations of 2025-09-07T07:36:42.2319736Z the same distribution type. It is parameterized by a `Categorical` 2025-09-07T07:36:42.2320262Z "selecting distribution" (over `k` component) and a component 2025-09-07T07:36:42.2320776Z distribution, i.e., a `Distribution` with a rightmost batch shape 2025-09-07T07:36:42.2321245Z (equal to `[k]`) which indexes each (batch of) component. 2025-09-07T07:36:42.2321694Z 2025-09-07T07:36:42.2321788Z Examples:: 2025-09-07T07:36:42.2321923Z 2025-09-07T07:36:42.2322037Z >>> # xdoctest: +SKIP("undefined vars") 2025-09-07T07:36:42.2322469Z >>> # Construct Gaussian Mixture Model in 1D consisting of 5 equally 2025-09-07T07:36:42.2322890Z >>> # weighted normal distributions 2025-09-07T07:36:42.2323231Z >>> mix = D.Categorical(torch.ones(5,)) 2025-09-07T07:36:42.2323605Z >>> comp = D.Normal(torch.randn(5,), torch.rand(5,)) 2025-09-07T07:36:42.2324141Z >>> gmm = MixtureSameFamily(mix, comp) 2025-09-07T07:36:42.2324367Z 2025-09-07T07:36:42.2324576Z >>> # Construct Gaussian Mixture Model in 2D consisting of 5 equally 2025-09-07T07:36:42.2325015Z >>> # weighted bivariate normal distributions 2025-09-07T07:36:42.2325373Z >>> mix = D.Categorical(torch.ones(5,)) 2025-09-07T07:36:42.2325709Z >>> comp = D.Independent(D.Normal( 2025-09-07T07:36:42.2326052Z ... torch.randn(5,2), torch.rand(5,2)), 1) 2025-09-07T07:36:42.2326401Z >>> gmm = MixtureSameFamily(mix, comp) 2025-09-07T07:36:42.2326634Z 2025-09-07T07:36:42.2326813Z >>> # Construct a batch of 3 Gaussian Mixture Models in 2D each 2025-09-07T07:36:42.2327364Z >>> # consisting of 5 random weighted bivariate normal distributions 2025-09-07T07:36:42.2327803Z >>> mix = D.Categorical(torch.rand(3,5)) 2025-09-07T07:36:42.2328131Z >>> comp = D.Independent(D.Normal( 2025-09-07T07:36:42.2328484Z ... torch.randn(3,5,2), torch.rand(3,5,2)), 1) 2025-09-07T07:36:42.2328898Z >>> gmm = MixtureSameFamily(mix, comp) 2025-09-07T07:36:42.2329119Z 2025-09-07T07:36:42.2329214Z Args: 2025-09-07T07:36:42.2329540Z mixture_distribution: `torch.distributions.Categorical`-like 2025-09-07T07:36:42.2330041Z instance. Manages the probability of selecting component. 2025-09-07T07:36:42.2330517Z The number of categories must match the rightmost batch 2025-09-07T07:36:42.2330993Z dimension of the `component_distribution`. Must have either 2025-09-07T07:36:42.2331448Z scalar `batch_shape` or `batch_shape` matching 2025-09-07T07:36:42.2331827Z `component_distribution.batch_shape[:-1]` 2025-09-07T07:36:42.2332293Z component_distribution: `torch.distributions.Distribution`-like 2025-09-07T07:36:42.2332811Z instance. Right-most batch dimension indexes component. 2025-09-07T07:36:42.2333137Z 2025-09-07T07:36:42.2333396Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:42.2333763Z 2025-09-07T07:36:42.2429546Z msg = Cannot scrape callname=RelaxedBernoulli in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/relaxed_bernoulli.py line=120. 2025-09-07T07:36:42.2430561Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:42.2430939Z 2025-09-07T07:36:42.2431120Z Creates a RelaxedBernoulli distribution, parametrized by 2025-09-07T07:36:42.2431605Z :attr:`temperature`, and either :attr:`probs` or :attr:`logits` 2025-09-07T07:36:42.2432131Z (but not both). This is a relaxed version of the `Bernoulli` distribution, 2025-09-07T07:36:42.2432650Z so the values are in (0, 1), and has reparametrizable samples. 2025-09-07T07:36:42.2432944Z 2025-09-07T07:36:42.2433048Z Example:: 2025-09-07T07:36:42.2433167Z 2025-09-07T07:36:42.2433306Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2025-09-07T07:36:42.2433688Z >>> m = RelaxedBernoulli(torch.tensor([2.2]), 2025-09-07T07:36:42.2434059Z ... torch.tensor([0.1, 0.2, 0.3, 0.99])) 2025-09-07T07:36:42.2434429Z >>> m.sample() 2025-09-07T07:36:42.2434675Z tensor([ 0.2951, 0.3442, 0.8918, 0.9021]) 2025-09-07T07:36:42.2434912Z 2025-09-07T07:36:42.2434993Z Args: 2025-09-07T07:36:42.2435245Z temperature (Tensor): relaxation temperature 2025-09-07T07:36:42.2435667Z probs (Number, Tensor): the probability of sampling `1` 2025-09-07T07:36:42.2436113Z logits (Number, Tensor): the log-odds of sampling `1` 2025-09-07T07:36:42.2436378Z 2025-09-07T07:36:42.2436695Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:42.2437076Z 2025-09-07T07:36:42.2444246Z msg = Cannot scrape callname=RelaxedOneHotCategorical in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/relaxed_categorical.py line=109. 2025-09-07T07:36:42.2445312Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:42.2445701Z 2025-09-07T07:36:42.2445914Z Creates a RelaxedOneHotCategorical distribution parametrized by 2025-09-07T07:36:42.2446449Z :attr:`temperature`, and either :attr:`probs` or :attr:`logits`. 2025-09-07T07:36:42.2446981Z This is a relaxed version of the :class:`OneHotCategorical` distribution, so 2025-09-07T07:36:42.2447516Z its samples are on simplex, and are reparametrizable. 2025-09-07T07:36:42.2447799Z 2025-09-07T07:36:42.2447888Z Example:: 2025-09-07T07:36:42.2448018Z 2025-09-07T07:36:42.2448154Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2025-09-07T07:36:42.2448563Z >>> m = RelaxedOneHotCategorical(torch.tensor([2.2]), 2025-09-07T07:36:42.2448949Z ... torch.tensor([0.1, 0.2, 0.3, 0.4])) 2025-09-07T07:36:42.2449354Z >>> m.sample() 2025-09-07T07:36:42.2449614Z tensor([ 0.1294, 0.2324, 0.3859, 0.2523]) 2025-09-07T07:36:42.2449838Z 2025-09-07T07:36:42.2449936Z Args: 2025-09-07T07:36:42.2450196Z temperature (Tensor): relaxation temperature 2025-09-07T07:36:42.2450555Z probs (Tensor): event probabilities 2025-09-07T07:36:42.2451010Z logits (Tensor): unnormalized log probability for each event 2025-09-07T07:36:42.2451308Z 2025-09-07T07:36:42.2451586Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:42.2451952Z 2025-09-07T07:36:42.6021139Z 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. 2025-09-07T07:36:42.6022290Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:42.6022876Z Return a new dict with new, potentially nested, key value pair 2025-09-07T07:36:42.6023206Z 2025-09-07T07:36:42.6023305Z >>> purchase = { 2025-09-07T07:36:42.6023566Z ... "name": "Alice", 2025-09-07T07:36:42.6024234Z ... "order": {"items": ["Apple", "Orange"], "costs": [0.50, 1.25]}, 2025-09-07T07:36:42.6024917Z ... "credit card": "5555-1234-1234-1234", 2025-09-07T07:36:42.6025375Z ... } 2025-09-07T07:36:42.6025699Z >>> assoc_in(purchase, ["order", "costs"], [0.25, 1.00]) # doctest: +SKIP 2025-09-07T07:36:42.6026143Z {'credit card': '5555-1234-1234-1234', 2025-09-07T07:36:42.6026458Z 'name': 'Alice', 2025-09-07T07:36:42.6026786Z 'order': {'costs': [0.25, 1.00], 'items': ['Apple', 'Orange']}} 2025-09-07T07:36:42.6027142Z 2025-09-07T07:36:42.6027514Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:42.6027885Z 2025-09-07T07:36:42.6028574Z 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. 2025-09-07T07:36:42.6029617Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:42.6030155Z Update value in a (potentially) nested dictionary 2025-09-07T07:36:42.6030413Z 2025-09-07T07:36:42.6030500Z inputs: 2025-09-07T07:36:42.6030749Z d - dictionary on which to operate 2025-09-07T07:36:42.6031191Z keys - list or tuple giving the location of the value to be changed in d 2025-09-07T07:36:42.6031655Z func - function to operate on that value 2025-09-07T07:36:42.6031881Z 2025-09-07T07:36:42.6032067Z If keys == [k0,..,kX] and d[k0]..[kX] == v, update_in returns a copy of the 2025-09-07T07:36:42.6032613Z original dictionary with v replaced by func(v), but does not mutate the 2025-09-07T07:36:42.6033068Z original dictionary. 2025-09-07T07:36:42.6033310Z 2025-09-07T07:36:42.6033531Z If k0 is not a key in d, update_in creates nested dictionaries to the depth 2025-09-07T07:36:42.6034073Z specified by the keys, with the innermost value set to func(default). 2025-09-07T07:36:42.6034460Z 2025-09-07T07:36:42.6034558Z >>> inc = lambda x: x + 1 2025-09-07T07:36:42.6034851Z >>> update_in({"a": 0}, ["a"], inc) 2025-09-07T07:36:42.6035148Z {'a': 1} 2025-09-07T07:36:42.6035271Z 2025-09-07T07:36:42.6035378Z >>> transaction = { 2025-09-07T07:36:42.6035626Z ... "name": "Alice", 2025-09-07T07:36:42.6035990Z ... "purchase": {"items": ["Apple", "Orange"], "costs": [0.50, 1.25]}, 2025-09-07T07:36:42.6036417Z ... "credit card": "5555-1234-1234-1234", 2025-09-07T07:36:42.6036731Z ... } 2025-09-07T07:36:42.6037057Z >>> update_in(transaction, ["purchase", "costs"], sum) # doctest: +SKIP 2025-09-07T07:36:42.6037496Z {'credit card': '5555-1234-1234-1234', 2025-09-07T07:36:42.6037803Z 'name': 'Alice', 2025-09-07T07:36:42.6038125Z 'purchase': {'costs': 1.75, 'items': ['Apple', 'Orange']}} 2025-09-07T07:36:42.6038402Z 2025-09-07T07:36:42.6038523Z >>> # updating a value when k0 is not in d 2025-09-07T07:36:42.6038958Z >>> update_in({}, [1, 2, 3], str, default="bar") 2025-09-07T07:36:42.6039292Z {1: {2: {3: 'bar'}}} 2025-09-07T07:36:42.6039572Z >>> update_in({1: "foo"}, [2, 3, 4], inc, 0) 2025-09-07T07:36:42.6039882Z {1: 'foo', 2: {3: {4: 1}}} 2025-09-07T07:36:42.6040148Z 2025-09-07T07:36:42.6040573Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:42.6040946Z 2025-09-07T07:36:42.6041636Z 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. 2025-09-07T07:36:42.6042663Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:42.6043215Z Returns coll[i0][i1]...[iX] where [i0, i1, ..., iX]==keys. 2025-09-07T07:36:42.6043494Z 2025-09-07T07:36:42.6043687Z If coll[i0][i1]...[iX] cannot be found, returns ``default``, unless 2025-09-07T07:36:42.6044176Z ``no_default`` is specified, then it raises KeyError or IndexError. 2025-09-07T07:36:42.6044503Z 2025-09-07T07:36:42.6044736Z ``get_in`` is a generalization of ``operator.getitem`` for nested data 2025-09-07T07:36:42.6045195Z structures such as dictionaries and lists. 2025-09-07T07:36:42.6045431Z 2025-09-07T07:36:42.6045537Z >>> transaction = { 2025-09-07T07:36:42.6045785Z ... "name": "Alice", 2025-09-07T07:36:42.6046148Z ... "purchase": {"items": ["Apple", "Orange"], "costs": [0.50, 1.25]}, 2025-09-07T07:36:42.6046570Z ... "credit card": "5555-1234-1234-1234", 2025-09-07T07:36:42.6046885Z ... } 2025-09-07T07:36:42.6047125Z >>> get_in(["purchase", "items", 0], transaction) 2025-09-07T07:36:42.6047458Z 'Apple' 2025-09-07T07:36:42.6047689Z >>> get_in(["name"], transaction) 2025-09-07T07:36:42.6047983Z 'Alice' 2025-09-07T07:36:42.6048219Z >>> get_in(["purchase", "total"], transaction) 2025-09-07T07:36:42.6048610Z >>> get_in(["purchase", "items", "apple"], transaction) 2025-09-07T07:36:42.6049008Z >>> get_in(["purchase", "items", 10], transaction) 2025-09-07T07:36:42.6049392Z >>> get_in(["purchase", "total"], transaction, 0) 2025-09-07T07:36:42.6049724Z 0 2025-09-07T07:36:42.6049933Z >>> get_in(["y"], {}, no_default=True) 2025-09-07T07:36:42.6050265Z Traceback (most recent call last): 2025-09-07T07:36:42.6050568Z ... 2025-09-07T07:36:42.6050766Z KeyError: 'y' 2025-09-07T07:36:42.6050918Z 2025-09-07T07:36:42.6051004Z See Also: 2025-09-07T07:36:42.6051222Z itertoolz.get 2025-09-07T07:36:42.6051473Z operator.getitem 2025-09-07T07:36:42.6051712Z 2025-09-07T07:36:42.6052075Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:42.6052455Z 2025-09-07T07:36:42.6053145Z 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. 2025-09-07T07:36:42.6054183Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:42.6054685Z Group a collection by a key function 2025-09-07T07:36:42.6054898Z 2025-09-07T07:36:42.6055064Z >>> names = ["Alice", "Bob", "Charlie", "Dan", "Edith", "Frank"] 2025-09-07T07:36:42.6055469Z >>> groupby(len, names) # doctest: +SKIP 2025-09-07T07:36:42.6055869Z {3: ['Bob', 'Dan'], 5: ['Alice', 'Edith', 'Frank'], 7: ['Charlie']} 2025-09-07T07:36:42.6056147Z 2025-09-07T07:36:42.6056266Z >>> iseven = lambda x: x % 2 == 0 2025-09-07T07:36:42.6056643Z >>> groupby(iseven, [1, 2, 3, 4, 5, 6, 7, 8]) # doctest: +SKIP 2025-09-07T07:36:42.6057025Z {False: [1, 3, 5, 7], True: [2, 4, 6, 8]} 2025-09-07T07:36:42.6057251Z 2025-09-07T07:36:42.6057391Z Non-callable keys imply grouping on a member. 2025-09-07T07:36:42.6057648Z 2025-09-07T07:36:42.6057735Z >>> groupby( 2025-09-07T07:36:42.6057962Z ... "gender", 2025-09-07T07:36:42.6058193Z ... [ 2025-09-07T07:36:42.6058439Z ... {"name": "Alice", "gender": "F"}, 2025-09-07T07:36:42.6058822Z ... {"name": "Bob", "gender": "M"}, 2025-09-07T07:36:42.6059173Z ... {"name": "Charlie", "gender": "M"}, 2025-09-07T07:36:42.6059481Z ... ], 2025-09-07T07:36:42.6059716Z ... ) # doctest:+SKIP 2025-09-07T07:36:42.6060004Z {'F': [{'gender': 'F', 'name': 'Alice'}], 2025-09-07T07:36:42.6060370Z 'M': [{'gender': 'M', 'name': 'Bob'}, 2025-09-07T07:36:42.6060689Z {'gender': 'M', 'name': 'Charlie'}]} 2025-09-07T07:36:42.6060929Z 2025-09-07T07:36:42.6061068Z Not to be confused with ``itertools.groupby`` 2025-09-07T07:36:42.6061324Z 2025-09-07T07:36:42.6061410Z See Also: 2025-09-07T07:36:42.6061631Z countby 2025-09-07T07:36:42.6061854Z 2025-09-07T07:36:42.6062214Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:42.6062597Z 2025-09-07T07:36:43.0011294Z msg = Cannot scrape callname=calculate_gain in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py line=142. 2025-09-07T07:36:43.0012210Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:43.0012989Z Return the recommended gain value for the given nonlinearity function. 2025-09-07T07:36:43.0013333Z 2025-09-07T07:36:43.0013454Z The values are as follows: 2025-09-07T07:36:43.0013648Z 2025-09-07T07:36:43.0013774Z ================= ==================================================== 2025-09-07T07:36:43.0014110Z nonlinearity gain 2025-09-07T07:36:43.0014402Z ================= ==================================================== 2025-09-07T07:36:43.0014744Z Linear / Identity :math:`1` 2025-09-07T07:36:43.0015031Z Conv{1,2,3}D :math:`1` 2025-09-07T07:36:43.0015296Z Sigmoid :math:`1` 2025-09-07T07:36:43.0015592Z Tanh :math:`\frac{5}{3}` 2025-09-07T07:36:43.0015912Z ReLU :math:`\sqrt{2}` 2025-09-07T07:36:43.0016310Z Leaky Relu :math:`\sqrt{\frac{2}{1 + \text{negative\_slope}^2}}` 2025-09-07T07:36:43.0016719Z SELU :math:`\frac{3}{4}` 2025-09-07T07:36:43.0017049Z ================= ==================================================== 2025-09-07T07:36:43.0017292Z 2025-09-07T07:36:43.0017403Z .. warning:: 2025-09-07T07:36:43.0017728Z In order to implement `Self-Normalizing Neural Networks`_ , 2025-09-07T07:36:43.0018278Z you should use ``nonlinearity='linear'`` instead of ``nonlinearity='selu'``. 2025-09-07T07:36:43.0018797Z This gives the initial weights a variance of ``1 / N``, 2025-09-07T07:36:43.0019304Z which is necessary to induce a stable fixed point in the forward pass. 2025-09-07T07:36:43.0019872Z In contrast, the default gain for ``SELU`` sacrifices the normalization 2025-09-07T07:36:43.0020474Z effect for more stable gradient flow in rectangular layers. 2025-09-07T07:36:43.0020775Z 2025-09-07T07:36:43.0020859Z Args: 2025-09-07T07:36:43.0021185Z nonlinearity: the non-linear function (`nn.functional` name) 2025-09-07T07:36:43.0021678Z param: optional parameter for the non-linear function 2025-09-07T07:36:43.0021959Z 2025-09-07T07:36:43.0022060Z Examples: 2025-09-07T07:36:43.0022315Z >>> gain = nn.init.calculate_gain( 2025-09-07T07:36:43.0022624Z ... "leaky_relu", 0.2 2025-09-07T07:36:43.0022948Z ... ) # leaky_relu with negative_slope=0.2 2025-09-07T07:36:43.0023187Z 2025-09-07T07:36:43.0023850Z .. _Self-Normalizing Neural Networks: https://papers.nips.cc/paper/2017/hash/5d44ee6f2c3f71b73125876103c8f6c4-Abstract.html 2025-09-07T07:36:43.0024529Z 2025-09-07T07:36:43.0024883Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:43.0025262Z 2025-09-07T07:36:43.0648635Z msg = Cannot scrape callname=SyncBatchNorm in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/batchnorm.py line=603. 2025-09-07T07:36:43.0649569Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:43.0650339Z Applies Batch Normalization over a N-Dimensional input. 2025-09-07T07:36:43.0650620Z 2025-09-07T07:36:43.0651020Z The N-D input is a mini-batch of [N-2]D inputs with additional channel dimension) as described in the paper 2025-09-07T07:36:43.0651712Z `Batch Normalization: Accelerating Deep Network Training by Reducing 2025-09-07T07:36:43.0652402Z Internal Covariate Shift `__ . 2025-09-07T07:36:43.0652724Z 2025-09-07T07:36:43.0652827Z .. math:: 2025-09-07T07:36:43.0652972Z 2025-09-07T07:36:43.0653192Z y = \frac{x - \mathrm{E}[x]}{ \sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta 2025-09-07T07:36:43.0653538Z 2025-09-07T07:36:43.0653770Z The mean and standard-deviation are calculated per-dimension over all 2025-09-07T07:36:43.0654349Z mini-batches of the same process groups. :math:`\gamma` and :math:`\beta` 2025-09-07T07:36:43.0654940Z are learnable parameter vectors of size `C` (where `C` is the input size). 2025-09-07T07:36:43.0655470Z By default, the elements of :math:`\gamma` are sampled from 2025-09-07T07:36:43.0656021Z :math:`\mathcal{U}(0, 1)` and the elements of :math:`\beta` are set to 0. 2025-09-07T07:36:43.0656601Z The standard-deviation is calculated via the biased estimator, equivalent to 2025-09-07T07:36:43.0657096Z `torch.var(input, unbiased=False)`. 2025-09-07T07:36:43.0657315Z 2025-09-07T07:36:43.0657549Z Also by default, during training this layer keeps running estimates of its 2025-09-07T07:36:43.0658144Z computed mean and variance, which are then used for normalization during 2025-09-07T07:36:43.0658742Z evaluation. The running estimates are kept with a default :attr:`momentum` 2025-09-07T07:36:43.0659190Z of 0.1. 2025-09-07T07:36:43.0659326Z 2025-09-07T07:36:43.0659564Z If :attr:`track_running_stats` is set to ``False``, this layer then does not 2025-09-07T07:36:43.0660119Z keep running estimates, and batch statistics are instead used during 2025-09-07T07:36:43.0660563Z evaluation time as well. 2025-09-07T07:36:43.0660759Z 2025-09-07T07:36:43.0660845Z .. note:: 2025-09-07T07:36:43.0661204Z This :attr:`momentum` argument is different from one used in optimizer 2025-09-07T07:36:43.0661759Z classes and the conventional notion of momentum. Mathematically, the 2025-09-07T07:36:43.0662238Z update rule for running statistics here is 2025-09-07T07:36:43.0662749Z :math:`\hat{x}_\text{new} = (1 - \text{momentum}) \times \hat{x} + \text{momentum} \times x_t`, 2025-09-07T07:36:43.0663343Z where :math:`\hat{x}` is the estimated statistic and :math:`x_t` is the 2025-09-07T07:36:43.0663771Z new observed value. 2025-09-07T07:36:43.0663942Z 2025-09-07T07:36:43.0664290Z Because the Batch Normalization is done for each channel in the ``C`` dimension, computing 2025-09-07T07:36:43.0664960Z statistics on ``(N, +)`` slices, it's common terminology to call this Volumetric Batch 2025-09-07T07:36:43.0665506Z Normalization or Spatio-temporal Batch Normalization. 2025-09-07T07:36:43.0665790Z 2025-09-07T07:36:43.0665948Z Currently :class:`SyncBatchNorm` only supports 2025-09-07T07:36:43.0666490Z :class:`~torch.nn.DistributedDataParallel` (DDP) with single GPU per process. Use 2025-09-07T07:36:43.0667128Z :meth:`torch.nn.SyncBatchNorm.convert_sync_batchnorm()` to convert 2025-09-07T07:36:43.0667653Z :attr:`BatchNorm*D` layer to :class:`SyncBatchNorm` before wrapping 2025-09-07T07:36:43.0668081Z Network with DDP. 2025-09-07T07:36:43.0668238Z 2025-09-07T07:36:43.0668339Z Args: 2025-09-07T07:36:43.0668631Z num_features: :math:`C` from an expected input of size 2025-09-07T07:36:43.0669001Z :math:`(N, C, +)` 2025-09-07T07:36:43.0669377Z eps: a value added to the denominator for numerical stability. 2025-09-07T07:36:43.0669787Z Default: ``1e-5`` 2025-09-07T07:36:43.0670164Z momentum: the value used for the running_mean and running_var 2025-09-07T07:36:43.0670713Z computation. Can be set to ``None`` for cumulative moving average 2025-09-07T07:36:43.0671174Z (i.e. simple average). Default: 0.1 2025-09-07T07:36:43.0671615Z affine: a boolean value that when set to ``True``, this module has 2025-09-07T07:36:43.0672129Z learnable affine parameters. Default: ``True`` 2025-09-07T07:36:43.0672611Z track_running_stats: a boolean value that when set to ``True``, this 2025-09-07T07:36:43.0673168Z module tracks the running mean and variance, and when set to ``False``, 2025-09-07T07:36:43.0673747Z this module does not track such statistics, and initializes statistics 2025-09-07T07:36:43.0674302Z buffers :attr:`running_mean` and :attr:`running_var` as ``None``. 2025-09-07T07:36:43.0674946Z When these buffers are ``None``, this module always uses batch statistics. 2025-09-07T07:36:43.0675442Z in both training and eval modes. Default: ``True`` 2025-09-07T07:36:43.0675966Z process_group: synchronization of stats happen within each process group 2025-09-07T07:36:43.0676602Z individually. Default behavior is synchronization across the whole 2025-09-07T07:36:43.0677035Z world 2025-09-07T07:36:43.0677171Z 2025-09-07T07:36:43.0677266Z Shape: 2025-09-07T07:36:43.0677480Z - Input: :math:`(N, C, +)` 2025-09-07T07:36:43.0677828Z - Output: :math:`(N, C, +)` (same shape as input) 2025-09-07T07:36:43.0678081Z 2025-09-07T07:36:43.0678179Z .. note:: 2025-09-07T07:36:43.0678550Z Synchronization of batchnorm statistics occurs only while training, i.e. 2025-09-07T07:36:43.0679104Z synchronization is disabled when ``model.eval()`` is set or if 2025-09-07T07:36:43.0679556Z ``self.training`` is otherwise ``False``. 2025-09-07T07:36:43.0679805Z 2025-09-07T07:36:43.0679895Z Examples:: 2025-09-07T07:36:43.0680027Z 2025-09-07T07:36:43.0680136Z >>> # xdoctest: +SKIP 2025-09-07T07:36:43.0680419Z >>> # With Learnable Parameters 2025-09-07T07:36:43.0680752Z >>> m = nn.SyncBatchNorm(100) 2025-09-07T07:36:43.0681086Z >>> # creating process group (optional) 2025-09-07T07:36:43.0681463Z >>> # ranks is a list of int identifying rank ids. 2025-09-07T07:36:43.0681825Z >>> ranks = list(range(8)) 2025-09-07T07:36:43.0682115Z >>> r1, r2 = ranks[:4], ranks[4:] 2025-09-07T07:36:43.0682480Z >>> # Note: every rank calls into new_group for every 2025-09-07T07:36:43.0682892Z >>> # process group created, even if that rank is not 2025-09-07T07:36:43.0683261Z >>> # part of the group. 2025-09-07T07:36:43.0683685Z >>> process_groups = [torch.distributed.new_group(pids) for pids in [r1, r2]] 2025-09-07T07:36:43.0684284Z >>> process_group = process_groups[0 if dist.get_rank() <= 3 else 1] 2025-09-07T07:36:43.0684724Z >>> # Without Learnable Parameters 2025-09-07T07:36:43.0685151Z >>> m = nn.BatchNorm3d(100, affine=False, process_group=process_group) 2025-09-07T07:36:43.0685590Z >>> input = torch.randn(20, 100, 35, 45, 10) 2025-09-07T07:36:43.0685922Z >>> output = m(input) 2025-09-07T07:36:43.0686113Z 2025-09-07T07:36:43.0686227Z >>> # network is nn.BatchNorm layer 2025-09-07T07:36:43.0686736Z >>> sync_bn_network = nn.SyncBatchNorm.convert_sync_batchnorm(network, process_group) 2025-09-07T07:36:43.0687302Z >>> # only single gpu per process is currently supported 2025-09-07T07:36:43.0687785Z >>> ddp_sync_bn_network = torch.nn.parallel.DistributedDataParallel( 2025-09-07T07:36:43.0688241Z >>> sync_bn_network, 2025-09-07T07:36:43.0688598Z >>> device_ids=[args.local_rank], 2025-09-07T07:36:43.0688974Z >>> output_device=args.local_rank) 2025-09-07T07:36:43.0689296Z 2025-09-07T07:36:43.0689660Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:43.0690075Z 2025-09-07T07:36:43.0690759Z 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=830. 2025-09-07T07:36:43.0691786Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:43.0692511Z Converts all :attr:`BatchNorm*D` layers in the model to :class:`torch.nn.SyncBatchNorm` layers. 2025-09-07T07:36:43.0692937Z 2025-09-07T07:36:43.0693034Z Args: 2025-09-07T07:36:43.0693395Z module (nn.Module): module containing one or more :attr:`BatchNorm*D` layers 2025-09-07T07:36:43.0693980Z process_group (optional): process group to scope synchronization, 2025-09-07T07:36:43.0694428Z default is the whole world 2025-09-07T07:36:43.0694644Z 2025-09-07T07:36:43.0694742Z Returns: 2025-09-07T07:36:43.0695125Z The original :attr:`module` with the converted :class:`torch.nn.SyncBatchNorm` 2025-09-07T07:36:43.0695719Z layers. If the original :attr:`module` is a :attr:`BatchNorm*D` layer, 2025-09-07T07:36:43.0696297Z a new :class:`torch.nn.SyncBatchNorm` layer object will be returned 2025-09-07T07:36:43.0696715Z instead. 2025-09-07T07:36:43.0696860Z 2025-09-07T07:36:43.0696962Z Example:: 2025-09-07T07:36:43.0697097Z 2025-09-07T07:36:43.0697212Z >>> # Network with nn.BatchNorm layer 2025-09-07T07:36:43.0697588Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2025-09-07T07:36:43.0697966Z >>> module = torch.nn.Sequential( 2025-09-07T07:36:43.0698318Z >>> torch.nn.Linear(20, 100), 2025-09-07T07:36:43.0698660Z >>> torch.nn.BatchNorm1d(100), 2025-09-07T07:36:43.0698989Z >>> ).cuda() 2025-09-07T07:36:43.0699300Z >>> # creating process group (optional) 2025-09-07T07:36:43.0699748Z >>> # ranks is a list of int identifying rank ids. 2025-09-07T07:36:43.0700104Z >>> ranks = list(range(8)) 2025-09-07T07:36:43.0700423Z >>> r1, r2 = ranks[:4], ranks[4:] 2025-09-07T07:36:43.0700792Z >>> # Note: every rank calls into new_group for every 2025-09-07T07:36:43.0701209Z >>> # process group created, even if that rank is not 2025-09-07T07:36:43.0701585Z >>> # part of the group. 2025-09-07T07:36:43.0701898Z >>> # xdoctest: +SKIP("distributed") 2025-09-07T07:36:43.0702382Z >>> process_groups = [torch.distributed.new_group(pids) for pids in [r1, r2]] 2025-09-07T07:36:43.0702959Z >>> process_group = process_groups[0 if dist.get_rank() <= 3 else 1] 2025-09-07T07:36:43.0703614Z >>> sync_bn_module = torch.nn.SyncBatchNorm.convert_sync_batchnorm(module, process_group) 2025-09-07T07:36:43.0704026Z 2025-09-07T07:36:43.0704109Z 2025-09-07T07:36:43.0704478Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:43.0704856Z 2025-09-07T07:36:43.0919163Z msg = Cannot scrape callname=Unflatten in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/flatten.py line=66. 2025-09-07T07:36:43.0920059Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:43.0920439Z 2025-09-07T07:36:43.0920757Z Unflattens a tensor dim expanding it to a desired shape. For use with :class:`~nn.Sequential`. 2025-09-07T07:36:43.0921182Z 2025-09-07T07:36:43.0921464Z * :attr:`dim` specifies the dimension of the input tensor to be unflattened, and it can 2025-09-07T07:36:43.0922072Z be either `int` or `str` when `Tensor` or `NamedTensor` is used, respectively. 2025-09-07T07:36:43.0922427Z 2025-09-07T07:36:43.0922744Z * :attr:`unflattened_size` is the new shape of the unflattened dimension of the tensor and it can be 2025-09-07T07:36:43.0923436Z a `tuple` of ints or a `list` of ints or `torch.Size` for `Tensor` input; a `NamedShape` 2025-09-07T07:36:43.0924288Z (tuple of `(name, size)` tuples) for `NamedTensor` input. 2025-09-07T07:36:43.0924568Z 2025-09-07T07:36:43.0924665Z Shape: 2025-09-07T07:36:43.0924999Z - Input: :math:`(*, S_{\text{dim}}, *)`, where :math:`S_{\text{dim}}` is the size at 2025-09-07T07:36:43.0925596Z dimension :attr:`dim` and :math:`*` means any number of dimensions including none. 2025-09-07T07:36:43.0926248Z - Output: :math:`(*, U_1, ..., U_n, *)`, where :math:`U` = :attr:`unflattened_size` and 2025-09-07T07:36:43.0926718Z :math:`\prod_{i=1}^n U_i = S_{\text{dim}}`. 2025-09-07T07:36:43.0926952Z 2025-09-07T07:36:43.0927035Z Args: 2025-09-07T07:36:43.0927308Z dim (Union[int, str]): Dimension to be unflattened 2025-09-07T07:36:43.0927927Z unflattened_size (Union[torch.Size, Tuple, List, NamedShape]): New shape of the unflattened dimension 2025-09-07T07:36:43.0928393Z 2025-09-07T07:36:43.0928512Z Examples: 2025-09-07T07:36:43.0928749Z >>> input = torch.randn(2, 50) 2025-09-07T07:36:43.0929045Z >>> # With tuple of ints 2025-09-07T07:36:43.0929332Z >>> m = nn.Sequential( 2025-09-07T07:36:43.0929607Z >>> nn.Linear(50, 50), 2025-09-07T07:36:43.0929945Z >>> nn.Unflatten(1, (2, 5, 5)) 2025-09-07T07:36:43.0930225Z >>> ) 2025-09-07T07:36:43.0930442Z >>> output = m(input) 2025-09-07T07:36:43.0930704Z >>> output.size() 2025-09-07T07:36:43.0930954Z torch.Size([2, 2, 5, 5]) 2025-09-07T07:36:43.0931212Z >>> # With torch.Size 2025-09-07T07:36:43.0931470Z >>> m = nn.Sequential( 2025-09-07T07:36:43.0931735Z >>> nn.Linear(50, 50), 2025-09-07T07:36:43.0932038Z >>> nn.Unflatten(1, torch.Size([2, 5, 5])) 2025-09-07T07:36:43.0932342Z >>> ) 2025-09-07T07:36:43.0932555Z >>> output = m(input) 2025-09-07T07:36:43.0932814Z >>> output.size() 2025-09-07T07:36:43.0933065Z torch.Size([2, 2, 5, 5]) 2025-09-07T07:36:43.0933347Z >>> # With namedshape (tuple of tuples) 2025-09-07T07:36:43.0933723Z >>> input = torch.randn(2, 50, names=("N", "features")) 2025-09-07T07:36:43.0934191Z >>> unflatten = nn.Unflatten("features", (("C", 2), ("H", 5), ("W", 5))) 2025-09-07T07:36:43.0934623Z >>> output = unflatten(input) 2025-09-07T07:36:43.0934904Z >>> output.size() 2025-09-07T07:36:43.0935160Z torch.Size([2, 2, 5, 5]) 2025-09-07T07:36:43.0935344Z 2025-09-07T07:36:43.0935596Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:43.0935962Z 2025-09-07T07:36:43.1290949Z msg = Cannot scrape callname=TripletMarginWithDistanceLoss in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py line=1798. 2025-09-07T07:36:43.1292091Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:43.1292846Z Creates a criterion that measures the triplet loss given input 2025-09-07T07:36:43.1293354Z tensors :math:`a`, :math:`p`, and :math:`n` (representing anchor, 2025-09-07T07:36:43.1293879Z positive, and negative examples, respectively), and a nonnegative, 2025-09-07T07:36:43.1294467Z real-valued function ("distance function") used to compute the relationship 2025-09-07T07:36:43.1295065Z between the anchor and positive example ("positive distance") and the 2025-09-07T07:36:43.1295556Z anchor and negative example ("negative distance"). 2025-09-07T07:36:43.1295818Z 2025-09-07T07:36:43.1296024Z The unreduced loss (i.e., with :attr:`reduction` set to ``'none'``) 2025-09-07T07:36:43.1296451Z can be described as: 2025-09-07T07:36:43.1296629Z 2025-09-07T07:36:43.1296730Z .. math:: 2025-09-07T07:36:43.1297004Z \ell(a, p, n) = L = \{l_1,\dots,l_N\}^\top, \quad 2025-09-07T07:36:43.1297401Z l_i = \max \{d(a_i, p_i) - d(a_i, n_i) + {\rm margin}, 0\} 2025-09-07T07:36:43.1297675Z 2025-09-07T07:36:43.1297918Z where :math:`N` is the batch size; :math:`d` is a nonnegative, real-valued function 2025-09-07T07:36:43.1298582Z quantifying the closeness of two tensors, referred to as the :attr:`distance_function`; 2025-09-07T07:36:43.1299299Z and :math:`margin` is a nonnegative margin representing the minimum difference 2025-09-07T07:36:43.1299912Z between the positive and negative distances that is required for the loss to 2025-09-07T07:36:43.1300497Z be 0. The input tensors have :math:`N` elements each and can be of any shape 2025-09-07T07:36:43.1301016Z that the distance function can handle. 2025-09-07T07:36:43.1301249Z 2025-09-07T07:36:43.1301362Z If :attr:`reduction` is not ``'none'`` 2025-09-07T07:36:43.1301687Z (default ``'mean'``), then: 2025-09-07T07:36:43.1301870Z 2025-09-07T07:36:43.1301966Z .. math:: 2025-09-07T07:36:43.1302177Z \ell(x, y) = 2025-09-07T07:36:43.1302419Z \begin{cases} 2025-09-07T07:36:43.1302777Z \operatorname{mean}(L), & \text{if reduction} = \text{`mean';}\\ 2025-09-07T07:36:43.1303341Z \operatorname{sum}(L), & \text{if reduction} = \text{`sum'.} 2025-09-07T07:36:43.1303779Z \end{cases} 2025-09-07T07:36:43.1303930Z 2025-09-07T07:36:43.1304230Z See also :class:`~torch.nn.TripletMarginLoss`, which computes the triplet 2025-09-07T07:36:43.1305349Z loss for input tensors using the :math:`l_p` distance as the distance function. 2025-09-07T07:36:43.1305859Z 2025-09-07T07:36:43.1305953Z Args: 2025-09-07T07:36:43.1306352Z distance_function (Callable, optional): A nonnegative, real-valued function that 2025-09-07T07:36:43.1306927Z quantifies the closeness of two tensors. If not specified, 2025-09-07T07:36:43.1307406Z `nn.PairwiseDistance` will be used. Default: ``None`` 2025-09-07T07:36:43.1307956Z margin (float, optional): A nonnegative margin representing the minimum difference 2025-09-07T07:36:43.1308629Z between the positive and negative distances required for the loss to be 0. Larger 2025-09-07T07:36:43.1309288Z margins penalize cases where the negative examples are not distant enough from the 2025-09-07T07:36:43.1309864Z anchors, relative to the positives. Default: :math:`1`. 2025-09-07T07:36:43.1310406Z swap (bool, optional): Whether to use the distance swap described in the paper 2025-09-07T07:36:43.1311037Z `Learning shallow convolutional feature descriptors with triplet losses` by 2025-09-07T07:36:43.1311657Z V. Balntas, E. Riba et al. If True, and if the positive example is closer to the 2025-09-07T07:36:43.1312269Z negative example than the anchor is, swaps the positive example and the anchor in 2025-09-07T07:36:43.1312794Z the loss computation. Default: ``False``. 2025-09-07T07:36:43.1313323Z reduction (str, optional): Specifies the (optional) reduction to apply to the output: 2025-09-07T07:36:43.1313977Z ``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: no reduction will be applied, 2025-09-07T07:36:43.1314558Z ``'mean'``: the sum of the output will be divided by the number of 2025-09-07T07:36:43.1315094Z elements in the output, ``'sum'``: the output will be summed. Default: ``'mean'`` 2025-09-07T07:36:43.1315465Z 2025-09-07T07:36:43.1315469Z 2025-09-07T07:36:43.1315584Z Shape: 2025-09-07T07:36:43.1316003Z - Input: :math:`(N, *)` where :math:`*` represents any number of additional dimensions 2025-09-07T07:36:43.1316497Z as supported by the distance function. 2025-09-07T07:36:43.1316975Z - Output: A Tensor of shape :math:`(N)` if :attr:`reduction` is ``'none'``, or a scalar 2025-09-07T07:36:43.1317441Z otherwise. 2025-09-07T07:36:43.1317598Z 2025-09-07T07:36:43.1317684Z Examples: 2025-09-07T07:36:43.1317812Z 2025-09-07T07:36:43.1317922Z >>> # Initialize embeddings 2025-09-07T07:36:43.1318240Z >>> embedding = nn.Embedding(1000, 128) 2025-09-07T07:36:43.1318582Z >>> anchor_ids = torch.randint(0, 1000, (1,)) 2025-09-07T07:36:43.1318951Z >>> positive_ids = torch.randint(0, 1000, (1,)) 2025-09-07T07:36:43.1319324Z >>> negative_ids = torch.randint(0, 1000, (1,)) 2025-09-07T07:36:43.1319717Z >>> anchor = embedding(anchor_ids) 2025-09-07T07:36:43.1320041Z >>> positive = embedding(positive_ids) 2025-09-07T07:36:43.1320386Z >>> negative = embedding(negative_ids) 2025-09-07T07:36:43.1320698Z >>> 2025-09-07T07:36:43.1320927Z >>> # Built-in Distance Function 2025-09-07T07:36:43.1321254Z >>> triplet_loss = \ 2025-09-07T07:36:43.1321704Z >>> nn.TripletMarginWithDistanceLoss(distance_function=nn.PairwiseDistance()) 2025-09-07T07:36:43.1322259Z >>> output = triplet_loss(anchor, positive, negative) 2025-09-07T07:36:43.1322638Z >>> output.backward() 2025-09-07T07:36:43.1322883Z >>> 2025-09-07T07:36:43.1323110Z >>> # Custom Distance Function 2025-09-07T07:36:43.1323419Z >>> def l_infinity(x1, x2): 2025-09-07T07:36:43.1324086Z >>> return torch.max(torch.abs(x1 - x2), dim=1).values 2025-09-07T07:36:43.1324455Z >>> 2025-09-07T07:36:43.1324768Z >>> # xdoctest: +SKIP("FIXME: Would call backwards a second time") 2025-09-07T07:36:43.1325178Z >>> triplet_loss = ( 2025-09-07T07:36:43.1325634Z >>> nn.TripletMarginWithDistanceLoss(distance_function=l_infinity, margin=1.5)) 2025-09-07T07:36:43.1326260Z >>> output = triplet_loss(anchor, positive, negative) 2025-09-07T07:36:43.1326631Z >>> output.backward() 2025-09-07T07:36:43.1326888Z >>> 2025-09-07T07:36:43.1327122Z >>> # Custom Distance Function (Lambda) 2025-09-07T07:36:43.1327430Z >>> triplet_loss = ( 2025-09-07T07:36:43.1327728Z >>> nn.TripletMarginWithDistanceLoss( 2025-09-07T07:36:43.1328183Z >>> distance_function=lambda x, y: 1.0 - F.cosine_similarity(x, y))) 2025-09-07T07:36:43.1328668Z >>> output = triplet_loss(anchor, positive, negative) 2025-09-07T07:36:43.1329022Z >>> output.backward() 2025-09-07T07:36:43.1329201Z 2025-09-07T07:36:43.1329291Z Reference: 2025-09-07T07:36:43.1329730Z V. Balntas, et al.: Learning shallow convolutional feature descriptors with triplet losses: 2025-09-07T07:36:43.1330385Z https://bmva-archive.org.uk/bmvc/2016/papers/paper119/index.html 2025-09-07T07:36:43.1330808Z 2025-09-07T07:36:43.1331165Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 17)) 2025-09-07T07:36:43.1331553Z 2025-09-07T07:36:43.1332104Z msg = Cannot scrape callname=CTCLoss in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py line=1933. 2025-09-07T07:36:43.1332981Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:43.1333494Z The Connectionist Temporal Classification loss. 2025-09-07T07:36:43.1333757Z 2025-09-07T07:36:43.1334136Z Calculates loss between a continuous (unsegmented) time series and a target sequence. CTCLoss sums over the 2025-09-07T07:36:43.1335043Z probability of possible alignments of input to target, producing a loss value which is differentiable 2025-09-07T07:36:43.1335856Z with respect to each input node. The alignment of input to target is assumed to be "many-to-one", which 2025-09-07T07:36:43.1336616Z limits the length of the target sequence such that it must be :math:`\leq` the input length. 2025-09-07T07:36:43.1337028Z 2025-09-07T07:36:43.1337111Z Args: 2025-09-07T07:36:43.1337398Z blank (int, optional): blank label. Default :math:`0`. 2025-09-07T07:36:43.1337943Z reduction (str, optional): Specifies the reduction to apply to the output: 2025-09-07T07:36:43.1338493Z ``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: no reduction will be applied, 2025-09-07T07:36:43.1339001Z ``'mean'``: the output losses will be divided by the target lengths and 2025-09-07T07:36:43.1339585Z then the mean over the batch is taken, ``'sum'``: the output losses will be summed. 2025-09-07T07:36:43.1340057Z Default: ``'mean'`` 2025-09-07T07:36:43.1340358Z zero_infinity (bool, optional): 2025-09-07T07:36:43.1340770Z Whether to zero infinite losses and the associated gradients. 2025-09-07T07:36:43.1341214Z Default: ``False`` 2025-09-07T07:36:43.1341583Z Infinite losses mainly occur when the inputs are too short 2025-09-07T07:36:43.1342001Z to be aligned to the targets. 2025-09-07T07:36:43.1342214Z 2025-09-07T07:36:43.1342310Z Shape: 2025-09-07T07:36:43.1342613Z - Log_probs: Tensor of size :math:`(T, N, C)` or :math:`(T, C)`, 2025-09-07T07:36:43.1343078Z where :math:`T = \text{input length}`, 2025-09-07T07:36:43.1343428Z :math:`N = \text{batch size}`, and 2025-09-07T07:36:43.1343808Z :math:`C = \text{number of classes (including blank)}`. 2025-09-07T07:36:43.1344291Z The logarithmized probabilities of the outputs (e.g. obtained with 2025-09-07T07:36:43.1344762Z :func:`torch.nn.functional.log_softmax`). 2025-09-07T07:36:43.1345141Z - Targets: Tensor of size :math:`(N, S)` or 2025-09-07T07:36:43.1345545Z :math:`(\operatorname{sum}(\text{target\_lengths}))`, 2025-09-07T07:36:43.1345986Z where :math:`N = \text{batch size}` and 2025-09-07T07:36:43.1346374Z :math:`S = \text{max target length, if shape is } (N, S)`. 2025-09-07T07:36:43.1346883Z It represents the target sequences. Each element in the target 2025-09-07T07:36:43.1347436Z sequence is a class index. And the target index cannot be blank (default=0). 2025-09-07T07:36:43.1347954Z In the :math:`(N, S)` form, targets are padded to the 2025-09-07T07:36:43.1348349Z length of the longest sequence, and stacked. 2025-09-07T07:36:43.1348786Z In the :math:`(\operatorname{sum}(\text{target\_lengths}))` form, 2025-09-07T07:36:43.1349235Z the targets are assumed to be un-padded and 2025-09-07T07:36:43.1349599Z concatenated within 1 dimension. 2025-09-07T07:36:43.1350015Z - Input_lengths: Tuple or tensor of size :math:`(N)` or :math:`()`, 2025-09-07T07:36:43.1350543Z where :math:`N = \text{batch size}`. It represents the lengths of the 2025-09-07T07:36:43.1351070Z inputs (must each be :math:`\leq T`). And the lengths are specified 2025-09-07T07:36:43.1351623Z for each sequence to achieve masking under the assumption that sequences 2025-09-07T07:36:43.1352087Z are padded to equal lengths. 2025-09-07T07:36:43.1352498Z - Target_lengths: Tuple or tensor of size :math:`(N)` or :math:`()`, 2025-09-07T07:36:43.1353045Z where :math:`N = \text{batch size}`. It represents lengths of the targets. 2025-09-07T07:36:43.1353609Z Lengths are specified for each sequence to achieve masking under the 2025-09-07T07:36:43.1354201Z assumption that sequences are padded to equal lengths. If target shape is 2025-09-07T07:36:43.1354823Z :math:`(N,S)`, target_lengths are effectively the stop index 2025-09-07T07:36:43.1355426Z :math:`s_n` for each target sequence, such that ``target_n = targets[n,0:s_n]`` for 2025-09-07T07:36:43.1355987Z each target in a batch. Lengths must each be :math:`\leq S` 2025-09-07T07:36:43.1356533Z If the targets are given as a 1d tensor that is the concatenation of individual 2025-09-07T07:36:43.1357135Z targets, the target_lengths must add up to the total length of the tensor. 2025-09-07T07:36:43.1357671Z - Output: scalar if :attr:`reduction` is ``'mean'`` (default) or 2025-09-07T07:36:43.1358214Z ``'sum'``. If :attr:`reduction` is ``'none'``, then :math:`(N)` if input is batched or 2025-09-07T07:36:43.1358768Z :math:`()` if input is unbatched, where :math:`N = \text{batch size}`. 2025-09-07T07:36:43.1359082Z 2025-09-07T07:36:43.1359181Z Examples: 2025-09-07T07:36:43.1359310Z 2025-09-07T07:36:43.1359429Z >>> # Target are to be padded 2025-09-07T07:36:43.1359738Z >>> T = 50 # Input sequence length 2025-09-07T07:36:43.1360095Z >>> C = 20 # Number of classes (including blank) 2025-09-07T07:36:43.1360440Z >>> N = 16 # Batch size 2025-09-07T07:36:43.1360856Z >>> S = 30 # Target sequence length of longest target in batch (padding length) 2025-09-07T07:36:43.1361412Z >>> S_min = 10 # Minimum target length, for demonstration purposes 2025-09-07T07:36:43.1373540Z >>> 2025-09-07T07:36:43.1373909Z >>> # Initialize random batch of input vectors, for *size = (T,N,C) 2025-09-07T07:36:43.1374567Z >>> input = torch.randn(T, N, C).log_softmax(2).detach().requires_grad_() 2025-09-07T07:36:43.1374993Z >>> 2025-09-07T07:36:43.1375318Z >>> # Initialize random batch of targets (0 = blank, 1:C = classes) 2025-09-07T07:36:43.1375856Z >>> target = torch.randint(low=1, high=C, size=(N, S), dtype=torch.long) 2025-09-07T07:36:43.1376266Z >>> 2025-09-07T07:36:43.1376615Z >>> input_lengths = torch.full(size=(N,), fill_value=T, dtype=torch.long) 2025-09-07T07:36:43.1377069Z >>> target_lengths = torch.randint( 2025-09-07T07:36:43.1377386Z ... low=S_min, 2025-09-07T07:36:43.1377632Z ... high=S, 2025-09-07T07:36:43.1377893Z ... size=(N,), 2025-09-07T07:36:43.1378164Z ... dtype=torch.long, 2025-09-07T07:36:43.1378442Z ... ) 2025-09-07T07:36:43.1378728Z >>> ctc_loss = nn.CTCLoss() 2025-09-07T07:36:43.1379118Z >>> loss = ctc_loss(input, target, input_lengths, target_lengths) 2025-09-07T07:36:43.1379527Z >>> loss.backward() 2025-09-07T07:36:43.1379789Z >>> 2025-09-07T07:36:43.1379983Z >>> 2025-09-07T07:36:43.1380223Z >>> # Target are to be un-padded 2025-09-07T07:36:43.1380556Z >>> T = 50 # Input sequence length 2025-09-07T07:36:43.1380948Z >>> C = 20 # Number of classes (including blank) 2025-09-07T07:36:43.1381285Z >>> N = 16 # Batch size 2025-09-07T07:36:43.1381555Z >>> 2025-09-07T07:36:43.1381877Z >>> # Initialize random batch of input vectors, for *size = (T,N,C) 2025-09-07T07:36:43.1382405Z >>> input = torch.randn(T, N, C).log_softmax(2).detach().requires_grad_() 2025-09-07T07:36:43.1382955Z >>> input_lengths = torch.full(size=(N,), fill_value=T, dtype=torch.long) 2025-09-07T07:36:43.1383367Z >>> 2025-09-07T07:36:43.1383682Z >>> # Initialize random batch of targets (0 = blank, 1:C = classes) 2025-09-07T07:36:43.1384234Z >>> target_lengths = torch.randint(low=1, high=T, size=(N,), dtype=torch.long) 2025-09-07T07:36:43.1384703Z >>> target = torch.randint( 2025-09-07T07:36:43.1384981Z ... low=1, 2025-09-07T07:36:43.1385223Z ... high=C, 2025-09-07T07:36:43.1385492Z ... size=(sum(target_lengths),), 2025-09-07T07:36:43.1385816Z ... dtype=torch.long, 2025-09-07T07:36:43.1386083Z ... ) 2025-09-07T07:36:43.1386317Z >>> ctc_loss = nn.CTCLoss() 2025-09-07T07:36:43.1386739Z >>> loss = ctc_loss(input, target, input_lengths, target_lengths) 2025-09-07T07:36:43.1387147Z >>> loss.backward() 2025-09-07T07:36:43.1387397Z >>> 2025-09-07T07:36:43.1387603Z >>> 2025-09-07T07:36:43.1387916Z >>> # Target are to be un-padded and unbatched (effectively N=1) 2025-09-07T07:36:43.1388335Z >>> T = 50 # Input sequence length 2025-09-07T07:36:43.1388680Z >>> C = 20 # Number of classes (including blank) 2025-09-07T07:36:43.1389033Z >>> 2025-09-07T07:36:43.1389356Z >>> # Initialize random batch of input vectors, for *size = (T,C) 2025-09-07T07:36:43.1389804Z >>> # xdoctest: +SKIP("FIXME: error in doctest") 2025-09-07T07:36:43.1390241Z >>> input = torch.randn(T, C).log_softmax(1).detach().requires_grad_() 2025-09-07T07:36:43.1390719Z >>> input_lengths = torch.tensor(T, dtype=torch.long) 2025-09-07T07:36:43.1391076Z >>> 2025-09-07T07:36:43.1391395Z >>> # Initialize random batch of targets (0 = blank, 1:C = classes) 2025-09-07T07:36:43.1391930Z >>> target_lengths = torch.randint(low=1, high=T, size=(), dtype=torch.long) 2025-09-07T07:36:43.1392393Z >>> target = torch.randint( 2025-09-07T07:36:43.1392719Z ... low=1, 2025-09-07T07:36:43.1392960Z ... high=C, 2025-09-07T07:36:43.1393208Z ... size=(target_lengths,), 2025-09-07T07:36:43.1393524Z ... dtype=torch.long, 2025-09-07T07:36:43.1393804Z ... ) 2025-09-07T07:36:43.1394039Z >>> ctc_loss = nn.CTCLoss() 2025-09-07T07:36:43.1394533Z >>> loss = ctc_loss(input, target, input_lengths, target_lengths) 2025-09-07T07:36:43.1394941Z >>> loss.backward() 2025-09-07T07:36:43.1395128Z 2025-09-07T07:36:43.1395216Z Reference: 2025-09-07T07:36:43.1395527Z A. Graves et al.: Connectionist Temporal Classification: 2025-09-07T07:36:43.1396059Z Labelling Unsegmented Sequence Data with Recurrent Neural Networks: 2025-09-07T07:36:43.1396577Z https://www.cs.toronto.edu/~graves/icml_2006.pdf 2025-09-07T07:36:43.1396859Z 2025-09-07T07:36:43.1396945Z Note: 2025-09-07T07:36:43.1397313Z In order to use CuDNN, the following must be satisfied: :attr:`targets` must be 2025-09-07T07:36:43.1397936Z in concatenated format, all :attr:`input_lengths` must be `T`. :math:`blank=0`, 2025-09-07T07:36:43.1398566Z :attr:`target_lengths` :math:`\leq 256`, the integer arguments must be of 2025-09-07T07:36:43.1399019Z dtype :attr:`torch.int32`. 2025-09-07T07:36:43.1399231Z 2025-09-07T07:36:43.1399502Z The regular implementation uses the (more common in PyTorch) `torch.long` dtype. 2025-09-07T07:36:43.1399889Z 2025-09-07T07:36:43.1399906Z 2025-09-07T07:36:43.1399986Z Note: 2025-09-07T07:36:43.1400357Z In some circumstances when using the CUDA backend with CuDNN, this operator 2025-09-07T07:36:43.1400971Z may select a nondeterministic algorithm to increase performance. If this is 2025-09-07T07:36:43.1401595Z undesirable, you can try to make the operation deterministic (potentially at 2025-09-07T07:36:43.1402198Z a performance cost) by setting ``torch.backends.cudnn.deterministic = 2025-09-07T07:36:43.1402642Z True``. 2025-09-07T07:36:43.1402977Z Please see the notes on :doc:`/notes/randomness` for background. 2025-09-07T07:36:43.1403364Z 2025-09-07T07:36:43.1403733Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:43.1404099Z 2025-09-07T07:36:43.1864698Z msg = Cannot scrape callname=MaxUnpool2d in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py line=410. 2025-09-07T07:36:43.1865644Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:43.1866186Z Computes a partial inverse of :class:`MaxPool2d`. 2025-09-07T07:36:43.1866447Z 2025-09-07T07:36:43.1866722Z :class:`MaxPool2d` is not fully invertible, since the non-maximal values are lost. 2025-09-07T07:36:43.1867241Z 2025-09-07T07:36:43.1867484Z :class:`MaxUnpool2d` takes in as input the output of :class:`MaxPool2d` 2025-09-07T07:36:43.1868065Z including the indices of the maximal values and computes a partial inverse 2025-09-07T07:36:43.1868585Z in which all non-maximal values are set to zero. 2025-09-07T07:36:43.1868852Z 2025-09-07T07:36:43.1868944Z Note: 2025-09-07T07:36:43.1869388Z This operation may behave nondeterministically when the input indices has repeat values. 2025-09-07T07:36:43.1870221Z See https://github.com/pytorch/pytorch/issues/80827 and :doc:`/notes/randomness` for more information. 2025-09-07T07:36:43.1870714Z 2025-09-07T07:36:43.1870961Z .. note:: :class:`MaxPool2d` can map several input sizes to the same output 2025-09-07T07:36:43.1871488Z sizes. Hence, the inversion process can get ambiguous. 2025-09-07T07:36:43.1871977Z To accommodate this, you can provide the needed output size 2025-09-07T07:36:43.1872499Z as an additional argument :attr:`output_size` in the forward call. 2025-09-07T07:36:43.1872939Z See the Inputs and Example below. 2025-09-07T07:36:43.1873187Z 2025-09-07T07:36:43.1873335Z Args: 2025-09-07T07:36:43.1873641Z kernel_size (int or tuple): Size of the max pooling window. 2025-09-07T07:36:43.1874117Z stride (int or tuple): Stride of the max pooling window. 2025-09-07T07:36:43.1874596Z It is set to :attr:`kernel_size` by default. 2025-09-07T07:36:43.1875018Z padding (int or tuple): Padding that was added to the input 2025-09-07T07:36:43.1875397Z 2025-09-07T07:36:43.1875483Z Inputs: 2025-09-07T07:36:43.1875739Z - `input`: the input Tensor to invert 2025-09-07T07:36:43.1876177Z - `indices`: the indices given out by :class:`~torch.nn.MaxPool2d` 2025-09-07T07:36:43.1876648Z - `output_size` (optional): the targeted output size 2025-09-07T07:36:43.1876929Z 2025-09-07T07:36:43.1877015Z Shape: 2025-09-07T07:36:43.1877333Z - Input: :math:`(N, C, H_{in}, W_{in})` or :math:`(C, H_{in}, W_{in})`. 2025-09-07T07:36:43.1877850Z - Output: :math:`(N, C, H_{out}, W_{out})` or :math:`(C, H_{out}, W_{out})`, where 2025-09-07T07:36:43.1878183Z 2025-09-07T07:36:43.1878287Z .. math:: 2025-09-07T07:36:43.1878749Z H_{out} = (H_{in} - 1) \times \text{stride[0]} - 2 \times \text{padding[0]} + \text{kernel\_size[0]} 2025-09-07T07:36:43.1879145Z 2025-09-07T07:36:43.1879232Z .. math:: 2025-09-07T07:36:43.1879629Z W_{out} = (W_{in} - 1) \times \text{stride[1]} - 2 \times \text{padding[1]} + \text{kernel\_size[1]} 2025-09-07T07:36:43.1880009Z 2025-09-07T07:36:43.1880188Z or as given by :attr:`output_size` in the call operator 2025-09-07T07:36:43.1880463Z 2025-09-07T07:36:43.1880565Z Example:: 2025-09-07T07:36:43.1880696Z 2025-09-07T07:36:43.1880857Z >>> pool = nn.MaxPool2d(2, stride=2, return_indices=True) 2025-09-07T07:36:43.1881258Z >>> unpool = nn.MaxUnpool2d(2, stride=2) 2025-09-07T07:36:43.1881629Z >>> input = torch.tensor([[[[ 1., 2., 3., 4.], 2025-09-07T07:36:43.1882001Z [ 5., 6., 7., 8.], 2025-09-07T07:36:43.1882337Z [ 9., 10., 11., 12.], 2025-09-07T07:36:43.1882658Z [13., 14., 15., 16.]]]]) 2025-09-07T07:36:43.1882998Z >>> output, indices = pool(input) 2025-09-07T07:36:43.1883331Z >>> unpool(output, indices) 2025-09-07T07:36:43.1883640Z tensor([[[[ 0., 0., 0., 0.], 2025-09-07T07:36:43.1883938Z [ 0., 6., 0., 8.], 2025-09-07T07:36:43.1884240Z [ 0., 0., 0., 0.], 2025-09-07T07:36:43.1884546Z [ 0., 14., 0., 16.]]]]) 2025-09-07T07:36:43.1884969Z >>> # Now using output_size to resolve an ambiguous size for the inverse 2025-09-07T07:36:43.1885430Z >>> input = torch.tensor([[[[ 1., 2., 3., 4., 5.], 2025-09-07T07:36:43.1885837Z [ 6., 7., 8., 9., 10.], 2025-09-07T07:36:43.1886178Z [11., 12., 13., 14., 15.], 2025-09-07T07:36:43.1886518Z [16., 17., 18., 19., 20.]]]]) 2025-09-07T07:36:43.1886854Z >>> output, indices = pool(input) 2025-09-07T07:36:43.1887253Z >>> # This call will not work without specifying output_size 2025-09-07T07:36:43.1887701Z >>> unpool(output, indices, output_size=input.size()) 2025-09-07T07:36:43.1888083Z tensor([[[[ 0., 0., 0., 0., 0.], 2025-09-07T07:36:43.1888398Z [ 0., 7., 0., 9., 0.], 2025-09-07T07:36:43.1888689Z [ 0., 0., 0., 0., 0.], 2025-09-07T07:36:43.1888997Z [ 0., 17., 0., 19., 0.]]]]) 2025-09-07T07:36:43.1889204Z 2025-09-07T07:36:43.1889221Z 2025-09-07T07:36:43.1889302Z 2025-09-07T07:36:43.1889666Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:43.1890034Z 2025-09-07T07:36:43.2151580Z msg = Cannot scrape callname=EmbeddingBag in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/sparse.py line=272. 2025-09-07T07:36:43.2152768Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:43.2153496Z Compute sums or means of 'bags' of embeddings, without instantiating the intermediate embeddings. 2025-09-07T07:36:43.2153937Z 2025-09-07T07:36:43.2154261Z For bags of constant length, no :attr:`per_sample_weights`, no indices equal to :attr:`padding_idx`, 2025-09-07T07:36:43.2154920Z and with 2D inputs, this class 2025-09-07T07:36:43.2155134Z 2025-09-07T07:36:43.2155440Z * with ``mode="sum"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.sum(dim=1)``, 2025-09-07T07:36:43.2156181Z * with ``mode="mean"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.mean(dim=1)``, 2025-09-07T07:36:43.2156950Z * with ``mode="max"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.max(dim=1)``. 2025-09-07T07:36:43.2157369Z 2025-09-07T07:36:43.2157736Z However, :class:`~torch.nn.EmbeddingBag` is much more time and memory efficient than using a chain of these 2025-09-07T07:36:43.2158305Z operations. 2025-09-07T07:36:43.2158441Z 2025-09-07T07:36:43.2158760Z EmbeddingBag also supports per-sample weights as an argument to the forward 2025-09-07T07:36:43.2159382Z pass. This scales the output of the Embedding before performing a weighted 2025-09-07T07:36:43.2159992Z reduction as specified by ``mode``. If :attr:`per_sample_weights` is passed, the 2025-09-07T07:36:43.2160603Z only supported ``mode`` is ``"sum"``, which computes a weighted sum according to 2025-09-07T07:36:43.2161071Z :attr:`per_sample_weights`. 2025-09-07T07:36:43.2161260Z 2025-09-07T07:36:43.2161343Z Args: 2025-09-07T07:36:43.2161649Z num_embeddings (int): size of the dictionary of embeddings 2025-09-07T07:36:43.2162125Z embedding_dim (int): the size of each embedding vector 2025-09-07T07:36:43.2162723Z max_norm (float, optional): If given, each embedding vector with norm larger than :attr:`max_norm` 2025-09-07T07:36:43.2163303Z is renormalized to have norm :attr:`max_norm`. 2025-09-07T07:36:43.2163928Z norm_type (float, optional): The p of the p-norm to compute for the :attr:`max_norm` option. Default ``2``. 2025-09-07T07:36:43.2164720Z scale_grad_by_freq (bool, optional): if given, this will scale gradients by the inverse of frequency of 2025-09-07T07:36:43.2165339Z the words in the mini-batch. Default ``False``. 2025-09-07T07:36:43.2165810Z Note: this option is not supported when ``mode="max"``. 2025-09-07T07:36:43.2166359Z mode (str, optional): ``"sum"``, ``"mean"`` or ``"max"``. Specifies the way to reduce the bag. 2025-09-07T07:36:43.2166997Z ``"sum"`` computes the weighted sum, taking :attr:`per_sample_weights` 2025-09-07T07:36:43.2167561Z into consideration. ``"mean"`` computes the average of the values 2025-09-07T07:36:43.2168079Z in the bag, ``"max"`` computes the max value over each bag. 2025-09-07T07:36:43.2168492Z Default: ``"mean"`` 2025-09-07T07:36:43.2169029Z sparse (bool, optional): if ``True``, gradient w.r.t. :attr:`weight` matrix will be a sparse tensor. See 2025-09-07T07:36:43.2169728Z Notes for more details regarding sparse gradients. Note: this option is not 2025-09-07T07:36:43.2170239Z supported when ``mode="max"``. 2025-09-07T07:36:43.2170860Z include_last_offset (bool, optional): if ``True``, :attr:`offsets` has one additional element, where the last element 2025-09-07T07:36:43.2171578Z is equivalent to the size of `indices`. This matches the CSR format. 2025-09-07T07:36:43.2172249Z padding_idx (int, optional): If specified, the entries at :attr:`padding_idx` do not contribute to the 2025-09-07T07:36:43.2173006Z gradient; therefore, the embedding vector at :attr:`padding_idx` is not updated 2025-09-07T07:36:43.2173657Z during training, i.e. it remains as a fixed "pad". For a newly constructed 2025-09-07T07:36:43.2174291Z EmbeddingBag, the embedding vector at :attr:`padding_idx` will default to all 2025-09-07T07:36:43.2174989Z zeros, but can be updated to another value to be used as the padding vector. 2025-09-07T07:36:43.2175593Z Note that the embedding vector at :attr:`padding_idx` is excluded from the 2025-09-07T07:36:43.2176070Z reduction. 2025-09-07T07:36:43.2176298Z 2025-09-07T07:36:43.2176387Z Attributes: 2025-09-07T07:36:43.2176844Z weight (Tensor): the learnable weights of the module of shape `(num_embeddings, embedding_dim)` 2025-09-07T07:36:43.2177425Z initialized from :math:`\mathcal{N}(0, 1)`. 2025-09-07T07:36:43.2177678Z 2025-09-07T07:36:43.2177780Z Examples:: 2025-09-07T07:36:43.2177954Z 2025-09-07T07:36:43.2178122Z >>> # an EmbeddingBag module containing 10 tensors of size 3 2025-09-07T07:36:43.2178564Z >>> embedding_sum = nn.EmbeddingBag(10, 3, mode='sum') 2025-09-07T07:36:43.2178967Z >>> # a batch of 2 samples of 4 indices each 2025-09-07T07:36:43.2179377Z >>> input = torch.tensor([1, 2, 4, 5, 4, 3, 2, 9], dtype=torch.long) 2025-09-07T07:36:43.2179834Z >>> offsets = torch.tensor([0, 4], dtype=torch.long) 2025-09-07T07:36:43.2180235Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2025-09-07T07:36:43.2180602Z >>> embedding_sum(input, offsets) 2025-09-07T07:36:43.2180944Z tensor([[-0.8861, -5.4350, -0.0523], 2025-09-07T07:36:43.2181255Z [ 1.1306, -2.5798, -1.0044]]) 2025-09-07T07:36:43.2181481Z 2025-09-07T07:36:43.2181590Z >>> # Example with padding_idx 2025-09-07T07:36:43.2182004Z >>> embedding_sum = nn.EmbeddingBag(10, 3, mode='sum', padding_idx=2) 2025-09-07T07:36:43.2182520Z >>> input = torch.tensor([2, 2, 2, 2, 4, 3, 2, 9], dtype=torch.long) 2025-09-07T07:36:43.2182961Z >>> offsets = torch.tensor([0, 4], dtype=torch.long) 2025-09-07T07:36:43.2183337Z >>> embedding_sum(input, offsets) 2025-09-07T07:36:43.2183661Z tensor([[ 0.0000, 0.0000, 0.0000], 2025-09-07T07:36:43.2183970Z [-0.7082, 3.2145, -2.6251]]) 2025-09-07T07:36:43.2184176Z 2025-09-07T07:36:43.2184347Z >>> # An EmbeddingBag can be loaded from an Embedding like so 2025-09-07T07:36:43.2184782Z >>> embedding = nn.Embedding(10, 3, padding_idx=2) 2025-09-07T07:36:43.2185230Z >>> embedding_sum = nn.EmbeddingBag.from_pretrained( 2025-09-07T07:36:43.2185609Z embedding.weight, 2025-09-07T07:36:43.2185936Z padding_idx=embedding.padding_idx, 2025-09-07T07:36:43.2186266Z mode='sum') 2025-09-07T07:36:43.2186523Z 2025-09-07T07:36:43.2186892Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:43.2187261Z 2025-09-07T07:36:43.2228730Z msg = Cannot scrape callname=Transformer.forward in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py line=186. 2025-09-07T07:36:43.2229759Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:43.2230350Z Take in and process masked source/target sequences. 2025-09-07T07:36:43.2230609Z 2025-09-07T07:36:43.2230703Z .. note:: 2025-09-07T07:36:43.2230853Z 2025-09-07T07:36:43.2231245Z If a boolean tensor is provided for any of the [src/tgt/memory]_mask arguments, positions with a ``True`` value are 2025-09-07T07:36:43.2231892Z not allowed to participate in the attention, 2025-09-07T07:36:43.2232338Z which is the opposite of the definition for :attr:`attn_mask` 2025-09-07T07:36:43.2232957Z in :func:`torch.nn.functional.scaled_dot_product_attention`. 2025-09-07T07:36:43.2233259Z 2025-09-07T07:36:43.2233346Z Args: 2025-09-07T07:36:43.2233620Z src: the sequence to the encoder (required). 2025-09-07T07:36:43.2234007Z tgt: the sequence to the decoder (required). 2025-09-07T07:36:43.2234540Z src_mask: the additive mask for the src sequence (optional). 2025-09-07T07:36:43.2235039Z tgt_mask: the additive mask for the tgt sequence (optional). 2025-09-07T07:36:43.2235557Z memory_mask: the additive mask for the encoder output (optional). 2025-09-07T07:36:43.2236118Z src_key_padding_mask: the Tensor mask for src keys per batch (optional). 2025-09-07T07:36:43.2236700Z tgt_key_padding_mask: the Tensor mask for tgt keys per batch (optional). 2025-09-07T07:36:43.2237311Z memory_key_padding_mask: the Tensor mask for memory keys per batch (optional). 2025-09-07T07:36:43.2237888Z src_is_causal: If specified, applies a causal mask as ``src_mask``. 2025-09-07T07:36:43.2238412Z Default: ``None``; try to detect a causal mask. 2025-09-07T07:36:43.2238768Z Warning: 2025-09-07T07:36:43.2239087Z ``src_is_causal`` provides a hint that ``src_mask`` is 2025-09-07T07:36:43.2239531Z the causal mask. Providing incorrect hints can result in 2025-09-07T07:36:43.2239986Z incorrect execution, including forward and backward 2025-09-07T07:36:43.2240368Z compatibility. 2025-09-07T07:36:43.2240757Z tgt_is_causal: If specified, applies a causal mask as ``tgt_mask``. 2025-09-07T07:36:43.2241216Z Default: ``None``; try to detect a causal mask. 2025-09-07T07:36:43.2241572Z Warning: 2025-09-07T07:36:43.2241895Z ``tgt_is_causal`` provides a hint that ``tgt_mask`` is 2025-09-07T07:36:43.2242346Z the causal mask. Providing incorrect hints can result in 2025-09-07T07:36:43.2242811Z incorrect execution, including forward and backward 2025-09-07T07:36:43.2243185Z compatibility. 2025-09-07T07:36:43.2243549Z memory_is_causal: If specified, applies a causal mask as 2025-09-07T07:36:43.2243942Z ``memory_mask``. 2025-09-07T07:36:43.2244231Z Default: ``False``. 2025-09-07T07:36:43.2244507Z Warning: 2025-09-07T07:36:43.2244800Z ``memory_is_causal`` provides a hint that 2025-09-07T07:36:43.2245217Z ``memory_mask`` is the causal mask. Providing incorrect 2025-09-07T07:36:43.2245661Z hints can result in incorrect execution, including 2025-09-07T07:36:43.2246091Z forward and backward compatibility. 2025-09-07T07:36:43.2246342Z 2025-09-07T07:36:43.2246429Z Shape: 2025-09-07T07:36:43.2246816Z - src: :math:`(S, E)` for unbatched input, :math:`(S, N, E)` if `batch_first=False` or 2025-09-07T07:36:43.2247301Z `(N, S, E)` if `batch_first=True`. 2025-09-07T07:36:43.2247780Z - tgt: :math:`(T, E)` for unbatched input, :math:`(T, N, E)` if `batch_first=False` or 2025-09-07T07:36:43.2248254Z `(N, T, E)` if `batch_first=True`. 2025-09-07T07:36:43.2248676Z - src_mask: :math:`(S, S)` or :math:`(N\cdot\text{num\_heads}, S, S)`. 2025-09-07T07:36:43.2249182Z - tgt_mask: :math:`(T, T)` or :math:`(N\cdot\text{num\_heads}, T, T)`. 2025-09-07T07:36:43.2249608Z - memory_mask: :math:`(T, S)`. 2025-09-07T07:36:43.2250058Z - src_key_padding_mask: :math:`(S)` for unbatched input otherwise :math:`(N, S)`. 2025-09-07T07:36:43.2250668Z - tgt_key_padding_mask: :math:`(T)` for unbatched input otherwise :math:`(N, T)`. 2025-09-07T07:36:43.2251288Z - memory_key_padding_mask: :math:`(S)` for unbatched input otherwise :math:`(N, S)`. 2025-09-07T07:36:43.2251661Z 2025-09-07T07:36:43.2252015Z Note: [src/tgt/memory]_mask ensures that position :math:`i` is allowed to attend the unmasked 2025-09-07T07:36:43.2252658Z positions. If a BoolTensor is provided, positions with ``True`` 2025-09-07T07:36:43.2253229Z are not allowed to attend while ``False`` values will be unchanged. If a FloatTensor 2025-09-07T07:36:43.2253815Z is provided, it will be added to the attention weight. 2025-09-07T07:36:43.2254403Z [src/tgt/memory]_key_padding_mask provides specified elements in the key to be ignored by 2025-09-07T07:36:43.2255040Z the attention. If a BoolTensor is provided, the positions with the 2025-09-07T07:36:43.2255681Z value of ``True`` will be ignored while the position with the value of ``False`` will be unchanged. 2025-09-07T07:36:43.2256111Z 2025-09-07T07:36:43.2256367Z - output: :math:`(T, E)` for unbatched input, :math:`(T, N, E)` if `batch_first=False` or 2025-09-07T07:36:43.2256861Z `(N, T, E)` if `batch_first=True`. 2025-09-07T07:36:43.2257098Z 2025-09-07T07:36:43.2257366Z Note: Due to the multi-head attention architecture in the transformer model, 2025-09-07T07:36:43.2257969Z the output sequence length of a transformer is same as the input sequence 2025-09-07T07:36:43.2258448Z (i.e. target) length of the decoder. 2025-09-07T07:36:43.2258674Z 2025-09-07T07:36:43.2258996Z where :math:`S` is the source sequence length, :math:`T` is the target sequence length, :math:`N` is the 2025-09-07T07:36:43.2259573Z batch size, :math:`E` is the feature number 2025-09-07T07:36:43.2259827Z 2025-09-07T07:36:43.2259912Z Examples: 2025-09-07T07:36:43.2260149Z >>> # xdoctest: +SKIP 2025-09-07T07:36:43.2260447Z >>> output = transformer_model( 2025-09-07T07:36:43.2260818Z ... src, tgt, src_mask=src_mask, tgt_mask=tgt_mask 2025-09-07T07:36:43.2261162Z ... ) 2025-09-07T07:36:43.2261385Z 2025-09-07T07:36:43.2261745Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:43.2262123Z 2025-09-07T07:36:43.2501723Z 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=1766. 2025-09-07T07:36:43.2502754Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:43.2503148Z 2025-09-07T07:36:43.2503379Z Context manager for training with uneven inputs across processes in DDP. 2025-09-07T07:36:43.2503749Z 2025-09-07T07:36:43.2503974Z This context manager will keep track of already-joined DDP processes, 2025-09-07T07:36:43.2504524Z and "shadow" the forward and backward passes by inserting collective 2025-09-07T07:36:43.2505218Z communication operations to match with the ones created by non-joined 2025-09-07T07:36:43.2505789Z DDP processes. This will ensure each collective call has a corresponding 2025-09-07T07:36:43.2506368Z call by already-joined DDP processes, preventing hangs or errors that 2025-09-07T07:36:43.2506911Z would otherwise happen when training with uneven inputs across 2025-09-07T07:36:43.2507467Z processes. Alternatively, if the flag ``throw_on_early_termination`` is 2025-09-07T07:36:43.2508018Z specified to be ``True``, all trainers will throw an error once one rank 2025-09-07T07:36:43.2508547Z runs out of inputs, allowing these errors to be caught and handled 2025-09-07T07:36:43.2508974Z according to application logic. 2025-09-07T07:36:43.2509167Z 2025-09-07T07:36:43.2509395Z Once all DDP processes have joined, the context manager will broadcast 2025-09-07T07:36:43.2509954Z the model corresponding to the last joined process to all processes to 2025-09-07T07:36:43.2510435Z ensure the model is the same across all processes 2025-09-07T07:36:43.2510807Z (which is guaranteed by DDP). 2025-09-07T07:36:43.2510995Z 2025-09-07T07:36:43.2511207Z To use this to enable training with uneven inputs across processes, 2025-09-07T07:36:43.2511804Z simply wrap this context manager around your training loop. No further 2025-09-07T07:36:43.2512309Z modifications to the model or data loading is required. 2025-09-07T07:36:43.2512603Z 2025-09-07T07:36:43.2512707Z .. warning:: 2025-09-07T07:36:43.2513053Z If the model or training loop this context manager is wrapped around 2025-09-07T07:36:43.2513619Z has additional distributed collective operations, such as 2025-09-07T07:36:43.2514101Z ``SyncBatchNorm`` in the model's forward pass, then the flag 2025-09-07T07:36:43.2514661Z ``throw_on_early_termination`` must be enabled. This is because this 2025-09-07T07:36:43.2515201Z context manager is not aware of non-DDP collective communication. 2025-09-07T07:36:43.2515705Z This flag will cause all ranks to throw when any one rank 2025-09-07T07:36:43.2516205Z exhausts inputs, allowing these errors to be caught and recovered 2025-09-07T07:36:43.2516620Z from across all ranks. 2025-09-07T07:36:43.2516811Z 2025-09-07T07:36:43.2516892Z Args: 2025-09-07T07:36:43.2517193Z divide_by_initial_world_size (bool): If ``True``, will divide 2025-09-07T07:36:43.2517751Z gradients by the initial ``world_size`` DDP training was launched 2025-09-07T07:36:43.2518279Z with. If ``False``, will compute the effective world size 2025-09-07T07:36:43.2518748Z (number of ranks that have not depleted their inputs yet) and 2025-09-07T07:36:43.2519202Z divide gradients by that during allreduce. Set 2025-09-07T07:36:43.2519662Z ``divide_by_initial_world_size=True`` to ensure every input 2025-09-07T07:36:43.2520153Z sample including the uneven inputs have equal weight in terms of 2025-09-07T07:36:43.2520649Z how much they contribute to the global gradient. This is 2025-09-07T07:36:43.2521109Z achieved by always dividing the gradient by the initial 2025-09-07T07:36:43.2521580Z ``world_size`` even when we encounter uneven inputs. If you set 2025-09-07T07:36:43.2522061Z this to ``False``, we divide the gradient by the remaining 2025-09-07T07:36:43.2522536Z number of nodes. This ensures parity with training on a smaller 2025-09-07T07:36:43.2523035Z ``world_size`` although it also means the uneven inputs would 2025-09-07T07:36:43.2523528Z contribute more towards the global gradient. Typically, you 2025-09-07T07:36:43.2524228Z would want to set this to ``True`` for cases where the last few 2025-09-07T07:36:43.2524719Z inputs of your training job are uneven. In extreme cases, where 2025-09-07T07:36:43.2525219Z there is a large discrepancy in the number of inputs, setting 2025-09-07T07:36:43.2525665Z this to ``False`` might provide better results. 2025-09-07T07:36:43.2526184Z enable (bool): Whether to enable uneven input detection or not. Pass 2025-09-07T07:36:43.2526672Z in ``enable=False`` to disable in cases where you know that 2025-09-07T07:36:43.2527150Z inputs are even across participating processes. Default is 2025-09-07T07:36:43.2527546Z ``True``. 2025-09-07T07:36:43.2527870Z throw_on_early_termination (bool): Whether to throw an error 2025-09-07T07:36:43.2528346Z or continue training when at least one rank has exhausted 2025-09-07T07:36:43.2528862Z inputs. If ``True``, will throw upon the first rank reaching end 2025-09-07T07:36:43.2529341Z of data. If ``False``, will continue training with a smaller 2025-09-07T07:36:43.2529963Z effective world size until all ranks are joined. Note that if 2025-09-07T07:36:43.2530768Z this flag is specified, then the flag 2025-09-07T07:36:43.2531165Z ``divide_by_initial_world_size`` would be ignored. Default 2025-09-07T07:36:43.2531554Z is ``False``. 2025-09-07T07:36:43.2531717Z 2025-09-07T07:36:43.2531724Z 2025-09-07T07:36:43.2531836Z Example:: 2025-09-07T07:36:43.2531957Z 2025-09-07T07:36:43.2532082Z >>> # xdoctest: +SKIP("Distributed") 2025-09-07T07:36:43.2532386Z >>> import torch 2025-09-07T07:36:43.2532749Z >>> import torch.distributed as dist 2025-09-07T07:36:43.2533065Z >>> import os 2025-09-07T07:36:43.2533334Z >>> import torch.multiprocessing as mp 2025-09-07T07:36:43.2533655Z >>> import torch.nn as nn 2025-09-07T07:36:43.2533954Z >>> # On each spawned worker 2025-09-07T07:36:43.2534289Z >>> def worker(rank): 2025-09-07T07:36:43.2534639Z >>> dist.init_process_group("nccl", rank=rank, world_size=2) 2025-09-07T07:36:43.2535034Z >>> torch.cuda.set_device(rank) 2025-09-07T07:36:43.2535393Z >>> model = nn.Linear(1, 1, bias=False).to(rank) 2025-09-07T07:36:43.2535821Z >>> model = torch.nn.parallel.DistributedDataParallel( 2025-09-07T07:36:43.2536250Z >>> model, device_ids=[rank], output_device=rank 2025-09-07T07:36:43.2536597Z >>> ) 2025-09-07T07:36:43.2536844Z >>> # Rank 1 gets one more input than rank 0. 2025-09-07T07:36:43.2537275Z >>> inputs = [torch.tensor([1]).float() for _ in range(10 + rank)] 2025-09-07T07:36:43.2537689Z >>> with model.join(): 2025-09-07T07:36:43.2537980Z >>> for _ in range(5): 2025-09-07T07:36:43.2538315Z >>> for inp in inputs: 2025-09-07T07:36:43.2538643Z >>> loss = model(inp).sum() 2025-09-07T07:36:43.2538977Z >>> loss.backward() 2025-09-07T07:36:43.2539378Z >>> # Without the join() API, the below synchronization will hang 2025-09-07T07:36:43.2539816Z >>> # blocking for rank 1's allreduce to complete. 2025-09-07T07:36:43.2540197Z >>> torch.cuda.synchronize(device=rank) 2025-09-07T07:36:43.2540442Z 2025-09-07T07:36:43.2540691Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:43.2541056Z 2025-09-07T07:36:43.2541817Z 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=2057. 2025-09-07T07:36:43.2542912Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:43.2543285Z 2025-09-07T07:36:43.2543602Z Register an optimizer in DDP to optimize parameter immediately after its gradient reduction. 2025-09-07T07:36:43.2544028Z 2025-09-07T07:36:43.2544230Z Registers an optimizer with DDP such that the optimization for a 2025-09-07T07:36:43.2544766Z parameter will run immediately when that parameter's gradient is 2025-09-07T07:36:43.2545288Z finished with reduction, instead of waiting for all parameters' 2025-09-07T07:36:43.2545828Z gradients to finish reduction. This can result in a training speedup 2025-09-07T07:36:43.2546382Z depending on your workload since the optimizer can run while gradient 2025-09-07T07:36:43.2546969Z reduction for other parameters are still ongoing. In addition, this has 2025-09-07T07:36:43.2547546Z the potential to reduce peak memory consumption during training, as it 2025-09-07T07:36:43.2548092Z only needs to load the per-parameter optimizer states of a single 2025-09-07T07:36:43.2548624Z parameter at a time, instead of loading all per-parameter optimizer 2025-09-07T07:36:43.2549032Z states at once. 2025-09-07T07:36:43.2549182Z 2025-09-07T07:36:43.2549262Z Args: 2025-09-07T07:36:43.2549573Z optim (Type): a ``torch.optim.Optimizer`` class to be registered 2025-09-07T07:36:43.2549985Z as a fused optimizer. 2025-09-07T07:36:43.2550309Z *args (Sequence[Any]): Arguments to forward to `optim`. 2025-09-07T07:36:43.2550804Z optim_params (Optional[Iterable[torch.Tensor]]): Set of parameters 2025-09-07T07:36:43.2551353Z to optimize, similar to `params` argument of traditional `torch.optim` 2025-09-07T07:36:43.2551895Z Optimizers. If this is omitted, all DDP model parameters will be 2025-09-07T07:36:43.2552306Z optimized. 2025-09-07T07:36:43.2552632Z **kwargs: (Dict[str, Any]): Keyword arguments to forward to `optim`. 2025-09-07T07:36:43.2552954Z 2025-09-07T07:36:43.2553044Z .. warning :: 2025-09-07T07:36:43.2553391Z _register_fused_optim should only be called once on a DDP instance, 2025-09-07T07:36:43.2553963Z and registering multiple fused optimizers for the same DDP model 2025-09-07T07:36:43.2554498Z is not currently supported. Please ping 2025-09-07T07:36:43.2554981Z https://github.com/pytorch/pytorch/issues/71595 if this is necessary 2025-09-07T07:36:43.2555461Z for your use case. 2025-09-07T07:36:43.2555618Z 2025-09-07T07:36:43.2555718Z .. warning :: 2025-09-07T07:36:43.2556031Z _register_fused_optim and register_comm_hook currently do not 2025-09-07T07:36:43.2556554Z compose together, meaning that custom DDP communication hooks are 2025-09-07T07:36:43.2557056Z not supported with overlapped optimizers. Please ping 2025-09-07T07:36:43.2557571Z https://github.com/pytorch/pytorch/issues/71595 if this is necessary 2025-09-07T07:36:43.2558011Z for your use case. 2025-09-07T07:36:43.2558162Z 2025-09-07T07:36:43.2558247Z .. warning :: 2025-09-07T07:36:43.2558601Z Gradient accumulation and DDP `no_sync` are currently not supported 2025-09-07T07:36:43.2559072Z with overlapped optimizer. Please ping 2025-09-07T07:36:43.2559558Z https://github.com/pytorch/pytorch/issues/71595 if this is necessary 2025-09-07T07:36:43.2559979Z for your use case. 2025-09-07T07:36:43.2560145Z 2025-09-07T07:36:43.2560232Z Example:: 2025-09-07T07:36:43.2560360Z 2025-09-07T07:36:43.2560490Z >>> # xdoctest: +SKIP("No rendezvous handler") 2025-09-07T07:36:43.2561036Z >>> torch.distributed.init_process_group(backend='nccl', world_size=4, init_method='...') 2025-09-07T07:36:43.2561648Z >>> net = torch.nn.parallel.DistributedDataParallel(model, pg) 2025-09-07T07:36:43.2562045Z >>> lr = 1e-2 2025-09-07T07:36:43.2562281Z >>> betas = (0.9, 0.99) 2025-09-07T07:36:43.2562542Z >>> eps = 1e-6 2025-09-07T07:36:43.2562897Z >>> net._register_fused_optim(torch.optim.Adam, lr, betas=betas, eps=eps) 2025-09-07T07:36:43.2563359Z >>> # Example with subset of parameters 2025-09-07T07:36:43.2563722Z >>> params_to_opt = [list(net.parameters())[0]] 2025-09-07T07:36:43.2564077Z >>> net._register_fused_optim( 2025-09-07T07:36:43.2564508Z ... torch.optim.Adam, lr, optim_params=params_to_opt, betas=betas, eps=eps 2025-09-07T07:36:43.2564932Z ... ) 2025-09-07T07:36:43.2565082Z 2025-09-07T07:36:43.2565334Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:43.2565712Z 2025-09-07T07:36:43.2785532Z 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=14. 2025-09-07T07:36:43.2786569Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:43.2787297Z Convert ``memory_format`` of ``nn.Conv2d.weight`` to ``memory_format``. 2025-09-07T07:36:43.2787630Z 2025-09-07T07:36:43.2787913Z The conversion recursively applies to nested ``nn.Module``, including ``module``. 2025-09-07T07:36:43.2788587Z Note that it only changes the memory_format, but not the semantics of each dimensions. 2025-09-07T07:36:43.2789236Z This function is used to facilitate the computation to adopt NHWC kernels, which 2025-09-07T07:36:43.2789921Z provides considerable speed up for fp16 data on CUDA devices with compute capability >= 7.0 2025-09-07T07:36:43.2790360Z 2025-09-07T07:36:43.2790451Z .. note:: 2025-09-07T07:36:43.2790819Z Calling ``model.to(memory_format=torch.channels_last)`` is more aggressive 2025-09-07T07:36:43.2791759Z than the utility function ``convert_conv2d_weight_memory_format``. Any 2025-09-07T07:36:43.2792383Z layer with 4d weight will be affected by ``model.to``, which does not 2025-09-07T07:36:43.2792942Z necessarily benefit from conversion to specified ``memory_format``. 2025-09-07T07:36:43.2793510Z One place we are confident in is that NHWC(channels_last) conversion for 2025-09-07T07:36:43.2794072Z convolution in cuDNN, as it is beneficial to run convolution in NHWC, 2025-09-07T07:36:43.2794726Z even in cases where we have to apply permutation to input tensors. 2025-09-07T07:36:43.2795060Z 2025-09-07T07:36:43.2795288Z Hence our strategy here is to convert only the weight of convolution to 2025-09-07T07:36:43.2795760Z channels_last. This ensures that; 2025-09-07T07:36:43.2796249Z 1. Fast convolution kernels will be used, the benefit of which could 2025-09-07T07:36:43.2796821Z outweigh overhead of permutation (if input is not in the same format). 2025-09-07T07:36:43.2797396Z 2. No unnecessary permutations are applied on layers that do not benefit 2025-09-07T07:36:43.2797865Z from memory_format conversion. 2025-09-07T07:36:43.2798076Z 2025-09-07T07:36:43.2798317Z The optimal case is that, layers between convolution layers are channels 2025-09-07T07:36:43.2798899Z last compatible. Input tensor would be permuted to channels last when it 2025-09-07T07:36:43.2799478Z encounters the first convolution layer and stay in that memory format. 2025-09-07T07:36:43.2800117Z Hence following convolutions will not need to permute its input tensor. 2025-09-07T07:36:43.2800483Z 2025-09-07T07:36:43.2800704Z In case where a channels last incompatible layer is between convolution 2025-09-07T07:36:43.2801265Z layers, we need to permute the input tensor back to contiguous format 2025-09-07T07:36:43.2801821Z for that layer. The input tensor will go through the remaining layers in 2025-09-07T07:36:43.2802378Z contiguous format and be permuted to channels last when it encounters 2025-09-07T07:36:43.2802934Z another convolution layer. There's no point in propagating that 2025-09-07T07:36:43.2803483Z permutation to an earlier layer, as most layers are quite agnostic to 2025-09-07T07:36:43.2803919Z ``memory_format``. 2025-09-07T07:36:43.2804086Z 2025-09-07T07:36:43.2804332Z This claim might change when PyTorch supports fusion of permutation, as 2025-09-07T07:36:43.2804895Z there might have been a better spot to fuse the permutation other than 2025-09-07T07:36:43.2805355Z immediately before a convolution. 2025-09-07T07:36:43.2805574Z 2025-09-07T07:36:43.2805668Z Args: 2025-09-07T07:36:43.2806004Z module (nn.Module): ``nn.Conv2d`` & ``nn.ConvTranspose2d`` or container 2025-09-07T07:36:43.2806425Z ``nn.Module`` 2025-09-07T07:36:43.2806786Z memory_format: user specified ``memory_format``, 2025-09-07T07:36:43.2807227Z e.g. ``torch.channels_last`` or ``torch.contiguous_format`` 2025-09-07T07:36:43.2807522Z 2025-09-07T07:36:43.2807616Z Returns: 2025-09-07T07:36:43.2807875Z The original module with updated ``nn.Conv2d`` 2025-09-07T07:36:43.2808138Z 2025-09-07T07:36:43.2808254Z Example: 2025-09-07T07:36:43.2808526Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2025-09-07T07:36:43.2808931Z >>> # xdoctest: +REQUIRES(env:CUBLAS_WORKSPACE_CONFIG) 2025-09-07T07:36:43.2809309Z >>> input = torch.randint( 2025-09-07T07:36:43.2809654Z ... 1, 10, (2, 8, 4, 4), dtype=torch.float16, device="cuda" 2025-09-07T07:36:43.2810005Z ... ) 2025-09-07T07:36:43.2810243Z >>> model = nn.Sequential( 2025-09-07T07:36:43.2810557Z >>> nn.Conv2d(8, 4, 3)).cuda().half() 2025-09-07T07:36:43.2810876Z >>> # This is identical to: 2025-09-07T07:36:43.2811316Z >>> # nn.utils.convert_conv2d_weight_memory_format(model, torch.channels_last) 2025-09-07T07:36:43.2811845Z >>> model = nn.utils.convert_conv2d_weight_memory_format( 2025-09-07T07:36:43.2812236Z ... model, torch.channels_last 2025-09-07T07:36:43.2812525Z ... ) 2025-09-07T07:36:43.2812756Z >>> out = model(input) 2025-09-07T07:36:43.2813026Z 2025-09-07T07:36:43.2813393Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:43.2813758Z 2025-09-07T07:36:43.2814427Z 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=93. 2025-09-07T07:36:43.2815490Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:43.2816072Z Convert ``memory_format`` of ``nn.Conv3d.weight`` to ``memory_format`` 2025-09-07T07:36:43.2816710Z The conversion recursively applies to nested ``nn.Module``, including ``module``. 2025-09-07T07:36:43.2817380Z Note that it only changes the memory_format, but not the semantics of each dimensions. 2025-09-07T07:36:43.2818039Z This function is used to facilitate the computation to adopt NHWC kernels, which 2025-09-07T07:36:43.2818729Z provides considerable speed up for fp16 data on CUDA devices with compute capability >= 7.0 2025-09-07T07:36:43.2819149Z 2025-09-07T07:36:43.2819237Z .. note:: 2025-09-07T07:36:43.2819616Z Calling ``model.to(memory_format=torch.channels_last_3d)`` is more aggressive 2025-09-07T07:36:43.2820207Z than the utility function ``convert_conv3d_weight_memory_format``. Any 2025-09-07T07:36:43.2820792Z layer with 4d weight will be affected by ``model.to``, which does not 2025-09-07T07:36:43.2821336Z necessarily benefit from conversion to specified ``memory_format``. 2025-09-07T07:36:43.2821916Z One place we are confident in is that NDHWC(channels_last_3d) conversion for 2025-09-07T07:36:43.2822496Z convolution in cuDNN, as it is beneficial to run convolution in NDHWC, 2025-09-07T07:36:43.2823037Z even in cases where we have to apply permutation to input tensors. 2025-09-07T07:36:43.2823355Z 2025-09-07T07:36:43.2823588Z Hence our strategy here is to convert only the weight of convolution to 2025-09-07T07:36:43.2824247Z channels_last_3d. This ensures that; 2025-09-07T07:36:43.2824695Z 1. Fast convolution kernels will be used, the benefit of which could 2025-09-07T07:36:43.2825266Z outweigh overhead of permutation (if input is not in the same format). 2025-09-07T07:36:43.2825862Z 2. No unnecessary permutations are applied on layers that do not benefit 2025-09-07T07:36:43.2826317Z from memory_format conversion. 2025-09-07T07:36:43.2826540Z 2025-09-07T07:36:43.2826762Z The optimal case is that, layers between convolution layers are channels 2025-09-07T07:36:43.2827378Z last compatible. Input tensor would be permuted to channels last when it 2025-09-07T07:36:43.2827958Z encounters the first convolution layer and stay in that memory format. 2025-09-07T07:36:43.2828543Z Hence following convolutions will not need to permute its input tensor. 2025-09-07T07:36:43.2828896Z 2025-09-07T07:36:43.2829198Z In case where a channels last incompatible layer is between convolution 2025-09-07T07:36:43.2829753Z layers, we need to permute the input tensor back to contiguous format 2025-09-07T07:36:43.2830312Z for that layer. The input tensor will go through the remaining layers in 2025-09-07T07:36:43.2830882Z contiguous format and be permuted to channels last when it encounters 2025-09-07T07:36:43.2831438Z another convolution layer. There's no point in propagating that 2025-09-07T07:36:43.2831974Z permutation to an earlier layer, as most layers are quite agnostic to 2025-09-07T07:36:43.2832412Z ``memory_format``. 2025-09-07T07:36:43.2832590Z 2025-09-07T07:36:43.2832820Z This claim might change when PyTorch supports fusion of permutation, as 2025-09-07T07:36:43.2833399Z there might have been a better spot to fuse the permutation other than 2025-09-07T07:36:43.2833869Z immediately before a convolution. 2025-09-07T07:36:43.2834092Z 2025-09-07T07:36:43.2834176Z Args: 2025-09-07T07:36:43.2834587Z module (nn.Module): ``nn.Conv3d`` & ``nn.ConvTranspose3d`` or container 2025-09-07T07:36:43.2835031Z ``nn.Module`` 2025-09-07T07:36:43.2835394Z memory_format: user specified ``memory_format``, 2025-09-07T07:36:43.2835878Z e.g. ``torch.channels_last`` or ``torch.contiguous_format`` 2025-09-07T07:36:43.2836182Z 2025-09-07T07:36:43.2836268Z Returns: 2025-09-07T07:36:43.2836545Z The original module with updated ``nn.Conv3d`` 2025-09-07T07:36:43.2836795Z 2025-09-07T07:36:43.2836928Z Example: 2025-09-07T07:36:43.2837179Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2025-09-07T07:36:43.2837586Z >>> # xdoctest: +REQUIRES(env:CUBLAS_WORKSPACE_CONFIG) 2025-09-07T07:36:43.2837970Z >>> input = torch.randint( 2025-09-07T07:36:43.2838330Z ... 1, 10, (2, 8, 4, 4, 4), dtype=torch.float16, device="cuda" 2025-09-07T07:36:43.2838690Z ... ) 2025-09-07T07:36:43.2838914Z >>> model = nn.Sequential( 2025-09-07T07:36:43.2839237Z >>> nn.Conv3d(8, 4, 3)).cuda().half() 2025-09-07T07:36:43.2839569Z >>> # This is identical to: 2025-09-07T07:36:43.2840025Z >>> # nn.utils.convert_conv3d_weight_memory_format(model, torch.channels_last_3d) 2025-09-07T07:36:43.2840558Z >>> model = nn.utils.convert_conv3d_weight_memory_format( 2025-09-07T07:36:43.2841010Z ... model, torch.channels_last_3d 2025-09-07T07:36:43.2841327Z ... ) 2025-09-07T07:36:43.2841558Z >>> out = model(input) 2025-09-07T07:36:43.2841817Z 2025-09-07T07:36:43.2842190Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:43.2842558Z 2025-09-07T07:36:43.2897046Z msg = Cannot scrape callname=register_parametrization in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/parametrize.py line=424. 2025-09-07T07:36:43.2898341Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:43.2898883Z Register a parametrization to a tensor in a module. 2025-09-07T07:36:43.2899149Z 2025-09-07T07:36:43.2899435Z Assume that ``tensor_name="weight"`` for simplicity. When accessing ``module.weight``, 2025-09-07T07:36:43.2900105Z the module will return the parametrized version ``parametrization(module.weight)``. 2025-09-07T07:36:43.2900785Z If the original tensor requires a gradient, the backward pass will differentiate 2025-09-07T07:36:43.2901461Z through :attr:`parametrization`, and the optimizer will update the tensor accordingly. 2025-09-07T07:36:43.2901870Z 2025-09-07T07:36:43.2902191Z The first time that a module registers a parametrization, this function will add an attribute 2025-09-07T07:36:43.2902873Z ``parametrizations`` to the module of type :class:`~ParametrizationList`. 2025-09-07T07:36:43.2903229Z 2025-09-07T07:36:43.2903480Z The list of parametrizations on the tensor ``weight`` will be accessible under 2025-09-07T07:36:43.2903979Z ``module.parametrizations.weight``. 2025-09-07T07:36:43.2904268Z 2025-09-07T07:36:43.2904400Z The original tensor will be accessible under 2025-09-07T07:36:43.2904794Z ``module.parametrizations.weight.original``. 2025-09-07T07:36:43.2905047Z 2025-09-07T07:36:43.2905322Z Parametrizations may be concatenated by registering several parametrizations 2025-09-07T07:36:43.2905796Z on the same attribute. 2025-09-07T07:36:43.2905978Z 2025-09-07T07:36:43.2906218Z The training mode of a registered parametrization is updated on registration 2025-09-07T07:36:43.2906724Z to match the training mode of the host module 2025-09-07T07:36:43.2906960Z 2025-09-07T07:36:43.2907274Z Parametrized parameters and buffers have an inbuilt caching system that can be activated 2025-09-07T07:36:43.2907831Z using the context manager :func:`cached`. 2025-09-07T07:36:43.2908060Z 2025-09-07T07:36:43.2908304Z A :attr:`parametrization` may optionally implement a method with signature 2025-09-07T07:36:43.2908675Z 2025-09-07T07:36:43.2908785Z .. code-block:: python 2025-09-07T07:36:43.2908967Z 2025-09-07T07:36:43.2909185Z def right_inverse(self, X: Tensor) -> Union[Tensor, Sequence[Tensor]] 2025-09-07T07:36:43.2909553Z 2025-09-07T07:36:43.2909825Z This method is called on the unparametrized tensor when the first parametrization 2025-09-07T07:36:43.2910423Z is registered to compute the initial value of the original tensor. 2025-09-07T07:36:43.2911044Z If this method is not implemented, the original tensor will be just the unparametrized tensor. 2025-09-07T07:36:43.2911513Z 2025-09-07T07:36:43.2911823Z If all the parametrizations registered on a tensor implement `right_inverse` it is possible 2025-09-07T07:36:43.2912542Z to initialize a parametrized tensor by assigning to it, as shown in the example below. 2025-09-07T07:36:43.2912948Z 2025-09-07T07:36:43.2913184Z It is possible for the first parametrization to depend on several inputs. 2025-09-07T07:36:43.2913776Z This may be implemented returning a tuple of tensors from ``right_inverse`` 2025-09-07T07:36:43.2914443Z (see the example implementation of a ``RankOne`` parametrization below). 2025-09-07T07:36:43.2914806Z 2025-09-07T07:36:43.2915142Z In this case, the unconstrained tensors are also located under ``module.parametrizations.weight`` 2025-09-07T07:36:43.2915769Z with names ``original0``, ``original1``,... 2025-09-07T07:36:43.2916004Z 2025-09-07T07:36:43.2916106Z .. note:: 2025-09-07T07:36:43.2916233Z 2025-09-07T07:36:43.2916511Z If unsafe=False (default) both the forward and right_inverse methods will be called 2025-09-07T07:36:43.2917040Z once to perform a number of consistency checks. 2025-09-07T07:36:43.2917569Z If unsafe=True, then right_inverse will be called if the tensor is not parametrized, 2025-09-07T07:36:43.2918091Z and nothing will be called otherwise. 2025-09-07T07:36:43.2918319Z 2025-09-07T07:36:43.2918419Z .. note:: 2025-09-07T07:36:43.2918544Z 2025-09-07T07:36:43.2918762Z In most situations, ``right_inverse`` will be a function such that 2025-09-07T07:36:43.2919194Z ``forward(right_inverse(X)) == X`` (see 2025-09-07T07:36:43.2919718Z `right inverse `_). 2025-09-07T07:36:43.2920381Z Sometimes, when the parametrization is not surjective, it may be reasonable 2025-09-07T07:36:43.2920852Z to relax this. 2025-09-07T07:36:43.2921023Z 2025-09-07T07:36:43.2921108Z .. warning:: 2025-09-07T07:36:43.2921255Z 2025-09-07T07:36:43.2921527Z If a parametrization depends on several inputs, :func:`~register_parametrization` 2025-09-07T07:36:43.2922180Z will register a number of new parameters. If such parametrization is registered 2025-09-07T07:36:43.2922830Z after the optimizer is created, these new parameters will need to be added manually 2025-09-07T07:36:43.2923420Z to the optimizer. See :meth:`torch.Optimizer.add_param_group`. 2025-09-07T07:36:43.2923912Z 2025-09-07T07:36:43.2924063Z Args: 2025-09-07T07:36:43.2924404Z module (nn.Module): module on which to register the parametrization 2025-09-07T07:36:43.2924951Z tensor_name (str): name of the parameter or buffer on which to register 2025-09-07T07:36:43.2925393Z the parametrization 2025-09-07T07:36:43.2925778Z parametrization (nn.Module): the parametrization to register 2025-09-07T07:36:43.2926191Z Keyword args: 2025-09-07T07:36:43.2926552Z unsafe (bool): a boolean flag that denotes whether the parametrization 2025-09-07T07:36:43.2927083Z may change the dtype and shape of the tensor. Default: `False` 2025-09-07T07:36:43.2927667Z Warning: the parametrization is not checked for consistency upon registration. 2025-09-07T07:36:43.2928161Z Enable this flag at your own risk. 2025-09-07T07:36:43.2928401Z 2025-09-07T07:36:43.2928482Z Raises: 2025-09-07T07:36:43.2928897Z ValueError: if the module does not have a parameter or a buffer named :attr:`tensor_name` 2025-09-07T07:36:43.2929347Z 2025-09-07T07:36:43.2929441Z Examples: 2025-09-07T07:36:43.2929711Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_LAPACK) 2025-09-07T07:36:43.2930113Z >>> import torch 2025-09-07T07:36:43.2930383Z >>> import torch.nn as nn 2025-09-07T07:36:43.2930718Z >>> import torch.nn.utils.parametrize as P 2025-09-07T07:36:43.2931045Z >>> 2025-09-07T07:36:43.2931278Z >>> class Symmetric(nn.Module): 2025-09-07T07:36:43.2931640Z >>> def forward(self, X): 2025-09-07T07:36:43.2932033Z >>> return X.triu() + X.triu(1).T # Return a symmetric matrix 2025-09-07T07:36:43.2932405Z >>> 2025-09-07T07:36:43.2932638Z >>> def right_inverse(self, A): 2025-09-07T07:36:43.2932997Z >>> return A.triu() 2025-09-07T07:36:43.2933281Z >>> 2025-09-07T07:36:43.2933488Z >>> m = nn.Linear(5, 5) 2025-09-07T07:36:43.2933850Z >>> P.register_parametrization(m, "weight", Symmetric()) 2025-09-07T07:36:43.2934387Z >>> print(torch.allclose(m.weight, m.weight.T)) # m.weight is now symmetric 2025-09-07T07:36:43.2934837Z True 2025-09-07T07:36:43.2935068Z >>> A = torch.rand(5, 5) 2025-09-07T07:36:43.2935359Z >>> A = A + A.T # A is now symmetric 2025-09-07T07:36:43.2935823Z >>> m.weight = A # Initialize the weight to be the symmetric matrix A 2025-09-07T07:36:43.2936268Z >>> print(torch.allclose(m.weight, A)) 2025-09-07T07:36:43.2936590Z True 2025-09-07T07:36:43.2936712Z 2025-09-07T07:36:43.2936819Z >>> class RankOne(nn.Module): 2025-09-07T07:36:43.2937137Z >>> def forward(self, x, y): 2025-09-07T07:36:43.2937497Z >>> # Form a rank 1 matrix multiplying two vectors 2025-09-07T07:36:43.2937892Z >>> return x.unsqueeze(-1) @ y.unsqueeze(-2) 2025-09-07T07:36:43.2938217Z >>> 2025-09-07T07:36:43.2938456Z >>> def right_inverse(self, Z): 2025-09-07T07:36:43.2938803Z >>> # Project Z onto the rank 1 matrices 2025-09-07T07:36:43.2939191Z >>> U, S, Vh = torch.linalg.svd(Z, full_matrices=False) 2025-09-07T07:36:43.2939577Z >>> # Return rescaled singular vectors 2025-09-07T07:36:43.2939937Z >>> s0_sqrt = S[0].sqrt().unsqueeze(-1) 2025-09-07T07:36:43.2940321Z >>> return U[..., :, 0] * s0_sqrt, Vh[..., 0, :] * s0_sqrt 2025-09-07T07:36:43.2940672Z >>> 2025-09-07T07:36:43.2940931Z >>> linear_rank_one = P.register_parametrization( 2025-09-07T07:36:43.2941326Z ... nn.Linear(4, 4), "weight", RankOne() 2025-09-07T07:36:43.2941646Z ... ) 2025-09-07T07:36:43.2941974Z >>> print(torch.linalg.matrix_rank(linear_rank_one.weight).item()) 2025-09-07T07:36:43.2942370Z 1 2025-09-07T07:36:43.2942500Z 2025-09-07T07:36:43.2942579Z 2025-09-07T07:36:43.2942943Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:43.2943341Z 2025-09-07T07:36:43.3004093Z 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=979. 2025-09-07T07:36:43.3004991Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:43.3005693Z Prune tensor by removing channels with the lowest L\ ``n``-norm along the specified dimension. 2025-09-07T07:36:43.3006120Z 2025-09-07T07:36:43.3006352Z Prunes tensor corresponding to parameter called ``name`` in ``module`` 2025-09-07T07:36:43.3006992Z by removing the specified ``amount`` of (currently unpruned) channels 2025-09-07T07:36:43.3007505Z along the specified ``dim`` with the lowest L\ ``n``-norm. 2025-09-07T07:36:43.3007996Z Modifies module in place (and also return the modified module) 2025-09-07T07:36:43.3008442Z by: 2025-09-07T07:36:43.3008560Z 2025-09-07T07:36:43.3008765Z 1) adding a named buffer called ``name+'_mask'`` corresponding to the 2025-09-07T07:36:43.3009317Z binary mask applied to the parameter ``name`` by the pruning method. 2025-09-07T07:36:43.3010262Z 2) replacing the parameter ``name`` by its pruned version, while the 2025-09-07T07:36:43.3010866Z original (unpruned) parameter is stored in a new parameter named 2025-09-07T07:36:43.3011280Z ``name+'_orig'``. 2025-09-07T07:36:43.3011462Z 2025-09-07T07:36:43.3011548Z Args: 2025-09-07T07:36:43.3011851Z module (nn.Module): module containing the tensor to prune 2025-09-07T07:36:43.3012366Z name (str): parameter name within ``module`` on which pruning 2025-09-07T07:36:43.3012759Z will act. 2025-09-07T07:36:43.3013083Z amount (int or float): quantity of parameters to prune. 2025-09-07T07:36:43.3013548Z If ``float``, should be between 0.0 and 1.0 and represent the 2025-09-07T07:36:43.3014043Z fraction of parameters to prune. If ``int``, it represents the 2025-09-07T07:36:43.3014497Z absolute number of parameters to prune. 2025-09-07T07:36:43.3014923Z n (int, float, inf, -inf, 'fro', 'nuc'): See documentation of valid 2025-09-07T07:36:43.3015395Z entries for argument ``p`` in :func:`torch.norm`. 2025-09-07T07:36:43.3015866Z dim (int): index of the dim along which we define channels to prune. 2025-09-07T07:36:43.3016512Z importance_scores (torch.Tensor): tensor of importance scores (of same 2025-09-07T07:36:43.3017065Z shape as module parameter) used to compute mask for pruning. 2025-09-07T07:36:43.3017591Z The values in this tensor indicate the importance of the corresponding 2025-09-07T07:36:43.3018072Z elements in the parameter being pruned. 2025-09-07T07:36:43.3018543Z If unspecified or None, the module parameter will be used in its place. 2025-09-07T07:36:43.3018888Z 2025-09-07T07:36:43.3018991Z Returns: 2025-09-07T07:36:43.3019332Z module (nn.Module): modified (i.e. pruned) version of the input module 2025-09-07T07:36:43.3019678Z 2025-09-07T07:36:43.3019764Z Examples: 2025-09-07T07:36:43.3020019Z >>> from torch.nn.utils import prune 2025-09-07T07:36:43.3020352Z >>> m = prune.ln_structured( 2025-09-07T07:36:43.3020728Z ... nn.Conv2d(5, 3, 2), "weight", amount=0.3, dim=1, n=float("-inf") 2025-09-07T07:36:43.3021111Z ... ) 2025-09-07T07:36:43.3021320Z 2025-09-07T07:36:43.3021682Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:43.3022044Z 2025-09-07T07:36:43.3022627Z 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=1026. 2025-09-07T07:36:43.3023542Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:43.3024119Z 2025-09-07T07:36:43.3024552Z Globally prunes tensors corresponding to all parameters in ``parameters`` by applying the specified ``pruning_method``. 2025-09-07T07:36:43.3025114Z 2025-09-07T07:36:43.3025312Z Modifies modules in place by: 2025-09-07T07:36:43.3025517Z 2025-09-07T07:36:43.3025724Z 1) adding a named buffer called ``name+'_mask'`` corresponding to the 2025-09-07T07:36:43.3026275Z binary mask applied to the parameter ``name`` by the pruning method. 2025-09-07T07:36:43.3026820Z 2) replacing the parameter ``name`` by its pruned version, while the 2025-09-07T07:36:43.3027337Z original (unpruned) parameter is stored in a new parameter named 2025-09-07T07:36:43.3027755Z ``name+'_orig'``. 2025-09-07T07:36:43.3027914Z 2025-09-07T07:36:43.3027995Z Args: 2025-09-07T07:36:43.3028312Z parameters (Iterable of (module, name) tuples): parameters of 2025-09-07T07:36:43.3028820Z the model to prune in a global fashion, i.e. by aggregating all 2025-09-07T07:36:43.3029328Z weights prior to deciding which ones to prune. module must be of 2025-09-07T07:36:43.3029800Z type :class:`nn.Module`, and name must be a string. 2025-09-07T07:36:43.3030289Z pruning_method (function): a valid pruning function from this module, 2025-09-07T07:36:43.3030802Z or a custom one implemented by the user that satisfies the 2025-09-07T07:36:43.3031310Z implementation guidelines and has ``PRUNING_TYPE='unstructured'``. 2025-09-07T07:36:43.3031941Z importance_scores (dict): a dictionary mapping (module, name) tuples to 2025-09-07T07:36:43.3032515Z the corresponding parameter's importance scores tensor. The tensor 2025-09-07T07:36:43.3032724Z should be the same shape as the parameter, and is used for computing 2025-09-07T07:36:43.3032875Z mask for pruning. 2025-09-07T07:36:43.3033080Z If unspecified or None, the parameter will be used in place of its 2025-09-07T07:36:43.3033191Z importance scores. 2025-09-07T07:36:43.3033316Z kwargs: other keyword arguments such as: 2025-09-07T07:36:43.3033510Z amount (int or float): quantity of parameters to prune across the 2025-09-07T07:36:43.3033634Z specified parameters. 2025-09-07T07:36:43.3033804Z If ``float``, should be between 0.0 and 1.0 and represent the 2025-09-07T07:36:43.3034011Z fraction of parameters to prune. If ``int``, it represents the 2025-09-07T07:36:43.3034136Z absolute number of parameters to prune. 2025-09-07T07:36:43.3034141Z 2025-09-07T07:36:43.3034226Z Raises: 2025-09-07T07:36:43.3034512Z TypeError: if ``PRUNING_TYPE != 'unstructured'`` 2025-09-07T07:36:43.3034518Z 2025-09-07T07:36:43.3034601Z Note: 2025-09-07T07:36:43.3034826Z Since global structured pruning doesn't make much sense unless the 2025-09-07T07:36:43.3035025Z norm is normalized by the size of the parameter, we now limit the 2025-09-07T07:36:43.3035221Z scope of global pruning to unstructured methods. 2025-09-07T07:36:43.3035225Z 2025-09-07T07:36:43.3035323Z Examples: 2025-09-07T07:36:43.3035475Z >>> from torch.nn.utils import prune 2025-09-07T07:36:43.3035605Z >>> from collections import OrderedDict 2025-09-07T07:36:43.3035706Z >>> net = nn.Sequential( 2025-09-07T07:36:43.3035798Z ... OrderedDict( 2025-09-07T07:36:43.3035905Z ... [ 2025-09-07T07:36:43.3036018Z ... ("first", nn.Linear(10, 4)), 2025-09-07T07:36:43.3036148Z ... ("second", nn.Linear(4, 1)), 2025-09-07T07:36:43.3036233Z ... ] 2025-09-07T07:36:43.3036317Z ... ) 2025-09-07T07:36:43.3036412Z ... ) 2025-09-07T07:36:43.3036521Z >>> parameters_to_prune = ( 2025-09-07T07:36:43.3036621Z ... (net.first, "weight"), 2025-09-07T07:36:43.3036738Z ... (net.second, "weight"), 2025-09-07T07:36:43.3036821Z ... ) 2025-09-07T07:36:43.3036944Z >>> prune.global_unstructured( 2025-09-07T07:36:43.3037051Z ... parameters_to_prune, 2025-09-07T07:36:43.3037178Z ... pruning_method=prune.L1Unstructured, 2025-09-07T07:36:43.3037284Z ... amount=10, 2025-09-07T07:36:43.3037366Z ... ) 2025-09-07T07:36:43.3037624Z >>> print(sum(torch.nn.utils.parameters_to_vector(net.buffers()) == 0)) 2025-09-07T07:36:43.3037711Z tensor(10) 2025-09-07T07:36:43.3037716Z 2025-09-07T07:36:43.3037720Z 2025-09-07T07:36:43.3037968Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:43.3037989Z 2025-09-07T07:36:43.3038513Z 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=1149. 2025-09-07T07:36:43.3038773Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:43.3039182Z Prune tensor corresponding to parameter called ``name`` in ``module`` by applying the pre-computed mask in ``mask``. 2025-09-07T07:36:43.3039187Z 2025-09-07T07:36:43.3039398Z Modifies module in place (and also return the modified module) by: 2025-09-07T07:36:43.3039403Z 2025-09-07T07:36:43.3039626Z 1) adding a named buffer called ``name+'_mask'`` corresponding to the 2025-09-07T07:36:43.3039846Z binary mask applied to the parameter ``name`` by the pruning method. 2025-09-07T07:36:43.3040071Z 2) replacing the parameter ``name`` by its pruned version, while the 2025-09-07T07:36:43.3040284Z original (unpruned) parameter is stored in a new parameter named 2025-09-07T07:36:43.3040410Z ``name+'_orig'``. 2025-09-07T07:36:43.3040414Z 2025-09-07T07:36:43.3040511Z Args: 2025-09-07T07:36:43.3040690Z module (nn.Module): module containing the tensor to prune 2025-09-07T07:36:43.3040886Z name (str): parameter name within ``module`` on which pruning 2025-09-07T07:36:43.3041006Z will act. 2025-09-07T07:36:43.3041177Z mask (Tensor): binary mask to be applied to the parameter. 2025-09-07T07:36:43.3041182Z 2025-09-07T07:36:43.3041278Z Returns: 2025-09-07T07:36:43.3041493Z module (nn.Module): modified (i.e. pruned) version of the input module 2025-09-07T07:36:43.3041497Z 2025-09-07T07:36:43.3041597Z Examples: 2025-09-07T07:36:43.3041711Z >>> from torch.nn.utils import prune 2025-09-07T07:36:43.3041822Z >>> m = prune.custom_from_mask( 2025-09-07T07:36:43.3042009Z ... nn.Linear(5, 3), name="bias", mask=torch.tensor([0, 1, 0]) 2025-09-07T07:36:43.3042095Z ... ) 2025-09-07T07:36:43.3042206Z >>> print(m.bias_mask) 2025-09-07T07:36:43.3042297Z tensor([0., 1., 0.]) 2025-09-07T07:36:43.3042302Z 2025-09-07T07:36:43.3042407Z 2025-09-07T07:36:43.3042690Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:43.3042696Z 2025-09-07T07:36:43.3070377Z msg = Cannot scrape callname=pad_packed_sequence in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/rnn.py line=350. 2025-09-07T07:36:43.3070648Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:43.3070787Z Pad a packed batch of variable length sequences. 2025-09-07T07:36:43.3070792Z 2025-09-07T07:36:43.3070979Z It is an inverse operation to :func:`pack_padded_sequence`. 2025-09-07T07:36:43.3070987Z 2025-09-07T07:36:43.3071265Z The returned Tensor's data will be of size ``T x B x *`` (if :attr:`batch_first` is ``False``) 2025-09-07T07:36:43.3071503Z or ``B x T x *`` (if :attr:`batch_first` is ``True``) , where ``T`` is the length of the longest 2025-09-07T07:36:43.3071632Z sequence and ``B`` is the batch size. 2025-09-07T07:36:43.3071638Z 2025-09-07T07:36:43.3071720Z Example: 2025-09-07T07:36:43.3071969Z >>> from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence 2025-09-07T07:36:43.3072117Z >>> seq = torch.tensor([[1, 2, 0], [3, 0, 0], [4, 5, 6]]) 2025-09-07T07:36:43.3072222Z >>> lens = [2, 1, 3] 2025-09-07T07:36:43.3072334Z >>> packed = pack_padded_sequence( 2025-09-07T07:36:43.3072484Z ... seq, lens, batch_first=True, enforce_sorted=False 2025-09-07T07:36:43.3072576Z ... ) 2025-09-07T07:36:43.3072660Z >>> packed 2025-09-07T07:36:43.3072934Z PackedSequence(data=tensor([4, 1, 3, 5, 2, 6]), batch_sizes=tensor([3, 2, 1]), 2025-09-07T07:36:43.3073163Z sorted_indices=tensor([2, 0, 1]), unsorted_indices=tensor([1, 2, 0])) 2025-09-07T07:36:43.3073406Z >>> seq_unpacked, lens_unpacked = pad_packed_sequence(packed, batch_first=True) 2025-09-07T07:36:43.3073513Z >>> seq_unpacked 2025-09-07T07:36:43.3073605Z tensor([[1, 2, 0], 2025-09-07T07:36:43.3073691Z [3, 0, 0], 2025-09-07T07:36:43.3073786Z [4, 5, 6]]) 2025-09-07T07:36:43.3073877Z >>> lens_unpacked 2025-09-07T07:36:43.3073988Z tensor([2, 1, 3]) 2025-09-07T07:36:43.3073992Z 2025-09-07T07:36:43.3074084Z .. note:: 2025-09-07T07:36:43.3074228Z :attr:`total_length` is useful to implement the 2025-09-07T07:36:43.3074540Z ``pack sequence -> recurrent network -> unpack sequence`` pattern in a 2025-09-07T07:36:43.3074755Z :class:`~torch.nn.Module` wrapped in :class:`~torch.nn.DataParallel`. 2025-09-07T07:36:43.3074999Z See :ref:`this FAQ section ` for 2025-09-07T07:36:43.3075081Z details. 2025-09-07T07:36:43.3075086Z 2025-09-07T07:36:43.3075179Z Args: 2025-09-07T07:36:43.3075342Z sequence (PackedSequence): batch to pad 2025-09-07T07:36:43.3075578Z batch_first (bool, optional): if ``True``, the output will be in ``B x T x *`` 2025-09-07T07:36:43.3075699Z format, ``T x B x *`` otherwise. 2025-09-07T07:36:43.3075888Z padding_value (float, optional): values for padded elements. 2025-09-07T07:36:43.3076155Z total_length (int, optional): if not ``None``, the output will be padded to 2025-09-07T07:36:43.3076388Z have length :attr:`total_length`. This method will throw :class:`ValueError` 2025-09-07T07:36:43.3076572Z if :attr:`total_length` is less than the max sequence length in 2025-09-07T07:36:43.3076680Z :attr:`sequence`. 2025-09-07T07:36:43.3076685Z 2025-09-07T07:36:43.3076769Z Returns: 2025-09-07T07:36:43.3076965Z Tuple of Tensor containing the padded sequence, and a Tensor 2025-09-07T07:36:43.3077161Z containing the list of lengths of each sequence in the batch. 2025-09-07T07:36:43.3077383Z Batch elements will be re-ordered as they were ordered originally when 2025-09-07T07:36:43.3077636Z the batch was passed to ``pack_padded_sequence`` or ``pack_sequence``. 2025-09-07T07:36:43.3077717Z 2025-09-07T07:36:43.3077975Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:43.3077982Z 2025-09-07T07:36:43.3760164Z msg = Cannot scrape callname=SequentialLR in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py line=811. 2025-09-07T07:36:43.3760463Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:43.3760793Z Contains a list of schedulers expected to be called sequentially during the optimization process. 2025-09-07T07:36:43.3760798Z 2025-09-07T07:36:43.3761191Z Specifically, the schedulers will be called according to the milestone points, which should provide exact 2025-09-07T07:36:43.3761407Z intervals by which each scheduler should be called at a given epoch. 2025-09-07T07:36:43.3761415Z 2025-09-07T07:36:43.3761512Z Args: 2025-09-07T07:36:43.3761651Z optimizer (Optimizer): Wrapped optimizer. 2025-09-07T07:36:43.3761795Z schedulers (list): List of chained schedulers. 2025-09-07T07:36:43.3762017Z milestones (list): List of integers that reflects milestone points. 2025-09-07T07:36:43.3762191Z last_epoch (int): The index of last epoch. Default: -1. 2025-09-07T07:36:43.3762195Z 2025-09-07T07:36:43.3762291Z Example: 2025-09-07T07:36:43.3762388Z >>> # xdoctest: +SKIP 2025-09-07T07:36:43.3762541Z >>> # Assuming optimizer uses lr = 0.05 for all groups 2025-09-07T07:36:43.3762659Z >>> # lr = 0.005 if epoch == 0 2025-09-07T07:36:43.3762757Z >>> # lr = 0.005 if epoch == 1 2025-09-07T07:36:43.3763030Z >>> # lr = 0.005 if epoch == 2 2025-09-07T07:36:43.3763117Z >>> # ... 2025-09-07T07:36:43.3763222Z >>> # lr = 0.05 if epoch == 20 2025-09-07T07:36:43.3763338Z >>> # lr = 0.045 if epoch == 21 2025-09-07T07:36:43.3763436Z >>> # lr = 0.0405 if epoch == 22 2025-09-07T07:36:43.3763655Z >>> scheduler1 = ConstantLR(optimizer, factor=0.1, total_iters=20) 2025-09-07T07:36:43.3763809Z >>> scheduler2 = ExponentialLR(optimizer, gamma=0.9) 2025-09-07T07:36:43.3763918Z >>> scheduler = SequentialLR( 2025-09-07T07:36:43.3764025Z ... optimizer, 2025-09-07T07:36:43.3764153Z ... schedulers=[scheduler1, scheduler2], 2025-09-07T07:36:43.3764251Z ... milestones=[20], 2025-09-07T07:36:43.3764345Z ... ) 2025-09-07T07:36:43.3764446Z >>> for epoch in range(100): 2025-09-07T07:36:43.3764549Z >>> train(...) 2025-09-07T07:36:43.3764640Z >>> validate(...) 2025-09-07T07:36:43.3764738Z >>> scheduler.step() 2025-09-07T07:36:43.3764743Z 2025-09-07T07:36:43.3764945Z .. image:: ../scripts/lr_scheduler_images/SequentialLR.png 2025-09-07T07:36:43.3765072Z 2025-09-07T07:36:43.3765336Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:43.3765340Z 2025-09-07T07:36:43.3795769Z msg = Cannot scrape callname=ReduceLROnPlateau in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py line=1236. 2025-09-07T07:36:43.3796139Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-09-07T07:36:43.3796318Z Reduce learning rate when a metric has stopped improving. 2025-09-07T07:36:43.3796323Z 2025-09-07T07:36:43.3796538Z Models often benefit from reducing the learning rate by a factor 2025-09-07T07:36:43.3796722Z of 2-10 once learning stagnates. This scheduler reads a metrics 2025-09-07T07:36:43.3796915Z quantity and if no improvement is seen for a 'patience' number 2025-09-07T07:36:43.3797050Z of epochs, the learning rate is reduced. 2025-09-07T07:36:43.3797055Z 2025-09-07T07:36:43.3797139Z Args: 2025-09-07T07:36:43.3797280Z optimizer (Optimizer): Wrapped optimizer. 2025-09-07T07:36:43.3797435Z mode (str): One of `min`, `max`. In `min` mode, lr will 2025-09-07T07:36:43.3797628Z be reduced when the quantity monitored has stopped 2025-09-07T07:36:43.3797801Z decreasing; in `max` mode it will be reduced when the 2025-09-07T07:36:43.3797993Z quantity monitored has stopped increasing. Default: 'min'. 2025-09-07T07:36:43.3798173Z factor (float): Factor by which the learning rate will be 2025-09-07T07:36:43.3798307Z reduced. new_lr = lr * factor. Default: 0.1. 2025-09-07T07:36:43.3798523Z patience (int): The number of allowed epochs with no improvement after 2025-09-07T07:36:43.3798661Z which the learning rate will be reduced. 2025-09-07T07:36:43.3798887Z For example, consider the case of having no patience (`patience = 0`). 2025-09-07T07:36:43.3799274Z In the first epoch, a baseline is established and is always considered good as there's no previous baseline. 2025-09-07T07:36:43.3799478Z In the second epoch, if the performance is worse than the baseline, 2025-09-07T07:36:43.3799638Z we have what is considered an intolerable epoch. 2025-09-07T07:36:43.3799902Z Since the count of intolerable epochs (1) is greater than the patience level (0), 2025-09-07T07:36:43.3800071Z the learning rate is reduced at the end of this epoch. 2025-09-07T07:36:43.3800405Z From the third epoch onwards, the learning rate continues to be reduced at the end of each epoch 2025-09-07T07:36:43.3800718Z if the performance is worse than the baseline. If the performance improves or remains the same, 2025-09-07T07:36:43.3800848Z the learning rate is not adjusted. 2025-09-07T07:36:43.3800973Z Default: 10. 2025-09-07T07:36:43.3801164Z threshold (float): Threshold for measuring the new optimum, 2025-09-07T07:36:43.3801334Z to only focus on significant changes. Default: 1e-4. 2025-09-07T07:36:43.3801505Z threshold_mode (str): One of `rel`, `abs`. In `rel` mode, 2025-09-07T07:36:43.3801677Z dynamic_threshold = best * ( 1 + threshold ) in 'max' 2025-09-07T07:36:43.3801820Z mode or best * ( 1 - threshold ) in `min` mode. 2025-09-07T07:36:43.3801977Z In `abs` mode, dynamic_threshold = best + threshold in 2025-09-07T07:36:43.3802169Z `max` mode or best - threshold in `min` mode. Default: 'rel'. 2025-09-07T07:36:43.3802340Z cooldown (int): Number of epochs to wait before resuming 2025-09-07T07:36:43.3802559Z normal operation after lr has been reduced. Default: 0. 2025-09-07T07:36:43.3802718Z min_lr (float or list): A scalar or a list of scalars. A 2025-09-07T07:36:43.3802891Z lower bound on the learning rate of all param groups 2025-09-07T07:36:43.3803025Z or each group respectively. Default: 0. 2025-09-07T07:36:43.3803198Z eps (float): Minimal decay applied to lr. If the difference 2025-09-07T07:36:43.3803414Z between new and old lr is smaller than eps, the update is 2025-09-07T07:36:43.3803521Z ignored. Default: 1e-8. 2025-09-07T07:36:43.3803526Z 2025-09-07T07:36:43.3803623Z Example: 2025-09-07T07:36:43.3803719Z >>> # xdoctest: +SKIP 2025-09-07T07:36:43.3803973Z >>> optimizer = torch.optim.SGD(model.parameters(), lr=0.1, momentum=0.9) 2025-09-07T07:36:43.3804136Z >>> scheduler = ReduceLROnPlateau(optimizer, "min") 2025-09-07T07:36:43.3804236Z >>> for epoch in range(10): 2025-09-07T07:36:43.3804338Z >>> train(...) 2025-09-07T07:36:43.3804443Z >>> val_loss = validate(...) 2025-09-07T07:36:43.3804591Z >>> # Note that step should be called after validate() 2025-09-07T07:36:43.3804753Z >>> scheduler.step(val_loss) 2025-09-07T07:36:43.3804758Z 2025-09-07T07:36:43.3805079Z .. image:: ../scripts/lr_scheduler_images/ReduceLROnPlateau.png 2025-09-07T07:36:43.3805230Z 2025-09-07T07:36:43.3805766Z Original Error: IndentationError('unexpected indent', ('', 8, 4, ' scheduler.step(val_loss)\n', 8, -1)) 2025-09-07T07:36:43.3805772Z 2025-09-07T07:36:43.3805879Z scheduler.step(val_loss) 2025-09-07T07:36:43.3805974Z ^ 2025-09-07T07:36:43.3806700Z msg = Cannot scrape callname=CyclicLR in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py line=1433. 2025-09-07T07:36:43.3807203Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:43.3807801Z Sets the learning rate of each parameter group according to cyclical learning rate policy (CLR). 2025-09-07T07:36:43.3807806Z 2025-09-07T07:36:43.3808103Z The policy cycles the learning rate between two boundaries with a constant frequency, 2025-09-07T07:36:43.3808360Z as detailed in the paper `Cyclical Learning Rates for Training Neural Networks`_. 2025-09-07T07:36:43.3808602Z The distance between the two boundaries can be scaled on a per-iteration 2025-09-07T07:36:43.3808702Z or per-cycle basis. 2025-09-07T07:36:43.3808707Z 2025-09-07T07:36:43.3808965Z Cyclical learning rate policy changes the learning rate after every batch. 2025-09-07T07:36:43.3809161Z `step` should be called after a batch has been used for training. 2025-09-07T07:36:43.3809167Z 2025-09-07T07:36:43.3809375Z This class has three built-in policies, as put forth in the paper: 2025-09-07T07:36:43.3809379Z 2025-09-07T07:36:43.3821368Z * "triangular": A basic triangular cycle without amplitude scaling. 2025-09-07T07:36:43.3822011Z * "triangular2": A basic triangular cycle that scales initial amplitude by half each cycle. 2025-09-07T07:36:43.3822705Z * "exp_range": A cycle that scales initial amplitude by :math:`\text{gamma}^{\text{cycle iterations}}` 2025-09-07T07:36:43.3822814Z at each cycle iteration. 2025-09-07T07:36:43.3822821Z 2025-09-07T07:36:43.3823080Z This implementation was adapted from the github repo: `bckenstler/CLR`_ 2025-09-07T07:36:43.3823090Z 2025-09-07T07:36:43.3823175Z Args: 2025-09-07T07:36:43.3823328Z optimizer (Optimizer): Wrapped optimizer. 2025-09-07T07:36:43.3823507Z base_lr (float or list): Initial learning rate which is the 2025-09-07T07:36:43.3823852Z lower boundary in the cycle for each parameter group. 2025-09-07T07:36:43.3824073Z max_lr (float or list): Upper learning rate boundaries in the cycle 2025-09-07T07:36:43.3824203Z for each parameter group. Functionally, 2025-09-07T07:36:43.3824376Z it defines the cycle amplitude (max_lr - base_lr). 2025-09-07T07:36:43.3824503Z The lr at any cycle is the sum of base_lr 2025-09-07T07:36:43.3824642Z and some scaling of the amplitude; therefore 2025-09-07T07:36:43.3824802Z max_lr may not actually be reached depending on 2025-09-07T07:36:43.3824900Z scaling function. 2025-09-07T07:36:43.3825083Z step_size_up (int): Number of training iterations in the 2025-09-07T07:36:43.3825295Z increasing half of a cycle. Default: 2000 2025-09-07T07:36:43.3825473Z step_size_down (int): Number of training iterations in the 2025-09-07T07:36:43.3825655Z decreasing half of a cycle. If step_size_down is None, 2025-09-07T07:36:43.3825832Z it is set to step_size_up. Default: None 2025-09-07T07:36:43.3826007Z mode (str): One of {triangular, triangular2, exp_range}. 2025-09-07T07:36:43.3826154Z Values correspond to policies detailed above. 2025-09-07T07:36:43.3826301Z If scale_fn is not None, this argument is ignored. 2025-09-07T07:36:43.3826417Z Default: 'triangular' 2025-09-07T07:36:43.3826581Z gamma (float): Constant in 'exp_range' scaling function: 2025-09-07T07:36:43.3826702Z gamma**(cycle iterations) 2025-09-07T07:36:43.3826796Z Default: 1.0 2025-09-07T07:36:43.3826984Z scale_fn (function): Custom scaling policy defined by a single 2025-09-07T07:36:43.3827119Z argument lambda function, where 2025-09-07T07:36:43.3827234Z 0 <= scale_fn(x) <= 1 for all x >= 0. 2025-09-07T07:36:43.3827411Z If specified, then 'mode' is ignored. 2025-09-07T07:36:43.3827505Z Default: None 2025-09-07T07:36:43.3827633Z scale_mode (str): {'cycle', 'iterations'}. 2025-09-07T07:36:43.3827774Z Defines whether scale_fn is evaluated on 2025-09-07T07:36:43.3827903Z cycle number or cycle iterations (training 2025-09-07T07:36:43.3828031Z iterations since start of cycle). 2025-09-07T07:36:43.3828126Z Default: 'cycle' 2025-09-07T07:36:43.3828322Z cycle_momentum (bool): If ``True``, momentum is cycled inversely 2025-09-07T07:36:43.3828521Z to learning rate between 'base_momentum' and 'max_momentum'. 2025-09-07T07:36:43.3828664Z Default: True 2025-09-07T07:36:43.3828897Z base_momentum (float or list): Lower momentum boundaries in the cycle 2025-09-07T07:36:43.3829102Z for each parameter group. Note that momentum is cycled inversely 2025-09-07T07:36:43.3829266Z to learning rate; at the peak of a cycle, momentum is 2025-09-07T07:36:43.3829415Z 'base_momentum' and learning rate is 'max_lr'. 2025-09-07T07:36:43.3829506Z Default: 0.8 2025-09-07T07:36:43.3829729Z max_momentum (float or list): Upper momentum boundaries in the cycle 2025-09-07T07:36:43.3829857Z for each parameter group. Functionally, 2025-09-07T07:36:43.3830049Z it defines the cycle amplitude (max_momentum - base_momentum). 2025-09-07T07:36:43.3830240Z The momentum at any cycle is the difference of max_momentum 2025-09-07T07:36:43.3830413Z and some scaling of the amplitude; therefore 2025-09-07T07:36:43.3830593Z base_momentum may not actually be reached depending on 2025-09-07T07:36:43.3830771Z scaling function. Note that momentum is cycled inversely 2025-09-07T07:36:43.3830980Z to learning rate; at the start of a cycle, momentum is 'max_momentum' 2025-09-07T07:36:43.3831118Z and learning rate is 'base_lr' 2025-09-07T07:36:43.3831209Z Default: 0.9 2025-09-07T07:36:43.3831474Z last_epoch (int): The index of the last batch. This parameter is used when 2025-09-07T07:36:43.3831690Z resuming a training job. Since `step()` should be invoked after each 2025-09-07T07:36:43.3831913Z batch instead of after each epoch, this number represents the total 2025-09-07T07:36:43.3832131Z number of *batches* computed, not the total number of epochs computed. 2025-09-07T07:36:43.3832335Z When last_epoch=-1, the schedule is started from the beginning. 2025-09-07T07:36:43.3832434Z Default: -1 2025-09-07T07:36:43.3832439Z 2025-09-07T07:36:43.3832522Z Example: 2025-09-07T07:36:43.3832631Z >>> # xdoctest: +SKIP 2025-09-07T07:36:43.3832855Z >>> optimizer = torch.optim.SGD(model.parameters(), lr=0.1, momentum=0.9) 2025-09-07T07:36:43.3833048Z >>> scheduler = torch.optim.lr_scheduler.CyclicLR( 2025-09-07T07:36:43.3833142Z ... optimizer, 2025-09-07T07:36:43.3833234Z ... base_lr=0.01, 2025-09-07T07:36:43.3833335Z ... max_lr=0.1, 2025-09-07T07:36:43.3833460Z ... step_size_up=10, 2025-09-07T07:36:43.3833542Z ... ) 2025-09-07T07:36:43.3833704Z >>> data_loader = torch.utils.data.DataLoader(...) 2025-09-07T07:36:43.3833805Z >>> for epoch in range(10): 2025-09-07T07:36:43.3833972Z >>> for batch in data_loader: 2025-09-07T07:36:43.3834122Z >>> train_batch(...) 2025-09-07T07:36:43.3834291Z >>> scheduler.step() 2025-09-07T07:36:43.3834299Z 2025-09-07T07:36:43.3834649Z .. image:: ../scripts/lr_scheduler_images/CyclicLR.png 2025-09-07T07:36:43.3834659Z 2025-09-07T07:36:43.3834999Z .. _Cyclical Learning Rates for Training Neural Networks: https://arxiv.org/abs/1506.01186 2025-09-07T07:36:43.3835237Z .. _bckenstler/CLR: https://github.com/bckenstler/CLR 2025-09-07T07:36:43.3835359Z 2025-09-07T07:36:43.3835846Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:43.3835853Z 2025-09-07T07:36:43.3836887Z msg = Cannot scrape callname=CosineAnnealingWarmRestarts in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py line=1725. 2025-09-07T07:36:43.3837152Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:43.3837418Z Set the learning rate of each parameter group using a cosine annealing schedule. 2025-09-07T07:36:43.3837422Z 2025-09-07T07:36:43.3837601Z The :math:`\eta_{max}` is set to the initial lr, :math:`T_{cur}` 2025-09-07T07:36:43.3837854Z is the number of epochs since the last restart and :math:`T_{i}` is the number 2025-09-07T07:36:43.3837986Z of epochs between two warm restarts in SGDR: 2025-09-07T07:36:43.3837993Z 2025-09-07T07:36:43.3838092Z .. math:: 2025-09-07T07:36:43.3838273Z \eta_t = \eta_{min} + \frac{1}{2}(\eta_{max} - \eta_{min})\left(1 + 2025-09-07T07:36:43.3838411Z \cos\left(\frac{T_{cur}}{T_{i}}\pi\right)\right) 2025-09-07T07:36:43.3838416Z 2025-09-07T07:36:43.3838594Z When :math:`T_{cur}=T_{i}`, set :math:`\eta_t = \eta_{min}`. 2025-09-07T07:36:43.3838781Z When :math:`T_{cur}=0` after restart, set :math:`\eta_t=\eta_{max}`. 2025-09-07T07:36:43.3838785Z 2025-09-07T07:36:43.3838899Z It has been proposed in 2025-09-07T07:36:43.3839067Z `SGDR: Stochastic Gradient Descent with Warm Restarts`_. 2025-09-07T07:36:43.3839072Z 2025-09-07T07:36:43.3839153Z Args: 2025-09-07T07:36:43.3839297Z optimizer (Optimizer): Wrapped optimizer. 2025-09-07T07:36:43.3839492Z T_0 (int): Number of iterations until the first restart. 2025-09-07T07:36:43.3839787Z T_mult (int, optional): A factor by which :math:`T_{i}` increases after a restart. Default: 1. 2025-09-07T07:36:43.3839973Z eta_min (float, optional): Minimum learning rate. Default: 0. 2025-09-07T07:36:43.3840187Z last_epoch (int, optional): The index of the last epoch. Default: -1. 2025-09-07T07:36:43.3840203Z 2025-09-07T07:36:43.3840368Z .. _SGDR\: Stochastic Gradient Descent with Warm Restarts: 2025-09-07T07:36:43.3840486Z https://arxiv.org/abs/1608.03983 2025-09-07T07:36:43.3840490Z 2025-09-07T07:36:43.3840587Z Example: 2025-09-07T07:36:43.3840688Z >>> # xdoctest: +SKIP 2025-09-07T07:36:43.3840881Z >>> optimizer = torch.optim.SGD(model.parameters(), lr=0.05) 2025-09-07T07:36:43.3841106Z >>> scheduler = torch.optim.lr_scheduler.CosineAnnealingWarmRestarts( 2025-09-07T07:36:43.3841207Z ... optimizer, T_0=20 2025-09-07T07:36:43.3841303Z ... ) 2025-09-07T07:36:43.3841404Z >>> for epoch in range(100): 2025-09-07T07:36:43.3841505Z >>> train(...) 2025-09-07T07:36:43.3841602Z >>> validate(...) 2025-09-07T07:36:43.3841752Z >>> scheduler.step() 2025-09-07T07:36:43.3841756Z 2025-09-07T07:36:43.3842002Z .. image:: ../scripts/lr_scheduler_images/CosineAnnealingWarmRestarts.png 2025-09-07T07:36:43.3842083Z 2025-09-07T07:36:43.3842342Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:43.3842374Z 2025-09-07T07:36:43.3842935Z msg = Cannot scrape callname=OneCycleLR in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py line=1875. 2025-09-07T07:36:43.3843207Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:43.3843509Z Sets the learning rate of each parameter group according to the 1cycle learning rate policy. 2025-09-07T07:36:43.3843513Z 2025-09-07T07:36:43.3843824Z The 1cycle policy anneals the learning rate from an initial learning rate to some maximum 2025-09-07T07:36:43.3844114Z learning rate and then from that maximum learning rate to some minimum learning rate much 2025-09-07T07:36:43.3844232Z lower than the initial learning rate. 2025-09-07T07:36:43.3844499Z This policy was initially described in the paper `Super-Convergence: 2025-09-07T07:36:43.3844707Z Very Fast Training of Neural Networks Using Large Learning Rates`_. 2025-09-07T07:36:43.3844713Z 2025-09-07T07:36:43.3844971Z The 1cycle learning rate policy changes the learning rate after every batch. 2025-09-07T07:36:43.3845169Z `step` should be called after a batch has been used for training. 2025-09-07T07:36:43.3845173Z 2025-09-07T07:36:43.3845294Z This scheduler is not chainable. 2025-09-07T07:36:43.3845298Z 2025-09-07T07:36:43.3845539Z Note also that the total number of steps in the cycle can be determined in one 2025-09-07T07:36:43.3845674Z of two ways (listed in order of precedence): 2025-09-07T07:36:43.3845679Z 2025-09-07T07:36:43.3845838Z #. A value for total_steps is explicitly provided. 2025-09-07T07:36:43.3846020Z #. A number of epochs (epochs) and a number of steps per epoch 2025-09-07T07:36:43.3846146Z (steps_per_epoch) are provided. 2025-09-07T07:36:43.3846310Z In this case, the number of total steps is inferred by 2025-09-07T07:36:43.3846431Z total_steps = epochs * steps_per_epoch 2025-09-07T07:36:43.3846436Z 2025-09-07T07:36:43.3846689Z You must either provide a value for total_steps or provide a value for both 2025-09-07T07:36:43.3846794Z epochs and steps_per_epoch. 2025-09-07T07:36:43.3846798Z 2025-09-07T07:36:43.3847112Z The default behaviour of this scheduler follows the fastai implementation of 1cycle, which 2025-09-07T07:36:43.3847404Z claims that "unpublished work has shown even better results by using only two phases". To 2025-09-07T07:36:43.3847681Z mimic the behaviour of the original paper instead, set ``three_phase=True``. 2025-09-07T07:36:43.3847686Z 2025-09-07T07:36:43.3847768Z Args: 2025-09-07T07:36:43.3847898Z optimizer (Optimizer): Wrapped optimizer. 2025-09-07T07:36:43.3848114Z max_lr (float or list): Upper learning rate boundaries in the cycle 2025-09-07T07:36:43.3848222Z for each parameter group. 2025-09-07T07:36:43.3848438Z total_steps (int): The total number of steps in the cycle. Note that 2025-09-07T07:36:43.3848646Z if a value is not provided here, then it must be inferred by providing 2025-09-07T07:36:43.3848776Z a value for epochs and steps_per_epoch. 2025-09-07T07:36:43.3848881Z Default: None 2025-09-07T07:36:43.3849080Z epochs (int): The number of epochs to train for. This is used along 2025-09-07T07:36:43.3849338Z with steps_per_epoch in order to infer the total number of steps in the cycle 2025-09-07T07:36:43.3849470Z if a value for total_steps is not provided. 2025-09-07T07:36:43.3849565Z Default: None 2025-09-07T07:36:43.3849802Z steps_per_epoch (int): The number of steps per epoch to train for. This is 2025-09-07T07:36:43.3850033Z used along with epochs in order to infer the total number of steps in the 2025-09-07T07:36:43.3850222Z cycle if a value for total_steps is not provided. 2025-09-07T07:36:43.3850312Z Default: None 2025-09-07T07:36:43.3850536Z pct_start (float): The percentage of the cycle (in number of steps) spent 2025-09-07T07:36:43.3850677Z increasing the learning rate. 2025-09-07T07:36:43.3850765Z Default: 0.3 2025-09-07T07:36:43.3850900Z anneal_strategy (str): {'cos', 'linear'} 2025-09-07T07:36:43.3851147Z Specifies the annealing strategy: "cos" for cosine annealing, "linear" for 2025-09-07T07:36:43.3851245Z linear annealing. 2025-09-07T07:36:43.3851353Z Default: 'cos' 2025-09-07T07:36:43.3851549Z cycle_momentum (bool): If ``True``, momentum is cycled inversely 2025-09-07T07:36:43.3851748Z to learning rate between 'base_momentum' and 'max_momentum'. 2025-09-07T07:36:43.3851840Z Default: True 2025-09-07T07:36:43.3852056Z base_momentum (float or list): Lower momentum boundaries in the cycle 2025-09-07T07:36:43.3852293Z for each parameter group. Note that momentum is cycled inversely 2025-09-07T07:36:43.3852454Z to learning rate; at the peak of a cycle, momentum is 2025-09-07T07:36:43.3852612Z 'base_momentum' and learning rate is 'max_lr'. 2025-09-07T07:36:43.3852704Z Default: 0.85 2025-09-07T07:36:43.3852918Z max_momentum (float or list): Upper momentum boundaries in the cycle 2025-09-07T07:36:43.3853060Z for each parameter group. Functionally, 2025-09-07T07:36:43.3853288Z it defines the cycle amplitude (max_momentum - base_momentum). 2025-09-07T07:36:43.3853427Z Note that momentum is cycled inversely 2025-09-07T07:36:43.3853636Z to learning rate; at the start of a cycle, momentum is 'max_momentum' 2025-09-07T07:36:43.3853746Z and learning rate is 'base_lr' 2025-09-07T07:36:43.3853853Z Default: 0.95 2025-09-07T07:36:43.3854034Z div_factor (float): Determines the initial learning rate via 2025-09-07T07:36:43.3854149Z initial_lr = max_lr/div_factor 2025-09-07T07:36:43.3854237Z Default: 25 2025-09-07T07:36:43.3854443Z final_div_factor (float): Determines the minimum learning rate via 2025-09-07T07:36:43.3854576Z min_lr = initial_lr/final_div_factor 2025-09-07T07:36:43.3854670Z Default: 1e4 2025-09-07T07:36:43.3854926Z three_phase (bool): If ``True``, use a third phase of the schedule to annihilate the 2025-09-07T07:36:43.3855176Z learning rate according to 'final_div_factor' instead of modifying the second 2025-09-07T07:36:43.3855440Z phase (the first two phases will be symmetrical about the step indicated by 2025-09-07T07:36:43.3855546Z 'pct_start'). 2025-09-07T07:36:43.3855765Z last_epoch (int): The index of the last batch. This parameter is used when 2025-09-07T07:36:43.3855998Z resuming a training job. Since `step()` should be invoked after each 2025-09-07T07:36:43.3856210Z batch instead of after each epoch, this number represents the total 2025-09-07T07:36:43.3856429Z number of *batches* computed, not the total number of epochs computed. 2025-09-07T07:36:43.3856636Z When last_epoch=-1, the schedule is started from the beginning. 2025-09-07T07:36:43.3856729Z Default: -1 2025-09-07T07:36:43.3856734Z 2025-09-07T07:36:43.3856834Z Example: 2025-09-07T07:36:43.3856930Z >>> # xdoctest: +SKIP 2025-09-07T07:36:43.3857077Z >>> data_loader = torch.utils.data.DataLoader(...) 2025-09-07T07:36:43.3857323Z >>> optimizer = torch.optim.SGD(model.parameters(), lr=1e-4, momentum=0.9) 2025-09-07T07:36:43.3857479Z >>> scheduler = torch.optim.lr_scheduler.OneCycleLR( 2025-09-07T07:36:43.3857703Z ... optimizer, max_lr=0.01, steps_per_epoch=len(data_loader), epochs=10 2025-09-07T07:36:43.3857813Z ... ) 2025-09-07T07:36:43.3857916Z >>> for epoch in range(10): 2025-09-07T07:36:43.3858037Z >>> for batch in data_loader: 2025-09-07T07:36:43.3858133Z >>> train_batch(...) 2025-09-07T07:36:43.3858245Z >>> optimizer.step() 2025-09-07T07:36:43.3858367Z >>> scheduler.step() 2025-09-07T07:36:43.3858371Z 2025-09-07T07:36:43.3858550Z .. image:: ../scripts/lr_scheduler_images/OneCycleLR.png 2025-09-07T07:36:43.3858555Z 2025-09-07T07:36:43.3858845Z .. _Super-Convergence\: Very Fast Training of Neural Networks Using Large Learning Rates: 2025-09-07T07:36:43.3858961Z https://arxiv.org/abs/1708.07120 2025-09-07T07:36:43.3859054Z 2025-09-07T07:36:43.3859308Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:43.3859312Z 2025-09-07T07:36:43.3966068Z msg = Cannot scrape callname=Optimizer.load_state_dict in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/optimizer.py line=868. 2025-09-07T07:36:43.3966337Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:43.3966554Z Load the optimizer state. 2025-09-07T07:36:43.3966560Z 2025-09-07T07:36:43.3966642Z Args: 2025-09-07T07:36:43.3966840Z state_dict (dict): optimizer state. Should be an object returned 2025-09-07T07:36:43.3966974Z from a call to :meth:`state_dict`. 2025-09-07T07:36:43.3966979Z 2025-09-07T07:36:43.3967071Z .. warning:: 2025-09-07T07:36:43.3967427Z Make sure this method is called after initializing :class:`torch.optim.lr_scheduler.LRScheduler`, 2025-09-07T07:36:43.3967637Z as calling it beforehand will overwrite the loaded learning rates. 2025-09-07T07:36:43.3967644Z 2025-09-07T07:36:43.3967729Z .. note:: 2025-09-07T07:36:43.3968034Z The names of the parameters (if they exist under the "param_names" key of each param group 2025-09-07T07:36:43.3968208Z in :meth:`state_dict`) will not affect the loading process. 2025-09-07T07:36:43.3968544Z To use the parameters' names for custom cases (such as when the parameters in the loaded state dict 2025-09-07T07:36:43.3968702Z differ from those initialized in the optimizer), 2025-09-07T07:36:43.3969018Z a custom ``register_load_state_dict_pre_hook`` should be implemented to adapt the loaded dict 2025-09-07T07:36:43.3969124Z accordingly. 2025-09-07T07:36:43.3969412Z If ``param_names`` exist in loaded state dict ``param_groups`` they will be saved and override 2025-09-07T07:36:43.3969740Z the current names, if present, in the optimizer state. If they do not exist in loaded state dict, 2025-09-07T07:36:43.3969946Z the optimizer ``param_names`` will remain unchanged. 2025-09-07T07:36:43.3969951Z 2025-09-07T07:36:43.3970051Z Example: 2025-09-07T07:36:43.3970151Z >>> # xdoctest: +SKIP 2025-09-07T07:36:43.3970283Z >>> model = torch.nn.Linear(10, 10) 2025-09-07T07:36:43.3970449Z >>> optim = torch.optim.SGD(model.parameters(), lr=3e-4) 2025-09-07T07:36:43.3970608Z >>> scheduler1 = torch.optim.lr_scheduler.LinearLR( 2025-09-07T07:36:43.3970710Z ... optim, 2025-09-07T07:36:43.3970811Z ... start_factor=0.1, 2025-09-07T07:36:43.3970904Z ... end_factor=1, 2025-09-07T07:36:43.3971015Z ... total_iters=20, 2025-09-07T07:36:43.3971097Z ... ) 2025-09-07T07:36:43.3971295Z >>> scheduler2 = torch.optim.lr_scheduler.CosineAnnealingLR( 2025-09-07T07:36:43.3971383Z ... optim, 2025-09-07T07:36:43.3971474Z ... T_max=80, 2025-09-07T07:36:43.3971581Z ... eta_min=3e-5, 2025-09-07T07:36:43.3971664Z ... ) 2025-09-07T07:36:43.3971818Z >>> lr = torch.optim.lr_scheduler.SequentialLR( 2025-09-07T07:36:43.3971908Z ... optim, 2025-09-07T07:36:43.3972085Z ... schedulers=[scheduler1, scheduler2], 2025-09-07T07:36:43.3972197Z ... milestones=[20], 2025-09-07T07:36:43.3972280Z ... ) 2025-09-07T07:36:43.3972442Z >>> lr.load_state_dict(torch.load("./save_seq.pt")) 2025-09-07T07:36:43.3972647Z >>> # now load the optimizer checkpoint after loading the LRScheduler 2025-09-07T07:36:43.3972862Z >>> optim.load_state_dict(torch.load("./save_optim.pt")) 2025-09-07T07:36:43.3972867Z 2025-09-07T07:36:43.3972960Z 2025-09-07T07:36:43.3973212Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:43.3973216Z 2025-09-07T07:36:43.4131320Z msg = Cannot scrape callname=AveragedModel in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/swa_utils.py line=120. 2025-09-07T07:36:43.4131794Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:43.4132174Z Implements averaged model for Stochastic Weight Averaging (SWA) and Exponential Moving Average (EMA). 2025-09-07T07:36:43.4132183Z 2025-09-07T07:36:43.4132514Z Stochastic Weight Averaging was proposed in `Averaging Weights Leads to 2025-09-07T07:36:43.4132747Z Wider Optima and Better Generalization`_ by Pavel Izmailov, Dmitrii 2025-09-07T07:36:43.4132967Z Podoprikhin, Timur Garipov, Dmitry Vetrov and Andrew Gordon Wilson 2025-09-07T07:36:43.4133053Z (UAI 2018). 2025-09-07T07:36:43.4133057Z 2025-09-07T07:36:43.4133285Z Exponential Moving Average is a variation of `Polyak averaging`_, 2025-09-07T07:36:43.4133525Z but using exponential weights instead of equal weights across iterations. 2025-09-07T07:36:43.4133529Z 2025-09-07T07:36:43.4133781Z AveragedModel class creates a copy of the provided module :attr:`model` 2025-09-07T07:36:43.4134001Z on the device :attr:`device` and allows to compute running averages of the 2025-09-07T07:36:43.4134109Z parameters of the :attr:`model`. 2025-09-07T07:36:43.4134128Z 2025-09-07T07:36:43.4134212Z Args: 2025-09-07T07:36:43.4134362Z model (torch.nn.Module): model to use with SWA/EMA 2025-09-07T07:36:43.4134609Z device (torch.device, optional): if provided, the averaged model will be 2025-09-07T07:36:43.4134715Z stored on the :attr:`device` 2025-09-07T07:36:43.4134928Z avg_fn (function, optional): the averaging function used to update 2025-09-07T07:36:43.4135173Z parameters; the function must take in the current value of the 2025-09-07T07:36:43.4135391Z :class:`AveragedModel` parameter, the current value of :attr:`model` 2025-09-07T07:36:43.4135595Z parameter, and the number of models already averaged; if None, 2025-09-07T07:36:43.4135823Z an equally weighted average is used (default: None) 2025-09-07T07:36:43.4136065Z multi_avg_fn (function, optional): the averaging function used to update 2025-09-07T07:36:43.4136300Z parameters inplace; the function must take in the current values of the 2025-09-07T07:36:43.4136570Z :class:`AveragedModel` parameters as a list, the current values of :attr:`model` 2025-09-07T07:36:43.4136809Z parameters as a list, and the number of models already averaged; if None, 2025-09-07T07:36:43.4136967Z an equally weighted average is used (default: None) 2025-09-07T07:36:43.4137186Z use_buffers (bool): if ``True``, it will compute running averages for 2025-09-07T07:36:43.4137408Z both the parameters and the buffers of the model. (default: ``False``) 2025-09-07T07:36:43.4137412Z 2025-09-07T07:36:43.4137497Z Example: 2025-09-07T07:36:43.4137643Z >>> # xdoctest: +SKIP("undefined variables") 2025-09-07T07:36:43.4137771Z >>> loader, optimizer, model, loss_fn = ... 2025-09-07T07:36:43.4137954Z >>> swa_model = torch.optim.swa_utils.AveragedModel(model) 2025-09-07T07:36:43.4138175Z >>> scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, 2025-09-07T07:36:43.4138323Z >>> T_max=300) 2025-09-07T07:36:43.4138430Z >>> swa_start = 160 2025-09-07T07:36:43.4138570Z >>> swa_scheduler = SWALR(optimizer, swa_lr=0.05) 2025-09-07T07:36:43.4138681Z >>> for i in range(300): 2025-09-07T07:36:43.4138843Z >>> for input, target in loader: 2025-09-07T07:36:43.4138953Z >>> optimizer.zero_grad() 2025-09-07T07:36:43.4139098Z >>> loss_fn(model(input), target).backward() 2025-09-07T07:36:43.4139204Z >>> optimizer.step() 2025-09-07T07:36:43.4139314Z >>> if i > swa_start: 2025-09-07T07:36:43.4139440Z >>> swa_model.update_parameters(model) 2025-09-07T07:36:43.4139550Z >>> swa_scheduler.step() 2025-09-07T07:36:43.4139652Z >>> else: 2025-09-07T07:36:43.4139774Z >>> scheduler.step() 2025-09-07T07:36:43.4139929Z >>> 2025-09-07T07:36:43.4140134Z >>> # Update bn statistics for the swa_model at the end 2025-09-07T07:36:43.4140308Z >>> torch.optim.swa_utils.update_bn(loader, swa_model) 2025-09-07T07:36:43.4140347Z 2025-09-07T07:36:43.4140721Z You can also use custom averaging functions with the `avg_fn` or `multi_avg_fn` parameters. 2025-09-07T07:36:43.4141041Z If no averaging function is provided, the default is to compute 2025-09-07T07:36:43.4141335Z equally-weighted average of the weights (SWA). 2025-09-07T07:36:43.4141343Z 2025-09-07T07:36:43.4141503Z Example: 2025-09-07T07:36:43.4141761Z >>> # xdoctest: +SKIP("undefined variables") 2025-09-07T07:36:43.4142006Z >>> # Compute exponential moving averages of the weights and buffers 2025-09-07T07:36:43.4142189Z >>> ema_model = torch.optim.swa_utils.AveragedModel(model, 2025-09-07T07:36:43.4142403Z >>> torch.optim.swa_utils.get_ema_multi_avg_fn(0.9), use_buffers=True) 2025-09-07T07:36:43.4142408Z 2025-09-07T07:36:43.4142501Z .. note:: 2025-09-07T07:36:43.4142732Z When using SWA/EMA with models containing Batch Normalization you may 2025-09-07T07:36:43.4142938Z need to update the activation statistics for Batch Normalization. 2025-09-07T07:36:43.4143181Z This can be done either by using the :meth:`torch.optim.swa_utils.update_bn` 2025-09-07T07:36:43.4143408Z or by setting :attr:`use_buffers` to `True`. The first approach updates the 2025-09-07T07:36:43.4143690Z statistics in a post-training step by passing data through the model. The 2025-09-07T07:36:43.4143944Z second does it during the parameter update phase by averaging all buffers. 2025-09-07T07:36:43.4144188Z Empirical evidence has shown that updating the statistics in normalization 2025-09-07T07:36:43.4144460Z layers increases accuracy, but you may wish to empirically test which 2025-09-07T07:36:43.4144614Z approach yields the best results in your problem. 2025-09-07T07:36:43.4144619Z 2025-09-07T07:36:43.4144717Z .. note:: 2025-09-07T07:36:43.4144968Z :attr:`avg_fn` and `multi_avg_fn` are not saved in the :meth:`state_dict` of the model. 2025-09-07T07:36:43.4144975Z 2025-09-07T07:36:43.4145059Z .. note:: 2025-09-07T07:36:43.4145267Z When :meth:`update_parameters` is called for the first time (i.e. 2025-09-07T07:36:43.4145452Z :attr:`n_averaged` is `0`) the parameters of `model` are copied 2025-09-07T07:36:43.4145666Z to the parameters of :class:`AveragedModel`. For every subsequent 2025-09-07T07:36:43.4145849Z call of :meth:`update_parameters` the function `avg_fn` is used 2025-09-07T07:36:43.4145953Z to update the parameters. 2025-09-07T07:36:43.4145957Z 2025-09-07T07:36:43.4146187Z .. _Averaging Weights Leads to Wider Optima and Better Generalization: 2025-09-07T07:36:43.4146314Z https://arxiv.org/abs/1803.05407 2025-09-07T07:36:43.4146560Z .. _There Are Many Consistent Explanations of Unlabeled Data: Why You Should 2025-09-07T07:36:43.4146676Z Average: 2025-09-07T07:36:43.4146788Z https://arxiv.org/abs/1806.05594 2025-09-07T07:36:43.4146999Z .. _SWALP: Stochastic Weight Averaging in Low-Precision Training: 2025-09-07T07:36:43.4147108Z https://arxiv.org/abs/1904.11943 2025-09-07T07:36:43.4147338Z .. _Stochastic Weight Averaging in Parallel: Large-Batch Training That 2025-09-07T07:36:43.4147463Z Generalizes Well: 2025-09-07T07:36:43.4147572Z https://arxiv.org/abs/2001.02312 2025-09-07T07:36:43.4147682Z .. _Polyak averaging: 2025-09-07T07:36:43.4147849Z https://paperswithcode.com/method/polyak-averaging 2025-09-07T07:36:43.4147943Z 2025-09-07T07:36:43.4148193Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:43.4148197Z 2025-09-07T07:36:43.4148708Z msg = Cannot scrape callname=SWALR in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/swa_utils.py line=375. 2025-09-07T07:36:43.4148964Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:43.4149178Z Anneals the learning rate in each parameter group to a fixed value. 2025-09-07T07:36:43.4149210Z 2025-09-07T07:36:43.4149452Z This learning rate scheduler is meant to be used with Stochastic Weight 2025-09-07T07:36:43.4149660Z Averaging (SWA) method (see `torch.optim.swa_utils.AveragedModel`). 2025-09-07T07:36:43.4149666Z 2025-09-07T07:36:43.4149762Z Args: 2025-09-07T07:36:43.4149934Z optimizer (torch.optim.Optimizer): wrapped optimizer 2025-09-07T07:36:43.4150152Z swa_lrs (float or list): the learning rate value for all param groups 2025-09-07T07:36:43.4150280Z together or separately for each group. 2025-09-07T07:36:43.4150480Z annealing_epochs (int): number of epochs in the annealing phase 2025-09-07T07:36:43.4150586Z (default: 10) 2025-09-07T07:36:43.4150797Z annealing_strategy (str): "cos" or "linear"; specifies the annealing 2025-09-07T07:36:43.4151018Z strategy: "cos" for cosine annealing, "linear" for linear annealing 2025-09-07T07:36:43.4151114Z (default: "cos") 2025-09-07T07:36:43.4151296Z last_epoch (int): the index of the last epoch (default: -1) 2025-09-07T07:36:43.4151301Z 2025-09-07T07:36:43.4151489Z The :class:`SWALR` scheduler can be used together with other 2025-09-07T07:36:43.4151707Z schedulers to switch to a constant learning rate late in the training 2025-09-07T07:36:43.4151820Z as in the example below. 2025-09-07T07:36:43.4151824Z 2025-09-07T07:36:43.4151906Z Example: 2025-09-07T07:36:43.4152032Z >>> # xdoctest: +SKIP("Undefined variables") 2025-09-07T07:36:43.4152157Z >>> loader, optimizer, model = ... 2025-09-07T07:36:43.4152264Z >>> lr_lambda = lambda epoch: 0.9 2025-09-07T07:36:43.4152522Z >>> scheduler = torch.optim.lr_scheduler.MultiplicativeLR(optimizer, 2025-09-07T07:36:43.4152623Z >>> lr_lambda=lr_lambda) 2025-09-07T07:36:43.4152794Z >>> swa_scheduler = torch.optim.swa_utils.SWALR(optimizer, 2025-09-07T07:36:43.4152983Z >>> anneal_strategy="linear", anneal_epochs=20, swa_lr=0.05) 2025-09-07T07:36:43.4153076Z >>> swa_start = 160 2025-09-07T07:36:43.4153185Z >>> for i in range(300): 2025-09-07T07:36:43.4153294Z >>> for input, target in loader: 2025-09-07T07:36:43.4153404Z >>> optimizer.zero_grad() 2025-09-07T07:36:43.4153550Z >>> loss_fn(model(input), target).backward() 2025-09-07T07:36:43.4153652Z >>> optimizer.step() 2025-09-07T07:36:43.4153758Z >>> if i > swa_start: 2025-09-07T07:36:43.4153862Z >>> swa_scheduler.step() 2025-09-07T07:36:43.4153946Z >>> else: 2025-09-07T07:36:43.4154059Z >>> scheduler.step() 2025-09-07T07:36:43.4154063Z 2025-09-07T07:36:43.4154282Z .. _Averaging Weights Leads to Wider Optima and Better Generalization: 2025-09-07T07:36:43.4154486Z https://arxiv.org/abs/1803.05407 2025-09-07T07:36:43.4154600Z 2025-09-07T07:36:43.4154854Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:43.4154858Z 2025-09-07T07:36:43.8893186Z msg = Cannot scrape callname=assert_close in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_comparison.py line=1331. 2025-09-07T07:36:43.8894571Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:43.8895104Z Asserts that ``actual`` and ``expected`` are close. 2025-09-07T07:36:43.8895365Z 2025-09-07T07:36:43.8895730Z If ``actual`` and ``expected`` are strided, non-quantized, real-valued, and finite, they are considered close if 2025-09-07T07:36:43.8896222Z 2025-09-07T07:36:43.8896326Z .. math:: 2025-09-07T07:36:43.8896469Z 2025-09-07T07:36:43.8896845Z \lvert \text{actual} - \text{expected} \rvert \le \texttt{atol} + \texttt{rtol} \cdot \lvert \text{expected} \rvert 2025-09-07T07:36:43.8897337Z 2025-09-07T07:36:43.8897690Z Non-finite values (``-inf`` and ``inf``) are only considered close if and only if they are equal. ``NaN``'s are 2025-09-07T07:36:43.8898428Z only considered equal to each other if ``equal_nan`` is ``True``. 2025-09-07T07:36:43.8898747Z 2025-09-07T07:36:43.8898958Z In addition, they are only considered close if they have the same 2025-09-07T07:36:43.8899276Z 2025-09-07T07:36:43.8899468Z - :attr:`~torch.Tensor.device` (if ``check_device`` is ``True``), 2025-09-07T07:36:43.8899908Z - ``dtype`` (if ``check_dtype`` is ``True``), 2025-09-07T07:36:43.8900293Z - ``layout`` (if ``check_layout`` is ``True``), and 2025-09-07T07:36:43.8900680Z - stride (if ``check_stride`` is ``True``). 2025-09-07T07:36:43.8900913Z 2025-09-07T07:36:43.8901211Z If either ``actual`` or ``expected`` is a meta tensor, only the attribute checks will be performed. 2025-09-07T07:36:43.8901639Z 2025-09-07T07:36:43.8902001Z If ``actual`` and ``expected`` are sparse (either having COO, CSR, CSC, BSR, or BSC layout), their strided members are 2025-09-07T07:36:43.8902864Z checked individually. Indices, namely ``indices`` for COO, ``crow_indices`` and ``col_indices`` for CSR and BSR, 2025-09-07T07:36:43.8903587Z or ``ccol_indices`` and ``row_indices`` for CSC and BSC layouts, respectively, 2025-09-07T07:36:43.8904320Z are always checked for equality whereas the values are checked for closeness according to the definition above. 2025-09-07T07:36:43.8904816Z 2025-09-07T07:36:43.8905106Z If ``actual`` and ``expected`` are quantized, they are considered close if they have the same 2025-09-07T07:36:43.8905848Z :meth:`~torch.Tensor.qscheme` and the result of :meth:`~torch.Tensor.dequantize` is close according to the 2025-09-07T07:36:43.8906417Z definition above. 2025-09-07T07:36:43.8906640Z 2025-09-07T07:36:43.8906995Z ``actual`` and ``expected`` can be :class:`~torch.Tensor`'s or any tensor-or-scalar-likes from which 2025-09-07T07:36:43.8907800Z :class:`torch.Tensor`'s can be constructed with :func:`torch.as_tensor`. Except for Python scalars the input types 2025-09-07T07:36:43.8908670Z have to be directly related. In addition, ``actual`` and ``expected`` can be :class:`~collections.abc.Sequence`'s 2025-09-07T07:36:43.8909541Z or :class:`~collections.abc.Mapping`'s in which case they are considered close if their structure matches and all 2025-09-07T07:36:43.8910272Z their elements are considered close according to the above definition. 2025-09-07T07:36:43.8910621Z 2025-09-07T07:36:43.8910725Z .. note:: 2025-09-07T07:36:43.8910855Z 2025-09-07T07:36:43.8911186Z Python scalars are an exception to the type relation requirement, because their :func:`type`, i.e. 2025-09-07T07:36:43.8911966Z :class:`int`, :class:`float`, and :class:`complex`, is equivalent to the ``dtype`` of a tensor-like. Thus, 2025-09-07T07:36:43.8912689Z Python scalars of different types can be checked, but require ``check_dtype=False``. 2025-09-07T07:36:43.8913140Z 2025-09-07T07:36:43.8913237Z Args: 2025-09-07T07:36:43.8913457Z actual (Any): Actual input. 2025-09-07T07:36:43.8913782Z expected (Any): Expected input. 2025-09-07T07:36:43.8914447Z allow_subclasses (bool): If ``True`` (default) and except for Python scalars, inputs of directly related types 2025-09-07T07:36:43.8915134Z are allowed. Otherwise type equality is required. 2025-09-07T07:36:43.8915782Z rtol (Optional[float]): Relative tolerance. If specified ``atol`` must also be specified. If omitted, default 2025-09-07T07:36:43.8916525Z values based on the :attr:`~torch.Tensor.dtype` are selected with the below table. 2025-09-07T07:36:43.8917280Z atol (Optional[float]): Absolute tolerance. If specified ``rtol`` must also be specified. If omitted, default 2025-09-07T07:36:43.8918033Z values based on the :attr:`~torch.Tensor.dtype` are selected with the below table. 2025-09-07T07:36:43.8918662Z equal_nan (Union[bool, str]): If ``True``, two ``NaN`` values will be considered equal. 2025-09-07T07:36:43.8919361Z check_device (bool): If ``True`` (default), asserts that corresponding tensors are on the same 2025-09-07T07:36:43.8920007Z :attr:`~torch.Tensor.device`. If this check is disabled, tensors on different 2025-09-07T07:36:43.8920614Z :attr:`~torch.Tensor.device`'s are moved to the CPU before being compared. 2025-09-07T07:36:43.8921332Z check_dtype (bool): If ``True`` (default), asserts that corresponding tensors have the same ``dtype``. If this 2025-09-07T07:36:43.8922150Z check is disabled, tensors with different ``dtype``'s are promoted to a common ``dtype`` (according to 2025-09-07T07:36:43.8922766Z :func:`torch.promote_types`) before being compared. 2025-09-07T07:36:43.8923386Z check_layout (bool): If ``True`` (default), asserts that corresponding tensors have the same ``layout``. If this 2025-09-07T07:36:43.8924379Z check is disabled, tensors with different ``layout``'s are converted to strided tensors before being 2025-09-07T07:36:43.8924923Z compared. 2025-09-07T07:36:43.8925443Z check_stride (bool): If ``True`` and corresponding tensors are strided, asserts that they have the same stride. 2025-09-07T07:36:43.8926285Z msg (Optional[Union[str, Callable[[str], str]]]): Optional error message to use in case a failure occurs during 2025-09-07T07:36:43.8927109Z the comparison. Can also passed as callable in which case it will be called with the generated message and 2025-09-07T07:36:43.8927696Z should return the new message. 2025-09-07T07:36:43.8927927Z 2025-09-07T07:36:43.8928009Z Raises: 2025-09-07T07:36:43.8928416Z ValueError: If no :class:`torch.Tensor` can be constructed from an input. 2025-09-07T07:36:43.8928922Z ValueError: If only ``rtol`` or ``atol`` is specified. 2025-09-07T07:36:43.8929531Z AssertionError: If corresponding inputs are not Python scalars and are not directly related. 2025-09-07T07:36:43.8930351Z AssertionError: If ``allow_subclasses`` is ``False``, but corresponding inputs are not Python scalars and have 2025-09-07T07:36:43.8930942Z different types. 2025-09-07T07:36:43.8931495Z AssertionError: If the inputs are :class:`~collections.abc.Sequence`'s, but their length does not match. 2025-09-07T07:36:43.8932335Z AssertionError: If the inputs are :class:`~collections.abc.Mapping`'s, but their set of keys do not match. 2025-09-07T07:36:43.8933147Z AssertionError: If corresponding tensors do not have the same :attr:`~torch.Tensor.shape`. 2025-09-07T07:36:43.8933883Z AssertionError: If ``check_layout`` is ``True``, but corresponding tensors do not have the same 2025-09-07T07:36:43.8934424Z :attr:`~torch.Tensor.layout`. 2025-09-07T07:36:43.8934864Z AssertionError: If only one of corresponding tensors is quantized. 2025-09-07T07:36:43.8935578Z AssertionError: If corresponding tensors are quantized, but have different :meth:`~torch.Tensor.qscheme`'s. 2025-09-07T07:36:43.8936428Z AssertionError: If ``check_device`` is ``True``, but corresponding tensors are not on the same 2025-09-07T07:36:43.8936959Z :attr:`~torch.Tensor.device`. 2025-09-07T07:36:43.8937511Z AssertionError: If ``check_dtype`` is ``True``, but corresponding tensors do not have the same ``dtype``. 2025-09-07T07:36:43.8938369Z AssertionError: If ``check_stride`` is ``True``, but corresponding strided tensors do not have the same stride. 2025-09-07T07:36:43.8939206Z AssertionError: If the values of corresponding tensors are not close according to the definition above. 2025-09-07T07:36:43.8939697Z 2025-09-07T07:36:43.8940064Z The following table displays the default ``rtol`` and ``atol`` for different ``dtype``'s. In case of mismatching 2025-09-07T07:36:43.8940697Z ``dtype``'s, the maximum of both tolerances is used. 2025-09-07T07:36:43.8940956Z 2025-09-07T07:36:43.8941100Z +---------------------------+------------+----------+ 2025-09-07T07:36:43.8941507Z | ``dtype`` | ``rtol`` | ``atol`` | 2025-09-07T07:36:43.8941845Z +===========================+============+==========+ 2025-09-07T07:36:43.8942213Z | :attr:`~torch.float16` | ``1e-3`` | ``1e-5`` | 2025-09-07T07:36:43.8942588Z +---------------------------+------------+----------+ 2025-09-07T07:36:43.8942962Z | :attr:`~torch.bfloat16` | ``1.6e-2`` | ``1e-5`` | 2025-09-07T07:36:43.8943319Z +---------------------------+------------+----------+ 2025-09-07T07:36:43.8943694Z | :attr:`~torch.float32` | ``1.3e-6`` | ``1e-5`` | 2025-09-07T07:36:43.8944068Z +---------------------------+------------+----------+ 2025-09-07T07:36:43.8944444Z | :attr:`~torch.float64` | ``1e-7`` | ``1e-7`` | 2025-09-07T07:36:43.8944798Z +---------------------------+------------+----------+ 2025-09-07T07:36:43.8945169Z | :attr:`~torch.complex32` | ``1e-3`` | ``1e-5`` | 2025-09-07T07:36:43.8945541Z +---------------------------+------------+----------+ 2025-09-07T07:36:43.8945914Z | :attr:`~torch.complex64` | ``1.3e-6`` | ``1e-5`` | 2025-09-07T07:36:43.8946285Z +---------------------------+------------+----------+ 2025-09-07T07:36:43.8946645Z | :attr:`~torch.complex128` | ``1e-7`` | ``1e-7`` | 2025-09-07T07:36:43.8947017Z +---------------------------+------------+----------+ 2025-09-07T07:36:43.8947386Z | :attr:`~torch.quint8` | ``1.3e-6`` | ``1e-5`` | 2025-09-07T07:36:43.8947753Z +---------------------------+------------+----------+ 2025-09-07T07:36:43.8948111Z | :attr:`~torch.quint2x4` | ``1.3e-6`` | ``1e-5`` | 2025-09-07T07:36:43.8948481Z +---------------------------+------------+----------+ 2025-09-07T07:36:43.8948878Z | :attr:`~torch.quint4x2` | ``1.3e-6`` | ``1e-5`` | 2025-09-07T07:36:43.8949246Z +---------------------------+------------+----------+ 2025-09-07T07:36:43.8949607Z | :attr:`~torch.qint8` | ``1.3e-6`` | ``1e-5`` | 2025-09-07T07:36:43.8949977Z +---------------------------+------------+----------+ 2025-09-07T07:36:43.8950344Z | :attr:`~torch.qint32` | ``1.3e-6`` | ``1e-5`` | 2025-09-07T07:36:43.8950712Z +---------------------------+------------+----------+ 2025-09-07T07:36:43.8951069Z | other | ``0.0`` | ``0.0`` | 2025-09-07T07:36:43.8951421Z +---------------------------+------------+----------+ 2025-09-07T07:36:43.8951665Z 2025-09-07T07:36:43.8951759Z .. note:: 2025-09-07T07:36:43.8951897Z 2025-09-07T07:36:43.8952281Z :func:`~torch.testing.assert_close` is highly configurable with strict default settings. Users are encouraged 2025-09-07T07:36:43.8953144Z to :func:`~functools.partial` it to fit their use case. For example, if an equality check is needed, one might 2025-09-07T07:36:43.8953885Z define an ``assert_equal`` that uses zero tolerances for every ``dtype`` by default: 2025-09-07T07:36:43.8954265Z 2025-09-07T07:36:43.8954469Z >>> import functools 2025-09-07T07:36:43.8954920Z >>> assert_equal = functools.partial(torch.testing.assert_close, rtol=0, atol=0) 2025-09-07T07:36:43.8955417Z >>> assert_equal(1e-9, 1e-10) 2025-09-07T07:36:43.8955753Z Traceback (most recent call last): 2025-09-07T07:36:43.8956106Z ... 2025-09-07T07:36:43.8956355Z AssertionError: Scalars are not equal! 2025-09-07T07:36:43.8956688Z 2025-09-07T07:36:43.8956953Z Expected 1e-10 but got 1e-09. 2025-09-07T07:36:43.8957298Z Absolute difference: 9.000000000000001e-10 2025-09-07T07:36:43.8957644Z Relative difference: 9.0 2025-09-07T07:36:43.8957844Z 2025-09-07T07:36:43.8957929Z Examples: 2025-09-07T07:36:43.8958171Z >>> # tensor to tensor comparison 2025-09-07T07:36:43.8958509Z >>> expected = torch.tensor([1e0, 1e-1, 1e-2]) 2025-09-07T07:36:43.8958885Z >>> actual = torch.acos(torch.cos(expected)) 2025-09-07T07:36:43.8959275Z >>> torch.testing.assert_close(actual, expected) 2025-09-07T07:36:43.8959525Z 2025-09-07T07:36:43.8959647Z >>> # scalar to scalar comparison 2025-09-07T07:36:43.8959984Z >>> import math 2025-09-07T07:36:43.8960252Z >>> expected = math.sqrt(2.0) 2025-09-07T07:36:43.8960567Z >>> actual = 2.0 / math.sqrt(2.0) 2025-09-07T07:36:43.8960929Z >>> torch.testing.assert_close(actual, expected) 2025-09-07T07:36:43.8961180Z 2025-09-07T07:36:43.8961314Z >>> # numpy array to numpy array comparison 2025-09-07T07:36:43.8961642Z >>> import numpy as np 2025-09-07T07:36:43.8961949Z >>> expected = np.array([1e0, 1e-1, 1e-2]) 2025-09-07T07:36:43.8962304Z >>> actual = np.arccos(np.cos(expected)) 2025-09-07T07:36:43.8962683Z >>> torch.testing.assert_close(actual, expected) 2025-09-07T07:36:43.8962930Z 2025-09-07T07:36:43.8963047Z >>> # sequence to sequence comparison 2025-09-07T07:36:43.8963376Z >>> import numpy as np 2025-09-07T07:36:43.8963814Z >>> # The types of the sequences do not have to match. They only have to have the same 2025-09-07T07:36:43.8964323Z >>> # length and their elements have to match. 2025-09-07T07:36:43.8964712Z >>> expected = [torch.tensor([1.0]), 2.0, np.array(3.0)] 2025-09-07T07:36:43.8965089Z >>> actual = tuple(expected) 2025-09-07T07:36:43.8965444Z >>> torch.testing.assert_close(actual, expected) 2025-09-07T07:36:43.8965700Z 2025-09-07T07:36:43.8965831Z >>> # mapping to mapping comparison 2025-09-07T07:36:43.8966187Z >>> from collections import OrderedDict 2025-09-07T07:36:43.8966511Z >>> import numpy as np 2025-09-07T07:36:43.8966811Z >>> foo = torch.tensor(1.0) 2025-09-07T07:36:43.8967108Z >>> bar = 2.0 2025-09-07T07:36:43.8967437Z >>> baz = np.array(3.0) 2025-09-07T07:36:43.8967878Z >>> # The types and a possible ordering of mappings do not have to match. They only 2025-09-07T07:36:43.8968459Z >>> # have to have the same set of keys and their elements have to match. 2025-09-07T07:36:43.8968997Z >>> expected = OrderedDict([("foo", foo), ("bar", bar), ("baz", baz)]) 2025-09-07T07:36:43.8969463Z >>> actual = {"baz": baz, "bar": bar, "foo": foo} 2025-09-07T07:36:43.8969852Z >>> torch.testing.assert_close(actual, expected) 2025-09-07T07:36:43.8970124Z 2025-09-07T07:36:43.8970249Z >>> expected = torch.tensor([1.0, 2.0, 3.0]) 2025-09-07T07:36:43.8970610Z >>> actual = expected.clone() 2025-09-07T07:36:43.8970994Z >>> # By default, directly related instances can be compared 2025-09-07T07:36:43.8971487Z >>> torch.testing.assert_close(torch.nn.Parameter(actual), expected) 2025-09-07T07:36:43.8972025Z >>> # This check can be made more strict with allow_subclasses=False 2025-09-07T07:36:43.8972494Z >>> torch.testing.assert_close( 2025-09-07T07:36:43.8972917Z ... torch.nn.Parameter(actual), expected, allow_subclasses=False 2025-09-07T07:36:43.8973360Z ... ) 2025-09-07T07:36:43.8973598Z Traceback (most recent call last): 2025-09-07T07:36:43.8973911Z ... 2025-09-07T07:36:43.8974240Z TypeError: No comparison pair was able to handle inputs of type 2025-09-07T07:36:43.8974786Z and . 2025-09-07T07:36:43.8975367Z >>> # If the inputs are not directly related, they are never considered close 2025-09-07T07:36:43.8975885Z >>> torch.testing.assert_close(actual.numpy(), expected) 2025-09-07T07:36:43.8976296Z Traceback (most recent call last): 2025-09-07T07:36:43.8976603Z ... 2025-09-07T07:36:43.8977008Z TypeError: No comparison pair was able to handle inputs of type 2025-09-07T07:36:43.8977526Z and . 2025-09-07T07:36:43.8977991Z >>> # Exceptions to these rules are Python scalars. They can be checked regardless of 2025-09-07T07:36:43.8978491Z >>> # their type if check_dtype=False. 2025-09-07T07:36:43.8978895Z >>> torch.testing.assert_close(1.0, 1, check_dtype=False) 2025-09-07T07:36:43.8979177Z 2025-09-07T07:36:43.8979308Z >>> # NaN != NaN by default. 2025-09-07T07:36:43.8979629Z >>> expected = torch.tensor(float("Nan")) 2025-09-07T07:36:43.8979976Z >>> actual = expected.clone() 2025-09-07T07:36:43.8980336Z >>> torch.testing.assert_close(actual, expected) 2025-09-07T07:36:43.8980696Z Traceback (most recent call last): 2025-09-07T07:36:43.8981006Z ... 2025-09-07T07:36:43.8981254Z AssertionError: Scalars are not close! 2025-09-07T07:36:43.8981582Z 2025-09-07T07:36:43.8981820Z Expected nan but got nan. 2025-09-07T07:36:43.8982167Z Absolute difference: nan (up to 1e-05 allowed) 2025-09-07T07:36:43.8982569Z Relative difference: nan (up to 1.3e-06 allowed) 2025-09-07T07:36:43.8983031Z >>> torch.testing.assert_close(actual, expected, equal_nan=True) 2025-09-07T07:36:43.8983348Z 2025-09-07T07:36:43.8983472Z >>> expected = torch.tensor([1.0, 2.0, 3.0]) 2025-09-07T07:36:43.8983832Z >>> actual = torch.tensor([1.0, 4.0, 5.0]) 2025-09-07T07:36:43.8984206Z >>> # The default error message can be overwritten. 2025-09-07T07:36:43.8984577Z >>> torch.testing.assert_close( 2025-09-07T07:36:43.8984967Z ... actual, expected, msg="Argh, the tensors are not close!" 2025-09-07T07:36:43.8985353Z ... ) 2025-09-07T07:36:43.8985598Z Traceback (most recent call last): 2025-09-07T07:36:43.8985905Z ... 2025-09-07T07:36:43.8986167Z AssertionError: Argh, the tensors are not close! 2025-09-07T07:36:43.8986650Z >>> # If msg is a callable, it can be used to augment the generated message with 2025-09-07T07:36:43.8987122Z >>> # extra information 2025-09-07T07:36:43.8987423Z >>> torch.testing.assert_close( 2025-09-07T07:36:43.8987838Z ... actual, expected, msg=lambda msg: f"Header\n\n{msg}\n\nFooter" 2025-09-07T07:36:43.8988231Z ... ) 2025-09-07T07:36:43.8988476Z Traceback (most recent call last): 2025-09-07T07:36:43.8988783Z ... 2025-09-07T07:36:43.8989015Z AssertionError: Header 2025-09-07T07:36:43.8989280Z 2025-09-07T07:36:43.8989537Z Tensor-likes are not close! 2025-09-07T07:36:43.8989830Z 2025-09-07T07:36:43.8990083Z Mismatched elements: 2 / 3 (66.7%) 2025-09-07T07:36:43.8990512Z Greatest absolute difference: 2.0 at index (1,) (up to 1e-05 allowed) 2025-09-07T07:36:43.8991083Z Greatest relative difference: 1.0 at index (1,) (up to 1.3e-06 allowed) 2025-09-07T07:36:43.8991518Z 2025-09-07T07:36:43.8991743Z Footer 2025-09-07T07:36:43.8991946Z 2025-09-07T07:36:43.8992308Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:43.8992687Z 2025-09-07T07:36:45.0658108Z 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=134. 2025-09-07T07:36:45.0659343Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:45.0659861Z Register a container-like type as pytree node. 2025-09-07T07:36:45.0660192Z 2025-09-07T07:36:45.0660280Z Args: 2025-09-07T07:36:45.0660595Z cls (type): A Python type to treat as an internal pytree node. 2025-09-07T07:36:45.0661180Z flatten_fn (callable): A function to be used during flattening, taking an instance of 2025-09-07T07:36:45.0661817Z ``cls`` and returning a pair, with (1) an iterable for the children to be flattened 2025-09-07T07:36:45.0662493Z recursively, and (2) some hashable auxiliary data to be stored in the treespec and to be 2025-09-07T07:36:45.0663027Z passed to the ``unflatten_fn``. 2025-09-07T07:36:45.0663534Z unflatten_fn (callable): A function taking two arguments: the auxiliary data that was 2025-09-07T07:36:45.0664199Z returned by ``flatten_fn`` and stored in the treespec, and the unflattened children. 2025-09-07T07:36:45.0664779Z The function should return an instance of ``cls``. 2025-09-07T07:36:45.0665325Z serialized_type_name (str, optional): A keyword argument used to specify the fully 2025-09-07T07:36:45.0665880Z qualified name used when serializing the tree spec. 2025-09-07T07:36:45.0666473Z to_dumpable_context (callable, optional): An optional keyword argument to custom specify how 2025-09-07T07:36:45.0667183Z to convert the context of the pytree to a custom json dumpable representation. This is 2025-09-07T07:36:45.0667845Z used for json serialization, which is being used in :mod:`torch.export` right now. 2025-09-07T07:36:45.0668548Z from_dumpable_context (callable, optional): An optional keyword argument to custom specify 2025-09-07T07:36:45.0669233Z how to convert the custom json dumpable representation of the context back to the 2025-09-07T07:36:45.0669887Z original context. This is used for json deserialization, which is being used in 2025-09-07T07:36:45.0670372Z :mod:`torch.export` right now. 2025-09-07T07:36:45.0670606Z 2025-09-07T07:36:45.0670715Z Example:: 2025-09-07T07:36:45.0670858Z 2025-09-07T07:36:45.0670960Z >>> # xdoctest: +SKIP 2025-09-07T07:36:45.0671286Z >>> # Registry a Python type with lambda functions 2025-09-07T07:36:45.0671651Z >>> register_pytree_node( 2025-09-07T07:36:45.0671921Z ... set, 2025-09-07T07:36:45.0672188Z ... lambda s: (sorted(s), None, None), 2025-09-07T07:36:45.0672545Z ... lambda children, _: set(children), 2025-09-07T07:36:45.0672862Z ... ) 2025-09-07T07:36:45.0673133Z 2025-09-07T07:36:45.0673504Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:45.0673884Z 2025-09-07T07:36:45.1223379Z msg = Cannot scrape callname=SelectiveCheckpointContext in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/checkpoint.py line=1226. 2025-09-07T07:36:45.1224542Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:45.1224922Z 2025-09-07T07:36:45.1225147Z Context passed to policy function during selective checkpointing. 2025-09-07T07:36:45.1225479Z 2025-09-07T07:36:45.1225717Z This class is used to pass relevant metadata to the policy function during 2025-09-07T07:36:45.1226315Z selective checkpointing. The metadata includes whether the current invocation 2025-09-07T07:36:45.1226862Z of the policy function is during recomputation or not. 2025-09-07T07:36:45.1227145Z 2025-09-07T07:36:45.1227233Z Example: 2025-09-07T07:36:45.1227466Z >>> # xdoctest: +SKIP(stub) 2025-09-07T07:36:45.1227732Z >>> 2025-09-07T07:36:45.1227976Z >>> def policy_fn(ctx, op, *args, **kwargs): 2025-09-07T07:36:45.1228322Z >>> print(ctx.is_recompute) 2025-09-07T07:36:45.1228769Z >>> 2025-09-07T07:36:45.1229145Z >>> context_fn = functools.partial(create_selective_checkpoint_contexts, policy_fn) 2025-09-07T07:36:45.1229735Z >>> 2025-09-07T07:36:45.1230222Z >>> out = torch.utils.checkpoint.checkpoint( 2025-09-07T07:36:45.1230692Z >>> fn, x, y, 2025-09-07T07:36:45.1231010Z >>> use_reentrant=False, 2025-09-07T07:36:45.1231301Z >>> context_fn=context_fn, 2025-09-07T07:36:45.1231589Z >>> ) 2025-09-07T07:36:45.1231706Z 2025-09-07T07:36:45.1231974Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:45.1232344Z 2025-09-07T07:36:45.1233014Z 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=1366. 2025-09-07T07:36:45.1234009Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:45.1234516Z 2025-09-07T07:36:45.1234758Z Helper to avoid recomputing certain ops during activation checkpointing. 2025-09-07T07:36:45.1235132Z 2025-09-07T07:36:45.1235405Z Use this with `torch.utils.checkpoint.checkpoint` to control which 2025-09-07T07:36:45.1235904Z operations are recomputed during the backward pass. 2025-09-07T07:36:45.1236173Z 2025-09-07T07:36:45.1236265Z Args: 2025-09-07T07:36:45.1236489Z policy_fn_or_list (Callable or List): 2025-09-07T07:36:45.1236891Z - If a policy function is provided, it should accept a 2025-09-07T07:36:45.1237415Z :class:`SelectiveCheckpointContext`, the :class:`OpOverload`, args and 2025-09-07T07:36:45.1237984Z kwargs to the op, and return a :class:`CheckpointPolicy` enum value 2025-09-07T07:36:45.1238549Z indicating whether the execution of the op should be recomputed or not. 2025-09-07T07:36:45.1239100Z - If a list of operations is provided, it is equivalent to a policy 2025-09-07T07:36:45.1239601Z returning `CheckpointPolicy.MUST_SAVE` for the specified 2025-09-07T07:36:45.1240119Z operations and `CheckpointPolicy.PREFER_RECOMPUTE` for all other 2025-09-07T07:36:45.1240542Z operations. 2025-09-07T07:36:45.1240890Z allow_cache_entry_mutation (bool, optional): By default, an error is 2025-09-07T07:36:45.1241435Z raised if any tensors cached by selective activation checkpoint are 2025-09-07T07:36:45.1241979Z mutated in order to ensure correctness. If set to `True`, this check 2025-09-07T07:36:45.1242405Z is disabled. 2025-09-07T07:36:45.1242631Z Returns: 2025-09-07T07:36:45.1242862Z A tuple of two context managers. 2025-09-07T07:36:45.1243080Z 2025-09-07T07:36:45.1243163Z Example: 2025-09-07T07:36:45.1243388Z >>> # xdoctest: +REQUIRES(LINUX) 2025-09-07T07:36:45.1243675Z >>> import functools 2025-09-07T07:36:45.1243976Z >>> 2025-09-07T07:36:45.1244214Z >>> x = torch.rand(10, 10, requires_grad=True) 2025-09-07T07:36:45.1244635Z >>> y = torch.rand(10, 10, requires_grad=True) 2025-09-07T07:36:45.1244943Z >>> 2025-09-07T07:36:45.1245150Z >>> ops_to_save = [ 2025-09-07T07:36:45.1245426Z >>> torch.ops.aten.mm.default, 2025-09-07T07:36:45.1245723Z >>> ] 2025-09-07T07:36:45.1245915Z >>> 2025-09-07T07:36:45.1246152Z >>> def policy_fn(ctx, op, *args, **kwargs): 2025-09-07T07:36:45.1246496Z >>> if op in ops_to_save: 2025-09-07T07:36:45.1246821Z >>> return CheckpointPolicy.MUST_SAVE 2025-09-07T07:36:45.1247143Z >>> else: 2025-09-07T07:36:45.1247426Z >>> return CheckpointPolicy.PREFER_RECOMPUTE 2025-09-07T07:36:45.1247767Z >>> 2025-09-07T07:36:45.1248152Z >>> context_fn = functools.partial(create_selective_checkpoint_contexts, policy_fn) 2025-09-07T07:36:45.1248615Z >>> 2025-09-07T07:36:45.1248825Z >>> # or equivalently 2025-09-07T07:36:45.1249266Z >>> context_fn = functools.partial(create_selective_checkpoint_contexts, ops_to_save) 2025-09-07T07:36:45.1249744Z >>> 2025-09-07T07:36:45.1249938Z >>> def fn(x, y): 2025-09-07T07:36:45.1250365Z >>> return torch.sigmoid(torch.matmul(torch.matmul(x, y), y)) * y 2025-09-07T07:36:45.1250770Z >>> 2025-09-07T07:36:45.1251046Z >>> out = torch.utils.checkpoint.checkpoint( 2025-09-07T07:36:45.1251379Z >>> fn, x, y, 2025-09-07T07:36:45.1251640Z >>> use_reentrant=False, 2025-09-07T07:36:45.1251978Z >>> context_fn=context_fn, 2025-09-07T07:36:45.1252270Z >>> ) 2025-09-07T07:36:45.1252386Z 2025-09-07T07:36:45.1252635Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:45.1253017Z 2025-09-07T07:36:45.1475213Z msg = Cannot scrape callname=CppExtension in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/cpp_extension.py line=1158. 2025-09-07T07:36:45.1476149Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:45.1476543Z 2025-09-07T07:36:45.1476688Z Create a :class:`setuptools.Extension` for C++. 2025-09-07T07:36:45.1476959Z 2025-09-07T07:36:45.1477209Z Convenience method that creates a :class:`setuptools.Extension` with the 2025-09-07T07:36:45.1477905Z bare minimum (but often sufficient) arguments to build a C++ extension. 2025-09-07T07:36:45.1478242Z 2025-09-07T07:36:45.1478467Z All arguments are forwarded to the :class:`setuptools.Extension` 2025-09-07T07:36:45.1478935Z constructor. Full list arguments can be found at 2025-09-07T07:36:45.1479534Z https://setuptools.pypa.io/en/latest/userguide/ext_modules.html#extension-api-reference 2025-09-07T07:36:45.1479987Z 2025-09-07T07:36:45.1480098Z .. warning:: 2025-09-07T07:36:45.1480461Z The PyTorch python API (as provided in libtorch_python) cannot be built 2025-09-07T07:36:45.1481025Z with the flag ``py_limited_api=True``. When this flag is passed, it is 2025-09-07T07:36:45.1481545Z the user's responsibility in their library to not use APIs from 2025-09-07T07:36:45.1482091Z libtorch_python (in particular pytorch/python bindings) and to only use 2025-09-07T07:36:45.1482736Z APIs from libtorch (aten objects, operators and the dispatcher). For 2025-09-07T07:36:45.1483298Z example, to give access to custom ops from python, the library should 2025-09-07T07:36:45.1483745Z register the ops through the dispatcher. 2025-09-07T07:36:45.1483985Z 2025-09-07T07:36:45.1484206Z Contrary to CPython setuptools, who does not define -DPy_LIMITED_API 2025-09-07T07:36:45.1484751Z as a compile flag when py_limited_api is specified as an option for 2025-09-07T07:36:45.1485277Z the "bdist_wheel" command in ``setup``, PyTorch does! We will specify 2025-09-07T07:36:45.1485814Z -DPy_LIMITED_API=min_supported_cpython to best enforce consistency, 2025-09-07T07:36:45.1486348Z safety, and sanity in order to encourage best practices. To target a 2025-09-07T07:36:45.1486960Z different version, set min_supported_cpython to the hexcode of the 2025-09-07T07:36:45.1487402Z CPython version of choice. 2025-09-07T07:36:45.1487621Z 2025-09-07T07:36:45.1487745Z Example: 2025-09-07T07:36:45.1487955Z >>> # xdoctest: +SKIP 2025-09-07T07:36:45.1488275Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2025-09-07T07:36:45.1488654Z >>> from setuptools import setup 2025-09-07T07:36:45.1489086Z >>> from torch.utils.cpp_extension import BuildExtension, CppExtension 2025-09-07T07:36:45.1489506Z >>> setup( 2025-09-07T07:36:45.1489744Z ... name='extension', 2025-09-07T07:36:45.1490025Z ... ext_modules=[ 2025-09-07T07:36:45.1490289Z ... CppExtension( 2025-09-07T07:36:45.1490560Z ... name='extension', 2025-09-07T07:36:45.1490883Z ... sources=['extension.cpp'], 2025-09-07T07:36:45.1491232Z ... extra_compile_args=['-g'], 2025-09-07T07:36:45.1491611Z ... extra_link_args=['-Wl,--no-as-needed', '-lm']) 2025-09-07T07:36:45.1491966Z ... ], 2025-09-07T07:36:45.1492231Z ... cmdclass={ 2025-09-07T07:36:45.1492501Z ... 'build_ext': BuildExtension 2025-09-07T07:36:45.1492794Z ... }) 2025-09-07T07:36:45.1493002Z 2025-09-07T07:36:45.1493252Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:45.1493634Z 2025-09-07T07:36:45.1494190Z msg = Cannot scrape callname=CUDAExtension in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/cpp_extension.py line=1228. 2025-09-07T07:36:45.1495173Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:45.1495550Z 2025-09-07T07:36:45.1495720Z Create a :class:`setuptools.Extension` for CUDA/C++. 2025-09-07T07:36:45.1495989Z 2025-09-07T07:36:45.1496228Z Convenience method that creates a :class:`setuptools.Extension` with the 2025-09-07T07:36:45.1496794Z bare minimum (but often sufficient) arguments to build a CUDA/C++ 2025-09-07T07:36:45.1497472Z extension. This includes the CUDA include path, library path and runtime 2025-09-07T07:36:45.1497913Z library. 2025-09-07T07:36:45.1498028Z 2025-09-07T07:36:45.1498232Z All arguments are forwarded to the :class:`setuptools.Extension` 2025-09-07T07:36:45.1498698Z constructor. Full list arguments can be found at 2025-09-07T07:36:45.1499321Z https://setuptools.pypa.io/en/latest/userguide/ext_modules.html#extension-api-reference 2025-09-07T07:36:45.1499760Z 2025-09-07T07:36:45.1499863Z .. warning:: 2025-09-07T07:36:45.1500225Z The PyTorch python API (as provided in libtorch_python) cannot be built 2025-09-07T07:36:45.1500770Z with the flag ``py_limited_api=True``. When this flag is passed, it is 2025-09-07T07:36:45.1501305Z the user's responsibility in their library to not use APIs from 2025-09-07T07:36:45.1501852Z libtorch_python (in particular pytorch/python bindings) and to only use 2025-09-07T07:36:45.1502423Z APIs from libtorch (aten objects, operators and the dispatcher). For 2025-09-07T07:36:45.1502965Z example, to give access to custom ops from python, the library should 2025-09-07T07:36:45.1503428Z register the ops through the dispatcher. 2025-09-07T07:36:45.1503670Z 2025-09-07T07:36:45.1503889Z Contrary to CPython setuptools, who does not define -DPy_LIMITED_API 2025-09-07T07:36:45.1504434Z as a compile flag when py_limited_api is specified as an option for 2025-09-07T07:36:45.1504965Z the "bdist_wheel" command in ``setup``, PyTorch does! We will specify 2025-09-07T07:36:45.1505491Z -DPy_LIMITED_API=min_supported_cpython to best enforce consistency, 2025-09-07T07:36:45.1506035Z safety, and sanity in order to encourage best practices. To target a 2025-09-07T07:36:45.1506841Z different version, set min_supported_cpython to the hexcode of the 2025-09-07T07:36:45.1507338Z CPython version of choice. 2025-09-07T07:36:45.1507552Z 2025-09-07T07:36:45.1507649Z Example: 2025-09-07T07:36:45.1507857Z >>> # xdoctest: +SKIP 2025-09-07T07:36:45.1508240Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2025-09-07T07:36:45.1508614Z >>> from setuptools import setup 2025-09-07T07:36:45.1509054Z >>> from torch.utils.cpp_extension import BuildExtension, CUDAExtension 2025-09-07T07:36:45.1509481Z >>> setup( 2025-09-07T07:36:45.1509722Z ... name='cuda_extension', 2025-09-07T07:36:45.1510025Z ... ext_modules=[ 2025-09-07T07:36:45.1510296Z ... CUDAExtension( 2025-09-07T07:36:45.1510596Z ... name='cuda_extension', 2025-09-07T07:36:45.1510983Z ... sources=['extension.cpp', 'extension_kernel.cu'], 2025-09-07T07:36:45.1511395Z ... extra_compile_args={'cxx': ['-g'], 2025-09-07T07:36:45.1511761Z ... 'nvcc': ['-O2']}, 2025-09-07T07:36:45.1512141Z ... extra_link_args=['-Wl,--no-as-needed', '-lcuda']) 2025-09-07T07:36:45.1512506Z ... ], 2025-09-07T07:36:45.1512729Z ... cmdclass={ 2025-09-07T07:36:45.1513000Z ... 'build_ext': BuildExtension 2025-09-07T07:36:45.1513298Z ... }) 2025-09-07T07:36:45.1513439Z 2025-09-07T07:36:45.1513539Z Compute capabilities: 2025-09-07T07:36:45.1513751Z 2025-09-07T07:36:45.1514050Z By default the extension will be compiled to run on all archs of the cards visible during the 2025-09-07T07:36:45.1514881Z building process of the extension, plus PTX. If down the road a new card is installed the 2025-09-07T07:36:45.1515596Z extension may need to be recompiled. If a visible card has a compute capability (CC) that's 2025-09-07T07:36:45.1516344Z newer than the newest version for which your nvcc can build fully-compiled binaries, PyTorch 2025-09-07T07:36:45.1517062Z will make nvcc fall back to building kernels with the newest version of PTX your nvcc does 2025-09-07T07:36:45.1517598Z support (see below for details on PTX). 2025-09-07T07:36:45.1517823Z 2025-09-07T07:36:45.1518146Z You can override the default behavior using `TORCH_CUDA_ARCH_LIST` to explicitly specify which 2025-09-07T07:36:45.1518688Z CCs you want the extension to support: 2025-09-07T07:36:45.1518918Z 2025-09-07T07:36:45.1519106Z ``TORCH_CUDA_ARCH_LIST="6.1 8.6" python build_my_extension.py`` 2025-09-07T07:36:45.1519653Z ``TORCH_CUDA_ARCH_LIST="5.2 6.0 6.1 7.0 7.5 8.0 8.6+PTX" python build_my_extension.py`` 2025-09-07T07:36:45.1520022Z 2025-09-07T07:36:45.1520378Z The +PTX option causes extension kernel binaries to include PTX instructions for the specified 2025-09-07T07:36:45.1521134Z CC. PTX is an intermediate representation that allows kernels to runtime-compile for any CC >= 2025-09-07T07:36:45.1521865Z the specified CC (for example, 8.6+PTX generates PTX that can runtime-compile for any GPU with 2025-09-07T07:36:45.1522580Z CC >= 8.6). This improves your binary's forward compatibility. However, relying on older PTX to 2025-09-07T07:36:45.1523319Z provide forward compat by runtime-compiling for newer CCs can modestly reduce performance on 2025-09-07T07:36:45.1524201Z those newer CCs. If you know exact CC(s) of the GPUs you want to target, you're always better 2025-09-07T07:36:45.1524922Z off specifying them individually. For example, if you want your extension to run on 8.0 and 8.6, 2025-09-07T07:36:45.1525671Z "8.0+PTX" would work functionally because it includes PTX that can runtime-compile for 8.6, but 2025-09-07T07:36:45.1526207Z "8.0 8.6" would be better. 2025-09-07T07:36:45.1526388Z 2025-09-07T07:36:45.1526686Z Note that while it's possible to include all supported archs, the more archs get included the 2025-09-07T07:36:45.1527400Z slower the building process will be, as it will build a separate kernel image for each arch. 2025-09-07T07:36:45.1527808Z 2025-09-07T07:36:45.1528149Z Note that CUDA-11.5 nvcc will hit internal compiler error while parsing torch/extension.h on Windows. 2025-09-07T07:36:45.1528810Z To workaround the issue, move python binding logic to pure C++ file. 2025-09-07T07:36:45.1529148Z 2025-09-07T07:36:45.1529236Z Example use: 2025-09-07T07:36:45.1529530Z #include 2025-09-07T07:36:45.1529861Z at::Tensor SigmoidAlphaBlendForwardCuda(....) 2025-09-07T07:36:45.1530114Z 2025-09-07T07:36:45.1530216Z Instead of: 2025-09-07T07:36:45.1530440Z #include 2025-09-07T07:36:45.1530790Z torch::Tensor SigmoidAlphaBlendForwardCuda(...) 2025-09-07T07:36:45.1531055Z 2025-09-07T07:36:45.1531346Z Currently open issue for nvcc bug: https://github.com/pytorch/pytorch/issues/69460 2025-09-07T07:36:45.1532256Z Complete workaround code example: https://github.com/facebookresearch/pytorch3d/commit/cb170ac024a949f1f9614ffe6af1c38d972f7d48 2025-09-07T07:36:45.1532879Z 2025-09-07T07:36:45.1533001Z Relocatable device code linking: 2025-09-07T07:36:45.1533197Z 2025-09-07T07:36:45.1533516Z If you want to reference device symbols across compilation units (across object files), 2025-09-07T07:36:45.1534186Z the object files need to be built with `relocatable device code` (-rdc=true or -dc). 2025-09-07T07:36:45.1534941Z An exception to this rule is "dynamic parallelism" (nested kernel launches) which is not used a lot anymore. 2025-09-07T07:36:45.1535756Z `Relocatable device code` is less optimized so it needs to be used only on object files that need it. 2025-09-07T07:36:45.1536583Z Using `-dlto` (Device Link Time Optimization) at the device code compilation step and `dlink` step 2025-09-07T07:36:45.1537206Z helps reduce the protentional perf degradation of `-rdc`. 2025-09-07T07:36:45.1537665Z Note that it needs to be used at both steps to be useful. 2025-09-07T07:36:45.1537983Z 2025-09-07T07:36:45.1538363Z If you have `rdc` objects you need to have an extra `-dlink` (device linking) step before the CPU symbol linking step. 2025-09-07T07:36:45.1539023Z There is also a case where `-dlink` is used without `-rdc`: 2025-09-07T07:36:45.1539550Z when an extension is linked against a static lib containing rdc-compiled objects 2025-09-07T07:36:45.1540136Z like the [NVSHMEM library](https://developer.nvidia.com/nvshmem). 2025-09-07T07:36:45.1540470Z 2025-09-07T07:36:45.1540672Z Note: Ninja is required to build a CUDA Extension with RDC linking. 2025-09-07T07:36:45.1540989Z 2025-09-07T07:36:45.1541091Z Example: 2025-09-07T07:36:45.1541343Z >>> # xdoctest: +SKIP 2025-09-07T07:36:45.1541652Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2025-09-07T07:36:45.1542008Z >>> CUDAExtension( 2025-09-07T07:36:45.1542380Z ... name='cuda_extension', 2025-09-07T07:36:45.1542747Z ... sources=['extension.cpp', 'extension_kernel.cu'], 2025-09-07T07:36:45.1543110Z ... dlink=True, 2025-09-07T07:36:45.1543398Z ... dlink_libraries=["dlink_lib"], 2025-09-07T07:36:45.1543754Z ... extra_compile_args={'cxx': ['-g'], 2025-09-07T07:36:45.1544118Z ... 'nvcc': ['-O2', '-rdc=true']}) 2025-09-07T07:36:45.1544355Z 2025-09-07T07:36:45.1544603Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:45.1544981Z 2025-09-07T07:36:45.1545534Z msg = Cannot scrape callname=SyclExtension in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/cpp_extension.py line=1420. 2025-09-07T07:36:45.1546461Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:45.1546857Z 2025-09-07T07:36:45.1547019Z Creates a :class:`setuptools.Extension` for SYCL/C++. 2025-09-07T07:36:45.1547289Z 2025-09-07T07:36:45.1547538Z Convenience method that creates a :class:`setuptools.Extension` with the 2025-09-07T07:36:45.1548103Z bare minimum (but often sufficient) arguments to build a SYCL/C++ 2025-09-07T07:36:45.1548501Z extension. 2025-09-07T07:36:45.1548634Z 2025-09-07T07:36:45.1548838Z All arguments are forwarded to the :class:`setuptools.Extension` 2025-09-07T07:36:45.1549247Z constructor. 2025-09-07T07:36:45.1549373Z 2025-09-07T07:36:45.1549472Z .. warning:: 2025-09-07T07:36:45.1549815Z The PyTorch python API (as provided in libtorch_python) cannot be built 2025-09-07T07:36:45.1550424Z with the flag ``py_limited_api=True``. When this flag is passed, it is 2025-09-07T07:36:45.1550956Z the user's responsibility in their library to not use APIs from 2025-09-07T07:36:45.1551502Z libtorch_python (in particular pytorch/python bindings) and to only use 2025-09-07T07:36:45.1552062Z APIs from libtorch (aten objects, operators and the dispatcher). For 2025-09-07T07:36:45.1552616Z example, to give access to custom ops from python, the library should 2025-09-07T07:36:45.1553077Z register the ops through the dispatcher. 2025-09-07T07:36:45.1553311Z 2025-09-07T07:36:45.1553543Z Contrary to CPython setuptools, who does not define -DPy_LIMITED_API 2025-09-07T07:36:45.1554087Z as a compile flag when py_limited_api is specified as an option for 2025-09-07T07:36:45.1554686Z the "bdist_wheel" command in ``setup``, PyTorch does! We will specify 2025-09-07T07:36:45.1555227Z -DPy_LIMITED_API=min_supported_cpython to best enforce consistency, 2025-09-07T07:36:45.1555774Z safety, and sanity in order to encourage best practices. To target a 2025-09-07T07:36:45.1556318Z different version, set min_supported_cpython to the hexcode of the 2025-09-07T07:36:45.1556743Z CPython version of choice. 2025-09-07T07:36:45.1556980Z 2025-09-07T07:36:45.1557066Z Example: 2025-09-07T07:36:45.1557286Z >>> # xdoctest: +SKIP 2025-09-07T07:36:45.1557612Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2025-09-07T07:36:45.1558100Z >>> from torch.utils.cpp_extension import BuildExtension, SyclExtension 2025-09-07T07:36:45.1558550Z >>> setup( 2025-09-07T07:36:45.1558786Z ... name='xpu_extension', 2025-09-07T07:36:45.1559072Z ... ext_modules=[ 2025-09-07T07:36:45.1559339Z ... SyclExtension( 2025-09-07T07:36:45.1559633Z ... name='xpu_extension', 2025-09-07T07:36:45.1560025Z ... sources=['extension.cpp', 'extension_kernel.cpp'], 2025-09-07T07:36:45.1560482Z ... extra_compile_args={'cxx': ['-g', '-std=c++20', '-fPIC']}) 2025-09-07T07:36:45.1560876Z ... ], 2025-09-07T07:36:45.1561102Z ... cmdclass={ 2025-09-07T07:36:45.1561378Z ... 'build_ext': BuildExtension 2025-09-07T07:36:45.1561675Z ... }) 2025-09-07T07:36:45.1561817Z 2025-09-07T07:36:45.1562115Z By default the extension will be compiled to run on all archs of the cards visible during the 2025-09-07T07:36:45.1562825Z building process of the extension. If down the road a new card is installed the 2025-09-07T07:36:45.1563456Z extension may need to be recompiled. You can override the default behavior using 2025-09-07T07:36:45.1564179Z `TORCH_XPU_ARCH_LIST` to explicitly specify which device architectures you want the extension 2025-09-07T07:36:45.1564706Z to support: 2025-09-07T07:36:45.1564845Z 2025-09-07T07:36:45.1565055Z ``TORCH_XPU_ARCH_LIST="pvc,xe-lpg" python build_my_extension.py`` 2025-09-07T07:36:45.1565363Z 2025-09-07T07:36:45.1565675Z Note that while it's possible to include all supported archs, the more archs get included the 2025-09-07T07:36:45.1566393Z slower the building process will be, as it will build a separate kernel image for each arch. 2025-09-07T07:36:45.1566800Z 2025-09-07T07:36:45.1566940Z Note: Ninja is required to build SyclExtension. 2025-09-07T07:36:45.1567201Z 2025-09-07T07:36:45.1567450Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:45.1567832Z 2025-09-07T07:36:45.1568403Z msg = Cannot scrape callname=load in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/cpp_extension.py line=1597. 2025-09-07T07:36:45.1569270Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:45.1569665Z 2025-09-07T07:36:45.1569806Z Load a PyTorch C++ extension just-in-time (JIT). 2025-09-07T07:36:45.1570069Z 2025-09-07T07:36:45.1570276Z To load an extension, a Ninja build file is emitted, which is used to 2025-09-07T07:36:45.1570811Z compile the given sources into a dynamic library. This library is 2025-09-07T07:36:45.1571390Z subsequently loaded into the current Python process as a module and 2025-09-07T07:36:45.1571890Z returned from this function, ready for use. 2025-09-07T07:36:45.1572126Z 2025-09-07T07:36:45.1572332Z By default, the directory to which the build file is emitted and the 2025-09-07T07:36:45.1572892Z resulting library compiled to is ``/torch_extensions/``, where 2025-09-07T07:36:45.1573448Z ```` is the temporary folder on the current platform and ```` 2025-09-07T07:36:45.1573984Z the name of the extension. This location can be overridden in two ways. 2025-09-07T07:36:45.1574513Z First, if the ``TORCH_EXTENSIONS_DIR`` environment variable is set, it 2025-09-07T07:36:45.1575056Z replaces ``/torch_extensions`` and all extensions will be compiled 2025-09-07T07:36:45.1575613Z into subfolders of this directory. Second, if the ``build_directory`` 2025-09-07T07:36:45.1576183Z argument to this function is supplied, it overrides the entire path, i.e. 2025-09-07T07:36:45.1576686Z the library will be compiled into that folder directly. 2025-09-07T07:36:45.1576969Z 2025-09-07T07:36:45.1577177Z To compile the sources, the default system compiler (``c++``) is used, 2025-09-07T07:36:45.1577785Z which can be overridden by setting the ``CXX`` environment variable. To pass 2025-09-07T07:36:45.1578377Z additional arguments to the compilation process, ``extra_cflags`` or 2025-09-07T07:36:45.1578941Z ``extra_ldflags`` can be provided. For example, to compile your extension 2025-09-07T07:36:45.1579481Z with optimizations, pass ``extra_cflags=['-O3']``. You can also use 2025-09-07T07:36:45.1579998Z ``extra_cflags`` to pass further include directories. 2025-09-07T07:36:45.1580268Z 2025-09-07T07:36:45.1580504Z CUDA support with mixed compilation is provided. Simply pass CUDA source 2025-09-07T07:36:45.1581046Z files (``.cu`` or ``.cuh``) along with other sources. Such files will be 2025-09-07T07:36:45.1581592Z detected and compiled with nvcc rather than the C++ compiler. This includes 2025-09-07T07:36:45.1582152Z passing the CUDA lib64 directory as a library directory, and linking 2025-09-07T07:36:45.1582634Z ``cudart``. You can pass additional flags to nvcc via 2025-09-07T07:36:45.1583107Z ``extra_cuda_cflags``, just like with ``extra_cflags`` for C++. Various 2025-09-07T07:36:45.1583675Z heuristics for finding the CUDA install directory are used, which usually 2025-09-07T07:36:45.1584256Z work fine. If not, setting the ``CUDA_HOME`` environment variable is the 2025-09-07T07:36:45.1584680Z safest option. 2025-09-07T07:36:45.1584827Z 2025-09-07T07:36:45.1585064Z SYCL support with mixed compilation is provided. Simply pass SYCL source 2025-09-07T07:36:45.1585618Z files (``.sycl``) along with other sources. Such files will be detected 2025-09-07T07:36:45.1586143Z and compiled with SYCL compiler (such as Intel DPC++ Compiler) rather 2025-09-07T07:36:45.1586685Z than the C++ compiler. You can pass additional flags to SYCL compiler 2025-09-07T07:36:45.1587202Z via ``extra_sycl_cflags``, just like with ``extra_cflags`` for C++. 2025-09-07T07:36:45.1587718Z SYCL compiler is expected to be found via system PATH environment 2025-09-07T07:36:45.1588187Z variable. 2025-09-07T07:36:45.1588304Z 2025-09-07T07:36:45.1588386Z Args: 2025-09-07T07:36:45.1588711Z name: The name of the extension to build. This MUST be the same as the 2025-09-07T07:36:45.1589147Z name of the pybind11 module! 2025-09-07T07:36:45.1589553Z sources: A list of relative or absolute paths to C++ source files. 2025-09-07T07:36:45.1590087Z extra_cflags: optional list of compiler flags to forward to the build. 2025-09-07T07:36:45.1590652Z extra_cuda_cflags: optional list of compiler flags to forward to nvcc 2025-09-07T07:36:45.1591098Z when building CUDA sources. 2025-09-07T07:36:45.1591521Z extra_sycl_cflags: optional list of compiler flags to forward to SYCL 2025-09-07T07:36:45.1591969Z compiler when building SYCL sources. 2025-09-07T07:36:45.1592447Z extra_ldflags: optional list of linker flags to forward to the build. 2025-09-07T07:36:45.1593004Z extra_include_paths: optional list of include directories to forward 2025-09-07T07:36:45.1593434Z to the build. 2025-09-07T07:36:45.1593772Z build_directory: optional path to use as build workspace. 2025-09-07T07:36:45.1594233Z verbose: If ``True``, turns on verbose logging of load steps. 2025-09-07T07:36:45.1594837Z with_cuda: Determines whether CUDA headers and libraries are added to 2025-09-07T07:36:45.1595337Z the build. If set to ``None`` (default), this value is 2025-09-07T07:36:45.1595822Z automatically determined based on the existence of ``.cu`` or 2025-09-07T07:36:45.1596297Z ``.cuh`` in ``sources``. Set it to `True`` to force CUDA headers 2025-09-07T07:36:45.1596698Z and libraries to be included. 2025-09-07T07:36:45.1597130Z with_sycl: Determines whether SYCL headers and libraries are added to 2025-09-07T07:36:45.1597627Z the build. If set to ``None`` (default), this value is 2025-09-07T07:36:45.1598119Z automatically determined based on the existence of ``.sycl`` in 2025-09-07T07:36:45.1598600Z ``sources``. Set it to `True`` to force SYCL headers and 2025-09-07T07:36:45.1599019Z libraries to be included. 2025-09-07T07:36:45.1599426Z is_python_module: If ``True`` (default), imports the produced shared 2025-09-07T07:36:45.1599941Z library as a Python module. If ``False``, behavior depends on 2025-09-07T07:36:45.1600334Z ``is_standalone``. 2025-09-07T07:36:45.1600750Z is_standalone: If ``False`` (default) loads the constructed extension 2025-09-07T07:36:45.1601271Z into the process as a plain dynamic library. If ``True``, build a 2025-09-07T07:36:45.1601689Z standalone executable. 2025-09-07T07:36:45.1601871Z 2025-09-07T07:36:45.1601953Z Returns: 2025-09-07T07:36:45.1602184Z If ``is_python_module`` is ``True``: 2025-09-07T07:36:45.1602577Z Returns the loaded PyTorch extension as a Python module. 2025-09-07T07:36:45.1602863Z 2025-09-07T07:36:45.1603077Z If ``is_python_module`` is ``False`` and ``is_standalone`` is ``False``: 2025-09-07T07:36:45.1603609Z Returns nothing. (The shared library is loaded into the process as 2025-09-07T07:36:45.1604020Z a side effect.) 2025-09-07T07:36:45.1604183Z 2025-09-07T07:36:45.1604291Z If ``is_standalone`` is ``True``. 2025-09-07T07:36:45.1604733Z Return the path to the executable. (On Windows, TORCH_LIB_PATH is 2025-09-07T07:36:45.1605232Z added to the PATH environment variable as a side effect.) 2025-09-07T07:36:45.1605521Z 2025-09-07T07:36:45.1605605Z Example: 2025-09-07T07:36:45.1605828Z >>> # xdoctest: +SKIP 2025-09-07T07:36:45.1606136Z >>> from torch.utils.cpp_extension import load 2025-09-07T07:36:45.1606480Z >>> module = load( 2025-09-07T07:36:45.1606731Z ... name='extension', 2025-09-07T07:36:45.1607078Z ... sources=['extension.cpp', 'extension_kernel.cu'], 2025-09-07T07:36:45.1607460Z ... extra_cflags=['-O2'], 2025-09-07T07:36:45.1616337Z ... verbose=True) 2025-09-07T07:36:45.1616544Z 2025-09-07T07:36:45.1616817Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:45.1617205Z 2025-09-07T07:36:45.1617763Z 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=1885. 2025-09-07T07:36:45.1618655Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:45.1619051Z 2025-09-07T07:36:45.1619261Z Load a PyTorch C++ extension just-in-time (JIT) from string sources. 2025-09-07T07:36:45.1619597Z 2025-09-07T07:36:45.1619829Z This function behaves exactly like :func:`load`, but takes its sources as 2025-09-07T07:36:45.1620407Z strings rather than filenames. These strings are stored to files in the 2025-09-07T07:36:45.1620966Z build directory, after which the behavior of :func:`load_inline` is 2025-09-07T07:36:45.1621387Z identical to :func:`load`. 2025-09-07T07:36:45.1621745Z 2025-09-07T07:36:45.1621832Z See `the 2025-09-07T07:36:45.1622289Z tests `_ 2025-09-07T07:36:45.1622865Z for good examples of using this function. 2025-09-07T07:36:45.1623092Z 2025-09-07T07:36:45.1623325Z Sources may omit two required parts of a typical non-inline C++ extension: 2025-09-07T07:36:45.1624128Z the necessary header includes, as well as the (pybind11) binding code. More 2025-09-07T07:36:45.1624737Z precisely, strings passed to ``cpp_sources`` are first concatenated into a 2025-09-07T07:36:45.1625290Z single ``.cpp`` file. This file is then prepended with ``#include 2025-09-07T07:36:45.1625699Z `` 2025-09-07T07:36:45.1625856Z 2025-09-07T07:36:45.1626080Z Furthermore, if the ``functions`` argument is supplied, bindings will be 2025-09-07T07:36:45.1626669Z automatically generated for each function specified. ``functions`` can 2025-09-07T07:36:45.1627248Z either be a list of function names, or a dictionary mapping from function 2025-09-07T07:36:45.1627812Z names to docstrings. If a list is given, the name of each function is used 2025-09-07T07:36:45.1628238Z as its docstring. 2025-09-07T07:36:45.1628479Z 2025-09-07T07:36:45.1628692Z The sources in ``cuda_sources`` are concatenated into a separate ``.cu`` 2025-09-07T07:36:45.1629202Z file and prepended with ``torch/types.h``, ``cuda.h`` and 2025-09-07T07:36:45.1629702Z ``cuda_runtime.h`` includes. The ``.cpp`` and ``.cu`` files are compiled 2025-09-07T07:36:45.1630296Z separately, but ultimately linked into a single library. Note that no 2025-09-07T07:36:45.1630853Z bindings are generated for functions in ``cuda_sources`` per se. To bind 2025-09-07T07:36:45.1631420Z to a CUDA kernel, you must create a C++ function that calls it, and either 2025-09-07T07:36:45.1631970Z declare or define this C++ function in one of the ``cpp_sources`` (and 2025-09-07T07:36:45.1632408Z include its name in ``functions``). 2025-09-07T07:36:45.1632613Z 2025-09-07T07:36:45.1632830Z The sources in ``sycl_sources`` are concatenated into a separate ``.sycl`` 2025-09-07T07:36:45.1633384Z file and prepended with ``torch/types.h``, ``sycl/sycl.hpp`` includes. 2025-09-07T07:36:45.1633919Z The ``.cpp`` and ``.sycl`` files are compiled separately, but ultimately 2025-09-07T07:36:45.1634567Z linked into a single library. Note that no bindings are generated for 2025-09-07T07:36:45.1635118Z functions in ``sycl_sources`` per se. To bind to a SYCL kernel, you must 2025-09-07T07:36:45.1635653Z create a C++ function that calls it, and either declare or define this 2025-09-07T07:36:45.1636171Z C++ function in one of the ``cpp_sources`` (and include its name 2025-09-07T07:36:45.1636578Z in ``functions``). 2025-09-07T07:36:45.1636722Z 2025-09-07T07:36:45.1636726Z 2025-09-07T07:36:45.1636730Z 2025-09-07T07:36:45.1636927Z See :func:`load` for a description of arguments omitted below. 2025-09-07T07:36:45.1637219Z 2025-09-07T07:36:45.1637315Z Args: 2025-09-07T07:36:45.1637642Z cpp_sources: A string, or list of strings, containing C++ source code. 2025-09-07T07:36:45.1638200Z cuda_sources: A string, or list of strings, containing CUDA source code. 2025-09-07T07:36:45.1638770Z sycl_sources: A string, or list of strings, containing SYCL source code. 2025-09-07T07:36:45.1639320Z functions: A list of function names for which to generate function 2025-09-07T07:36:45.1639843Z bindings. If a dictionary is given, it should map function names to 2025-09-07T07:36:45.1640361Z docstrings (which are otherwise just the function names). 2025-09-07T07:36:45.1640882Z with_cuda: Determines whether CUDA headers and libraries are added to 2025-09-07T07:36:45.1641384Z the build. If set to ``None`` (default), this value is 2025-09-07T07:36:45.1641868Z automatically determined based on whether ``cuda_sources`` is 2025-09-07T07:36:45.1642328Z provided. Set it to ``True`` to force CUDA headers 2025-09-07T07:36:45.1642750Z and libraries to be included. 2025-09-07T07:36:45.1643189Z with_sycl: Determines whether SYCL headers and libraries are added to 2025-09-07T07:36:45.1643688Z the build. If set to ``None`` (default), this value is 2025-09-07T07:36:45.1644155Z automatically determined based on whether ``sycl_sources`` is 2025-09-07T07:36:45.1644629Z provided. Set it to ``True`` to force SYCL headers 2025-09-07T07:36:45.1645009Z and libraries to be included. 2025-09-07T07:36:45.1645430Z with_pytorch_error_handling: Determines whether pytorch error and 2025-09-07T07:36:45.1645942Z warning macros are handled by pytorch instead of pybind. To do 2025-09-07T07:36:45.1646479Z this, each function ``foo`` is called via an intermediary ``_safe_foo`` 2025-09-07T07:36:45.1647021Z function. This redirection might cause issues in obscure cases 2025-09-07T07:36:45.1647530Z of cpp. This flag should be set to ``False`` when this redirect 2025-09-07T07:36:45.1647931Z causes issues. 2025-09-07T07:36:45.1648329Z no_implicit_headers: If ``True``, skips automatically adding headers, most notably 2025-09-07T07:36:45.1648932Z ``#include `` and ``#include `` lines. 2025-09-07T07:36:45.1649466Z Use this option to improve cold start times when you 2025-09-07T07:36:45.1650005Z already include the necessary headers in your source code. Default: ``False``. 2025-09-07T07:36:45.1650380Z 2025-09-07T07:36:45.1650468Z Example: 2025-09-07T07:36:45.1650768Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2025-09-07T07:36:45.1651185Z >>> from torch.utils.cpp_extension import load_inline 2025-09-07T07:36:45.1651547Z >>> source = """ 2025-09-07T07:36:45.1651843Z at::Tensor sin_add(at::Tensor x, at::Tensor y) { 2025-09-07T07:36:45.1652198Z return x.sin() + y.sin(); 2025-09-07T07:36:45.1652469Z } 2025-09-07T07:36:45.1652670Z """ 2025-09-07T07:36:45.1652917Z >>> module = load_inline(name='inline_extension', 2025-09-07T07:36:45.1653289Z ... cpp_sources=[source], 2025-09-07T07:36:45.1653643Z ... functions=['sin_add']) 2025-09-07T07:36:45.1653873Z 2025-09-07T07:36:45.1653974Z .. note:: 2025-09-07T07:36:45.1654334Z Since load_inline will just-in-time compile the source code, please ensure 2025-09-07T07:36:45.1654941Z that you have the right toolchains installed in the runtime. For example, 2025-09-07T07:36:45.1655516Z when loading C++, make sure a C++ compiler is available. If you're loading 2025-09-07T07:36:45.1656110Z a CUDA extension, you will need to additionally install the corresponding CUDA 2025-09-07T07:36:45.1656727Z toolkit (nvcc and any other dependencies your code has). Compiling toolchains 2025-09-07T07:36:45.1657323Z are not included when you install torch and must be additionally installed. 2025-09-07T07:36:45.1657690Z 2025-09-07T07:36:45.1657944Z During compiling, by default, the Ninja backend uses #CPUS + 2 workers to build 2025-09-07T07:36:45.1658536Z the extension. This may use up too many resources on some systems. One 2025-09-07T07:36:45.1659096Z can control the number of workers by setting the `MAX_JOBS` environment 2025-09-07T07:36:45.1659562Z variable to a non-negative number. 2025-09-07T07:36:45.1659777Z 2025-09-07T07:36:45.1660026Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:45.1660405Z 2025-09-07T07:36:45.1698748Z 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. 2025-09-07T07:36:45.1699752Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:45.1700140Z 2025-09-07T07:36:45.1700433Z This class is a wrapper around a c++ component throughput_benchmark::ThroughputBenchmark. 2025-09-07T07:36:45.1700848Z 2025-09-07T07:36:45.1701211Z This wrapper on the throughput_benchmark::ThroughputBenchmark component is responsible 2025-09-07T07:36:45.1701881Z for executing a PyTorch module (nn.Module or ScriptModule) under an inference 2025-09-07T07:36:45.1702471Z server like load. It can emulate multiple calling threads to a single module 2025-09-07T07:36:45.1703074Z provided. In the future we plan to enhance this component to support inter and 2025-09-07T07:36:45.1703685Z intra-op parallelism as well as multiple models running in a single process. 2025-09-07T07:36:45.1704049Z 2025-09-07T07:36:45.1704318Z Please note that even though nn.Module is supported, it might incur an overhead 2025-09-07T07:36:45.1704919Z from the need to hold GIL every time we execute Python code or pass around 2025-09-07T07:36:45.1705490Z inputs as Python objects. As soon as you have a ScriptModule version of your 2025-09-07T07:36:45.1706086Z model for inference deployment it is better to switch to using it in this 2025-09-07T07:36:45.1706534Z benchmark. 2025-09-07T07:36:45.1706657Z 2025-09-07T07:36:45.1706761Z Example:: 2025-09-07T07:36:45.1706883Z 2025-09-07T07:36:45.1707009Z >>> # xdoctest: +SKIP("undefined vars") 2025-09-07T07:36:45.1707365Z >>> from torch.utils import ThroughputBenchmark 2025-09-07T07:36:45.1707749Z >>> bench = ThroughputBenchmark(my_module) 2025-09-07T07:36:45.1708185Z >>> # Pre-populate benchmark's data set with the inputs 2025-09-07T07:36:45.1708570Z >>> for input in inputs: 2025-09-07T07:36:45.1708963Z ... # Both args and kwargs work, same as any PyTorch Module / ScriptModule 2025-09-07T07:36:45.1709436Z ... bench.add_input(input[0], x2=input[1]) 2025-09-07T07:36:45.1709911Z >>> # Inputs supplied above are randomly used during the execution 2025-09-07T07:36:45.1710333Z >>> stats = bench.benchmark( 2025-09-07T07:36:45.1710634Z ... num_calling_threads=4, 2025-09-07T07:36:45.1710977Z ... num_warmup_iters = 100, 2025-09-07T07:36:45.1711283Z ... num_iters = 1000, 2025-09-07T07:36:45.1711551Z ... ) 2025-09-07T07:36:45.1711838Z >>> print("Avg latency (ms): {}".format(stats.latency_avg_ms)) 2025-09-07T07:36:45.1712309Z >>> print("Number of iterations: {}".format(stats.num_iters)) 2025-09-07T07:36:45.1712621Z 2025-09-07T07:36:45.1712869Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:45.1713235Z 2025-09-07T07:36:45.3925730Z 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. 2025-09-07T07:36:45.3926745Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:45.3927342Z Sampler that restricts data loading to a subset of the dataset. 2025-09-07T07:36:45.3927656Z 2025-09-07T07:36:45.3927791Z It is especially useful in conjunction with 2025-09-07T07:36:45.3928288Z :class:`torch.nn.parallel.DistributedDataParallel`. In such a case, each 2025-09-07T07:36:45.3928921Z process can pass a :class:`~torch.utils.data.DistributedSampler` instance as a 2025-09-07T07:36:45.3929540Z :class:`~torch.utils.data.DataLoader` sampler, and load a subset of the 2025-09-07T07:36:45.3930023Z original dataset that is exclusive to it. 2025-09-07T07:36:45.3930258Z 2025-09-07T07:36:45.3930371Z .. note:: 2025-09-07T07:36:45.3930739Z Dataset is assumed to be of constant size and that any instance of it always 2025-09-07T07:36:45.3931246Z returns the same elements in the same order. 2025-09-07T07:36:45.3931490Z 2025-09-07T07:36:45.3931587Z Args: 2025-09-07T07:36:45.3931818Z dataset: Dataset used for sampling. 2025-09-07T07:36:45.3932261Z num_replicas (int, optional): Number of processes participating in 2025-09-07T07:36:45.3932844Z distributed training. By default, :attr:`world_size` is retrieved from the 2025-09-07T07:36:45.3933334Z current distributed group. 2025-09-07T07:36:45.3933778Z rank (int, optional): Rank of the current process within :attr:`num_replicas`. 2025-09-07T07:36:45.3934423Z By default, :attr:`rank` is retrieved from the current distributed 2025-09-07T07:36:45.3934838Z group. 2025-09-07T07:36:45.3935212Z shuffle (bool, optional): If ``True`` (default), sampler will shuffle the 2025-09-07T07:36:45.3935655Z indices. 2025-09-07T07:36:45.3935988Z seed (int, optional): random seed used to shuffle the sampler if 2025-09-07T07:36:45.3936501Z :attr:`shuffle=True`. This number should be identical across all 2025-09-07T07:36:45.3937006Z processes in the distributed group. Default: ``0``. 2025-09-07T07:36:45.3937508Z drop_last (bool, optional): if ``True``, then the sampler will drop the 2025-09-07T07:36:45.3938048Z tail of the data to make it evenly divisible across the number of 2025-09-07T07:36:45.3938550Z replicas. If ``False``, the sampler will add extra indices to make 2025-09-07T07:36:45.3939076Z the data evenly divisible across the replicas. Default: ``False``. 2025-09-07T07:36:45.3939417Z 2025-09-07T07:36:45.3939507Z .. warning:: 2025-09-07T07:36:45.3939841Z In distributed mode, calling the :meth:`set_epoch` method at 2025-09-07T07:36:45.3940387Z the beginning of each epoch **before** creating the :class:`DataLoader` iterator 2025-09-07T07:36:45.3941086Z is necessary to make shuffling work properly across multiple epochs. Otherwise, 2025-09-07T07:36:45.3941599Z the same ordering will be always used. 2025-09-07T07:36:45.3941827Z 2025-09-07T07:36:45.3941934Z Example:: 2025-09-07T07:36:45.3942126Z 2025-09-07T07:36:45.3942239Z >>> # xdoctest: +SKIP 2025-09-07T07:36:45.3942634Z >>> sampler = DistributedSampler(dataset) if is_distributed else None 2025-09-07T07:36:45.3943149Z >>> loader = DataLoader(dataset, shuffle=(sampler is None), 2025-09-07T07:36:45.3943557Z ... sampler=sampler) 2025-09-07T07:36:45.3943922Z >>> for epoch in range(start_epoch, n_epochs): 2025-09-07T07:36:45.3944279Z ... if is_distributed: 2025-09-07T07:36:45.3944579Z ... sampler.set_epoch(epoch) 2025-09-07T07:36:45.3944895Z ... train(loader) 2025-09-07T07:36:45.3945155Z 2025-09-07T07:36:45.3945508Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:45.3945884Z 2025-09-07T07:36:45.3986629Z msg = Cannot scrape callname=WeightedRandomSampler in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/sampler.py line=227. 2025-09-07T07:36:45.3987658Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:45.3988292Z Samples elements from ``[0,..,len(weights)-1]`` with given probabilities (weights). 2025-09-07T07:36:45.3988760Z 2025-09-07T07:36:45.3988844Z Args: 2025-09-07T07:36:45.3989282Z weights (sequence) : a sequence of weights, not necessary summing up to one 2025-09-07T07:36:45.3989776Z num_samples (int): number of samples to draw 2025-09-07T07:36:45.3990242Z replacement (bool): if ``True``, samples are drawn with replacement. 2025-09-07T07:36:45.3990784Z If not, they are drawn without replacement, which means that when a 2025-09-07T07:36:45.3991334Z sample index is drawn for a row, it cannot be drawn again for that row. 2025-09-07T07:36:45.3991836Z generator (Generator): Generator used in sampling. 2025-09-07T07:36:45.3992103Z 2025-09-07T07:36:45.3992189Z Example: 2025-09-07T07:36:45.3992462Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2025-09-07T07:36:45.3992805Z >>> list( 2025-09-07T07:36:45.3993060Z ... WeightedRandomSampler( 2025-09-07T07:36:45.3993404Z ... [0.1, 0.9, 0.4, 0.7, 3.0, 0.6], 5, replacement=True 2025-09-07T07:36:45.3993746Z ... ) 2025-09-07T07:36:45.3993968Z ... ) 2025-09-07T07:36:45.3994183Z [4, 4, 1, 4, 5] 2025-09-07T07:36:45.3994468Z >>> list( 2025-09-07T07:36:45.3994787Z ... WeightedRandomSampler( 2025-09-07T07:36:45.3995151Z ... [0.9, 0.4, 0.05, 0.2, 0.3, 0.1], 5, replacement=False 2025-09-07T07:36:45.3995502Z ... ) 2025-09-07T07:36:45.3995714Z ... ) 2025-09-07T07:36:45.3995941Z [0, 1, 4, 3, 2] 2025-09-07T07:36:45.3996178Z 2025-09-07T07:36:45.3996549Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:45.3996914Z 2025-09-07T07:36:45.3997460Z msg = Cannot scrape callname=BatchSampler in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/sampler.py line=300. 2025-09-07T07:36:45.3998378Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:45.3998934Z Wraps another sampler to yield a mini-batch of indices. 2025-09-07T07:36:45.3999224Z 2025-09-07T07:36:45.3999307Z Args: 2025-09-07T07:36:45.3999657Z sampler (Sampler or Iterable): Base sampler. Can be any iterable object 2025-09-07T07:36:45.4000119Z batch_size (int): Size of mini-batch. 2025-09-07T07:36:45.4000604Z drop_last (bool): If ``True``, the sampler will drop the last batch if 2025-09-07T07:36:45.4001062Z its size would be less than ``batch_size`` 2025-09-07T07:36:45.4001382Z 2025-09-07T07:36:45.4001486Z Example: 2025-09-07T07:36:45.4001691Z >>> list( 2025-09-07T07:36:45.4001935Z ... BatchSampler( 2025-09-07T07:36:45.4002328Z ... SequentialSampler(range(10)), batch_size=3, drop_last=False 2025-09-07T07:36:45.4002739Z ... ) 2025-09-07T07:36:45.4002982Z ... ) 2025-09-07T07:36:45.4003213Z [[0, 1, 2], [3, 4, 5], [6, 7, 8], [9]] 2025-09-07T07:36:45.4003509Z >>> list( 2025-09-07T07:36:45.4003904Z ... BatchSampler(SequentialSampler(range(10)), batch_size=3, drop_last=True) 2025-09-07T07:36:45.4004368Z ... ) 2025-09-07T07:36:45.4004577Z [[0, 1, 2], [3, 4, 5], [6, 7, 8]] 2025-09-07T07:36:45.4004865Z 2025-09-07T07:36:45.4005230Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:45.4005595Z 2025-09-07T07:36:45.4180442Z msg = Cannot scrape callname=IterDataPipe in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/datapipe.py line=56. 2025-09-07T07:36:45.4181604Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:45.4181981Z 2025-09-07T07:36:45.4182100Z Iterable-style DataPipe. 2025-09-07T07:36:45.4182274Z 2025-09-07T07:36:45.4182532Z All DataPipes that represent an iterable of data samples should subclass this. 2025-09-07T07:36:45.4183173Z This style of DataPipes is particularly useful when data come from a stream, or 2025-09-07T07:36:45.4183904Z when the number of samples is too large to fit them all in memory. ``IterDataPipe`` is lazily initialized and its 2025-09-07T07:36:45.4184678Z elements are computed only when ``next()`` is called on the iterator of an ``IterDataPipe``. 2025-09-07T07:36:45.4185088Z 2025-09-07T07:36:45.4185329Z All subclasses should overwrite :meth:`__iter__`, which would return an 2025-09-07T07:36:45.4186011Z iterator of samples in this DataPipe. Calling ``__iter__`` of an ``IterDataPipe`` automatically invokes its 2025-09-07T07:36:45.4186851Z method ``reset()``, which by default performs no operation. When writing a custom ``IterDataPipe``, users should 2025-09-07T07:36:45.4187618Z override ``reset()`` if necessary. The common usages include resetting buffers, pointers, 2025-09-07T07:36:45.4188216Z and various state variables within the custom ``IterDataPipe``. 2025-09-07T07:36:45.4188527Z 2025-09-07T07:36:45.4188620Z Note: 2025-09-07T07:36:45.4188937Z Only `one` iterator can be valid for each ``IterDataPipe`` at a time, 2025-09-07T07:36:45.4189618Z and the creation a second iterator will invalidate the first one. This constraint is necessary because 2025-09-07T07:36:45.4190462Z some ``IterDataPipe`` have internal buffers, whose states can become invalid if there are multiple iterators. 2025-09-07T07:36:45.4191283Z The code example below presents details on how this constraint looks in practice. 2025-09-07T07:36:45.4192035Z If you have any feedback related to this constraint, please see `GitHub IterDataPipe Single Iterator Issue`_. 2025-09-07T07:36:45.4192533Z 2025-09-07T07:36:45.4192818Z These DataPipes can be invoked in two ways, using the class constructor or applying their 2025-09-07T07:36:45.4193590Z functional form onto an existing ``IterDataPipe`` (recommended, available to most but not all DataPipes). 2025-09-07T07:36:45.4194451Z You can chain multiple `IterDataPipe` together to form a pipeline that will perform multiple 2025-09-07T07:36:45.4194994Z operations in succession. 2025-09-07T07:36:45.4195200Z 2025-09-07T07:36:45.4195336Z .. _GitHub IterDataPipe Single Iterator Issue: 2025-09-07T07:36:45.4195743Z https://github.com/pytorch/data/issues/45 2025-09-07T07:36:45.4195998Z 2025-09-07T07:36:45.4196079Z Note: 2025-09-07T07:36:45.4196423Z When a subclass is used with :class:`~torch.utils.data.DataLoader`, each 2025-09-07T07:36:45.4197045Z item in the DataPipe will be yielded from the :class:`~torch.utils.data.DataLoader` 2025-09-07T07:36:45.4197641Z iterator. When :attr:`num_workers > 0`, each worker process will have a 2025-09-07T07:36:45.4198275Z different copy of the DataPipe object, so it is often desired to configure 2025-09-07T07:36:45.4198872Z each copy independently to avoid having duplicate data returned from the 2025-09-07T07:36:45.4199470Z workers. :func:`~torch.utils.data.get_worker_info`, when called in a worker 2025-09-07T07:36:45.4200115Z process, returns information about the worker. It can be used in either the 2025-09-07T07:36:45.4200738Z dataset's :meth:`__iter__` method or the :class:`~torch.utils.data.DataLoader` 's 2025-09-07T07:36:45.4201298Z :attr:`worker_init_fn` option to modify each copy's behavior. 2025-09-07T07:36:45.4201602Z 2025-09-07T07:36:45.4201705Z Examples: 2025-09-07T07:36:45.4201927Z General Usage: 2025-09-07T07:36:45.4202165Z >>> # xdoctest: +SKIP 2025-09-07T07:36:45.4202562Z >>> from torchdata.datapipes.iter import IterableWrapper, Mapper 2025-09-07T07:36:45.4203011Z >>> dp = IterableWrapper(range(10)) 2025-09-07T07:36:45.4203429Z >>> map_dp_1 = Mapper(dp, lambda x: x + 1) # Using class constructor 2025-09-07T07:36:45.4203856Z >>> map_dp_2 = dp.map( 2025-09-07T07:36:45.4204136Z ... lambda x: x + 1 2025-09-07T07:36:45.4204447Z ... ) # Using functional form (recommended) 2025-09-07T07:36:45.4204788Z >>> list(map_dp_1) 2025-09-07T07:36:45.4205043Z [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] 2025-09-07T07:36:45.4205340Z >>> list(map_dp_2) 2025-09-07T07:36:45.4205604Z [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] 2025-09-07T07:36:45.4205951Z >>> filter_dp = map_dp_1.filter(lambda x: x % 2 == 0) 2025-09-07T07:36:45.4206300Z >>> list(filter_dp) 2025-09-07T07:36:45.4206561Z [2, 4, 6, 8, 10] 2025-09-07T07:36:45.4206843Z Single Iterator Constraint Example: 2025-09-07T07:36:45.4207267Z >>> from torchdata.datapipes.iter import IterableWrapper, Mapper 2025-09-07T07:36:45.4207712Z >>> source_dp = IterableWrapper(range(10)) 2025-09-07T07:36:45.4208060Z >>> it1 = iter(source_dp) 2025-09-07T07:36:45.4208347Z >>> list(it1) 2025-09-07T07:36:45.4208595Z [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] 2025-09-07T07:36:45.4208882Z >>> it1 = iter(source_dp) 2025-09-07T07:36:45.4209160Z >>> it2 = iter( 2025-09-07T07:36:45.4209407Z ... source_dp 2025-09-07T07:36:45.4209726Z ... ) # The creation of a new iterator invalidates `it1` 2025-09-07T07:36:45.4210073Z >>> next(it2) 2025-09-07T07:36:45.4210305Z 0 2025-09-07T07:36:45.4210610Z >>> next(it1) # Further usage of `it1` will raise a `RunTimeError` 2025-09-07T07:36:45.4210909Z 2025-09-07T07:36:45.4211175Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:45.4211576Z 2025-09-07T07:36:45.4391611Z msg = Cannot scrape callname=DemultiplexerIterDataPipe in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/combining.py line=375. 2025-09-07T07:36:45.4392749Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:45.4393126Z 2025-09-07T07:36:45.4393585Z Splits the input DataPipe into multiple child DataPipes, using the given classification function (functional name: ``demux``). 2025-09-07T07:36:45.4394176Z 2025-09-07T07:36:45.4394398Z A list of the child DataPipes is returned from this operation. 2025-09-07T07:36:45.4394788Z 2025-09-07T07:36:45.4394875Z Args: 2025-09-07T07:36:45.4395141Z datapipe: Iterable DataPipe being filtered 2025-09-07T07:36:45.4395578Z num_instances: number of instances of the DataPipe to create 2025-09-07T07:36:45.4396254Z classifier_fn: a function that maps values to an integer within the range ``[0, num_instances - 1]`` or ``None`` 2025-09-07T07:36:45.4397077Z drop_none: defaults to ``False``, if ``True``, the function will skip over elements classified as ``None`` 2025-09-07T07:36:45.4397838Z buffer_size: this defines the maximum number of inputs that the buffer can hold across all child 2025-09-07T07:36:45.4398700Z DataPipes while waiting for their values to be yielded. 2025-09-07T07:36:45.4399170Z Defaults to ``1000``. Use ``-1`` for the unlimited buffer. 2025-09-07T07:36:45.4399452Z 2025-09-07T07:36:45.4399542Z Examples: 2025-09-07T07:36:45.4399876Z >>> # xdoctest: +REQUIRES(module:torchdata) 2025-09-07T07:36:45.4400292Z >>> from torchdata.datapipes.iter import IterableWrapper 2025-09-07T07:36:45.4400679Z >>> def odd_or_even(n): 2025-09-07T07:36:45.4400941Z ... return n % 2 2025-09-07T07:36:45.4401236Z >>> source_dp = IterableWrapper(range(5)) 2025-09-07T07:36:45.4401684Z >>> dp1, dp2 = source_dp.demux(num_instances=2, classifier_fn=odd_or_even) 2025-09-07T07:36:45.4402104Z >>> list(dp1) 2025-09-07T07:36:45.4402321Z [0, 2, 4] 2025-09-07T07:36:45.4402533Z >>> list(dp2) 2025-09-07T07:36:45.4402756Z [1, 3] 2025-09-07T07:36:45.4403115Z >>> # It can also filter out any element that gets `None` from the `classifier_fn` 2025-09-07T07:36:45.4403569Z >>> def odd_or_even_no_zero(n): 2025-09-07T07:36:45.4403926Z ... return n % 2 if n != 0 else None 2025-09-07T07:36:45.4404252Z >>> dp1, dp2 = source_dp.demux( 2025-09-07T07:36:45.4404671Z ... num_instances=2, classifier_fn=odd_or_even_no_zero, drop_none=True 2025-09-07T07:36:45.4405078Z ... ) 2025-09-07T07:36:45.4405357Z >>> list(dp1) 2025-09-07T07:36:45.4405586Z [2, 4] 2025-09-07T07:36:45.4405791Z >>> list(dp2) 2025-09-07T07:36:45.4405999Z [1, 3] 2025-09-07T07:36:45.4406125Z 2025-09-07T07:36:45.4406376Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:45.4406754Z 2025-09-07T07:36:45.4407471Z msg = Cannot scrape callname=MultiplexerIterDataPipe in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/combining.py line=594. 2025-09-07T07:36:45.4408539Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:45.4408918Z 2025-09-07T07:36:45.4409242Z Yields one element at a time from each of the input Iterable DataPipes (functional name: ``mux``). 2025-09-07T07:36:45.4409667Z 2025-09-07T07:36:45.4410028Z As in, one element from the 1st input DataPipe, then one element from the 2nd DataPipe in the next iteration, 2025-09-07T07:36:45.4410687Z and so on. It ends when the shortest input DataPipe is exhausted. 2025-09-07T07:36:45.4410981Z 2025-09-07T07:36:45.4411060Z Args: 2025-09-07T07:36:45.4411584Z datapipes: Iterable DataPipes that will take turn to yield their elements, until the shortest DataPipe is exhausted 2025-09-07T07:36:45.4412121Z 2025-09-07T07:36:45.4412206Z Example: 2025-09-07T07:36:45.4412447Z >>> # xdoctest: +REQUIRES(module:torchdata) 2025-09-07T07:36:45.4413544Z >>> from torchdata.datapipes.iter import IterableWrapper 2025-09-07T07:36:45.4413946Z >>> dp1, dp2, dp3 = ( 2025-09-07T07:36:45.4414237Z ... IterableWrapper(range(3)), 2025-09-07T07:36:45.4414578Z ... IterableWrapper(range(10, 15)), 2025-09-07T07:36:45.4414908Z ... IterableWrapper(range(20, 25)), 2025-09-07T07:36:45.4415221Z ... ) 2025-09-07T07:36:45.4415445Z >>> list(dp1.mux(dp2, dp3)) 2025-09-07T07:36:45.4415732Z [0, 10, 20, 1, 11, 21, 2, 12, 22] 2025-09-07T07:36:45.4415926Z 2025-09-07T07:36:45.4416175Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:45.4416555Z 2025-09-07T07:36:45.4417322Z msg = Cannot scrape callname=ZipperIterDataPipe in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/combining.py line=665. 2025-09-07T07:36:45.4418361Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:45.4418749Z 2025-09-07T07:36:45.4419057Z Aggregates elements into a tuple from each of the input DataPipes (functional name: ``zip``). 2025-09-07T07:36:45.4419482Z 2025-09-07T07:36:45.4419725Z The output is stopped as soon as the shortest input DataPipe is exhausted. 2025-09-07T07:36:45.4420108Z 2025-09-07T07:36:45.4420207Z Args: 2025-09-07T07:36:45.4420465Z *datapipes: Iterable DataPipes being aggregated 2025-09-07T07:36:45.4420736Z 2025-09-07T07:36:45.4420819Z Example: 2025-09-07T07:36:45.4421066Z >>> # xdoctest: +REQUIRES(module:torchdata) 2025-09-07T07:36:45.4421507Z >>> from torchdata.datapipes.iter import IterableWrapper 2025-09-07T07:36:45.4421892Z >>> dp1, dp2, dp3 = ( 2025-09-07T07:36:45.4422162Z ... IterableWrapper(range(5)), 2025-09-07T07:36:45.4422494Z ... IterableWrapper(range(10, 15)), 2025-09-07T07:36:45.4422837Z ... IterableWrapper(range(20, 25)), 2025-09-07T07:36:45.4423145Z ... ) 2025-09-07T07:36:45.4423359Z >>> list(dp1.zip(dp2, dp3)) 2025-09-07T07:36:45.4423852Z [(0, 10, 20), (1, 11, 21), (2, 12, 22), (3, 13, 23), (4, 14, 24)] 2025-09-07T07:36:45.4424096Z 2025-09-07T07:36:45.4424359Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:45.4424730Z 2025-09-07T07:36:45.4425509Z msg = Cannot scrape callname=FileOpenerIterDataPipe in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/fileopener.py line=18. 2025-09-07T07:36:45.4426573Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:45.4426950Z 2025-09-07T07:36:45.4427330Z Given pathnames, opens files and yield pathname and file stream in a tuple (functional name: ``open_files``). 2025-09-07T07:36:45.4427818Z 2025-09-07T07:36:45.4427897Z Args: 2025-09-07T07:36:45.4428180Z datapipe: Iterable datapipe that provides pathnames 2025-09-07T07:36:45.4428634Z mode: An optional string that specifies the mode in which 2025-09-07T07:36:45.4429137Z the file is opened by ``open()``. It defaults to ``r``, other options are 2025-09-07T07:36:45.4429622Z ``b`` for reading in binary mode and ``t`` for text mode. 2025-09-07T07:36:45.4430098Z encoding: An optional string that specifies the encoding of the 2025-09-07T07:36:45.4430680Z underlying file. It defaults to ``None`` to match the default encoding of ``open``. 2025-09-07T07:36:45.4431187Z length: Nominal length of the datapipe 2025-09-07T07:36:45.4431413Z 2025-09-07T07:36:45.4431508Z Note: 2025-09-07T07:36:45.4431886Z The opened file handles will be closed by Python's GC periodically. Users can choose 2025-09-07T07:36:45.4432391Z to close them explicitly. 2025-09-07T07:36:45.4432578Z 2025-09-07T07:36:45.4432678Z Example: 2025-09-07T07:36:45.4432897Z >>> # xdoctest: +SKIP 2025-09-07T07:36:45.4433210Z >>> from torchdata.datapipes.iter import ( 2025-09-07T07:36:45.4433547Z ... FileLister, 2025-09-07T07:36:45.4433800Z ... FileOpener, 2025-09-07T07:36:45.4434051Z ... StreamReader, 2025-09-07T07:36:45.4434384Z ... ) 2025-09-07T07:36:45.4434737Z >>> dp = FileLister(root=".").filter(lambda fname: fname.endswith(".txt")) 2025-09-07T07:36:45.4435177Z >>> dp = FileOpener(dp) 2025-09-07T07:36:45.4435466Z >>> dp = StreamReader(dp) 2025-09-07T07:36:45.4435728Z >>> list(dp) 2025-09-07T07:36:45.4435967Z [('./abc.txt', 'abc')] 2025-09-07T07:36:45.4436137Z 2025-09-07T07:36:45.4436405Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:45.4436777Z 2025-09-07T07:36:45.4451011Z msg = Cannot scrape callname=GrouperIterDataPipe in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/grouping.py line=155. 2025-09-07T07:36:45.4452056Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:45.4452435Z 2025-09-07T07:36:45.4452848Z Groups data from IterDataPipe by keys from ``group_key_fn``, yielding a ``DataChunk`` with batch size up to ``group_size``. 2025-09-07T07:36:45.4453390Z 2025-09-07T07:36:45.4453497Z (functional name: ``groupby``). 2025-09-07T07:36:45.4453699Z 2025-09-07T07:36:45.4454086Z The samples are read sequentially from the source ``datapipe``, and a batch of samples belonging to the same group 2025-09-07T07:36:45.4454968Z will be yielded as soon as the size of the batch reaches ``group_size``. When the buffer is full, 2025-09-07T07:36:45.4455694Z the DataPipe will yield the largest batch with the same key, provided that its size is larger 2025-09-07T07:36:45.4456521Z than ``guaranteed_group_size``. If its size is smaller, it will be dropped if ``drop_remaining=True``. 2025-09-07T07:36:45.4456962Z 2025-09-07T07:36:45.4457342Z After iterating through the entirety of source ``datapipe``, everything not dropped due to the buffer capacity 2025-09-07T07:36:45.4458169Z will be yielded from the buffer, even if the group sizes are smaller than ``guaranteed_group_size``. 2025-09-07T07:36:45.4458615Z 2025-09-07T07:36:45.4458697Z Args: 2025-09-07T07:36:45.4458952Z datapipe: Iterable datapipe to be grouped 2025-09-07T07:36:45.4459465Z group_key_fn: Function used to generate group key from the data of the source datapipe 2025-09-07T07:36:45.4460087Z keep_key: Option to yield the matching key along with the items in a tuple, 2025-09-07T07:36:45.4460645Z resulting in `(key, [items])` otherwise returning [items] 2025-09-07T07:36:45.4461090Z buffer_size: The size of buffer for ungrouped data 2025-09-07T07:36:45.4461628Z group_size: The max size of each group, a batch is yielded as soon as it reaches this size 2025-09-07T07:36:45.4462356Z guaranteed_group_size: The guaranteed minimum group size to be yielded in case the buffer is full 2025-09-07T07:36:45.4463167Z drop_remaining: Specifies if the group smaller than ``guaranteed_group_size`` will be dropped from buffer 2025-09-07T07:36:45.4463748Z when the buffer is full 2025-09-07T07:36:45.4463932Z 2025-09-07T07:36:45.4464034Z Example: 2025-09-07T07:36:45.4464252Z >>> import os 2025-09-07T07:36:45.4464477Z >>> # xdoctest: +SKIP 2025-09-07T07:36:45.4464826Z >>> from torchdata.datapipes.iter import IterableWrapper 2025-09-07T07:36:45.4465223Z >>> def group_fn(file): 2025-09-07T07:36:45.4465544Z ... return os.path.basename(file).split(".")[0] 2025-09-07T07:36:45.4465901Z >>> source_dp = IterableWrapper( 2025-09-07T07:36:45.4466270Z ... ["a.png", "b.png", "a.json", "b.json", "a.jpg", "c.json"] 2025-09-07T07:36:45.4466626Z ... ) 2025-09-07T07:36:45.4466888Z >>> dp0 = source_dp.groupby(group_key_fn=group_fn) 2025-09-07T07:36:45.4467217Z >>> list(dp0) 2025-09-07T07:36:45.4467553Z [['a.png', 'a.json', 'a.jpg'], ['b.png', 'b.json'], ['c.json']] 2025-09-07T07:36:45.4468021Z >>> # A group is yielded as soon as its size equals to `group_size` 2025-09-07T07:36:45.4468510Z >>> dp1 = source_dp.groupby(group_key_fn=group_fn, group_size=2) 2025-09-07T07:36:45.4468887Z >>> list(dp1) 2025-09-07T07:36:45.4469225Z [['a.png', 'a.json'], ['b.png', 'b.json'], ['a.jpg'], ['c.json']] 2025-09-07T07:36:45.4469852Z >>> # Scenario where `buffer` is full, and group 'a' needs to be yielded since its size > `guaranteed_group_size` 2025-09-07T07:36:45.4470428Z >>> dp2 = source_dp.groupby( 2025-09-07T07:36:45.4470728Z ... group_key_fn=group_fn, 2025-09-07T07:36:45.4471019Z ... buffer_size=3, 2025-09-07T07:36:45.4471286Z ... group_size=3, 2025-09-07T07:36:45.4471563Z ... guaranteed_group_size=2, 2025-09-07T07:36:45.4471853Z ... ) 2025-09-07T07:36:45.4472049Z >>> list(dp2) 2025-09-07T07:36:45.4472393Z [['a.png', 'a.json'], ['b.png', 'b.json'], ['a.jpg'], ['c.json']] 2025-09-07T07:36:45.4472665Z 2025-09-07T07:36:45.4472929Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:45.4473296Z 2025-09-07T07:36:45.5885158Z gathering tests 2025-09-07T07:36:45.5896429Z running 732 test(s) 2025-09-07T07:36:45.5916403Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::typename:0, line 1082 <- wrt source file 2025-09-07T07:36:45.5924642Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::typename:0 2025-09-07T07:36:45.5925929Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::is_tensor:0, line 1118 <- wrt source file 2025-09-07T07:36:45.5929695Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::is_tensor:0 2025-09-07T07:36:45.5931105Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::set_default_device:0, line 1203 <- wrt source file 2025-09-07T07:36:45.5932413Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::set_default_device:0 2025-09-07T07:36:45.5933689Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::set_default_tensor_type:0, line 1252 <- wrt source file 2025-09-07T07:36:45.5935132Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::set_default_tensor_type:0 2025-09-07T07:36:45.5936483Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::set_default_dtype:0, line 1289 <- wrt source file 2025-09-07T07:36:45.5937758Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::set_default_dtype:0 2025-09-07T07:36:45.5939310Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::use_deterministic_algorithms:0, line 1444 <- wrt source file 2025-09-07T07:36:45.5940763Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::use_deterministic_algorithms:0 2025-09-07T07:36:45.5942192Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::compile:0, line 2568 <- wrt source file 2025-09-07T07:36:45.5943451Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::compile:0 2025-09-07T07:36:45.5944677Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::_is_device_backend_autoload_enabled:0, line 2841 <- wrt source file 2025-09-07T07:36:45.5946047Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::_is_device_backend_autoload_enabled:0 2025-09-07T07:36:45.5947377Z * 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 2025-09-07T07:36:45.5948726Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_C.cpython-313-x86_64-linux-gnu.so::Generator:0 2025-09-07T07:36:45.5950129Z * 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 2025-09-07T07:36:45.5951510Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_C.cpython-313-x86_64-linux-gnu.so::_LinAlgError:0 2025-09-07T07:36:45.5952735Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_custom_ops.py::custom_op:0, line 55 <- wrt source file 2025-09-07T07:36:45.5953882Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_custom_ops.py::custom_op:0 2025-09-07T07:36:45.5955104Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_custom_ops.py::impl:0, line 138 <- wrt source file 2025-09-07T07:36:45.5956198Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_custom_ops.py::impl:0 2025-09-07T07:36:45.5957306Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_custom_ops.py::impl_abstract:0, line 208 <- wrt source file 2025-09-07T07:36:45.6365145Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_custom_ops.py::impl_abstract:0 2025-09-07T07:36:45.6366409Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_namedtensor_internals.py::update_names:0, line 118 <- wrt source file 2025-09-07T07:36:45.6367904Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_namedtensor_internals.py::update_names:0 2025-09-07T07:36:45.6369168Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py::Tensor.register_hook:0, line 649 <- wrt source file 2025-09-07T07:36:45.6378369Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py::Tensor.register_hook:0 2025-09-07T07:36:45.6379666Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py::Tensor.register_post_accumulate_grad_hook:0, line 706 <- wrt source file 2025-09-07T07:36:45.6399737Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py::Tensor.register_post_accumulate_grad_hook:0 2025-09-07T07:36:45.6401036Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py::Tensor.refine_names:0, line 1333 <- wrt source file 2025-09-07T07:36:45.6512948Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py::Tensor.refine_names:0 2025-09-07T07:36:45.6516020Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py::Tensor.align_to:0, line 1378 <- wrt source file 2025-09-07T07:36:45.6521247Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py::Tensor.align_to:0 2025-09-07T07:36:45.6522397Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py::Tensor.rename:0, line 1451 <- wrt source file 2025-09-07T07:36:45.6528834Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py::Tensor.rename:0 2025-09-07T07:36:45.6530000Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py::Tensor.to_sparse_coo:0, line 1481 <- wrt source file 2025-09-07T07:36:45.6534825Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py::Tensor.to_sparse_coo:0 2025-09-07T07:36:45.6536001Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor_str.py::set_printoptions:0, line 53 <- wrt source file 2025-09-07T07:36:45.6557003Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor_str.py::set_printoptions:0 2025-09-07T07:36:45.6558209Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::broadcast_tensors:0, line 64 <- wrt source file 2025-09-07T07:36:45.6564013Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::broadcast_tensors:0 2025-09-07T07:36:45.6565224Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::broadcast_shapes:0, line 92 <- wrt source file 2025-09-07T07:36:45.6567552Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::broadcast_shapes:0 2025-09-07T07:36:45.6568698Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::split:0, line 144 <- wrt source file 2025-09-07T07:36:45.6581247Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::split:0 2025-09-07T07:36:45.6582353Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::einsum:0, line 258 <- wrt source file 2025-09-07T07:36:45.6599354Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::einsum:0 2025-09-07T07:36:45.6600560Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::_unique_consecutive_impl:0, line 992 <- wrt source file 2025-09-07T07:36:45.6612346Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::_unique_consecutive_impl:0 2025-09-07T07:36:45.6613680Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::tensordot:0, line 1267 <- wrt source file 2025-09-07T07:36:45.6624906Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::tensordot:0 2025-09-07T07:36:45.6626185Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::cartesian_prod:0, line 1351 <- wrt source file 2025-09-07T07:36:45.6632655Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::cartesian_prod:0 2025-09-07T07:36:45.6633824Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::block_diag:0, line 1385 <- wrt source file 2025-09-07T07:36:45.6644636Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::block_diag:0 2025-09-07T07:36:45.6645852Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::cdist:0, line 1441 <- wrt source file 2025-09-07T07:36:45.6660655Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::cdist:0 2025-09-07T07:36:45.6661784Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::atleast_1d:0, line 1482 <- wrt source file 2025-09-07T07:36:45.6679991Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::atleast_1d:0 2025-09-07T07:36:45.6681142Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::atleast_2d:0, line 1520 <- wrt source file 2025-09-07T07:36:45.6699766Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::atleast_2d:0 2025-09-07T07:36:45.6700910Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::atleast_3d:0, line 1560 <- wrt source file 2025-09-07T07:36:45.6724610Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::atleast_3d:0 2025-09-07T07:36:45.6725719Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::norm:0, line 1735 <- wrt source file 2025-09-07T07:36:45.6762108Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::norm:0 2025-09-07T07:36:45.6763252Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::unravel_index:0, line 1903 <- wrt source file 2025-09-07T07:36:45.6791657Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::unravel_index:0 2025-09-07T07:36:45.6792870Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::chain_matmul:0, line 2003 <- wrt source file 2025-09-07T07:36:45.6794418Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::chain_matmul:0 2025-09-07T07:36:45.6795551Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::_lu_impl:0, line 2104 <- wrt source file 2025-09-07T07:36:45.6796695Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::_lu_impl:0 2025-09-07T07:36:45.6797753Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/hub.py::list:0, line 473 <- wrt source file 2025-09-07T07:36:45.6798896Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/hub.py::list:0 2025-09-07T07:36:45.6799954Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/hub.py::help:0, line 533 <- wrt source file 2025-09-07T07:36:45.6800977Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/hub.py::help:0 2025-09-07T07:36:45.6802140Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::Library.define:0, line 153 <- wrt source file 2025-09-07T07:36:45.6803306Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::Library.define:0 2025-09-07T07:36:45.6804583Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::Library._impl_with_aoti_compile:0, line 247 <- wrt source file 2025-09-07T07:36:45.6811578Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::Library._impl_with_aoti_compile:0 2025-09-07T07:36:45.6812821Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::Library.impl:0, line 307 <- wrt source file 2025-09-07T07:36:45.6815936Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::Library.impl:0 2025-09-07T07:36:45.6817101Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::define:0, line 512 <- wrt source file 2025-09-07T07:36:45.6828125Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::define:0 2025-09-07T07:36:45.6829185Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::impl:0, line 618 <- wrt source file 2025-09-07T07:36:45.6844529Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::impl:0 2025-09-07T07:36:45.6845889Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::register_kernel:0, line 799 <- wrt source file 2025-09-07T07:36:45.6847071Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::register_kernel:0 2025-09-07T07:36:45.6848217Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::register_autocast:0, line 867 <- wrt source file 2025-09-07T07:36:45.6849422Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::register_autocast:0 2025-09-07T07:36:45.6850632Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::register_torch_dispatch:0, line 1232 <- wrt source file 2025-09-07T07:36:45.6937497Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::register_torch_dispatch:0 2025-09-07T07:36:45.6938692Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::register_vmap:0, line 1321 <- wrt source file 2025-09-07T07:36:45.7087045Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::register_vmap:0 2025-09-07T07:36:45.7088252Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/overrides.py::get_ignored_functions:0, line 116 <- wrt source file 2025-09-07T07:36:45.7093608Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/overrides.py::get_ignored_functions:0 2025-09-07T07:36:45.7094862Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/overrides.py::get_testing_overrides:0, line 423 <- wrt source file 2025-09-07T07:36:45.7131132Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/overrides.py::get_testing_overrides:0 2025-09-07T07:36:45.7132368Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/overrides.py::wrap_torch_function:0, line 1578 <- wrt source file 2025-09-07T07:36:45.7134576Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/overrides.py::wrap_torch_function:0 2025-09-07T07:36:45.7135809Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/overrides.py::handle_torch_function:0, line 1713 <- wrt source file 2025-09-07T07:36:45.7137324Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/overrides.py::handle_torch_function:0 2025-09-07T07:36:45.7138588Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/overrides.py::is_tensor_method_or_property:0, line 1961 <- wrt source file 2025-09-07T07:36:45.7163557Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/overrides.py::is_tensor_method_or_property:0 2025-09-07T07:36:45.7164809Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/overrides.py::is_tensor_like:0, line 1980 <- wrt source file 2025-09-07T07:36:45.7170717Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/overrides.py::is_tensor_like:0 2025-09-07T07:36:45.7171991Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/quasirandom.py::SobolEngine:0, line 39 <- wrt source file 2025-09-07T07:36:45.7173286Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/quasirandom.py::SobolEngine:0 2025-09-07T07:36:45.7174655Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/serialization.py::add_safe_globals:0, line 299 <- wrt source file 2025-09-07T07:36:45.7175922Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/serialization.py::add_safe_globals:0 2025-09-07T07:36:45.7177119Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/serialization.py::safe_globals:0, line 324 <- wrt source file 2025-09-07T07:36:45.7178490Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/serialization.py::safe_globals:0 2025-09-07T07:36:45.7179815Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/serialization.py::skip_data:0, line 400 <- wrt source file 2025-09-07T07:36:45.7181015Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/serialization.py::skip_data:0 2025-09-07T07:36:45.7182304Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/serialization.py::register_package:0, line 472 <- wrt source file 2025-09-07T07:36:45.7183573Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/serialization.py::register_package:0 2025-09-07T07:36:45.7184743Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/serialization.py::save:0, line 950 <- wrt source file 2025-09-07T07:36:45.7185886Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/serialization.py::save:0 2025-09-07T07:36:45.7187138Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/torch_version.py::TorchVersion:0, line 19 <- wrt source file 2025-09-07T07:36:45.7188353Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/torch_version.py::TorchVersion:0 2025-09-07T07:36:45.7189585Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_inductor/__init__.py::list_mode_options:0, line 320 <- wrt source file 2025-09-07T07:36:45.7190861Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_inductor/__init__.py::list_mode_options:0 2025-09-07T07:36:45.7192104Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_inductor/__init__.py::list_options:0, line 357 <- wrt source file 2025-09-07T07:36:45.7196526Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_inductor/__init__.py::list_options:0 2025-09-07T07:36:45.7197828Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/accelerator/__init__.py::current_accelerator:0, line 113 <- wrt source file 2025-09-07T07:36:45.7202304Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/accelerator/__init__.py::current_accelerator:0 2025-09-07T07:36:45.7203804Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/accelerator/__init__.py::device_index:0, line 249 <- wrt source file 2025-09-07T07:36:45.7205098Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/accelerator/__init__.py::device_index:0 2025-09-07T07:36:45.7206369Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::allow_in_graph:0, line 127 <- wrt source file 2025-09-07T07:36:45.7207616Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::allow_in_graph:0 2025-09-07T07:36:45.7208877Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::substitute_in_graph:0, line 183 <- wrt source file 2025-09-07T07:36:45.9794940Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::substitute_in_graph:0 2025-09-07T07:36:45.9796459Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::wrap_numpy:0, line 413 <- wrt source file 2025-09-07T07:36:45.9797699Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::wrap_numpy:0 2025-09-07T07:36:45.9798978Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::is_compiling:0, line 445 <- wrt source file 2025-09-07T07:36:45.9800206Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::is_compiling:0 2025-09-07T07:36:45.9801525Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::is_dynamo_compiling:0, line 466 <- wrt source file 2025-09-07T07:36:45.9802839Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::is_dynamo_compiling:0 2025-09-07T07:36:45.9804084Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::is_exporting:0, line 484 <- wrt source file 2025-09-07T07:36:45.9805526Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::is_exporting:0 2025-09-07T07:36:45.9807085Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::save_cache_artifacts:0, line 499 <- wrt source file 2025-09-07T07:36:45.9808534Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::save_cache_artifacts:0 2025-09-07T07:36:45.9810081Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::load_cache_artifacts:0, line 514 <- wrt source file 2025-09-07T07:36:45.9811656Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::load_cache_artifacts:0 2025-09-07T07:36:45.9813066Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/__init__.py::_compile_kernel:0, line 1760 <- wrt source file 2025-09-07T07:36:45.9814448Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/__init__.py::_compile_kernel:0 2025-09-07T07:36:45.9816011Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/export/__init__.py::save:0, line 349 <- wrt source file 2025-09-07T07:36:45.9817157Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/export/__init__.py::save:0 2025-09-07T07:36:45.9818263Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/export/__init__.py::load:0, line 419 <- wrt source file 2025-09-07T07:36:45.9819392Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/export/__init__.py::load:0 2025-09-07T07:36:45.9820685Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/export/__init__.py::register_dataclass:0, line 576 <- wrt source file 2025-09-07T07:36:45.9821968Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/export/__init__.py::register_dataclass:0 2025-09-07T07:36:45.9823316Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/futures/__init__.py::Future.add_done_callback:0, line 197 <- wrt source file 2025-09-07T07:36:45.9824868Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/futures/__init__.py::Future.add_done_callback:0 2025-09-07T07:36:45.9826174Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/futures/__init__.py::Future.set_exception:0, line 261 <- wrt source file 2025-09-07T07:36:45.9827481Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/futures/__init__.py::Future.set_exception:0 2025-09-07T07:36:45.9828807Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/futures/__init__.py::collect_all:0, line 295 <- wrt source file 2025-09-07T07:36:45.9830188Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/futures/__init__.py::collect_all:0 2025-09-07T07:36:45.9831366Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/jit/__init__.py::annotate:0, line 147 <- wrt source file 2025-09-07T07:36:45.9832497Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/jit/__init__.py::annotate:0 2025-09-07T07:36:45.9833733Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/monitor/__init__.py::TensorboardEventHandler:0, line 22 <- wrt source file 2025-09-07T07:36:45.9835160Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/monitor/__init__.py::TensorboardEventHandler:0 2025-09-07T07:36:45.9836437Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nested/__init__.py::as_nested_tensor:0, line 61 <- wrt source file 2025-09-07T07:36:45.9873081Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nested/__init__.py::as_nested_tensor:0 2025-09-07T07:36:45.9875559Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nested/__init__.py::nested_tensor:0, line 240 <- wrt source file 2025-09-07T07:36:45.9878482Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nested/__init__.py::nested_tensor:0 2025-09-07T07:36:45.9880926Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nested/__init__.py::narrow:0, line 315 <- wrt source file 2025-09-07T07:36:45.9952328Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nested/__init__.py::narrow:0 2025-09-07T07:36:45.9954824Z * 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 2025-09-07T07:36:45.9963468Z W0907 07:36:45.995000 1450 site-packages/torch/fx/_symbolic_trace.py:52] is_fx_tracing will return true for both fx.symbolic_trace and torch.export. Please use is_fx_tracing_symbolic_tracing() for specifically fx.symbolic_trace or torch.compiler.is_compiling() for specifically torch.export/compile. 2025-09-07T07:36:45.9984992Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nested/__init__.py::nested_tensor_from_jagged:0 2025-09-07T07:36:45.9987499Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nested/__init__.py::masked_select:0, line 481 <- wrt source file 2025-09-07T07:36:46.0009518Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nested/__init__.py::masked_select:0 2025-09-07T07:36:46.0012073Z * 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 2025-09-07T07:36:46.0022727Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/sparse/__init__.py::check_sparse_tensor_invariants:0 2025-09-07T07:36:46.0025612Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_dynamo/decorators.py::substitute_in_graph:0, line 349 <- wrt source file 2025-09-07T07:36:46.0028248Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_dynamo/decorators.py::substitute_in_graph:0 2025-09-07T07:36:46.0030968Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_dynamo/variables/base.py::VariableTracker.python_type:0, line 322 <- wrt source file 2025-09-07T07:36:46.0033869Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_dynamo/variables/base.py::VariableTracker.python_type:0 2025-09-07T07:36:46.0036833Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_export/utils.py::register_module_as_pytree_input_node:0, line 1410 <- wrt source file 2025-09-07T07:36:46.0039693Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_export/utils.py::register_module_as_pytree_input_node:0 2025-09-07T07:36:46.0042693Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_export/wrappers.py::mark_subclass_constructor_exportable_experimental:0, line 158 <- wrt source file 2025-09-07T07:36:46.0045864Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_export/wrappers.py::mark_subclass_constructor_exportable_experimental:0 2025-09-07T07:36:46.0048664Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/aot_autograd.py::aot_function:0, line 768 <- wrt source file 2025-09-07T07:36:46.0488916Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/aot_autograd.py::aot_function:0 2025-09-07T07:36:46.0491647Z * 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 2025-09-07T07:36:46.0494494Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/benchmark_utils.py::benchmark_utilization:0 2025-09-07T07:36:46.0497091Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::vjp:0, line 233 <- wrt source file 2025-09-07T07:36:46.0548995Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::vjp:0 2025-09-07T07:36:46.0551463Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::jacrev:0, line 475 <- wrt source file 2025-09-07T07:36:46.0636127Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::jacrev:0 2025-09-07T07:36:46.0638612Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::jvp:0, line 1023 <- wrt source file 2025-09-07T07:36:46.1443228Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::jvp:0 2025-09-07T07:36:46.1445826Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::jacfwd:0, line 1181 <- wrt source file 2025-09-07T07:36:46.1532974Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::jacfwd:0 2025-09-07T07:36:46.1535522Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::hessian:0, line 1341 <- wrt source file 2025-09-07T07:36:46.1560300Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::hessian:0 2025-09-07T07:36:46.1562942Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::functionalize:0, line 1505 <- wrt source file 2025-09-07T07:36:46.1565809Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::functionalize:0 2025-09-07T07:36:46.1568447Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::linearize:0, line 1704 <- wrt source file 2025-09-07T07:36:46.1819916Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::linearize:0 2025-09-07T07:36:46.1822549Z * 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 2025-09-07T07:36:46.1825596Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/functional_call.py::functional_call:0 2025-09-07T07:36:46.1828156Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/fx_minifier.py::minifier:0, line 194 <- wrt source file 2025-09-07T07:36:46.1830657Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/fx_minifier.py::minifier:0 2025-09-07T07:36:46.1833444Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/_aot_autograd/schemas.py::CompilerWrapper.post_compile:0, line 1131 <- wrt source file 2025-09-07T07:36:46.1836595Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/_aot_autograd/schemas.py::CompilerWrapper.post_compile:0 2025-09-07T07:36:46.1839667Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/_aot_autograd/schemas.py::InductorWrapper.post_compile:0, line 1186 <- wrt source file 2025-09-07T07:36:46.1842788Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/_aot_autograd/schemas.py::InductorWrapper.post_compile:0 2025-09-07T07:36:46.1845711Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_higher_order_ops/associative_scan.py::associative_scan:0, line 186 <- wrt source file 2025-09-07T07:36:46.1848611Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_higher_order_ops/associative_scan.py::associative_scan:0 2025-09-07T07:36:46.1851622Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_higher_order_ops/associative_scan.py::generic_associative_scan:0, line 322 <- wrt source file 2025-09-07T07:36:46.1854692Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_higher_order_ops/associative_scan.py::generic_associative_scan:0 2025-09-07T07:36:46.1857325Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_higher_order_ops/cond.py::cond:0, line 155 <- wrt source file 2025-09-07T07:36:46.1859705Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_higher_order_ops/cond.py::cond:0 2025-09-07T07:36:46.1862215Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_higher_order_ops/flat_apply.py::FlatApply.__call__:0, line 80 <- wrt source file 2025-09-07T07:36:46.1864981Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_higher_order_ops/flat_apply.py::FlatApply.__call__:0 2025-09-07T07:36:46.1867472Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_higher_order_ops/map.py::map:0, line 79 <- wrt source file 2025-09-07T07:36:46.1869813Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_higher_order_ops/map.py::map:0 2025-09-07T07:36:46.1871734Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_higher_order_ops/scan.py::scan:0, line 156 <- wrt source file 2025-09-07T07:36:46.1872943Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_higher_order_ops/scan.py::scan:0 2025-09-07T07:36:46.1874241Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_higher_order_ops/scan.py::ScanAutogradOp:0, line 474 <- wrt source file 2025-09-07T07:36:46.1875655Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_higher_order_ops/scan.py::ScanAutogradOp:0 2025-09-07T07:36:46.1876964Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_inductor/codecache.py::WritableTempFile:0, line 374 <- wrt source file 2025-09-07T07:36:46.1878292Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_inductor/codecache.py::WritableTempFile:0 2025-09-07T07:36:46.1879730Z * 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 1721 <- wrt source file 2025-09-07T07:36:46.1881259Z * 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 2025-09-07T07:36:46.1882696Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_inductor/select_algorithm.py::add_preprocessing_fn:0, line 3473 <- wrt source file 2025-09-07T07:36:46.1884117Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_inductor/select_algorithm.py::add_preprocessing_fn:0 2025-09-07T07:36:46.1885621Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_inductor/template_heuristics/registry.py::register_template_heuristic:0, line 54 <- wrt source file 2025-09-07T07:36:46.1887241Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_inductor/template_heuristics/registry.py::register_template_heuristic:0 2025-09-07T07:36:46.1888607Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/custom_ops.py::custom_op:0, line 98 <- wrt source file 2025-09-07T07:36:46.2356466Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/custom_ops.py::custom_op:0 2025-09-07T07:36:46.2357795Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/custom_ops.py::CustomOpDef.set_kernel_enabled:0, line 238 <- wrt source file 2025-09-07T07:36:46.2442167Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/custom_ops.py::CustomOpDef.set_kernel_enabled:0 2025-09-07T07:36:46.2443589Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_kernel:0, line 307 <- wrt source file 2025-09-07T07:36:46.2445018Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_kernel:0 2025-09-07T07:36:46.2446419Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_autograd:0, line 541 <- wrt source file 2025-09-07T07:36:46.2608722Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_autograd:0 2025-09-07T07:36:46.2610113Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_vmap:0, line 709 <- wrt source file 2025-09-07T07:36:46.2776539Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_vmap:0 2025-09-07T07:36:46.2777921Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_autocast:0, line 795 <- wrt source file 2025-09-07T07:36:46.2779480Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_autocast:0 2025-09-07T07:36:46.2780956Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/fake_class_registry.py::register_fake_class:0, line 230 <- wrt source file 2025-09-07T07:36:46.2782372Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/fake_class_registry.py::register_fake_class:0 2025-09-07T07:36:46.2783756Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/fake_impl.py::FakeImplCtx.new_dynamic_size:0, line 175 <- wrt source file 2025-09-07T07:36:46.2850317Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/fake_impl.py::FakeImplCtx.new_dynamic_size:0 2025-09-07T07:36:46.2851702Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/infer_schema.py::infer_schema:0, line 51 <- wrt source file 2025-09-07T07:36:46.2856575Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/infer_schema.py::infer_schema:0 2025-09-07T07:36:46.2857792Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_logging/_internal.py::set_logs:0, line 459 <- wrt source file 2025-09-07T07:36:46.2859005Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_logging/_internal.py::set_logs:0 2025-09-07T07:36:46.2860226Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_equal:0, line 171 <- wrt source file 2025-09-07T07:36:46.2897832Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_equal:0 2025-09-07T07:36:46.2899122Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_array_less:0, line 1008 <- wrt source file 2025-09-07T07:36:46.2953986Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_array_less:0 2025-09-07T07:36:46.2955329Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_string_equal:0, line 1073 <- wrt source file 2025-09-07T07:36:46.2956674Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_string_equal:0 2025-09-07T07:36:46.2958086Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_allclose:0, line 1294 <- wrt source file 2025-09-07T07:36:46.2973574Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_allclose:0 2025-09-07T07:36:46.2974948Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_array_almost_equal_nulp:0, line 1360 <- wrt source file 2025-09-07T07:36:46.2977384Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_array_almost_equal_nulp:0 2025-09-07T07:36:46.2978796Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_array_max_ulp:0, line 1423 <- wrt source file 2025-09-07T07:36:46.2981489Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_array_max_ulp:0 2025-09-07T07:36:46.2982775Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::nulp_diff:0, line 1468 <- wrt source file 2025-09-07T07:36:46.2984028Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::nulp_diff:0 2025-09-07T07:36:46.2985351Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_warns:0, line 1578 <- wrt source file 2025-09-07T07:36:46.2987146Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_warns:0 2025-09-07T07:36:46.2988432Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/context.py::TorchRefsMode:0, line 95 <- wrt source file 2025-09-07T07:36:46.2989644Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/context.py::TorchRefsMode:0 2025-09-07T07:36:46.2990867Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/amp/grad_scaler.py::GradScaler:0, line 64 <- wrt source file 2025-09-07T07:36:46.2992070Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/amp/grad_scaler.py::GradScaler:0 2025-09-07T07:36:46.2993546Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/intrinsic/qat/modules/linear_relu.py::LinearReLU:0, line 30 <- wrt source file 2025-09-07T07:36:46.2995108Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/intrinsic/qat/modules/linear_relu.py::LinearReLU:0 2025-09-07T07:36:46.2996686Z * 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 24 <- wrt source file 2025-09-07T07:36:46.2998383Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/intrinsic/quantized/dynamic/modules/linear_relu.py::LinearReLU:0 2025-09-07T07:36:46.2999986Z * 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 2025-09-07T07:36:46.3001596Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py::LinearReLU:0 2025-09-07T07:36:46.3003193Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py::LinearLeakyReLU:0, line 67 <- wrt source file 2025-09-07T07:36:46.3004843Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py::LinearLeakyReLU:0 2025-09-07T07:36:46.3006426Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py::LinearTanh:0, line 142 <- wrt source file 2025-09-07T07:36:46.3008061Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py::LinearTanh:0 2025-09-07T07:36:46.3009515Z * 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 2025-09-07T07:36:46.3016935Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantizable/modules/rnn.py::LSTMCell:0 2025-09-07T07:36:46.3018276Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantizable/modules/rnn.py::LSTM:0, line 413 <- wrt source file 2025-09-07T07:36:46.3056701Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantizable/modules/rnn.py::LSTM:0 2025-09-07T07:36:46.3058572Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/functional.py::conv1d:0, line 211 <- wrt source file 2025-09-07T07:36:46.3059882Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/functional.py::conv1d:0 2025-09-07T07:36:46.3061161Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/functional.py::conv2d:0, line 283 <- wrt source file 2025-09-07T07:36:46.3062561Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/functional.py::conv2d:0 2025-09-07T07:36:46.3063926Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/functional.py::conv3d:0, line 359 <- wrt source file 2025-09-07T07:36:46.3065233Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/functional.py::conv3d:0 2025-09-07T07:36:46.3066537Z * 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 2025-09-07T07:36:46.3067907Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/__init__.py::Quantize:0 2025-09-07T07:36:46.3069304Z * 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 2025-09-07T07:36:46.3070719Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/__init__.py::DeQuantize:0 2025-09-07T07:36:46.3072115Z * 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 2025-09-07T07:36:46.3073549Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::Conv1d:0 2025-09-07T07:36:46.3075018Z * 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 2025-09-07T07:36:46.3076453Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::Conv2d:0 2025-09-07T07:36:46.3077845Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::Conv3d:0, line 209 <- wrt source file 2025-09-07T07:36:46.3079277Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::Conv3d:0 2025-09-07T07:36:46.3080742Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::ConvTranspose1d:0, line 296 <- wrt source file 2025-09-07T07:36:46.3082293Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::ConvTranspose1d:0 2025-09-07T07:36:46.3083889Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::ConvTranspose2d:0, line 378 <- wrt source file 2025-09-07T07:36:46.3085458Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::ConvTranspose2d:0 2025-09-07T07:36:46.3086979Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::ConvTranspose3d:0, line 460 <- wrt source file 2025-09-07T07:36:46.3088516Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::ConvTranspose3d:0 2025-09-07T07:36:46.3090365Z * 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 2025-09-07T07:36:46.3093272Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/linear.py::Linear:0 2025-09-07T07:36:46.3096039Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::LSTM:0, line 515 <- wrt source file 2025-09-07T07:36:46.3098849Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::LSTM:0 2025-09-07T07:36:46.3101556Z * 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 2025-09-07T07:36:46.3104346Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::GRU:0 2025-09-07T07:36:46.3107104Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::RNNCell:0, line 1206 <- wrt source file 2025-09-07T07:36:46.3109953Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::RNNCell:0 2025-09-07T07:36:46.3112777Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::LSTMCell:0, line 1273 <- wrt source file 2025-09-07T07:36:46.3115812Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::LSTMCell:0 2025-09-07T07:36:46.3118616Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::GRUCell:0, line 1326 <- wrt source file 2025-09-07T07:36:46.3121468Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::GRUCell:0 2025-09-07T07:36:46.3124383Z * 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 2025-09-07T07:36:46.3127170Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/activation.py::ReLU6:0 2025-09-07T07:36:46.3129807Z * 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 2025-09-07T07:36:46.3132453Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/conv.py::Conv2d:0 2025-09-07T07:36:46.3135023Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/conv.py::Conv3d:0, line 635 <- wrt source file 2025-09-07T07:36:46.3137623Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/conv.py::Conv3d:0 2025-09-07T07:36:46.3140336Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/conv.py::ConvTranspose1d:0, line 892 <- wrt source file 2025-09-07T07:36:46.3143297Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/conv.py::ConvTranspose1d:0 2025-09-07T07:36:46.3146099Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/conv.py::ConvTranspose2d:0, line 1014 <- wrt source file 2025-09-07T07:36:46.3148976Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/conv.py::ConvTranspose2d:0 2025-09-07T07:36:46.3151793Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/conv.py::ConvTranspose3d:0, line 1140 <- wrt source file 2025-09-07T07:36:46.3154687Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/conv.py::ConvTranspose3d:0 2025-09-07T07:36:46.3157539Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/embedding_ops.py::Embedding:0, line 111 <- wrt source file 2025-09-07T07:36:46.3160497Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/embedding_ops.py::Embedding:0 2025-09-07T07:36:46.3163477Z * 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 2025-09-07T07:36:46.3166495Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/embedding_ops.py::EmbeddingBag:0 2025-09-07T07:36:46.3169636Z * 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 2025-09-07T07:36:46.3172831Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/functional_modules.py::FloatFunctional:0 2025-09-07T07:36:46.3175885Z * 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 2025-09-07T07:36:46.3178999Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/functional_modules.py::QFunctional:0 2025-09-07T07:36:46.3181873Z * 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 2025-09-07T07:36:46.3184576Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/linear.py::Linear:0 2025-09-07T07:36:46.3187656Z * 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 2025-09-07T07:36:46.3191239Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/pruning/_experimental/data_sparsifier/base_data_sparsifier.py::BaseDataSparsifier:0 2025-09-07T07:36:46.3194420Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/pruning/scheduler/lambda_scheduler.py::LambdaSL:0, line 24 <- wrt source file 2025-09-07T07:36:46.3197324Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/pruning/scheduler/lambda_scheduler.py::LambdaSL:0 2025-09-07T07:36:46.3200238Z * 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 2025-09-07T07:36:46.3203287Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/pruning/sparsifier/base_sparsifier.py::BaseSparsifier:0 2025-09-07T07:36:46.3206163Z * 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 2025-09-07T07:36:46.3208911Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fuse_modules.py::fuse_modules:0 2025-09-07T07:36:46.3211684Z * 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 2025-09-07T07:36:46.3214612Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_conv_bn:0 2025-09-07T07:36:46.3217522Z * 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 2025-09-07T07:36:46.3220548Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_conv_bn_relu:0 2025-09-07T07:36:46.3223485Z * 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 2025-09-07T07:36:46.3226596Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_linear_bn:0 2025-09-07T07:36:46.3229672Z * 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 2025-09-07T07:36:46.3232889Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_convtranspose_bn:0 2025-09-07T07:36:46.3247929Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/observer.py::_with_args:0, line 110 <- wrt source file 2025-09-07T07:36:46.3250599Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/observer.py::_with_args:0 2025-09-07T07:36:46.3253294Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/observer.py::_with_callable_args:0, line 132 <- wrt source file 2025-09-07T07:36:46.3256108Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/observer.py::_with_callable_args:0 2025-09-07T07:36:46.3258875Z * 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 2025-09-07T07:36:46.3261490Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_fx.py::fuse_fx:0 2025-09-07T07:36:46.3264067Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_fx.py::prepare_fx:0, line 288 <- wrt source file 2025-09-07T07:36:46.3266780Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_fx.py::prepare_fx:0 2025-09-07T07:36:46.3269479Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_fx.py::prepare_qat_fx:0, line 427 <- wrt source file 2025-09-07T07:36:46.3272252Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_fx.py::prepare_qat_fx:0 2025-09-07T07:36:46.3274981Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_fx.py::convert_fx:0, line 608 <- wrt source file 2025-09-07T07:36:46.3277662Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_fx.py::convert_fx:0 2025-09-07T07:36:46.3280418Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_fx.py::convert_to_reference_fx:0, line 668 <- wrt source file 2025-09-07T07:36:46.3283496Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_fx.py::convert_to_reference_fx:0 2025-09-07T07:36:46.3286539Z * 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 720 <- wrt source file 2025-09-07T07:36:46.3289765Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_fx.py::_convert_to_reference_decomposed_fx:0 2025-09-07T07:36:46.3292697Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_pt2e.py::prepare_pt2e:0, line 51 <- wrt source file 2025-09-07T07:36:46.3295482Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_pt2e.py::prepare_pt2e:0 2025-09-07T07:36:46.3298239Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_pt2e.py::prepare_qat_pt2e:0, line 130 <- wrt source file 2025-09-07T07:36:46.3301100Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_pt2e.py::prepare_qat_pt2e:0 2025-09-07T07:36:46.3304998Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_pt2e.py::convert_pt2e:0, line 228 <- wrt source file 2025-09-07T07:36:46.3307773Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_pt2e.py::convert_pt2e:0 2025-09-07T07:36:46.3310513Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/utils.py::get_combined_dict:0, line 172 <- wrt source file 2025-09-07T07:36:46.3313188Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/utils.py::get_combined_dict:0 2025-09-07T07:36:46.3315912Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/utils.py::_get_path_of_module:0, line 544 <- wrt source file 2025-09-07T07:36:46.3318629Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/utils.py::_get_path_of_module:0 2025-09-07T07:36:46.3321371Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/utils.py::_get_signature_locals:0, line 566 <- wrt source file 2025-09-07T07:36:46.3324286Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/utils.py::_get_signature_locals:0 2025-09-07T07:36:46.3326993Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/utils.py::_get_default_kwargs:0, line 580 <- wrt source file 2025-09-07T07:36:46.3329717Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/utils.py::_get_default_kwargs:0 2025-09-07T07:36:46.3332360Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/utils.py::_normalize_kwargs:0, line 602 <- wrt source file 2025-09-07T07:36:46.3335049Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/utils.py::_normalize_kwargs:0 2025-09-07T07:36:46.3337652Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/utils.py::_get_num_pos_args:0, line 729 <- wrt source file 2025-09-07T07:36:46.3340301Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/utils.py::_get_num_pos_args:0 2025-09-07T07:36:46.3343193Z * 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 2025-09-07T07:36:46.3346479Z * 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 2025-09-07T07:36:46.3349610Z * 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 2025-09-07T07:36:46.3352791Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/pt2e/_affine_quantization.py::_get_reduction_params:0 2025-09-07T07:36:46.3355948Z * 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 2025-09-07T07:36:46.3359117Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/pt2e/_affine_quantization.py::_register_custom_op:0 2025-09-07T07:36:46.3362183Z * 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 189 <- wrt source file 2025-09-07T07:36:46.3365283Z * 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 2025-09-07T07:36:46.3368497Z * 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 436 <- wrt source file 2025-09-07T07:36:46.3371754Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/pt2e/utils.py::_replace_literals_with_new_placeholders:0 2025-09-07T07:36:46.3374690Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/anomaly_mode.py::detect_anomaly:0, line 28 <- wrt source file 2025-09-07T07:36:46.3377274Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/anomaly_mode.py::detect_anomaly:0 2025-09-07T07:36:46.3379740Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/forward_ad.py::make_dual:0, line 82 <- wrt source file 2025-09-07T07:36:46.3382172Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/forward_ad.py::make_dual:0 2025-09-07T07:36:46.3384659Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/forward_ad.py::unpack_dual:0, line 151 <- wrt source file 2025-09-07T07:36:46.3387142Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/forward_ad.py::unpack_dual:0 2025-09-07T07:36:46.3389583Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/forward_ad.py::dual_level:0, line 187 <- wrt source file 2025-09-07T07:36:46.3392036Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/forward_ad.py::dual_level:0 2025-09-07T07:36:46.3394705Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/function.py::FunctionCtx.save_for_backward:0, line 71 <- wrt source file 2025-09-07T07:36:46.3397574Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/function.py::FunctionCtx.save_for_backward:0 2025-09-07T07:36:46.3400369Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/function.py::FunctionCtx.save_for_forward:0, line 115 <- wrt source file 2025-09-07T07:36:46.3403213Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/function.py::FunctionCtx.save_for_forward:0 2025-09-07T07:36:46.3405938Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/function.py::FunctionCtx.mark_dirty:0, line 167 <- wrt source file 2025-09-07T07:36:46.3408655Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/function.py::FunctionCtx.mark_dirty:0 2025-09-07T07:36:46.3411524Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/function.py::FunctionCtx.mark_non_differentiable:0, line 214 <- wrt source file 2025-09-07T07:36:46.3414572Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/function.py::FunctionCtx.mark_non_differentiable:0 2025-09-07T07:36:46.3417517Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/function.py::FunctionCtx.set_materialize_grads:0, line 243 <- wrt source file 2025-09-07T07:36:46.3420480Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/function.py::FunctionCtx.set_materialize_grads:0 2025-09-07T07:36:46.3423099Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/function.py::Function:0, line 485 <- wrt source file 2025-09-07T07:36:46.3425626Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/function.py::Function:0 2025-09-07T07:36:46.3427971Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/functional.py::vjp:0, line 293 <- wrt source file 2025-09-07T07:36:46.3430405Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/functional.py::vjp:0 2025-09-07T07:36:46.3432750Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/functional.py::jvp:0, line 395 <- wrt source file 2025-09-07T07:36:46.3435133Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/functional.py::jvp:0 2025-09-07T07:36:46.3437592Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/functional.py::jacobian:0, line 630 <- wrt source file 2025-09-07T07:36:46.3440055Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/functional.py::jacobian:0 2025-09-07T07:36:46.3442519Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/functional.py::hessian:0, line 894 <- wrt source file 2025-09-07T07:36:46.3444954Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/functional.py::hessian:0 2025-09-07T07:36:46.3447352Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/functional.py::vhp:0, line 1010 <- wrt source file 2025-09-07T07:36:46.3449697Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/functional.py::vhp:0 2025-09-07T07:36:46.3452023Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/functional.py::hvp:0, line 1109 <- wrt source file 2025-09-07T07:36:46.3454382Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/functional.py::hvp:0 2025-09-07T07:36:46.3456682Z * 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 2025-09-07T07:36:46.3459040Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/grad_mode.py::no_grad:0 2025-09-07T07:36:46.3461418Z * 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 2025-09-07T07:36:46.3463853Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/grad_mode.py::enable_grad:0 2025-09-07T07:36:46.3466305Z * 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 2025-09-07T07:36:46.3468874Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/grad_mode.py::set_grad_enabled:0 2025-09-07T07:36:46.3471582Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/grad_mode.py::inference_mode:0, line 246 <- wrt source file 2025-09-07T07:36:46.3474108Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/grad_mode.py::inference_mode:0 2025-09-07T07:36:46.3476518Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/graph.py::Node.name:0, line 53 <- wrt source file 2025-09-07T07:36:46.3478842Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/graph.py::Node.name:0 2025-09-07T07:36:46.3481253Z * 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 2025-09-07T07:36:46.3483780Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/graph.py::Node.register_hook:0 2025-09-07T07:36:46.3486305Z * 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 2025-09-07T07:36:46.3488929Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/graph.py::Node.register_prehook:0 2025-09-07T07:36:46.3491501Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/graph.py::saved_tensors_hooks:0, line 283 <- wrt source file 2025-09-07T07:36:46.3494040Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/graph.py::saved_tensors_hooks:0 2025-09-07T07:36:46.3496504Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/graph.py::save_on_cpu:0, line 353 <- wrt source file 2025-09-07T07:36:46.3498892Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/graph.py::save_on_cpu:0 2025-09-07T07:36:46.3501413Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/graph.py::disable_saved_tensors_hooks:0, line 410 <- wrt source file 2025-09-07T07:36:46.3504146Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/graph.py::disable_saved_tensors_hooks:0 2025-09-07T07:36:46.3506811Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/graph.py::register_multi_grad_hook:0, line 487 <- wrt source file 2025-09-07T07:36:46.3509486Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/graph.py::register_multi_grad_hook:0 2025-09-07T07:36:46.3512192Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/graph.py::allow_mutation_on_saved_tensors:0, line 753 <- wrt source file 2025-09-07T07:36:46.3515034Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/graph.py::allow_mutation_on_saved_tensors:0 2025-09-07T07:36:46.3517598Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/profiler.py::profile:0, line 182 <- wrt source file 2025-09-07T07:36:46.3519997Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/profiler.py::profile:0 2025-09-07T07:36:46.3522338Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/profiler.py::emit_itt:0, line 880 <- wrt source file 2025-09-07T07:36:46.3524869Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/profiler.py::emit_itt:0 2025-09-07T07:36:46.3527232Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/profiler.py::emit_nvtx:0, line 953 <- wrt source file 2025-09-07T07:36:46.3529656Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/profiler.py::emit_nvtx:0 2025-09-07T07:36:46.3532074Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/gds.py::gds_register_buffer:0, line 42 <- wrt source file 2025-09-07T07:36:46.3534485Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/gds.py::gds_register_buffer:0 2025-09-07T07:36:46.3536847Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/gds.py::gds_deregister_buffer:0, line 58 <- wrt source file 2025-09-07T07:36:46.3539258Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/gds.py::gds_deregister_buffer:0 2025-09-07T07:36:46.3541514Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/gds.py::GdsFile:0, line 85 <- wrt source file 2025-09-07T07:36:46.3543692Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/gds.py::GdsFile:0 2025-09-07T07:36:46.3545967Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/jiterator.py::_create_jit_fn:0, line 114 <- wrt source file 2025-09-07T07:36:46.3548378Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/jiterator.py::_create_jit_fn:0 2025-09-07T07:36:46.3550748Z * 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 2025-09-07T07:36:46.3553237Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/jiterator.py::_create_jit_fn:1 2025-09-07T07:36:46.3555658Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/jiterator.py::_create_jit_fn:2, line 140 <- wrt source file 2025-09-07T07:36:46.3558169Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/jiterator.py::_create_jit_fn:2 2025-09-07T07:36:46.3560699Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/jiterator.py::_create_multi_output_jit_fn:0, line 173 <- wrt source file 2025-09-07T07:36:46.3563411Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/jiterator.py::_create_multi_output_jit_fn:0 2025-09-07T07:36:46.3565858Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/profiler.py::profile:0, line 75 <- wrt source file 2025-09-07T07:36:46.3568282Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/profiler.py::profile:0 2025-09-07T07:36:46.3570671Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/_distributed_c10d.py::__doc__:0, line 11 <- wrt source file 2025-09-07T07:36:46.3573274Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/_distributed_c10d.py::__doc__:0 2025-09-07T07:36:46.3575835Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/device_mesh.py::DeviceMesh:0, line 410 <- wrt source file 2025-09-07T07:36:46.3578429Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/device_mesh.py::DeviceMesh:0 2025-09-07T07:36:46.3581118Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/device_mesh.py::DeviceMesh.get_local_rank:0, line 955 <- wrt source file 2025-09-07T07:36:46.3584021Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/device_mesh.py::DeviceMesh.get_local_rank:0 2025-09-07T07:36:46.3586785Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/device_mesh.py::init_device_mesh:0, line 1101 <- wrt source file 2025-09-07T07:36:46.3589499Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/device_mesh.py::init_device_mesh:0 2025-09-07T07:36:46.3592289Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::_coalescing_manager:0, line 2573 <- wrt source file 2025-09-07T07:36:46.3595280Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::_coalescing_manager:0 2025-09-07T07:36:46.3598101Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::_time_estimator:0, line 2675 <- wrt source file 2025-09-07T07:36:46.3600914Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::_time_estimator:0 2025-09-07T07:36:46.3603690Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::all_gather_object:0, line 3146 <- wrt source file 2025-09-07T07:36:46.3606549Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::all_gather_object:0 2025-09-07T07:36:46.3609355Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::send_object_list:0, line 3380 <- wrt source file 2025-09-07T07:36:46.3612173Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::send_object_list:0 2025-09-07T07:36:46.3615001Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::recv_object_list:0, line 3497 <- wrt source file 2025-09-07T07:36:46.3617823Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::recv_object_list:0 2025-09-07T07:36:46.3620712Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::broadcast_object_list:0, line 3643 <- wrt source file 2025-09-07T07:36:46.3623786Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::broadcast_object_list:0 2025-09-07T07:36:46.3626656Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::scatter_object_list:0, line 3766 <- wrt source file 2025-09-07T07:36:46.3629570Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::scatter_object_list:0 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* DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/autograd/__init__.py::context:0, line 47 <- wrt source file 2025-09-07T07:36:46.3685291Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/autograd/__init__.py::context:0 2025-09-07T07:36:46.3688091Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/_composable/checkpoint_activation.py::checkpoint:0, line 53 <- wrt source file 2025-09-07T07:36:46.3691184Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/_composable/checkpoint_activation.py::checkpoint:0 2025-09-07T07:36:46.3694044Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/_composable/contract.py::contract:0, line 66 <- wrt source file 2025-09-07T07:36:46.3696777Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/_composable/contract.py::contract:0 2025-09-07T07:36:46.3699495Z * DOCTEST 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file 2025-09-07T07:36:46.4160299Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/bernoulli.py::Bernoulli:0 2025-09-07T07:36:46.4162693Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/beta.py::Beta:0, line 21 <- wrt source file 2025-09-07T07:36:46.4165039Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/beta.py::Beta:0 2025-09-07T07:36:46.4167434Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/binomial.py::Binomial:0, line 31 <- wrt source file 2025-09-07T07:36:46.4169948Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/binomial.py::Binomial:0 2025-09-07T07:36:46.4172512Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/categorical.py::Categorical:0, line 42 <- wrt source file 2025-09-07T07:36:46.4175288Z * SUCCESS: 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2025-09-07T07:36:46.4368278Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/export/dynamic_shapes.py::Dim:0, line 103 <- wrt source file 2025-09-07T07:36:46.4370650Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/export/dynamic_shapes.py::Dim:0 2025-09-07T07:36:46.4373158Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/export/dynamic_shapes.py::ShapesCollection:0, line 715 <- wrt source file 2025-09-07T07:36:46.4375848Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/export/dynamic_shapes.py::ShapesCollection:0 2025-09-07T07:36:46.4378484Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/export/dynamic_shapes.py::ShapesCollection:1, line 731 <- wrt source file 2025-09-07T07:36:46.4381183Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/export/dynamic_shapes.py::ShapesCollection:1 2025-09-07T07:36:46.4383829Z * DOCTEST : 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2025-09-07T07:36:46.4532203Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/unification_tools.py::first:0 2025-09-07T07:36:46.4535125Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/utils.py::transitive_get:0, line 15 <- wrt source file 2025-09-07T07:36:46.4538056Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/utils.py::transitive_get:0 2025-09-07T07:36:46.4540871Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/utils.py::_toposort:0, line 42 <- wrt source file 2025-09-07T07:36:46.4543700Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/utils.py::_toposort:0 2025-09-07T07:36:46.4546515Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/utils.py::reverse_dict:0, line 70 <- wrt source file 2025-09-07T07:36:46.4549426Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/utils.py::reverse_dict:0 2025-09-07T07:36:46.4552200Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/utils.py::freeze:0, line 95 <- wrt source file 2025-09-07T07:36:46.4555058Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/utils.py::freeze:0 2025-09-07T07:36:46.4557929Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/variable.py::variables:0, line 67 <- wrt source file 2025-09-07T07:36:46.4560876Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/variable.py::variables:0 2025-09-07T07:36:46.4563910Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/multipledispatch/core.py::dispatch:0, line 20 <- wrt source file 2025-09-07T07:36:46.4567147Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/multipledispatch/core.py::dispatch:0 2025-09-07T07:36:46.4570463Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/multipledispatch/dispatcher.py::Dispatcher:0, line 113 <- wrt source file 2025-09-07T07:36:46.4573938Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/multipledispatch/dispatcher.py::Dispatcher:0 2025-09-07T07:36:46.4577433Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/multipledispatch/dispatcher.py::Dispatcher.register:0, line 138 <- wrt source file 2025-09-07T07:36:46.4581199Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/multipledispatch/dispatcher.py::Dispatcher.register:0 2025-09-07T07:36:46.4584800Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/multipledispatch/dispatcher.py::Dispatcher.add:0, line 191 <- wrt source file 2025-09-07T07:36:46.4588382Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/multipledispatch/dispatcher.py::Dispatcher.add:0 2025-09-07T07:36:46.4591951Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/multipledispatch/dispatcher.py::Dispatcher.dispatch:0, line 304 <- wrt source file 2025-09-07T07:36:46.4595716Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/multipledispatch/dispatcher.py::Dispatcher.dispatch:0 2025-09-07T07:36:46.4599271Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/multipledispatch/dispatcher.py::str_signature:0, line 434 <- wrt source file 2025-09-07T07:36:46.4602794Z * SUCCESS: 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/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::reverse_dict:0, line 68 <- wrt source file 2025-09-07T07:36:46.4622699Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::reverse_dict:0 2025-09-07T07:36:46.4626148Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::groupby:0, line 87 <- wrt source file 2025-09-07T07:36:46.4629410Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::groupby:0 2025-09-07T07:36:46.4632626Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::typename:0, line 117 <- wrt source file 2025-09-07T07:36:46.4635950Z * SUCCESS: 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/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/passes/graph_drawer.py::FxGraphDrawer.get_dot_graph:0, line 129 <- wrt source file 2025-09-07T07:36:46.4655423Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/passes/graph_drawer.py::FxGraphDrawer.get_dot_graph:0 2025-09-07T07:36:46.4658077Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/passes/shape_prop.py::ShapeProp:0, line 99 <- wrt source file 2025-09-07T07:36:46.4660539Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/passes/shape_prop.py::ShapeProp:0 2025-09-07T07:36:46.4663011Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/passes/split_module.py::split_module:0, line 89 <- wrt source file 2025-09-07T07:36:46.4665633Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/passes/split_module.py::split_module:0 2025-09-07T07:36:46.4668720Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/passes/utils/matcher_with_name_node_map_utils.py::SubgraphMatcherWithNameNodeMap:0, line 51 <- wrt source file 2025-09-07T07:36:46.4672289Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/passes/utils/matcher_with_name_node_map_utils.py::SubgraphMatcherWithNameNodeMap:0 2025-09-07T07:36:46.4675402Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/jit/_check.py::AttributeTypeIsSupportedChecker:0, line 36 <- wrt source file 2025-09-07T07:36:46.4678175Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/jit/_check.py::AttributeTypeIsSupportedChecker:0 2025-09-07T07:36:46.4680861Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/jit/mobile/__init__.py::_load_for_lite_interpreter:0, line 22 <- wrt source file 2025-09-07T07:36:46.4683615Z * SKIPPED: 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2025-09-07T07:36:46.5355608Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::ctc_loss:0, line 3051 <- wrt source file 2025-09-07T07:36:46.5368143Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::ctc_loss:0 2025-09-07T07:36:46.5370432Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::nll_loss:0, line 3121 <- wrt source file 2025-09-07T07:36:46.5374933Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::nll_loss:0 2025-09-07T07:36:46.5377264Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::cross_entropy:0, line 3430 <- wrt source file 2025-09-07T07:36:46.5384489Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::cross_entropy:0 2025-09-07T07:36:46.5386948Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::binary_cross_entropy:0, line 3495 <- wrt source file 2025-09-07T07:36:46.5391361Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::binary_cross_entropy:0 2025-09-07T07:36:46.5394129Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::binary_cross_entropy_with_logits:0, line 3565 <- wrt source file 2025-09-07T07:36:46.5398594Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::binary_cross_entropy_with_logits:0 2025-09-07T07:36:46.5401091Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::pad:0, line 5263 <- wrt source file 2025-09-07T07:36:46.5408530Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::pad:0 2025-09-07T07:36:46.5410693Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/grad.py::conv1d_input:0, line 32 <- wrt source file 2025-09-07T07:36:46.5416832Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/grad.py::conv1d_input:0 2025-09-07T07:36:46.5419038Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/grad.py::conv1d_weight:0, line 79 <- wrt source file 2025-09-07T07:36:46.5422485Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/grad.py::conv1d_weight:0 2025-09-07T07:36:46.5424967Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/grad.py::conv2d_input:0, line 130 <- wrt source file 2025-09-07T07:36:46.5430609Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/grad.py::conv2d_input:0 2025-09-07T07:36:46.5432953Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/grad.py::conv2d_weight:0, line 177 <- wrt source file 2025-09-07T07:36:46.5435770Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/grad.py::conv2d_weight:0 2025-09-07T07:36:46.5437964Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/grad.py::conv3d_input:0, line 228 <- wrt source file 2025-09-07T07:36:46.5468819Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/grad.py::conv3d_input:0 2025-09-07T07:36:46.5471751Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/grad.py::conv3d_weight:0, line 275 <- wrt source file 2025-09-07T07:36:46.5490295Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/grad.py::conv3d_weight:0 2025-09-07T07:36:46.5492859Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::uniform_:0, line 230 <- wrt source file 2025-09-07T07:36:46.5495218Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::uniform_:0 2025-09-07T07:36:46.5497298Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::normal_:0, line 257 <- wrt source file 2025-09-07T07:36:46.5499444Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::normal_:0 2025-09-07T07:36:46.5501606Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::trunc_normal_:0, line 292 <- wrt source file 2025-09-07T07:36:46.5503870Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::trunc_normal_:0 2025-09-07T07:36:46.5506066Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::constant_:0, line 306 <- wrt source file 2025-09-07T07:36:46.5508276Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::constant_:0 2025-09-07T07:36:46.5510363Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::ones_:0, line 323 <- wrt source file 2025-09-07T07:36:46.5512462Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::ones_:0 2025-09-07T07:36:46.5514664Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::zeros_:0, line 336 <- wrt source file 2025-09-07T07:36:46.5516803Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::zeros_:0 2025-09-07T07:36:46.5518855Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::eye_:0, line 352 <- wrt source file 2025-09-07T07:36:46.5520947Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::eye_:0 2025-09-07T07:36:46.5522991Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::dirac_:0, line 374 <- wrt source file 2025-09-07T07:36:46.5525271Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::dirac_:0 2025-09-07T07:36:46.5527453Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::xavier_uniform_:0, line 460 <- wrt source file 2025-09-07T07:36:46.5529747Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::xavier_uniform_:0 2025-09-07T07:36:46.5531991Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::xavier_normal_:0, line 492 <- wrt source file 2025-09-07T07:36:46.5534347Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::xavier_normal_:0 2025-09-07T07:36:46.5536586Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::kaiming_uniform_:0, line 543 <- wrt source file 2025-09-07T07:36:46.5538970Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::kaiming_uniform_:0 2025-09-07T07:36:46.5541227Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::kaiming_normal_:0, line 608 <- wrt source file 2025-09-07T07:36:46.5543516Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::kaiming_normal_:0 2025-09-07T07:36:46.5545739Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::orthogonal_:0, line 647 <- wrt source file 2025-09-07T07:36:46.5548022Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::orthogonal_:0 2025-09-07T07:36:46.5550173Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::sparse_:0, line 700 <- wrt source file 2025-09-07T07:36:46.5552318Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::sparse_:0 2025-09-07T07:36:46.5554636Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/attention/__init__.py::sdpa_kernel:0, line 120 <- wrt source file 2025-09-07T07:36:46.5557149Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/attention/__init__.py::sdpa_kernel:0 2025-09-07T07:36:46.5559584Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/attention/bias.py::CausalBias:0, line 95 <- wrt source file 2025-09-07T07:36:46.5562015Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/attention/bias.py::CausalBias:0 2025-09-07T07:36:46.5564457Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Threshold:0, line 72 <- wrt source file 2025-09-07T07:36:46.5566976Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Threshold:0 2025-09-07T07:36:46.5569390Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::ReLU:0, line 120 <- wrt source file 2025-09-07T07:36:46.5571795Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::ReLU:0 2025-09-07T07:36:46.5574247Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::RReLU:0, line 185 <- wrt source file 2025-09-07T07:36:46.5576671Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::RReLU:0 2025-09-07T07:36:46.5579085Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Hardtanh:0, line 247 <- wrt source file 2025-09-07T07:36:46.5581581Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Hardtanh:0 2025-09-07T07:36:46.5583976Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::ReLU6:0, line 318 <- wrt source file 2025-09-07T07:36:46.5586396Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::ReLU6:0 2025-09-07T07:36:46.5588792Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Sigmoid:0, line 349 <- wrt source file 2025-09-07T07:36:46.5591235Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Sigmoid:0 2025-09-07T07:36:46.5593798Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Hardsigmoid:0, line 384 <- wrt source file 2025-09-07T07:36:46.5596421Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Hardsigmoid:0 2025-09-07T07:36:46.5598887Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Tanh:0, line 420 <- wrt source file 2025-09-07T07:36:46.5601290Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Tanh:0 2025-09-07T07:36:46.5603632Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::SiLU:0, line 456 <- wrt source file 2025-09-07T07:36:46.5606025Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::SiLU:0 2025-09-07T07:36:46.5608420Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Mish:0, line 501 <- wrt source file 2025-09-07T07:36:46.5610838Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Mish:0 2025-09-07T07:36:46.5613264Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Hardswish:0, line 552 <- wrt source file 2025-09-07T07:36:46.5615775Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Hardswish:0 2025-09-07T07:36:46.5618190Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::ELU:0, line 598 <- wrt source file 2025-09-07T07:36:46.5620575Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::ELU:0 2025-09-07T07:36:46.5622920Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::CELU:0, line 646 <- wrt source file 2025-09-07T07:36:46.5625463Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::CELU:0 2025-09-07T07:36:46.5627816Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::SELU:0, line 705 <- wrt source file 2025-09-07T07:36:46.5630218Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::SELU:0 2025-09-07T07:36:46.5632611Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::GLU:0, line 751 <- wrt source file 2025-09-07T07:36:46.5635033Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::GLU:0 2025-09-07T07:36:46.5637387Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::GELU:0, line 799 <- wrt source file 2025-09-07T07:36:46.5639803Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::GELU:0 2025-09-07T07:36:46.5642246Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Hardshrink:0, line 848 <- wrt source file 2025-09-07T07:36:46.5644817Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Hardshrink:0 2025-09-07T07:36:46.5647312Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::LeakyReLU:0, line 903 <- wrt source file 2025-09-07T07:36:46.5649827Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::LeakyReLU:0 2025-09-07T07:36:46.5652314Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::LogSigmoid:0, line 945 <- wrt source file 2025-09-07T07:36:46.5654926Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::LogSigmoid:0 2025-09-07T07:36:46.5657392Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Softplus:0, line 981 <- wrt source file 2025-09-07T07:36:46.5659945Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Softplus:0 2025-09-07T07:36:46.5662430Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Softshrink:0, line 1030 <- wrt source file 2025-09-07T07:36:46.5664969Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Softshrink:0 2025-09-07T07:36:46.5668108Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::MultiheadAttention:0, line 1144 <- wrt source file 2025-09-07T07:36:46.5671494Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::MultiheadAttention:0 2025-09-07T07:36:46.5674055Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::PReLU:0, line 1609 <- wrt source file 2025-09-07T07:36:46.5676558Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::PReLU:0 2025-09-07T07:36:46.5678942Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Softsign:0, line 1660 <- wrt source file 2025-09-07T07:36:46.5681444Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Softsign:0 2025-09-07T07:36:46.5683938Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Tanhshrink:0, line 1686 <- wrt source file 2025-09-07T07:36:46.5686505Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Tanhshrink:0 2025-09-07T07:36:46.5688976Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Softmin:0, line 1724 <- wrt source file 2025-09-07T07:36:46.5691470Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Softmin:0 2025-09-07T07:36:46.5693915Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Softmax:0, line 1788 <- wrt source file 2025-09-07T07:36:46.5696435Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Softmax:0 2025-09-07T07:36:46.5698890Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Softmax2d:0, line 1835 <- wrt source file 2025-09-07T07:36:46.5701422Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Softmax2d:0 2025-09-07T07:36:46.5703915Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::LogSoftmax:0, line 1874 <- wrt source file 2025-09-07T07:36:46.5706473Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::LogSoftmax:0 2025-09-07T07:36:46.5708964Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/batchnorm.py::BatchNorm1d:0, line 332 <- wrt source file 2025-09-07T07:36:46.5711524Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/batchnorm.py::BatchNorm1d:0 2025-09-07T07:36:46.5714013Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/batchnorm.py::BatchNorm2d:0, line 443 <- wrt source file 2025-09-07T07:36:46.5890753Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/batchnorm.py::BatchNorm2d:0 2025-09-07T07:36:46.5893566Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/batchnorm.py::BatchNorm3d:0, line 554 <- wrt source file 2025-09-07T07:36:46.8121216Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/batchnorm.py::BatchNorm3d:0 2025-09-07T07:36:46.8287691Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/channelshuffle.py::ChannelShuffle:0, line 21 <- wrt source file 2025-09-07T07:36:46.8308971Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/channelshuffle.py::ChannelShuffle:0 2025-09-07T07:36:46.8311564Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::Sequential:0, line 81 <- wrt source file 2025-09-07T07:36:46.8314419Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::Sequential:0 2025-09-07T07:36:46.8317003Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::Sequential.append:0, line 260 <- wrt source file 2025-09-07T07:36:46.8319700Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::Sequential.append:0 2025-09-07T07:36:46.8322352Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::Sequential.insert:0, line 283 <- wrt source file 2025-09-07T07:36:46.8326890Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::Sequential.insert:0 2025-09-07T07:36:46.8329542Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::Sequential.extend:0, line 314 <- wrt source file 2025-09-07T07:36:46.8334675Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::Sequential.extend:0 2025-09-07T07:36:46.8337233Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::ModuleList:0, line 343 <- wrt source file 2025-09-07T07:36:46.8339743Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::ModuleList:0 2025-09-07T07:36:46.8342214Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::ModuleDict:0, line 523 <- wrt source file 2025-09-07T07:36:46.8344832Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::ModuleDict:0 2025-09-07T07:36:46.8347361Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::ParameterList:0, line 653 <- wrt source file 2025-09-07T07:36:46.8349954Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::ParameterList:0 2025-09-07T07:36:46.8352497Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::ParameterDict:0, line 808 <- wrt source file 2025-09-07T07:36:46.8355182Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::ParameterDict:0 2025-09-07T07:36:46.8357764Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/distance.py::PairwiseDistance:0, line 38 <- wrt source file 2025-09-07T07:36:46.8360428Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/distance.py::PairwiseDistance:0 2025-09-07T07:36:46.8363023Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/distance.py::CosineSimilarity:0, line 81 <- wrt source file 2025-09-07T07:36:46.8365738Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/distance.py::CosineSimilarity:0 2025-09-07T07:36:46.8368205Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/dropout.py::Dropout:0, line 60 <- wrt source file 2025-09-07T07:36:46.8370690Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/dropout.py::Dropout:0 2025-09-07T07:36:46.8373065Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/dropout.py::Dropout1d:0, line 108 <- wrt source file 2025-09-07T07:36:46.8375532Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/dropout.py::Dropout1d:0 2025-09-07T07:36:46.8377938Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/dropout.py::Dropout2d:0, line 163 <- wrt source file 2025-09-07T07:36:46.8386208Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/dropout.py::Dropout2d:0 2025-09-07T07:36:46.8389003Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/dropout.py::Dropout3d:0, line 211 <- wrt source file 2025-09-07T07:36:46.8457165Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/dropout.py::Dropout3d:0 2025-09-07T07:36:46.8460037Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/dropout.py::AlphaDropout:0, line 257 <- wrt source file 2025-09-07T07:36:46.8462721Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/dropout.py::AlphaDropout:0 2025-09-07T07:36:46.8465300Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/dropout.py::FeatureAlphaDropout:0, line 309 <- wrt source file 2025-09-07T07:36:46.8532016Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/dropout.py::FeatureAlphaDropout:0 2025-09-07T07:36:46.8534904Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/flatten.py::Flatten:0, line 30 <- wrt source file 2025-09-07T07:36:46.8538080Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/flatten.py::Flatten:0 2025-09-07T07:36:46.8540408Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/fold.py::Fold:0, line 224 <- wrt source file 2025-09-07T07:36:46.8544064Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/fold.py::Fold:0 2025-09-07T07:36:46.8546299Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/fold.py::Unfold:0, line 395 <- wrt source file 2025-09-07T07:36:46.8559476Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/fold.py::Unfold:0 2025-09-07T07:36:46.8562027Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm1d:0, line 187 <- wrt source file 2025-09-07T07:36:46.8571993Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm1d:0 2025-09-07T07:36:46.8574679Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm2d:0, line 303 <- wrt source file 2025-09-07T07:36:46.8742866Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm2d:0 2025-09-07T07:36:46.8745882Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm3d:0, line 419 <- wrt source file 2025-09-07T07:36:47.1132524Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm3d:0 2025-09-07T07:36:47.1299104Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/lazy.py::LazyModuleMixin:0, line 77 <- wrt source file 2025-09-07T07:36:47.1301942Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/lazy.py::LazyModuleMixin:0 2025-09-07T07:36:47.1304348Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/linear.py::Identity:0, line 34 <- wrt source file 2025-09-07T07:36:47.1307518Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/linear.py::Identity:0 2025-09-07T07:36:47.1309849Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/linear.py::Linear:0, line 83 <- wrt source file 2025-09-07T07:36:47.1316648Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/linear.py::Linear:0 2025-09-07T07:36:47.1319065Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/linear.py::Bilinear:0, line 191 <- wrt source file 2025-09-07T07:36:47.1338661Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/linear.py::Bilinear:0 2025-09-07T07:36:47.1341236Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::L1Loss:0, line 115 <- wrt source file 2025-09-07T07:36:47.1348014Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::L1Loss:0 2025-09-07T07:36:47.1350601Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::NLLLoss:0, line 215 <- wrt source file 2025-09-07T07:36:47.1377649Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::NLLLoss:0 2025-09-07T07:36:47.1380465Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::PoissonNLLLoss:0, line 329 <- wrt source file 2025-09-07T07:36:47.1384719Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::PoissonNLLLoss:0 2025-09-07T07:36:47.1387171Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::GaussianNLLLoss:0, line 418 <- wrt source file 2025-09-07T07:36:47.1399883Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::GaussianNLLLoss:0 2025-09-07T07:36:47.1402482Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::KLDivLoss:0, line 535 <- wrt source file 2025-09-07T07:36:47.1408226Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::KLDivLoss:0 2025-09-07T07:36:47.1410540Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::MSELoss:0, line 617 <- wrt source file 2025-09-07T07:36:47.1414559Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::MSELoss:0 2025-09-07T07:36:47.1416838Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::BCELoss:0, line 703 <- wrt source file 2025-09-07T07:36:47.1421837Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::BCELoss:0 2025-09-07T07:36:47.1424409Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::BCEWithLogitsLoss:0, line 778 <- wrt source file 2025-09-07T07:36:47.1434869Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::BCEWithLogitsLoss:0 2025-09-07T07:36:47.1437597Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::BCEWithLogitsLoss:1, line 826 <- wrt source file 2025-09-07T07:36:47.1440680Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::BCEWithLogitsLoss:1 2025-09-07T07:36:47.1443313Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::MultiLabelMarginLoss:0, line 974 <- wrt source file 2025-09-07T07:36:47.1449531Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::MultiLabelMarginLoss:0 2025-09-07T07:36:47.1452109Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::CrossEntropyLoss:0, line 1306 <- wrt source file 2025-09-07T07:36:47.1458129Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::CrossEntropyLoss:0 2025-09-07T07:36:47.1460734Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::CrossEntropyLoss:1, line 1333 <- wrt source file 2025-09-07T07:36:47.1463314Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::CrossEntropyLoss:1 2025-09-07T07:36:47.1465892Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::CosineEmbeddingLoss:0, line 1495 <- wrt source file 2025-09-07T07:36:47.1470228Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::CosineEmbeddingLoss:0 2025-09-07T07:36:47.1472804Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::MarginRankingLoss:0, line 1562 <- wrt source file 2025-09-07T07:36:47.1478352Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::MarginRankingLoss:0 2025-09-07T07:36:47.1480861Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::MultiMarginLoss:0, line 1643 <- wrt source file 2025-09-07T07:36:47.1487017Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::MultiMarginLoss:0 2025-09-07T07:36:47.1489518Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::TripletMarginLoss:0, line 1745 <- wrt source file 2025-09-07T07:36:47.1498580Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::TripletMarginLoss:0 2025-09-07T07:36:47.1501350Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.register_buffer:0, line 551 <- wrt source file 2025-09-07T07:36:47.1504091Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.register_buffer:0 2025-09-07T07:36:47.1506677Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.apply:0, line 1039 <- wrt source file 2025-09-07T07:36:47.1514836Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.apply:0 2025-09-07T07:36:47.1517279Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.to:0, line 1290 <- wrt source file 2025-09-07T07:36:47.1521577Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.to:0 2025-09-07T07:36:47.1524211Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.state_dict:0, line 2229 <- wrt source file 2025-09-07T07:36:47.1526838Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.state_dict:0 2025-09-07T07:36:47.1529532Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.parameters:0, line 2670 <- wrt source file 2025-09-07T07:36:47.1532137Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.parameters:0 2025-09-07T07:36:47.1534832Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.named_parameters:0, line 2698 <- wrt source file 2025-09-07T07:36:47.1537539Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.named_parameters:0 2025-09-07T07:36:47.1540133Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.buffers:0, line 2725 <- wrt source file 2025-09-07T07:36:47.1542753Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.buffers:0 2025-09-07T07:36:47.1545381Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.named_buffers:0, line 2752 <- wrt source file 2025-09-07T07:36:47.1548022Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.named_buffers:0 2025-09-07T07:36:47.1550649Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.named_children:0, line 2783 <- wrt source file 2025-09-07T07:36:47.1567350Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.named_children:0 2025-09-07T07:36:47.1570410Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.modules:0, line 2807 <- wrt source file 2025-09-07T07:36:47.1573825Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.modules:0 2025-09-07T07:36:47.1577207Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.named_modules:0, line 2845 <- wrt source file 2025-09-07T07:36:47.1580816Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.named_modules:0 2025-09-07T07:36:47.1584413Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/normalization.py::LocalResponseNorm:0, line 38 <- wrt source file 2025-09-07T07:36:47.1588134Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/normalization.py::LocalResponseNorm:0 2025-09-07T07:36:47.1591116Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/normalization.py::LayerNorm:0, line 163 <- wrt source file 2025-09-07T07:36:47.1593725Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/normalization.py::LayerNorm:0 2025-09-07T07:36:47.1596362Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/normalization.py::GroupNorm:0, line 274 <- wrt source file 2025-09-07T07:36:47.1598948Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/normalization.py::GroupNorm:0 2025-09-07T07:36:47.1601457Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/normalization.py::RMSNorm:0, line 367 <- wrt source file 2025-09-07T07:36:47.1604000Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/normalization.py::RMSNorm:0 2025-09-07T07:36:47.1606485Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::CircularPad1d:0, line 70 <- wrt source file 2025-09-07T07:36:47.1609008Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::CircularPad1d:0 2025-09-07T07:36:47.1611558Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::CircularPad2d:0, line 122 <- wrt source file 2025-09-07T07:36:47.1624382Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::CircularPad2d:0 2025-09-07T07:36:47.1626974Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::CircularPad3d:0, line 187 <- wrt source file 2025-09-07T07:36:47.7106712Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::CircularPad3d:0 2025-09-07T07:36:47.7395904Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ConstantPad1d:0, line 241 <- wrt source file 2025-09-07T07:36:47.7404660Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ConstantPad1d:0 2025-09-07T07:36:47.7407414Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ConstantPad2d:0, line 294 <- wrt source file 2025-09-07T07:36:47.7411475Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ConstantPad2d:0 2025-09-07T07:36:47.7413983Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ConstantPad3d:0, line 350 <- wrt source file 2025-09-07T07:36:47.7434657Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ConstantPad3d:0 2025-09-07T07:36:47.7437218Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ReflectionPad1d:0, line 395 <- wrt source file 2025-09-07T07:36:47.7442094Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ReflectionPad1d:0 2025-09-07T07:36:47.7444657Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ReflectionPad2d:0, line 439 <- wrt source file 2025-09-07T07:36:47.7447784Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ReflectionPad2d:0 2025-09-07T07:36:47.7450340Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ReflectionPad3d:0, line 497 <- wrt source file 2025-09-07T07:36:47.7452927Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ReflectionPad3d:0 2025-09-07T07:36:47.7455645Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ReplicationPad1d:0, line 556 <- wrt source file 2025-09-07T07:36:47.7458279Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ReplicationPad1d:0 2025-09-07T07:36:47.7460851Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ReplicationPad2d:0, line 600 <- wrt source file 2025-09-07T07:36:47.7464160Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ReplicationPad2d:0 2025-09-07T07:36:47.7466729Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ReplicationPad3d:0, line 658 <- wrt source file 2025-09-07T07:36:48.2210881Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ReplicationPad3d:0 2025-09-07T07:36:48.2502354Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ZeroPad1d:0, line 692 <- wrt source file 2025-09-07T07:36:48.2512150Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ZeroPad1d:0 2025-09-07T07:36:48.2514935Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ZeroPad2d:0, line 750 <- wrt source file 2025-09-07T07:36:48.2518992Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ZeroPad2d:0 2025-09-07T07:36:48.2521519Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ZeroPad3d:0, line 812 <- wrt source file 2025-09-07T07:36:48.2542375Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ZeroPad3d:0 2025-09-07T07:36:48.2544841Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pixelshuffle.py::PixelShuffle:0, line 40 <- wrt source file 2025-09-07T07:36:48.2548603Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pixelshuffle.py::PixelShuffle:0 2025-09-07T07:36:48.2551404Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pixelshuffle.py::PixelUnshuffle:0, line 99 <- wrt source file 2025-09-07T07:36:48.2554709Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pixelshuffle.py::PixelUnshuffle:0 2025-09-07T07:36:48.2557303Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::MaxPool1d:0, line 129 <- wrt source file 2025-09-07T07:36:48.2559953Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::MaxPool1d:0 2025-09-07T07:36:48.2562372Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::MaxPool2d:0, line 207 <- wrt source file 2025-09-07T07:36:48.2601324Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::MaxPool2d:0 2025-09-07T07:36:48.2603785Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::MaxPool3d:0, line 291 <- wrt source file 2025-09-07T07:36:48.4419752Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::MaxPool3d:0 2025-09-07T07:36:48.4477861Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::MaxUnpool1d:0, line 366 <- wrt source file 2025-09-07T07:36:48.4489944Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::MaxUnpool1d:0 2025-09-07T07:36:48.4492701Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::MaxUnpool3d:0, line 550 <- wrt source file 2025-09-07T07:36:48.5161146Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::MaxUnpool3d:0 2025-09-07T07:36:48.5163645Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AvgPool1d:0, line 642 <- wrt source file 2025-09-07T07:36:48.5171108Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AvgPool1d:0 2025-09-07T07:36:48.5173527Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AvgPool2d:0, line 738 <- wrt source file 2025-09-07T07:36:48.5208437Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AvgPool2d:0 2025-09-07T07:36:48.5210870Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AvgPool3d:0, line 855 <- wrt source file 2025-09-07T07:36:48.6742809Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AvgPool3d:0 2025-09-07T07:36:48.6801085Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::FractionalMaxPool2d:0, line 946 <- wrt source file 2025-09-07T07:36:48.6846645Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::FractionalMaxPool2d:0 2025-09-07T07:36:48.6849346Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::FractionalMaxPool3d:0, line 1033 <- wrt source file 2025-09-07T07:36:48.7437691Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::FractionalMaxPool3d:0 2025-09-07T07:36:48.7440258Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::LPPool1d:0, line 1152 <- wrt source file 2025-09-07T07:36:48.7445874Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::LPPool1d:0 2025-09-07T07:36:48.7448268Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::LPPool2d:0, line 1204 <- wrt source file 2025-09-07T07:36:48.7494895Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::LPPool2d:0 2025-09-07T07:36:48.7497954Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::LPPool3d:0, line 1264 <- wrt source file 2025-09-07T07:36:48.9313153Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::LPPool3d:0 2025-09-07T07:36:48.9371701Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool1d:0, line 1320 <- wrt source file 2025-09-07T07:36:48.9377630Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool1d:0 2025-09-07T07:36:48.9380267Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool2d:0, line 1355 <- wrt source file 2025-09-07T07:36:48.9387073Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool2d:0 2025-09-07T07:36:48.9389744Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool3d:0, line 1399 <- wrt source file 2025-09-07T07:36:48.9407493Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool3d:0 2025-09-07T07:36:48.9410127Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool1d:0, line 1447 <- wrt source file 2025-09-07T07:36:48.9413111Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool1d:0 2025-09-07T07:36:48.9415755Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool2d:0, line 1481 <- wrt source file 2025-09-07T07:36:48.9419894Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool2d:0 2025-09-07T07:36:48.9422521Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool3d:0, line 1521 <- wrt source file 2025-09-07T07:36:48.9437072Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool3d:0 2025-09-07T07:36:48.9439462Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/rnn.py::RNN:0, line 595 <- wrt source file 2025-09-07T07:36:48.9448832Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/rnn.py::RNN:0 2025-09-07T07:36:48.9451041Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/rnn.py::LSTM:0, line 953 <- wrt source file 2025-09-07T07:36:48.9869156Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/rnn.py::LSTM:0 2025-09-07T07:36:48.9871378Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/rnn.py::GRU:0, line 1288 <- wrt source file 2025-09-07T07:36:48.9886776Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/rnn.py::GRU:0 2025-09-07T07:36:48.9889033Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/rnn.py::RNNCell:0, line 1537 <- wrt source file 2025-09-07T07:36:48.9898650Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/rnn.py::RNNCell:0 2025-09-07T07:36:48.9900927Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/rnn.py::LSTMCell:0, line 1659 <- wrt source file 2025-09-07T07:36:48.9909357Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/rnn.py::LSTMCell:0 2025-09-07T07:36:48.9911656Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/rnn.py::GRUCell:0, line 1773 <- wrt source file 2025-09-07T07:36:48.9923267Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/rnn.py::GRUCell:0 2025-09-07T07:36:48.9925719Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/sparse.py::Embedding:0, line 71 <- wrt source file 2025-09-07T07:36:48.9936926Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/sparse.py::Embedding:0 2025-09-07T07:36:48.9939512Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/sparse.py::Embedding.from_pretrained:0, line 243 <- wrt source file 2025-09-07T07:36:48.9942307Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/sparse.py::Embedding.from_pretrained:0 2025-09-07T07:36:48.9945093Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/sparse.py::EmbeddingBag.from_pretrained:0, line 521 <- wrt source file 2025-09-07T07:36:48.9948614Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/sparse.py::EmbeddingBag.from_pretrained:0 2025-09-07T07:36:48.9951307Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py::Transformer:0, line 90 <- wrt source file 2025-09-07T07:36:49.7355578Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py::Transformer:0 2025-09-07T07:36:49.7370504Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py::TransformerEncoder:0, line 336 <- wrt source file 2025-09-07T07:36:49.8394623Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py::TransformerEncoder:0 2025-09-07T07:36:49.8398970Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py::TransformerDecoder:0, line 562 <- wrt source file 2025-09-07T07:36:50.0613510Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py::TransformerDecoder:0 2025-09-07T07:36:50.0621126Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py::TransformerEncoderLayer:0, line 686 <- wrt source file 2025-09-07T07:36:50.0904806Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py::TransformerEncoderLayer:0 2025-09-07T07:36:50.0907863Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py::TransformerDecoderLayer:0, line 995 <- wrt source file 2025-09-07T07:36:50.1509060Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py::TransformerDecoderLayer:0 2025-09-07T07:36:50.1511918Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/upsampling.py::Upsample:0, line 77 <- wrt source file 2025-09-07T07:36:50.1534624Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/upsampling.py::Upsample:0 2025-09-07T07:36:50.1537232Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/upsampling.py::UpsamplingNearest2d:0, line 229 <- wrt source file 2025-09-07T07:36:50.1547060Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/upsampling.py::UpsamplingNearest2d:0 2025-09-07T07:36:50.1549825Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/upsampling.py::UpsamplingBilinear2d:0, line 279 <- wrt source file 2025-09-07T07:36:50.1555914Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/upsampling.py::UpsamplingBilinear2d:0 2025-09-07T07:36:50.1558659Z * 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 2025-09-07T07:36:50.1561400Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/data_parallel.py::DataParallel:0 2025-09-07T07:36:50.1564223Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel:0, line 642 <- wrt source file 2025-09-07T07:36:50.1567207Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel:0 2025-09-07T07:36:50.1570223Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.no_sync:0, line 1446 <- wrt source file 2025-09-07T07:36:50.1573379Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.no_sync:0 2025-09-07T07:36:50.1576598Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.register_comm_hook:0, line 1999 <- wrt source file 2025-09-07T07:36:50.1579945Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.register_comm_hook:0 2025-09-07T07:36:50.1583364Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.register_comm_hook:1, line 2009 <- wrt source file 2025-09-07T07:36:50.1586740Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.register_comm_hook:1 2025-09-07T07:36:50.1590189Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel._register_builtin_comm_hook:0, line 2044 <- wrt source file 2025-09-07T07:36:50.1593769Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel._register_builtin_comm_hook:0 2025-09-07T07:36:50.1596923Z * 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 2025-09-07T07:36:50.1599792Z * 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 2025-09-07T07:36:50.1602343Z * 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 2025-09-07T07:36:50.1604781Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/init.py::skip_init:0 2025-09-07T07:36:50.1607261Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/parametrizations.py::orthogonal:0, line 265 <- wrt source file 2025-09-07T07:36:50.1610059Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/parametrizations.py::orthogonal:0 2025-09-07T07:36:50.1612735Z * 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 2025-09-07T07:36:50.1615436Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/parametrizations.py::weight_norm:0 2025-09-07T07:36:50.1618086Z * 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 2025-09-07T07:36:50.1620852Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/parametrizations.py::spectral_norm:0 2025-09-07T07:36:50.1623351Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py::identity:0, line 849 <- wrt source file 2025-09-07T07:36:50.1625880Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py::identity:0 2025-09-07T07:36:50.1628311Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py::random_unstructured:0, line 885 <- wrt source file 2025-09-07T07:36:50.1630882Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py::random_unstructured:0 2025-09-07T07:36:50.1633351Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py::l1_unstructured:0, line 928 <- wrt source file 2025-09-07T07:36:50.1635883Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py::l1_unstructured:0 2025-09-07T07:36:50.1638333Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py::random_structured:0, line 968 <- wrt source file 2025-09-07T07:36:50.1640858Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py::random_structured:0 2025-09-07T07:36:50.1643239Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py::remove:0, line 1197 <- wrt source file 2025-09-07T07:36:50.1645633Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py::remove:0 2025-09-07T07:36:50.1647944Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py::is_pruned:0, line 1225 <- wrt source file 2025-09-07T07:36:50.1650322Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py::is_pruned:0 2025-09-07T07:36:50.1652637Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/rnn.py::pad_sequence:0, line 439 <- wrt source file 2025-09-07T07:36:50.1655007Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/rnn.py::pad_sequence:0 2025-09-07T07:36:50.1657353Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/rnn.py::unpad_sequence:0, line 500 <- wrt source file 2025-09-07T07:36:50.1659770Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/rnn.py::unpad_sequence:0 2025-09-07T07:36:50.1662123Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/rnn.py::pack_sequence:0, line 556 <- wrt source file 2025-09-07T07:36:50.1664566Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/rnn.py::pack_sequence:0 2025-09-07T07:36:50.1666956Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/rnn.py::unpack_sequence:0, line 584 <- wrt source file 2025-09-07T07:36:50.1669468Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/rnn.py::unpack_sequence:0 2025-09-07T07:36:50.1671981Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/spectral_norm.py::spectral_norm:0, line 314 <- wrt source file 2025-09-07T07:36:50.1674623Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/spectral_norm.py::spectral_norm:0 2025-09-07T07:36:50.1677251Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/spectral_norm.py::remove_spectral_norm:0, line 346 <- wrt source file 2025-09-07T07:36:50.1680016Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/spectral_norm.py::remove_spectral_norm:0 2025-09-07T07:36:50.1682715Z * 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 2025-09-07T07:36:50.1685331Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/stateless.py::functional_call:0 2025-09-07T07:36:50.1687817Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/weight_norm.py::weight_norm:0, line 134 <- wrt source file 2025-09-07T07:36:50.1690329Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/weight_norm.py::weight_norm:0 2025-09-07T07:36:50.1692889Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/weight_norm.py::remove_weight_norm:0, line 156 <- wrt source file 2025-09-07T07:36:50.1695566Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/weight_norm.py::remove_weight_norm:0 2025-09-07T07:36:50.1698270Z * 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 2025-09-07T07:36:50.1701224Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/_expanded_weights/conv_utils.py::unfold3d:0 2025-09-07T07:36:50.1704352Z * 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 2025-09-07T07:36:50.1707941Z * 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 2025-09-07T07:36:50.1710834Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::LambdaLR:0, line 283 <- wrt source file 2025-09-07T07:36:50.1713242Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::LambdaLR:0 2025-09-07T07:36:50.1715746Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::MultiplicativeLR:0, line 391 <- wrt source file 2025-09-07T07:36:50.1718355Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::MultiplicativeLR:0 2025-09-07T07:36:50.1720790Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::StepLR:0, line 494 <- wrt source file 2025-09-07T07:36:50.1723162Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::StepLR:0 2025-09-07T07:36:50.1725672Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::MultiStepLR:0, line 550 <- wrt source file 2025-09-07T07:36:50.1728255Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::MultiStepLR:0 2025-09-07T07:36:50.1730672Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::ConstantLR:0, line 611 <- wrt source file 2025-09-07T07:36:50.1733202Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::ConstantLR:0 2025-09-07T07:36:50.1735559Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::LinearLR:0, line 686 <- wrt source file 2025-09-07T07:36:50.1737951Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::LinearLR:0 2025-09-07T07:36:50.1740376Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::ExponentialLR:0, line 776 <- wrt source file 2025-09-07T07:36:50.1742959Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::ExponentialLR:0 2025-09-07T07:36:50.1745423Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::PolynomialLR:0, line 974 <- wrt source file 2025-09-07T07:36:50.1747919Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::PolynomialLR:0 2025-09-07T07:36:50.1750436Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::CosineAnnealingLR:0, line 1065 <- wrt source file 2025-09-07T07:36:50.1753081Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::CosineAnnealingLR:0 2025-09-07T07:36:50.1755702Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::ChainedScheduler:0, line 1137 <- wrt source file 2025-09-07T07:36:50.1758321Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::ChainedScheduler:0 2025-09-07T07:36:50.1761099Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::CosineAnnealingWarmRestarts.step:0, line 1806 <- wrt source file 2025-09-07T07:36:50.1764077Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::CosineAnnealingWarmRestarts.step:0 2025-09-07T07:36:50.1767019Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::CosineAnnealingWarmRestarts.step:1, line 1822 <- wrt source file 2025-09-07T07:36:50.1770070Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::CosineAnnealingWarmRestarts.step:1 2025-09-07T07:36:50.1772660Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/swa_utils.py::update_bn:0, line 337 <- wrt source file 2025-09-07T07:36:50.1775028Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/swa_utils.py::update_bn:0 2025-09-07T07:36:50.1777384Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/package/glob_group.py::GlobGroup:0, line 22 <- wrt source file 2025-09-07T07:36:50.1779818Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/package/glob_group.py::GlobGroup:0 2025-09-07T07:36:50.1782562Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/profiler/profiler.py::_KinetoProfile.toggle_collection_dynamic:0, line 295 <- wrt source file 2025-09-07T07:36:50.1785667Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/profiler/profiler.py::_KinetoProfile.toggle_collection_dynamic:0 2025-09-07T07:36:50.1788391Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/profiler/profiler.py::profile:0, line 616 <- wrt source file 2025-09-07T07:36:50.1790760Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/profiler/profiler.py::profile:0 2025-09-07T07:36:50.1793379Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/sparse/semi_structured.py::to_sparse_semi_structured:0, line 339 <- wrt source file 2025-09-07T07:36:50.1796309Z * SKIPPED: 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/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_internal/common_utils.py::reparametrize:0 2025-09-07T07:36:50.1814865Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_internal/common_utils.py::decorateIf:0, line 825 <- wrt source file 2025-09-07T07:36:50.1817576Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_internal/common_utils.py::decorateIf:0 2025-09-07T07:36:50.1820419Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_internal/common_utils.py::random_symmetric_psd_matrix:0, line 4734 <- wrt source file 2025-09-07T07:36:50.1823479Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_internal/common_utils.py::random_symmetric_psd_matrix:0 2025-09-07T07:36:50.1826601Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_internal/common_utils.py::random_hermitian_psd_matrix:0, line 4748 <- wrt source file 2025-09-07T07:36:50.1829669Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_internal/common_utils.py::random_hermitian_psd_matrix:0 2025-09-07T07:36:50.1832740Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_internal/common_utils.py::random_hermitian_pd_matrix:0, line 4778 <- wrt source file 2025-09-07T07:36:50.1835865Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_internal/common_utils.py::random_hermitian_pd_matrix:0 2025-09-07T07:36:50.1838716Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_internal/logging_utils.py::logs_to_string:0, line 194 <- wrt source file 2025-09-07T07:36:50.1841508Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_internal/logging_utils.py::logs_to_string:0 2025-09-07T07:36:50.1844302Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_internal/logging_utils.py::multiple_logs_to_string:0, line 220 <- wrt source file 2025-09-07T07:36:50.1847266Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_internal/logging_utils.py::multiple_logs_to_string:0 2025-09-07T07:36:50.1850396Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_internal/distributed/_tensor/common_dtensor.py::skip_unless_torch_gpu:0, line 331 <- wrt source file 2025-09-07T07:36:50.1853884Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_internal/distributed/_tensor/common_dtensor.py::skip_unless_torch_gpu:0 2025-09-07T07:36:50.1857296Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_internal/optests/autograd_registration.py::autograd_registration_check:0, line 29 <- wrt source file 2025-09-07T07:36:50.1860747Z * SUCCESS: 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/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::tree_unflatten:0 2025-09-07T07:36:50.1878208Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::tree_iter:0, line 387 <- wrt source file 2025-09-07T07:36:50.1880564Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::tree_iter:0 2025-09-07T07:36:50.1882926Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::tree_leaves:0, line 422 <- wrt source file 2025-09-07T07:36:50.1885324Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::tree_leaves:0 2025-09-07T07:36:50.1887723Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::tree_structure:0, line 457 <- wrt source file 2025-09-07T07:36:50.1890198Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::tree_structure:0 2025-09-07T07:36:50.1892597Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::tree_map:0, line 494 <- wrt source file 2025-09-07T07:36:50.1894951Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::tree_map:0 2025-09-07T07:36:50.1897359Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::broadcast_prefix:0, line 893 <- wrt source file 2025-09-07T07:36:50.1899907Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::broadcast_prefix:0 2025-09-07T07:36:50.1902390Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_pytree.py::register_dataclass:0, line 303 <- wrt source file 2025-09-07T07:36:50.1904889Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_pytree.py::register_dataclass:0 2025-09-07T07:36:50.1907334Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_pytree.py::register_constant:0, line 419 <- wrt source file 2025-09-07T07:36:50.1909789Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_pytree.py::register_constant:0 2025-09-07T07:36:50.1912206Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_pytree.py::tree_is_leaf:0, line 1026 <- wrt source file 2025-09-07T07:36:50.1914593Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_pytree.py::tree_is_leaf:0 2025-09-07T07:36:50.1916910Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_pytree.py::tree_map:0, line 1345 <- wrt source file 2025-09-07T07:36:50.1919195Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_pytree.py::tree_map:0 2025-09-07T07:36:50.1921781Z * 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 2025-09-07T07:36:50.1924920Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/backend_registration.py::rename_privateuse1_backend:0 2025-09-07T07:36:50.1928076Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/backend_registration.py::generate_methods_for_privateuse1_backend:0, line 375 <- wrt source file 2025-09-07T07:36:50.1931341Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/backend_registration.py::generate_methods_for_privateuse1_backend:0 2025-09-07T07:36:50.1934306Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/backend_registration.py::_get_custom_mod_func:0, line 410 <- wrt source file 2025-09-07T07:36:50.1937144Z * SKIPPED: 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/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/dlpack.py::from_dlpack:0 2025-09-07T07:36:50.1955469Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_sympy/functions.py::MinMaxBase._collapse_arguments:0, line 724 <- wrt source file 2025-09-07T07:36:50.2257178Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_sympy/functions.py::MinMaxBase._collapse_arguments:0 2025-09-07T07:36:50.2259951Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/dataset.py::IterableDataset:0, line 94 <- wrt source file 2025-09-07T07:36:50.2262574Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/dataset.py::IterableDataset:0 2025-09-07T07:36:50.2265088Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/dataset.py::StackDataset:0, line 219 <- wrt source file 2025-09-07T07:36:50.2267619Z * SKIPPED: 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2025-09-07T07:36:50.2317885Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/combinatorics.py::ShufflerIterDataPipe:0 2025-09-07T07:36:50.2321067Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/combining.py::ConcaterIterDataPipe:0, line 38 <- wrt source file 2025-09-07T07:36:50.2324415Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/combining.py::ConcaterIterDataPipe:0 2025-09-07T07:36:50.2327510Z * 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 2025-09-07T07:36:50.2330621Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/combining.py::ForkerIterDataPipe:0 2025-09-07T07:36:50.2333639Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/combining.py::_ChildDataPipe:0, line 304 <- wrt source file 2025-09-07T07:36:50.2336659Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/combining.py::_ChildDataPipe:0 2025-09-07T07:36:50.2339832Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/filelister.py::FileListerIterDataPipe:0, line 30 <- wrt source file 2025-09-07T07:36:50.2343074Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/filelister.py::FileListerIterDataPipe:0 2025-09-07T07:36:50.2346260Z * 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 2025-09-07T07:36:50.2349398Z * SKIPPED: 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25 <- wrt source file 2025-09-07T07:36:50.2368648Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/streamreader.py::StreamReaderIterDataPipe:0 2025-09-07T07:36:50.2371884Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/utils.py::IterableWrapperIterDataPipe:0, line 29 <- wrt source file 2025-09-07T07:36:50.2375126Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/utils.py::IterableWrapperIterDataPipe:0 2025-09-07T07:36:50.2378219Z * 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 2025-09-07T07:36:50.2381269Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/map/callable.py::MapperMapDataPipe:0 2025-09-07T07:36:50.2384459Z * 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 2025-09-07T07:36:50.2387724Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/map/combinatorics.py::ShufflerIterDataPipe:0 2025-09-07T07:36:50.2390832Z * 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 2025-09-07T07:36:50.2393942Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/map/combining.py::ConcaterMapDataPipe:0 2025-09-07T07:36:50.2397020Z * 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 2025-09-07T07:36:50.2400101Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/map/combining.py::ZipperMapDataPipe:0 2025-09-07T07:36:50.2403112Z * 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 2025-09-07T07:36:50.2406224Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/map/grouping.py::BatcherMapDataPipe:0 2025-09-07T07:36:50.2409313Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/map/utils.py::SequenceWrapperMapDataPipe:0, line 29 <- wrt source file 2025-09-07T07:36:50.2412515Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/map/utils.py::SequenceWrapperMapDataPipe:0 2025-09-07T07:36:50.2415555Z * 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 2025-09-07T07:36:50.2418580Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/utils/common.py::validate_input_col:0 2025-09-07T07:36:50.2421519Z * 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 2025-09-07T07:36:50.2424682Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/utils/decoder.py::basichandlers:0 2025-09-07T07:36:50.2427504Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/hipify/hipify_python.py::find_closure_group:0, line 439 <- wrt source file 2025-09-07T07:36:50.6357226Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/hipify/hipify_python.py::find_closure_group:0 2025-09-07T07:36:50.6358722Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/hipify/hipify_python.py::replace_extern_shared:0, line 535 <- wrt source file 2025-09-07T07:36:50.6360219Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/hipify/hipify_python.py::replace_extern_shared:0 2025-09-07T07:36:50.6361666Z * 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 2025-09-07T07:36:50.6363151Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.__init__:0 2025-09-07T07:36:50.6364598Z * 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 2025-09-07T07:36:50.6366104Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_hparams:0 2025-09-07T07:36:50.6367783Z * 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 2025-09-07T07:36:50.6369301Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_scalar:0 2025-09-07T07:36:50.6370780Z * 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 2025-09-07T07:36:50.6372277Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_scalars:0 2025-09-07T07:36:50.6373725Z * 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 2025-09-07T07:36:50.6375211Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_tensor:0 2025-09-07T07:36:50.6376685Z * 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 2025-09-07T07:36:50.6378278Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_histogram:0 2025-09-07T07:36:50.6379830Z * 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 2025-09-07T07:36:50.6381450Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_histogram_raw:0 2025-09-07T07:36:50.6382940Z * 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 2025-09-07T07:36:50.6384405Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_image:0 2025-09-07T07:36:50.6385856Z * 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 2025-09-07T07:36:50.6387407Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_images:0 2025-09-07T07:36:50.6388870Z * 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 2025-09-07T07:36:50.6390346Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_text:0 2025-09-07T07:36:50.6391831Z * 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 2025-09-07T07:36:50.6393343Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_embedding:0 2025-09-07T07:36:50.6394942Z * 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 2025-09-07T07:36:50.6397212Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_pr_curve:0 2025-09-07T07:36:50.6399837Z * 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 2025-09-07T07:36:50.6402537Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_custom_scalars_multilinechart:0 2025-09-07T07:36:50.6405216Z * 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 2025-09-07T07:36:50.6408016Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_custom_scalars_marginchart:0 2025-09-07T07:36:50.6410782Z * 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 2025-09-07T07:36:50.6414259Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_custom_scalars:0 2025-09-07T07:36:50.6417003Z * 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 2025-09-07T07:36:50.6419440Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_mesh:0 2025-09-07T07:36:50.6420780Z ============ 2025-09-07T07:36:50.6421334Z Finished doctests 2025-09-07T07:36:50.6421734Z 338 / 732 passed 2025-09-07T07:36:50.6422158Z  2025-09-07T07:36:50.6422686Z === Found 146 parse-time warnings === 2025-09-07T07:36:50.6423451Z --- Parse Warning: 1 / 146 --- 2025-09-07T07:36:50.6425550Z /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=1493. 2025-09-07T07:36:50.6427815Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.6428657Z 2025-09-07T07:36:50.6429075Z dim_order(ambiguity_check=False) -> tuple 2025-09-07T07:36:50.6429611Z 2025-09-07T07:36:50.6430029Z Returns the uniquely determined tuple of int describing the dim order or 2025-09-07T07:36:50.6430867Z physical layout of :attr:`self`. 2025-09-07T07:36:50.6431318Z 2025-09-07T07:36:50.6431969Z The dim order represents how dimensions are laid out in memory of dense tensors, 2025-09-07T07:36:50.6433107Z starting from the outermost to the innermost dimension. 2025-09-07T07:36:50.6433802Z 2025-09-07T07:36:50.6434300Z Note that the dim order may not always be uniquely determined. 2025-09-07T07:36:50.6435663Z If `ambiguity_check` is True, this function raises a RuntimeError when the dim order cannot be uniquely determined; 2025-09-07T07:36:50.6437341Z If `ambiguity_check` is a list of memory formats, this function raises a RuntimeError when tensor can not be interpreted 2025-09-07T07:36:50.6438692Z into exactly one of the given memory formats, or it cannot be uniquely determined. 2025-09-07T07:36:50.6439875Z If `ambiguity_check` is False, it will return one of legal dim order(s) without checking its uniqueness. 2025-09-07T07:36:50.6440779Z Otherwise, it will raise TypeError. 2025-09-07T07:36:50.6441253Z 2025-09-07T07:36:50.6441626Z Args: 2025-09-07T07:36:50.6442413Z ambiguity_check (bool or List[torch.memory_format]): The check method for ambiguity of dim order. 2025-09-07T07:36:50.6443296Z 2025-09-07T07:36:50.6443616Z Examples:: 2025-09-07T07:36:50.6443896Z 2025-09-07T07:36:50.6444198Z >>> torch.empty((2, 3, 5, 7)).dim_order() 2025-09-07T07:36:50.6444671Z (0, 1, 2, 3) 2025-09-07T07:36:50.6445154Z >>> torch.empty((2, 3, 5, 7)).transpose(1, 2).dim_order() 2025-09-07T07:36:50.6445718Z (0, 2, 1, 3) 2025-09-07T07:36:50.6446205Z >>> torch.empty((2, 3, 5, 7), memory_format=torch.channels_last).dim_order() 2025-09-07T07:36:50.6446746Z (0, 2, 3, 1) 2025-09-07T07:36:50.6447085Z >>> torch.empty((1, 2, 3, 4)).dim_order() 2025-09-07T07:36:50.6447556Z (0, 1, 2, 3) 2025-09-07T07:36:50.6448028Z >>> try: 2025-09-07T07:36:50.6448537Z ... torch.empty((1, 2, 3, 4)).dim_order(ambiguity_check=True) 2025-09-07T07:36:50.6449035Z ... except RuntimeError as e: 2025-09-07T07:36:50.6449502Z ... print(e) 2025-09-07T07:36:50.6450245Z The tensor does not have unique dim order, or cannot map to exact one of the given memory formats. 2025-09-07T07:36:50.6451103Z >>> torch.empty((1, 2, 3, 4)).dim_order( 2025-09-07T07:36:50.6451747Z ... ambiguity_check=[torch.contiguous_format, torch.channels_last] 2025-09-07T07:36:50.6452522Z ... ) # It can be mapped to contiguous format 2025-09-07T07:36:50.6452959Z (0, 1, 2, 3) 2025-09-07T07:36:50.6453198Z >>> try: 2025-09-07T07:36:50.6453758Z ... torch.empty((1, 2, 3, 4)).dim_order(ambiguity_check="ILLEGAL") 2025-09-07T07:36:50.6454357Z ... except TypeError as e: 2025-09-07T07:36:50.6454791Z ... print(e) 2025-09-07T07:36:50.6455368Z The ambiguity_check argument must be a bool or a list of memory formats. 2025-09-07T07:36:50.6456097Z 2025-09-07T07:36:50.6456384Z .. warning:: 2025-09-07T07:36:50.6456714Z The dim_order tensor API is experimental and subject to change. 2025-09-07T07:36:50.6457360Z 2025-09-07T07:36:50.6457975Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.6458663Z 2025-09-07T07:36:50.6458921Z warnings.warn(msg) 2025-09-07T07:36:50.6459291Z 2025-09-07T07:36:50.6459825Z --- Parse Warning: 2 / 146 --- 2025-09-07T07:36:50.6461349Z /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=397. 2025-09-07T07:36:50.6463335Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.6464313Z Creates grids of coordinates specified by the 1D inputs in `attr`:tensors. 2025-09-07T07:36:50.6465006Z 2025-09-07T07:36:50.6465431Z This is helpful when you want to visualize data over some 2025-09-07T07:36:50.6466080Z range of inputs. See below for a plotting example. 2025-09-07T07:36:50.6466599Z 2025-09-07T07:36:50.6467061Z Given :math:`N` 1D tensors :math:`T_0 \ldots T_{N-1}` as 2025-09-07T07:36:50.6467808Z inputs with corresponding sizes :math:`S_0 \ldots S_{N-1}`, 2025-09-07T07:36:50.6468481Z this creates :math:`N` N-dimensional tensors :math:`G_0 \ldots 2025-09-07T07:36:50.6469152Z G_{N-1}`, each with shape :math:`(S_0, ..., S_{N-1})` where 2025-09-07T07:36:50.6469881Z the output :math:`G_i` is constructed by expanding :math:`T_i` 2025-09-07T07:36:50.6470614Z to the result shape. 2025-09-07T07:36:50.6470950Z 2025-09-07T07:36:50.6471217Z .. note:: 2025-09-07T07:36:50.6471618Z 0D inputs are treated equivalently to 1D inputs of a 2025-09-07T07:36:50.6472120Z single element. 2025-09-07T07:36:50.6472397Z 2025-09-07T07:36:50.6472655Z .. warning:: 2025-09-07T07:36:50.6473132Z `torch.meshgrid(*tensors)` currently has the same behavior 2025-09-07T07:36:50.6473857Z as calling `numpy.meshgrid(*arrays, indexing='ij')`. 2025-09-07T07:36:50.6474261Z 2025-09-07T07:36:50.6474742Z In the future `torch.meshgrid` will transition to 2025-09-07T07:36:50.6475311Z `indexing='xy'` as the default. 2025-09-07T07:36:50.6475769Z 2025-09-07T07:36:50.6476243Z https://github.com/pytorch/pytorch/issues/50276 tracks 2025-09-07T07:36:50.6477062Z this issue with the goal of migrating to NumPy's behavior. 2025-09-07T07:36:50.6477664Z 2025-09-07T07:36:50.6478008Z .. seealso:: 2025-09-07T07:36:50.6478268Z 2025-09-07T07:36:50.6478799Z :func:`torch.cartesian_prod` has the same effect but it 2025-09-07T07:36:50.6479493Z collects the data in a tensor of vectors. 2025-09-07T07:36:50.6480036Z 2025-09-07T07:36:50.6480364Z Args: 2025-09-07T07:36:50.6480844Z tensors (list of Tensor): list of scalars or 1 dimensional tensors. Scalars will be 2025-09-07T07:36:50.6481398Z treated as tensors of size :math:`(1,)` automatically 2025-09-07T07:36:50.6481887Z 2025-09-07T07:36:50.6482267Z indexing: (str, optional): the indexing mode, either "xy" 2025-09-07T07:36:50.6482966Z or "ij", defaults to "ij". See warning for future changes. 2025-09-07T07:36:50.6483491Z 2025-09-07T07:36:50.6483786Z If "xy" is selected, the first dimension corresponds 2025-09-07T07:36:50.6484253Z to the cardinality of the second input and the second 2025-09-07T07:36:50.6484864Z dimension corresponds to the cardinality of the first 2025-09-07T07:36:50.6485406Z input. 2025-09-07T07:36:50.6485761Z 2025-09-07T07:36:50.6486175Z If "ij" is selected, the dimensions are in the same 2025-09-07T07:36:50.6486731Z order as the cardinality of the inputs. 2025-09-07T07:36:50.6488372Z 2025-09-07T07:36:50.6488687Z Returns: 2025-09-07T07:36:50.6489174Z seq (sequence of Tensors): If the input has :math:`N` 2025-09-07T07:36:50.6489786Z tensors of size :math:`S_0 \ldots S_{N-1}``, then the 2025-09-07T07:36:50.6490589Z output will also have :math:`N` tensors, where each tensor 2025-09-07T07:36:50.6491268Z is of shape :math:`(S_0, ..., S_{N-1})`. 2025-09-07T07:36:50.6491809Z 2025-09-07T07:36:50.6492124Z Example:: 2025-09-07T07:36:50.6492509Z 2025-09-07T07:36:50.6492861Z >>> x = torch.tensor([1, 2, 3]) 2025-09-07T07:36:50.6493436Z >>> y = torch.tensor([4, 5, 6]) 2025-09-07T07:36:50.6493935Z 2025-09-07T07:36:50.6494399Z Observe the element-wise pairings across the grid, (1, 4), 2025-09-07T07:36:50.6494999Z (1, 5), ..., (3, 6). This is the same thing as the 2025-09-07T07:36:50.6495595Z cartesian product. 2025-09-07T07:36:50.6496245Z >>> grid_x, grid_y = torch.meshgrid(x, y, indexing='ij') 2025-09-07T07:36:50.6496827Z >>> grid_x 2025-09-07T07:36:50.6497252Z tensor([[1, 1, 1], 2025-09-07T07:36:50.6497667Z [2, 2, 2], 2025-09-07T07:36:50.6498064Z [3, 3, 3]]) 2025-09-07T07:36:50.6498429Z >>> grid_y 2025-09-07T07:36:50.6498868Z tensor([[4, 5, 6], 2025-09-07T07:36:50.6499244Z [4, 5, 6], 2025-09-07T07:36:50.6499593Z [4, 5, 6]]) 2025-09-07T07:36:50.6499990Z 2025-09-07T07:36:50.6500278Z This correspondence can be seen when these grids are 2025-09-07T07:36:50.6500785Z stacked properly. 2025-09-07T07:36:50.6501376Z >>> torch.equal(torch.cat(tuple(torch.dstack([grid_x, grid_y]))), 2025-09-07T07:36:50.6501894Z ... torch.cartesian_prod(x, y)) 2025-09-07T07:36:50.6502233Z True 2025-09-07T07:36:50.6502456Z 2025-09-07T07:36:50.6502750Z `torch.meshgrid` is commonly used to produce a grid for 2025-09-07T07:36:50.6503143Z plotting. 2025-09-07T07:36:50.6503447Z >>> # xdoctest: +REQUIRES(module:matplotlib) 2025-09-07T07:36:50.6503938Z >>> # xdoctest: +REQUIRES(env:DOCTEST_SHOW) 2025-09-07T07:36:50.6504558Z >>> import matplotlib.pyplot as plt 2025-09-07T07:36:50.6504965Z >>> xs = torch.linspace(-5, 5, steps=100) 2025-09-07T07:36:50.6505337Z >>> ys = torch.linspace(-5, 5, steps=100) 2025-09-07T07:36:50.6505707Z >>> x, y = torch.meshgrid(xs, ys, indexing='xy') 2025-09-07T07:36:50.6506294Z >>> z = torch.sin(torch.sqrt(x * x + y * y)) 2025-09-07T07:36:50.6506669Z >>> ax = plt.axes(projection='3d') 2025-09-07T07:36:50.6507093Z >>> ax.plot_surface(x.numpy(), y.numpy(), z.numpy()) 2025-09-07T07:36:50.6507558Z >>> plt.show() 2025-09-07T07:36:50.6507830Z 2025-09-07T07:36:50.6508085Z .. image:: ../_static/img/meshgrid.png 2025-09-07T07:36:50.6508419Z :width: 512 2025-09-07T07:36:50.6508664Z 2025-09-07T07:36:50.6508867Z 2025-09-07T07:36:50.6509375Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.6509938Z 2025-09-07T07:36:50.6510137Z warnings.warn(msg) 2025-09-07T07:36:50.6510420Z 2025-09-07T07:36:50.6510932Z --- Parse Warning: 3 / 146 --- 2025-09-07T07:36:50.6512119Z /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=793. 2025-09-07T07:36:50.6513351Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.6514158Z unique(input, sorted=True, return_inverse=False, return_counts=False, dim=None) -> tuple[Tensor, Tensor, Tensor] 2025-09-07T07:36:50.6514844Z 2025-09-07T07:36:50.6515112Z Returns the unique elements of the input tensor. 2025-09-07T07:36:50.6515463Z 2025-09-07T07:36:50.6515905Z .. note:: This function is different from :func:`torch.unique_consecutive` in the sense that 2025-09-07T07:36:50.6516535Z this function also eliminates non-consecutive duplicate values. 2025-09-07T07:36:50.6516949Z 2025-09-07T07:36:50.6517299Z .. note:: Currently in the CUDA implementation and the CPU implementation, 2025-09-07T07:36:50.6517944Z `torch.unique` always sort the tensor at the beginning regardless of the `sort` argument. 2025-09-07T07:36:50.6518638Z Sorting could be slow, so if your input tensor is already sorted, it is recommended to use 2025-09-07T07:36:50.6519230Z :func:`torch.unique_consecutive` which avoids the sorting. 2025-09-07T07:36:50.6519615Z 2025-09-07T07:36:50.6519812Z Args: 2025-09-07T07:36:50.6520083Z input (Tensor): the input tensor 2025-09-07T07:36:50.6520523Z sorted (bool): Whether to sort the unique elements in ascending order 2025-09-07T07:36:50.6520972Z before returning as output. 2025-09-07T07:36:50.6521407Z return_inverse (bool): Whether to also return the indices for where 2025-09-07T07:36:50.6521950Z elements in the original input ended up in the returned unique list. 2025-09-07T07:36:50.6522519Z return_counts (bool): Whether to also return the counts for each unique 2025-09-07T07:36:50.6522953Z element. 2025-09-07T07:36:50.6523323Z dim (int, optional): the dimension to operate upon. If ``None``, the 2025-09-07T07:36:50.6523999Z unique of the flattened input is returned. Otherwise, each of the 2025-09-07T07:36:50.6524527Z tensors indexed by the given dimension is treated as one of the 2025-09-07T07:36:50.6525077Z elements to apply the unique operation upon. See examples for more 2025-09-07T07:36:50.6525533Z details. Default: ``None`` 2025-09-07T07:36:50.6525846Z 2025-09-07T07:36:50.6526034Z Returns: 2025-09-07T07:36:50.6526468Z (Tensor, Tensor (optional), Tensor (optional)): A tensor or a tuple of tensors containing 2025-09-07T07:36:50.6526960Z 2025-09-07T07:36:50.6527267Z - **output** (*Tensor*): the output list of unique scalar elements. 2025-09-07T07:36:50.6527716Z - **inverse_indices** (*Tensor*): (optional) if 2025-09-07T07:36:50.6528160Z :attr:`return_inverse` is True, there will be an additional 2025-09-07T07:36:50.6528739Z returned tensor (same shape as input) representing the indices 2025-09-07T07:36:50.6529272Z for where elements in the original input map to in the output; 2025-09-07T07:36:50.6529787Z otherwise, this function will only return a single tensor. 2025-09-07T07:36:50.6530217Z - **counts** (*Tensor*): (optional) if 2025-09-07T07:36:50.6530637Z :attr:`return_counts` is True, there will be an additional 2025-09-07T07:36:50.6531129Z returned tensor (same shape as output or output.size(dim), 2025-09-07T07:36:50.6531632Z if dim was specified) representing the number of occurrences 2025-09-07T07:36:50.6532045Z for each unique value or tensor. 2025-09-07T07:36:50.6532363Z 2025-09-07T07:36:50.6532569Z Example:: 2025-09-07T07:36:50.6532795Z 2025-09-07T07:36:50.6533110Z >>> output = torch.unique(torch.tensor([1, 3, 2, 3], dtype=torch.long)) 2025-09-07T07:36:50.6533540Z >>> output 2025-09-07T07:36:50.6533791Z tensor([1, 2, 3]) 2025-09-07T07:36:50.6534052Z 2025-09-07T07:36:50.6534281Z >>> output, inverse_indices = torch.unique( 2025-09-07T07:36:50.6534811Z ... torch.tensor([1, 3, 2, 3], dtype=torch.long), sorted=True, return_inverse=True) 2025-09-07T07:36:50.6535261Z >>> output 2025-09-07T07:36:50.6535513Z tensor([1, 2, 3]) 2025-09-07T07:36:50.6535778Z >>> inverse_indices 2025-09-07T07:36:50.6536113Z tensor([0, 2, 1, 2]) 2025-09-07T07:36:50.6536381Z 2025-09-07T07:36:50.6536628Z >>> output, inverse_indices = torch.unique( 2025-09-07T07:36:50.6537149Z ... torch.tensor([[1, 3], [2, 3]], dtype=torch.long), sorted=True, return_inverse=True) 2025-09-07T07:36:50.6537600Z >>> output 2025-09-07T07:36:50.6537852Z tensor([1, 2, 3]) 2025-09-07T07:36:50.6538129Z >>> inverse_indices 2025-09-07T07:36:50.6538408Z tensor([[0, 2], 2025-09-07T07:36:50.6538658Z [1, 2]]) 2025-09-07T07:36:50.6538911Z 2025-09-07T07:36:50.6539120Z >>> a = torch.tensor([ 2025-09-07T07:36:50.6539393Z ... [ 2025-09-07T07:36:50.6539638Z ... [1, 1, 0, 0], 2025-09-07T07:36:50.6539967Z ... [1, 1, 0, 0], 2025-09-07T07:36:50.6540256Z ... [0, 0, 1, 1], 2025-09-07T07:36:50.6540535Z ... ], 2025-09-07T07:36:50.6540772Z ... [ 2025-09-07T07:36:50.6541018Z ... [0, 0, 1, 1], 2025-09-07T07:36:50.6541305Z ... [0, 0, 1, 1], 2025-09-07T07:36:50.6541583Z ... [1, 1, 1, 1], 2025-09-07T07:36:50.6541866Z ... ], 2025-09-07T07:36:50.6542108Z ... [ 2025-09-07T07:36:50.6542357Z ... [1, 1, 0, 0], 2025-09-07T07:36:50.6542632Z ... [1, 1, 0, 0], 2025-09-07T07:36:50.6542928Z ... [0, 0, 1, 1], 2025-09-07T07:36:50.6543211Z ... ], 2025-09-07T07:36:50.6543454Z ... ]) 2025-09-07T07:36:50.6543665Z 2025-09-07T07:36:50.6544001Z >>> # If we call `torch.unique(a, dim=0)`, each of the tensors `a[idx, :, :]` 2025-09-07T07:36:50.6544561Z >>> # will be compared. We can see that `a[0, :, :]` and `a[2, :, :]` match 2025-09-07T07:36:50.6545038Z >>> # each other, so one of them will be removed. 2025-09-07T07:36:50.6545406Z >>> (a[0, :, :] == a[2, :, :]).all() 2025-09-07T07:36:50.6545710Z tensor(True) 2025-09-07T07:36:50.6546000Z >>> a_unique_dim0 = torch.unique(a, dim=0) 2025-09-07T07:36:50.6546335Z >>> a_unique_dim0 2025-09-07T07:36:50.6546612Z tensor([[[0, 0, 1, 1], 2025-09-07T07:36:50.6546884Z [0, 0, 1, 1], 2025-09-07T07:36:50.6547165Z [1, 1, 1, 1]], 2025-09-07T07:36:50.6547447Z [[1, 1, 0, 0], 2025-09-07T07:36:50.6547761Z [1, 1, 0, 0], 2025-09-07T07:36:50.6548028Z [0, 0, 1, 1]]]) 2025-09-07T07:36:50.6548312Z 2025-09-07T07:36:50.6548636Z >>> # Notice which sub-tensors from `a` match with the sub-tensors from 2025-09-07T07:36:50.6549073Z >>> # `a_unique_dim0`: 2025-09-07T07:36:50.6549382Z >>> (a_unique_dim0[0, :, :] == a[1, :, :]).all() 2025-09-07T07:36:50.6549717Z tensor(True) 2025-09-07T07:36:50.6550010Z >>> (a_unique_dim0[1, :, :] == a[0, :, :]).all() 2025-09-07T07:36:50.6550349Z tensor(True) 2025-09-07T07:36:50.6550620Z 2025-09-07T07:36:50.6550940Z >>> # For `torch.unique(a, dim=1)`, each of the tensors `a[:, idx, :]` are 2025-09-07T07:36:50.6551455Z >>> # compared. `a[:, 0, :]` and `a[:, 1, :]` match each other, so one of 2025-09-07T07:36:50.6551870Z >>> # them will be removed. 2025-09-07T07:36:50.6552206Z >>> (a[:, 0, :] == a[:, 1, :]).all() 2025-09-07T07:36:50.6552603Z tensor(True) 2025-09-07T07:36:50.6552876Z >>> torch.unique(a, dim=1) 2025-09-07T07:36:50.6553180Z tensor([[[0, 0, 1, 1], 2025-09-07T07:36:50.6553491Z [1, 1, 0, 0]], 2025-09-07T07:36:50.6553771Z [[1, 1, 1, 1], 2025-09-07T07:36:50.6554055Z [0, 0, 1, 1]], 2025-09-07T07:36:50.6554406Z [[0, 0, 1, 1], 2025-09-07T07:36:50.6554679Z [1, 1, 0, 0]]]) 2025-09-07T07:36:50.6554996Z 2025-09-07T07:36:50.6555323Z >>> # For `torch.unique(a, dim=2)`, the tensors `a[:, :, idx]` are compared. 2025-09-07T07:36:50.6555821Z >>> # `a[:, :, 0]` and `a[:, :, 1]` match each other. Also, `a[:, :, 2]` and 2025-09-07T07:36:50.6556265Z >>> # `a[:, :, 3]` match each other as well. So in this case, two of the 2025-09-07T07:36:50.6556684Z >>> # sub-tensors will be removed. 2025-09-07T07:36:50.6557027Z >>> (a[:, :, 0] == a[:, :, 1]).all() 2025-09-07T07:36:50.6557341Z tensor(True) 2025-09-07T07:36:50.6557650Z >>> (a[:, :, 2] == a[:, :, 3]).all() 2025-09-07T07:36:50.6557952Z tensor(True) 2025-09-07T07:36:50.6558222Z >>> torch.unique(a, dim=2) 2025-09-07T07:36:50.6558528Z tensor([[[0, 1], 2025-09-07T07:36:50.6558822Z [0, 1], 2025-09-07T07:36:50.6559071Z [1, 0]], 2025-09-07T07:36:50.6559330Z [[1, 0], 2025-09-07T07:36:50.6559592Z [1, 0], 2025-09-07T07:36:50.6559850Z [1, 1]], 2025-09-07T07:36:50.6560101Z [[0, 1], 2025-09-07T07:36:50.6560359Z [0, 1], 2025-09-07T07:36:50.6560628Z [1, 0]]]) 2025-09-07T07:36:50.6560875Z 2025-09-07T07:36:50.6561251Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.6561712Z 2025-09-07T07:36:50.6561921Z warnings.warn(msg) 2025-09-07T07:36:50.6562158Z 2025-09-07T07:36:50.6562504Z --- Parse Warning: 4 / 146 --- 2025-09-07T07:36:50.6563546Z /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=565. 2025-09-07T07:36:50.6564726Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.6565195Z 2025-09-07T07:36:50.6565467Z Load a model from a github repo or a local directory. 2025-09-07T07:36:50.6565822Z 2025-09-07T07:36:50.6566162Z Note: Loading a model is the typical use case, but this can also be used to 2025-09-07T07:36:50.6566727Z for loading other objects such as tokenizers, loss functions, etc. 2025-09-07T07:36:50.6567127Z 2025-09-07T07:36:50.6567408Z If ``source`` is 'github', ``repo_or_dir`` is expected to be 2025-09-07T07:36:50.6567902Z of the form ``repo_owner/repo_name[:ref]`` with an optional 2025-09-07T07:36:50.6568294Z ref (a tag or a branch). 2025-09-07T07:36:50.6568550Z 2025-09-07T07:36:50.6568836Z If ``source`` is 'local', ``repo_or_dir`` is expected to be a 2025-09-07T07:36:50.6569228Z path to a local directory. 2025-09-07T07:36:50.6569501Z 2025-09-07T07:36:50.6569687Z Args: 2025-09-07T07:36:50.6569941Z repo_or_dir (str): If ``source`` is 'github', 2025-09-07T07:36:50.6570479Z this should correspond to a github repo with format ``repo_owner/repo_name[:ref]`` with 2025-09-07T07:36:50.6571192Z an optional ref (tag or branch), for example 'pytorch/vision:0.10'. If ``ref`` is not specified, 2025-09-07T07:36:50.6571878Z the default branch is assumed to be ``main`` if it exists, and otherwise ``master``. 2025-09-07T07:36:50.6572460Z If ``source`` is 'local' then it should be a path to a local directory. 2025-09-07T07:36:50.6572991Z model (str): the name of a callable (entrypoint) defined in the 2025-09-07T07:36:50.6573416Z repo/dir's ``hubconf.py``. 2025-09-07T07:36:50.6573833Z *args (optional): the corresponding args for callable ``model``. 2025-09-07T07:36:50.6600421Z source (str, optional): 'github' or 'local'. Specifies how 2025-09-07T07:36:50.6600943Z ``repo_or_dir`` is to be interpreted. Default is 'github'. 2025-09-07T07:36:50.6601462Z trust_repo (bool, str or None): ``"check"``, ``True``, ``False`` or ``None``. 2025-09-07T07:36:50.6602149Z This parameter was introduced in v1.12 and helps ensuring that users 2025-09-07T07:36:50.6602627Z only run code from repos that they trust. 2025-09-07T07:36:50.6602964Z 2025-09-07T07:36:50.6603284Z - If ``False``, a prompt will ask the user whether the repo should 2025-09-07T07:36:50.6603707Z be trusted. 2025-09-07T07:36:50.6604059Z - If ``True``, the repo will be added to the trusted list and loaded 2025-09-07T07:36:50.6604518Z without requiring explicit confirmation. 2025-09-07T07:36:50.6604943Z - If ``"check"``, the repo will be checked against the list of 2025-09-07T07:36:50.6605461Z trusted repos in the cache. If it is not present in that list, the 2025-09-07T07:36:50.6606066Z behaviour will fall back onto the ``trust_repo=False`` option. 2025-09-07T07:36:50.6606563Z - If ``None``: this will raise a warning, inviting the user to set 2025-09-07T07:36:50.6607062Z ``trust_repo`` to either ``False``, ``True`` or ``"check"``. This 2025-09-07T07:36:50.6607585Z is only present for backward compatibility and will be removed in 2025-09-07T07:36:50.6608009Z v2.0. 2025-09-07T07:36:50.6608228Z 2025-09-07T07:36:50.6608557Z Default is ``None`` and will eventually change to ``"check"`` in v2.0. 2025-09-07T07:36:50.6609108Z force_reload (bool, optional): whether to force a fresh download of 2025-09-07T07:36:50.6609639Z the github repo unconditionally. Does not have any effect if 2025-09-07T07:36:50.6610120Z ``source = 'local'``. Default is ``False``. 2025-09-07T07:36:50.6610561Z verbose (bool, optional): If ``False``, mute messages about hitting 2025-09-07T07:36:50.6611101Z local caches. Note that the message about first download cannot be 2025-09-07T07:36:50.6611606Z muted. Does not have any effect if ``source = 'local'``. 2025-09-07T07:36:50.6611999Z Default is ``True``. 2025-09-07T07:36:50.6612477Z skip_validation (bool, optional): if ``False``, torchhub will check that the branch or commit 2025-09-07T07:36:50.6613185Z specified by the ``github`` argument properly belongs to the repo owner. This will make 2025-09-07T07:36:50.6613862Z requests to the GitHub API; you can specify a non-default GitHub token by setting the 2025-09-07T07:36:50.6614469Z ``GITHUB_TOKEN`` environment variable. Default is ``False``. 2025-09-07T07:36:50.6614986Z **kwargs (optional): the corresponding kwargs for callable ``model``. 2025-09-07T07:36:50.6615397Z 2025-09-07T07:36:50.6615600Z Returns: 2025-09-07T07:36:50.6615924Z The output of the ``model`` callable when called with the given 2025-09-07T07:36:50.6616336Z ``*args`` and ``**kwargs``. 2025-09-07T07:36:50.6616608Z 2025-09-07T07:36:50.6616812Z Example: 2025-09-07T07:36:50.6617083Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_HUB) 2025-09-07T07:36:50.6617434Z >>> # from a github repo 2025-09-07T07:36:50.6617721Z >>> repo = "pytorch/vision" 2025-09-07T07:36:50.6618032Z >>> model = torch.hub.load( 2025-09-07T07:36:50.6618420Z ... repo, "resnet50", weights="ResNet50_Weights.IMAGENET1K_V1" 2025-09-07T07:36:50.6618812Z ... ) 2025-09-07T07:36:50.6619037Z >>> # from a local directory 2025-09-07T07:36:50.6619376Z >>> path = "/some/local/path/pytorch/vision" 2025-09-07T07:36:50.6619721Z >>> # xdoctest: +SKIP 2025-09-07T07:36:50.6620150Z >>> model = torch.hub.load(path, "resnet50", weights="ResNet50_Weights.DEFAULT") 2025-09-07T07:36:50.6620625Z 2025-09-07T07:36:50.6621000Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.6621455Z 2025-09-07T07:36:50.6621673Z warnings.warn(msg) 2025-09-07T07:36:50.6621919Z 2025-09-07T07:36:50.6622293Z --- Parse Warning: 5 / 146 --- 2025-09-07T07:36:50.6623393Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=_load_local in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/hub.py line=657. 2025-09-07T07:36:50.6624800Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.6625300Z 2025-09-07T07:36:50.6625582Z Load a model from a local directory with a ``hubconf.py``. 2025-09-07T07:36:50.6625963Z 2025-09-07T07:36:50.6626169Z Args: 2025-09-07T07:36:50.6626476Z hubconf_dir (str): path to a local directory that contains a 2025-09-07T07:36:50.6626889Z ``hubconf.py``. 2025-09-07T07:36:50.6627256Z model (str): name of an entrypoint defined in the directory's 2025-09-07T07:36:50.6627661Z ``hubconf.py``. 2025-09-07T07:36:50.6628124Z *args (optional): the corresponding args for callable ``model``. 2025-09-07T07:36:50.6628647Z **kwargs (optional): the corresponding kwargs for callable ``model``. 2025-09-07T07:36:50.6629081Z 2025-09-07T07:36:50.6629288Z Returns: 2025-09-07T07:36:50.6629595Z a single model with corresponding pretrained weights. 2025-09-07T07:36:50.6629954Z 2025-09-07T07:36:50.6630155Z Example: 2025-09-07T07:36:50.6630405Z >>> # xdoctest: +SKIP("stub local path") 2025-09-07T07:36:50.6630772Z >>> path = "/some/local/path/pytorch/vision" 2025-09-07T07:36:50.6631109Z >>> model = _load_local( 2025-09-07T07:36:50.6631388Z ... path, 2025-09-07T07:36:50.6631633Z ... "resnet50", 2025-09-07T07:36:50.6631947Z ... weights="ResNet50_Weights.IMAGENET1K_V1", 2025-09-07T07:36:50.6632281Z ... ) 2025-09-07T07:36:50.6632493Z 2025-09-07T07:36:50.6632866Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.6633326Z 2025-09-07T07:36:50.6633525Z warnings.warn(msg) 2025-09-07T07:36:50.6633778Z 2025-09-07T07:36:50.6634103Z --- Parse Warning: 6 / 146 --- 2025-09-07T07:36:50.6635280Z /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=696. 2025-09-07T07:36:50.6636499Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.6637100Z Download object at the given URL to a local path. 2025-09-07T07:36:50.6637453Z 2025-09-07T07:36:50.6637657Z Args: 2025-09-07T07:36:50.6637907Z url (str): URL of the object to download 2025-09-07T07:36:50.6638390Z dst (str): Full path where object will be saved, e.g. ``/tmp/temporary_file`` 2025-09-07T07:36:50.6639098Z hash_prefix (str, optional): If not None, the SHA256 downloaded file should start with ``hash_prefix``. 2025-09-07T07:36:50.6639713Z Default: None 2025-09-07T07:36:50.6640151Z progress (bool, optional): whether or not to display a progress bar to stderr 2025-09-07T07:36:50.6640620Z Default: True 2025-09-07T07:36:50.6640886Z 2025-09-07T07:36:50.6641091Z Example: 2025-09-07T07:36:50.6641375Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_HUB) 2025-09-07T07:36:50.6641732Z >>> # xdoctest: +REQUIRES(POSIX) 2025-09-07T07:36:50.6642079Z >>> torch.hub.download_url_to_file( 2025-09-07T07:36:50.6642554Z ... "https://s3.amazonaws.com/pytorch/models/resnet18-5c106cde.pth", 2025-09-07T07:36:50.6643009Z ... "/tmp/temporary_file", 2025-09-07T07:36:50.6643301Z ... ) 2025-09-07T07:36:50.6643589Z 2025-09-07T07:36:50.6643793Z 2025-09-07T07:36:50.6644176Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.6644625Z 2025-09-07T07:36:50.6644837Z warnings.warn(msg) 2025-09-07T07:36:50.6645091Z 2025-09-07T07:36:50.6645457Z --- Parse Warning: 7 / 146 --- 2025-09-07T07:36:50.6646559Z /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=821. 2025-09-07T07:36:50.6647901Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.6648453Z Loads the Torch serialized object at the given URL. 2025-09-07T07:36:50.6648808Z 2025-09-07T07:36:50.6649114Z If downloaded file is a zip file, it will be automatically 2025-09-07T07:36:50.6649526Z decompressed. 2025-09-07T07:36:50.6649772Z 2025-09-07T07:36:50.6650100Z If the object is already present in `model_dir`, it's deserialized and 2025-09-07T07:36:50.6650558Z returned. 2025-09-07T07:36:50.6650905Z The default value of ``model_dir`` is ``/checkpoints`` where 2025-09-07T07:36:50.6651432Z ``hub_dir`` is the directory returned by :func:`~torch.hub.get_dir`. 2025-09-07T07:36:50.6651832Z 2025-09-07T07:36:50.6652032Z Args: 2025-09-07T07:36:50.6652285Z url (str): URL of the object to download 2025-09-07T07:36:50.6652722Z model_dir (str, optional): directory in which to save the object 2025-09-07T07:36:50.6653391Z map_location (optional): a function or a dict specifying how to remap storage locations (see torch.load) 2025-09-07T07:36:50.6654127Z progress (bool, optional): whether or not to display a progress bar to stderr. 2025-09-07T07:36:50.6654608Z Default: True 2025-09-07T07:36:50.6655114Z check_hash(bool, optional): If True, the filename part of the URL should follow the naming convention 2025-09-07T07:36:50.6655796Z ``filename-.ext`` where ```` is the first eight or more 2025-09-07T07:36:50.6656370Z digits of the SHA256 hash of the contents of the file. The hash is used to 2025-09-07T07:36:50.6656921Z ensure unique names and to verify the contents of the file. 2025-09-07T07:36:50.6657332Z Default: False 2025-09-07T07:36:50.6657844Z file_name (str, optional): name for the downloaded file. Filename from ``url`` will be used if not set. 2025-09-07T07:36:50.6658631Z weights_only(bool, optional): If True, only weights will be loaded and no complex pickled objects. 2025-09-07T07:36:50.6659377Z Recommended for untrusted sources. See :func:`~torch.load` for more details. 2025-09-07T07:36:50.6659840Z 2025-09-07T07:36:50.6660031Z Example: 2025-09-07T07:36:50.6660314Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_HUB) 2025-09-07T07:36:50.6660721Z >>> state_dict = torch.hub.load_state_dict_from_url( 2025-09-07T07:36:50.6661212Z ... "https://s3.amazonaws.com/pytorch/models/resnet18-5c106cde.pth" 2025-09-07T07:36:50.6661628Z ... ) 2025-09-07T07:36:50.6661854Z 2025-09-07T07:36:50.6662053Z 2025-09-07T07:36:50.6662434Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.6662878Z 2025-09-07T07:36:50.6663088Z warnings.warn(msg) 2025-09-07T07:36:50.6663342Z 2025-09-07T07:36:50.6663655Z --- Parse Warning: 8 / 146 --- 2025-09-07T07:36:50.6664768Z /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=375. 2025-09-07T07:36:50.6666000Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-09-07T07:36:50.6666646Z Registers the function implementation as the fallback for the given key. 2025-09-07T07:36:50.6667098Z 2025-09-07T07:36:50.6667435Z This function only works for a library with global namespace ("_"). 2025-09-07T07:36:50.6667846Z 2025-09-07T07:36:50.6668079Z Args: 2025-09-07T07:36:50.6668501Z fn: function used as fallback for the given dispatch key or :func:`~fallthrough_kernel` 2025-09-07T07:36:50.6669024Z to register a fallthrough. 2025-09-07T07:36:50.6669569Z dispatch_key: dispatch key that the input function should be registered for. By default, it uses 2025-09-07T07:36:50.6670187Z the dispatch key that the library was created with. 2025-09-07T07:36:50.6670859Z with_keyset: flag controlling if the current dispatcher call keyset should be passed as the first argument 2025-09-07T07:36:50.6671687Z to :attr:`fn` when calling. This should be used to create the appropriate keyset for redispatch calls. 2025-09-07T07:36:50.6672227Z 2025-09-07T07:36:50.6672471Z Example:: 2025-09-07T07:36:50.6672710Z 2025-09-07T07:36:50.6672942Z >>> my_lib = Library("_", "IMPL") 2025-09-07T07:36:50.6673313Z >>> def fallback_kernel(op, *args, **kwargs): 2025-09-07T07:36:50.6673689Z >>> # Handle all autocast ops generically 2025-09-07T07:36:50.6674035Z >>> # ... 2025-09-07T07:36:50.6674439Z >>> my_lib.fallback(fallback_kernel, "Autocast") 2025-09-07T07:36:50.6674794Z 2025-09-07T07:36:50.6675555Z Original Error: IndentationError('expected an indented block after function definition on line 2', ('', 5, 1, 'my_lib.fallback(fallback_kernel, "Autocast")\n', 5, 7)) 2025-09-07T07:36:50.6676390Z 2025-09-07T07:36:50.6676640Z my_lib.fallback(fallback_kernel, "Autocast") 2025-09-07T07:36:50.6676973Z ^ 2025-09-07T07:36:50.6677191Z warnings.warn(msg) 2025-09-07T07:36:50.6677430Z 2025-09-07T07:36:50.6677742Z --- Parse Warning: 9 / 146 --- 2025-09-07T07:36:50.6678834Z /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=948. 2025-09-07T07:36:50.6680059Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-09-07T07:36:50.6680650Z Register a FakeTensor implementation ("fake impl") for this operator. 2025-09-07T07:36:50.6681089Z 2025-09-07T07:36:50.6681386Z Also sometimes known as a "meta kernel", "abstract impl". 2025-09-07T07:36:50.6681769Z 2025-09-07T07:36:50.6682161Z An "FakeTensor implementation" specifies the behavior of this operator on 2025-09-07T07:36:50.6682757Z Tensors that carry no data ("FakeTensor"). Given some input Tensors with 2025-09-07T07:36:50.6683344Z certain properties (sizes/strides/storage_offset/device), it specifies 2025-09-07T07:36:50.6683855Z what the properties of the output Tensors are. 2025-09-07T07:36:50.6684199Z 2025-09-07T07:36:50.6684533Z The FakeTensor implementation has the same signature as the operator. 2025-09-07T07:36:50.6685098Z It is run for both FakeTensors and meta tensors. To write a FakeTensor 2025-09-07T07:36:50.6685648Z implementation, assume that all Tensor inputs to the operator are 2025-09-07T07:36:50.6686189Z regular CPU/CUDA/Meta tensors, but they do not have storage, and 2025-09-07T07:36:50.6686706Z you are trying to return regular CPU/CUDA/Meta tensor(s) as output. 2025-09-07T07:36:50.6687266Z The FakeTensor implementation must consist of only PyTorch operations 2025-09-07T07:36:50.6687810Z (and may not directly access the storage or data of any input or 2025-09-07T07:36:50.6688228Z intermediate Tensors). 2025-09-07T07:36:50.6688513Z 2025-09-07T07:36:50.6688785Z This API may be used as a decorator (see examples). 2025-09-07T07:36:50.6689145Z 2025-09-07T07:36:50.6689402Z For a detailed guide on custom ops, please see 2025-09-07T07:36:50.6689877Z https://pytorch.org/tutorials/advanced/custom_ops_landing_page.html 2025-09-07T07:36:50.6690341Z 2025-09-07T07:36:50.6690541Z Args: 2025-09-07T07:36:50.6690900Z op_name: Operator name (along with the overload) or OpOverload object. 2025-09-07T07:36:50.6691368Z func: Fake tensor implementation. 2025-09-07T07:36:50.6691788Z lib (Optional[Library]): Library to register the fake tensor to. 2025-09-07T07:36:50.6692323Z allow_override: Flag controlling if we want to override an 2025-09-07T07:36:50.6692808Z existing registered fake impl. This is by default off, 2025-09-07T07:36:50.6693285Z and will error you're trying to register a fake impl to 2025-09-07T07:36:50.6693749Z an operator that already has a fake impl. This also only 2025-09-07T07:36:50.6694241Z applies if the custom operator was not created via 2025-09-07T07:36:50.6694714Z torch.library.custom_op, as overriding and existing fake 2025-09-07T07:36:50.6695209Z impl is already allowed. 2025-09-07T07:36:50.6695525Z 2025-09-07T07:36:50.6695718Z Examples: 2025-09-07T07:36:50.6695958Z >>> import torch 2025-09-07T07:36:50.6696242Z >>> import numpy as np 2025-09-07T07:36:50.6696538Z >>> from torch import Tensor 2025-09-07T07:36:50.6696840Z >>> 2025-09-07T07:36:50.6697199Z >>> # Example 1: an operator without data-dependent output shape 2025-09-07T07:36:50.6697729Z >>> @torch.library.custom_op("mylib::custom_linear", mutates_args=()) 2025-09-07T07:36:50.6698283Z >>> def custom_linear(x: Tensor, weight: Tensor, bias: Tensor) -> Tensor: 2025-09-07T07:36:50.6698796Z >>> raise NotImplementedError("Implementation goes here") 2025-09-07T07:36:50.6699181Z >>> 2025-09-07T07:36:50.6699485Z >>> @torch.library.register_fake("mylib::custom_linear") 2025-09-07T07:36:50.6699875Z >>> def _(x, weight, bias): 2025-09-07T07:36:50.6700179Z >>> assert x.dim() == 2 2025-09-07T07:36:50.6700503Z >>> assert weight.dim() == 2 2025-09-07T07:36:50.6700834Z >>> assert bias.dim() == 1 2025-09-07T07:36:50.6701184Z >>> assert x.shape[1] == weight.shape[1] 2025-09-07T07:36:50.6701547Z >>> assert weight.shape[0] == bias.shape[0] 2025-09-07T07:36:50.6701983Z >>> assert x.device == weight.device 2025-09-07T07:36:50.6702303Z >>> 2025-09-07T07:36:50.6702556Z >>> return (x @ weight.t()) + bias 2025-09-07T07:36:50.6702856Z >>> 2025-09-07T07:36:50.6703167Z >>> with torch._subclasses.fake_tensor.FakeTensorMode(): 2025-09-07T07:36:50.6703562Z >>> x = torch.randn(2, 3) 2025-09-07T07:36:50.6703886Z >>> w = torch.randn(3, 3) 2025-09-07T07:36:50.6704202Z >>> b = torch.randn(3) 2025-09-07T07:36:50.6704539Z >>> y = torch.ops.mylib.custom_linear(x, w, b) 2025-09-07T07:36:50.6704880Z >>> 2025-09-07T07:36:50.6705124Z >>> assert y.shape == (2, 3) 2025-09-07T07:36:50.6705421Z >>> 2025-09-07T07:36:50.6705723Z >>> # Example 2: an operator with data-dependent output shape 2025-09-07T07:36:50.6706243Z >>> @torch.library.custom_op("mylib::custom_nonzero", mutates_args=()) 2025-09-07T07:36:50.6706719Z >>> def custom_nonzero(x: Tensor) -> Tensor: 2025-09-07T07:36:50.6707082Z >>> x_np = x.numpy(force=True) 2025-09-07T07:36:50.6707430Z >>> res = np.stack(np.nonzero(x_np), axis=1) 2025-09-07T07:36:50.6707854Z >>> return torch.tensor(res, device=x.device) 2025-09-07T07:36:50.6708184Z >>> 2025-09-07T07:36:50.6708499Z >>> @torch.library.register_fake("mylib::custom_nonzero") 2025-09-07T07:36:50.6708872Z >>> def _(x): 2025-09-07T07:36:50.6709193Z >>> # Number of nonzero-elements is data-dependent. 2025-09-07T07:36:50.6709648Z >>> # Since we cannot peek at the data in an fake impl, 2025-09-07T07:36:50.6710081Z >>> # we use the ctx object to construct a new symint that 2025-09-07T07:36:50.6710493Z >>> # represents the data-dependent size. 2025-09-07T07:36:50.6710849Z >>> ctx = torch.library.get_ctx() 2025-09-07T07:36:50.6711203Z >>> nnz = ctx.new_dynamic_size() 2025-09-07T07:36:50.6711546Z >>> shape = [nnz, x.dim()] 2025-09-07T07:36:50.6711915Z >>> result = x.new_empty(shape, dtype=torch.int64) 2025-09-07T07:36:50.6712273Z >>> return result 2025-09-07T07:36:50.6712557Z >>> 2025-09-07T07:36:50.6712928Z >>> from torch.fx.experimental.proxy_tensor import make_fx 2025-09-07T07:36:50.6713319Z >>> 2025-09-07T07:36:50.6713563Z >>> x = torch.tensor([0, 1, 2, 3, 4, 0]) 2025-09-07T07:36:50.6714037Z >>> trace = make_fx(torch.ops.mylib.custom_nonzero, tracing_mode="symbolic")(x) 2025-09-07T07:36:50.6714626Z >>> trace.print_readable() 2025-09-07T07:36:50.6714925Z >>> 2025-09-07T07:36:50.6715270Z >>> assert torch.allclose(trace(x), torch.ops.mylib.custom_nonzero(x)) 2025-09-07T07:36:50.6715700Z 2025-09-07T07:36:50.6715899Z 2025-09-07T07:36:50.6716553Z Original Error: IndentationError('expected an indented block after function definition on line 37', ('', 38, 1, '_._ = None\n', 38, 2)) 2025-09-07T07:36:50.6717272Z 2025-09-07T07:36:50.6717471Z _._ = None 2025-09-07T07:36:50.6717688Z ^ 2025-09-07T07:36:50.6717910Z warnings.warn(msg) 2025-09-07T07:36:50.6718153Z 2025-09-07T07:36:50.6718490Z --- Parse Warning: 10 / 146 --- 2025-09-07T07:36:50.6719623Z /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=1083. 2025-09-07T07:36:50.6720878Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.6721414Z Register a backward formula for this custom op. 2025-09-07T07:36:50.6721745Z 2025-09-07T07:36:50.6722067Z In order for an operator to work with autograd, you need to register 2025-09-07T07:36:50.6722493Z a backward formula: 2025-09-07T07:36:50.6722933Z 1. You must tell us how to compute gradients during the backward pass 2025-09-07T07:36:50.6723377Z by providing us a "backward" function. 2025-09-07T07:36:50.6724002Z 2. If you need any values from the forward to compute gradients, you can 2025-09-07T07:36:50.6724490Z use `setup_context` to save values for backward. 2025-09-07T07:36:50.6724842Z 2025-09-07T07:36:50.6725174Z ``backward`` runs during the backward pass. It accepts ``(ctx, *grads)``: 2025-09-07T07:36:50.6725722Z - ``grads`` is one or more gradients. The number of gradients matches 2025-09-07T07:36:50.6726177Z the number of outputs of the operator. 2025-09-07T07:36:50.6726631Z The ``ctx`` object is `the same ctx object `_ used by 2025-09-07T07:36:50.6727218Z :class:`torch.autograd.Function`. The semantics of ``backward_fn`` are the 2025-09-07T07:36:50.6727729Z same as :meth:`torch.autograd.Function.backward`. 2025-09-07T07:36:50.6728083Z 2025-09-07T07:36:50.6728411Z ``setup_context(ctx, inputs, output)`` runs during the forward pass. 2025-09-07T07:36:50.6728974Z Please save quantities needed for backward onto the ``ctx`` object via 2025-09-07T07:36:50.6729672Z either :meth:`torch.autograd.function.FunctionCtx.save_for_backward` 2025-09-07T07:36:50.6730231Z or assigning them as attributes of ``ctx``. If your custom op has 2025-09-07T07:36:50.6730766Z kwarg-only arguments, we expect the signature of ``setup_context`` 2025-09-07T07:36:50.6731340Z to be ``setup_context(ctx, inputs, keyword_only_inputs, output)``. 2025-09-07T07:36:50.6731726Z 2025-09-07T07:36:50.6732060Z Both ``setup_context_fn`` and ``backward_fn`` must be traceable. That is, 2025-09-07T07:36:50.6732627Z they may not directly access :meth:`torch.Tensor.data_ptr` and they must 2025-09-07T07:36:50.6733215Z not depend on or mutate global state. If you need a non-traceable backward, 2025-09-07T07:36:50.6733804Z you can make it a separate custom_op that you call inside ``backward_fn``. 2025-09-07T07:36:50.6734220Z 2025-09-07T07:36:50.6734559Z If you need different autograd behavior on different devices, then we 2025-09-07T07:36:50.6735137Z recommend creating two different custom operators, one for each device 2025-09-07T07:36:50.6735751Z that needs different behavior, and switching between them at runtime. 2025-09-07T07:36:50.6736167Z 2025-09-07T07:36:50.6736368Z Examples: 2025-09-07T07:36:50.6736611Z >>> import torch 2025-09-07T07:36:50.6736893Z >>> import numpy as np 2025-09-07T07:36:50.6737187Z >>> from torch import Tensor 2025-09-07T07:36:50.6737487Z >>> 2025-09-07T07:36:50.6737835Z >>> @torch.library.custom_op("mylib::numpy_sin", mutates_args=()) 2025-09-07T07:36:50.6738277Z >>> def numpy_sin(x: Tensor) -> Tensor: 2025-09-07T07:36:50.6738609Z >>> x_np = x.cpu().numpy() 2025-09-07T07:36:50.6738933Z >>> y_np = np.sin(x_np) 2025-09-07T07:36:50.6739310Z >>> return torch.from_numpy(y_np).to(device=x.device) 2025-09-07T07:36:50.6739679Z >>> 2025-09-07T07:36:50.6739962Z >>> def setup_context(ctx, inputs, output) -> Tensor: 2025-09-07T07:36:50.6740327Z >>> x, = inputs 2025-09-07T07:36:50.6740622Z >>> ctx.save_for_backward(x) 2025-09-07T07:36:50.6740925Z >>> 2025-09-07T07:36:50.6741154Z >>> def backward(ctx, grad): 2025-09-07T07:36:50.6741476Z >>> x, = ctx.saved_tensors 2025-09-07T07:36:50.6741793Z >>> return grad * x.cos() 2025-09-07T07:36:50.6742091Z >>> 2025-09-07T07:36:50.6742338Z >>> torch.library.register_autograd( 2025-09-07T07:36:50.6742760Z ... "mylib::numpy_sin", backward, setup_context=setup_context 2025-09-07T07:36:50.6743149Z ... ) 2025-09-07T07:36:50.6743373Z >>> 2025-09-07T07:36:50.6743679Z >>> x = torch.randn(3, requires_grad=True) 2025-09-07T07:36:50.6744012Z >>> y = numpy_sin(x) 2025-09-07T07:36:50.6744375Z >>> (grad_x,) = torch.autograd.grad(y, x, torch.ones_like(y)) 2025-09-07T07:36:50.6744805Z >>> assert torch.allclose(grad_x, x.cos()) 2025-09-07T07:36:50.6745137Z >>> 2025-09-07T07:36:50.6745427Z >>> # Example with a keyword-only arg 2025-09-07T07:36:50.6745873Z >>> @torch.library.custom_op("mylib::numpy_mul", mutates_args=()) 2025-09-07T07:36:50.6746351Z >>> def numpy_mul(x: Tensor, *, val: float) -> Tensor: 2025-09-07T07:36:50.6746729Z >>> x_np = x.cpu().numpy() 2025-09-07T07:36:50.6747033Z >>> y_np = x_np * val 2025-09-07T07:36:50.6747396Z >>> return torch.from_numpy(y_np).to(device=x.device) 2025-09-07T07:36:50.6747752Z >>> 2025-09-07T07:36:50.6748117Z >>> def setup_context(ctx, inputs, keyword_only_inputs, output) -> Tensor: 2025-09-07T07:36:50.6748595Z >>> ctx.val = keyword_only_inputs["val"] 2025-09-07T07:36:50.6748907Z >>> 2025-09-07T07:36:50.6749156Z >>> def backward(ctx, grad): 2025-09-07T07:36:50.6749514Z >>> return grad * ctx.val 2025-09-07T07:36:50.6749811Z >>> 2025-09-07T07:36:50.6750059Z >>> torch.library.register_autograd( 2025-09-07T07:36:50.6750477Z ... "mylib::numpy_mul", backward, setup_context=setup_context 2025-09-07T07:36:50.6750909Z ... ) 2025-09-07T07:36:50.6751130Z >>> 2025-09-07T07:36:50.6751380Z >>> x = torch.randn(3, requires_grad=True) 2025-09-07T07:36:50.6751727Z >>> y = numpy_mul(x, val=3.14) 2025-09-07T07:36:50.6752112Z >>> (grad_x,) = torch.autograd.grad(y, x, torch.ones_like(y)) 2025-09-07T07:36:50.6752581Z >>> assert torch.allclose(grad_x, torch.full_like(x, 3.14)) 2025-09-07T07:36:50.6752945Z 2025-09-07T07:36:50.6753145Z 2025-09-07T07:36:50.6753521Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.6753980Z 2025-09-07T07:36:50.6754185Z warnings.warn(msg) 2025-09-07T07:36:50.6754535Z 2025-09-07T07:36:50.6754871Z --- Parse Warning: 11 / 146 --- 2025-09-07T07:36:50.6756007Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=get_kernel in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py line=1482. 2025-09-07T07:36:50.6757196Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-09-07T07:36:50.6757783Z Returns the computed kernel for a given operator and dispatch key. 2025-09-07T07:36:50.6758199Z 2025-09-07T07:36:50.6758547Z This function retrieves the kernel that would be executed for a given 2025-09-07T07:36:50.6759133Z operator and dispatch key combination. The returned SafeKernelFunction 2025-09-07T07:36:50.6759673Z can be used to call the kernel in a boxed fashion. The intended use 2025-09-07T07:36:50.6760212Z case for this function is to retrieve the original kernel for a given 2025-09-07T07:36:50.6760773Z dispatch key and then register another kernel to the same dispatch key 2025-09-07T07:36:50.6761285Z that calls into the original kernel for certain cases. 2025-09-07T07:36:50.6761640Z 2025-09-07T07:36:50.6761841Z Args: 2025-09-07T07:36:50.6762174Z op: Operator name (along with the overload) or OpOverload object 2025-09-07T07:36:50.6762718Z Can be a string (e.g., "aten::add.Tensor"), an OpOverload, or a CustomOpDef. 2025-09-07T07:36:50.6763315Z dispatch_key (str | torch.DispatchKey): The dispatch key to get the kernel for. 2025-09-07T07:36:50.6763895Z Can be a string (e.g., "CPU", "CUDA") or a DispatchKey enum value. 2025-09-07T07:36:50.6764297Z 2025-09-07T07:36:50.6764530Z Returns: 2025-09-07T07:36:50.6764913Z torch._C._SafeKernelFunction: A safe kernel function that can be used to 2025-09-07T07:36:50.6765362Z call the kernel. 2025-09-07T07:36:50.6765643Z 2025-09-07T07:36:50.6765844Z Raises: 2025-09-07T07:36:50.6766128Z RuntimeError: If the operator does not exist. 2025-09-07T07:36:50.6766460Z 2025-09-07T07:36:50.6766656Z Example: 2025-09-07T07:36:50.6766912Z >>> # Get the CPU kernel for torch.add 2025-09-07T07:36:50.6767344Z >>> kernel = torch.library.get_kernel("aten::add.Tensor", "CPU") 2025-09-07T07:36:50.6767729Z >>> 2025-09-07T07:36:50.6768000Z >>> # You can also use DispatchKey enum 2025-09-07T07:36:50.6768496Z >>> kernel = torch.library.get_kernel("aten::add.Tensor", torch.DispatchKey.CPU) 2025-09-07T07:36:50.6768960Z >>> 2025-09-07T07:36:50.6769202Z >>> # Or use an OpOverload directly 2025-09-07T07:36:50.6769665Z >>> kernel = torch.library.get_kernel(torch.ops.aten.add.Tensor, "CPU") 2025-09-07T07:36:50.6770098Z >>> 2025-09-07T07:36:50.6770443Z >>> # Example: Using get_kernel in a custom op with conditional dispatch 2025-09-07T07:36:50.6770927Z >>> # Get the original kernel for torch.sin 2025-09-07T07:36:50.6771388Z >>> original_sin_kernel = torch.library.get_kernel("aten::sin", "CPU") 2025-09-07T07:36:50.6771808Z >>> 2025-09-07T07:36:50.6772194Z >>> # If input has negative values, use original sin, otherwise return zeros 2025-09-07T07:36:50.6772687Z >>> def conditional_sin_impl(dispatch_keys, x): 2025-09-07T07:36:50.6773030Z >>> if (x < 0).any(): 2025-09-07T07:36:50.6773414Z >>> return original_sin_kernel.call_boxed(dispatch_keys, x) 2025-09-07T07:36:50.6773802Z >>> else: 2025-09-07T07:36:50.6774083Z >>> return torch.zeros_like(x) 2025-09-07T07:36:50.6774391Z >>> 2025-09-07T07:36:50.6774661Z >>> lib = torch.library.Library("aten", "IMPL") 2025-09-07T07:36:50.6775176Z >>> # with_keyset=True so the first argument to the impl is the current DispatchKeySet 2025-09-07T07:36:50.6775756Z >>> which needs to be the first argument to ``kernel.call_boxed`` 2025-09-07T07:36:50.6776280Z >>> lib.impl("sin", conditional_sin_impl, "CPU", with_keyset=True) 2025-09-07T07:36:50.6776680Z >>> 2025-09-07T07:36:50.6776932Z >>> # Test the conditional behavior 2025-09-07T07:36:50.6777289Z >>> x_positive = torch.tensor([1.0, 2.0]) 2025-09-07T07:36:50.6777631Z >>> x_mixed = torch.tensor([-1.0, 2.0]) 2025-09-07T07:36:50.6777971Z >>> torch.sin(x_positive) 2025-09-07T07:36:50.6778272Z tensor([0., 0.]) 2025-09-07T07:36:50.6778556Z >>> torch.sin(x_mixed) 2025-09-07T07:36:50.6778842Z tensor([-0.8415, 0.9093]) 2025-09-07T07:36:50.6779127Z 2025-09-07T07:36:50.6779731Z Original Error: SyntaxError('invalid syntax', ('', 23, 7, 'which needs to be the first argument to ``kernel.call_boxed``\n', 23, 12)) 2025-09-07T07:36:50.6780412Z 2025-09-07T07:36:50.6780710Z which needs to be the first argument to ``kernel.call_boxed`` 2025-09-07T07:36:50.6781085Z ^ 2025-09-07T07:36:50.6781305Z warnings.warn(msg) 2025-09-07T07:36:50.6781559Z 2025-09-07T07:36:50.6781869Z --- Parse Warning: 12 / 146 --- 2025-09-07T07:36:50.6782948Z /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=1571. 2025-09-07T07:36:50.6784155Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.6784763Z Given an operator and some sample arguments, tests if the operator is 2025-09-07T07:36:50.6785239Z registered correctly. 2025-09-07T07:36:50.6785495Z 2025-09-07T07:36:50.6785813Z That is, when you use the torch.library/TORCH_LIBRARY APIs to create a 2025-09-07T07:36:50.6786396Z custom op, you specified metadata (e.g. mutability info) about the custom op 2025-09-07T07:36:50.6786993Z and these APIs require that the functions you pass them satisfy certain 2025-09-07T07:36:50.6787570Z properties (e.g. no data pointer access in the fake/meta/abstract kernel) 2025-09-07T07:36:50.6788062Z ``opcheck`` tests these metadata and properties. 2025-09-07T07:36:50.6788402Z 2025-09-07T07:36:50.6788670Z Concretely, we test the following: 2025-09-07T07:36:50.6788974Z 2025-09-07T07:36:50.6789256Z - test_schema: If the schema matches the implementation of 2025-09-07T07:36:50.6789778Z the operator. For example: if the schema specifies a Tensor is mutated, 2025-09-07T07:36:50.6790339Z then we check the implementation mutates the Tensor. If the schema 2025-09-07T07:36:50.6790858Z specifies that we return a new Tensor, then we check that the 2025-09-07T07:36:50.6791373Z implementation returns a new Tensor (instead of an existing one or 2025-09-07T07:36:50.6791849Z a view of an existing one). 2025-09-07T07:36:50.6792263Z - test_autograd_registration: If the operator supports training 2025-09-07T07:36:50.6792781Z (autograd): we check that its autograd formula is registered via 2025-09-07T07:36:50.6793338Z torch.library.register_autograd or a manual registration to one 2025-09-07T07:36:50.6793860Z or more DispatchKey::Autograd keys. Any other DispatchKey-based 2025-09-07T07:36:50.6794320Z registrations may lead to undefined behavior. 2025-09-07T07:36:50.6794838Z - test_faketensor: If the operator has a FakeTensor kernel 2025-09-07T07:36:50.6795308Z (and if it is correct). The FakeTensor kernel is necessary ( 2025-09-07T07:36:50.6795814Z but not sufficient) for the operator to work with PyTorch compilation 2025-09-07T07:36:50.6796372Z APIs (torch.compile/export/FX). We check that a FakeTensor kernel 2025-09-07T07:36:50.6796904Z (also sometimes known as a meta kernel) was registered for the 2025-09-07T07:36:50.6797458Z operator and that it is correct. This test takes the result of 2025-09-07T07:36:50.6797967Z running the operator on real tensors and the result of running 2025-09-07T07:36:50.6798465Z the operator on FakeTensors and checks that they have the same 2025-09-07T07:36:50.6798938Z Tensor metadata (sizes/strides/dtype/device/etc). 2025-09-07T07:36:50.6799415Z - test_aot_dispatch_dynamic: If the operator has correct behavior 2025-09-07T07:36:50.6799921Z with PyTorch compilation APIs (torch.compile/export/FX). 2025-09-07T07:36:50.6800424Z This checks that the outputs (and gradients, if applicable) are the 2025-09-07T07:36:50.6800919Z same under eager-mode PyTorch and torch.compile. 2025-09-07T07:36:50.6801387Z This test is a superset of ``test_faketensor`` and is an e2e test; 2025-09-07T07:36:50.6801871Z other things it tests are that the operator supports 2025-09-07T07:36:50.6802378Z functionalization and that the backward pass (if it exists) also 2025-09-07T07:36:50.6802847Z supports FakeTensor and functionalization. 2025-09-07T07:36:50.6803185Z 2025-09-07T07:36:50.6803504Z For best results, please call ``opcheck`` multiple times with a 2025-09-07T07:36:50.6804010Z representative set of inputs. If your operator supports 2025-09-07T07:36:50.6804534Z autograd, please use ``opcheck`` with inputs with ``requires_grad = True``; 2025-09-07T07:36:50.6805120Z if your operator supports multiple devices (e.g. CPU and CUDA), please 2025-09-07T07:36:50.6805629Z use ``opcheck`` with inputs on all supported devices. 2025-09-07T07:36:50.6805990Z 2025-09-07T07:36:50.6806211Z Args: 2025-09-07T07:36:50.6806524Z op: The operator. Must either be a function decorated with 2025-09-07T07:36:50.6807038Z :func:`torch.library.custom_op` or an OpOverload/OpOverloadPacket 2025-09-07T07:36:50.6807592Z found in torch.ops.* (e.g. torch.ops.aten.sin, torch.ops.mylib.foo) 2025-09-07T07:36:50.6808038Z args: The args to the operator 2025-09-07T07:36:50.6808378Z kwargs: The kwargs to the operator 2025-09-07T07:36:50.6808796Z test_utils: Tests that we should run. Default: all of them. 2025-09-07T07:36:50.6809263Z Example: ("test_schema", "test_faketensor") 2025-09-07T07:36:50.6809711Z raise_exception: If we should raise an exception on the first 2025-09-07T07:36:50.6810181Z error. If False, we will return a dict with information 2025-09-07T07:36:50.6810583Z on if each test passed or not. 2025-09-07T07:36:50.6811056Z rtol (Optional[float]): Relative tolerance for floating point comparisons. 2025-09-07T07:36:50.6811564Z If specified ``atol`` must also be specified. 2025-09-07T07:36:50.6812003Z If omitted, default values based on the ``dtype`` are selected 2025-09-07T07:36:50.6812519Z (see the table in :func:`torch.testing.assert_close`). 2025-09-07T07:36:50.6813049Z atol (Optional[float]): Absolute tolerance for floating point comparisons. 2025-09-07T07:36:50.6813555Z If specified ``rtol`` must also be specified. 2025-09-07T07:36:50.6814074Z If omitted, default values based on the ``dtype`` are selected 2025-09-07T07:36:50.6814545Z (see the table in :func:`torch.testing.assert_close`). 2025-09-07T07:36:50.6814909Z 2025-09-07T07:36:50.6815122Z .. warning:: 2025-09-07T07:36:50.6815354Z 2025-09-07T07:36:50.6815674Z opcheck and :func:`torch.autograd.gradcheck` test different things; 2025-09-07T07:36:50.6816226Z opcheck tests if your usage of torch.library APIs is correct while 2025-09-07T07:36:50.6816774Z :func:`torch.autograd.gradcheck` tests if your autograd formula is 2025-09-07T07:36:50.6817334Z mathematically correct. Use both to test custom ops that support 2025-09-07T07:36:50.6817770Z gradient computation. 2025-09-07T07:36:50.6818083Z 2025-09-07T07:36:50.6818287Z Example: 2025-09-07T07:36:50.6818513Z 2025-09-07T07:36:50.6818771Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2025-09-07T07:36:50.6819236Z >>> @torch.library.custom_op("mylib::numpy_mul", mutates_args=()) 2025-09-07T07:36:50.6819710Z >>> def numpy_mul(x: Tensor, y: float) -> Tensor: 2025-09-07T07:36:50.6820087Z >>> x_np = x.numpy(force=True) 2025-09-07T07:36:50.6820400Z >>> z_np = x_np * y 2025-09-07T07:36:50.6820738Z >>> return torch.from_numpy(z_np).to(x.device) 2025-09-07T07:36:50.6821083Z >>> 2025-09-07T07:36:50.6821336Z >>> @numpy_mul.register_fake 2025-09-07T07:36:50.6821634Z >>> def _(x, y): 2025-09-07T07:36:50.6821929Z >>> return torch.empty_like(x) 2025-09-07T07:36:50.6822241Z >>> 2025-09-07T07:36:50.6822512Z >>> def setup_context(ctx, inputs, output): 2025-09-07T07:36:50.6822841Z >>> y, = inputs 2025-09-07T07:36:50.6823115Z >>> ctx.y = y 2025-09-07T07:36:50.6823377Z >>> 2025-09-07T07:36:50.6823787Z >>> def backward(ctx, grad): 2025-09-07T07:36:50.6824107Z >>> return grad * ctx.y, None 2025-09-07T07:36:50.6824415Z >>> 2025-09-07T07:36:50.6824773Z >>> numpy_mul.register_autograd(backward, setup_context=setup_context) 2025-09-07T07:36:50.6825203Z >>> 2025-09-07T07:36:50.6825445Z >>> sample_inputs = [ 2025-09-07T07:36:50.6825736Z >>> (torch.randn(3), 3.14), 2025-09-07T07:36:50.6826167Z >>> (torch.randn(2, 3, device='cuda'), 2.718), 2025-09-07T07:36:50.6826564Z >>> (torch.randn(1, 10, requires_grad=True), 1.234), 2025-09-07T07:36:50.6827023Z >>> (torch.randn(64, 64, device='cuda', requires_grad=True), 90.18), 2025-09-07T07:36:50.6827414Z >>> ] 2025-09-07T07:36:50.6827654Z >>> 2025-09-07T07:36:50.6827902Z >>> for args in sample_inputs: 2025-09-07T07:36:50.6828262Z >>> torch.library.opcheck(numpy_mul, args) 2025-09-07T07:36:50.6828587Z 2025-09-07T07:36:50.6828794Z 2025-09-07T07:36:50.6829171Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.6829633Z 2025-09-07T07:36:50.6829834Z warnings.warn(msg) 2025-09-07T07:36:50.6830084Z 2025-09-07T07:36:50.6830429Z --- Parse Warning: 13 / 146 --- 2025-09-07T07:36:50.6831523Z /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=1285. 2025-09-07T07:36:50.6832727Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.6833501Z load(f, map_location=None, pickle_module=pickle, *, weights_only=True, mmap=None, **pickle_load_args) 2025-09-07T07:36:50.6834041Z 2025-09-07T07:36:50.6834383Z Loads an object saved with :func:`torch.save` from a file. 2025-09-07T07:36:50.6834831Z 2025-09-07T07:36:50.6835172Z :func:`torch.load` uses Python's unpickling facilities but treats storages, 2025-09-07T07:36:50.6835778Z which underlie tensors, specially. They are first deserialized on the 2025-09-07T07:36:50.6836340Z CPU and are then moved to the device they were saved from. If this fails 2025-09-07T07:36:50.6836910Z (e.g. because the run time system doesn't have certain devices), an exception 2025-09-07T07:36:50.6837496Z is raised. However, storages can be dynamically remapped to an alternative 2025-09-07T07:36:50.6838034Z set of devices using the :attr:`map_location` argument. 2025-09-07T07:36:50.6838402Z 2025-09-07T07:36:50.6838756Z If :attr:`map_location` is a callable, it will be called once for each serialized 2025-09-07T07:36:50.6839373Z storage with two arguments: storage and location. The storage argument 2025-09-07T07:36:50.6839955Z will be the initial deserialization of the storage, residing on the CPU. 2025-09-07T07:36:50.6840530Z Each serialized storage has a location tag associated with it which 2025-09-07T07:36:50.6841083Z identifies the device it was saved from, and this tag is the second 2025-09-07T07:36:50.6841673Z argument passed to :attr:`map_location`. The builtin location tags are ``'cpu'`` 2025-09-07T07:36:50.6842266Z for CPU tensors and ``'cuda:device_id'`` (e.g. ``'cuda:2'``) for CUDA tensors. 2025-09-07T07:36:50.6842821Z :attr:`map_location` should return either ``None`` or a storage. If 2025-09-07T07:36:50.6843399Z :attr:`map_location` returns a storage, it will be used as the final deserialized 2025-09-07T07:36:50.6844025Z object, already moved to the right device. Otherwise, :func:`torch.load` will 2025-09-07T07:36:50.6844641Z fall back to the default behavior, as if :attr:`map_location` wasn't specified. 2025-09-07T07:36:50.6845115Z 2025-09-07T07:36:50.6845470Z If :attr:`map_location` is a :class:`torch.device` object or a string containing 2025-09-07T07:36:50.6846068Z a device tag, it indicates the location where all tensors should be loaded. 2025-09-07T07:36:50.6846502Z 2025-09-07T07:36:50.6846865Z Otherwise, if :attr:`map_location` is a dict, it will be used to remap location tags 2025-09-07T07:36:50.6847452Z appearing in the file (keys), to ones that specify where to put the 2025-09-07T07:36:50.6847880Z storages (values). 2025-09-07T07:36:50.6848168Z 2025-09-07T07:36:50.6848495Z User extensions can register their own location tags and tagging and 2025-09-07T07:36:50.6849099Z deserialization methods using :func:`torch.serialization.register_package`. 2025-09-07T07:36:50.6849566Z 2025-09-07T07:36:50.6849924Z See :ref:`layout-control` for more advanced tools to manipulate a checkpoint. 2025-09-07T07:36:50.6850357Z 2025-09-07T07:36:50.6850559Z Args: 2025-09-07T07:36:50.6851020Z f: a file-like object (has to implement :meth:`read`, :meth:`readline`, :meth:`tell`, and :meth:`seek`), 2025-09-07T07:36:50.6851643Z or a string or os.PathLike object containing a file name 2025-09-07T07:36:50.6852261Z map_location: a function, :class:`torch.device`, string or a dict specifying how to remap storage 2025-09-07T07:36:50.6852789Z locations 2025-09-07T07:36:50.6853187Z pickle_module: module used for unpickling metadata and objects (has to 2025-09-07T07:36:50.6853710Z match the :attr:`pickle_module` used to serialize file) 2025-09-07T07:36:50.6854216Z weights_only: Indicates whether unpickler should be restricted to 2025-09-07T07:36:50.6854731Z loading only tensors, primitive types, dictionaries 2025-09-07T07:36:50.6855231Z and any types added via :func:`torch.serialization.add_safe_globals`. 2025-09-07T07:36:50.6855707Z See :ref:`weights-only` for more details. 2025-09-07T07:36:50.6856293Z mmap: Indicates whether the file should be mapped rather than loading all the storages into memory. 2025-09-07T07:36:50.6857120Z Typically, tensor storages in the file will first be moved from disk to CPU memory, after which they 2025-09-07T07:36:50.6857908Z are moved to the location that they were tagged with when saving, or specified by ``map_location``. This 2025-09-07T07:36:50.6858704Z second step is a no-op if the final location is CPU. When the ``mmap`` flag is set, instead of copying the 2025-09-07T07:36:50.6859494Z tensor storages from disk to CPU memory in the first step, ``f`` is mapped, which means tensor storages 2025-09-07T07:36:50.6860106Z will be lazily loaded when their data is accessed. 2025-09-07T07:36:50.6860648Z pickle_load_args: (Python 3 only) optional keyword arguments passed over to 2025-09-07T07:36:50.6861228Z :func:`pickle_module.load` and :func:`pickle_module.Unpickler`, e.g., 2025-09-07T07:36:50.6861675Z :attr:`errors=...`. 2025-09-07T07:36:50.6861958Z 2025-09-07T07:36:50.6862169Z .. warning:: 2025-09-07T07:36:50.6862523Z :func:`torch.load()` unless `weights_only` parameter is set to `True`, 2025-09-07T07:36:50.6863062Z uses ``pickle`` module implicitly, which is known to be insecure. 2025-09-07T07:36:50.6863659Z It is possible to construct malicious pickle data which will execute arbitrary code 2025-09-07T07:36:50.6864295Z during unpickling. Never load data that could have come from an untrusted 2025-09-07T07:36:50.6864954Z source in an unsafe mode, or that could have been tampered with. **Only load data you trust**. 2025-09-07T07:36:50.6865439Z 2025-09-07T07:36:50.6865640Z .. note:: 2025-09-07T07:36:50.6866045Z When you call :func:`torch.load()` on a file which contains GPU tensors, those tensors 2025-09-07T07:36:50.6866691Z will be loaded to GPU by default. You can call ``torch.load(.., map_location='cpu')`` 2025-09-07T07:36:50.6867338Z and then :meth:`load_state_dict` to avoid GPU RAM surge when loading a model checkpoint. 2025-09-07T07:36:50.6867813Z 2025-09-07T07:36:50.6868009Z .. note:: 2025-09-07T07:36:50.6868402Z By default, we decode byte strings as ``utf-8``. This is to avoid a common error 2025-09-07T07:36:50.6869010Z case ``UnicodeDecodeError: 'ascii' codec can't decode byte 0x...`` 2025-09-07T07:36:50.6869560Z when loading files saved by Python 2 in Python 3. If this default 2025-09-07T07:36:50.6870145Z is incorrect, you may use an extra :attr:`encoding` keyword argument to specify how 2025-09-07T07:36:50.6870773Z these objects should be loaded, e.g., :attr:`encoding='latin1'` decodes them 2025-09-07T07:36:50.6871375Z to strings using ``latin1`` encoding, and :attr:`encoding='bytes'` keeps them 2025-09-07T07:36:50.6871952Z as byte arrays which can be decoded later with ``byte_array.decode(...)``. 2025-09-07T07:36:50.6872384Z 2025-09-07T07:36:50.6872584Z Example: 2025-09-07T07:36:50.6872860Z >>> # xdoctest: +SKIP("undefined filepaths") 2025-09-07T07:36:50.6873235Z >>> torch.load("tensors.pt", weights_only=True) 2025-09-07T07:36:50.6873604Z # Load all tensors onto the CPU 2025-09-07T07:36:50.6873924Z >>> torch.load( 2025-09-07T07:36:50.6874197Z ... "tensors.pt", 2025-09-07T07:36:50.6874600Z ... map_location=torch.device("cpu"), 2025-09-07T07:36:50.6874950Z ... weights_only=True, 2025-09-07T07:36:50.6875287Z ... ) 2025-09-07T07:36:50.6875574Z # Load all tensors onto the CPU, using a function 2025-09-07T07:36:50.6875924Z >>> torch.load( 2025-09-07T07:36:50.6876204Z ... "tensors.pt", 2025-09-07T07:36:50.6876537Z ... map_location=lambda storage, loc: storage, 2025-09-07T07:36:50.6876935Z ... weights_only=True, 2025-09-07T07:36:50.6877225Z ... ) 2025-09-07T07:36:50.6877460Z # Load all tensors onto GPU 1 2025-09-07T07:36:50.6877773Z >>> torch.load( 2025-09-07T07:36:50.6878042Z ... "tensors.pt", 2025-09-07T07:36:50.6878393Z ... map_location=lambda storage, loc: storage.cuda(1), 2025-09-07T07:36:50.6878764Z ... weights_only=True, 2025-09-07T07:36:50.6879092Z ... ) # type: ignore[attr-defined] 2025-09-07T07:36:50.6879446Z # Map tensors from GPU 1 to GPU 0 2025-09-07T07:36:50.6879768Z >>> torch.load( 2025-09-07T07:36:50.6880029Z ... "tensors.pt", 2025-09-07T07:36:50.6880352Z ... map_location={"cuda:1": "cuda:0"}, 2025-09-07T07:36:50.6880728Z ... weights_only=True, 2025-09-07T07:36:50.6881024Z ... ) 2025-09-07T07:36:50.6881276Z # Load tensor from io.BytesIO object 2025-09-07T07:36:50.6881771Z # Loading from a buffer setting weights_only=False, warning this can be unsafe 2025-09-07T07:36:50.6882274Z >>> with open("tensor.pt", "rb") as f: 2025-09-07T07:36:50.6882632Z ... buffer = io.BytesIO(f.read()) 2025-09-07T07:36:50.6882984Z >>> torch.load(buffer, weights_only=False) 2025-09-07T07:36:50.6883389Z # Load a module with 'ascii' encoding for unpickling 2025-09-07T07:36:50.6883918Z # Loading from a module setting weights_only=False, warning this can be unsafe 2025-09-07T07:36:50.6884488Z >>> torch.load("module.pt", encoding="ascii", weights_only=False) 2025-09-07T07:36:50.6884884Z 2025-09-07T07:36:50.6885265Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.6885715Z 2025-09-07T07:36:50.6885935Z warnings.warn(msg) 2025-09-07T07:36:50.6886175Z 2025-09-07T07:36:50.6886513Z --- Parse Warning: 14 / 146 --- 2025-09-07T07:36:50.6887767Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=compute_required_storage_length in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/__init__.py line=1877. 2025-09-07T07:36:50.6889152Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.6889758Z Computes the minimum storage size to hold the given tensor geometry. 2025-09-07T07:36:50.6890197Z 2025-09-07T07:36:50.6890399Z Example 2025-09-07T07:36:50.6890623Z ======= 2025-09-07T07:36:50.6890832Z 2025-09-07T07:36:50.6891164Z This is the size of a newly allocated tensor's storage, in units of elements 2025-09-07T07:36:50.6891604Z 2025-09-07T07:36:50.6891826Z >>> t = torch.empty((10, 20)) 2025-09-07T07:36:50.6892285Z >>> compute_required_storage_length(t.shape, t.stride(), t.storage_offset()) 2025-09-07T07:36:50.6892725Z 200 2025-09-07T07:36:50.6892938Z 2025-09-07T07:36:50.6893157Z >>> # xdoctest: +SKIP(failing) 2025-09-07T07:36:50.6893509Z >>> t2 = torch.empty_strided((1, 2, 3), (5, 7, 11)) 2025-09-07T07:36:50.6893876Z >>> size = compute_required_storage_length( 2025-09-07T07:36:50.6894254Z ... t2.shape, t2.stride(), t2.storage_offset() 2025-09-07T07:36:50.6894589Z ... ) 2025-09-07T07:36:50.6894822Z >>> size == t.storage().size() 2025-09-07T07:36:50.6895106Z True 2025-09-07T07:36:50.6895317Z 2025-09-07T07:36:50.6895628Z A valid tensor may have a larger storage size, but never smaller 2025-09-07T07:36:50.6896027Z 2025-09-07T07:36:50.6896267Z >>> slice = torch.empty(100)[20:40] 2025-09-07T07:36:50.6896598Z >>> slice.storage().size() 2025-09-07T07:36:50.6896884Z 100 2025-09-07T07:36:50.6897087Z 2025-09-07T07:36:50.6897302Z >>> compute_required_storage_length( 2025-09-07T07:36:50.6897699Z ... slice.shape, slice.stride(), slice.storage_offset() 2025-09-07T07:36:50.6898098Z ... ) 2025-09-07T07:36:50.6898312Z 40 2025-09-07T07:36:50.6898501Z 2025-09-07T07:36:50.6898697Z 2025-09-07T07:36:50.6899071Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.6899525Z 2025-09-07T07:36:50.6899719Z warnings.warn(msg) 2025-09-07T07:36:50.6899967Z 2025-09-07T07:36:50.6900281Z --- Parse Warning: 15 / 146 --- 2025-09-07T07:36:50.6901420Z /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=66. 2025-09-07T07:36:50.6902697Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-09-07T07:36:50.6903324Z Check if the current accelerator is available at runtime: it was build, all the 2025-09-07T07:36:50.6903915Z required drivers are available and at least one device is visible. 2025-09-07T07:36:50.6904403Z See :ref:`accelerator` for details. 2025-09-07T07:36:50.6904748Z 2025-09-07T07:36:50.6904934Z Returns: 2025-09-07T07:36:50.6905358Z bool: A boolean indicating if there is an available :ref:`accelerator`. 2025-09-07T07:36:50.6905839Z 2025-09-07T07:36:50.6906202Z .. note:: This API delegates to the device-specific version of `is_available`. 2025-09-07T07:36:50.6906819Z On CUDA, when the environment variable ``PYTORCH_NVML_BASED_CUDA_CHECK=1`` is set, 2025-09-07T07:36:50.6907080Z this function will NOT poison fork. Otherwise, it will. For more details, see 2025-09-07T07:36:50.6907226Z :ref:`multiprocessing-poison-fork-note`. 2025-09-07T07:36:50.6907320Z 2025-09-07T07:36:50.6907408Z Example:: 2025-09-07T07:36:50.6907486Z 2025-09-07T07:36:50.6907764Z >>> assert torch.accelerator.is_available() "No available accelerators detected." 2025-09-07T07:36:50.6907846Z 2025-09-07T07:36:50.6908426Z Original Error: SyntaxError('invalid syntax', ('', 1, 41, 'assert torch.accelerator.is_available() "No available accelerators detected."\n', 1, 78)) 2025-09-07T07:36:50.6908505Z 2025-09-07T07:36:50.6908763Z assert torch.accelerator.is_available() "No available accelerators detected." 2025-09-07T07:36:50.6908901Z ^ 2025-09-07T07:36:50.6909000Z warnings.warn(msg) 2025-09-07T07:36:50.6909089Z 2025-09-07T07:36:50.6909278Z --- Parse Warning: 16 / 146 --- 2025-09-07T07:36:50.6910188Z /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=212. 2025-09-07T07:36:50.6910446Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-09-07T07:36:50.6910654Z Wait for all kernels in all streams on the given device to complete. 2025-09-07T07:36:50.6910745Z 2025-09-07T07:36:50.6910826Z Args: 2025-09-07T07:36:50.6911146Z device (:class:`torch.device`, str, int, optional): device for which to synchronize. It must match 2025-09-07T07:36:50.6911392Z the current :ref:`accelerator` device type. If not given, 2025-09-07T07:36:50.6911589Z use :func:`torch.accelerator.current_device_index` by default. 2025-09-07T07:36:50.6911681Z 2025-09-07T07:36:50.6911994Z .. note:: This function is a no-op if the current :ref:`accelerator` is not initialized. 2025-09-07T07:36:50.6912104Z 2025-09-07T07:36:50.6912205Z Example:: 2025-09-07T07:36:50.6912283Z 2025-09-07T07:36:50.6912440Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2025-09-07T07:36:50.6912707Z >>> assert torch.accelerator.is_available() "No available accelerators detected." 2025-09-07T07:36:50.6912879Z >>> start_event = torch.Event(enable_timing=True) 2025-09-07T07:36:50.6913023Z >>> end_event = torch.Event(enable_timing=True) 2025-09-07T07:36:50.6913125Z >>> start_event.record() 2025-09-07T07:36:50.6913373Z >>> tensor = torch.randn(100, device=torch.accelerator.current_accelerator()) 2025-09-07T07:36:50.6913477Z >>> sum = torch.sum(tensor) 2025-09-07T07:36:50.6913577Z >>> end_event.record() 2025-09-07T07:36:50.6913716Z >>> torch.accelerator.synchronize() 2025-09-07T07:36:50.6913884Z >>> elapsed_time_ms = start_event.elapsed_time(end_event) 2025-09-07T07:36:50.6913980Z 2025-09-07T07:36:50.6914671Z Original Error: SyntaxError('invalid syntax', ('', 2, 41, 'assert torch.accelerator.is_available() "No available accelerators detected."\n', 2, 78)) 2025-09-07T07:36:50.6914755Z 2025-09-07T07:36:50.6915033Z assert torch.accelerator.is_available() "No available accelerators detected." 2025-09-07T07:36:50.6915135Z ^ 2025-09-07T07:36:50.6915252Z warnings.warn(msg) 2025-09-07T07:36:50.6915332Z 2025-09-07T07:36:50.6915528Z --- Parse Warning: 17 / 146 --- 2025-09-07T07:36:50.6916392Z /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=434. 2025-09-07T07:36:50.6916639Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-09-07T07:36:50.6916770Z Retrieves the CUDA runtime API module. 2025-09-07T07:36:50.6916852Z 2025-09-07T07:36:50.6916932Z 2025-09-07T07:36:50.6917199Z This function initializes the CUDA runtime environment if it is not already 2025-09-07T07:36:50.6917429Z initialized and returns the CUDA runtime API module (_cudart). The CUDA 2025-09-07T07:36:50.6917665Z runtime API module provides access to various CUDA runtime functions. 2025-09-07T07:36:50.6917745Z 2025-09-07T07:36:50.6917830Z Args: 2025-09-07T07:36:50.6917929Z ``None`` 2025-09-07T07:36:50.6918013Z 2025-09-07T07:36:50.6918113Z Returns: 2025-09-07T07:36:50.6918260Z module: The CUDA runtime API module (_cudart). 2025-09-07T07:36:50.6918340Z 2025-09-07T07:36:50.6918438Z Raises: 2025-09-07T07:36:50.6918698Z RuntimeError: If CUDA cannot be re-initialized in a forked subprocess. 2025-09-07T07:36:50.6919071Z AssertionError: If PyTorch is not compiled with CUDA support or if libcudart functions are unavailable. 2025-09-07T07:36:50.6919153Z 2025-09-07T07:36:50.6919286Z Example of CUDA operations with profiling: 2025-09-07T07:36:50.6919399Z >>> import torch 2025-09-07T07:36:50.6919535Z >>> from torch.cuda import cudart, check_error 2025-09-07T07:36:50.6919643Z >>> import os 2025-09-07T07:36:50.6919728Z >>> 2025-09-07T07:36:50.6919847Z >>> os.environ["CUDA_PROFILE"] = "1" 2025-09-07T07:36:50.6919942Z >>> 2025-09-07T07:36:50.6920079Z >>> def perform_cuda_operations_with_streams(): 2025-09-07T07:36:50.6920206Z >>> stream = torch.cuda.Stream() 2025-09-07T07:36:50.6920329Z >>> with torch.cuda.stream(stream): 2025-09-07T07:36:50.6920458Z >>> x = torch.randn(100, 100, device='cuda') 2025-09-07T07:36:50.6920595Z >>> y = torch.randn(100, 100, device='cuda') 2025-09-07T07:36:50.6920699Z >>> z = torch.mul(x, y) 2025-09-07T07:36:50.6920817Z >>> return z 2025-09-07T07:36:50.6920911Z >>> 2025-09-07T07:36:50.6921026Z >>> torch.cuda.synchronize() 2025-09-07T07:36:50.6921177Z >>> print("====== Start nsys profiling ======") 2025-09-07T07:36:50.6921315Z >>> check_error(cudart().cudaProfilerStart()) 2025-09-07T07:36:50.6921482Z >>> with torch.autograd.profiler.emit_nvtx(): 2025-09-07T07:36:50.6921648Z >>> result = perform_cuda_operations_with_streams() 2025-09-07T07:36:50.6921775Z >>> print("CUDA operations completed.") 2025-09-07T07:36:50.6921952Z >>> check_error(torch.cuda.cudart().cudaProfilerStop()) 2025-09-07T07:36:50.6922077Z >>> print("====== End nsys profiling ======") 2025-09-07T07:36:50.6922159Z 2025-09-07T07:36:50.6922372Z To run this example and save the profiling information, execute: 2025-09-07T07:36:50.6922733Z >>> $ nvprof --profile-from-start off --csv --print-summary -o trace_name.prof -f -- python cudart_test.py 2025-09-07T07:36:50.6922827Z 2025-09-07T07:36:50.6923100Z This command profiles the CUDA operations in the provided script and saves 2025-09-07T07:36:50.6923296Z the profiling information to a file named `trace_name.prof`. 2025-09-07T07:36:50.6923540Z The `--profile-from-start off` option ensures that profiling starts only 2025-09-07T07:36:50.6923884Z after the `cudaProfilerStart` call in the script. 2025-09-07T07:36:50.6924117Z The `--csv` and `--print-summary` options format the profiling output as a 2025-09-07T07:36:50.6924247Z CSV file and print a summary, respectively. 2025-09-07T07:36:50.6924503Z The `-o` option specifies the output file name, and the `-f` option forces the 2025-09-07T07:36:50.6924657Z overwrite of the output file if it already exists. 2025-09-07T07:36:50.6924737Z 2025-09-07T07:36:50.6925391Z 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)) 2025-09-07T07:36:50.6925474Z 2025-09-07T07:36:50.6925835Z $ nvprof --profile-from-start off --csv --print-summary -o trace_name.prof -f -- python cudart_test.py 2025-09-07T07:36:50.6925916Z ^ 2025-09-07T07:36:50.6926013Z warnings.warn(msg) 2025-09-07T07:36:50.6926104Z 2025-09-07T07:36:50.6926304Z --- Parse Warning: 18 / 146 --- 2025-09-07T07:36:50.6927198Z /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=101. 2025-09-07T07:36:50.6927537Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.6927620Z 2025-09-07T07:36:50.6927861Z Append the given callback function to this ``Future``, which will be run 2025-09-07T07:36:50.6928068Z when the ``Future`` is completed. Multiple callbacks can be added to 2025-09-07T07:36:50.6928288Z the same ``Future``, but the order in which they will be executed cannot 2025-09-07T07:36:50.6928473Z be guaranteed (to enforce a certain order consider chaining: 2025-09-07T07:36:50.6928675Z ``fut.then(cb1).then(cb2)``). The callback must take one argument, which 2025-09-07T07:36:50.6928897Z is the reference to this ``Future``. The callback function can use the 2025-09-07T07:36:50.6929102Z :meth:`value` method to get the value. Note that if this ``Future`` is 2025-09-07T07:36:50.6929338Z already completed, the given callback will be run immediately inline. 2025-09-07T07:36:50.6929417Z 2025-09-07T07:36:50.6929609Z If the ``Future``'s value contains tensors that reside on GPUs, the 2025-09-07T07:36:50.6929839Z callback might be invoked while the async kernels that are populating 2025-09-07T07:36:50.6930061Z those tensors haven't yet finished executing on the device. However, the 2025-09-07T07:36:50.6930331Z callback will be invoked with some dedicated streams set as current 2025-09-07T07:36:50.6930533Z (fetched from a global pool) which will be synchronized with those 2025-09-07T07:36:50.6930758Z kernels. Hence any operation performed by the callback on these tensors 2025-09-07T07:36:50.6931012Z will be scheduled on the device after the kernels complete. In other 2025-09-07T07:36:50.6931206Z words, as long as the callback doesn't switch streams, it can safely 2025-09-07T07:36:50.6931442Z manipulate the result without any additional synchronization. This is 2025-09-07T07:36:50.6931602Z similar to the non-blocking behavior of :meth:`wait`. 2025-09-07T07:36:50.6931683Z 2025-09-07T07:36:50.6931908Z Similarly, if the callback returns a value that contains tensors that 2025-09-07T07:36:50.6932100Z reside on a GPU, it can do so even if the kernels that are producing 2025-09-07T07:36:50.6932330Z these tensors are still running on the device, as long as the callback 2025-09-07T07:36:50.6932536Z didn't change streams during its execution. If one wants to change 2025-09-07T07:36:50.6932792Z streams, one must be careful to re-synchronize them with the original 2025-09-07T07:36:50.6933009Z streams, that is, those that were current when the callback was invoked. 2025-09-07T07:36:50.6933089Z 2025-09-07T07:36:50.6933181Z Args: 2025-09-07T07:36:50.6933387Z callback(``Callable``): a ``Callable`` that takes this ``Future`` as 2025-09-07T07:36:50.6933499Z the only argument. 2025-09-07T07:36:50.6933589Z 2025-09-07T07:36:50.6933673Z Returns: 2025-09-07T07:36:50.6933849Z A new ``Future`` object that holds the return value of the 2025-09-07T07:36:50.6934027Z ``callback`` and will be marked as completed when the given 2025-09-07T07:36:50.6934124Z ``callback`` finishes. 2025-09-07T07:36:50.6934214Z 2025-09-07T07:36:50.6934388Z .. note:: Note that if the callback function throws, either 2025-09-07T07:36:50.6934610Z through the original future being completed with an exception and 2025-09-07T07:36:50.6934803Z calling ``fut.wait()``, or through other code in the callback, the 2025-09-07T07:36:50.6935004Z future returned by ``then`` will be marked appropriately with the 2025-09-07T07:36:50.6935214Z encountered error. However, if this callback later completes 2025-09-07T07:36:50.6935427Z additional futures, those futures are not marked as completed with 2025-09-07T07:36:50.6935636Z an error and the user is responsible for handling completion/waiting 2025-09-07T07:36:50.6935748Z on those futures independently. 2025-09-07T07:36:50.6935827Z 2025-09-07T07:36:50.6935923Z Example:: 2025-09-07T07:36:50.6936027Z 2025-09-07T07:36:50.6936185Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_FUTURES) 2025-09-07T07:36:50.6936283Z >>> def callback(fut): 2025-09-07T07:36:50.6936423Z ... print(f"RPC return value is {fut.wait()}.") 2025-09-07T07:36:50.6936545Z >>> fut = torch.futures.Future() 2025-09-07T07:36:50.6936715Z >>> # The inserted callback will print the return value when 2025-09-07T07:36:50.6936850Z >>> # receiving the response from "worker1" 2025-09-07T07:36:50.6936955Z >>> cb_fut = fut.then(callback) 2025-09-07T07:36:50.6937057Z >>> chain_cb_fut = cb_fut.then( 2025-09-07T07:36:50.6937219Z ... lambda x : print(f"Chained cb done. {x.wait()}") 2025-09-07T07:36:50.6937301Z ... ) 2025-09-07T07:36:50.6937414Z >>> fut.set_result(5) 2025-09-07T07:36:50.6937513Z RPC return value is 5. 2025-09-07T07:36:50.6937608Z Chained cb done. None 2025-09-07T07:36:50.6937700Z 2025-09-07T07:36:50.6937960Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.6938050Z 2025-09-07T07:36:50.6938146Z warnings.warn(msg) 2025-09-07T07:36:50.6938252Z 2025-09-07T07:36:50.6938462Z --- Parse Warning: 19 / 146 --- 2025-09-07T07:36:50.6939364Z /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=211. 2025-09-07T07:36:50.6939665Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.6939746Z 2025-09-07T07:36:50.6939949Z Set the result for this ``Future``, which will mark this ``Future`` as 2025-09-07T07:36:50.6940177Z completed and trigger all attached callbacks. Note that a ``Future`` 2025-09-07T07:36:50.6940290Z cannot be marked completed twice. 2025-09-07T07:36:50.6940382Z 2025-09-07T07:36:50.6940598Z If the result contains tensors that reside on GPUs, this method can be 2025-09-07T07:36:50.6940804Z called even if the asynchronous kernels that are populating those 2025-09-07T07:36:50.6941036Z tensors haven't yet completed running on the device, provided that the 2025-09-07T07:36:50.6941288Z streams on which those kernels were enqueued are set as the current ones 2025-09-07T07:36:50.6941511Z when this method is called. Put simply, it's safe to call this method 2025-09-07T07:36:50.6941723Z immediately after launching those kernels, without any additional 2025-09-07T07:36:50.6941946Z synchronization, as long as one doesn't change streams in between. This 2025-09-07T07:36:50.6942179Z method will record events on all the relevant current streams and will 2025-09-07T07:36:50.6942380Z use them to ensure proper scheduling for all the consumers of this 2025-09-07T07:36:50.6942477Z ``Future``. 2025-09-07T07:36:50.6942558Z 2025-09-07T07:36:50.6942638Z Args: 2025-09-07T07:36:50.6942809Z result (object): the result object of this ``Future``. 2025-09-07T07:36:50.6942888Z 2025-09-07T07:36:50.6942984Z Example:: 2025-09-07T07:36:50.6943064Z 2025-09-07T07:36:50.6943210Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_FUTURES) 2025-09-07T07:36:50.6943316Z >>> import threading 2025-09-07T07:36:50.6943405Z >>> import time 2025-09-07T07:36:50.6943517Z >>> def slow_set_future(fut, value): 2025-09-07T07:36:50.6943624Z ... time.sleep(0.5) 2025-09-07T07:36:50.6943730Z ... fut.set_result(value) 2025-09-07T07:36:50.6943854Z >>> fut = torch.futures.Future() 2025-09-07T07:36:50.6943955Z >>> t = threading.Thread( 2025-09-07T07:36:50.6944055Z ... target=slow_set_future, 2025-09-07T07:36:50.6944177Z ... args=(fut, torch.ones(2) * 3) 2025-09-07T07:36:50.6944257Z ... ) 2025-09-07T07:36:50.6944354Z >>> t.start() 2025-09-07T07:36:50.6944477Z >>> print(fut.wait()) 2025-09-07T07:36:50.6944567Z tensor([3., 3.]) 2025-09-07T07:36:50.6944704Z >>> t.join() 2025-09-07T07:36:50.6972296Z 2025-09-07T07:36:50.6972680Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.6972791Z 2025-09-07T07:36:50.6972891Z warnings.warn(msg) 2025-09-07T07:36:50.6972992Z 2025-09-07T07:36:50.6973261Z --- Parse Warning: 20 / 146 --- 2025-09-07T07:36:50.6974157Z /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=145. 2025-09-07T07:36:50.6974441Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.6974661Z Compiles compute shader from source and allows one to invoke kernels 2025-09-07T07:36:50.6974824Z defined there from the comfort of Python runtime 2025-09-07T07:36:50.6974929Z Example:: 2025-09-07T07:36:50.6975009Z 2025-09-07T07:36:50.6975161Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_MPS) 2025-09-07T07:36:50.6975280Z >>> lib = torch.mps.compile_shader( 2025-09-07T07:36:50.6975793Z ... "kernel void full(device float* out, constant float& val, uint idx [[thread_position_in_grid]]) { out[idx] = val; }" 2025-09-07T07:36:50.6975878Z ... ) 2025-09-07T07:36:50.6975996Z >>> x = torch.zeros(16, device="mps") 2025-09-07T07:36:50.6976146Z >>> lib.full(x, 3.14) 2025-09-07T07:36:50.6976227Z 2025-09-07T07:36:50.6976497Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.6976581Z 2025-09-07T07:36:50.6976682Z warnings.warn(msg) 2025-09-07T07:36:50.6976774Z 2025-09-07T07:36:50.6976958Z --- Parse Warning: 21 / 146 --- 2025-09-07T07:36:50.6977804Z /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. 2025-09-07T07:36:50.6978066Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.6978228Z Return the sum of each row of the given sparse tensor. 2025-09-07T07:36:50.6978355Z 2025-09-07T07:36:50.6978582Z Returns the sum of each row of the sparse tensor :attr:`input` in the given 2025-09-07T07:36:50.6978791Z dimensions :attr:`dim`. If :attr:`dim` is a list of dimensions, 2025-09-07T07:36:50.6979000Z reduce over all of them. When sum over all ``sparse_dim``, this method 2025-09-07T07:36:50.6979152Z returns a dense tensor instead of a sparse tensor. 2025-09-07T07:36:50.6979244Z 2025-09-07T07:36:50.6979501Z All summed :attr:`dim` are squeezed (see :func:`torch.squeeze`), resulting an output 2025-09-07T07:36:50.6979708Z tensor having :attr:`dim` fewer dimensions than :attr:`input`. 2025-09-07T07:36:50.6979786Z 2025-09-07T07:36:50.6980005Z During backward, only gradients at ``nnz`` locations of :attr:`input` 2025-09-07T07:36:50.6980257Z will propagate back. Note that the gradients of :attr:`input` is coalesced. 2025-09-07T07:36:50.6980341Z 2025-09-07T07:36:50.6980439Z Args: 2025-09-07T07:36:50.6980564Z input (Tensor): the input sparse tensor 2025-09-07T07:36:50.6980836Z dim (int or tuple of ints): a dimension or a list of dimensions to reduce. Default: reduce 2025-09-07T07:36:50.6980950Z over all dims. 2025-09-07T07:36:50.6981203Z dtype (:class:`torch.dtype`, optional): the desired data type of returned Tensor. 2025-09-07T07:36:50.6981331Z Default: dtype of :attr:`input`. 2025-09-07T07:36:50.6981408Z 2025-09-07T07:36:50.6981497Z Example:: 2025-09-07T07:36:50.6981584Z 2025-09-07T07:36:50.6981668Z >>> nnz = 3 2025-09-07T07:36:50.6981803Z >>> dims = [5, 5, 2, 3] 2025-09-07T07:36:50.6981964Z >>> I = torch.cat([torch.randint(0, dims[0], size=(nnz,)), 2025-09-07T07:36:50.6982146Z torch.randint(0, dims[1], size=(nnz,))], 0).reshape(2, nnz) 2025-09-07T07:36:50.6982280Z >>> V = torch.randn(nnz, dims[2], dims[3]) 2025-09-07T07:36:50.6982384Z >>> size = torch.Size(dims) 2025-09-07T07:36:50.6982534Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2025-09-07T07:36:50.6982663Z >>> S = torch.sparse_coo_tensor(I, V, size) 2025-09-07T07:36:50.6982746Z >>> S 2025-09-07T07:36:50.6982870Z tensor(indices=tensor([[2, 0, 3], 2025-09-07T07:36:50.6982965Z [2, 4, 1]]), 2025-09-07T07:36:50.6983106Z values=tensor([[[-0.6438, -1.6467, 1.4004], 2025-09-07T07:36:50.6983217Z [ 0.3411, 0.0918, -0.2312]], 2025-09-07T07:36:50.6983293Z 2025-09-07T07:36:50.6983416Z [[ 0.5348, 0.0634, -2.0494], 2025-09-07T07:36:50.6983523Z [-0.7125, -1.0646, 2.1844]], 2025-09-07T07:36:50.6983627Z 2025-09-07T07:36:50.6983750Z [[ 0.1276, 0.1874, -0.6334], 2025-09-07T07:36:50.6983862Z [-1.9682, -0.5340, 0.7483]]]), 2025-09-07T07:36:50.6984013Z size=(5, 5, 2, 3), nnz=3, layout=torch.sparse_coo) 2025-09-07T07:36:50.6984120Z 2025-09-07T07:36:50.6984310Z # when sum over only part of sparse_dims, return a sparse tensor 2025-09-07T07:36:50.6984435Z >>> torch.sparse.sum(S, [1, 3]) 2025-09-07T07:36:50.6984549Z tensor(indices=tensor([[0, 2, 3]]), 2025-09-07T07:36:50.6984672Z values=tensor([[-1.4512, 0.4073], 2025-09-07T07:36:50.6984775Z [-0.8901, 0.2017], 2025-09-07T07:36:50.6984881Z [-0.3183, -1.7539]]), 2025-09-07T07:36:50.6985026Z size=(5, 2), nnz=3, layout=torch.sparse_coo) 2025-09-07T07:36:50.6985104Z 2025-09-07T07:36:50.6985269Z # when sum over all sparse dim, return a dense tensor 2025-09-07T07:36:50.6985375Z # with summed dims squeezed 2025-09-07T07:36:50.6985514Z >>> torch.sparse.sum(S, [0, 1, 3]) 2025-09-07T07:36:50.6985626Z tensor([-2.6596, -1.1450]) 2025-09-07T07:36:50.6985708Z 2025-09-07T07:36:50.6985971Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.6986053Z 2025-09-07T07:36:50.6986148Z warnings.warn(msg) 2025-09-07T07:36:50.6986240Z 2025-09-07T07:36:50.6986431Z --- Parse Warning: 22 / 146 --- 2025-09-07T07:36:50.6987344Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=as_sparse_gradcheck in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/sparse/__init__.py line=550. 2025-09-07T07:36:50.6987603Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.6987787Z Decorate function, to extend gradcheck for sparse tensors. 2025-09-07T07:36:50.6987879Z 2025-09-07T07:36:50.6988093Z Decorator for torch.autograd.gradcheck or its functools.partial 2025-09-07T07:36:50.6988316Z variants that extends the gradcheck function with support to input 2025-09-07T07:36:50.6988491Z functions that operate on or/and return sparse tensors. 2025-09-07T07:36:50.6988573Z 2025-09-07T07:36:50.6988795Z The specified gradcheck function itself is guaranteed to operate 2025-09-07T07:36:50.6988898Z on strided tensors only. 2025-09-07T07:36:50.6988989Z 2025-09-07T07:36:50.6989075Z For example: 2025-09-07T07:36:50.6989155Z 2025-09-07T07:36:50.6989404Z >>> gradcheck = torch.sparse.as_sparse_gradcheck(torch.autograd.gradcheck) 2025-09-07T07:36:50.6989516Z >>> x = ( 2025-09-07T07:36:50.6989682Z ... torch.tensor([[0, 1], [2, 3]], dtype=torch.float64) 2025-09-07T07:36:50.6989778Z ... .to_sparse_coo() 2025-09-07T07:36:50.6989884Z ... .requires_grad_(True) 2025-09-07T07:36:50.6989975Z ... ) 2025-09-07T07:36:50.6990106Z >>> gradcheck(lambda x: x.to_sparse_csr(), x) 2025-09-07T07:36:50.6990188Z True 2025-09-07T07:36:50.6990295Z 2025-09-07T07:36:50.6990546Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.6990638Z 2025-09-07T07:36:50.6990733Z warnings.warn(msg) 2025-09-07T07:36:50.6990809Z 2025-09-07T07:36:50.6991009Z --- Parse Warning: 23 / 146 --- 2025-09-07T07:36:50.6991854Z /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. 2025-09-07T07:36:50.6992128Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.6992207Z 2025-09-07T07:36:50.6992426Z vmap is the vectorizing map; ``vmap(func)`` returns a new function that 2025-09-07T07:36:50.6992662Z maps ``func`` over some dimension of the inputs. Semantically, vmap 2025-09-07T07:36:50.6992877Z pushes the map into PyTorch operations called by ``func``, effectively 2025-09-07T07:36:50.6992996Z vectorizing those operations. 2025-09-07T07:36:50.6993101Z 2025-09-07T07:36:50.6993310Z vmap is useful for handling batch dimensions: one can write a function 2025-09-07T07:36:50.6993519Z ``func`` that runs on examples and then lift it to a function that can 2025-09-07T07:36:50.6993732Z take batches of examples with ``vmap(func)``. vmap can also be used to 2025-09-07T07:36:50.6993913Z compute batched gradients when composed with autograd. 2025-09-07T07:36:50.6993989Z 2025-09-07T07:36:50.6994075Z .. note:: 2025-09-07T07:36:50.6994275Z :func:`torch.vmap` is aliased to :func:`torch.func.vmap` for 2025-09-07T07:36:50.6994494Z convenience. Use whichever one you'd like. 2025-09-07T07:36:50.6994593Z 2025-09-07T07:36:50.6994676Z Args: 2025-09-07T07:36:50.6994887Z func (function): A Python function that takes one or more arguments. 2025-09-07T07:36:50.6995060Z Must return one or more Tensors. 2025-09-07T07:36:50.6995261Z in_dims (int or nested structure): Specifies which dimension of the 2025-09-07T07:36:50.6995447Z inputs should be mapped over. ``in_dims`` should have a 2025-09-07T07:36:50.6995637Z structure like the inputs. If the ``in_dim`` for a particular 2025-09-07T07:36:50.6995819Z input is None, then that indicates there is no map dimension. 2025-09-07T07:36:50.6995919Z Default: 0. 2025-09-07T07:36:50.6996108Z out_dims (int or Tuple[int]): Specifies where the mapped dimension 2025-09-07T07:36:50.6996311Z should appear in the outputs. If ``out_dims`` is a Tuple, then 2025-09-07T07:36:50.6996464Z it should have one element per output. Default: 0. 2025-09-07T07:36:50.6996646Z randomness (str): Specifies whether the randomness in this 2025-09-07T07:36:50.6996869Z vmap should be the same or different across batches. If 'different', 2025-09-07T07:36:50.6997068Z the randomness for each batch will be different. If 'same', the 2025-09-07T07:36:50.6997293Z randomness will be the same across batches. If 'error', any calls to 2025-09-07T07:36:50.6997502Z random functions will error. Default: 'error'. WARNING: this flag 2025-09-07T07:36:50.6997714Z only applies to random PyTorch operations and does not apply to 2025-09-07T07:36:50.6997850Z Python's random module or numpy randomness. 2025-09-07T07:36:50.6998071Z chunk_size (None or int): If None (default), apply a single vmap over inputs. 2025-09-07T07:36:50.6998323Z If not None, then compute the vmap :attr:`chunk_size` samples at a time. 2025-09-07T07:36:50.6998584Z Note that :attr:`chunk_size=1` is equivalent to computing the vmap with a for-loop. 2025-09-07T07:36:50.6998862Z If you run into memory issues computing the vmap, please try a non-None chunk_size. 2025-09-07T07:36:50.6998944Z 2025-09-07T07:36:50.6999027Z Returns: 2025-09-07T07:36:50.6999225Z Returns a new "batched" function. It takes the same inputs as 2025-09-07T07:36:50.6999408Z ``func``, except each input has an extra dimension at the index 2025-09-07T07:36:50.6999609Z specified by ``in_dims``. It takes returns the same outputs as 2025-09-07T07:36:50.6999793Z ``func``, except each output has an extra dimension at the index 2025-09-07T07:36:50.6999897Z specified by ``out_dims``. 2025-09-07T07:36:50.6999990Z 2025-09-07T07:36:50.7000073Z .. warning: 2025-09-07T07:36:50.7000283Z :func:`vmap` works best with functional-style code. Please do not 2025-09-07T07:36:50.7000468Z perform any side-effects in ``func``, with the exception of 2025-09-07T07:36:50.7000699Z in-place PyTorch operations. Examples of side-effects include mutating 2025-09-07T07:36:50.7000961Z Python data structures and assigning values to variables not captured 2025-09-07T07:36:50.7001050Z in ``func``. 2025-09-07T07:36:50.7001140Z 2025-09-07T07:36:50.7001375Z One example of using :func:`vmap` is to compute batched dot products. PyTorch 2025-09-07T07:36:50.7001616Z doesn't provide a batched ``torch.dot`` API; instead of unsuccessfully 2025-09-07T07:36:50.7001846Z rummaging through docs, use :func:`vmap` to construct a new function. 2025-09-07T07:36:50.7001926Z 2025-09-07T07:36:50.7002041Z >>> torch.dot # [D], [D] -> [] 2025-09-07T07:36:50.7002246Z >>> batched_dot = torch.func.vmap(torch.dot) # [N, D], [N, D] -> [N] 2025-09-07T07:36:50.7002376Z >>> x, y = torch.randn(2, 5), torch.randn(2, 5) 2025-09-07T07:36:50.7002485Z >>> batched_dot(x, y) 2025-09-07T07:36:50.7002566Z 2025-09-07T07:36:50.7002792Z :func:`vmap` can be helpful in hiding batch dimensions, leading to a simpler 2025-09-07T07:36:50.7002912Z model authoring experience. 2025-09-07T07:36:50.7002990Z 2025-09-07T07:36:50.7003151Z >>> batch_size, feature_size = 3, 5 2025-09-07T07:36:50.7003325Z >>> weights = torch.randn(feature_size, requires_grad=True) 2025-09-07T07:36:50.7003407Z >>> 2025-09-07T07:36:50.7003525Z >>> def model(feature_vec): 2025-09-07T07:36:50.7003653Z >>> # Very simple linear model with activation 2025-09-07T07:36:50.7003792Z >>> return feature_vec.dot(weights).relu() 2025-09-07T07:36:50.7003875Z >>> 2025-09-07T07:36:50.7004021Z >>> examples = torch.randn(batch_size, feature_size) 2025-09-07T07:36:50.7004155Z >>> result = torch.vmap(model)(examples) 2025-09-07T07:36:50.7004235Z 2025-09-07T07:36:50.7004496Z :func:`vmap` can also help vectorize computations that were previously difficult 2025-09-07T07:36:50.7004729Z or impossible to batch. One example is higher-order gradient computation. 2025-09-07T07:36:50.7004959Z The PyTorch autograd engine computes vjps (vector-Jacobian products). 2025-09-07T07:36:50.7005200Z Computing a full Jacobian matrix for some function f: R^N -> R^N usually 2025-09-07T07:36:50.7005446Z requires N calls to ``autograd.grad``, one per Jacobian row. Using :func:`vmap`, 2025-09-07T07:36:50.7005693Z we can vectorize the whole computation, computing the Jacobian in a single 2025-09-07T07:36:50.7005795Z call to ``autograd.grad``. 2025-09-07T07:36:50.7005874Z 2025-09-07T07:36:50.7005971Z >>> # Setup 2025-09-07T07:36:50.7006056Z >>> N = 5 2025-09-07T07:36:50.7006162Z >>> f = lambda x: x**2 2025-09-07T07:36:50.7006286Z >>> x = torch.randn(N, requires_grad=True) 2025-09-07T07:36:50.7006368Z >>> y = f(x) 2025-09-07T07:36:50.7006512Z >>> I_N = torch.eye(N) 2025-09-07T07:36:50.7006592Z >>> 2025-09-07T07:36:50.7006705Z >>> # Sequential approach 2025-09-07T07:36:50.7006917Z >>> jacobian_rows = [torch.autograd.grad(y, x, v, retain_graph=True)[0] 2025-09-07T07:36:50.7007029Z >>> for v in I_N.unbind()] 2025-09-07T07:36:50.7007165Z >>> jacobian = torch.stack(jacobian_rows) 2025-09-07T07:36:50.7007245Z >>> 2025-09-07T07:36:50.7007412Z >>> # vectorized gradient computation 2025-09-07T07:36:50.7007519Z >>> def get_vjp(v): 2025-09-07T07:36:50.7007638Z >>> return torch.autograd.grad(y, x, v) 2025-09-07T07:36:50.7007767Z >>> jacobian = torch.vmap(get_vjp)(I_N) 2025-09-07T07:36:50.7007847Z 2025-09-07T07:36:50.7008106Z :func:`vmap` can also be nested, producing an output with multiple batched dimensions 2025-09-07T07:36:50.7008198Z 2025-09-07T07:36:50.7008302Z >>> torch.dot # [D], [D] -> [] 2025-09-07T07:36:50.7008423Z >>> batched_dot = torch.vmap( 2025-09-07T07:36:50.7008568Z ... torch.vmap(torch.dot) 2025-09-07T07:36:50.7008686Z ... ) # [N1, N0, D], [N1, N0, D] -> [N1, N0] 2025-09-07T07:36:50.7008864Z >>> x, y = torch.randn(2, 3, 5), torch.randn(2, 3, 5) 2025-09-07T07:36:50.7008993Z >>> batched_dot(x, y) # tensor of size [2, 3] 2025-09-07T07:36:50.7009086Z 2025-09-07T07:36:50.7009328Z If the inputs are not batched along the first dimension, ``in_dims`` specifies 2025-09-07T07:36:50.7009503Z the dimension that each inputs are batched along as 2025-09-07T07:36:50.7009593Z 2025-09-07T07:36:50.7009694Z >>> torch.dot # [N], [N] -> [] 2025-09-07T07:36:50.7009925Z >>> batched_dot = torch.vmap(torch.dot, in_dims=1) # [N, D], [N, D] -> [D] 2025-09-07T07:36:50.7010047Z >>> x, y = torch.randn(2, 5), torch.randn(2, 5) 2025-09-07T07:36:50.7010136Z >>> batched_dot( 2025-09-07T07:36:50.7010230Z ... x, y 2025-09-07T07:36:50.7010420Z ... ) # output is [5] instead of [2] if batched along the 0th dimension 2025-09-07T07:36:50.7010509Z 2025-09-07T07:36:50.7010765Z If there are multiple inputs each of which is batched along different dimensions, 2025-09-07T07:36:50.7010964Z ``in_dims`` must be a tuple with the batch dimension for each input as 2025-09-07T07:36:50.7011076Z 2025-09-07T07:36:50.7011179Z >>> torch.dot # [D], [D] -> [] 2025-09-07T07:36:50.7011421Z >>> batched_dot = torch.vmap(torch.dot, in_dims=(0, None)) # [N, D], [D] -> [N] 2025-09-07T07:36:50.7011544Z >>> x, y = torch.randn(2, 5), torch.randn(5) 2025-09-07T07:36:50.7011634Z >>> batched_dot( 2025-09-07T07:36:50.7011725Z ... x, y 2025-09-07T07:36:50.7011915Z ... ) # second arg doesn't have a batch dim because in_dim[1] was None 2025-09-07T07:36:50.7012004Z 2025-09-07T07:36:50.7012238Z If the input is a Python struct, ``in_dims`` must be a tuple containing a struct 2025-09-07T07:36:50.7012348Z matching the shape of the input: 2025-09-07T07:36:50.7012438Z 2025-09-07T07:36:50.7012573Z >>> f = lambda dict: torch.dot(dict["x"], dict["y"]) 2025-09-07T07:36:50.7012695Z >>> x, y = torch.randn(2, 5), torch.randn(5) 2025-09-07T07:36:50.7012804Z >>> input = {"x": x, "y": y} 2025-09-07T07:36:50.7012980Z >>> batched_dot = torch.vmap(f, in_dims=({"x": 0, "y": None},)) 2025-09-07T07:36:50.7013092Z >>> batched_dot(input) 2025-09-07T07:36:50.7013171Z 2025-09-07T07:36:50.7013445Z By default, the output is batched along the first dimension. However, it can be batched 2025-09-07T07:36:50.7013583Z along any dimension by using ``out_dims`` 2025-09-07T07:36:50.7013661Z 2025-09-07T07:36:50.7013767Z >>> f = lambda x: x**2 2025-09-07T07:36:50.7013870Z >>> x = torch.randn(2, 5) 2025-09-07T07:36:50.7013993Z >>> batched_pow = torch.vmap(f, out_dims=1) 2025-09-07T07:36:50.7014128Z >>> batched_pow(x) # [5, 2] 2025-09-07T07:36:50.7014208Z 2025-09-07T07:36:50.7014509Z For any function that uses kwargs, the returned function will not batch the kwargs but will 2025-09-07T07:36:50.7014598Z accept kwargs 2025-09-07T07:36:50.7014679Z 2025-09-07T07:36:50.7014788Z >>> x = torch.randn([2, 5]) 2025-09-07T07:36:50.7014886Z >>> def fn(x, scale=4.): 2025-09-07T07:36:50.7014993Z >>> return x * scale 2025-09-07T07:36:50.7015072Z >>> 2025-09-07T07:36:50.7015182Z >>> batched_pow = torch.vmap(fn) 2025-09-07T07:36:50.7015336Z >>> assert torch.allclose(batched_pow(x), x * 4) 2025-09-07T07:36:50.7015557Z >>> batched_pow(x, scale=x) # scale is not batched, output has shape [2, 2, 5] 2025-09-07T07:36:50.7015636Z 2025-09-07T07:36:50.7015734Z .. note:: 2025-09-07T07:36:50.7015957Z vmap does not provide general autobatching or handle variable-length 2025-09-07T07:36:50.7016084Z sequences out of the box. 2025-09-07T07:36:50.7016160Z 2025-09-07T07:36:50.7016414Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.7016505Z 2025-09-07T07:36:50.7016624Z warnings.warn(msg) 2025-09-07T07:36:50.7016741Z 2025-09-07T07:36:50.7016965Z --- Parse Warning: 24 / 146 --- 2025-09-07T07:36:50.7017808Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=grad in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/apis.py line=306. 2025-09-07T07:36:50.7018112Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.7018348Z ``grad`` operator helps computing gradients of ``func`` with respect to the 2025-09-07T07:36:50.7018557Z input(s) specified by ``argnums``. This operator can be nested to 2025-09-07T07:36:50.7018668Z compute higher-order gradients. 2025-09-07T07:36:50.7018747Z 2025-09-07T07:36:50.7018844Z Args: 2025-09-07T07:36:50.7019053Z func (Callable): A Python function that takes one or more arguments. 2025-09-07T07:36:50.7019324Z Must return a single-element Tensor. If specified ``has_aux`` equals ``True``, 2025-09-07T07:36:50.7019617Z function can return a tuple of single-element Tensor and other auxiliary objects: 2025-09-07T07:36:50.7019713Z ``(output, aux)``. 2025-09-07T07:36:50.7019999Z argnums (int or Tuple[int]): Specifies arguments to compute gradients with respect to. 2025-09-07T07:36:50.7020205Z ``argnums`` can be single integer or tuple of integers. Default: 0. 2025-09-07T07:36:50.7020431Z has_aux (bool): Flag indicating that ``func`` returns a tensor and other 2025-09-07T07:36:50.7020594Z auxiliary objects: ``(output, aux)``. Default: False. 2025-09-07T07:36:50.7020687Z 2025-09-07T07:36:50.7020770Z Returns: 2025-09-07T07:36:50.7021053Z Function to compute gradients with respect to its inputs. By default, the output of 2025-09-07T07:36:50.7021295Z the function is the gradient tensor(s) with respect to the first argument. 2025-09-07T07:36:50.7021566Z If specified ``has_aux`` equals ``True``, tuple of gradients and output auxiliary objects 2025-09-07T07:36:50.7021826Z is returned. If ``argnums`` is a tuple of integers, a tuple of output gradients with 2025-09-07T07:36:50.7021969Z respect to each ``argnums`` value is returned. 2025-09-07T07:36:50.7022050Z 2025-09-07T07:36:50.7022165Z Example of using ``grad``: 2025-09-07T07:36:50.7022241Z 2025-09-07T07:36:50.7022347Z >>> # xdoctest: +SKIP 2025-09-07T07:36:50.7022463Z >>> from torch.func import grad 2025-09-07T07:36:50.7022557Z >>> x = torch.randn([]) 2025-09-07T07:36:50.7022692Z >>> cos_x = grad(lambda x: torch.sin(x))(x) 2025-09-07T07:36:50.7022842Z >>> assert torch.allclose(cos_x, x.cos()) 2025-09-07T07:36:50.7022934Z >>> 2025-09-07T07:36:50.7023040Z >>> # Second-order gradients 2025-09-07T07:36:50.7023185Z >>> neg_sin_x = grad(grad(lambda x: torch.sin(x)))(x) 2025-09-07T07:36:50.7023334Z >>> assert torch.allclose(neg_sin_x, -x.sin()) 2025-09-07T07:36:50.7023410Z 2025-09-07T07:36:50.7023847Z When composed with ``vmap``, ``grad`` can be used to compute per-sample-gradients: 2025-09-07T07:36:50.7023926Z 2025-09-07T07:36:50.7024022Z >>> # xdoctest: +SKIP 2025-09-07T07:36:50.7024158Z >>> from torch.func import grad, vmap 2025-09-07T07:36:50.7024269Z >>> batch_size, feature_size = 3, 5 2025-09-07T07:36:50.7024348Z >>> 2025-09-07T07:36:50.7024479Z >>> def model(weights, feature_vec): 2025-09-07T07:36:50.7024609Z >>> # Very simple linear model with activation 2025-09-07T07:36:50.7024735Z >>> assert feature_vec.dim() == 1 2025-09-07T07:36:50.7024860Z >>> return feature_vec.dot(weights).relu() 2025-09-07T07:36:50.7024939Z >>> 2025-09-07T07:36:50.7025092Z >>> def compute_loss(weights, example, target): 2025-09-07T07:36:50.7025278Z >>> y = model(weights, example) 2025-09-07T07:36:50.7025424Z >>> return ((y - target) ** 2).mean() # MSELoss 2025-09-07T07:36:50.7025506Z >>> 2025-09-07T07:36:50.7025678Z >>> weights = torch.randn(feature_size, requires_grad=True) 2025-09-07T07:36:50.7025892Z >>> examples = torch.randn(batch_size, feature_size) 2025-09-07T07:36:50.7026007Z >>> targets = torch.randn(batch_size) 2025-09-07T07:36:50.7026140Z >>> inputs = (weights, examples, targets) 2025-09-07T07:36:50.7026371Z >>> grad_weight_per_example = vmap(grad(compute_loss), in_dims=(None, 0, 0))( 2025-09-07T07:36:50.7026460Z ... *inputs 2025-09-07T07:36:50.7026551Z ... ) 2025-09-07T07:36:50.7026628Z 2025-09-07T07:36:50.7026815Z Example of using ``grad`` with ``has_aux`` and ``argnums``: 2025-09-07T07:36:50.7026892Z 2025-09-07T07:36:50.7026986Z >>> # xdoctest: +SKIP 2025-09-07T07:36:50.7027109Z >>> from torch.func import grad 2025-09-07T07:36:50.7027215Z >>> def my_loss_func(y, y_pred): 2025-09-07T07:36:50.7027387Z >>> loss_per_sample = (0.5 * y_pred - y) ** 2 2025-09-07T07:36:50.7027503Z >>> loss = loss_per_sample.mean() 2025-09-07T07:36:50.7027630Z >>> return loss, (y_pred, loss_per_sample) 2025-09-07T07:36:50.7027720Z >>> 2025-09-07T07:36:50.7027869Z >>> fn = grad(my_loss_func, argnums=(0, 1), has_aux=True) 2025-09-07T07:36:50.7027979Z >>> y_true = torch.rand(4) 2025-09-07T07:36:50.7028122Z >>> y_preds = torch.rand(4, requires_grad=True) 2025-09-07T07:36:50.7028226Z >>> out = fn(y_true, y_preds) 2025-09-07T07:36:50.7028480Z >>> # > output is ((grads w.r.t y_true, grads w.r.t y_preds), (y_pred, loss_per_sample)) 2025-09-07T07:36:50.7028557Z 2025-09-07T07:36:50.7028654Z .. note:: 2025-09-07T07:36:50.7028822Z Using PyTorch ``torch.no_grad`` together with ``grad``. 2025-09-07T07:36:50.7028903Z 2025-09-07T07:36:50.7029060Z Case 1: Using ``torch.no_grad`` inside a function: 2025-09-07T07:36:50.7029141Z 2025-09-07T07:36:50.7029243Z >>> # xdoctest: +SKIP 2025-09-07T07:36:50.7029345Z >>> def f(x): 2025-09-07T07:36:50.7029454Z >>> with torch.no_grad(): 2025-09-07T07:36:50.7029558Z >>> c = x ** 2 2025-09-07T07:36:50.7029652Z >>> return x - c 2025-09-07T07:36:50.7029729Z 2025-09-07T07:36:50.7029942Z In this case, ``grad(f)(x)`` will respect the inner ``torch.no_grad``. 2025-09-07T07:36:50.7030018Z 2025-09-07T07:36:50.7030238Z Case 2: Using ``grad`` inside ``torch.no_grad`` context manager: 2025-09-07T07:36:50.7030318Z 2025-09-07T07:36:50.7030418Z >>> # xdoctest: +SKIP 2025-09-07T07:36:50.7030529Z >>> with torch.no_grad(): 2025-09-07T07:36:50.7030623Z >>> grad(f)(x) 2025-09-07T07:36:50.7030698Z 2025-09-07T07:36:50.7030927Z In this case, ``grad`` will respect the inner ``torch.no_grad``, but not the 2025-09-07T07:36:50.7031144Z outer one. This is because ``grad`` is a "function transform": its result 2025-09-07T07:36:50.7031373Z should not depend on the result of a context manager outside of ``f``. 2025-09-07T07:36:50.7031449Z 2025-09-07T07:36:50.7031528Z 2025-09-07T07:36:50.7031795Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.7031875Z 2025-09-07T07:36:50.7031977Z warnings.warn(msg) 2025-09-07T07:36:50.7032053Z 2025-09-07T07:36:50.7032269Z --- Parse Warning: 25 / 146 --- 2025-09-07T07:36:50.7033238Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=CustomOpDef.register_fake in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/custom_ops.py line=397. 2025-09-07T07:36:50.7033512Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-09-07T07:36:50.7033699Z Register a FakeTensor implementation for this custom op. 2025-09-07T07:36:50.7033774Z 2025-09-07T07:36:50.7034027Z This is necessary to get the operator to work efficiently with torch.compile. 2025-09-07T07:36:50.7034136Z 2025-09-07T07:36:50.7034431Z The Fake impl (sometimes also known as a meta kernel or abstract impl) 2025-09-07T07:36:50.7034666Z specifies the behavior of this operator on Tensors that carry no data. 2025-09-07T07:36:50.7034814Z Given some input Tensors with certain properties 2025-09-07T07:36:50.7035077Z (sizes/strides/storage_offset/device), it specifies what the properties of 2025-09-07T07:36:50.7035177Z the output Tensors are. 2025-09-07T07:36:50.7035254Z 2025-09-07T07:36:50.7035459Z Please see :func:`torch.library.register_fake` for more details. 2025-09-07T07:36:50.7035537Z 2025-09-07T07:36:50.7035617Z Args: 2025-09-07T07:36:50.7035828Z fn (Callable): The function to register as the FakeTensor 2025-09-07T07:36:50.7035929Z implementation. 2025-09-07T07:36:50.7036017Z 2025-09-07T07:36:50.7036101Z Examples: 2025-09-07T07:36:50.7036193Z >>> import torch 2025-09-07T07:36:50.7036306Z >>> import numpy as np 2025-09-07T07:36:50.7036414Z >>> from torch import Tensor 2025-09-07T07:36:50.7036507Z >>> 2025-09-07T07:36:50.7036695Z >>> # Example 1: an operator without data-dependent output shape 2025-09-07T07:36:50.7036886Z >>> @torch.library.custom_op("mylib::linear", mutates_args=()) 2025-09-07T07:36:50.7037083Z >>> def linear(x: Tensor, weight: Tensor, bias: Tensor) -> Tensor: 2025-09-07T07:36:50.7037200Z >>> return (x @ weight.t()) + bias 2025-09-07T07:36:50.7037288Z >>> 2025-09-07T07:36:50.7037391Z >>> @linear.register_fake 2025-09-07T07:36:50.7037494Z >>> def _(x, weight, bias): 2025-09-07T07:36:50.7037604Z >>> assert x.dim() == 2 2025-09-07T07:36:50.7037713Z >>> assert weight.dim() == 2 2025-09-07T07:36:50.7037827Z >>> assert bias.dim() == 1 2025-09-07T07:36:50.7037949Z >>> assert x.shape[1] == weight.shape[1] 2025-09-07T07:36:50.7038078Z >>> assert weight.shape[0] == bias.shape[0] 2025-09-07T07:36:50.7038203Z >>> assert x.device == weight.device 2025-09-07T07:36:50.7038348Z >>> return x.new_empty(x.size(0), weight.size(0)) 2025-09-07T07:36:50.7038461Z >>> 2025-09-07T07:36:50.7038563Z >>> x = torch.randn(2, 2) 2025-09-07T07:36:50.7038674Z >>> weight = torch.randn(2, 2) 2025-09-07T07:36:50.7038783Z >>> bias = torch.randn(2) 2025-09-07T07:36:50.7038916Z >>> # xdoctest: +SKIP("Requires Python <= 3.11") 2025-09-07T07:36:50.7039113Z >>> out = torch.compile(linear, fullgraph=True)(x, weight, bias) 2025-09-07T07:36:50.7039247Z >>> # xdoctest: +SKIP("Requires Python <= 3.11") 2025-09-07T07:36:50.7039497Z >>> assert torch.allclose(out, torch.nn.functional.linear(x, weight, bias)) 2025-09-07T07:36:50.7039594Z >>> 2025-09-07T07:36:50.7039765Z >>> # Example 2: an operator with data-dependent output shape 2025-09-07T07:36:50.7039963Z >>> @torch.library.custom_op("mylib::nonzero", mutates_args=()) 2025-09-07T07:36:50.7040082Z >>> def nonzero(x: Tensor) -> Tensor: 2025-09-07T07:36:50.7040182Z >>> x_np = x.cpu().numpy() 2025-09-07T07:36:50.7040318Z >>> res = np.stack(np.nonzero(x_np), axis=1) 2025-09-07T07:36:50.7040476Z >>> return torch.tensor(res, device=x.device) 2025-09-07T07:36:50.7040563Z >>> 2025-09-07T07:36:50.7040672Z >>> @nonzero.register_fake 2025-09-07T07:36:50.7040758Z >>> def _(x): 2025-09-07T07:36:50.7040909Z >>> # Number of nonzero-elements is data-dependent. 2025-09-07T07:36:50.7041097Z >>> # Since we cannot peek at the data in an abstract impl, 2025-09-07T07:36:50.7041259Z >>> # we use the ctx object to construct a new symint that 2025-09-07T07:36:50.7041385Z >>> # represents the data-dependent size. 2025-09-07T07:36:50.7041500Z >>> ctx = torch.library.get_ctx() 2025-09-07T07:36:50.7041621Z >>> nnz = ctx.new_dynamic_size() 2025-09-07T07:36:50.7041726Z >>> shape = [nnz, x.dim()] 2025-09-07T07:36:50.7041874Z >>> result = x.new_empty(shape, dtype=torch.int64) 2025-09-07T07:36:50.7041969Z >>> return result 2025-09-07T07:36:50.7042046Z >>> 2025-09-07T07:36:50.7042167Z >>> x = torch.tensor([0, 1, 2, 0, 0, 1]) 2025-09-07T07:36:50.7042352Z >>> # xdoctest: +SKIP("Requires Python <= 3.11") 2025-09-07T07:36:50.7042507Z >>> out = torch.compile(nonzero, fullgraph=True)(x) 2025-09-07T07:36:50.7042639Z >>> # xdoctest: +SKIP("Requires Python <= 3.11") 2025-09-07T07:36:50.7042763Z >>> assert torch.allclose(out, x.nonzero()) 2025-09-07T07:36:50.7042847Z 2025-09-07T07:36:50.7042926Z 2025-09-07T07:36:50.7043464Z Original Error: IndentationError('expected an indented block after function definition on line 36', ('', 37, 1, '_._ = None\n', 37, 2)) 2025-09-07T07:36:50.7043544Z 2025-09-07T07:36:50.7043620Z _._ = None 2025-09-07T07:36:50.7043713Z ^ 2025-09-07T07:36:50.7043807Z warnings.warn(msg) 2025-09-07T07:36:50.7043899Z 2025-09-07T07:36:50.7044113Z --- Parse Warning: 26 / 146 --- 2025-09-07T07:36:50.7045086Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=unsafe_generate_fake_kernels in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/fake_profile.py line=94. 2025-09-07T07:36:50.7045360Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.7045438Z 2025-09-07T07:36:50.7045672Z Registers a fake kernel based on the given operator profiles. This fake 2025-09-07T07:36:50.7045917Z kernel registration will override any existing fake kernel registrations. 2025-09-07T07:36:50.7045993Z 2025-09-07T07:36:50.7046212Z The input is a dictionary mapping operator names to a set of operator 2025-09-07T07:36:50.7046471Z profiles, which we will use to generate fake kernels. The operator profiles 2025-09-07T07:36:50.7046678Z are a record of the input and output tensor metadata. Based on this 2025-09-07T07:36:50.7046920Z information we will match a given input to the recorded profile, and return 2025-09-07T07:36:50.7047138Z an output with the same metadata as in the recorded profile. If a profile 2025-09-07T07:36:50.7047287Z doesn't exist then an exception will be thrown. 2025-09-07T07:36:50.7047364Z 2025-09-07T07:36:50.7047596Z The fake kernel generation is considered unsafe because it relies on the 2025-09-07T07:36:50.7047818Z rigid, pre-defined operator profiles that do not account for potential 2025-09-07T07:36:50.7048058Z variations in output behavior. Specifically, the generated kernels assume a 2025-09-07T07:36:50.7048314Z fixed relationship between input and output ranks. However, in reality, it's 2025-09-07T07:36:50.7048585Z possible that data-dependent operations may produce outputs of different 2025-09-07T07:36:50.7048811Z ranks even when given inputs of the same rank. The generated fake kernels 2025-09-07T07:36:50.7049021Z are inflexible and unable to accommodate these nuances, making them 2025-09-07T07:36:50.7049139Z potentially unsafe. 2025-09-07T07:36:50.7049224Z 2025-09-07T07:36:50.7049305Z Args: 2025-09-07T07:36:50.7049528Z op_profiles (dict[str, set[OpProfile]]): A dictionary mapping operator 2025-09-07T07:36:50.7049724Z name to a set of operator profiles from which we will generate fake 2025-09-07T07:36:50.7049829Z kernels. 2025-09-07T07:36:50.7049912Z 2025-09-07T07:36:50.7049994Z Examples: 2025-09-07T07:36:50.7050077Z 2025-09-07T07:36:50.7050240Z >>> # Example: Registering an op-profile from draft-export 2025-09-07T07:36:50.7050329Z >>> import torch 2025-09-07T07:36:50.7050499Z >>> from torch.export._draft_export import draft_export 2025-09-07T07:36:50.7050579Z >>> 2025-09-07T07:36:50.7050764Z >>> @torch.library.custom_op("mylib::foo", mutates_args=()) 2025-09-07T07:36:50.7050886Z >>> def foo(x: Tensor, y: Tensor) -> Tensor: 2025-09-07T07:36:50.7050976Z >>> return x + y 2025-09-07T07:36:50.7051064Z >>> 2025-09-07T07:36:50.7051170Z >>> class M(torch.nn.Module): 2025-09-07T07:36:50.7051301Z >>> def forward(self, a, b): 2025-09-07T07:36:50.7051440Z >>> res = torch.ops.mylib.foo(a, b) # no fake impl 2025-09-07T07:36:50.7051531Z >>> return res 2025-09-07T07:36:50.7051618Z >>> 2025-09-07T07:36:50.7051784Z >>> ep = draft_export(M(), (torch.ones(3, 4), torch.ones(3, 4)) 2025-09-07T07:36:50.7051860Z >>> 2025-09-07T07:36:50.7052163Z >>> with torch._library.fake_profile.unsafe_generate_fake_kernels(ep._report.op_profiles): 2025-09-07T07:36:50.7052283Z >>> decomp = ep.run_decompositions() 2025-09-07T07:36:50.7052369Z 2025-09-07T07:36:50.7052446Z 2025-09-07T07:36:50.7052691Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.7052779Z 2025-09-07T07:36:50.7052871Z warnings.warn(msg) 2025-09-07T07:36:50.7052955Z 2025-09-07T07:36:50.7053144Z --- Parse Warning: 27 / 146 --- 2025-09-07T07:36:50.7053999Z /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=96. 2025-09-07T07:36:50.7054272Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.7054519Z Create a custom operator whose implementation is backed by 1+ triton kernels. 2025-09-07T07:36:50.7054600Z 2025-09-07T07:36:50.7054801Z This is a more structured way of using triton kernels with PyTorch. 2025-09-07T07:36:50.7055075Z Prefer using triton kernels with no ``torch.library`` custom operator wrappers 2025-09-07T07:36:50.7055334Z (like :func:`torch.library.custom_op`, :func:`torch.library.triton_op`) because 2025-09-07T07:36:50.7055423Z that is simpler; 2025-09-07T07:36:50.7055680Z only use :func:`torch.library.custom_op`/:func:`torch.library.triton_op` if you 2025-09-07T07:36:50.7055904Z want to create an operator that behaves like PyTorch built-in operators. 2025-09-07T07:36:50.7056120Z For example, you may use a ``torch.library`` wrapper API to define the 2025-09-07T07:36:50.7056335Z behavior of the triton kernel when passed a tensor subclass or under 2025-09-07T07:36:50.7056435Z a TorchDispatchMode. 2025-09-07T07:36:50.7056517Z 2025-09-07T07:36:50.7056760Z Use :func:`torch.library.triton_op` instead of :func:`torch.library.custom_op` 2025-09-07T07:36:50.7056864Z when the implementation 2025-09-07T07:36:50.7057079Z consists of 1+ triton kernels. :func:`torch.library.custom_op` treats 2025-09-07T07:36:50.7057242Z custom operators as opaque (:func:`torch.compile` and 2025-09-07T07:36:50.7057473Z :func:`torch.export.export` will never trace into them), but ``triton_op`` 2025-09-07T07:36:50.7057726Z makes the implementation visible to these subsystems, allowing them 2025-09-07T07:36:50.7057844Z to optimize the triton kernel(s). 2025-09-07T07:36:50.7057921Z 2025-09-07T07:36:50.7058107Z Note that ``fn`` must only consist of calls to PyTorch-understood 2025-09-07T07:36:50.7058372Z operators and triton kernels. Any triton kernels called inside ``fn`` 2025-09-07T07:36:50.7058561Z must be wrapped in a call to :func:`torch.library.wrap_triton`. 2025-09-07T07:36:50.7058649Z 2025-09-07T07:36:50.7058731Z Args: 2025-09-07T07:36:50.7058952Z name (str): A name for the custom op that looks like "{namespace}::{name}", 2025-09-07T07:36:50.7059173Z e.g. "mylib::my_linear". The name is used as the op's stable identifier 2025-09-07T07:36:50.7059337Z in PyTorch subsystems (e.g. torch.export, FX graphs). 2025-09-07T07:36:50.7059579Z To avoid name collisions, please use your project name as the namespace; 2025-09-07T07:36:50.7059784Z e.g. all custom ops in pytorch/fbgemm use "fbgemm" as the namespace. 2025-09-07T07:36:50.7060081Z mutates_args (Iterable[str] or "unknown"): The names of args that the function mutates. 2025-09-07T07:36:50.7060332Z This MUST be accurate, otherwise, the behavior is undefined. If "unknown", 2025-09-07T07:36:50.7060594Z it pessimistically assumes that all inputs to the operator are being mutated. 2025-09-07T07:36:50.7060792Z schema (None | str): A schema string for the operator. If None 2025-09-07T07:36:50.7060997Z (recommended) we'll infer a schema for the operator from its type 2025-09-07T07:36:50.7061202Z annotations. We recommend letting us infer a schema unless you 2025-09-07T07:36:50.7061332Z have a specific reason not to. 2025-09-07T07:36:50.7061483Z Example: "(Tensor x, int y) -> (Tensor, Tensor)". 2025-09-07T07:36:50.7061579Z 2025-09-07T07:36:50.7061667Z Example:: 2025-09-07T07:36:50.7061745Z 2025-09-07T07:36:50.7061898Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2025-09-07T07:36:50.7061992Z >>> import torch 2025-09-07T07:36:50.7062159Z >>> from torch.library import triton_op, wrap_triton 2025-09-07T07:36:50.7062245Z >>> 2025-09-07T07:36:50.7062339Z >>> import triton 2025-09-07T07:36:50.7062474Z >>> from triton import language as tl 2025-09-07T07:36:50.7062601Z >>> 2025-09-07T07:36:50.7062706Z >>> @triton.jit 2025-09-07T07:36:50.7062800Z >>> def add_kernel( 2025-09-07T07:36:50.7062891Z >>> in_ptr0, 2025-09-07T07:36:50.7062994Z >>> in_ptr1, 2025-09-07T07:36:50.7063106Z >>> out_ptr, 2025-09-07T07:36:50.7063208Z >>> n_elements, 2025-09-07T07:36:50.7063323Z >>> BLOCK_SIZE: "tl.constexpr", 2025-09-07T07:36:50.7063408Z >>> ): 2025-09-07T07:36:50.7063533Z >>> pid = tl.program_id(axis=0) 2025-09-07T07:36:50.7063682Z >>> block_start = pid * BLOCK_SIZE 2025-09-07T07:36:50.7063831Z >>> offsets = block_start + tl.arange(0, BLOCK_SIZE) 2025-09-07T07:36:50.7063951Z >>> mask = offsets < n_elements 2025-09-07T07:36:50.7064082Z >>> x = tl.load(in_ptr0 + offsets, mask=mask) 2025-09-07T07:36:50.7064218Z >>> y = tl.load(in_ptr1 + offsets, mask=mask) 2025-09-07T07:36:50.7064314Z >>> output = x + y 2025-09-07T07:36:50.7064457Z >>> tl.store(out_ptr + offsets, output, mask=mask) 2025-09-07T07:36:50.7064550Z >>> 2025-09-07T07:36:50.7064680Z >>> @triton_op("mylib::add", mutates_args={}) 2025-09-07T07:36:50.7064872Z >>> def add(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor: 2025-09-07T07:36:50.7064987Z >>> output = torch.empty_like(x) 2025-09-07T07:36:50.7065124Z >>> n_elements = output.numel() 2025-09-07T07:36:50.7065219Z >>> 2025-09-07T07:36:50.7065319Z >>> def grid(meta): 2025-09-07T07:36:50.7065501Z >>> return (triton.cdiv(n_elements, meta["BLOCK_SIZE"]),) 2025-09-07T07:36:50.7065582Z >>> 2025-09-07T07:36:50.7065785Z >>> # NB: we need to wrap the triton kernel in a call to wrap_triton 2025-09-07T07:36:50.7065980Z >>> wrap_triton(add_kernel)[grid](x, y, output, n_elements, 16) 2025-09-07T07:36:50.7066076Z >>> return output 2025-09-07T07:36:50.7066168Z >>> 2025-09-07T07:36:50.7066262Z >>> @torch.compile 2025-09-07T07:36:50.7066351Z >>> def f(x, y): 2025-09-07T07:36:50.7066460Z >>> return add(x, y) 2025-09-07T07:36:50.7066540Z >>> 2025-09-07T07:36:50.7066669Z >>> x = torch.randn(3, device="cuda") 2025-09-07T07:36:50.7066781Z >>> y = torch.randn(3, device="cuda") 2025-09-07T07:36:50.7066862Z >>> 2025-09-07T07:36:50.7066959Z >>> z = f(x, y) 2025-09-07T07:36:50.7067096Z >>> assert torch.allclose(z, x + y) 2025-09-07T07:36:50.7067185Z 2025-09-07T07:36:50.7067263Z 2025-09-07T07:36:50.7067515Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.7067604Z 2025-09-07T07:36:50.7067698Z warnings.warn(msg) 2025-09-07T07:36:50.7067776Z 2025-09-07T07:36:50.7067986Z --- Parse Warning: 28 / 146 --- 2025-09-07T07:36:50.7068858Z /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=296. 2025-09-07T07:36:50.7069129Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.7069319Z Allows capture of a triton kernel into a graph via make_fx or 2025-09-07T07:36:50.7069440Z non-strict ``torch.export``. 2025-09-07T07:36:50.7069517Z 2025-09-07T07:36:50.7069700Z These technologies perform Dispatcher-based tracing (via 2025-09-07T07:36:50.7069908Z ``__torch_dispatch__``) and cannot see calls to raw triton kernels. 2025-09-07T07:36:50.7070104Z The ``wrap_triton`` API wraps a triton kernel into a callable that 2025-09-07T07:36:50.7070231Z can actually be traced into a graph. 2025-09-07T07:36:50.7070309Z 2025-09-07T07:36:50.7070518Z Please use this API together with :func:`torch.library.triton_op`. 2025-09-07T07:36:50.7070608Z 2025-09-07T07:36:50.7070693Z Examples: 2025-09-07T07:36:50.7070771Z 2025-09-07T07:36:50.7070883Z >>> # xdoctest: +SKIP 2025-09-07T07:36:50.7071000Z >>> import torch 2025-09-07T07:36:50.7071104Z >>> import triton 2025-09-07T07:36:50.7071222Z >>> from triton import language as tl 2025-09-07T07:36:50.7071402Z >>> from torch.fx.experimental.proxy_tensor import make_fx 2025-09-07T07:36:50.7071542Z >>> from torch.library import wrap_triton 2025-09-07T07:36:50.7071623Z >>> 2025-09-07T07:36:50.7071721Z >>> @triton.jit 2025-09-07T07:36:50.7071811Z >>> def add_kernel( 2025-09-07T07:36:50.7071901Z >>> in_ptr0, 2025-09-07T07:36:50.7071998Z >>> in_ptr1, 2025-09-07T07:36:50.7072083Z >>> out_ptr, 2025-09-07T07:36:50.7072240Z >>> n_elements, 2025-09-07T07:36:50.7072353Z >>> BLOCK_SIZE: "tl.constexpr", 2025-09-07T07:36:50.7072434Z >>> ): 2025-09-07T07:36:50.7072556Z >>> pid = tl.program_id(axis=0) 2025-09-07T07:36:50.7072672Z >>> block_start = pid * BLOCK_SIZE 2025-09-07T07:36:50.7072819Z >>> offsets = block_start + tl.arange(0, BLOCK_SIZE) 2025-09-07T07:36:50.7072941Z >>> mask = offsets < n_elements 2025-09-07T07:36:50.7073095Z >>> x = tl.load(in_ptr0 + offsets, mask=mask) 2025-09-07T07:36:50.7073235Z >>> y = tl.load(in_ptr1 + offsets, mask=mask) 2025-09-07T07:36:50.7073332Z >>> output = x + y 2025-09-07T07:36:50.7073473Z >>> tl.store(out_ptr + offsets, output, mask=mask) 2025-09-07T07:36:50.7073591Z >>> 2025-09-07T07:36:50.7073685Z >>> def add(x, y): 2025-09-07T07:36:50.7073811Z >>> output = torch.empty_like(x) 2025-09-07T07:36:50.7073920Z >>> n_elements = output.numel() 2025-09-07T07:36:50.7074002Z >>> 2025-09-07T07:36:50.7074111Z >>> def grid_fn(meta): 2025-09-07T07:36:50.7074277Z >>> return (triton.cdiv(n_elements, meta["BLOCK_SIZE"]),) 2025-09-07T07:36:50.7074457Z >>> 2025-09-07T07:36:50.7074654Z >>> wrap_triton(add_kernel)[grid_fn](x, y, output, n_elements, 16) 2025-09-07T07:36:50.7074749Z >>> return output 2025-09-07T07:36:50.7074846Z >>> 2025-09-07T07:36:50.7074962Z >>> x = torch.randn(3, device="cuda") 2025-09-07T07:36:50.7075120Z >>> y = torch.randn(3, device="cuda") 2025-09-07T07:36:50.7075221Z >>> gm = make_fx(add)(x, y) 2025-09-07T07:36:50.7075316Z >>> print(gm.code) 2025-09-07T07:36:50.7075439Z >>> # def forward(self, x_1, y_1): 2025-09-07T07:36:50.7075674Z >>> # empty_like = torch.ops.aten.empty_like.default(x_1, pin_memory = False) 2025-09-07T07:36:50.7075924Z >>> # triton_kernel_wrapper_mutation_proxy = triton_kernel_wrapper_mutation( 2025-09-07T07:36:50.7076053Z >>> # kernel_idx = 0, constant_args_idx = 0, 2025-09-07T07:36:50.7076168Z >>> # grid = [(1, 1, 1)], kwargs = { 2025-09-07T07:36:50.7076331Z >>> # 'in_ptr0': x_1, 'in_ptr1': y_1, 'out_ptr': empty_like, 2025-09-07T07:36:50.7076452Z >>> # 'n_elements': 3, 'BLOCK_SIZE': 16 2025-09-07T07:36:50.7076552Z >>> # }) 2025-09-07T07:36:50.7076652Z >>> # return empty_like 2025-09-07T07:36:50.7076733Z 2025-09-07T07:36:50.7076823Z 2025-09-07T07:36:50.7077075Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.7077165Z 2025-09-07T07:36:50.7077259Z warnings.warn(msg) 2025-09-07T07:36:50.7077336Z 2025-09-07T07:36:50.7077554Z --- Parse Warning: 29 / 146 --- 2025-09-07T07:36:50.7078477Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=print_assert_equal in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py line=286. 2025-09-07T07:36:50.7078780Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.7078858Z 2025-09-07T07:36:50.7079113Z Test if two objects are equal, and print an error message if test fails. 2025-09-07T07:36:50.7079202Z 2025-09-07T07:36:50.7079346Z The test is performed with ``actual == desired``. 2025-09-07T07:36:50.7079438Z 2025-09-07T07:36:50.7079523Z Parameters 2025-09-07T07:36:50.7079608Z ---------- 2025-09-07T07:36:50.7079709Z test_string : str 2025-09-07T07:36:50.7079838Z The message supplied to AssertionError. 2025-09-07T07:36:50.7079922Z actual : object 2025-09-07T07:36:50.7080081Z The object to test for equality against `desired`. 2025-09-07T07:36:50.7080167Z desired : object 2025-09-07T07:36:50.7080278Z The expected result. 2025-09-07T07:36:50.7080357Z 2025-09-07T07:36:50.7080439Z Examples 2025-09-07T07:36:50.7080536Z -------- 2025-09-07T07:36:50.7080650Z >>> np.testing.print_assert_equal( 2025-09-07T07:36:50.7080777Z ... "Test XYZ of func xyz", [0, 1], [0, 1] 2025-09-07T07:36:50.7080871Z ... ) # doctest: +SKIP 2025-09-07T07:36:50.7080981Z >>> np.testing.print_assert_equal( 2025-09-07T07:36:50.7081135Z ... "Test XYZ of func xyz", [0, 1], [0, 2] 2025-09-07T07:36:50.7081227Z ... ) # doctest: +SKIP 2025-09-07T07:36:50.7081350Z Traceback (most recent call last): 2025-09-07T07:36:50.7081431Z ... 2025-09-07T07:36:50.7081560Z AssertionError: Test XYZ of func xyz failed 2025-09-07T07:36:50.7081681Z ACTUAL: 2025-09-07T07:36:50.7081763Z [0, 1] 2025-09-07T07:36:50.7081846Z DESIRED: 2025-09-07T07:36:50.7081939Z [0, 2] 2025-09-07T07:36:50.7082020Z 2025-09-07T07:36:50.7082112Z 2025-09-07T07:36:50.7082365Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.7082441Z 2025-09-07T07:36:50.7082548Z warnings.warn(msg) 2025-09-07T07:36:50.7082626Z 2025-09-07T07:36:50.7082814Z --- Parse Warning: 30 / 146 --- 2025-09-07T07:36:50.7083751Z /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=331. 2025-09-07T07:36:50.7084054Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.7084145Z 2025-09-07T07:36:50.7084349Z Raises an AssertionError if two items are not equal up to desired 2025-09-07T07:36:50.7084448Z precision. 2025-09-07T07:36:50.7084524Z 2025-09-07T07:36:50.7084698Z .. note:: It is recommended to use one of `assert_allclose`, 2025-09-07T07:36:50.7084887Z `assert_array_almost_equal_nulp` or `assert_array_max_ulp` 2025-09-07T07:36:50.7085074Z instead of this function for more consistent floating point 2025-09-07T07:36:50.7085179Z comparisons. 2025-09-07T07:36:50.7085258Z 2025-09-07T07:36:50.7085472Z The test verifies that the elements of `actual` and `desired` satisfy. 2025-09-07T07:36:50.7085560Z 2025-09-07T07:36:50.7085717Z ``abs(desired-actual) < float64(1.5 * 10**(-decimal))`` 2025-09-07T07:36:50.7085810Z 2025-09-07T07:36:50.7086038Z That is a looser test than originally documented, but agrees with what the 2025-09-07T07:36:50.7086267Z actual implementation in `assert_array_almost_equal` did up to rounding 2025-09-07T07:36:50.7086511Z vagaries. An exception is raised at conflicting values. For ndarrays this 2025-09-07T07:36:50.7086630Z delegates to assert_array_almost_equal 2025-09-07T07:36:50.7086722Z 2025-09-07T07:36:50.7086807Z Parameters 2025-09-07T07:36:50.7086893Z ---------- 2025-09-07T07:36:50.7086996Z actual : array_like 2025-09-07T07:36:50.7087095Z The object to check. 2025-09-07T07:36:50.7087188Z desired : array_like 2025-09-07T07:36:50.7087300Z The expected object. 2025-09-07T07:36:50.7087430Z decimal : int, optional 2025-09-07T07:36:50.7087554Z Desired precision, default is 7. 2025-09-07T07:36:50.7087648Z err_msg : str, optional 2025-09-07T07:36:50.7087802Z The error message to be printed in case of failure. 2025-09-07T07:36:50.7087910Z verbose : bool, optional 2025-09-07T07:36:50.7088115Z If True, the conflicting values are appended to the error message. 2025-09-07T07:36:50.7088206Z 2025-09-07T07:36:50.7088287Z Raises 2025-09-07T07:36:50.7088368Z ------ 2025-09-07T07:36:50.7088475Z AssertionError 2025-09-07T07:36:50.7088666Z If actual and desired are not equal up to specified precision. 2025-09-07T07:36:50.7088746Z 2025-09-07T07:36:50.7088842Z See Also 2025-09-07T07:36:50.7088925Z -------- 2025-09-07T07:36:50.7089171Z assert_allclose: Compare two array_like objects for equality with desired 2025-09-07T07:36:50.7089295Z relative and/or absolute precision. 2025-09-07T07:36:50.7089501Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2025-09-07T07:36:50.7089594Z 2025-09-07T07:36:50.7089675Z Examples 2025-09-07T07:36:50.7089768Z -------- 2025-09-07T07:36:50.7089958Z >>> from torch._numpy.testing import assert_almost_equal 2025-09-07T07:36:50.7090086Z >>> assert_almost_equal(2.3333333333333, 2.33333334) 2025-09-07T07:36:50.7090266Z >>> assert_almost_equal(2.3333333333333, 2.33333334, decimal=10) 2025-09-07T07:36:50.7090379Z Traceback (most recent call last): 2025-09-07T07:36:50.7090495Z ... 2025-09-07T07:36:50.7090587Z AssertionError: 2025-09-07T07:36:50.7090708Z Arrays are not almost equal to 10 decimals 2025-09-07T07:36:50.7090809Z ACTUAL: 2.3333333333333 2025-09-07T07:36:50.7090898Z DESIRED: 2.33333334 2025-09-07T07:36:50.7090973Z 2025-09-07T07:36:50.7091078Z >>> assert_almost_equal( 2025-09-07T07:36:50.7091274Z ... np.array([1.0, 2.3333333333333]), np.array([1.0, 2.33333334]), decimal=9 2025-09-07T07:36:50.7091363Z ... ) 2025-09-07T07:36:50.7091471Z Traceback (most recent call last): 2025-09-07T07:36:50.7091548Z ... 2025-09-07T07:36:50.7091649Z AssertionError: 2025-09-07T07:36:50.7091769Z Arrays are not almost equal to 9 decimals 2025-09-07T07:36:50.7091862Z 2025-09-07T07:36:50.7091990Z Mismatched elements: 1 / 2 (50%) 2025-09-07T07:36:50.7092118Z Max absolute difference: 6.666699636781459e-09 2025-09-07T07:36:50.7092259Z Max relative difference: 2.8571569790287484e-09 2025-09-07T07:36:50.7092395Z x: torch.ndarray([1.0000, 2.3333], dtype=float64) 2025-09-07T07:36:50.7092541Z y: torch.ndarray([1.0000, 2.3333], dtype=float64) 2025-09-07T07:36:50.7092620Z 2025-09-07T07:36:50.7092693Z 2025-09-07T07:36:50.7092954Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.7093032Z 2025-09-07T07:36:50.7093120Z warnings.warn(msg) 2025-09-07T07:36:50.7093208Z 2025-09-07T07:36:50.7093401Z --- Parse Warning: 31 / 146 --- 2025-09-07T07:36:50.7094344Z /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=457. 2025-09-07T07:36:50.7094606Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.7094693Z 2025-09-07T07:36:50.7094912Z Raises an AssertionError if two items are not equal up to significant 2025-09-07T07:36:50.7094993Z digits. 2025-09-07T07:36:50.7095075Z 2025-09-07T07:36:50.7095246Z .. note:: It is recommended to use one of `assert_allclose`, 2025-09-07T07:36:50.7095432Z `assert_array_almost_equal_nulp` or `assert_array_max_ulp` 2025-09-07T07:36:50.7095616Z instead of this function for more consistent floating point 2025-09-07T07:36:50.7095709Z comparisons. 2025-09-07T07:36:50.7095822Z 2025-09-07T07:36:50.7096005Z Given two numbers, check that they are approximately equal. 2025-09-07T07:36:50.7096214Z Approximately equal is defined as the number of significant digits 2025-09-07T07:36:50.7096310Z that agree. 2025-09-07T07:36:50.7096389Z 2025-09-07T07:36:50.7096480Z Parameters 2025-09-07T07:36:50.7096565Z ---------- 2025-09-07T07:36:50.7096651Z actual : scalar 2025-09-07T07:36:50.7096756Z The object to check. 2025-09-07T07:36:50.7096842Z desired : scalar 2025-09-07T07:36:50.7096946Z The expected object. 2025-09-07T07:36:50.7097046Z significant : int, optional 2025-09-07T07:36:50.7097157Z Desired precision, default is 7. 2025-09-07T07:36:50.7097259Z err_msg : str, optional 2025-09-07T07:36:50.7097411Z The error message to be printed in case of failure. 2025-09-07T07:36:50.7097519Z verbose : bool, optional 2025-09-07T07:36:50.7097719Z If True, the conflicting values are appended to the error message. 2025-09-07T07:36:50.7097798Z 2025-09-07T07:36:50.7097886Z Raises 2025-09-07T07:36:50.7097966Z ------ 2025-09-07T07:36:50.7098053Z AssertionError 2025-09-07T07:36:50.7098258Z If actual and desired are not equal up to specified precision. 2025-09-07T07:36:50.7098365Z 2025-09-07T07:36:50.7098457Z See Also 2025-09-07T07:36:50.7098542Z -------- 2025-09-07T07:36:50.7098774Z assert_allclose: Compare two array_like objects for equality with desired 2025-09-07T07:36:50.7098911Z relative and/or absolute precision. 2025-09-07T07:36:50.7099137Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2025-09-07T07:36:50.7099227Z 2025-09-07T07:36:50.7099307Z Examples 2025-09-07T07:36:50.7099386Z -------- 2025-09-07T07:36:50.7099510Z >>> np.testing.assert_approx_equal( 2025-09-07T07:36:50.7099615Z ... 0.12345677777777e-20, 0.1234567e-20 2025-09-07T07:36:50.7099719Z ... ) # doctest: +SKIP 2025-09-07T07:36:50.7099831Z >>> np.testing.assert_approx_equal( 2025-09-07T07:36:50.7099916Z ... 0.12345670e-20, 2025-09-07T07:36:50.7100035Z ... 0.12345671e-20, # doctest: +SKIP 2025-09-07T07:36:50.7100126Z ... significant=8, 2025-09-07T07:36:50.7100205Z ... ) 2025-09-07T07:36:50.7100324Z >>> np.testing.assert_approx_equal( 2025-09-07T07:36:50.7100433Z ... 0.12345670e-20, 2025-09-07T07:36:50.7100548Z ... 0.12345672e-20, # doctest: +SKIP 2025-09-07T07:36:50.7100639Z ... significant=8, 2025-09-07T07:36:50.7100719Z ... ) 2025-09-07T07:36:50.7100836Z Traceback (most recent call last): 2025-09-07T07:36:50.7100914Z ... 2025-09-07T07:36:50.7101013Z AssertionError: 2025-09-07T07:36:50.7101140Z Items are not equal to 8 significant digits: 2025-09-07T07:36:50.7101229Z ACTUAL: 1.234567e-21 2025-09-07T07:36:50.7101327Z DESIRED: 1.2345672e-21 2025-09-07T07:36:50.7101405Z 2025-09-07T07:36:50.7101565Z the evaluated condition that raises the exception is 2025-09-07T07:36:50.7101652Z 2025-09-07T07:36:50.7101837Z >>> abs(0.12345670e-20 / 1e-21 - 0.12345672e-20 / 1e-21) >= 10 ** -(8 - 1) 2025-09-07T07:36:50.7101926Z True 2025-09-07T07:36:50.7102005Z 2025-09-07T07:36:50.7102081Z 2025-09-07T07:36:50.7102343Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.7102424Z 2025-09-07T07:36:50.7102530Z warnings.warn(msg) 2025-09-07T07:36:50.7102607Z 2025-09-07T07:36:50.7102794Z --- Parse Warning: 32 / 146 --- 2025-09-07T07:36:50.7103734Z /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=744. 2025-09-07T07:36:50.7103995Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.7104080Z 2025-09-07T07:36:50.7104314Z Raises an AssertionError if two array_like objects are not equal. 2025-09-07T07:36:50.7104391Z 2025-09-07T07:36:50.7104607Z Given two array_like objects, check that the shape is equal and all 2025-09-07T07:36:50.7104825Z elements of these objects are equal (but see the Notes for the special 2025-09-07T07:36:50.7105035Z handling of a scalar). An exception is raised at shape mismatch or 2025-09-07T07:36:50.7105251Z conflicting values. In contrast to the standard usage in numpy, NaNs 2025-09-07T07:36:50.7105463Z are compared like numbers, no assertion is raised if both objects have 2025-09-07T07:36:50.7105577Z NaNs in the same positions. 2025-09-07T07:36:50.7105655Z 2025-09-07T07:36:50.7105886Z The usual caution for verifying equality with floating point numbers is 2025-09-07T07:36:50.7105967Z advised. 2025-09-07T07:36:50.7106044Z 2025-09-07T07:36:50.7106138Z Parameters 2025-09-07T07:36:50.7106224Z ---------- 2025-09-07T07:36:50.7106321Z x : array_like 2025-09-07T07:36:50.7106425Z The actual object to check. 2025-09-07T07:36:50.7106510Z y : array_like 2025-09-07T07:36:50.7106627Z The desired, expected object. 2025-09-07T07:36:50.7106721Z err_msg : str, optional 2025-09-07T07:36:50.7106902Z The error message to be printed in case of failure. 2025-09-07T07:36:50.7107010Z verbose : bool, optional 2025-09-07T07:36:50.7107212Z If True, the conflicting values are appended to the error message. 2025-09-07T07:36:50.7107316Z strict : bool, optional 2025-09-07T07:36:50.7107537Z If True, raise an AssertionError when either the shape or the data 2025-09-07T07:36:50.7107712Z type of the array_like objects does not match. The special 2025-09-07T07:36:50.7107921Z handling for scalars mentioned in the Notes section is disabled. 2025-09-07T07:36:50.7107999Z 2025-09-07T07:36:50.7108092Z Raises 2025-09-07T07:36:50.7108172Z ------ 2025-09-07T07:36:50.7108261Z AssertionError 2025-09-07T07:36:50.7108404Z If actual and desired objects are not equal. 2025-09-07T07:36:50.7108481Z 2025-09-07T07:36:50.7108570Z See Also 2025-09-07T07:36:50.7108650Z -------- 2025-09-07T07:36:50.7108889Z assert_allclose: Compare two array_like objects for equality with desired 2025-09-07T07:36:50.7109024Z relative and/or absolute precision. 2025-09-07T07:36:50.7109252Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2025-09-07T07:36:50.7109341Z 2025-09-07T07:36:50.7109421Z Notes 2025-09-07T07:36:50.7109505Z ----- 2025-09-07T07:36:50.7109694Z When one of `x` and `y` is a scalar and the other is array_like, the 2025-09-07T07:36:50.7109916Z function checks that each element of the array_like object is equal to 2025-09-07T07:36:50.7110137Z the scalar. This behaviour can be disabled with the `strict` parameter. 2025-09-07T07:36:50.7110228Z 2025-09-07T07:36:50.7110309Z Examples 2025-09-07T07:36:50.7110400Z -------- 2025-09-07T07:36:50.7110534Z The first assert does not raise an exception: 2025-09-07T07:36:50.7110611Z 2025-09-07T07:36:50.7110735Z >>> np.testing.assert_array_equal( 2025-09-07T07:36:50.7110875Z ... [1.0, 2.33333, np.nan], [np.exp(0), 2.33333, np.nan] 2025-09-07T07:36:50.7110969Z ... ) 2025-09-07T07:36:50.7111052Z 2025-09-07T07:36:50.7111281Z Use `assert_allclose` or one of the nulp (number of floating point values) 2025-09-07T07:36:50.7111407Z functions for these cases instead: 2025-09-07T07:36:50.7111488Z 2025-09-07T07:36:50.7111609Z >>> np.testing.assert_allclose( 2025-09-07T07:36:50.7111802Z ... [1.0, np.pi, np.nan], [1, np.sqrt(np.pi) ** 2, np.nan], rtol=1e-10, atol=0 2025-09-07T07:36:50.7111883Z ... ) 2025-09-07T07:36:50.7111972Z 2025-09-07T07:36:50.7112171Z As mentioned in the Notes section, `assert_array_equal` has special 2025-09-07T07:36:50.7112394Z handling for scalars. Here the test checks that each value in `x` is 3: 2025-09-07T07:36:50.7112512Z 2025-09-07T07:36:50.7112619Z >>> x = np.full((2, 5), fill_value=3) 2025-09-07T07:36:50.7112745Z >>> np.testing.assert_array_equal(x, 3) 2025-09-07T07:36:50.7112822Z 2025-09-07T07:36:50.7113038Z Use `strict` to raise an AssertionError when comparing a scalar with an 2025-09-07T07:36:50.7113132Z array: 2025-09-07T07:36:50.7113208Z 2025-09-07T07:36:50.7113369Z >>> np.testing.assert_array_equal(x, 3, strict=True) 2025-09-07T07:36:50.7113479Z Traceback (most recent call last): 2025-09-07T07:36:50.7113559Z ... 2025-09-07T07:36:50.7113662Z AssertionError: 2025-09-07T07:36:50.7113757Z Arrays are not equal 2025-09-07T07:36:50.7113855Z 2025-09-07T07:36:50.7113954Z (shapes (2, 5), () mismatch) 2025-09-07T07:36:50.7114058Z x: torch.ndarray([[3, 3, 3, 3, 3], 2025-09-07T07:36:50.7114159Z [3, 3, 3, 3, 3]]) 2025-09-07T07:36:50.7114250Z y: torch.ndarray(3) 2025-09-07T07:36:50.7114325Z 2025-09-07T07:36:50.7114660Z The `strict` parameter also ensures that the array data types match: 2025-09-07T07:36:50.7114736Z 2025-09-07T07:36:50.7114841Z >>> x = np.array([2, 2, 2]) 2025-09-07T07:36:50.7114968Z >>> y = np.array([2.0, 2.0, 2.0], dtype=np.float32) 2025-09-07T07:36:50.7115148Z >>> np.testing.assert_array_equal(x, y, strict=True) 2025-09-07T07:36:50.7115275Z Traceback (most recent call last): 2025-09-07T07:36:50.7115353Z ... 2025-09-07T07:36:50.7115453Z AssertionError: 2025-09-07T07:36:50.7115545Z Arrays are not equal 2025-09-07T07:36:50.7115659Z 2025-09-07T07:36:50.7115811Z (dtypes dtype("int64"), dtype("float32") mismatch) 2025-09-07T07:36:50.7115911Z x: torch.ndarray([2, 2, 2]) 2025-09-07T07:36:50.7116023Z y: torch.ndarray([2., 2., 2.]) 2025-09-07T07:36:50.7116102Z 2025-09-07T07:36:50.7116353Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.7116439Z 2025-09-07T07:36:50.7116531Z warnings.warn(msg) 2025-09-07T07:36:50.7116605Z 2025-09-07T07:36:50.7116826Z --- Parse Warning: 33 / 146 --- 2025-09-07T07:36:50.7117832Z /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=851. 2025-09-07T07:36:50.7118105Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.7118188Z 2025-09-07T07:36:50.7118410Z Raises an AssertionError if two objects are not equal up to desired 2025-09-07T07:36:50.7118497Z precision. 2025-09-07T07:36:50.7118573Z 2025-09-07T07:36:50.7118758Z .. note:: It is recommended to use one of `assert_allclose`, 2025-09-07T07:36:50.7120231Z `assert_array_almost_equal_nulp` or `assert_array_max_ulp` 2025-09-07T07:36:50.7120431Z instead of this function for more consistent floating point 2025-09-07T07:36:50.7120527Z comparisons. 2025-09-07T07:36:50.7120604Z 2025-09-07T07:36:50.7120853Z The test verifies identical shapes and that the elements of ``actual`` and 2025-09-07T07:36:50.7120944Z ``desired`` satisfy. 2025-09-07T07:36:50.7121020Z 2025-09-07T07:36:50.7121162Z ``abs(desired-actual) < 1.5 * 10**(-decimal)`` 2025-09-07T07:36:50.7121241Z 2025-09-07T07:36:50.7121478Z That is a looser test than originally documented, but agrees with what the 2025-09-07T07:36:50.7121715Z actual implementation did up to rounding vagaries. An exception is raised 2025-09-07T07:36:50.7121944Z at shape mismatch or conflicting values. In contrast to the standard usage 2025-09-07T07:36:50.7122171Z in numpy, NaNs are compared like numbers, no assertion is raised if both 2025-09-07T07:36:50.7122291Z objects have NaNs in the same positions. 2025-09-07T07:36:50.7122381Z 2025-09-07T07:36:50.7122464Z Parameters 2025-09-07T07:36:50.7122547Z ---------- 2025-09-07T07:36:50.7122673Z x : array_like 2025-09-07T07:36:50.7122774Z The actual object to check. 2025-09-07T07:36:50.7122869Z y : array_like 2025-09-07T07:36:50.7122974Z The desired, expected object. 2025-09-07T07:36:50.7123069Z decimal : int, optional 2025-09-07T07:36:50.7123192Z Desired precision, default is 6. 2025-09-07T07:36:50.7123287Z err_msg : str, optional 2025-09-07T07:36:50.7123450Z The error message to be printed in case of failure. 2025-09-07T07:36:50.7123546Z verbose : bool, optional 2025-09-07T07:36:50.7123911Z If True, the conflicting values are appended to the error message. 2025-09-07T07:36:50.7124002Z 2025-09-07T07:36:50.7124084Z Raises 2025-09-07T07:36:50.7124167Z ------ 2025-09-07T07:36:50.7124269Z AssertionError 2025-09-07T07:36:50.7124463Z If actual and desired are not equal up to specified precision. 2025-09-07T07:36:50.7124555Z 2025-09-07T07:36:50.7124640Z See Also 2025-09-07T07:36:50.7124723Z -------- 2025-09-07T07:36:50.7124978Z assert_allclose: Compare two array_like objects for equality with desired 2025-09-07T07:36:50.7125105Z relative and/or absolute precision. 2025-09-07T07:36:50.7125406Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2025-09-07T07:36:50.7125486Z 2025-09-07T07:36:50.7125569Z Examples 2025-09-07T07:36:50.7125666Z -------- 2025-09-07T07:36:50.7125796Z the first assert does not raise an exception 2025-09-07T07:36:50.7125929Z 2025-09-07T07:36:50.7126180Z >>> np.testing.assert_array_almost_equal([1.0, 2.333, np.nan], [1.0, 2.333, np.nan]) 2025-09-07T07:36:50.7126259Z 2025-09-07T07:36:50.7126394Z >>> np.testing.assert_array_almost_equal( 2025-09-07T07:36:50.7126542Z ... [1.0, 2.33333, np.nan], [1.0, 2.33339, np.nan], decimal=5 2025-09-07T07:36:50.7126619Z ... ) 2025-09-07T07:36:50.7126740Z Traceback (most recent call last): 2025-09-07T07:36:50.7126822Z ... 2025-09-07T07:36:50.7126919Z AssertionError: 2025-09-07T07:36:50.7127037Z Arrays are not almost equal to 5 decimals 2025-09-07T07:36:50.7127118Z 2025-09-07T07:36:50.7127237Z Mismatched elements: 1 / 3 (33.3%) 2025-09-07T07:36:50.7127366Z Max absolute difference: 5.999999999994898e-05 2025-09-07T07:36:50.7127545Z Max relative difference: 2.5713661239633743e-05 2025-09-07T07:36:50.7127705Z x: torch.ndarray([1.0000, 2.3333, nan], dtype=float64) 2025-09-07T07:36:50.7127859Z y: torch.ndarray([1.0000, 2.3334, nan], dtype=float64) 2025-09-07T07:36:50.7127948Z 2025-09-07T07:36:50.7128066Z >>> np.testing.assert_array_almost_equal( 2025-09-07T07:36:50.7128207Z ... [1.0, 2.33333, np.nan], [1.0, 2.33333, 5], decimal=5 2025-09-07T07:36:50.7128285Z ... ) 2025-09-07T07:36:50.7128393Z Traceback (most recent call last): 2025-09-07T07:36:50.7128484Z ... 2025-09-07T07:36:50.7128576Z AssertionError: 2025-09-07T07:36:50.7128706Z Arrays are not almost equal to 5 decimals 2025-09-07T07:36:50.7128789Z 2025-09-07T07:36:50.7128892Z x and y nan location mismatch: 2025-09-07T07:36:50.7129063Z x: torch.ndarray([1.0000, 2.3333, nan], dtype=float64) 2025-09-07T07:36:50.7129217Z y: torch.ndarray([1.0000, 2.3333, 5.0000], dtype=float64) 2025-09-07T07:36:50.7129294Z 2025-09-07T07:36:50.7129383Z 2025-09-07T07:36:50.7129635Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.7129722Z 2025-09-07T07:36:50.7129818Z warnings.warn(msg) 2025-09-07T07:36:50.7129893Z 2025-09-07T07:36:50.7130118Z --- Parse Warning: 34 / 146 --- 2025-09-07T07:36:50.7131072Z /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=1848. 2025-09-07T07:36:50.7131381Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.7131594Z Context manager that resets warning registry for catching warnings 2025-09-07T07:36:50.7131671Z 2025-09-07T07:36:50.7131928Z Warnings can be slippery, because, whenever a warning is triggered, Python 2025-09-07T07:36:50.7132151Z adds a ``__warningregistry__`` member to the *calling* module. This makes 2025-09-07T07:36:50.7132395Z it impossible to retrigger the warning in this module, whatever you put in 2025-09-07T07:36:50.7132635Z the warnings filters. This context manager accepts a sequence of `modules` 2025-09-07T07:36:50.7132783Z as a keyword argument to its constructor and: 2025-09-07T07:36:50.7132862Z 2025-09-07T07:36:50.7133087Z * stores and removes any ``__warningregistry__`` entries in given `modules` 2025-09-07T07:36:50.7133182Z on entry; 2025-09-07T07:36:50.7133365Z * resets ``__warningregistry__`` to its previous state on exit. 2025-09-07T07:36:50.7133443Z 2025-09-07T07:36:50.7133678Z This makes it possible to trigger any warning afresh inside the context 2025-09-07T07:36:50.7133858Z manager without disturbing the state of warnings outside. 2025-09-07T07:36:50.7133980Z 2025-09-07T07:36:50.7134215Z For compatibility with Python 3.0, please consider all arguments to be 2025-09-07T07:36:50.7134308Z keyword-only. 2025-09-07T07:36:50.7134396Z 2025-09-07T07:36:50.7134484Z Parameters 2025-09-07T07:36:50.7134601Z ---------- 2025-09-07T07:36:50.7134700Z record : bool, optional 2025-09-07T07:36:50.7134885Z Specifies whether warnings should be captured by a custom 2025-09-07T07:36:50.7135127Z implementation of ``warnings.showwarning()`` and be appended to a list 2025-09-07T07:36:50.7135351Z returned by the context manager. Otherwise None is returned by the 2025-09-07T07:36:50.7135592Z context manager. The objects appended to the list are arguments whose 2025-09-07T07:36:50.7135758Z attributes mirror the arguments to ``showwarning()``. 2025-09-07T07:36:50.7135860Z modules : sequence, optional 2025-09-07T07:36:50.7136087Z Sequence of modules for which to reset warnings registry on entry and 2025-09-07T07:36:50.7136302Z restore on exit. To work correctly, all 'ignore' filters should 2025-09-07T07:36:50.7136421Z filter by one of these modules. 2025-09-07T07:36:50.7136498Z 2025-09-07T07:36:50.7136578Z Examples 2025-09-07T07:36:50.7136671Z -------- 2025-09-07T07:36:50.7136765Z >>> import warnings 2025-09-07T07:36:50.7136964Z >>> with np.testing.clear_and_catch_warnings( # doctest: +SKIP 2025-09-07T07:36:50.7137080Z ... modules=[np.core.fromnumeric] 2025-09-07T07:36:50.7137160Z ... ): 2025-09-07T07:36:50.7137295Z ... warnings.simplefilter("always") 2025-09-07T07:36:50.7137521Z ... warnings.filterwarnings("ignore", module="np.core.fromnumeric") 2025-09-07T07:36:50.7137705Z ... # do something that raises a warning but ignore those in 2025-09-07T07:36:50.7137810Z ... # np.core.fromnumeric 2025-09-07T07:36:50.7137892Z 2025-09-07T07:36:50.7138153Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.7138236Z 2025-09-07T07:36:50.7138331Z warnings.warn(msg) 2025-09-07T07:36:50.7138422Z 2025-09-07T07:36:50.7138619Z --- Parse Warning: 35 / 146 --- 2025-09-07T07:36:50.7139557Z /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. 2025-09-07T07:36:50.7139820Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.7140041Z Applies a 1D convolution over a quantized input signal composed of 2025-09-07T07:36:50.7140188Z several quantized input planes. 2025-09-07T07:36:50.7140269Z 2025-09-07T07:36:50.7140492Z For details on input arguments, parameters, and implementation see 2025-09-07T07:36:50.7140599Z :class:`~torch.nn.Conv1d`. 2025-09-07T07:36:50.7140692Z 2025-09-07T07:36:50.7140782Z .. note:: 2025-09-07T07:36:50.7140981Z Only `zeros` is supported for the :attr:`padding_mode` argument. 2025-09-07T07:36:50.7141076Z 2025-09-07T07:36:50.7141161Z .. note:: 2025-09-07T07:36:50.7141340Z Only `torch.quint8` is supported for the input data type. 2025-09-07T07:36:50.7141440Z 2025-09-07T07:36:50.7141518Z 2025-09-07T07:36:50.7141620Z Attributes: 2025-09-07T07:36:50.7141827Z weight (Tensor): packed tensor derived from the learnable weight 2025-09-07T07:36:50.7141931Z parameter. 2025-09-07T07:36:50.7142090Z scale (Tensor): scalar for the output scale 2025-09-07T07:36:50.7142254Z zero_point (Tensor): scalar for the output zero point 2025-09-07T07:36:50.7142347Z 2025-09-07T07:36:50.7142498Z See :class:`~torch.nn.Conv1d` for other attributes. 2025-09-07T07:36:50.7142602Z 2025-09-07T07:36:50.7142705Z Examples:: 2025-09-07T07:36:50.7142787Z 2025-09-07T07:36:50.7142952Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_QENGINE) 2025-09-07T07:36:50.7143082Z >>> m = nn.quantized.Conv1d(16, 33, 3, stride=2) 2025-09-07T07:36:50.7143218Z >>> input = torch.randn(20, 16, 100) 2025-09-07T07:36:50.7143394Z >>> # quantize input to quint8 2025-09-07T07:36:50.7143492Z >>> # xdoctest: +SKIP 2025-09-07T07:36:50.7143717Z >>> q_input = torch.quantize_per_tensor(input, scale=1.0, zero_point=0, 2025-09-07T07:36:50.7143842Z ... dtype=torch.quint8) 2025-09-07T07:36:50.7143941Z >>> output = m(q_input) 2025-09-07T07:36:50.7144035Z 2025-09-07T07:36:50.7144113Z 2025-09-07T07:36:50.7144362Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.7144451Z 2025-09-07T07:36:50.7144546Z warnings.warn(msg) 2025-09-07T07:36:50.7144634Z 2025-09-07T07:36:50.7144845Z --- Parse Warning: 36 / 146 --- 2025-09-07T07:36:50.7145743Z /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=12. 2025-09-07T07:36:50.7146017Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.7146142Z A quantized long short-term memory (LSTM). 2025-09-07T07:36:50.7146231Z 2025-09-07T07:36:50.7146509Z For the description and the argument types, please, refer to :class:`~torch.nn.LSTM` 2025-09-07T07:36:50.7146597Z 2025-09-07T07:36:50.7146685Z Attributes: 2025-09-07T07:36:50.7146806Z layers : instances of the `_LSTMLayer` 2025-09-07T07:36:50.7146893Z 2025-09-07T07:36:50.7146977Z .. note:: 2025-09-07T07:36:50.7147188Z To access the weights and biases, you need to access them per layer. 2025-09-07T07:36:50.7147373Z See examples in :class:`~torch.ao.nn.quantizable.LSTM` 2025-09-07T07:36:50.7147452Z 2025-09-07T07:36:50.7147553Z Examples:: 2025-09-07T07:36:50.7147649Z >>> # xdoctest: +SKIP 2025-09-07T07:36:50.7147759Z >>> custom_module_config = { 2025-09-07T07:36:50.7147909Z ... 'float_to_observed_custom_module_class': { 2025-09-07T07:36:50.7148033Z ... nn.LSTM: nn.quantizable.LSTM, 2025-09-07T07:36:50.7148132Z ... }, 2025-09-07T07:36:50.7148280Z ... 'observed_to_quantized_custom_module_class': { 2025-09-07T07:36:50.7148449Z ... nn.quantizable.LSTM: nn.quantized.LSTM, 2025-09-07T07:36:50.7148545Z ... } 2025-09-07T07:36:50.7148625Z ... } 2025-09-07T07:36:50.7148849Z >>> tq.prepare(model, prepare_custom_module_class=custom_module_config) 2025-09-07T07:36:50.7149062Z >>> tq.convert(model, convert_custom_module_class=custom_module_config) 2025-09-07T07:36:50.7149141Z 2025-09-07T07:36:50.7149400Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.7149478Z 2025-09-07T07:36:50.7149584Z warnings.warn(msg) 2025-09-07T07:36:50.7149659Z 2025-09-07T07:36:50.7149843Z --- Parse Warning: 37 / 146 --- 2025-09-07T07:36:50.7151038Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ActivationSparsifier in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/pruning/_experimental/activation_sparsifier/activation_sparsifier.py line=16. 2025-09-07T07:36:50.7151300Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.7151389Z 2025-09-07T07:36:50.7151649Z The Activation sparsifier class aims to sparsify/prune activations in a neural 2025-09-07T07:36:50.7151903Z network. The idea is to attach the sparsifier to a layer (or layers) and it 2025-09-07T07:36:50.7152162Z zeroes out the activations based on the mask_fn (or sparsification function) 2025-09-07T07:36:50.7152254Z input by the user. 2025-09-07T07:36:50.7152489Z The mask_fn is applied once all the inputs are aggregated and reduced i.e. 2025-09-07T07:36:50.7152681Z mask = mask_fn(reduce_fn(aggregate_fn(activations))) 2025-09-07T07:36:50.7152758Z 2025-09-07T07:36:50.7152855Z Note:: 2025-09-07T07:36:50.7153175Z The sparsification mask is computed on the input **before it goes through the attached layer**. 2025-09-07T07:36:50.7153267Z 2025-09-07T07:36:50.7153347Z Args: 2025-09-07T07:36:50.7153442Z model (nn.Module): 2025-09-07T07:36:50.7153676Z The model whose layers will be sparsified. The layers that needs to be 2025-09-07T07:36:50.7153917Z sparsified should be added separately using the register_layer() function 2025-09-07T07:36:50.7154046Z aggregate_fn (Optional, Callable): 2025-09-07T07:36:50.7154330Z default aggregate_fn that is used if not specified while registering the layer. 2025-09-07T07:36:50.7154574Z specifies how inputs should be aggregated over time. 2025-09-07T07:36:50.7154876Z The aggregate_fn should usually take 2 torch tensors and return the aggregated tensor. 2025-09-07T07:36:50.7154960Z Example 2025-09-07T07:36:50.7155147Z def add_agg_fn(tensor1, tensor2): return tensor1 + tensor2 2025-09-07T07:36:50.7155267Z reduce_fn (Optional, Callable): 2025-09-07T07:36:50.7155509Z default reduce_fn that is used if not specified while registering the layer. 2025-09-07T07:36:50.7155776Z reduce_fn will be called on the aggregated tensor i.e. the tensor obtained after 2025-09-07T07:36:50.7155895Z calling agg_fn() on all inputs. 2025-09-07T07:36:50.7155999Z Example 2025-09-07T07:36:50.7156196Z def mean_reduce_fn(agg_tensor): return agg_tensor.mean(dim=0) 2025-09-07T07:36:50.7156310Z mask_fn (Optional, Callable): 2025-09-07T07:36:50.7156633Z default mask_fn that is used to create the sparsification mask using the tensor obtained after 2025-09-07T07:36:50.7156886Z calling the reduce_fn(). This is used by default if a custom one is passed in the 2025-09-07T07:36:50.7157000Z register_layer(). 2025-09-07T07:36:50.7157347Z Note that the mask_fn() definition should contain the sparse arguments that is passed in sparse_config 2025-09-07T07:36:50.7157452Z arguments. 2025-09-07T07:36:50.7157593Z features (Optional, list): 2025-09-07T07:36:50.7157724Z default selected features to sparsify. 2025-09-07T07:36:50.7158000Z If this is non-empty, then the mask_fn will be applied for each feature of the input. 2025-09-07T07:36:50.7158097Z For example, 2025-09-07T07:36:50.7158362Z mask = [mask_fn(reduce_fn(aggregated_fn(input[feature])) for feature in features] 2025-09-07T07:36:50.7158474Z feature_dim (Optional, int): 2025-09-07T07:36:50.7158747Z default dimension of input features. Again, features along this dim will be chosen 2025-09-07T07:36:50.7158862Z for sparsification. 2025-09-07T07:36:50.7158962Z sparse_config (Dict): 2025-09-07T07:36:50.7159189Z Default configuration for the mask_fn. This config will be passed 2025-09-07T07:36:50.7159288Z with the mask_fn() 2025-09-07T07:36:50.7159365Z 2025-09-07T07:36:50.7159466Z Example: 2025-09-07T07:36:50.7159559Z >>> # xdoctest: +SKIP 2025-09-07T07:36:50.7159656Z >>> model = SomeModel() 2025-09-07T07:36:50.7159907Z >>> act_sparsifier = ActivationSparsifier(...) # init activation sparsifier 2025-09-07T07:36:50.7160040Z >>> # Initialize aggregate_fn 2025-09-07T07:36:50.7160145Z >>> def agg_fn(x, y): 2025-09-07T07:36:50.7160234Z >>> return x + y 2025-09-07T07:36:50.7160312Z >>> 2025-09-07T07:36:50.7160424Z >>> # Initialize reduce_fn 2025-09-07T07:36:50.7160541Z >>> def reduce_fn(x): 2025-09-07T07:36:50.7160662Z >>> return torch.mean(x, dim=0) 2025-09-07T07:36:50.7160741Z >>> 2025-09-07T07:36:50.7160837Z >>> # Initialize mask_fn 2025-09-07T07:36:50.7160941Z >>> def mask_fn(data): 2025-09-07T07:36:50.7161084Z >>> return torch.eye(data.shape).to(data.device) 2025-09-07T07:36:50.7161176Z >>> 2025-09-07T07:36:50.7161255Z >>> 2025-09-07T07:36:50.7161371Z >>> act_sparsifier.register_layer( 2025-09-07T07:36:50.7161478Z ... model.some_layer, 2025-09-07T07:36:50.7161576Z ... aggregate_fn=agg_fn, 2025-09-07T07:36:50.7161673Z ... reduce_fn=reduce_fn, 2025-09-07T07:36:50.7161777Z ... mask_fn=mask_fn, 2025-09-07T07:36:50.7161853Z ... ) 2025-09-07T07:36:50.7161970Z >>> 2025-09-07T07:36:50.7162070Z >>> # start training process 2025-09-07T07:36:50.7162161Z >>> for _ in [...]: 2025-09-07T07:36:50.7162265Z >>> # epoch starts 2025-09-07T07:36:50.7162434Z >>> # model.forward(), compute_loss() and model.backwards() 2025-09-07T07:36:50.7162537Z >>> # epoch ends 2025-09-07T07:36:50.7162644Z >>> act_sparsifier.step() 2025-09-07T07:36:50.7162744Z >>> # end training process 2025-09-07T07:36:50.7162862Z >>> sparsifier.squash_mask() 2025-09-07T07:36:50.7162939Z 2025-09-07T07:36:50.7163209Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.7163286Z 2025-09-07T07:36:50.7163381Z warnings.warn(msg) 2025-09-07T07:36:50.7163471Z 2025-09-07T07:36:50.7163677Z --- Parse Warning: 38 / 146 --- 2025-09-07T07:36:50.7164896Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=BaseDataScheduler.get_schedule_param in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/pruning/_experimental/data_scheduler/base_data_scheduler.py line=91. 2025-09-07T07:36:50.7165156Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.7165234Z 2025-09-07T07:36:50.7165451Z Abstract method that needs to be implemented by the child class. 2025-09-07T07:36:50.7165702Z The expected return type should is a dictionary of name to schedule_param value 2025-09-07T07:36:50.7166008Z The returned values will be updated in sparsifier when the scheduler step() function 2025-09-07T07:36:50.7166092Z is called. 2025-09-07T07:36:50.7166171Z 2025-09-07T07:36:50.7166263Z Example: 2025-09-07T07:36:50.7166375Z >>> def get_schedule_param(self): 2025-09-07T07:36:50.7166480Z ... new_param = {} 2025-09-07T07:36:50.7166633Z ... for name in self.sparsifier.data_groups.keys(): 2025-09-07T07:36:50.7166732Z ... new_param[name] = ( 2025-09-07T07:36:50.7166952Z ... self.sparsifier.data_groups[name][self.schedule_param] * 0.5 2025-09-07T07:36:50.7167038Z ... ) 2025-09-07T07:36:50.7167146Z ... return new_param 2025-09-07T07:36:50.7167224Z 2025-09-07T07:36:50.7167566Z When the step() function is called, the value in self.sparsifier.data_groups[name][self.schedule_param] 2025-09-07T07:36:50.7167670Z would be halved 2025-09-07T07:36:50.7167748Z 2025-09-07T07:36:50.7168000Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.7168093Z 2025-09-07T07:36:50.7168190Z warnings.warn(msg) 2025-09-07T07:36:50.7168281Z 2025-09-07T07:36:50.7168468Z --- Parse Warning: 39 / 146 --- 2025-09-07T07:36:50.7169573Z /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=229. 2025-09-07T07:36:50.7169846Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.7170049Z Squashes the sparse masks into the appropriate tensors. 2025-09-07T07:36:50.7170141Z 2025-09-07T07:36:50.7170348Z If either the `params_to_keep` or `params_to_keep_per_layer` is set, 2025-09-07T07:36:50.7170545Z the module will have a `sparse_params` dict attached to it. 2025-09-07T07:36:50.7170626Z 2025-09-07T07:36:50.7170709Z Args: 2025-09-07T07:36:50.7170908Z params_to_keep: List of keys to save in the module or a dict 2025-09-07T07:36:50.7171068Z representing the modules and keys that will have 2025-09-07T07:36:50.7171211Z sparsity parameters saved 2025-09-07T07:36:50.7171449Z params_to_keep_per_layer: Dict to specify the params that should be 2025-09-07T07:36:50.7171601Z saved for specific layers. The keys in the dict 2025-09-07T07:36:50.7171769Z should be the module fqn, while the values should 2025-09-07T07:36:50.7171927Z be a list of strings with the names of the variables 2025-09-07T07:36:50.7172064Z to save in the `sparse_params` 2025-09-07T07:36:50.7172142Z 2025-09-07T07:36:50.7172232Z Examples: 2025-09-07T07:36:50.7172376Z >>> # xdoctest: +SKIP("locals are undefined") 2025-09-07T07:36:50.7172498Z >>> # Don't save any sparse params 2025-09-07T07:36:50.7172620Z >>> sparsifier.squash_mask() 2025-09-07T07:36:50.7172758Z >>> hasattr(model.submodule1, "sparse_params") 2025-09-07T07:36:50.7172842Z False 2025-09-07T07:36:50.7172929Z 2025-09-07T07:36:50.7173046Z >>> # Keep sparse params per layer 2025-09-07T07:36:50.7173153Z >>> sparsifier.squash_mask( 2025-09-07T07:36:50.7173281Z ... params_to_keep_per_layer={ 2025-09-07T07:36:50.7173415Z ... "submodule1.linear1": ("foo", "bar"), 2025-09-07T07:36:50.7173551Z ... "submodule2.linear42": ("baz",), 2025-09-07T07:36:50.7173683Z ... } 2025-09-07T07:36:50.7173765Z ... ) 2025-09-07T07:36:50.7173928Z >>> print(model.submodule1.linear1.sparse_params) 2025-09-07T07:36:50.7174026Z {'foo': 42, 'bar': 24} 2025-09-07T07:36:50.7174220Z >>> print(model.submodule2.linear42.sparse_params) 2025-09-07T07:36:50.7174311Z {'baz': 0.1} 2025-09-07T07:36:50.7174388Z 2025-09-07T07:36:50.7174523Z >>> # Keep sparse params for all layers 2025-09-07T07:36:50.7174693Z >>> sparsifier.squash_mask(params_to_keep=("foo", "bar")) 2025-09-07T07:36:50.7174856Z >>> print(model.submodule1.linear1.sparse_params) 2025-09-07T07:36:50.7174951Z {'foo': 42, 'bar': 24} 2025-09-07T07:36:50.7175106Z >>> print(model.submodule2.linear42.sparse_params) 2025-09-07T07:36:50.7175212Z {'foo': 42, 'bar': 24} 2025-09-07T07:36:50.7175290Z 2025-09-07T07:36:50.7175496Z >>> # Keep some sparse params for all layers, and specific ones for 2025-09-07T07:36:50.7175594Z >>> # some other layers 2025-09-07T07:36:50.7175704Z >>> sparsifier.squash_mask( 2025-09-07T07:36:50.7175834Z ... params_to_keep=("foo", "bar"), 2025-09-07T07:36:50.7176026Z ... params_to_keep_per_layer={"submodule2.linear42": ("baz",)}, 2025-09-07T07:36:50.7176124Z ... ) 2025-09-07T07:36:50.7176299Z >>> print(model.submodule1.linear1.sparse_params) 2025-09-07T07:36:50.7176395Z {'foo': 42, 'bar': 24} 2025-09-07T07:36:50.7176564Z >>> print(model.submodule2.linear42.sparse_params) 2025-09-07T07:36:50.7176670Z {'foo': 42, 'bar': 24, 'baz': 0.1} 2025-09-07T07:36:50.7176825Z 2025-09-07T07:36:50.7177078Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.7177154Z 2025-09-07T07:36:50.7177260Z warnings.warn(msg) 2025-09-07T07:36:50.7177343Z 2025-09-07T07:36:50.7177543Z --- Parse Warning: 40 / 146 --- 2025-09-07T07:36:50.7178588Z /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. 2025-09-07T07:36:50.7178850Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.7178944Z 2025-09-07T07:36:50.7179223Z Config object that specifies the supported data types passed as arguments to 2025-09-07T07:36:50.7179472Z quantize ops in the reference model spec, for input and output activations, 2025-09-07T07:36:50.7179567Z weights, and biases. 2025-09-07T07:36:50.7179646Z 2025-09-07T07:36:50.7179814Z For example, consider the following reference model: 2025-09-07T07:36:50.7179891Z 2025-09-07T07:36:50.7180060Z quant1 - [dequant1 - fp32_linear - quant2] - dequant2 2025-09-07T07:36:50.7180136Z 2025-09-07T07:36:50.7180346Z The pattern in the square brackets refers to the reference pattern of 2025-09-07T07:36:50.7180632Z statically quantized linear. Setting the input dtype as `torch.quint8` 2025-09-07T07:36:50.7180857Z in the DTypeConfig means we pass in `torch.quint8` as the dtype argument 2025-09-07T07:36:50.7181087Z to the first quantize op (quant1). Similarly, setting the output dtype as 2025-09-07T07:36:50.7181307Z `torch.quint8` means we pass in `torch.quint8` as the dtype argument to 2025-09-07T07:36:50.7181413Z the second quantize op (quant2). 2025-09-07T07:36:50.7181503Z 2025-09-07T07:36:50.7181720Z Note that the dtype here does not refer to the interface dtypes of the 2025-09-07T07:36:50.7181935Z op. For example, the "input dtype" here is not the dtype of the input 2025-09-07T07:36:50.7182148Z tensor passed to the quantized linear op. Though it can still be the 2025-09-07T07:36:50.7182344Z same as the interface dtype, this is not always the case, e.g. the 2025-09-07T07:36:50.7182572Z interface dtype is fp32 in dynamic quantization but the "input dtype" 2025-09-07T07:36:50.7182807Z specified in the DTypeConfig would still be quint8. The semantics of 2025-09-07T07:36:50.7183027Z dtypes here are the same as the semantics of the dtypes specified in 2025-09-07T07:36:50.7183116Z the observers. 2025-09-07T07:36:50.7183196Z 2025-09-07T07:36:50.7183412Z These dtypes are matched against the ones specified in the user's 2025-09-07T07:36:50.7183626Z QConfig. If there is a match, and the QConfig satisfies the constraints 2025-09-07T07:36:50.7183854Z specified in the DTypeConfig (if any), then we will quantize the given 2025-09-07T07:36:50.7184076Z pattern using this DTypeConfig. Otherwise, the QConfig is ignored and 2025-09-07T07:36:50.7184188Z the pattern will not be quantized. 2025-09-07T07:36:50.7184277Z 2025-09-07T07:36:50.7184373Z Example usage:: 2025-09-07T07:36:50.7184462Z 2025-09-07T07:36:50.7184565Z >>> # xdoctest: +SKIP(failing) 2025-09-07T07:36:50.7184671Z >>> dtype_config1 = DTypeConfig( 2025-09-07T07:36:50.7184789Z ... input_dtype=torch.quint8, 2025-09-07T07:36:50.7184901Z ... output_dtype=torch.quint8, 2025-09-07T07:36:50.7185020Z ... weight_dtype=torch.qint8, 2025-09-07T07:36:50.7185122Z ... bias_dtype=torch.float) 2025-09-07T07:36:50.7185236Z 2025-09-07T07:36:50.7185355Z >>> dtype_config2 = DTypeConfig( 2025-09-07T07:36:50.7185484Z ... input_dtype=DTypeWithConstraints( 2025-09-07T07:36:50.7185583Z ... dtype=torch.quint8, 2025-09-07T07:36:50.7185700Z ... quant_min_lower_bound=0, 2025-09-07T07:36:50.7185837Z ... quant_max_upper_bound=255, 2025-09-07T07:36:50.7185929Z ... ), 2025-09-07T07:36:50.7186056Z ... output_dtype=DTypeWithConstraints( 2025-09-07T07:36:50.7186154Z ... dtype=torch.quint8, 2025-09-07T07:36:50.7186272Z ... quant_min_lower_bound=0, 2025-09-07T07:36:50.7186383Z ... quant_max_upper_bound=255, 2025-09-07T07:36:50.7186474Z ... ), 2025-09-07T07:36:50.7186600Z ... weight_dtype=DTypeWithConstraints( 2025-09-07T07:36:50.7186699Z ... dtype=torch.qint8, 2025-09-07T07:36:50.7186875Z ... quant_min_lower_bound=-128, 2025-09-07T07:36:50.7186986Z ... quant_max_upper_bound=127, 2025-09-07T07:36:50.7187079Z ... ), 2025-09-07T07:36:50.7187203Z ... bias_dtype=torch.float) 2025-09-07T07:36:50.7187284Z 2025-09-07T07:36:50.7187404Z >>> dtype_config1.input_dtype 2025-09-07T07:36:50.7187494Z torch.quint8 2025-09-07T07:36:50.7187573Z 2025-09-07T07:36:50.7187687Z >>> dtype_config2.input_dtype 2025-09-07T07:36:50.7187773Z torch.quint8 2025-09-07T07:36:50.7187861Z 2025-09-07T07:36:50.7187996Z >>> dtype_config2.input_dtype_with_constraints 2025-09-07T07:36:50.7188541Z DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=0, quant_max_upper_bound=255, scale_min_lower_bound=None, scale_max_upper_bound=None) 2025-09-07T07:36:50.7188631Z 2025-09-07T07:36:50.7188885Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.7188974Z 2025-09-07T07:36:50.7189070Z warnings.warn(msg) 2025-09-07T07:36:50.7189149Z 2025-09-07T07:36:50.7189351Z --- Parse Warning: 41 / 146 --- 2025-09-07T07:36:50.7190387Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ModelReport in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fx/_model_report/model_report.py line=24. 2025-09-07T07:36:50.7190659Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.7190736Z 2025-09-07T07:36:50.7191040Z The ModelReport class aims to provide users an easy way to diagnose issues that they run into 2025-09-07T07:36:50.7191348Z with their models. The class works with all traceable GraphModules to help diagnose issues, 2025-09-07T07:36:50.7191673Z though the requirements on the type of model more-so depends on the specific report the user 2025-09-07T07:36:50.7191986Z is trying to generate. With respect to the reports, the ModelReport class is initialized with 2025-09-07T07:36:50.7192260Z a set of Detector classes, each of which generate reports on quantization configuration 2025-09-07T07:36:50.7192376Z issues a use might have. 2025-09-07T07:36:50.7192453Z 2025-09-07T07:36:50.7192582Z Currently supports generating reports on: 2025-09-07T07:36:50.7192823Z - Suggestions for per-channel vs. per-tensor quantization (nn.Module) 2025-09-07T07:36:50.7193090Z - Suggestions for dynamic vs static quantization for linear layers (Graph Modules) 2025-09-07T07:36:50.7193395Z - Suggestions for input-weight equalization for linear and conv layers (Graph Modules) 2025-09-07T07:36:50.7193604Z - Suggestions for outlier detection for all layers (Graph Modules) 2025-09-07T07:36:50.7193684Z 2025-09-07T07:36:50.7194105Z The ModelReport class has the primary functionality of inserting observers (primarily the ModelReportObserver) 2025-09-07T07:36:50.7194561Z where needed for each detector to gather the information it needs, and then after calibration, the ModelReport 2025-09-07T07:36:50.7194986Z class compiles the report generated by each Detector class into a single report to return to the user. It also 2025-09-07T07:36:50.7195197Z has the capability to remove all the observers it inserted as well. 2025-09-07T07:36:50.7195300Z 2025-09-07T07:36:50.7195597Z * :attr:`_model` The model we wish to generate the report for. Must be a traceable GraphModule 2025-09-07T07:36:50.7195674Z 2025-09-07T07:36:50.7196061Z * :attr:`_desired_report_detectors` The set of Detectors representing desired reports from the ModelReport class 2025-09-07T07:36:50.7196377Z Make sure that these are all unique types of detectors [do not have more than 1 of the same class] 2025-09-07T07:36:50.7196454Z 2025-09-07T07:36:50.7196754Z * :attr:`_desired_detector_names` The set of detector names of the _desired_report_detectors. 2025-09-07T07:36:50.7196978Z This set is generated by calling the get_detector_name() of each detector 2025-09-07T07:36:50.7197070Z 2025-09-07T07:36:50.7197431Z * :attr:`_detector_name_to_observer_fqns` The mapping from each detector to fqns of observers of interest 2025-09-07T07:36:50.7197759Z The purpose of this is to keep track of what observers were inserted for each detector, so that they 2025-09-07T07:36:50.7197876Z can be removed at the end if desired 2025-09-07T07:36:50.7197951Z 2025-09-07T07:36:50.7198281Z * :attr:`_prepared_flag` A boolean flag that keeps track of whether we have prepared the model or not 2025-09-07T07:36:50.7198511Z This is to ensure we only insert observers once with the ModelReport instance 2025-09-07T07:36:50.7198600Z 2025-09-07T07:36:50.7198855Z * :attr:`_removed_observers` A boolean to track if we have removed observers already 2025-09-07T07:36:50.7199139Z The purpose is to ensure we don't attempt to remove observers twice with the same ModelReport 2025-09-07T07:36:50.7199702Z instance. This also allows the functionality where we can generate the report multiple times 2025-09-07T07:36:50.7199944Z as long as we haven't removed the observers yet. 2025-09-07T07:36:50.7200065Z 2025-09-07T07:36:50.7200179Z Note: 2025-09-07T07:36:50.7200554Z This class was initially designed to work with the Fx Graph Mode workflow in mind. However, 2025-09-07T07:36:50.7200886Z full functionality is available as long as there is a traceable GraphModule that is being used. 2025-09-07T07:36:50.7201361Z One method to get a traceable GraphModule without going through the Fx workflow is to use 2025-09-07T07:36:50.7201514Z the QuantizationTracer class. 2025-09-07T07:36:50.7201666Z 2025-09-07T07:36:50.7201800Z General Flow for Fx workflow: 2025-09-07T07:36:50.7202250Z 1.) Initialize ModelReport object with reports of interest by passing in initialized detector objects and model 2025-09-07T07:36:50.7202460Z 2.) Prepare your model with prepare_fx 2025-09-07T07:36:50.7202761Z 3.) Call model_report.prepare_detailed_calibration to add relevant observers 2025-09-07T07:36:50.7202946Z 4.) Calibrate your model with data 2025-09-07T07:36:50.7203335Z 5.) Call model_report.generate_report on your model to generate report and optionally remove added observers 2025-09-07T07:36:50.7203453Z Optional 2025-09-07T07:36:50.7203778Z 6.) Call model_report.generate_visualizer to get a ModelReportVisualizer instance 2025-09-07T07:36:50.7204087Z 7.) To help in parsing report information and debugging, view report info as a: 2025-09-07T07:36:50.7204299Z - Table 2025-09-07T07:36:50.7204419Z - Histogram 2025-09-07T07:36:50.7204539Z - Line plot 2025-09-07T07:36:50.7204930Z 8.) Call model_report.generate_qconfigs to generate the qconfigs based on the report suggestions 2025-09-07T07:36:50.7205021Z 2025-09-07T07:36:50.7205272Z Example (with QuantizationTracer): 2025-09-07T07:36:50.7205400Z >>> # xdoctest: +SKIP 2025-09-07T07:36:50.7205566Z >>> # get the necessary qconfig 2025-09-07T07:36:50.7205755Z >>> config = PrepareCustomConfig() 2025-09-07T07:36:50.7205942Z >>> skipped_module_names, skipped_module_classes = ( 2025-09-07T07:36:50.7206192Z ... get_skipped_module_name_and_classes(config, False) 2025-09-07T07:36:50.7206348Z ... ) 2025-09-07T07:36:50.7206456Z 2025-09-07T07:36:50.7206660Z >>> # initialize our model and get GraphModule 2025-09-07T07:36:50.7206788Z >>> model = SomeModel() 2025-09-07T07:36:50.7207102Z >>> tracer = QuantizationTracer(skipped_module_names, skipped_module_classes) 2025-09-07T07:36:50.7207331Z >>> graph_module = GraphModule(model, tracer.trace(model)) 2025-09-07T07:36:50.7207459Z 2025-09-07T07:36:50.7207688Z >>> # get our set of detectors and ModelReport instance 2025-09-07T07:36:50.7207816Z >>> detector_set = set( 2025-09-07T07:36:50.7207977Z ... [ 2025-09-07T07:36:50.7208129Z ... DynamicStaticDetector(tolerance=0.5), 2025-09-07T07:36:50.7208433Z ... InputWeightEqualizationDetector(ratio_threshold=0.7), 2025-09-07T07:36:50.7208605Z ... ] 2025-09-07T07:36:50.7208715Z ... ) 2025-09-07T07:36:50.7209004Z >>> tracer_reporter = ModelReport(graph_module, tracer_detector_set) 2025-09-07T07:36:50.7209119Z 2025-09-07T07:36:50.7209284Z >>> # now we insert the observers and calibrate the model 2025-09-07T07:36:50.7209660Z >>> tracer_model_with_observers = tracer_reporter.prepare_detailed_calibration() 2025-09-07T07:36:50.7209825Z >>> for i in range(num_callibration_batches): 2025-09-07T07:36:50.7210038Z >>> example_input = get_callibration_input() 2025-09-07T07:36:50.7210208Z >>> tracer_model_with_observers(example_input) 2025-09-07T07:36:50.7210316Z 2025-09-07T07:36:50.7210659Z >>> # finally we generate the reports and optionally remove the observers we inserted 2025-09-07T07:36:50.7210860Z >>> reports = tracer_reporter.generate_model_report( 2025-09-07T07:36:50.7211049Z ... remove_inserted_observers=True 2025-09-07T07:36:50.7211169Z ... ) 2025-09-07T07:36:50.7211278Z 2025-09-07T07:36:50.7211556Z >>> # Optional: we can generate the qconfig mapping based on the suggestions 2025-09-07T07:36:50.7211774Z >>> qconfigs = model_report.generate_qconfig_mapping() 2025-09-07T07:36:50.7211942Z 2025-09-07T07:36:50.7212223Z >>> # Optional: we can generate the equalization mapping based on the suggestions 2025-09-07T07:36:50.7212427Z >>> qconfigs = model_report.generate_equalization_mapping() 2025-09-07T07:36:50.7212609Z 2025-09-07T07:36:50.7212929Z >>> # Optional: we get a ModelReportVisualizer instance to do any visualizations desired 2025-09-07T07:36:50.7213256Z >>> model_report_visualizer = tracer_reporter.generate_visualizer() 2025-09-07T07:36:50.7213374Z 2025-09-07T07:36:50.7213484Z 2025-09-07T07:36:50.7213812Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.7213921Z 2025-09-07T07:36:50.7214106Z warnings.warn(msg) 2025-09-07T07:36:50.7214232Z 2025-09-07T07:36:50.7214495Z --- Parse Warning: 42 / 146 --- 2025-09-07T07:36:50.7215837Z /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. 2025-09-07T07:36:50.7216129Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.7216260Z 2025-09-07T07:36:50.7216600Z Takes in optional filter values and generates two tables with desired information. 2025-09-07T07:36:50.7216777Z 2025-09-07T07:36:50.7217017Z The generated tables are presented in both a list-of-lists format 2025-09-07T07:36:50.7217174Z 2025-09-07T07:36:50.7217458Z The reason for the two tables are that they handle different things: 2025-09-07T07:36:50.7217631Z 1.) the first table handles all tensor level information 2025-09-07T07:36:50.7217989Z 2.) the second table handles and displays all channel based information 2025-09-07T07:36:50.7218130Z 2025-09-07T07:36:50.7218481Z The reasoning for this is that having all the info in one table can make it ambiguous which collected 2025-09-07T07:36:50.7218908Z statistics are global, and which are actually per-channel, so it's better to split it up into two 2025-09-07T07:36:50.7219292Z tables. This also makes the information much easier to digest given the plethora of statistics collected 2025-09-07T07:36:50.7219472Z 2025-09-07T07:36:50.7219619Z Tensor table columns: 2025-09-07T07:36:50.7219841Z idx layer_fqn feature_1 feature_2 feature_3 .... feature_n 2025-09-07T07:36:50.7220073Z ---- --------- --------- --------- --------- --------- 2025-09-07T07:36:50.7220182Z 2025-09-07T07:36:50.7220377Z Per-Channel table columns: 2025-09-07T07:36:50.7220665Z idx layer_fqn channel feature_1 feature_2 feature_3 .... feature_n 2025-09-07T07:36:50.7220878Z ---- --------- ------- --------- --------- --------- --------- 2025-09-07T07:36:50.7221034Z 2025-09-07T07:36:50.7221147Z Args: 2025-09-07T07:36:50.7221498Z feature_filter (str, optional): Filters the features presented to only those that 2025-09-07T07:36:50.7221619Z contain this filter substring 2025-09-07T07:36:50.7221846Z Default = "", results in all the features being printed 2025-09-07T07:36:50.7222200Z module_fqn_filter (str, optional): Only includes modules that contains this string 2025-09-07T07:36:50.7222479Z Default = "", results in all the modules in the reports to be visible in the table 2025-09-07T07:36:50.7222640Z 2025-09-07T07:36:50.7222781Z Returns a dictionary with two keys: 2025-09-07T07:36:50.7222967Z (Dict[str, Tuple[List, List]]) A dict containing two keys: 2025-09-07T07:36:50.7223221Z "tensor_level_info", "channel_level_info" 2025-09-07T07:36:50.7223362Z Each key maps to a tuple with: 2025-09-07T07:36:50.7223578Z A list of the headers of each table 2025-09-07T07:36:50.7223982Z A list of lists containing the table information row by row 2025-09-07T07:36:50.7224185Z The 0th index row will contain the headers of the columns 2025-09-07T07:36:50.7224411Z The rest of the rows will contain data 2025-09-07T07:36:50.7224538Z 2025-09-07T07:36:50.7224704Z Example Use: 2025-09-07T07:36:50.7224953Z >>> # xdoctest: +SKIP("undefined variables") 2025-09-07T07:36:50.7225136Z >>> mod_report_visualizer.generate_filtered_tables( 2025-09-07T07:36:50.7225382Z ... feature_filter="per_channel_min", module_fqn_filter="block1" 2025-09-07T07:36:50.7225727Z ... ) # generates table with per_channel_min info for all modules in block 1 of the model 2025-09-07T07:36:50.7225897Z 2025-09-07T07:36:50.7226192Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.7226306Z 2025-09-07T07:36:50.7226476Z warnings.warn(msg) 2025-09-07T07:36:50.7226565Z 2025-09-07T07:36:50.7226964Z --- Parse Warning: 43 / 146 --- 2025-09-07T07:36:50.7228279Z /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=399. 2025-09-07T07:36:50.7228623Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.7228733Z 2025-09-07T07:36:50.7229084Z Takes in optional filter values and prints out formatted tables of the information. 2025-09-07T07:36:50.7229248Z 2025-09-07T07:36:50.7229642Z The reason for the two tables printed out instead of one large one are that they handle different things: 2025-09-07T07:36:50.7229914Z 1.) the first table handles all tensor level information 2025-09-07T07:36:50.7230199Z 2.) the second table handles and displays all channel based information 2025-09-07T07:36:50.7230307Z 2025-09-07T07:36:50.7230682Z The reasoning for this is that having all the info in one table can make it ambiguous which collected 2025-09-07T07:36:50.7231076Z statistics are global, and which are actually per-channel, so it's better to split it up into two 2025-09-07T07:36:50.7231530Z tables. This also makes the information much easier to digest given the plethora of statistics collected 2025-09-07T07:36:50.7231641Z 2025-09-07T07:36:50.7231768Z Tensor table columns: 2025-09-07T07:36:50.7232033Z idx layer_fqn feature_1 feature_2 feature_3 .... feature_n 2025-09-07T07:36:50.7232238Z ---- --------- --------- --------- --------- --------- 2025-09-07T07:36:50.7232446Z 2025-09-07T07:36:50.7232585Z Per-Channel table columns: 2025-09-07T07:36:50.7232693Z 2025-09-07T07:36:50.7232985Z idx layer_fqn channel feature_1 feature_2 feature_3 .... feature_n 2025-09-07T07:36:50.7233180Z ---- --------- ------- --------- --------- --------- --------- 2025-09-07T07:36:50.7233347Z 2025-09-07T07:36:50.7233475Z Args: 2025-09-07T07:36:50.7233769Z feature_filter (str, optional): Filters the features presented to only those that 2025-09-07T07:36:50.7233986Z contain this filter substring 2025-09-07T07:36:50.7234181Z Default = "", results in all the features being printed 2025-09-07T07:36:50.7234601Z module_fqn_filter (str, optional): Only includes modules that contains this string 2025-09-07T07:36:50.7234918Z Default = "", results in all the modules in the reports to be visible in the table 2025-09-07T07:36:50.7235124Z 2025-09-07T07:36:50.7235246Z Example Use: 2025-09-07T07:36:50.7235407Z >>> # xdoctest: +SKIP("undefined variables") 2025-09-07T07:36:50.7235653Z >>> mod_report_visualizer.generate_table_visualization( 2025-09-07T07:36:50.7235858Z ... feature_filter="per_channel_min", module_fqn_filter="block1" 2025-09-07T07:36:50.7236067Z ... ) 2025-09-07T07:36:50.7236287Z >>> # prints out neatly formatted table with per_channel_min info 2025-09-07T07:36:50.7236443Z >>> # for all modules in block 1 of the model 2025-09-07T07:36:50.7236594Z 2025-09-07T07:36:50.7236917Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.7237090Z 2025-09-07T07:36:50.7237232Z warnings.warn(msg) 2025-09-07T07:36:50.7237340Z 2025-09-07T07:36:50.7237630Z --- Parse Warning: 44 / 146 --- 2025-09-07T07:36:50.7238954Z /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=564. 2025-09-07T07:36:50.7239271Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.7239416Z 2025-09-07T07:36:50.7239702Z Takes in a feature and optional module_filter and plots of the desired data. 2025-09-07T07:36:50.7239854Z 2025-09-07T07:36:50.7240163Z For per channel features, it averages the value across the channels and plots a point 2025-09-07T07:36:50.7240496Z per module. The reason for this is that for models with hundreds of channels, it can 2025-09-07T07:36:50.7240783Z be hard to differentiate one channel line from another, and so the point of generating 2025-09-07T07:36:50.7249781Z a single average point per module is to give a sense of general trends that encourage 2025-09-07T07:36:50.7250066Z further deep dives. 2025-09-07T07:36:50.7250164Z 2025-09-07T07:36:50.7250244Z Note: 2025-09-07T07:36:50.7250541Z Only features in the report that have tensor value data are plottable by this class 2025-09-07T07:36:50.7250746Z When the tensor information is plotted, it will plot: 2025-09-07T07:36:50.7250884Z idx as the x val, feature value as the y_val 2025-09-07T07:36:50.7251059Z When the channel information is plotted, it will plot: 2025-09-07T07:36:50.7251328Z the first idx of each module as the x val, feature value as the y_val [for each channel] 2025-09-07T07:36:50.7251572Z The reason for this is that we want to be able to compare values across the 2025-09-07T07:36:50.7251807Z channels for same layer, and it will be hard if values are staggered by idx 2025-09-07T07:36:50.7251976Z This means each module is represented by only 1 x value 2025-09-07T07:36:50.7252078Z Args: 2025-09-07T07:36:50.7252336Z feature_filter (str): Filters the features presented to only those that 2025-09-07T07:36:50.7252460Z contain this filter substring 2025-09-07T07:36:50.7252718Z module_fqn_filter (str, optional): Only includes modules that contains this string 2025-09-07T07:36:50.7252962Z Default = "", results in all the modules in the reports to be visible in the table 2025-09-07T07:36:50.7253057Z 2025-09-07T07:36:50.7253145Z Example Use: 2025-09-07T07:36:50.7253286Z >>> # xdoctest: +SKIP("undefined variables") 2025-09-07T07:36:50.7253445Z >>> mod_report_visualizer.generate_plot_visualization( 2025-09-07T07:36:50.7253641Z ... feature_filter="per_channel_min", module_fqn_filter="block1" 2025-09-07T07:36:50.7253734Z ... ) 2025-09-07T07:36:50.7253912Z >>> # outputs line plot of per_channel_min information for all 2025-09-07T07:36:50.7254109Z >>> # modules in block1 of model each channel gets it's own line, 2025-09-07T07:36:50.7254285Z >>> # and it's plotted across the in-order modules on the x-axis 2025-09-07T07:36:50.7254364Z 2025-09-07T07:36:50.7254628Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.7254707Z 2025-09-07T07:36:50.7254813Z warnings.warn(msg) 2025-09-07T07:36:50.7254894Z 2025-09-07T07:36:50.7255137Z --- Parse Warning: 45 / 146 --- 2025-09-07T07:36:50.7256494Z /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=643. 2025-09-07T07:36:50.7256755Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.7256849Z 2025-09-07T07:36:50.7257128Z Takes in a feature and optional module_filter and plots the histogram of desired data. 2025-09-07T07:36:50.7257208Z 2025-09-07T07:36:50.7257308Z Note: 2025-09-07T07:36:50.7257574Z Only features in the report that have tensor value data can be viewed as a histogram 2025-09-07T07:36:50.7257850Z If you want to plot a histogram from all the channel values of a specific feature for 2025-09-07T07:36:50.7258095Z a specific model, make sure to specify both the model and the feature properly 2025-09-07T07:36:50.7258343Z in the filters and you should be able to see a distribution of the channel data 2025-09-07T07:36:50.7258434Z 2025-09-07T07:36:50.7258513Z Args: 2025-09-07T07:36:50.7258789Z feature_filter (str, optional): Filters the features presented to only those that 2025-09-07T07:36:50.7258897Z contain this filter substring 2025-09-07T07:36:50.7259056Z Default = "", results in all the features being printed 2025-09-07T07:36:50.7259346Z module_fqn_filter (str, optional): Only includes modules that contains this string 2025-09-07T07:36:50.7259590Z Default = "", results in all the modules in the reports to be visible in the table 2025-09-07T07:36:50.7259819Z num_bins (int, optional): The number of bins to create the histogram with 2025-09-07T07:36:50.7260030Z Default = 10, the values will be split into 10 equal sized bins 2025-09-07T07:36:50.7260109Z 2025-09-07T07:36:50.7260208Z Example Use: 2025-09-07T07:36:50.7260305Z >>> # xdoctest: +SKIP 2025-09-07T07:36:50.7260613Z >>> mod_report_visualizer.generategenerate_histogram_visualization_plot_visualization( 2025-09-07T07:36:50.7260805Z ... feature_filter="per_channel_min", module_fqn_filter="block1" 2025-09-07T07:36:50.7260884Z ... ) 2025-09-07T07:36:50.7261170Z # outputs histogram of per_channel_min information for all modules in block1 of model 2025-09-07T07:36:50.7261426Z information is gathered across all channels for all modules in block 1 for the 2025-09-07T07:36:50.7261680Z per_channel_min and is displayed in a histogram of equally sized bins 2025-09-07T07:36:50.7261760Z 2025-09-07T07:36:50.7262013Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.7262107Z 2025-09-07T07:36:50.7262228Z warnings.warn(msg) 2025-09-07T07:36:50.7262316Z 2025-09-07T07:36:50.7262506Z --- Parse Warning: 46 / 146 --- 2025-09-07T07:36:50.7263412Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=record_function in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/profiler.py line=734. 2025-09-07T07:36:50.7263687Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.7264074Z Context manager/function decorator that adds a label to a code block/function when running autograd profiler. 2025-09-07T07:36:50.7264272Z Label will only appear if CPU activity tracing is enabled. 2025-09-07T07:36:50.7264350Z 2025-09-07T07:36:50.7264496Z It is useful when tracing the code profile. 2025-09-07T07:36:50.7264574Z 2025-09-07T07:36:50.7264656Z Args: 2025-09-07T07:36:50.7264813Z name (str): Label assigned to the block of code. 2025-09-07T07:36:50.7264994Z node_id (int): ID of node, for distributed profiling. Unset in 2025-09-07T07:36:50.7265100Z non-distributed cases. 2025-09-07T07:36:50.7265192Z 2025-09-07T07:36:50.7265275Z Example: 2025-09-07T07:36:50.7265468Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_AUTOGRAD_PROFILER) 2025-09-07T07:36:50.7265644Z >>> x = torch.randn((1, 1), requires_grad=True) 2025-09-07T07:36:50.7265801Z >>> with torch.autograd.profiler.profile() as prof: 2025-09-07T07:36:50.7265906Z ... y = x**2 2025-09-07T07:36:50.7266065Z ... with torch.autograd.profiler.record_function( 2025-09-07T07:36:50.7266170Z ... "label-z" 2025-09-07T07:36:50.7266274Z ... ): # label the block 2025-09-07T07:36:50.7266365Z ... z = y**3 2025-09-07T07:36:50.7266469Z ... y.backward() 2025-09-07T07:36:50.7266573Z >>> # xdoctest: +IGNORE_WANT 2025-09-07T07:36:50.7266722Z >>> # NOTE: some columns were removed for brevity 2025-09-07T07:36:50.7266926Z >>> print(prof.key_averages().table(sort_by="self_cpu_time_total")) 2025-09-07T07:36:50.7267121Z ----------------------------------- --------------- --------------- --------------- 2025-09-07T07:36:50.7267318Z Name Self CPU total % CPU time avg Number of Calls 2025-09-07T07:36:50.7267513Z ----------------------------------- --------------- --------------- --------------- 2025-09-07T07:36:50.7267659Z pow 60.77% 47.470us 3 2025-09-07T07:36:50.7267813Z mul 21.73% 25.465us 2 2025-09-07T07:36:50.7267997Z PowBackward0 12.03% 121.891us 1 2025-09-07T07:36:50.7268249Z torch::autograd::AccumulateGrad 2.70% 6.324us 1 2025-09-07T07:36:50.7268390Z label-z 2.13% 12.421us 1 2025-09-07T07:36:50.7268602Z torch::autograd::GraphRoot 0.64% 1.503us 1 2025-09-07T07:36:50.7268790Z ----------------------------------- --------------- --------------- --------------- 2025-09-07T07:36:50.7268913Z Self CPU time total: 234.344us 2025-09-07T07:36:50.7269014Z CUDA time total: 0.000us 2025-09-07T07:36:50.7269090Z 2025-09-07T07:36:50.7269183Z 2025-09-07T07:36:50.7269434Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.7269527Z 2025-09-07T07:36:50.7269620Z warnings.warn(msg) 2025-09-07T07:36:50.7269727Z 2025-09-07T07:36:50.7269929Z --- Parse Warning: 47 / 146 --- 2025-09-07T07:36:50.7270893Z /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=701. 2025-09-07T07:36:50.7271154Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-09-07T07:36:50.7271235Z 2025-09-07T07:36:50.7271502Z Slice the current DeviceMesh based on the mesh_dim_names given to create a submesh. 2025-09-07T07:36:50.7271781Z The submesh created consists of the dimensions and the communicators indicated by 2025-09-07T07:36:50.7271875Z ``mesh_dim_names`` 2025-09-07T07:36:50.7271967Z 2025-09-07T07:36:50.7272050Z Args: 2025-09-07T07:36:50.7272284Z mesh_dim_names (Union[str, Tuple[str]]): the name or the tuple of names of the 2025-09-07T07:36:50.7272482Z mesh dimension of the DeviceMesh to create the submesh for. 2025-09-07T07:36:50.7272564Z Returns: 2025-09-07T07:36:50.7272681Z A :class:`DeviceMesh` object 2025-09-07T07:36:50.7272758Z 2025-09-07T07:36:50.7273038Z The following program runs on each process/rank in an SPMD manner in a world size of 8. 2025-09-07T07:36:50.7273151Z In the first example: 2025-09-07T07:36:50.7273404Z Calling mesh_2d["tp"] on rank 0, 1, 2, 3 returns a 1D submesh of DeviceMesh:([0, 1, 2, 3]). 2025-09-07T07:36:50.7273662Z Calling mesh_2d["tp"] on rank 4, 5, 6, 7 returns a 1D submesh of DeviceMesh:([4, 5, 6, 7]). 2025-09-07T07:36:50.7273911Z Calling mesh_2d["dp"] on rank 0, 4 returns a 1D submesh of DeviceMesh:([0, 4]). 2025-09-07T07:36:50.7274131Z Calling mesh_2d["dp"] on rank 1, 5 returns a 1D submesh of DeviceMesh:([1, 5]). 2025-09-07T07:36:50.7274464Z Calling mesh_2d["dp"] on rank 2, 6 returns a 1D submesh of DeviceMesh:([2, 6]). 2025-09-07T07:36:50.7274685Z Calling mesh_2d["dp"] on rank 3, 7 returns a 1D submesh of DeviceMesh:([3, 7]). 2025-09-07T07:36:50.7274774Z 2025-09-07T07:36:50.7274872Z In the second example: 2025-09-07T07:36:50.7275137Z Calling mesh_3d["dp", "cp"] on rank 0, 1, 4, 5 returns a 2D submesh of DeviceMesh:([[0, 1], [4, 5]]). 2025-09-07T07:36:50.7275414Z Calling mesh_3d["dp", "cp"] on rank 2, 3, 6, 7 returns a 2D submesh of DeviceMesh:([[2, 3], [6, 7]]). 2025-09-07T07:36:50.7275674Z Calling mesh_3d["cp", "dp"] on rank 0, 1, 4, 5 returns a 2D submesh of DeviceMesh:([[0, 4], [1, 5]]). 2025-09-07T07:36:50.7275947Z Calling mesh_3d["cp", "dp"] on rank 2, 3, 6, 7 returns a 2D submesh of DeviceMesh:([[2, 6], [3, 7]]). 2025-09-07T07:36:50.7276023Z 2025-09-07T07:36:50.7276111Z Example:: 2025-09-07T07:36:50.7276202Z 2025-09-07T07:36:50.7276342Z >>> # xdoctest: +SKIP("no rank") 2025-09-07T07:36:50.7276526Z >>> from torch.distributed.device_mesh import DeviceMesh 2025-09-07T07:36:50.7276608Z >>> 2025-09-07T07:36:50.7276806Z >>> # Initialize a 2D device mesh as (2, 4) to represent the topology 2025-09-07T07:36:50.7276983Z >>> # of cross-host(dim 0), and within-host (dim 1). 2025-09-07T07:36:50.7277235Z >>> mesh_2d = init_device_mesh(device_type="cuda", (2,4), mesh_dim_names=("dp", "tp")) 2025-09-07T07:36:50.7277343Z >>> tp_mesh = mesh_2d["tp"] 2025-09-07T07:36:50.7277438Z >>> dp_mesh = mesh_2d["dp"] 2025-09-07T07:36:50.7277519Z >>> 2025-09-07T07:36:50.7277625Z >>> # Initialize a 3D mesh. 2025-09-07T07:36:50.7277902Z >>> mesh_3d = init_device_mesh(device_type="cuda", (2,2,2), mesh_dim_names=("dp", "pp", "cp")) 2025-09-07T07:36:50.7278217Z >>> # The order of the mesh_dim_names provided deteremines the order of dimensions in the submesh. 2025-09-07T07:36:50.7278331Z >>> dp_cp_mesh = mesh_3d["dp", "cp"] 2025-09-07T07:36:50.7278441Z >>> cp_dp_mesh = mesh_3d["cp", "dp"] 2025-09-07T07:36:50.7278558Z 2025-09-07T07:36:50.7279231Z 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)) 2025-09-07T07:36:50.7279325Z 2025-09-07T07:36:50.7279570Z mesh_2d = init_device_mesh(device_type="cuda", (2,4), mesh_dim_names=("dp", "tp")) 2025-09-07T07:36:50.7279687Z ^ 2025-09-07T07:36:50.7279797Z warnings.warn(msg) 2025-09-07T07:36:50.7279874Z 2025-09-07T07:36:50.7280085Z --- Parse Warning: 48 / 146 --- 2025-09-07T07:36:50.7281056Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=batch_isend_irecv in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py line=2705. 2025-09-07T07:36:50.7281334Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.7281413Z 2025-09-07T07:36:50.7281665Z Send or Receive a batch of tensors asynchronously and return a list of requests. 2025-09-07T07:36:50.7281757Z 2025-09-07T07:36:50.7282002Z Process each of the operations in ``p2p_op_list`` and return the corresponding 2025-09-07T07:36:50.7282208Z requests. NCCL, Gloo, and UCC backend are currently supported. 2025-09-07T07:36:50.7282285Z 2025-09-07T07:36:50.7282367Z Args: 2025-09-07T07:36:50.7282605Z p2p_op_list: A list of point-to-point operations(type of each operator is 2025-09-07T07:36:50.7282859Z ``torch.distributed.P2POp``). The order of the isend/irecv in the list 2025-09-07T07:36:50.7283082Z matters and it needs to match with corresponding isend/irecv on the 2025-09-07T07:36:50.7283173Z remote end. 2025-09-07T07:36:50.7283250Z 2025-09-07T07:36:50.7283346Z Returns: 2025-09-07T07:36:50.7283590Z A list of distributed request objects returned by calling the corresponding 2025-09-07T07:36:50.7283686Z op in the op_list. 2025-09-07T07:36:50.7283777Z 2025-09-07T07:36:50.7283860Z Examples: 2025-09-07T07:36:50.7283980Z >>> # xdoctest: +SKIP("no rank") 2025-09-07T07:36:50.7284166Z >>> send_tensor = torch.arange(2, dtype=torch.float32) + 2 * rank 2025-09-07T07:36:50.7284317Z >>> recv_tensor = torch.randn(2, dtype=torch.float32) 2025-09-07T07:36:50.7284542Z >>> send_op = dist.P2POp(dist.isend, send_tensor, (rank + 1) % world_size) 2025-09-07T07:36:50.7284646Z >>> recv_op = dist.P2POp( 2025-09-07T07:36:50.7284849Z ... dist.irecv, recv_tensor, (rank - 1 + world_size) % world_size 2025-09-07T07:36:50.7284930Z ... ) 2025-09-07T07:36:50.7285064Z >>> reqs = batch_isend_irecv([send_op, recv_op]) 2025-09-07T07:36:50.7285198Z >>> for req in reqs: 2025-09-07T07:36:50.7285289Z >>> req.wait() 2025-09-07T07:36:50.7285390Z >>> recv_tensor 2025-09-07T07:36:50.7285486Z tensor([2, 3]) # Rank 0 2025-09-07T07:36:50.7285578Z tensor([0, 1]) # Rank 1 2025-09-07T07:36:50.7285694Z 2025-09-07T07:36:50.7285936Z .. note:: Note that when this API is used with the NCCL PG backend, users must set 2025-09-07T07:36:50.7286168Z the current GPU device with `torch.cuda.set_device`, otherwise it will 2025-09-07T07:36:50.7286277Z lead to unexpected hang issues. 2025-09-07T07:36:50.7286354Z 2025-09-07T07:36:50.7286577Z In addition, if this API is the first collective call in the ``group`` 2025-09-07T07:36:50.7286794Z passed to ``dist.P2POp``, all ranks of the ``group`` must participate in 2025-09-07T07:36:50.7287029Z this API call; otherwise, the behavior is undefined. If this API call is 2025-09-07T07:36:50.7287247Z not the first collective call in the ``group``, batched P2P operations 2025-09-07T07:36:50.7287463Z involving only a subset of ranks of the ``group`` are allowed. 2025-09-07T07:36:50.7287554Z 2025-09-07T07:36:50.7287805Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.7287895Z 2025-09-07T07:36:50.7287991Z warnings.warn(msg) 2025-09-07T07:36:50.7288070Z 2025-09-07T07:36:50.7288270Z --- Parse Warning: 49 / 146 --- 2025-09-07T07:36:50.7289210Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=all_reduce in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py line=2837. 2025-09-07T07:36:50.7289485Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.7289564Z 2025-09-07T07:36:50.7289828Z Reduces the tensor data across all machines in a way that all get the final result. 2025-09-07T07:36:50.7289923Z 2025-09-07T07:36:50.7290149Z After the call ``tensor`` is going to be bitwise identical in all processes. 2025-09-07T07:36:50.7290239Z 2025-09-07T07:36:50.7290347Z Complex tensors are supported. 2025-09-07T07:36:50.7290426Z 2025-09-07T07:36:50.7290523Z Args: 2025-09-07T07:36:50.7290726Z tensor (Tensor): Input and output of the collective. The function 2025-09-07T07:36:50.7290841Z operates in-place. 2025-09-07T07:36:50.7290961Z op (optional): One of the values from 2025-09-07T07:36:50.7291076Z ``torch.distributed.ReduceOp`` 2025-09-07T07:36:50.7291289Z enum. Specifies an operation used for element-wise reductions. 2025-09-07T07:36:50.7291544Z group (ProcessGroup, optional): The process group to work on. If None, 2025-09-07T07:36:50.7291681Z the default process group will be used. 2025-09-07T07:36:50.7291873Z async_op (bool, optional): Whether this op should be an async op 2025-09-07T07:36:50.7291952Z 2025-09-07T07:36:50.7292044Z Returns: 2025-09-07T07:36:50.7292184Z Async work handle, if async_op is set to True. 2025-09-07T07:36:50.7292328Z None, if not async_op or if not part of the group 2025-09-07T07:36:50.7292416Z 2025-09-07T07:36:50.7292500Z Examples: 2025-09-07T07:36:50.7292617Z >>> # xdoctest: +SKIP("no rank") 2025-09-07T07:36:50.7292746Z >>> # All tensors below are of torch.int64 type. 2025-09-07T07:36:50.7292859Z >>> # We have 2 process groups, 2 ranks. 2025-09-07T07:36:50.7292988Z >>> device = torch.device(f"cuda:{rank}") 2025-09-07T07:36:50.7293210Z >>> tensor = torch.arange(2, dtype=torch.int64, device=device) + 1 + 2 * rank 2025-09-07T07:36:50.7293305Z >>> tensor 2025-09-07T07:36:50.7293422Z tensor([1, 2], device='cuda:0') # Rank 0 2025-09-07T07:36:50.7293532Z tensor([3, 4], device='cuda:1') # Rank 1 2025-09-07T07:36:50.7293676Z >>> dist.all_reduce(tensor, op=ReduceOp.SUM) 2025-09-07T07:36:50.7293845Z >>> tensor 2025-09-07T07:36:50.7293967Z tensor([4, 6], device='cuda:0') # Rank 0 2025-09-07T07:36:50.7294080Z tensor([4, 6], device='cuda:1') # Rank 1 2025-09-07T07:36:50.7294156Z 2025-09-07T07:36:50.7294301Z >>> # All tensors below are of torch.cfloat type. 2025-09-07T07:36:50.7294443Z >>> # We have 2 process groups, 2 ranks. 2025-09-07T07:36:50.7294555Z >>> tensor = torch.tensor( 2025-09-07T07:36:50.7294700Z ... [1 + 1j, 2 + 2j], dtype=torch.cfloat, device=device 2025-09-07T07:36:50.7294795Z ... ) + 2 * rank * (1 + 1j) 2025-09-07T07:36:50.7294891Z >>> tensor 2025-09-07T07:36:50.7295026Z tensor([1.+1.j, 2.+2.j], device='cuda:0') # Rank 0 2025-09-07T07:36:50.7295179Z tensor([3.+3.j, 4.+4.j], device='cuda:1') # Rank 1 2025-09-07T07:36:50.7295313Z >>> dist.all_reduce(tensor, op=ReduceOp.SUM) 2025-09-07T07:36:50.7295393Z >>> tensor 2025-09-07T07:36:50.7295541Z tensor([4.+4.j, 6.+6.j], device='cuda:0') # Rank 0 2025-09-07T07:36:50.7295706Z tensor([4.+4.j, 6.+6.j], device='cuda:1') # Rank 1 2025-09-07T07:36:50.7295786Z 2025-09-07T07:36:50.7295877Z 2025-09-07T07:36:50.7296128Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.7296228Z 2025-09-07T07:36:50.7296322Z warnings.warn(msg) 2025-09-07T07:36:50.7296400Z 2025-09-07T07:36:50.7296604Z --- Parse Warning: 50 / 146 --- 2025-09-07T07:36:50.7297561Z /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=3201. 2025-09-07T07:36:50.7297838Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.7297917Z 2025-09-07T07:36:50.7298136Z Gathers picklable objects from the whole group in a single process. 2025-09-07T07:36:50.7298230Z 2025-09-07T07:36:50.7298461Z Similar to :func:`gather`, but Python objects can be passed in. Note that the 2025-09-07T07:36:50.7298619Z object must be picklable in order to be gathered. 2025-09-07T07:36:50.7298695Z 2025-09-07T07:36:50.7298779Z Args: 2025-09-07T07:36:50.7298921Z obj (Any): Input object. Must be picklable. 2025-09-07T07:36:50.7299127Z object_gather_list (list[Any]): Output list. On the ``dst`` rank, it 2025-09-07T07:36:50.7299323Z should be correctly sized as the size of the group for this 2025-09-07T07:36:50.7299539Z collective and will contain the output. Must be ``None`` on non-dst 2025-09-07T07:36:50.7299643Z ranks. (default is ``None``) 2025-09-07T07:36:50.7300002Z dst (int, optional): Destination rank on global process group (regardless of ``group`` argument). 2025-09-07T07:36:50.7300196Z (If both ``dst`` and ``group_dst`` are None, default is global rank 0) 2025-09-07T07:36:50.7300439Z group: (ProcessGroup, optional): The process group to work on. If None, 2025-09-07T07:36:50.7300627Z the default process group will be used. Default is ``None``. 2025-09-07T07:36:50.7300985Z group_dst (int, optional): Destination rank on ``group``. Invalid to specify both ``dst`` and ``group_dst`` 2025-09-07T07:36:50.7301066Z 2025-09-07T07:36:50.7301149Z Returns: 2025-09-07T07:36:50.7301344Z None. On the ``dst`` rank, ``object_gather_list`` will contain the 2025-09-07T07:36:50.7301448Z output of the collective. 2025-09-07T07:36:50.7301525Z 2025-09-07T07:36:50.7301748Z .. note:: Note that this API differs slightly from the gather collective 2025-09-07T07:36:50.7301966Z since it does not provide an async_op handle and thus will be a blocking 2025-09-07T07:36:50.7302061Z call. 2025-09-07T07:36:50.7302140Z 2025-09-07T07:36:50.7302368Z .. note:: For NCCL-based processed groups, internal tensor representations 2025-09-07T07:36:50.7302616Z of objects must be moved to the GPU device before communication takes 2025-09-07T07:36:50.7302761Z place. In this case, the device used is given by 2025-09-07T07:36:50.7302992Z ``torch.cuda.current_device()`` and it is the user's responsibility to 2025-09-07T07:36:50.7303224Z ensure that this is set so that each rank has an individual GPU, via 2025-09-07T07:36:50.7303343Z ``torch.cuda.set_device()``. 2025-09-07T07:36:50.7303476Z 2025-09-07T07:36:50.7303563Z .. warning:: 2025-09-07T07:36:50.7303813Z Object collectives have a number of serious performance and scalability 2025-09-07T07:36:50.7303986Z limitations. See :ref:`object_collectives` for details. 2025-09-07T07:36:50.7304068Z 2025-09-07T07:36:50.7304162Z .. warning:: 2025-09-07T07:36:50.7304356Z :func:`gather_object` uses ``pickle`` module implicitly, which is 2025-09-07T07:36:50.7304593Z known to be insecure. It is possible to construct malicious pickle data 2025-09-07T07:36:50.7304834Z which will execute arbitrary code during unpickling. Only call this 2025-09-07T07:36:50.7304953Z function with data you trust. 2025-09-07T07:36:50.7305031Z 2025-09-07T07:36:50.7305114Z .. warning:: 2025-09-07T07:36:50.7305336Z Calling :func:`gather_object` with GPU tensors is not well supported 2025-09-07T07:36:50.7305558Z and inefficient as it incurs GPU -> CPU transfer since tensors would be 2025-09-07T07:36:50.7305734Z pickled. Please consider using :func:`gather` instead. 2025-09-07T07:36:50.7305810Z 2025-09-07T07:36:50.7305895Z Example:: 2025-09-07T07:36:50.7306039Z >>> # xdoctest: +SKIP("need process group init") 2025-09-07T07:36:50.7306218Z >>> # Note: Process group initialization omitted on each rank. 2025-09-07T07:36:50.7306346Z >>> import torch.distributed as dist 2025-09-07T07:36:50.7306449Z >>> # Assumes world_size of 3. 2025-09-07T07:36:50.7306620Z >>> gather_objects = ["foo", 12, {1: 2}] # any picklable object 2025-09-07T07:36:50.7306754Z >>> output = [None for _ in gather_objects] 2025-09-07T07:36:50.7306852Z >>> dist.gather_object( 2025-09-07T07:36:50.7306970Z ... gather_objects[dist.get_rank()], 2025-09-07T07:36:50.7307113Z ... output if dist.get_rank() == 0 else None, 2025-09-07T07:36:50.7307194Z ... dst=0 2025-09-07T07:36:50.7307288Z ... ) 2025-09-07T07:36:50.7307374Z >>> # On rank 0 2025-09-07T07:36:50.7307457Z >>> output 2025-09-07T07:36:50.7307556Z ['foo', 12, {1: 2}] 2025-09-07T07:36:50.7307634Z 2025-09-07T07:36:50.7307894Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.7307999Z 2025-09-07T07:36:50.7308092Z warnings.warn(msg) 2025-09-07T07:36:50.7308180Z 2025-09-07T07:36:50.7308369Z --- Parse Warning: 51 / 146 --- 2025-09-07T07:36:50.7309321Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=all_gather in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py line=3849. 2025-09-07T07:36:50.7309582Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.7309661Z 2025-09-07T07:36:50.7309811Z Gathers tensors from the whole group in a list. 2025-09-07T07:36:50.7309888Z 2025-09-07T07:36:50.7310039Z Complex and uneven sized tensors are supported. 2025-09-07T07:36:50.7310115Z 2025-09-07T07:36:50.7310194Z Args: 2025-09-07T07:36:50.7310383Z tensor_list (list[Tensor]): Output list. It should contain 2025-09-07T07:36:50.7310597Z correctly-sized tensors to be used for output of the collective. 2025-09-07T07:36:50.7310732Z Uneven sized tensors are supported. 2025-09-07T07:36:50.7310918Z tensor (Tensor): Tensor to be broadcast from current process. 2025-09-07T07:36:50.7311175Z group (ProcessGroup, optional): The process group to work on. If None, 2025-09-07T07:36:50.7311314Z the default process group will be used. 2025-09-07T07:36:50.7311505Z async_op (bool, optional): Whether this op should be an async op 2025-09-07T07:36:50.7311622Z 2025-09-07T07:36:50.7311704Z Returns: 2025-09-07T07:36:50.7311845Z Async work handle, if async_op is set to True. 2025-09-07T07:36:50.7312000Z None, if not async_op or if not part of the group 2025-09-07T07:36:50.7312077Z 2025-09-07T07:36:50.7312163Z Examples: 2025-09-07T07:36:50.7312312Z >>> # xdoctest: +SKIP("need process group init") 2025-09-07T07:36:50.7312445Z >>> # All tensors below are of torch.int64 dtype. 2025-09-07T07:36:50.7312577Z >>> # We have 2 process groups, 2 ranks. 2025-09-07T07:36:50.7312696Z >>> device = torch.device(f"cuda:{rank}") 2025-09-07T07:36:50.7312787Z >>> tensor_list = [ 2025-09-07T07:36:50.7313008Z ... torch.zeros(2, dtype=torch.int64, device=device) for _ in range(2) 2025-09-07T07:36:50.7313096Z ... ] 2025-09-07T07:36:50.7313218Z >>> tensor_list 2025-09-07T07:36:50.7313419Z [tensor([0, 0], device='cuda:0'), tensor([0, 0], device='cuda:0')] # Rank 0 2025-09-07T07:36:50.7313617Z [tensor([0, 0], device='cuda:1'), tensor([0, 0], device='cuda:1')] # Rank 1 2025-09-07T07:36:50.7313852Z >>> tensor = torch.arange(2, dtype=torch.int64, device=device) + 1 + 2 * rank 2025-09-07T07:36:50.7313935Z >>> tensor 2025-09-07T07:36:50.7314058Z tensor([1, 2], device='cuda:0') # Rank 0 2025-09-07T07:36:50.7314170Z tensor([3, 4], device='cuda:1') # Rank 1 2025-09-07T07:36:50.7314287Z >>> dist.all_gather(tensor_list, tensor) 2025-09-07T07:36:50.7314485Z >>> tensor_list 2025-09-07T07:36:50.7314681Z [tensor([1, 2], device='cuda:0'), tensor([3, 4], device='cuda:0')] # Rank 0 2025-09-07T07:36:50.7314888Z [tensor([1, 2], device='cuda:1'), tensor([3, 4], device='cuda:1')] # Rank 1 2025-09-07T07:36:50.7314967Z 2025-09-07T07:36:50.7315107Z >>> # All tensors below are of torch.cfloat dtype. 2025-09-07T07:36:50.7315236Z >>> # We have 2 process groups, 2 ranks. 2025-09-07T07:36:50.7315330Z >>> tensor_list = [ 2025-09-07T07:36:50.7315554Z ... torch.zeros(2, dtype=torch.cfloat, device=device) for _ in range(2) 2025-09-07T07:36:50.7315634Z ... ] 2025-09-07T07:36:50.7315721Z >>> tensor_list 2025-09-07T07:36:50.7315981Z [tensor([0.+0.j, 0.+0.j], device='cuda:0'), tensor([0.+0.j, 0.+0.j], device='cuda:0')] # Rank 0 2025-09-07T07:36:50.7316219Z [tensor([0.+0.j, 0.+0.j], device='cuda:1'), tensor([0.+0.j, 0.+0.j], device='cuda:1')] # Rank 1 2025-09-07T07:36:50.7316360Z >>> tensor = torch.tensor( 2025-09-07T07:36:50.7316506Z ... [1 + 1j, 2 + 2j], dtype=torch.cfloat, device=device 2025-09-07T07:36:50.7316597Z ... ) + 2 * rank * (1 + 1j) 2025-09-07T07:36:50.7316692Z >>> tensor 2025-09-07T07:36:50.7316832Z tensor([1.+1.j, 2.+2.j], device='cuda:0') # Rank 0 2025-09-07T07:36:50.7316976Z tensor([3.+3.j, 4.+4.j], device='cuda:1') # Rank 1 2025-09-07T07:36:50.7317092Z >>> dist.all_gather(tensor_list, tensor) 2025-09-07T07:36:50.7317182Z >>> tensor_list 2025-09-07T07:36:50.7317427Z [tensor([1.+1.j, 2.+2.j], device='cuda:0'), tensor([3.+3.j, 4.+4.j], device='cuda:0')] # Rank 0 2025-09-07T07:36:50.7317661Z [tensor([1.+1.j, 2.+2.j], device='cuda:1'), tensor([3.+3.j, 4.+4.j], device='cuda:1')] # Rank 1 2025-09-07T07:36:50.7317751Z 2025-09-07T07:36:50.7317831Z 2025-09-07T07:36:50.7318085Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.7318173Z 2025-09-07T07:36:50.7318264Z warnings.warn(msg) 2025-09-07T07:36:50.7318340Z 2025-09-07T07:36:50.7318550Z --- Parse Warning: 52 / 146 --- 2025-09-07T07:36:50.7319546Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=all_to_all_single in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py line=4555. 2025-09-07T07:36:50.7319811Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.7319913Z 2025-09-07T07:36:50.7320175Z Split input tensor and then scatter the split list to all processes in a group. 2025-09-07T07:36:50.7320252Z 2025-09-07T07:36:50.7320507Z Later the received tensors are concatenated from all the processes in the group 2025-09-07T07:36:50.7320632Z and returned as a single output tensor. 2025-09-07T07:36:50.7320709Z 2025-09-07T07:36:50.7320827Z Complex tensors are supported. 2025-09-07T07:36:50.7320902Z 2025-09-07T07:36:50.7320980Z Args: 2025-09-07T07:36:50.7321151Z output (Tensor): Gathered concatenated output tensor. 2025-09-07T07:36:50.7321275Z input (Tensor): Input tensor to scatter. 2025-09-07T07:36:50.7321526Z output_split_sizes: (list[Int], optional): Output split sizes for dim 0 2025-09-07T07:36:50.7321768Z if specified None or empty, dim 0 of ``output`` tensor must divide 2025-09-07T07:36:50.7321872Z equally by ``world_size``. 2025-09-07T07:36:50.7322087Z input_split_sizes: (list[Int], optional): Input split sizes for dim 0 2025-09-07T07:36:50.7322282Z if specified None or empty, dim 0 of ``input`` tensor must divide 2025-09-07T07:36:50.7322391Z equally by ``world_size``. 2025-09-07T07:36:50.7322616Z group (ProcessGroup, optional): The process group to work on. If None, 2025-09-07T07:36:50.7322740Z the default process group will be used. 2025-09-07T07:36:50.7322943Z async_op (bool, optional): Whether this op should be an async op. 2025-09-07T07:36:50.7323020Z 2025-09-07T07:36:50.7323099Z Returns: 2025-09-07T07:36:50.7323245Z Async work handle, if async_op is set to True. 2025-09-07T07:36:50.7323388Z None, if not async_op or if not part of the group. 2025-09-07T07:36:50.7323472Z 2025-09-07T07:36:50.7323562Z .. warning:: 2025-09-07T07:36:50.7323917Z `all_to_all_single` is experimental and subject to change. 2025-09-07T07:36:50.7324006Z 2025-09-07T07:36:50.7324088Z Examples: 2025-09-07T07:36:50.7324212Z >>> # xdoctest: +SKIP("Undefined rank") 2025-09-07T07:36:50.7324324Z >>> input = torch.arange(4) + rank * 4 2025-09-07T07:36:50.7324407Z >>> input 2025-09-07T07:36:50.7324514Z tensor([0, 1, 2, 3]) # Rank 0 2025-09-07T07:36:50.7324611Z tensor([4, 5, 6, 7]) # Rank 1 2025-09-07T07:36:50.7324714Z tensor([8, 9, 10, 11]) # Rank 2 2025-09-07T07:36:50.7324891Z tensor([12, 13, 14, 15]) # Rank 3 2025-09-07T07:36:50.7325034Z >>> output = torch.empty([4], dtype=torch.int64) 2025-09-07T07:36:50.7325161Z >>> dist.all_to_all_single(output, input) 2025-09-07T07:36:50.7325245Z >>> output 2025-09-07T07:36:50.7325347Z tensor([0, 4, 8, 12]) # Rank 0 2025-09-07T07:36:50.7325442Z tensor([1, 5, 9, 13]) # Rank 1 2025-09-07T07:36:50.7325535Z tensor([2, 6, 10, 14]) # Rank 2 2025-09-07T07:36:50.7325634Z tensor([3, 7, 11, 15]) # Rank 3 2025-09-07T07:36:50.7325712Z 2025-09-07T07:36:50.7325869Z >>> # Essentially, it is similar to following operation: 2025-09-07T07:36:50.7326019Z >>> scatter_list = list(input.chunk(world_size)) 2025-09-07T07:36:50.7326153Z >>> gather_list = list(output.chunk(world_size)) 2025-09-07T07:36:50.7326267Z >>> for i in range(world_size): 2025-09-07T07:36:50.7326495Z >>> dist.scatter(gather_list[i], scatter_list if i == rank else [], src = i) 2025-09-07T07:36:50.7326568Z 2025-09-07T07:36:50.7326692Z >>> # Another example with uneven split 2025-09-07T07:36:50.7326774Z >>> input 2025-09-07T07:36:50.7326973Z tensor([0, 1, 2, 3, 4, 5]) # Rank 0 2025-09-07T07:36:50.7327132Z tensor([10, 11, 12, 13, 14, 15, 16, 17, 18]) # Rank 1 2025-09-07T07:36:50.7327283Z tensor([20, 21, 22, 23, 24]) # Rank 2 2025-09-07T07:36:50.7327494Z tensor([30, 31, 32, 33, 34, 35, 36]) # Rank 3 2025-09-07T07:36:50.7327587Z >>> input_splits 2025-09-07T07:36:50.7327717Z [2, 2, 1, 1] # Rank 0 2025-09-07T07:36:50.7327837Z [3, 2, 2, 2] # Rank 1 2025-09-07T07:36:50.7327963Z [2, 1, 1, 1] # Rank 2 2025-09-07T07:36:50.7328081Z [2, 2, 2, 1] # Rank 3 2025-09-07T07:36:50.7328173Z >>> output_splits 2025-09-07T07:36:50.7328299Z [2, 3, 2, 2] # Rank 0 2025-09-07T07:36:50.7328452Z [2, 2, 1, 2] # Rank 1 2025-09-07T07:36:50.7328578Z [1, 2, 1, 2] # Rank 2 2025-09-07T07:36:50.7328694Z [1, 2, 1, 1] # Rank 3 2025-09-07T07:36:50.7328787Z >>> output = ... 2025-09-07T07:36:50.7329000Z >>> dist.all_to_all_single(output, input, output_splits, input_splits) 2025-09-07T07:36:50.7329082Z >>> output 2025-09-07T07:36:50.7329242Z tensor([ 0, 1, 10, 11, 12, 20, 21, 30, 31]) # Rank 0 2025-09-07T07:36:50.7329390Z tensor([ 2, 3, 13, 14, 22, 32, 33]) # Rank 1 2025-09-07T07:36:50.7329539Z tensor([ 4, 15, 16, 23, 34, 35]) # Rank 2 2025-09-07T07:36:50.7329695Z tensor([ 5, 17, 18, 24, 36]) # Rank 3 2025-09-07T07:36:50.7329775Z 2025-09-07T07:36:50.7329858Z 2025-09-07T07:36:50.7330017Z >>> # Another example with tensors of torch.cfloat type. 2025-09-07T07:36:50.7330119Z >>> input = torch.tensor( 2025-09-07T07:36:50.7330265Z ... [1 + 1j, 2 + 2j, 3 + 3j, 4 + 4j], dtype=torch.cfloat 2025-09-07T07:36:50.7330358Z ... ) + 4 * rank * (1 + 1j) 2025-09-07T07:36:50.7330447Z >>> input 2025-09-07T07:36:50.7330617Z tensor([1+1j, 2+2j, 3+3j, 4+4j]) # Rank 0 2025-09-07T07:36:50.7330783Z tensor([5+5j, 6+6j, 7+7j, 8+8j]) # Rank 1 2025-09-07T07:36:50.7330966Z tensor([9+9j, 10+10j, 11+11j, 12+12j]) # Rank 2 2025-09-07T07:36:50.7331174Z tensor([13+13j, 14+14j, 15+15j, 16+16j]) # Rank 3 2025-09-07T07:36:50.7331320Z >>> output = torch.empty([4], dtype=torch.int64) 2025-09-07T07:36:50.7331441Z >>> dist.all_to_all_single(output, input) 2025-09-07T07:36:50.7331524Z >>> output 2025-09-07T07:36:50.7331703Z tensor([1+1j, 5+5j, 9+9j, 13+13j]) # Rank 0 2025-09-07T07:36:50.7331875Z tensor([2+2j, 6+6j, 10+10j, 14+14j]) # Rank 1 2025-09-07T07:36:50.7332058Z tensor([3+3j, 7+7j, 11+11j, 15+15j]) # Rank 2 2025-09-07T07:36:50.7332224Z tensor([4+4j, 8+8j, 12+12j, 16+16j]) # Rank 3 2025-09-07T07:36:50.7332301Z 2025-09-07T07:36:50.7332558Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.7332637Z 2025-09-07T07:36:50.7332740Z warnings.warn(msg) 2025-09-07T07:36:50.7332814Z 2025-09-07T07:36:50.7333024Z --- Parse Warning: 53 / 146 --- 2025-09-07T07:36:50.7333969Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=all_to_all in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py line=4697. 2025-09-07T07:36:50.7334258Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.7334343Z 2025-09-07T07:36:50.7334711Z Scatters list of input tensors to all processes in a group and return gathered list of tensors in output list. 2025-09-07T07:36:50.7334813Z 2025-09-07T07:36:50.7334928Z Complex tensors are supported. 2025-09-07T07:36:50.7335004Z 2025-09-07T07:36:50.7335092Z Args: 2025-09-07T07:36:50.7335311Z output_tensor_list (list[Tensor]): List of tensors to be gathered one 2025-09-07T07:36:50.7335396Z per rank. 2025-09-07T07:36:50.7335635Z input_tensor_list (list[Tensor]): List of tensors to scatter one per rank. 2025-09-07T07:36:50.7335862Z group (ProcessGroup, optional): The process group to work on. If None, 2025-09-07T07:36:50.7335996Z the default process group will be used. 2025-09-07T07:36:50.7336191Z async_op (bool, optional): Whether this op should be an async op. 2025-09-07T07:36:50.7336266Z 2025-09-07T07:36:50.7336380Z Returns: 2025-09-07T07:36:50.7336516Z Async work handle, if async_op is set to True. 2025-09-07T07:36:50.7336667Z None, if not async_op or if not part of the group. 2025-09-07T07:36:50.7336747Z 2025-09-07T07:36:50.7336834Z .. warning:: 2025-09-07T07:36:50.7336994Z `all_to_all` is experimental and subject to change. 2025-09-07T07:36:50.7337070Z 2025-09-07T07:36:50.7337155Z Examples: 2025-09-07T07:36:50.7337281Z >>> # xdoctest: +SKIP("Undefined rank") 2025-09-07T07:36:50.7337390Z >>> input = torch.arange(4) + rank * 4 2025-09-07T07:36:50.7337506Z >>> input = list(input.chunk(4)) 2025-09-07T07:36:50.7337589Z >>> input 2025-09-07T07:36:50.7337756Z [tensor([0]), tensor([1]), tensor([2]), tensor([3])] # Rank 0 2025-09-07T07:36:50.7337931Z [tensor([4]), tensor([5]), tensor([6]), tensor([7])] # Rank 1 2025-09-07T07:36:50.7338093Z [tensor([8]), tensor([9]), tensor([10]), tensor([11])] # Rank 2 2025-09-07T07:36:50.7338267Z [tensor([12]), tensor([13]), tensor([14]), tensor([15])] # Rank 3 2025-09-07T07:36:50.7338445Z >>> output = list(torch.empty([4], dtype=torch.int64).chunk(4)) 2025-09-07T07:36:50.7338560Z >>> dist.all_to_all(output, input) 2025-09-07T07:36:50.7338649Z >>> output 2025-09-07T07:36:50.7338810Z [tensor([0]), tensor([4]), tensor([8]), tensor([12])] # Rank 0 2025-09-07T07:36:50.7338975Z [tensor([1]), tensor([5]), tensor([9]), tensor([13])] # Rank 1 2025-09-07T07:36:50.7339134Z [tensor([2]), tensor([6]), tensor([10]), tensor([14])] # Rank 2 2025-09-07T07:36:50.7339318Z [tensor([3]), tensor([7]), tensor([11]), tensor([15])] # Rank 3 2025-09-07T07:36:50.7339403Z 2025-09-07T07:36:50.7339562Z >>> # Essentially, it is similar to following operation: 2025-09-07T07:36:50.7339670Z >>> scatter_list = input 2025-09-07T07:36:50.7339765Z >>> gather_list = output 2025-09-07T07:36:50.7339872Z >>> for i in range(world_size): 2025-09-07T07:36:50.7340101Z >>> dist.scatter(gather_list[i], scatter_list if i == rank else [], src=i) 2025-09-07T07:36:50.7340179Z 2025-09-07T07:36:50.7340270Z >>> input 2025-09-07T07:36:50.7340419Z tensor([0, 1, 2, 3, 4, 5]) # Rank 0 2025-09-07T07:36:50.7340573Z tensor([10, 11, 12, 13, 14, 15, 16, 17, 18]) # Rank 1 2025-09-07T07:36:50.7340732Z tensor([20, 21, 22, 23, 24]) # Rank 2 2025-09-07T07:36:50.7340890Z tensor([30, 31, 32, 33, 34, 35, 36]) # Rank 3 2025-09-07T07:36:50.7340993Z >>> input_splits 2025-09-07T07:36:50.7341112Z [2, 2, 1, 1] # Rank 0 2025-09-07T07:36:50.7341254Z [3, 2, 2, 2] # Rank 1 2025-09-07T07:36:50.7341386Z [2, 1, 1, 1] # Rank 2 2025-09-07T07:36:50.7341503Z [2, 2, 2, 1] # Rank 3 2025-09-07T07:36:50.7341632Z >>> output_splits 2025-09-07T07:36:50.7341750Z [2, 3, 2, 2] # Rank 0 2025-09-07T07:36:50.7341877Z [2, 2, 1, 2] # Rank 1 2025-09-07T07:36:50.7341991Z [1, 2, 1, 2] # Rank 2 2025-09-07T07:36:50.7342108Z [1, 2, 1, 1] # Rank 3 2025-09-07T07:36:50.7342246Z >>> input = list(input.split(input_splits)) 2025-09-07T07:36:50.7342329Z >>> input 2025-09-07T07:36:50.7342539Z [tensor([0, 1]), tensor([2, 3]), tensor([4]), tensor([5])] # Rank 0 2025-09-07T07:36:50.7342740Z [tensor([10, 11, 12]), tensor([13, 14]), tensor([15, 16]), tensor([17, 18])] # Rank 1 2025-09-07T07:36:50.7342968Z [tensor([20, 21]), tensor([22]), tensor([23]), tensor([24])] # Rank 2 2025-09-07T07:36:50.7343177Z [tensor([30, 31]), tensor([32, 33]), tensor([34, 35]), tensor([36])] # Rank 3 2025-09-07T07:36:50.7343273Z >>> output = ... 2025-09-07T07:36:50.7343395Z >>> dist.all_to_all(output, input) 2025-09-07T07:36:50.7343475Z >>> output 2025-09-07T07:36:50.7343671Z [tensor([0, 1]), tensor([10, 11, 12]), tensor([20, 21]), tensor([30, 31])] # Rank 0 2025-09-07T07:36:50.7343879Z [tensor([2, 3]), tensor([13, 14]), tensor([22]), tensor([32, 33])] # Rank 1 2025-09-07T07:36:50.7344080Z [tensor([4]), tensor([15, 16]), tensor([23]), tensor([34, 35])] # Rank 2 2025-09-07T07:36:50.7344288Z [tensor([5]), tensor([17, 18]), tensor([24]), tensor([36])] # Rank 3 2025-09-07T07:36:50.7344367Z 2025-09-07T07:36:50.7344529Z >>> # Another example with tensors of torch.cfloat type. 2025-09-07T07:36:50.7344643Z >>> input = torch.tensor( 2025-09-07T07:36:50.7344778Z ... [1 + 1j, 2 + 2j, 3 + 3j, 4 + 4j], dtype=torch.cfloat 2025-09-07T07:36:50.7344884Z ... ) + 4 * rank * (1 + 1j) 2025-09-07T07:36:50.7344991Z >>> input = list(input.chunk(4)) 2025-09-07T07:36:50.7345070Z >>> input 2025-09-07T07:36:50.7345288Z [tensor([1+1j]), tensor([2+2j]), tensor([3+3j]), tensor([4+4j])] # Rank 0 2025-09-07T07:36:50.7345493Z [tensor([5+5j]), tensor([6+6j]), tensor([7+7j]), tensor([8+8j])] # Rank 1 2025-09-07T07:36:50.7345743Z [tensor([9+9j]), tensor([10+10j]), tensor([11+11j]), tensor([12+12j])] # Rank 2 2025-09-07T07:36:50.7345962Z [tensor([13+13j]), tensor([14+14j]), tensor([15+15j]), tensor([16+16j])] # Rank 3 2025-09-07T07:36:50.7346143Z >>> output = list(torch.empty([4], dtype=torch.int64).chunk(4)) 2025-09-07T07:36:50.7346261Z >>> dist.all_to_all(output, input) 2025-09-07T07:36:50.7346344Z >>> output 2025-09-07T07:36:50.7346561Z [tensor([1+1j]), tensor([5+5j]), tensor([9+9j]), tensor([13+13j])] # Rank 0 2025-09-07T07:36:50.7346767Z [tensor([2+2j]), tensor([6+6j]), tensor([10+10j]), tensor([14+14j])] # Rank 1 2025-09-07T07:36:50.7346973Z [tensor([3+3j]), tensor([7+7j]), tensor([11+11j]), tensor([15+15j])] # Rank 2 2025-09-07T07:36:50.7347183Z [tensor([4+4j]), tensor([8+8j]), tensor([12+12j]), tensor([16+16j])] # Rank 3 2025-09-07T07:36:50.7347261Z 2025-09-07T07:36:50.7347346Z 2025-09-07T07:36:50.7347600Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.7347676Z 2025-09-07T07:36:50.7347776Z warnings.warn(msg) 2025-09-07T07:36:50.7347849Z 2025-09-07T07:36:50.7348082Z --- Parse Warning: 54 / 146 --- 2025-09-07T07:36:50.7348933Z /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. 2025-09-07T07:36:50.7349193Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.7349309Z 2025-09-07T07:36:50.7349430Z Module ``torch.distributed.launch``. 2025-09-07T07:36:50.7349518Z 2025-09-07T07:36:50.7349760Z ``torch.distributed.launch`` is a module that spawns up multiple distributed 2025-09-07T07:36:50.7349912Z training processes on each of the training nodes. 2025-09-07T07:36:50.7350003Z 2025-09-07T07:36:50.7350088Z .. warning:: 2025-09-07T07:36:50.7350175Z 2025-09-07T07:36:50.7350420Z This module is going to be deprecated in favor of :ref:`torchrun `. 2025-09-07T07:36:50.7350496Z 2025-09-07T07:36:50.7350744Z The utility can be used for single-node distributed training, in which one or 2025-09-07T07:36:50.7351007Z more processes per node will be spawned. The utility can be used for either 2025-09-07T07:36:50.7351235Z CPU training or GPU training. If the utility is used for GPU training, 2025-09-07T07:36:50.7351479Z each distributed process will be operating on a single GPU. This can achieve 2025-09-07T07:36:50.7351711Z well-improved single-node training performance. It can also be used in 2025-09-07T07:36:50.7351984Z multi-node distributed training, by spawning up multiple processes on each node 2025-09-07T07:36:50.7352209Z for well-improved multi-node distributed training performance as well. 2025-09-07T07:36:50.7352445Z This will especially be beneficial for systems with multiple Infiniband 2025-09-07T07:36:50.7352696Z interfaces that have direct-GPU support, since all of them can be utilized for 2025-09-07T07:36:50.7352809Z aggregated communication bandwidth. 2025-09-07T07:36:50.7352896Z 2025-09-07T07:36:50.7353126Z In both cases of single-node distributed training or multi-node distributed 2025-09-07T07:36:50.7353368Z training, this utility will launch the given number of processes per node 2025-09-07T07:36:50.7353588Z (``--nproc-per-node``). If used for GPU training, this number needs to be less 2025-09-07T07:36:50.7353803Z or equal to the number of GPUs on the current system (``nproc_per_node``), 2025-09-07T07:36:50.7354013Z and each process will be operating on a single GPU from *GPU 0 to 2025-09-07T07:36:50.7354115Z GPU (nproc_per_node - 1)*. 2025-09-07T07:36:50.7354202Z 2025-09-07T07:36:50.7354300Z **How to use this module:** 2025-09-07T07:36:50.7354466Z 2025-09-07T07:36:50.7354664Z 1. Single-Node multi-process distributed training 2025-09-07T07:36:50.7354740Z 2025-09-07T07:36:50.7354834Z :: 2025-09-07T07:36:50.7354910Z 2025-09-07T07:36:50.7355143Z python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE 2025-09-07T07:36:50.7355352Z YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3 and all other 2025-09-07T07:36:50.7355482Z arguments of your training script) 2025-09-07T07:36:50.7355571Z 2025-09-07T07:36:50.7355777Z 2. Multi-Node multi-process distributed training: (e.g. two nodes) 2025-09-07T07:36:50.7355856Z 2025-09-07T07:36:50.7355943Z 2025-09-07T07:36:50.7356085Z Node 1: *(IP: 192.168.1.1, and has a free port: 1234)* 2025-09-07T07:36:50.7356161Z 2025-09-07T07:36:50.7356250Z :: 2025-09-07T07:36:50.7356327Z 2025-09-07T07:36:50.7356570Z python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE 2025-09-07T07:36:50.7356726Z --nnodes=2 --node-rank=0 --master-addr="192.168.1.1" 2025-09-07T07:36:50.7356935Z --master-port=1234 YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3 2025-09-07T07:36:50.7357097Z and all other arguments of your training script) 2025-09-07T07:36:50.7357214Z 2025-09-07T07:36:50.7357304Z Node 2: 2025-09-07T07:36:50.7357381Z 2025-09-07T07:36:50.7357459Z :: 2025-09-07T07:36:50.7357547Z 2025-09-07T07:36:50.7357774Z python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE 2025-09-07T07:36:50.7357936Z --nnodes=2 --node-rank=1 --master-addr="192.168.1.1" 2025-09-07T07:36:50.7358164Z --master-port=1234 YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3 2025-09-07T07:36:50.7358311Z and all other arguments of your training script) 2025-09-07T07:36:50.7358403Z 2025-09-07T07:36:50.7358566Z 3. To look up what optional arguments this module offers: 2025-09-07T07:36:50.7358653Z 2025-09-07T07:36:50.7358729Z :: 2025-09-07T07:36:50.7358808Z 2025-09-07T07:36:50.7358953Z python -m torch.distributed.launch --help 2025-09-07T07:36:50.7359028Z 2025-09-07T07:36:50.7359102Z 2025-09-07T07:36:50.7359205Z **Important Notices:** 2025-09-07T07:36:50.7359284Z 2025-09-07T07:36:50.7359477Z 1. This utility and multi-process distributed (single-node or 2025-09-07T07:36:50.7359745Z multi-node) GPU training currently only achieves the best performance using 2025-09-07T07:36:50.7359995Z the NCCL distributed backend. Thus NCCL backend is the recommended backend to 2025-09-07T07:36:50.7360102Z use for GPU training. 2025-09-07T07:36:50.7360181Z 2025-09-07T07:36:50.7360403Z 2. In your training program, you must parse the command-line argument: 2025-09-07T07:36:50.7360632Z ``--local-rank=LOCAL_PROCESS_RANK``, which will be provided by this module. 2025-09-07T07:36:50.7360857Z If your training program uses GPUs, you should ensure that your code only 2025-09-07T07:36:50.7361061Z runs on the GPU device of LOCAL_PROCESS_RANK. This can be done by: 2025-09-07T07:36:50.7361137Z 2025-09-07T07:36:50.7361254Z Parsing the local_rank argument 2025-09-07T07:36:50.7361330Z 2025-09-07T07:36:50.7361411Z :: 2025-09-07T07:36:50.7361497Z 2025-09-07T07:36:50.7361592Z >>> # xdoctest: +SKIP 2025-09-07T07:36:50.7361693Z >>> import argparse 2025-09-07T07:36:50.7361818Z >>> parser = argparse.ArgumentParser() 2025-09-07T07:36:50.7362006Z >>> parser.add_argument("--local-rank", "--local_rank", type=int) 2025-09-07T07:36:50.7362123Z >>> args = parser.parse_args() 2025-09-07T07:36:50.7362241Z 2025-09-07T07:36:50.7362360Z Set your device to local rank using either 2025-09-07T07:36:50.7362443Z 2025-09-07T07:36:50.7362520Z :: 2025-09-07T07:36:50.7362601Z 2025-09-07T07:36:50.7362799Z >>> torch.cuda.set_device(args.local_rank) # before your code runs 2025-09-07T07:36:50.7362873Z 2025-09-07T07:36:50.7362957Z or 2025-09-07T07:36:50.7363057Z 2025-09-07T07:36:50.7363146Z :: 2025-09-07T07:36:50.7363223Z 2025-09-07T07:36:50.7363355Z >>> with torch.cuda.device(args.local_rank): 2025-09-07T07:36:50.7363458Z >>> # your code to run 2025-09-07T07:36:50.7363542Z >>> ... 2025-09-07T07:36:50.7363621Z 2025-09-07T07:36:50.7363733Z .. versionchanged:: 2.0.0 2025-09-07T07:36:50.7363811Z 2025-09-07T07:36:50.7364063Z The launcher will passes the ``--local-rank=`` argument to your script. 2025-09-07T07:36:50.7364298Z From PyTorch 2.0.0 onwards, the dashed ``--local-rank`` is preferred over the 2025-09-07T07:36:50.7364439Z previously used underscored ``--local_rank``. 2025-09-07T07:36:50.7364528Z 2025-09-07T07:36:50.7364762Z For backward compatibility, it may be necessary for users to handle both 2025-09-07T07:36:50.7365035Z cases in their argument parsing code. This means including both ``"--local-rank"`` 2025-09-07T07:36:50.7365251Z and ``"--local_rank"`` in the argument parser. If only ``"--local_rank"`` is 2025-09-07T07:36:50.7365498Z provided, the launcher will trigger an error: "error: unrecognized arguments: 2025-09-07T07:36:50.7365768Z --local-rank=". For training code that only supports PyTorch 2.0.0+, 2025-09-07T07:36:50.7365919Z including ``"--local-rank"`` should be sufficient. 2025-09-07T07:36:50.7366006Z 2025-09-07T07:36:50.7366236Z 3. In your training program, you are supposed to call the following function 2025-09-07T07:36:50.7366498Z at the beginning to start the distributed backend. It is strongly recommended 2025-09-07T07:36:50.7366724Z that ``init_method=env://``. Other init methods (e.g. ``tcp://``) may work, 2025-09-07T07:36:50.7366910Z but ``env://`` is the one that is officially supported by this module. 2025-09-07T07:36:50.7366994Z 2025-09-07T07:36:50.7367074Z :: 2025-09-07T07:36:50.7367147Z 2025-09-07T07:36:50.7367356Z >>> torch.distributed.init_process_group(backend='YOUR BACKEND', 2025-09-07T07:36:50.7367495Z >>> init_method='env://') 2025-09-07T07:36:50.7367580Z 2025-09-07T07:36:50.7367818Z 4. In your training program, you can either use regular distributed functions 2025-09-07T07:36:50.7368072Z or use :func:`torch.nn.parallel.DistributedDataParallel` module. If your 2025-09-07T07:36:50.7368290Z training program uses GPUs for training and you would like to use 2025-09-07T07:36:50.7368482Z :func:`torch.nn.parallel.DistributedDataParallel` module, 2025-09-07T07:36:50.7368597Z here is how to configure it. 2025-09-07T07:36:50.7368673Z 2025-09-07T07:36:50.7368753Z :: 2025-09-07T07:36:50.7368843Z 2025-09-07T07:36:50.7369032Z >>> model = torch.nn.parallel.DistributedDataParallel(model, 2025-09-07T07:36:50.7369183Z >>> device_ids=[args.local_rank], 2025-09-07T07:36:50.7369324Z >>> output_device=args.local_rank) 2025-09-07T07:36:50.7369403Z 2025-09-07T07:36:50.7369660Z Please ensure that ``device_ids`` argument is set to be the only GPU device id 2025-09-07T07:36:50.7369938Z that your code will be operating on. This is generally the local rank of the 2025-09-07T07:36:50.7370192Z process. In other words, the ``device_ids`` needs to be ``[args.local_rank]``, 2025-09-07T07:36:50.7370410Z and ``output_device`` needs to be ``args.local_rank`` in order to use this 2025-09-07T07:36:50.7370494Z utility 2025-09-07T07:36:50.7370587Z 2025-09-07T07:36:50.7370830Z 5. Another way to pass ``local_rank`` to the subprocesses via environment variable 2025-09-07T07:36:50.7371057Z ``LOCAL_RANK``. This behavior is enabled when you launch the script with 2025-09-07T07:36:50.7371277Z ``--use-env=True``. You must adjust the subprocess example above to replace 2025-09-07T07:36:50.7371498Z ``args.local_rank`` with ``os.environ['LOCAL_RANK']``; the launcher 2025-09-07T07:36:50.7371680Z will not pass ``--local-rank`` when you specify this flag. 2025-09-07T07:36:50.7371760Z 2025-09-07T07:36:50.7371863Z .. warning:: 2025-09-07T07:36:50.7371944Z 2025-09-07T07:36:50.7372144Z ``local_rank`` is NOT globally unique: it is only unique per process 2025-09-07T07:36:50.7372345Z on a machine. Thus, don't use it to decide if you should, e.g., 2025-09-07T07:36:50.7372466Z write to a networked filesystem. See 2025-09-07T07:36:50.7372698Z https://github.com/pytorch/pytorch/issues/12042 for an example of 2025-09-07T07:36:50.7372862Z how things can go wrong if you don't do this correctly. 2025-09-07T07:36:50.7372941Z 2025-09-07T07:36:50.7373032Z 2025-09-07T07:36:50.7373109Z 2025-09-07T07:36:50.7373197Z 2025-09-07T07:36:50.7373446Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.7373522Z 2025-09-07T07:36:50.7373631Z warnings.warn(msg) 2025-09-07T07:36:50.7373707Z 2025-09-07T07:36:50.7373924Z --- Parse Warning: 55 / 146 --- 2025-09-07T07:36:50.7374990Z /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. 2025-09-07T07:36:50.7375283Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.7375450Z 2025-09-07T07:36:50.7375689Z Creates an :class:`ShardedTensor` from local shards and the global metadata. 2025-09-07T07:36:50.7375844Z Needs to be called on all ranks in an SPMD fashion. 2025-09-07T07:36:50.7375921Z 2025-09-07T07:36:50.7376049Z Args: 2025-09-07T07:36:50.7376333Z local_shards (List[:class `torch.distributed._shard.sharded_tensor.Shard`]): A list 2025-09-07T07:36:50.7376502Z of shards that represent the local shards on this rank. 2025-09-07T07:36:50.7376742Z global_size (int...): a list, tuple, or `torch.Size` of integers defining the 2025-09-07T07:36:50.7376861Z shape of the overall sharded tensor. 2025-09-07T07:36:50.7376939Z 2025-09-07T07:36:50.7377043Z Keyword args: 2025-09-07T07:36:50.7377329Z process_group (ProcessGroup, optional): The process group to work on. If None, 2025-09-07T07:36:50.7377464Z the default process group will be used. 2025-09-07T07:36:50.7377634Z init_rrefs (bool, optional): Whether or not to initialize 2025-09-07T07:36:50.7377840Z :class:`torch.distributed.rpc.RRef`s pointing to remote shards. 2025-09-07T07:36:50.7378040Z Need to initialize the RPC Framework if specified as ``True``. 2025-09-07T07:36:50.7378137Z Default: ``False``. 2025-09-07T07:36:50.7378226Z 2025-09-07T07:36:50.7378306Z Returns: 2025-09-07T07:36:50.7378456Z A :class:`ShardedTensor` object handle on this rank 2025-09-07T07:36:50.7378548Z 2025-09-07T07:36:50.7378627Z 2025-09-07T07:36:50.7378712Z Examples: 2025-09-07T07:36:50.7378972Z Suppose we want construct a sharded tensor on two ranks, global size = (10, 5), 2025-09-07T07:36:50.7379158Z each shard have a (5, 5) local tensor, we can do it like below: 2025-09-07T07:36:50.7379248Z 2025-09-07T07:36:50.7379334Z on rank 0: 2025-09-07T07:36:50.7379456Z >>> # xdoctest: +SKIP("not distributed") 2025-09-07T07:36:50.7379587Z >>> local_shard_metadata = ShardMetadata( 2025-09-07T07:36:50.7379691Z >>> shard_offsets=[0, 0], 2025-09-07T07:36:50.7379808Z >>> shard_lengths=[5, 5], 2025-09-07T07:36:50.7379916Z >>> placement="rank:0/cuda:0" 2025-09-07T07:36:50.7379995Z >>> ) 2025-09-07T07:36:50.7380199Z >>> local_shards = [Shard(torch.randn(5, 5), local_shard_metadata)] 2025-09-07T07:36:50.7380388Z >>> sharded_tensor = init_from_local_shards(local_shards, [10, 5]) 2025-09-07T07:36:50.7380503Z 2025-09-07T07:36:50.7380586Z on rank 1: 2025-09-07T07:36:50.7380703Z >>> # xdoctest: +SKIP("not distributed") 2025-09-07T07:36:50.7380835Z >>> local_shard_metadata = ShardMetadata( 2025-09-07T07:36:50.7380935Z >>> shard_offsets=[5, 0], 2025-09-07T07:36:50.7381044Z >>> shard_lengths=[5, 5], 2025-09-07T07:36:50.7381148Z >>> placement="rank:1/cuda:1" 2025-09-07T07:36:50.7381229Z >>> ) 2025-09-07T07:36:50.7381430Z >>> local_shards = [Shard(torch.randn(5, 5), local_shard_metadata)] 2025-09-07T07:36:50.7381622Z >>> sharded_tensor = init_from_local_shards(local_shards, [10, 5]) 2025-09-07T07:36:50.7381709Z 2025-09-07T07:36:50.7381957Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.7382033Z 2025-09-07T07:36:50.7382139Z warnings.warn(msg) 2025-09-07T07:36:50.7382213Z 2025-09-07T07:36:50.7382407Z --- Parse Warning: 56 / 146 --- 2025-09-07T07:36:50.7383513Z /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=835. 2025-09-07T07:36:50.7383805Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.7383898Z 2025-09-07T07:36:50.7384158Z Initialize a ShardedTensor given only one local tensor, global sharded tensor 2025-09-07T07:36:50.7384314Z size and sharding spec on each rank. 2025-09-07T07:36:50.7384392Z 2025-09-07T07:36:50.7384473Z Args: 2025-09-07T07:36:50.7384708Z local_tensor (Tensor): Single tensor of local shard stored in each rank. 2025-09-07T07:36:50.7384964Z sharding_spec (:class:`torch.distributed._shard.sharding_spec.ShardingSpec`): 2025-09-07T07:36:50.7385143Z The specification describing how to shard the Tensor. 2025-09-07T07:36:50.7385311Z global_size (Sequence[int]): Size of the sharded tensor. 2025-09-07T07:36:50.7385560Z process_group (ProcessGroup, optional): The process group to aggregate on. 2025-09-07T07:36:50.7385667Z Default: None 2025-09-07T07:36:50.7385859Z init_rrefs (bool, optional): Whether or not to initialize 2025-09-07T07:36:50.7386074Z :class:`torch.distributed.rpc.RRef`s pointing to remote shards. 2025-09-07T07:36:50.7386263Z Need to initialize the RPC Framework if specified as ``True``. 2025-09-07T07:36:50.7386360Z Default: ``False``. 2025-09-07T07:36:50.7386450Z 2025-09-07T07:36:50.7386532Z Returns: 2025-09-07T07:36:50.7386772Z A :class:`ShardedTensor` sharded based on the given sharding_spec with local 2025-09-07T07:36:50.7386896Z tensor stored in the current rank. 2025-09-07T07:36:50.7386974Z 2025-09-07T07:36:50.7387067Z Examples: 2025-09-07T07:36:50.7387163Z >>> # xdoctest: +SKIP 2025-09-07T07:36:50.7387294Z >>> # All tensors below are of torch.int64 type. 2025-09-07T07:36:50.7387418Z >>> # We have 2 process groups, 2 ranks. 2025-09-07T07:36:50.7387592Z >>> tensor = torch.arange(2, dtype=torch.int64) + 1 + 2 * rank 2025-09-07T07:36:50.7387800Z >>> local_tensor = torch.unsqueeze(torch.cat([tensor, tensor + 2])) 2025-09-07T07:36:50.7387889Z >>> local_tensor 2025-09-07T07:36:50.7387984Z tensor([[1, 2, 3, 4]]) # Rank 0 2025-09-07T07:36:50.7388092Z tensor([[3, 4, 5, 6]]) # Rank 1 2025-09-07T07:36:50.7388185Z >>> sharding_dim = 0 2025-09-07T07:36:50.7388315Z >>> sharding_spec = ChunkShardingSpec( 2025-09-07T07:36:50.7388411Z dim=sharding_dim, 2025-09-07T07:36:50.7388502Z placements=[ 2025-09-07T07:36:50.7388604Z "rank:0/cuda:0", 2025-09-07T07:36:50.7388694Z "rank:1/cuda:1", 2025-09-07T07:36:50.7388786Z ], 2025-09-07T07:36:50.7388902Z ) 2025-09-07T07:36:50.7389034Z >>> st = ShardedTensor._init_from_local_tensor( 2025-09-07T07:36:50.7389167Z ... local_tensor, sharding_spec, [2, 4] 2025-09-07T07:36:50.7389246Z ... ) 2025-09-07T07:36:50.7389327Z >>> st 2025-09-07T07:36:50.7389428Z ShardedTensor( 2025-09-07T07:36:50.7389534Z ShardedTensorMetadata( 2025-09-07T07:36:50.7389642Z shards_metadata=[ 2025-09-07T07:36:50.7389913Z ShardMetadata(shard_offsets=[0, 0], shard_sizes=[1, 4], placement=rank:0/cuda:0), 2025-09-07T07:36:50.7390174Z ShardMetadata(shard_offsets=[1, 0], shard_sizes=[1, 4], placement=rank:1/cuda:1), 2025-09-07T07:36:50.7390265Z ], 2025-09-07T07:36:50.7390365Z size=torch.Size([2, 4]) 2025-09-07T07:36:50.7390456Z ) 2025-09-07T07:36:50.7390548Z >>> st.local_tensor() 2025-09-07T07:36:50.7390643Z tensor([1, 2, 3, 4]) # Rank 0 2025-09-07T07:36:50.7390749Z tensor([3, 4, 5, 6]) # Rank 1 2025-09-07T07:36:50.7390825Z 2025-09-07T07:36:50.7391105Z Warning: This API is experimental and subject to change. It lacks of a fully across 2025-09-07T07:36:50.7391370Z rank validations, and we only validate the local shard on the current rank. 2025-09-07T07:36:50.7391585Z We fully rely on the user to ensure local tensor is sharded based on the 2025-09-07T07:36:50.7391698Z sharding spec. 2025-09-07T07:36:50.7391774Z 2025-09-07T07:36:50.7392063Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.7392141Z 2025-09-07T07:36:50.7392235Z warnings.warn(msg) 2025-09-07T07:36:50.7392324Z 2025-09-07T07:36:50.7392519Z --- Parse Warning: 57 / 146 --- 2025-09-07T07:36:50.7393578Z /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=1076. 2025-09-07T07:36:50.7393839Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.7393919Z 2025-09-07T07:36:50.7394181Z Reshard a sharded tensor given the ``resharding_spec``. For now, we only support 2025-09-07T07:36:50.7394301Z single local shard. 2025-09-07T07:36:50.7394477Z 2025-09-07T07:36:50.7394700Z If ``resharding_spec`` is same as the original one, this becomes a no-op. 2025-09-07T07:36:50.7394943Z If only ``resharding_spec`` shares the same sharding dim with the original one, 2025-09-07T07:36:50.7395065Z we swap local shards directly. 2025-09-07T07:36:50.7395324Z For more generic cases, we merge different shards across different ranks and split 2025-09-07T07:36:50.7395585Z the local shards based on the ``resharding_spec`` via `all_to_all` collective API. 2025-09-07T07:36:50.7395661Z 2025-09-07T07:36:50.7395743Z Args: 2025-09-07T07:36:50.7396045Z resharding_spec (:class:`torch.distributed._shard.sharding_spec.ShardingSpec`): The 2025-09-07T07:36:50.7396206Z specification describing how the tensor is sharded. 2025-09-07T07:36:50.7396297Z 2025-09-07T07:36:50.7396379Z Returns: 2025-09-07T07:36:50.7396579Z A :class:`ShardedTensor` object whose local shards are resharded. 2025-09-07T07:36:50.7396665Z 2025-09-07T07:36:50.7396749Z Examples: 2025-09-07T07:36:50.7396856Z >>> # xdoctest: +SKIP 2025-09-07T07:36:50.7396978Z >>> # We have 2 process groups, 2 ranks. 2025-09-07T07:36:50.7397151Z >>> tensor = torch.arange(4, dtype=torch.int64) + 1 + 2 * rank 2025-09-07T07:36:50.7397283Z >>> tensor = torch.stack([tensor, tensor]) 2025-09-07T07:36:50.7397364Z >>> tensor 2025-09-07T07:36:50.7397491Z tensor([[1, 2, 3, 4], [1, 2, 3, 4]]) # Rank 0 2025-09-07T07:36:50.7397607Z tensor([[3, 4, 5, 6], [3, 4, 5, 6]]) # Rank 1 2025-09-07T07:36:50.7397750Z tensor([[5, 6, 7, 8], [5, 6, 7, 8]]) # Rank 2 2025-09-07T07:36:50.7397885Z tensor([[7, 8, 9, 10], [7, 8, 9, 10]]) # Rank 3 2025-09-07T07:36:50.7397981Z >>> sharding_dim = 0 2025-09-07T07:36:50.7398103Z >>> spec = ChunkShardingSpec( 2025-09-07T07:36:50.7398201Z dim=sharding_dim, 2025-09-07T07:36:50.7398296Z placements=[ 2025-09-07T07:36:50.7398403Z "rank:0/cuda:0", 2025-09-07T07:36:50.7398496Z "rank:1/cuda:1", 2025-09-07T07:36:50.7398588Z "rank:2/cuda:2", 2025-09-07T07:36:50.7398692Z "rank:3/cuda:3", 2025-09-07T07:36:50.7398772Z ], 2025-09-07T07:36:50.7398865Z ) 2025-09-07T07:36:50.7398970Z >>> current_offsets = [0] * 2 2025-09-07T07:36:50.7399075Z >>> current_offsets[0] = rank * 2 2025-09-07T07:36:50.7399200Z >>> shard_metadata = ShardMetadata( 2025-09-07T07:36:50.7399354Z shard_offsets=copy.deepcopy(current_offsets), 2025-09-07T07:36:50.7399475Z shard_sizes=tensor.size(), 2025-09-07T07:36:50.7399599Z placement=spec.placements[rank], 2025-09-07T07:36:50.7399704Z ) 2025-09-07T07:36:50.7399807Z >>> local_shards = [ 2025-09-07T07:36:50.7399890Z Shard( 2025-09-07T07:36:50.7400003Z tensor=tensor, 2025-09-07T07:36:50.7400114Z metadata=shard_metadata, 2025-09-07T07:36:50.7400195Z ) 2025-09-07T07:36:50.7400316Z ] 2025-09-07T07:36:50.7400544Z >>> st = ShardedTensor._init_from_local_shards(local_shards, tensor.size()) 2025-09-07T07:36:50.7400638Z >>> sharding_dim = 1 2025-09-07T07:36:50.7400774Z >>> resharding_spec = ChunkShardingSpec( 2025-09-07T07:36:50.7400871Z dim=sharding_dim, 2025-09-07T07:36:50.7400979Z placements=[ 2025-09-07T07:36:50.7401073Z "rank:0/cuda:0", 2025-09-07T07:36:50.7401164Z "rank:1/cuda:1", 2025-09-07T07:36:50.7401268Z "rank:2/cuda:2", 2025-09-07T07:36:50.7401357Z "rank:3/cuda:3", 2025-09-07T07:36:50.7401451Z ], 2025-09-07T07:36:50.7401530Z ) 2025-09-07T07:36:50.7401635Z >>> st.reshard(resharding_spec) 2025-09-07T07:36:50.7401782Z >>> tensor = st.local_shards()[0].tensor 2025-09-07T07:36:50.7401863Z >>> tensor 2025-09-07T07:36:50.7402013Z tensor([[1], [1], [3], [3], [5], [5], [7], [7]]) # Rank 0 2025-09-07T07:36:50.7402151Z tensor([[2], [2], [4], [4], [6], [6], [8], [8]]) # Rank 1 2025-09-07T07:36:50.7402285Z tensor([[3], [3], [5], [5], [7], [7], [9], [9]]) # Rank 2 2025-09-07T07:36:50.7402435Z tensor([[4], [4], [6], [6], [8], [8], [10], [10]]) # Rank 3 2025-09-07T07:36:50.7402512Z 2025-09-07T07:36:50.7402770Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.7402845Z 2025-09-07T07:36:50.7402945Z warnings.warn(msg) 2025-09-07T07:36:50.7403036Z 2025-09-07T07:36:50.7403234Z --- Parse Warning: 58 / 146 --- 2025-09-07T07:36:50.7404235Z /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. 2025-09-07T07:36:50.7404496Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.7404573Z 2025-09-07T07:36:50.7404801Z Representation of a sharding plan, describes how to shard a module 2025-09-07T07:36:50.7405071Z across hosts. `plan` is used to shard module parameters according to the spec provided, 2025-09-07T07:36:50.7405368Z `output_plan` and `return_local_tensor` are optional, they are used to specify the output 2025-09-07T07:36:50.7405639Z layout of a module with a spec, and when to convert back to data parallel fashion. 2025-09-07T07:36:50.7405716Z 2025-09-07T07:36:50.7405812Z Args: 2025-09-07T07:36:50.7406082Z plan (Dict[str, Union[:class:`torch.distributed._shard.sharding_spec.ShardingSpec`, 2025-09-07T07:36:50.7406258Z :class:`torch.distributed._shard.sharder.Sharder`]): 2025-09-07T07:36:50.7406528Z a dict describes how to shard a module, there're currently two ways to shard a module: 2025-09-07T07:36:50.7406781Z 1. directly shard a module parameter by a `ShardingSpec`, keyed by the name of 2025-09-07T07:36:50.7406916Z a parameter to a `ShardingSpec`. 2025-09-07T07:36:50.7407169Z 2. shard a submodule by applying a `Sharder` on it, keyed by the name of a module 2025-09-07T07:36:50.7407277Z to a `Sharder` object. 2025-09-07T07:36:50.7407596Z output_plan (Dict[str, :class:`torch.distributed._shard.sharding_spec.ShardingSpec`), optional): 2025-09-07T07:36:50.7407850Z a dict specifies the layout of a module's output which produces a ShardedTensor, 2025-09-07T07:36:50.7408082Z keyed by the name of module to ShardingSpec("" in key means the root module). 2025-09-07T07:36:50.7408195Z Default: `None` 2025-09-07T07:36:50.7408451Z return_local_tensor (List[str], optional): a list of string, each element enables 2025-09-07T07:36:50.7408687Z a module's sharded output to be returned as a Tensor from its local shards to 2025-09-07T07:36:50.7408947Z ensure further processing in a data parallel fashion. ("" in list means the 2025-09-07T07:36:50.7409063Z root module). 2025-09-07T07:36:50.7409149Z Default: None 2025-09-07T07:36:50.7409243Z Example: 2025-09-07T07:36:50.7409528Z Suppose we want to shard a module with two linear layers and then run it with DDP, we also 2025-09-07T07:36:50.7409827Z want to convert the output of the second linear layer back to DDP, we can do it as follows: 2025-09-07T07:36:50.7409906Z 2025-09-07T07:36:50.7410073Z >>> # xdoctest: +REQUIRES(module:torch._C._distributed_c10d) 2025-09-07T07:36:50.7410191Z >>> class MyModule(nn.Module): 2025-09-07T07:36:50.7410299Z >>> def __init__(self) -> None: 2025-09-07T07:36:50.7410409Z >>> super().__init__() 2025-09-07T07:36:50.7410535Z >>> self.fc1 = nn.Linear() 2025-09-07T07:36:50.7410636Z >>> self.gelu = nn.GELU() 2025-09-07T07:36:50.7410746Z >>> self.fc2 = nn.Linear() 2025-09-07T07:36:50.7410852Z >>> self.relu = nn.Linear() 2025-09-07T07:36:50.7410933Z >>> 2025-09-07T07:36:50.7411049Z >>> def forward(self, input): 2025-09-07T07:36:50.7411219Z >>> return self.relu(self.fc2(self.gelu(self.fc1(input)))) 2025-09-07T07:36:50.7411308Z 2025-09-07T07:36:50.7411383Z 2025-09-07T07:36:50.7411509Z >>> # xdoctest: +SKIP("Undefined spec1, spec2) 2025-09-07T07:36:50.7411634Z >>> sharding_plan = ShardingPlan( 2025-09-07T07:36:50.7411718Z >>> plan={ 2025-09-07T07:36:50.7411829Z >>> "fc1.weight": spec1, 2025-09-07T07:36:50.7411927Z >>> "fc2.weight": spec2 2025-09-07T07:36:50.7412009Z >>> }, 2025-09-07T07:36:50.7412115Z >>> output_plan={ 2025-09-07T07:36:50.7412211Z >>> "fc2": output_spec 2025-09-07T07:36:50.7412304Z >>> }, 2025-09-07T07:36:50.7412410Z >>> return_local_tensor=["fc2"] 2025-09-07T07:36:50.7412493Z >>> ) 2025-09-07T07:36:50.7412581Z 2025-09-07T07:36:50.7412833Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.7412909Z 2025-09-07T07:36:50.7413016Z warnings.warn(msg) 2025-09-07T07:36:50.7413093Z 2025-09-07T07:36:50.7413297Z --- Parse Warning: 59 / 146 --- 2025-09-07T07:36:50.7414435Z /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. 2025-09-07T07:36:50.7414707Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.7414784Z 2025-09-07T07:36:50.7414891Z Run post-localSGD algorithm. 2025-09-07T07:36:50.7414980Z 2025-09-07T07:36:50.7415215Z This DDP communication hook is used for running post-localSGD algorithm, 2025-09-07T07:36:50.7415374Z by combining with a model averaging component (e.g., 2025-09-07T07:36:50.7415712Z :class:`~torch.distributed.algorithms.model_averaging.averagers.PeriodicModelAverager`) 2025-09-07T07:36:50.7415822Z that runs after the optimizer step. 2025-09-07T07:36:50.7415915Z 2025-09-07T07:36:50.7415996Z Args: 2025-09-07T07:36:50.7416215Z state (PostLocalSGDState): State information to run post-localSGD. 2025-09-07T07:36:50.7416503Z Users mainly need to tune ``start_localSGD_iter`` to determine when to start local SGD. 2025-09-07T07:36:50.7416926Z bucket (dist.GradBucket): Bucket that stores a 1D flattened gradient tensor that batches multiple per-variable tensors. 2025-09-07T07:36:50.7417209Z Note that since DDP comm hook only supports single process single device mode, 2025-09-07T07:36:50.7417361Z only exactly one tensor is stored in this bucket. 2025-09-07T07:36:50.7417449Z 2025-09-07T07:36:50.7417529Z Returns: 2025-09-07T07:36:50.7417794Z Future handler of the communication, which updates the gradients in place. 2025-09-07T07:36:50.7417882Z 2025-09-07T07:36:50.7417972Z Example:: 2025-09-07T07:36:50.7418067Z >>> # xdoctest: +SKIP 2025-09-07T07:36:50.7418328Z >>> state = PostLocalSGDState(process_group=process_group, subgroup=subgroup, 2025-09-07T07:36:50.7418442Z start_localSGD_iter=10) 2025-09-07T07:36:50.7418624Z >>> ddp_model.register_comm_hook(state, post_localSGD_hook) 2025-09-07T07:36:50.7418957Z >>> # Also need to establish a model averaging module and run model averaging after ``optimizer.step()``. 2025-09-07T07:36:50.7419313Z >>> # Please refer to the examples in ``torch.distributed.algorithms.model_averaging.averagers`` module. 2025-09-07T07:36:50.7419391Z 2025-09-07T07:36:50.7419677Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.7419765Z 2025-09-07T07:36:50.7419861Z warnings.warn(msg) 2025-09-07T07:36:50.7419936Z 2025-09-07T07:36:50.7420134Z --- Parse Warning: 60 / 146 --- 2025-09-07T07:36:50.7421207Z /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. 2025-09-07T07:36:50.7421478Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.7421556Z 2025-09-07T07:36:50.7421673Z Implement PowerSGD algorithm. 2025-09-07T07:36:50.7421750Z 2025-09-07T07:36:50.7421975Z This DDP communication hook implements PowerSGD gradient compression 2025-09-07T07:36:50.7422226Z algorithm described in the `paper `_. 2025-09-07T07:36:50.7422466Z Once gradient tensors are aggregated across all workers, this hook applies 2025-09-07T07:36:50.7422583Z compression as follows: 2025-09-07T07:36:50.7422660Z 2025-09-07T07:36:50.7423098Z 1. Views the input flattened 1D gradient tensor as a list of per-parameter tensors, and divides all the tensors into two groups: 2025-09-07T07:36:50.7423185Z 2025-09-07T07:36:50.7423602Z 1.1 The tensors that should be compressed before allreduce, because the compression can give enough saving in bandwidth. 2025-09-07T07:36:50.7423861Z 2025-09-07T07:36:50.7424345Z 1.2 Rest of the tensors will be directly allreduced without compression, including all the vector tensors (for biases). 2025-09-07T07:36:50.7424423Z 2025-09-07T07:36:50.7424543Z 2. Handles uncompressed tensors: 2025-09-07T07:36:50.7424623Z 2025-09-07T07:36:50.7425139Z 2.1. Allocate contiguous memory for those uncompressed tensors, and allreduces all the uncompressed tensors as a batch, without compression; 2025-09-07T07:36:50.7425231Z 2025-09-07T07:36:50.7425571Z 2.2. Copies the individual uncompressed tensors from the contiguous memory back to the input tensor. 2025-09-07T07:36:50.7425663Z 2025-09-07T07:36:50.7425896Z 3. Handles the tensors that should be compressed by PowerSGD compression: 2025-09-07T07:36:50.7425973Z 2025-09-07T07:36:50.7426224Z 3.1. For each tensor M, creates two low-rank tensors P and Q for decomposing M, 2025-09-07T07:36:50.7426532Z such that M = PQ^T, where Q is initialized from a standard normal distribution and orthogonalized; 2025-09-07T07:36:50.7426624Z 2025-09-07T07:36:50.7426767Z 3.2. Computes each P in Ps, which is equal to MQ; 2025-09-07T07:36:50.7426842Z 2025-09-07T07:36:50.7426958Z 3.3. Allreduces Ps as a batch; 2025-09-07T07:36:50.7427073Z 2025-09-07T07:36:50.7427195Z 3.4. Orthogonalizes each P in Ps; 2025-09-07T07:36:50.7427271Z 2025-09-07T07:36:50.7427465Z 3.5. Computes each Q in Qs, which is approximately equal to M^TP; 2025-09-07T07:36:50.7427552Z 2025-09-07T07:36:50.7427688Z 3.6. Allreduces Qs as a batch; 2025-09-07T07:36:50.7427773Z 2025-09-07T07:36:50.7428068Z 3.7. Computes each M among all the compressed tensors, which is approximately equal to PQ^T. 2025-09-07T07:36:50.7428144Z 2025-09-07T07:36:50.7428570Z Note that this communication hook enforces vanilla allreduce for the first ``state.start_powerSGD_iter`` iterations. 2025-09-07T07:36:50.7428854Z This not only gives the user more control over the tradeoff between speedup and accuracy, 2025-09-07T07:36:50.7429297Z but also helps abstract away some complexity of the internal optimization of DDP for future communication hook developers. 2025-09-07T07:36:50.7429385Z 2025-09-07T07:36:50.7429467Z Args: 2025-09-07T07:36:50.7429954Z state (PowerSGDState): State information to configure the compression rate and support error feedback, warm start, etc. 2025-09-07T07:36:50.7430312Z To tune the compression configs, mainly need to tune ``matrix_approximation_rank``, ``start_powerSGD_iter`` 2025-09-07T07:36:50.7430437Z and ``min_compression_rate``. 2025-09-07T07:36:50.7430854Z bucket (dist.GradBucket): Bucket that stores a 1D flattened gradient tensor that batches multiple per-variable tensors. 2025-09-07T07:36:50.7431119Z Note that since DDP comm hook only supports single process single device mode, 2025-09-07T07:36:50.7431273Z only exactly one tensor is stored in this bucket. 2025-09-07T07:36:50.7431351Z 2025-09-07T07:36:50.7431450Z Returns: 2025-09-07T07:36:50.7431692Z Future handler of the communication, which updates the gradients in place. 2025-09-07T07:36:50.7431770Z 2025-09-07T07:36:50.7431874Z Example:: 2025-09-07T07:36:50.7431973Z >>> # xdoctest: +SKIP 2025-09-07T07:36:50.7432264Z >>> state = PowerSGDState(process_group=process_group, matrix_approximation_rank=1, 2025-09-07T07:36:50.7432428Z start_powerSGD_iter=10, min_compression_rate=0.5) 2025-09-07T07:36:50.7432588Z >>> ddp_model.register_comm_hook(state, powerSGD_hook) 2025-09-07T07:36:50.7432683Z 2025-09-07T07:36:50.7432935Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.7433029Z 2025-09-07T07:36:50.7433127Z warnings.warn(msg) 2025-09-07T07:36:50.7433204Z 2025-09-07T07:36:50.7433420Z --- Parse Warning: 61 / 146 --- 2025-09-07T07:36:50.7434634Z /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. 2025-09-07T07:36:50.7434912Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.7434992Z 2025-09-07T07:36:50.7435177Z Averages parameters periodically after the warm-up stage. 2025-09-07T07:36:50.7435272Z 2025-09-07T07:36:50.7435535Z This can be used for running `post-local SGD `_, 2025-09-07T07:36:50.7435743Z by running :class:`~torch.nn.DistributedDataParallel` (DDP) 2025-09-07T07:36:50.7435980Z using the subgroups created by :meth:`~torch.distributed.new_subgroups`. 2025-09-07T07:36:50.7436058Z 2025-09-07T07:36:50.7436157Z Args: 2025-09-07T07:36:50.7436360Z period (int): The number of steps per model averaging. 2025-09-07T07:36:50.7436642Z Usually the period should be greater than ``1`` to reduce the communication cost. 2025-09-07T07:36:50.7436772Z Otherwise, only DDP needs to be used. 2025-09-07T07:36:50.7437006Z warmup_steps (int): The number of warm-up steps. During this stage, 2025-09-07T07:36:50.7437140Z model averaging is skipped. 2025-09-07T07:36:50.7437326Z process_group: The process group to be used for all-reduce. 2025-09-07T07:36:50.7437481Z If ``None``, the default process group, which 2025-09-07T07:36:50.7437734Z is created by :func:`torch.distributed.init_process_group`, 2025-09-07T07:36:50.7437870Z will be used. (default: ``None``) 2025-09-07T07:36:50.7437952Z 2025-09-07T07:36:50.7438042Z Example:: 2025-09-07T07:36:50.7438138Z 2025-09-07T07:36:50.7438266Z >>> # xdoctest: +SKIP("undefined variables") 2025-09-07T07:36:50.7438355Z >>> import torch 2025-09-07T07:36:50.7438485Z >>> import torch.distributed as dist 2025-09-07T07:36:50.7438797Z >>> import torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook as post_localSGD 2025-09-07T07:36:50.7439073Z >>> import torch.distributed.algorithms.model_averaging.averagers as averagers 2025-09-07T07:36:50.7439189Z >>> import torch.nn as nn 2025-09-07T07:36:50.7439294Z >>> 2025-09-07T07:36:50.7439481Z >>> dist.init_process_group("nccl", rank=rank, world_size=16) 2025-09-07T07:36:50.7439592Z >>> torch.cuda.set_device(rank) 2025-09-07T07:36:50.7439736Z >>> module = nn.Linear(1, 1, bias=False).cuda() 2025-09-07T07:36:50.7439885Z >>> model = nn.parallel.DistributedDataParallel( 2025-09-07T07:36:50.7440018Z >>> module, device_ids=[rank], output_device=rank 2025-09-07T07:36:50.7440109Z >>> ) 2025-09-07T07:36:50.7440250Z >>> # Register a post-localSGD communication hook. 2025-09-07T07:36:50.7440557Z >>> state = PostLocalSGDState(process_group=None, subgroup=None, start_localSGD_iter=100) 2025-09-07T07:36:50.7440716Z >>> model.register_comm_hook(state, post_localSGD_hook) 2025-09-07T07:36:50.7440795Z >>> 2025-09-07T07:36:50.7441074Z >>> # In the first 100 steps, run global gradient averaging like normal DDP at every step. 2025-09-07T07:36:50.7441230Z >>> # After 100 steps, run model averaging every 4 steps. 2025-09-07T07:36:50.7441558Z >>> # Note that ``warmup_steps`` must be the same as ``start_localSGD_iter`` used in ``PostLocalSGDState``. 2025-09-07T07:36:50.7441806Z >>> averager = averagers.PeriodicModelAverager(period=4, warmup_steps=100) 2025-09-07T07:36:50.7441911Z >>> for step in range(0, 200): 2025-09-07T07:36:50.7442026Z >>> optimizer.zero_grad() 2025-09-07T07:36:50.7442141Z >>> loss = loss_fn(output, labels) 2025-09-07T07:36:50.7442251Z >>> loss.backward() 2025-09-07T07:36:50.7442350Z >>> optimizer.step() 2025-09-07T07:36:50.7442572Z >>> # Will average model parameters globally every 4 steps. Thus, 2025-09-07T07:36:50.7442790Z >>> # inter-node communication only occurs every 4 iterations after 2025-09-07T07:36:50.7442913Z >>> # the initial ``warmup_steps`` period. 2025-09-07T07:36:50.7443087Z >>> averager.average_parameters(model.parameters()) 2025-09-07T07:36:50.7443165Z 2025-09-07T07:36:50.7443416Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.7443506Z 2025-09-07T07:36:50.7443601Z warnings.warn(msg) 2025-09-07T07:36:50.7443687Z 2025-09-07T07:36:50.7443887Z --- Parse Warning: 62 / 146 --- 2025-09-07T07:36:50.7445110Z /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. 2025-09-07T07:36:50.7445379Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.7445458Z 2025-09-07T07:36:50.7445802Z Runs hierarchical model averaging (`hierarchical SGD `_). 2025-09-07T07:36:50.7445908Z 2025-09-07T07:36:50.7446217Z Process groups of different sizes are organized in a hierarchy, and they average parameters 2025-09-07T07:36:50.7446431Z by using different periods concurrently after the warm-up stage. 2025-09-07T07:36:50.7446866Z This is an extension of :class:`~torch.distributed.algorithms.model_averaging.averagers.PeriodicModelAverager` 2025-09-07T07:36:50.7447213Z that supports `post-local SGD `_, which essentially only supports 2025-09-07T07:36:50.7447520Z a two-level hierarchy: the intra-machine level and the global level, where the intra-machine 2025-09-07T07:36:50.7447885Z level is usually embedded in :meth:`~torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook`. 2025-09-07T07:36:50.7448186Z Similarly, the process groups within this class do not have such an intra-machine process 2025-09-07T07:36:50.7448461Z subgroup, which should be embedded by the post-local SGD communication hook instead. 2025-09-07T07:36:50.7448552Z 2025-09-07T07:36:50.7448660Z Args: 2025-09-07T07:36:50.7448936Z period_group_size_dict: An ordered dict mapping keys of model averaging period to 2025-09-07T07:36:50.7449135Z process group size, used for initializing process groups of 2025-09-07T07:36:50.7449361Z different sizes in a hierarchy to average parameters concurrently. 2025-09-07T07:36:50.7449587Z Particularly, at each iteration, there will be at most a single 2025-09-07T07:36:50.7449818Z process group that runs averaging -- the period of such group should 2025-09-07T07:36:50.7450046Z have the largest period which the current step can be divided by. 2025-09-07T07:36:50.7450210Z For example, if the dict has three keys: 2, 4, and 8, 2025-09-07T07:36:50.7450428Z then this means totally three process groups will be created to 2025-09-07T07:36:50.7450637Z average parameters every 2, 4, and 8 iterations, respectively. 2025-09-07T07:36:50.7450824Z At the 4th iteration, only the second process group will run 2025-09-07T07:36:50.7451010Z averaging, because the first process group should be a 2025-09-07T07:36:50.7451227Z subset of the second process group, and no need to execute the first 2025-09-07T07:36:50.7451360Z process group redundantly. 2025-09-07T07:36:50.7451580Z On the other hand, the third process group can only be triggered 2025-09-07T07:36:50.7451800Z every 8 iterations, so it will not be triggered at the 4th iteration. 2025-09-07T07:36:50.7452113Z warmup_steps (int): The number of warm-up steps. During this stage, model averaging is skipped. 2025-09-07T07:36:50.7452558Z process_group (ProcessGroup, optional): The overall process group containing all the processes that runs model averaging. 2025-09-07T07:36:50.7452740Z If ``None``, the default process group, which is created 2025-09-07T07:36:50.7452946Z by :func:`torch.distributed.init_process_group`, will be used. 2025-09-07T07:36:50.7453073Z (default: ``None``) 2025-09-07T07:36:50.7453153Z 2025-09-07T07:36:50.7453241Z Example:: 2025-09-07T07:36:50.7453369Z >>> # xdoctest: +SKIP('undefined rank') 2025-09-07T07:36:50.7453489Z >>> from collections import OrderedDict 2025-09-07T07:36:50.7453595Z >>> import torch 2025-09-07T07:36:50.7453709Z >>> import torch.distributed as dist 2025-09-07T07:36:50.7453980Z >>> from torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook import ( 2025-09-07T07:36:50.7454117Z >>> PostLocalSGDState, 2025-09-07T07:36:50.7454219Z >>> post_localSGD_hook, 2025-09-07T07:36:50.7454300Z >>> ) 2025-09-07T07:36:50.7454684Z >>> import torch.distributed.algorithms.model_averaging.hierarchical_model_averager as hierarchicalSGD 2025-09-07T07:36:50.7454821Z >>> import torch.nn as nn 2025-09-07T07:36:50.7454915Z >>> 2025-09-07T07:36:50.7455091Z >>> dist.init_process_group("nccl", rank=rank, world_size=16) 2025-09-07T07:36:50.7455199Z >>> torch.cuda.set_device(rank) 2025-09-07T07:36:50.7455344Z >>> module = nn.Linear(1, 1, bias=False).to(rank) 2025-09-07T07:36:50.7455494Z >>> model = nn.parallel.DistributedDataParallel( 2025-09-07T07:36:50.7455642Z >>> module, device_ids=[rank], output_device=rank 2025-09-07T07:36:50.7455724Z >>> ) 2025-09-07T07:36:50.7455865Z >>> # Register a post-localSGD communication hook. 2025-09-07T07:36:50.7456153Z >>> # Assume that each machine has 4 GPUs, then each intra-machine subgroup has a size of 4. 2025-09-07T07:36:50.7456989Z >>> subgroup, _ = dist.new_subgroups() 2025-09-07T07:36:50.7457315Z >>> state = PostLocalSGDState(process_group=None, subgroup=subgroup, start_localSGD_iter=100) 2025-09-07T07:36:50.7457478Z >>> model.register_comm_hook(state, post_localSGD_hook) 2025-09-07T07:36:50.7457558Z >>> 2025-09-07T07:36:50.7457850Z >>> # Average parameters among each group of 8 processes every 4 iterations, and among all 2025-09-07T07:36:50.7457972Z >>> # the 16 processes every 16 iterations. 2025-09-07T07:36:50.7458165Z >>> averager = hierarchicalSGD.HierarchicalModelAverager( 2025-09-07T07:36:50.7458396Z >>> period_group_size_dict=OrderedDict([(4, 8), (16, 16)]), warmup_steps=100) 2025-09-07T07:36:50.7458722Z >>> # Note that ``warmup_steps`` must be the same as ``start_localSGD_iter`` used in ``PostLocalSGDState``. 2025-09-07T07:36:50.7458988Z >>> # In the first 100 steps, run global gradient averaging like normal DDP at every step. 2025-09-07T07:36:50.7459140Z >>> # After 100 steps, run model averaging at two levels. 2025-09-07T07:36:50.7459255Z >>> for step in range(0, 200): 2025-09-07T07:36:50.7459360Z >>> optimizer.zero_grad() 2025-09-07T07:36:50.7459484Z >>> loss = loss_fn(output, labels) 2025-09-07T07:36:50.7459580Z >>> loss.backward() 2025-09-07T07:36:50.7459678Z >>> optimizer.step() 2025-09-07T07:36:50.7459875Z >>> # Average parameters after ``optimizer.step()``. 2025-09-07T07:36:50.7460160Z >>> # Thus, the inter-node communication only occurs periodically after ``warmup_steps``. 2025-09-07T07:36:50.7460356Z >>> averager.average_parameters(model.parameters()) 2025-09-07T07:36:50.7460437Z 2025-09-07T07:36:50.7460526Z .. warning :: 2025-09-07T07:36:50.7460791Z The last group size in the dict must be the size of the provided ``process_group``, 2025-09-07T07:36:50.7463684Z which indicates model averaging at the highest level of the hierarchy. 2025-09-07T07:36:50.7463990Z If ``process_group`` is not provided, then the last group size should be equal to the world size. 2025-09-07T07:36:50.7464085Z 2025-09-07T07:36:50.7464175Z .. warning :: 2025-09-07T07:36:50.7464419Z `HierarchicalModelAverager` is experimental and subject to change. 2025-09-07T07:36:50.7464497Z 2025-09-07T07:36:50.7464750Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.7464841Z 2025-09-07T07:36:50.7464936Z warnings.warn(msg) 2025-09-07T07:36:50.7465025Z 2025-09-07T07:36:50.7465262Z --- Parse Warning: 63 / 146 --- 2025-09-07T07:36:50.7466348Z /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. 2025-09-07T07:36:50.7466705Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.7466783Z 2025-09-07T07:36:50.7467084Z StorageReader for reading a Torch Save file. This reader will read the entire checkpoint 2025-09-07T07:36:50.7467331Z on the coordinator rank, and then broadcast and shard each tensor to all ranks. 2025-09-07T07:36:50.7467409Z 2025-09-07T07:36:50.7467604Z . N.B. Intended to be used with DynamicMetaLoadPlanner 2025-09-07T07:36:50.7467681Z 2025-09-07T07:36:50.7467782Z .. warning:: 2025-09-07T07:36:50.7467954Z Current implementation only supports loading Tensors. 2025-09-07T07:36:50.7468038Z 2025-09-07T07:36:50.7468167Z >>> # xdoctest: +SKIP("undefined vars") 2025-09-07T07:36:50.7468263Z >>> sd = {"mode": model} 2025-09-07T07:36:50.7468350Z >>> dcp.load( 2025-09-07T07:36:50.7468446Z >>> sd, 2025-09-07T07:36:50.7468598Z >>> storage_reader=BroadcastingTorchSaveReader(), 2025-09-07T07:36:50.7468766Z >>> planner=DynamicMetaLoadPlanner(), 2025-09-07T07:36:50.7468882Z >>> checkpoint_id="path_to_model.pt" 2025-09-07T07:36:50.7468961Z >>> ) 2025-09-07T07:36:50.7469053Z 2025-09-07T07:36:50.7469306Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.7469400Z 2025-09-07T07:36:50.7469497Z warnings.warn(msg) 2025-09-07T07:36:50.7469575Z 2025-09-07T07:36:50.7469777Z --- Parse Warning: 64 / 146 --- 2025-09-07T07:36:50.7470831Z /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. 2025-09-07T07:36:50.7471107Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.7471189Z 2025-09-07T07:36:50.7471558Z Extension of DefaultLoadPlanner, which creates a new Metadata object based on the passed in state dict, 2025-09-07T07:36:50.7471900Z avoiding the need to read metadata from disk. This is useful when reading formats which don't have a 2025-09-07T07:36:50.7472016Z metadata file, like Torch Save files. 2025-09-07T07:36:50.7472111Z 2025-09-07T07:36:50.7472289Z . N.B. Intended to be used with BroadcastingTorchSaveReader 2025-09-07T07:36:50.7472369Z 2025-09-07T07:36:50.7472473Z .. warning:: 2025-09-07T07:36:50.7472644Z Current implementation only supports loading Tensors. 2025-09-07T07:36:50.7472736Z 2025-09-07T07:36:50.7472852Z >>> # xdoctest: +SKIP("undefined vars") 2025-09-07T07:36:50.7472973Z >>> sd = {"mode": model} 2025-09-07T07:36:50.7473073Z >>> dcp.load( 2025-09-07T07:36:50.7473156Z >>> sd, 2025-09-07T07:36:50.7473317Z >>> storage_reader=BroadcastingTorchSaveReader(), 2025-09-07T07:36:50.7473439Z >>> planner=DynamicMetaLoadPlanner(), 2025-09-07T07:36:50.7473551Z >>> checkpoint_id="path_to_model.pt" 2025-09-07T07:36:50.7473712Z >>> ) 2025-09-07T07:36:50.7473789Z 2025-09-07T07:36:50.7474042Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.7474133Z 2025-09-07T07:36:50.7474227Z warnings.warn(msg) 2025-09-07T07:36:50.7474317Z 2025-09-07T07:36:50.7474599Z --- Parse Warning: 65 / 146 --- 2025-09-07T07:36:50.7475666Z /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. 2025-09-07T07:36:50.7475938Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.7476018Z 2025-09-07T07:36:50.7476238Z Load a state_dict in conjunction with FSDP sharded optimizer state. 2025-09-07T07:36:50.7476354Z 2025-09-07T07:36:50.7476524Z This is the current recommended way to checkpoint FSDP. 2025-09-07T07:36:50.7476629Z >>> # xdoctest: +SKIP 2025-09-07T07:36:50.7476780Z >>> import torch.distributed.checkpoint as dist_cp 2025-09-07T07:36:50.7476878Z >>> # Save 2025-09-07T07:36:50.7476976Z >>> model: torch.nn.Model 2025-09-07T07:36:50.7477094Z >>> optim_params = model.parameters() 2025-09-07T07:36:50.7477246Z >>> optim = torch.optim.SGD(optim_params, lr=0.01) 2025-09-07T07:36:50.7477326Z >>> # Save 2025-09-07T07:36:50.7477553Z >>> with FSDP.state_dict_type(model, StateDictType.SHARDED_STATE_DICT): 2025-09-07T07:36:50.7477645Z >>> state_dict = { 2025-09-07T07:36:50.7477800Z >>> "optimizer": FSDP.optim_state_dict(model, optim), 2025-09-07T07:36:50.7477921Z >>> "model": model.state_dict() 2025-09-07T07:36:50.7478001Z >>> } 2025-09-07T07:36:50.7478117Z >>> dist_cp.save_state_dict( 2025-09-07T07:36:50.7478219Z >>> state_dict=optim_state, 2025-09-07T07:36:50.7478422Z >>> storage_writer=dist_cp.FileSystemWriter("checkpoint"), 2025-09-07T07:36:50.7478566Z >>> planner=dist_cp.DefaultSavePlanner(), 2025-09-07T07:36:50.7478650Z >>> ) 2025-09-07T07:36:50.7478744Z >>> 2025-09-07T07:36:50.7478827Z >>> # Load 2025-09-07T07:36:50.7479058Z >>> with FSDP.state_dict_type(model_tp, StateDictType.SHARDED_STATE_DICT): 2025-09-07T07:36:50.7479201Z >>> model_state_dict = model_tp.state_dict() 2025-09-07T07:36:50.7479294Z >>> checkpoint = { 2025-09-07T07:36:50.7479400Z >>> "model": model_state_dict 2025-09-07T07:36:50.7479492Z >>> } 2025-09-07T07:36:50.7479595Z >>> dist_cp.load_state_dict( 2025-09-07T07:36:50.7479710Z >>> state_dict=checkpoint, 2025-09-07T07:36:50.7479898Z >>> storage_reader=dist_cp.FileSystemReader(checkpoint_file), 2025-09-07T07:36:50.7480030Z >>> planner=dist_cp.DefaultLoadPlanner(), 2025-09-07T07:36:50.7480122Z >>> ) 2025-09-07T07:36:50.7480275Z >>> model.load_state_dict(checkpoint["model_state"]) 2025-09-07T07:36:50.7480367Z >>> 2025-09-07T07:36:50.7480537Z >>> optim_state = dist_cp.load_sharded_optimizer_state_dict( 2025-09-07T07:36:50.7480634Z >>> model_state_dict, 2025-09-07T07:36:50.7480754Z >>> optimizer_key="optimizer", 2025-09-07T07:36:50.7480927Z >>> storage_reader=dist_cp.FileSystemReader("checkpoint"), 2025-09-07T07:36:50.7481018Z >>> ) 2025-09-07T07:36:50.7481099Z >>> 2025-09-07T07:36:50.7481243Z >>> flattened_osd = FSDP.optim_state_dict_to_load( 2025-09-07T07:36:50.7481381Z >>> model, optim, optim_state["optimizer"] 2025-09-07T07:36:50.7481484Z >>> ) 2025-09-07T07:36:50.7481574Z >>> 2025-09-07T07:36:50.7481696Z >>> optim.load_state_dict(flattened_osd) 2025-09-07T07:36:50.7481774Z 2025-09-07T07:36:50.7482036Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.7482160Z 2025-09-07T07:36:50.7482258Z warnings.warn(msg) 2025-09-07T07:36:50.7482352Z 2025-09-07T07:36:50.7482539Z --- Parse Warning: 66 / 146 --- 2025-09-07T07:36:50.7483515Z /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=122. 2025-09-07T07:36:50.7483778Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.7483868Z 2025-09-07T07:36:50.7484156Z Abstract class defining the protocol used by save_state_dict to plan the save process. 2025-09-07T07:36:50.7484236Z 2025-09-07T07:36:50.7484542Z SavePlanners are stateful objects that can be used to customize the whole save process. 2025-09-07T07:36:50.7484620Z 2025-09-07T07:36:50.7484937Z SavePlanner acts as an access proxy to the state_dict, so any transformation done to it 2025-09-07T07:36:50.7485054Z will be visible to the whole process. 2025-09-07T07:36:50.7485133Z 2025-09-07T07:36:50.7485418Z A planner subclass can expect the following sequence of calls during save_state_dict: 2025-09-07T07:36:50.7485496Z 2025-09-07T07:36:50.7485617Z 1) set_up_planner - called on all ranks. 2025-09-07T07:36:50.7485755Z Signals the start of a checkpoint save. 2025-09-07T07:36:50.7485832Z 2025-09-07T07:36:50.7485963Z 2) create_local_plan - called on all ranks. 2025-09-07T07:36:50.7486246Z Process the state_dict and produces a `SavePlan` that will be sent for global planning. 2025-09-07T07:36:50.7486322Z 2025-09-07T07:36:50.7486513Z 3) create_global_plan - called on the coordinator rank only. 2025-09-07T07:36:50.7486708Z Takes the SavePlan from all ranks and make any global decision. 2025-09-07T07:36:50.7486799Z 2025-09-07T07:36:50.7486907Z 4) finish_plan - called on all ranks. 2025-09-07T07:36:50.7487147Z This gives each rank a chance to adjust to global planning decisions. 2025-09-07T07:36:50.7487237Z 2025-09-07T07:36:50.7487385Z 5) resolve_data - called multiple times on each rank 2025-09-07T07:36:50.7487597Z Lookups a value on the `state_dict` for the storage layer to write. 2025-09-07T07:36:50.7487674Z 2025-09-07T07:36:50.7487980Z Users are recommended to extend DefaultSavePlanner instead of this interface directly as 2025-09-07T07:36:50.7488171Z most changes can be expressed by changes in a single method. 2025-09-07T07:36:50.7488249Z 2025-09-07T07:36:50.7488441Z There are 3 usual patterns of extension: 2025-09-07T07:36:50.7488519Z 2025-09-07T07:36:50.7488774Z Rewriting state_dict. This is the simplest way to extend the save process as it 2025-09-07T07:36:50.7489011Z doesn't requite understanding the intrincacies of how SavePlan works: 2025-09-07T07:36:50.7489089Z 2025-09-07T07:36:50.7489214Z >>> # xdoctest: +SKIP("undefined vars") 2025-09-07T07:36:50.7489350Z >>> class RenamePlanner(DefaultSavePlanner): 2025-09-07T07:36:50.7489448Z >>> def set_up_planner( 2025-09-07T07:36:50.7489541Z >>> self, 2025-09-07T07:36:50.7489651Z >>> state_dict: STATE_DICT_TYPE, 2025-09-07T07:36:50.7489786Z >>> storage_meta: Optional[StorageMeta], 2025-09-07T07:36:50.7489888Z >>> is_coordinator: bool, 2025-09-07T07:36:50.7489975Z >>> ) -> None: 2025-09-07T07:36:50.7490092Z >>> # prefix all keys with `foo_`` 2025-09-07T07:36:50.7490383Z >>> super().set_up_planner({"foo_" + k: v for k, v in state_dict.items()}, storage_meta, is_coordinator) 2025-09-07T07:36:50.7490459Z 2025-09-07T07:36:50.7490825Z Modifying local plan and lookup in tandem. This is useful when fine control of how data is persisted 2025-09-07T07:36:50.7490905Z 2025-09-07T07:36:50.7491031Z >>> # xdoctest: +SKIP("undefined vars") 2025-09-07T07:36:50.7491159Z >>> class FP16Planner(DefaultSavePlanner): 2025-09-07T07:36:50.7491312Z >>> def create_local_plan(self): 2025-09-07T07:36:50.7491445Z >>> plan = super().create_local_plan() 2025-09-07T07:36:50.7491540Z >>> for p in plan: 2025-09-07T07:36:50.7491668Z >>> if p.tensor_data is not None: 2025-09-07T07:36:50.7491825Z >>> p.tensor_data.properties.dtype = torch.float16 2025-09-07T07:36:50.7491916Z >>> return plan 2025-09-07T07:36:50.7492008Z >>> 2025-09-07T07:36:50.7492123Z >>> def resolve_data(self, write_item): 2025-09-07T07:36:50.7492259Z >>> item = super().resolve_data(write_item) 2025-09-07T07:36:50.7492533Z >>> return item if write_item.type == WriteItemType.BYTE_IO else item.to(torch.float16) 2025-09-07T07:36:50.7492612Z 2025-09-07T07:36:50.7492966Z Using the global planning step to make central decisions that can't be made individually by each rank 2025-09-07T07:36:50.7493070Z 2025-09-07T07:36:50.7493196Z >>> # xdoctest: +SKIP("undefined vars") 2025-09-07T07:36:50.7493309Z >>> from itertools import zip_longest 2025-09-07T07:36:50.7493419Z >>> from dataclasses import replace 2025-09-07T07:36:50.7493600Z >>> class DDPLoadBalancingPlanner(DefaultSavePlanner): 2025-09-07T07:36:50.7493873Z >>> # This uses the default local plan behavior of having all non-sharded writes in rank 0 2025-09-07T07:36:50.7494015Z >>> # This sample doesn't handle ShardedTensors 2025-09-07T07:36:50.7494143Z >>> def create_global_plan(self, all_plans): 2025-09-07T07:36:50.7494290Z >>> iters = [iter(all_plans[0].items)] * len(all_plans) 2025-09-07T07:36:50.7494403Z >>> items_per_rank = [ 2025-09-07T07:36:50.7494540Z >>> [item for item in items if item is not None] 2025-09-07T07:36:50.7494706Z >>> for items in zip(*zip_longest(*iters), strict=True) 2025-09-07T07:36:50.7494790Z >>> ] 2025-09-07T07:36:50.7494883Z >>> all_plans = [ 2025-09-07T07:36:50.7495005Z >>> replace(plan, items=items) 2025-09-07T07:36:50.7495223Z >>> for plan, items in zip(all_plans, items_per_rank, strict=True) 2025-09-07T07:36:50.7495315Z >>> ] 2025-09-07T07:36:50.7495453Z >>> return super().create_global_plan(all_plans) 2025-09-07T07:36:50.7495531Z 2025-09-07T07:36:50.7495806Z Finally, some planners need to save additional metadata in the checkpoint, this is 2025-09-07T07:36:50.7496074Z accomplished by having each rank contribute their data items in the local plan and 2025-09-07T07:36:50.7496198Z the global planner aggregate them: 2025-09-07T07:36:50.7496276Z 2025-09-07T07:36:50.7496390Z >>> # xdoctest: +SKIP("undefined vars") 2025-09-07T07:36:50.7496590Z >>> class SaveExtraDataPlanner(DefaultSavePlanner): 2025-09-07T07:36:50.7496718Z >>> def create_local_plan(self) -> SavePlan: 2025-09-07T07:36:50.7496859Z >>> plan = super().create_local_plan() 2025-09-07T07:36:50.7497023Z >>> return replace(plan, planner_data="per-rank-data") 2025-09-07T07:36:50.7497104Z >>> 2025-09-07T07:36:50.7497415Z >>> def create_global_plan(self, all_plans: List[SavePlan]) -> Tuple[List[SavePlan], Metadata]: 2025-09-07T07:36:50.7497609Z >>> global_plan, metadata = super().create_global_plan(all_plans) 2025-09-07T07:36:50.7497777Z >>> merged_data = [p.planner_data for p in global_plan] 2025-09-07T07:36:50.7497946Z >>> metadata = replace(metadata, planner_data=merged_data) 2025-09-07T07:36:50.7498055Z >>> return global_plan, metadata 2025-09-07T07:36:50.7498145Z 2025-09-07T07:36:50.7498423Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.7498513Z 2025-09-07T07:36:50.7498610Z warnings.warn(msg) 2025-09-07T07:36:50.7498690Z 2025-09-07T07:36:50.7498900Z --- Parse Warning: 67 / 146 --- 2025-09-07T07:36:50.7499866Z /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=305. 2025-09-07T07:36:50.7500166Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.7500241Z 2025-09-07T07:36:50.7500527Z Abstract class defining the protocol used by load_state_dict to plan the load process. 2025-09-07T07:36:50.7500620Z 2025-09-07T07:36:50.7500905Z LoadPlanner are stateful objects that can be used to customize the whole load process. 2025-09-07T07:36:50.7500995Z 2025-09-07T07:36:50.7501275Z LoadPlanner acts as an access proxy to the state_dict, so any transformation done to it 2025-09-07T07:36:50.7501387Z will be visible to the whole process. 2025-09-07T07:36:50.7501479Z 2025-09-07T07:36:50.7501750Z A planner subclass can expect the following sequence of calls during load_state_dict: 2025-09-07T07:36:50.7501864Z 2025-09-07T07:36:50.7501985Z 1) set_up_planner - called on all ranks. 2025-09-07T07:36:50.7502114Z Signals the start of loading a checkpoint. 2025-09-07T07:36:50.7502204Z 2025-09-07T07:36:50.7502327Z 2) create_local_plan - called on all ranks. 2025-09-07T07:36:50.7502621Z Process the state_dict and produces a `LoadPlan` that will be sent for global planning. 2025-09-07T07:36:50.7502698Z 2025-09-07T07:36:50.7502877Z 3) create_global_plan - called on the coordinator rank only. 2025-09-07T07:36:50.7503084Z Takes the LoadPlan from all ranks and make any global decision. 2025-09-07T07:36:50.7503160Z 2025-09-07T07:36:50.7503304Z 4) load_bytes - called multiple times on each rank 2025-09-07T07:36:50.7503479Z This is called once per non-tensor value in state_dict. 2025-09-07T07:36:50.7503559Z 2025-09-07T07:36:50.7503789Z 5) resolve_tensor and commit_tensor - called multiple times on each rank 2025-09-07T07:36:50.7503969Z They are called in pair for each Tensor value in state_dict. 2025-09-07T07:36:50.7504076Z 2025-09-07T07:36:50.7504391Z Users are recommended to extend DefaultLoadPlanner instead of this interface directly as 2025-09-07T07:36:50.7504574Z most changes can be expressed by changes in a single method. 2025-09-07T07:36:50.7504661Z 2025-09-07T07:36:50.7504786Z There are two usual patterns of extension: 2025-09-07T07:36:50.7504860Z 2025-09-07T07:36:50.7505121Z Rewriting state_dict. This is the simplest way to extend the load process as it 2025-09-07T07:36:50.7505371Z doesn't requite understanding the intrincacies of how LoadPlan works. We need 2025-09-07T07:36:50.7505604Z to keep a reference to the original state_dict as load happens in place so 2025-09-07T07:36:50.7505727Z we need to be able to perform it in place 2025-09-07T07:36:50.7505804Z 2025-09-07T07:36:50.7505929Z >>> # xdoctest: +SKIP("undefined vars") 2025-09-07T07:36:50.7506058Z >>> class RenamePlanner(DefaultLoadPlanner): 2025-09-07T07:36:50.7506166Z >>> def set_up_planner( 2025-09-07T07:36:50.7506245Z >>> self, 2025-09-07T07:36:50.7506353Z >>> state_dict: STATE_DICT_TYPE, 2025-09-07T07:36:50.7506464Z >>> metadata: Metadata, 2025-09-07T07:36:50.7506565Z >>> is_coordinator: bool, 2025-09-07T07:36:50.7506660Z >>> ) -> None: 2025-09-07T07:36:50.7506787Z >>> self.original_state_dict = state_dict 2025-09-07T07:36:50.7506958Z >>> state_dict = {"foo_" + k: v for k, v in state_dict.items()} 2025-09-07T07:36:50.7507047Z >>> 2025-09-07T07:36:50.7507162Z >>> if self.flatten_sharded_tensors: 2025-09-07T07:36:50.7507349Z >>> state_dict = _flatten_sharded_tensors(state_dict) 2025-09-07T07:36:50.7507427Z >>> 2025-09-07T07:36:50.7507533Z >>> if self.flatten_state_dict: 2025-09-07T07:36:50.7507725Z >>> state_dict, self.mappings = flatten_state_dict(state_dict) 2025-09-07T07:36:50.7507802Z >>> 2025-09-07T07:36:50.7507942Z >>> self.state_dict = state_dict 2025-09-07T07:36:50.7508057Z >>> self.metadata = metadata 2025-09-07T07:36:50.7508179Z >>> self.is_coordinator = is_coordinator 2025-09-07T07:36:50.7508267Z >>> 2025-09-07T07:36:50.7508387Z >>> def load_bytes(self, read_item, value): 2025-09-07T07:36:50.7508489Z >>> # Remove the "foo_" prefix 2025-09-07T07:36:50.7508819Z >>> self.original_state_dict[read_item.dest_index.fqn[4:]] = torch.load(value, weights_only=False) 2025-09-07T07:36:50.7508896Z 2025-09-07T07:36:50.7508984Z 2025-09-07T07:36:50.7509246Z Modifying resolve_tensor and commit_tensor to handle load time transformation. 2025-09-07T07:36:50.7509322Z 2025-09-07T07:36:50.7509441Z >>> # xdoctest: +SKIP("undefined vars") 2025-09-07T07:36:50.7509594Z >>> class MetaModelMaterialize(DefaultSavePlanner): 2025-09-07T07:36:50.7509743Z >>> def resolve_tensor(self, read_item): 2025-09-07T07:36:50.7509877Z >>> tensor = super().resolve_tensor(read_item) 2025-09-07T07:36:50.7510017Z >>> return torch.empty_like(tensor, device="cpu") 2025-09-07T07:36:50.7510102Z >>> 2025-09-07T07:36:50.7510231Z >>> def commit_tensor(self, read_item, tensor): 2025-09-07T07:36:50.7510391Z >>> self.state_dict[read_item.dest_index.fqn] = tensor 2025-09-07T07:36:50.7510466Z 2025-09-07T07:36:50.7510718Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.7510803Z 2025-09-07T07:36:50.7510898Z warnings.warn(msg) 2025-09-07T07:36:50.7510986Z 2025-09-07T07:36:50.7511182Z --- Parse Warning: 68 / 146 --- 2025-09-07T07:36:50.7512172Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=get_state_dict in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/checkpoint/state_dict.py line=1118. 2025-09-07T07:36:50.7512484Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.7512564Z 2025-09-07T07:36:50.7512737Z Return the model state_dict and optimizers state_dict. 2025-09-07T07:36:50.7512817Z 2025-09-07T07:36:50.7513035Z ``get_state_dict`` can process any module that is parallelized by PyTorch 2025-09-07T07:36:50.7513299Z FSDP/fully_shard, DDP/replicate, tensor_parallel/parallelize_module, and any 2025-09-07T07:36:50.7513545Z combination of these parallelisms. The main functions of ``get_state_dict`` 2025-09-07T07:36:50.7513765Z are: 1.) returning a model and optimizer state_dict that can be resharded 2025-09-07T07:36:50.7513974Z with a different number of trainers and/or different parallelisms. 2025-09-07T07:36:50.7514220Z 2.) hiding the parallelism-specific state_dict APIs. Users don't have to call 2025-09-07T07:36:50.7514318Z these APIs. 2025-09-07T07:36:50.7514523Z 3.) sanity checking the result state_dict. 2025-09-07T07:36:50.7514614Z 2025-09-07T07:36:50.7514822Z The keys of the result state dictionary are the canonical FQNs (Fully 2025-09-07T07:36:50.7515056Z Qualified Names). A canonical FQN refers to the FQN based on a parameter's 2025-09-07T07:36:50.7515304Z position in an nn.Module hierarchy. More specifically, a canonical FQN to a 2025-09-07T07:36:50.7515506Z parameter is the FQN returned by ``module.named_parameters()`` or 2025-09-07T07:36:50.7515750Z ``module.named_buffers()`` when the module is not distributed by any 2025-09-07T07:36:50.7516003Z parallelisms. Since the optimizer internally uses parameter IDs to represent 2025-09-07T07:36:50.7516247Z a parameter, there will be a conversion from the parameter IDs to the 2025-09-07T07:36:50.7516374Z canonical FQNs when calling this API. 2025-09-07T07:36:50.7516451Z 2025-09-07T07:36:50.7516673Z ``get_state_dict`` can also process a module that is not parallelized. In 2025-09-07T07:36:50.7516897Z such a case, ``get_state_dict`` only performs one function -- converting the 2025-09-07T07:36:50.7517097Z optimizer parameter IDs to the canonical FQNs. 2025-09-07T07:36:50.7517185Z 2025-09-07T07:36:50.7517267Z Example: 2025-09-07T07:36:50.7517369Z >>> # xdoctest: +SKIP 2025-09-07T07:36:50.7517459Z >>> import torch 2025-09-07T07:36:50.7517693Z >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP 2025-09-07T07:36:50.7517902Z >>> from torch.nn.parallel import DistributedDataParallel as DDP 2025-09-07T07:36:50.7518125Z >>> from torch.distributed.checkpoint.state_dict import get_state_dict 2025-09-07T07:36:50.7518214Z 2025-09-07T07:36:50.7518341Z >>> fsdp_model = FSDP(copy.deepcopy(model)) 2025-09-07T07:36:50.7518520Z >>> fsdp_optim = torch.optim.Adam(model.parameters(), lr=1e-3) 2025-09-07T07:36:50.7518653Z >>> ddp_model = DDP(copy.deepcopy(model)) 2025-09-07T07:36:50.7518850Z >>> ddp_optim = torch.optim.Adam(model.parameters(), lr=1e-3) 2025-09-07T07:36:50.7518943Z 2025-09-07T07:36:50.7519022Z 2025-09-07T07:36:50.7519249Z >>> ddp_state_dict, ddp_optim_state_dict = get_state_dict(ddp_model, ddp_optim) 2025-09-07T07:36:50.7519431Z >>> fsdp_state_dict, fsdp_optim_state_dict = get_state_dict( 2025-09-07T07:36:50.7519535Z ... fsdp_model, fsdp_optim 2025-09-07T07:36:50.7519626Z ... ) 2025-09-07T07:36:50.7519703Z 2025-09-07T07:36:50.7519911Z >>> # if we simply call ddp_model.state_dict() and fsdp_model.state_dict(), 2025-09-07T07:36:50.7520018Z >>> # the asserts will fail. 2025-09-07T07:36:50.7520141Z >>> assert ddp_state_dict == fsdp_state_dict 2025-09-07T07:36:50.7520296Z >>> assert ddp_optim_state == fsdp_optim_state_dict 2025-09-07T07:36:50.7520373Z 2025-09-07T07:36:50.7520448Z 2025-09-07T07:36:50.7520537Z Args: 2025-09-07T07:36:50.7520680Z model (nn.Module): the nn.Module to the model. 2025-09-07T07:36:50.7520861Z optimizers (Union[None, Optimizer, Iterable[Optimizer]]): 2025-09-07T07:36:50.7521052Z The optimizers that are used to optimize ``model``. 2025-09-07T07:36:50.7521334Z submodules (deprecated): Optional[set[nn.Module]]: only return the model parameters 2025-09-07T07:36:50.7521458Z that belong to the submodules. 2025-09-07T07:36:50.7521624Z options (StateDictOptions): the options to control how 2025-09-07T07:36:50.7521825Z model state_dict and optimizer state_dict should be returned. See 2025-09-07T07:36:50.7521953Z `StateDictOptions` for the details. 2025-09-07T07:36:50.7522028Z 2025-09-07T07:36:50.7522120Z Returns: 2025-09-07T07:36:50.7522309Z ``Tuple`` that contain model state_dict and optimizer state_dict. 2025-09-07T07:36:50.7522387Z 2025-09-07T07:36:50.7522621Z :rtype: typing.Tuple[typing.Dict[str, ValueType], OptimizerStateType] 2025-09-07T07:36:50.7522703Z 2025-09-07T07:36:50.7522967Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.7523050Z 2025-09-07T07:36:50.7523146Z warnings.warn(msg) 2025-09-07T07:36:50.7523235Z 2025-09-07T07:36:50.7523436Z --- Parse Warning: 69 / 146 --- 2025-09-07T07:36:50.7524639Z /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=69. 2025-09-07T07:36:50.7524905Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.7524982Z 2025-09-07T07:36:50.7525264Z Load a checkpoint into a distributed state dict in SPMD style. 2025-09-07T07:36:50.7525343Z 2025-09-07T07:36:50.7525573Z Each rank must have the same keys in their ``state_dict`` provided to this 2025-09-07T07:36:50.7525801Z API. Mismatched keys may result in hangs or errors. If unsure, you can use 2025-09-07T07:36:50.7526063Z the ``utils._assert_same_keys`` API to check (but may incur communication 2025-09-07T07:36:50.7531425Z costs). 2025-09-07T07:36:50.7531530Z 2025-09-07T07:36:50.7531734Z Each rank will try to read the least amount of data necessary 2025-09-07T07:36:50.7531986Z to fulfill the requested `state_dict`. When loading :class:`ShardedTensor` 2025-09-07T07:36:50.7532230Z or :class:`DTensor` instances, each rank only reads data for their local shards. 2025-09-07T07:36:50.7532325Z 2025-09-07T07:36:50.7532588Z For each ``Stateful`` object (having both a ``state_dict`` and a ``load_state_dict``), 2025-09-07T07:36:50.7532861Z load will first call ``state_dict`` before attempting deserialization, followed by 2025-09-07T07:36:50.7533027Z ``load_state_dict`` once the deserialization is complete. 2025-09-07T07:36:50.7533282Z For each non-``Stateful`` object, load will deserialize the object, and then replace 2025-09-07T07:36:50.7533551Z it in the ``state_dict`` with the deserialized object. 2025-09-07T07:36:50.7533635Z 2025-09-07T07:36:50.7533745Z .. warning:: 2025-09-07T07:36:50.7533913Z All tensors in ``state_dict`` must be allocated on their 2025-09-07T07:36:50.7534078Z destination device *prior to* calling this function. 2025-09-07T07:36:50.7534169Z 2025-09-07T07:36:50.7534397Z All non-tensor data is loaded using `torch.load()` and modified in place 2025-09-07T07:36:50.7534486Z on state_dict. 2025-09-07T07:36:50.7534579Z 2025-09-07T07:36:50.7534664Z .. warning:: 2025-09-07T07:36:50.7534879Z Users must call `load_state_dict` on the root module to ensure load 2025-09-07T07:36:50.7535065Z pos-processing and non-tensor data properly propagates. 2025-09-07T07:36:50.7535146Z 2025-09-07T07:36:50.7535240Z .. note: 2025-09-07T07:36:50.7535460Z If no process group is initialized, this function will assume the intent 2025-09-07T07:36:50.7535697Z is to load a checkpoint into the local process. This can be useful in the 2025-09-07T07:36:50.7535986Z case of local inference, and when using regular Tensors (as opposed to DTensor 2025-09-07T07:36:50.7536085Z or ShardedTensor) 2025-09-07T07:36:50.7536173Z 2025-09-07T07:36:50.7536254Z .. note: 2025-09-07T07:36:50.7536405Z Rank 0 is assumed to be the coordinator rank. 2025-09-07T07:36:50.7536482Z 2025-09-07T07:36:50.7536562Z Args: 2025-09-07T07:36:50.7536785Z state_dict (Dict[str, Any]): The state_dict to load the checkpoint into. 2025-09-07T07:36:50.7536928Z checkpoint_id (Union[str, os.PathLike, None]): 2025-09-07T07:36:50.7537153Z The ID of this checkpoint instance. The meaning of the checkpoint_id 2025-09-07T07:36:50.7537355Z depends on the storage. It can be a path to a folder or to a file. 2025-09-07T07:36:50.7537519Z It can also be a key if the storage is a key-value store. 2025-09-07T07:36:50.7537632Z (Default: ``None``) 2025-09-07T07:36:50.7537767Z storage_reader (Optional[StorageReader]): 2025-09-07T07:36:50.7537984Z Instance of StorageWriter used to perform reads. If this is not 2025-09-07T07:36:50.7538187Z specified, DCP will automatically infer the reader based on the 2025-09-07T07:36:50.7538385Z checkpoint_id. If checkpoint_id is also None, an exception will 2025-09-07T07:36:50.7538503Z be raised. (Default: ``None``) 2025-09-07T07:36:50.7538617Z planner (Optional[LoadPlanner]): 2025-09-07T07:36:50.7538821Z Instance of LoadPlanner. If this is not specified, the default 2025-09-07T07:36:50.7538978Z planner will be used. (Default: ``None``) 2025-09-07T07:36:50.7539104Z process_group (Optional[ProcessGroup]): 2025-09-07T07:36:50.7539294Z ProcessGroup to be used for cross-rank synchronization. 2025-09-07T07:36:50.7539388Z (Default: ``None``) 2025-09-07T07:36:50.7539609Z no_dist (bool): If ``True``, this function will assume the intent is to load 2025-09-07T07:36:50.7539895Z a checkpoint without using cross-rank synchronization. (Default: ``False``) 2025-09-07T07:36:50.7539977Z Returns: 2025-09-07T07:36:50.7540070Z None. 2025-09-07T07:36:50.7540149Z 2025-09-07T07:36:50.7540242Z Examples 2025-09-07T07:36:50.7540338Z >>> # xdoctest: +SKIP 2025-09-07T07:36:50.7540436Z >>> my_model = MyModule() 2025-09-07T07:36:50.7540583Z >>> optimizer = Adagrad(my_model.parameters()) 2025-09-07T07:36:50.7540711Z >>> model_state_dict = my_model.state_dict() 2025-09-07T07:36:50.7540951Z >>> fs_storage_reader = torch.distributed.checkpoint.FileSystemReader( 2025-09-07T07:36:50.7541049Z ... "/checkpoint/1" 2025-09-07T07:36:50.7541130Z ... ) 2025-09-07T07:36:50.7541220Z 2025-09-07T07:36:50.7541372Z >>> torch.distributed.checkpoint.load_state_dict( 2025-09-07T07:36:50.7541508Z >>> state_dict=model_state_dict, 2025-09-07T07:36:50.7541636Z >>> storage_reader=fs_storage_reader, 2025-09-07T07:36:50.7541714Z >>> ) 2025-09-07T07:36:50.7541804Z 2025-09-07T07:36:50.7542001Z >>> # module.load_state_dict() function might have customized steps 2025-09-07T07:36:50.7542126Z >>> # to flush the state_dict, must call it to 2025-09-07T07:36:50.7542240Z >>> # ensure correct behavior. 2025-09-07T07:36:50.7542369Z >>> my_model.load_state_dict(model_state_dict) 2025-09-07T07:36:50.7542461Z 2025-09-07T07:36:50.7542542Z .. note:: 2025-09-07T07:36:50.7542748Z load_state_dict uses collectives to coordinate reads across ranks. 2025-09-07T07:36:50.7542974Z For NCCL-based process groups, internal tensor representations of 2025-09-07T07:36:50.7543203Z objects must be moved to the GPU device before communication takes place. 2025-09-07T07:36:50.7543434Z In this case, the device used is given by ``torch.cuda.current_device()`` 2025-09-07T07:36:50.7543682Z and it is the user's responsibility to ensure that this is set so that each 2025-09-07T07:36:50.7543868Z rank has an individual GPU, via ``torch.cuda.set_device()``. 2025-09-07T07:36:50.7543958Z 2025-09-07T07:36:50.7544208Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.7544300Z 2025-09-07T07:36:50.7544395Z warnings.warn(msg) 2025-09-07T07:36:50.7544474Z 2025-09-07T07:36:50.7544724Z --- Parse Warning: 70 / 146 --- 2025-09-07T07:36:50.7545698Z /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=97. 2025-09-07T07:36:50.7545975Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.7546054Z 2025-09-07T07:36:50.7546171Z Save a distributed model in SPMD style. 2025-09-07T07:36:50.7546266Z 2025-09-07T07:36:50.7546457Z This function is different from ``torch.save()`` as it handles 2025-09-07T07:36:50.7546726Z ``ShardedTensor`` , and ``DTensor`` by having each rank only save their local shards. 2025-09-07T07:36:50.7546802Z 2025-09-07T07:36:50.7547060Z For each ``Stateful`` object (having both a ``state_dict`` and a ``load_state_dict``), 2025-09-07T07:36:50.7547220Z save will call ``state_dict`` before serialization. 2025-09-07T07:36:50.7547298Z 2025-09-07T07:36:50.7547393Z .. warning:: 2025-09-07T07:36:50.7547625Z There is no guarantees of Backwards Compatibility across PyTorch versions 2025-09-07T07:36:50.7547755Z for saved state_dicts. 2025-09-07T07:36:50.7547845Z 2025-09-07T07:36:50.7547932Z .. warning:: 2025-09-07T07:36:50.7548149Z If using the `process_group` argument, make sure that only its ranks 2025-09-07T07:36:50.7548351Z call `save_state_dict` and that all data in state_dict belong to it. 2025-09-07T07:36:50.7548462Z 2025-09-07T07:36:50.7548556Z .. note:: 2025-09-07T07:36:50.7548812Z When saving checkpoint for FSDP's `ShardingStrategy.HYBRID_SHARD`, only one of 2025-09-07T07:36:50.7549080Z the shard_group should be calling `save_state_dict` and the corresponding process 2025-09-07T07:36:50.7549184Z group needs to be passed in. 2025-09-07T07:36:50.7549260Z 2025-09-07T07:36:50.7549355Z .. note:: 2025-09-07T07:36:50.7549619Z If no process group is available, this function assumes the intention is to save the 2025-09-07T07:36:50.7549741Z state_dict in the local process. 2025-09-07T07:36:50.7549815Z 2025-09-07T07:36:50.7549897Z .. note: 2025-09-07T07:36:50.7550049Z Rank 0 is assumed to be the coordinator rank. 2025-09-07T07:36:50.7550126Z 2025-09-07T07:36:50.7550206Z 2025-09-07T07:36:50.7550297Z Args: 2025-09-07T07:36:50.7550476Z state_dict (Dict[str, Any]): The state_dict to save. 2025-09-07T07:36:50.7550634Z checkpoint_id (Union[str, os.PathLike, None]): 2025-09-07T07:36:50.7550841Z The ID of this checkpoint instance. The meaning of the checkpoint_id 2025-09-07T07:36:50.7551040Z depends on the storage. It can be a path to a folder or to a file. 2025-09-07T07:36:50.7551216Z It can also be a key if the storage is a key-value store. 2025-09-07T07:36:50.7551313Z (Default: ``None``) 2025-09-07T07:36:50.7551451Z storage_writer (Optional[StorageWriter]): 2025-09-07T07:36:50.7551656Z Instance of StorageWriter used to perform writes. If this is not 2025-09-07T07:36:50.7551862Z specified, DCP will automatically infer the writer based on the 2025-09-07T07:36:50.7552070Z checkpoint_id. If checkpoint_id is also None, an exception will 2025-09-07T07:36:50.7552175Z be raised. (Default: ``None``) 2025-09-07T07:36:50.7552300Z planner (Optional[SavePlanner]): 2025-09-07T07:36:50.7552521Z Instance of SavePlanner. If this is not specified, the default 2025-09-07T07:36:50.7552649Z planner will be used. (Default: ``None``) 2025-09-07T07:36:50.7552787Z process_group (Optional[ProcessGroup]): 2025-09-07T07:36:50.7552967Z ProcessGroup to be used for cross-rank synchronization. 2025-09-07T07:36:50.7553072Z (Default: ``None``) 2025-09-07T07:36:50.7553161Z no_dist (bool): 2025-09-07T07:36:50.7553328Z If ``True``, this function will assume the intent is to load 2025-09-07T07:36:50.7553461Z a checkpoint on a single rank/process. 2025-09-07T07:36:50.7553555Z (Default: ``False``) 2025-09-07T07:36:50.7553821Z use_collectives (bool): If ``False``, this function will assume the intent is to save 2025-09-07T07:36:50.7553994Z a checkpoint without using cross-rank synchronization. 2025-09-07T07:36:50.7554090Z (Default: ``True``) 2025-09-07T07:36:50.7554321Z This configuration is experimental and should be used with caution. 2025-09-07T07:36:50.7554689Z It will change the format of the saved checkpoint and may not be backward compatible. 2025-09-07T07:36:50.7554778Z 2025-09-07T07:36:50.7554860Z Returns: 2025-09-07T07:36:50.7555016Z Metadata: Metadata object for the saved checkpoint. 2025-09-07T07:36:50.7555105Z 2025-09-07T07:36:50.7555185Z Example: 2025-09-07T07:36:50.7555295Z >>> # xdoctest: +SKIP 2025-09-07T07:36:50.7555397Z >>> my_model = MyModule() 2025-09-07T07:36:50.7555475Z 2025-09-07T07:36:50.7555594Z >>> state_dict = {"model": my_model} 2025-09-07T07:36:50.7555671Z 2025-09-07T07:36:50.7555933Z >>> fs_storage_writer = torch.distributed.checkpoint.FileSystemWriter( 2025-09-07T07:36:50.7556043Z ... "/checkpoint/1" 2025-09-07T07:36:50.7556125Z ... ) 2025-09-07T07:36:50.7556264Z >>> torch.distributed.checkpoint.save( 2025-09-07T07:36:50.7556369Z >>> state_dict=state_dict, 2025-09-07T07:36:50.7556549Z >>> storage_writer=fs_storage_writer, 2025-09-07T07:36:50.7556639Z >>> ) 2025-09-07T07:36:50.7556717Z 2025-09-07T07:36:50.7556811Z .. note:: 2025-09-07T07:36:50.7557025Z save_state_dict uses collectives to coordinate writes across ranks. 2025-09-07T07:36:50.7557239Z For NCCL-based process groups, internal tensor representations of 2025-09-07T07:36:50.7557480Z objects must be moved to the GPU device before communication takes place. 2025-09-07T07:36:50.7557699Z In this case, the device used is given by ``torch.cuda.current_device()`` 2025-09-07T07:36:50.7557916Z and it is the user's responsibility to ensure that this is set so that 2025-09-07T07:36:50.7558113Z each rank has an individual GPU, via ``torch.cuda.set_device()``. 2025-09-07T07:36:50.7558191Z 2025-09-07T07:36:50.7558455Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.7558561Z 2025-09-07T07:36:50.7558668Z warnings.warn(msg) 2025-09-07T07:36:50.7558746Z 2025-09-07T07:36:50.7558956Z --- Parse Warning: 71 / 146 --- 2025-09-07T07:36:50.7559971Z /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=230. 2025-09-07T07:36:50.7560233Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.7560511Z Asynchronous version of ``save``. This code first de-stages the state_dict on to the 2025-09-07T07:36:50.7560801Z staging storage (defaults to CPU memory), and then calls the `save` in a separate thread. 2025-09-07T07:36:50.7560896Z 2025-09-07T07:36:50.7560986Z .. warning:: 2025-09-07T07:36:50.7561146Z This feature is experimental and subject to change. 2025-09-07T07:36:50.7561299Z MUST CALL CLOSE AFTER LAST CHECKPOINT IS SAVED 2025-09-07T07:36:50.7561422Z 2025-09-07T07:36:50.7561502Z Args: 2025-09-07T07:36:50.7561667Z state_dict (Dict[str, Any]): The state_dict to save. 2025-09-07T07:36:50.7561810Z checkpoint_id (Union[str, os.PathLike, None]): 2025-09-07T07:36:50.7562035Z The ID of this checkpoint instance. The meaning of the checkpoint_id 2025-09-07T07:36:50.7562236Z depends on the storage. It can be a path to a folder or to a file. 2025-09-07T07:36:50.7562406Z It can also be a key if the storage is a key-value store. 2025-09-07T07:36:50.7562516Z (Default: ``None``) 2025-09-07T07:36:50.7562650Z storage_writer (Optional[StorageWriter]): 2025-09-07T07:36:50.7562872Z Instance of StorageWriter used to perform 'stage' and 'save'. If 2025-09-07T07:36:50.7563113Z this is not specified, DCP will automatically infer the writer based on the 2025-09-07T07:36:50.7563330Z checkpoint_id. If checkpoint_id is also None, an exception will 2025-09-07T07:36:50.7563440Z be raised. (Default: ``None``) 2025-09-07T07:36:50.7563559Z planner (Optional[SavePlanner]): 2025-09-07T07:36:50.7563769Z Instance of SavePlanner. If this is not specified, the default 2025-09-07T07:36:50.7563901Z planner will be used. (Default: ``None``) 2025-09-07T07:36:50.7564047Z process_group (Optional[ProcessGroup]): 2025-09-07T07:36:50.7564228Z ProcessGroup to be used for cross-rank synchronization. 2025-09-07T07:36:50.7564326Z (Default: ``None``) 2025-09-07T07:36:50.7564522Z async_checkpointer_type (AsyncCheckpointerType): 2025-09-07T07:36:50.7564694Z whether to do checkpoint in separate thread or process 2025-09-07T07:36:50.7564847Z (Default: ``AsyncCheckpointerType.THREAD``) 2025-09-07T07:36:50.7564956Z async_stager (AsyncStager): 2025-09-07T07:36:50.7565244Z provides staging implementation. If storage_writer implements AsyncStager 2025-09-07T07:36:50.7565466Z and async_stager is provided, async_stager will be used for staging 2025-09-07T07:36:50.7565559Z no_dist (bool): 2025-09-07T07:36:50.7565737Z If ``True``, this function will assume the intent is to save 2025-09-07T07:36:50.7565864Z a checkpoint on a single rank/process. 2025-09-07T07:36:50.7565963Z (Default: ``False``) 2025-09-07T07:36:50.7566286Z use_collectives: If False, Save the checkpoint without rank coordination. (Default: ``True``) 2025-09-07T07:36:50.7566514Z This configuration is experimental and should be used with caution. 2025-09-07T07:36:50.7566794Z It will change the format of the saved checkpoint and may not be backward compatible. 2025-09-07T07:36:50.7566899Z 2025-09-07T07:36:50.7566984Z Returns: 2025-09-07T07:36:50.7567214Z Future: A future holding the resultant Metadata object from `save`. 2025-09-07T07:36:50.7567290Z 2025-09-07T07:36:50.7567384Z Example: 2025-09-07T07:36:50.7567481Z >>> # xdoctest: +SKIP 2025-09-07T07:36:50.7567580Z >>> my_model = MyModule() 2025-09-07T07:36:50.7567671Z 2025-09-07T07:36:50.7567785Z >>> state_dict = {"model": my_model} 2025-09-07T07:36:50.7567860Z 2025-09-07T07:36:50.7568099Z >>> fs_storage_writer = torch.distributed.checkpoint.FileSystemWriter( 2025-09-07T07:36:50.7568194Z ... "/checkpoint/1" 2025-09-07T07:36:50.7568286Z ... ) 2025-09-07T07:36:50.7568494Z >>> checkpoint_future = torch.distributed.checkpoint.async_save( 2025-09-07T07:36:50.7568602Z >>> state_dict=state_dict, 2025-09-07T07:36:50.7568733Z >>> storage_writer=fs_storage_writer, 2025-09-07T07:36:50.7568814Z >>> ) 2025-09-07T07:36:50.7568902Z >>> 2025-09-07T07:36:50.7569026Z >>> # ... do some work ... 2025-09-07T07:36:50.7569108Z >>> 2025-09-07T07:36:50.7569229Z >>> checkpoint_future.result() 2025-09-07T07:36:50.7569308Z 2025-09-07T07:36:50.7569399Z 2025-09-07T07:36:50.7569650Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.7569727Z 2025-09-07T07:36:50.7569835Z warnings.warn(msg) 2025-09-07T07:36:50.7569912Z 2025-09-07T07:36:50.7570108Z --- Parse Warning: 72 / 146 --- 2025-09-07T07:36:50.7571187Z /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=94. 2025-09-07T07:36:50.7571447Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.7571539Z 2025-09-07T07:36:50.7571744Z Initialize rendezvous event object and record its operations. 2025-09-07T07:36:50.7571834Z 2025-09-07T07:36:50.7571915Z Args: 2025-09-07T07:36:50.7572044Z run_id (str): The run id of the rendezvous. 2025-09-07T07:36:50.7572202Z message (str): The message describing the event. 2025-09-07T07:36:50.7572457Z node_state (NodeState): The state of the node (INIT, RUNNING, SUCCEEDED, FAILED). 2025-09-07T07:36:50.7572651Z name (str): Event name. (E.g. Current action being performed). 2025-09-07T07:36:50.7572767Z hostname (str): Hostname of the node. 2025-09-07T07:36:50.7572911Z pid (Optional[int]): The process id of the node. 2025-09-07T07:36:50.7573197Z master_endpoint (str): The master endpoint for the rendezvous store, if known. 2025-09-07T07:36:50.7573464Z local_id (Optional[int]): The local_id of the node, if defined in dynamic_rendezvous.py 2025-09-07T07:36:50.7573642Z rank (Optional[int]): The rank of the node, if known. 2025-09-07T07:36:50.7573751Z Returns: 2025-09-07T07:36:50.7573834Z None 2025-09-07T07:36:50.7573929Z Example: 2025-09-07T07:36:50.7574056Z >>> # See DynamicRendezvousHandler class 2025-09-07T07:36:50.7574146Z >>> def _record( 2025-09-07T07:36:50.7574243Z ... self, 2025-09-07T07:36:50.7574337Z ... message: str, 2025-09-07T07:36:50.7574483Z ... node_state: NodeState = NodeState.RUNNING, 2025-09-07T07:36:50.7574591Z ... rank: Optional[int] = None, 2025-09-07T07:36:50.7574677Z ... ) -> None: 2025-09-07T07:36:50.7574804Z ... construct_and_record_rdzv_event( 2025-09-07T07:36:50.7574968Z ... name=f"{self.__class__.__name__}.{get_method_name()}", 2025-09-07T07:36:50.7575095Z ... run_id=self._settings.run_id, 2025-09-07T07:36:50.7575191Z ... message=message, 2025-09-07T07:36:50.7575319Z ... node_state=node_state, 2025-09-07T07:36:50.7575447Z ... hostname=self._this_node.addr, 2025-09-07T07:36:50.7575558Z ... pid=self._this_node.pid, 2025-09-07T07:36:50.7575691Z ... local_id=self._this_node.local_id, 2025-09-07T07:36:50.7575784Z ... rank=rank, 2025-09-07T07:36:50.7575868Z ... ) 2025-09-07T07:36:50.7575956Z 2025-09-07T07:36:50.7576207Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.7576293Z 2025-09-07T07:36:50.7576386Z warnings.warn(msg) 2025-09-07T07:36:50.7576463Z 2025-09-07T07:36:50.7576661Z --- Parse Warning: 73 / 146 --- 2025-09-07T07:36:50.7577587Z /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. 2025-09-07T07:36:50.7577860Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.7577941Z 2025-09-07T07:36:50.7578153Z This configures FSDP-native mixed precision training. 2025-09-07T07:36:50.7578241Z 2025-09-07T07:36:50.7578327Z Attributes: 2025-09-07T07:36:50.7578569Z param_dtype (Optional[torch.dtype]): This specifies the dtype for model 2025-09-07T07:36:50.7578765Z parameters during forward and backward and thus the dtype for 2025-09-07T07:36:50.7578987Z forward and backward computation. Outside forward and backward, the 2025-09-07T07:36:50.7579190Z *sharded* parameters are kept in full precision (e.g. for the 2025-09-07T07:36:50.7579397Z optimizer step), and for model checkpointing, the parameters are 2025-09-07T07:36:50.7579562Z always saved in full precision. (Default: ``None``) 2025-09-07T07:36:50.7579773Z reduce_dtype (Optional[torch.dtype]): This specifies the dtype for 2025-09-07T07:36:50.7579987Z gradient reduction (i.e. reduce-scatter or all-reduce). If this is 2025-09-07T07:36:50.7580182Z ``None`` but ``param_dtype`` is not ``None``, then this takes on 2025-09-07T07:36:50.7580381Z the ``param_dtype`` value, still running gradient reduction in low 2025-09-07T07:36:50.7580605Z precision. This is permitted to differ from ``param_dtype``, e.g. 2025-09-07T07:36:50.7580802Z to force gradient reduction to run in full precision. (Default: 2025-09-07T07:36:50.7580889Z ``None``) 2025-09-07T07:36:50.7581105Z buffer_dtype (Optional[torch.dtype]): This specifies the dtype for 2025-09-07T07:36:50.7581301Z buffers. FSDP does not shard buffers. Rather, FSDP casts them to 2025-09-07T07:36:50.7581550Z ``buffer_dtype`` in the first forward pass and keeps them in that 2025-09-07T07:36:50.7581757Z dtype thereafter. For model checkpointing, the buffers are saved 2025-09-07T07:36:50.7581950Z in full precision except for ``LOCAL_STATE_DICT``. (Default: 2025-09-07T07:36:50.7582062Z ``None``) 2025-09-07T07:36:50.7582259Z keep_low_precision_grads (bool): If ``False``, then FSDP upcasts 2025-09-07T07:36:50.7582484Z gradients to full precision after the backward pass in preparation 2025-09-07T07:36:50.7582684Z for the optimizer step. If ``True``, then FSDP keeps the gradients 2025-09-07T07:36:50.7582890Z in the dtype used for gradient reduction, which can save memory if 2025-09-07T07:36:50.7583090Z using a custom optimizer that supports running in low precision. 2025-09-07T07:36:50.7583186Z (Default: ``False``) 2025-09-07T07:36:50.7583412Z cast_forward_inputs (bool): If ``True``, then this FSDP module casts 2025-09-07T07:36:50.7583610Z its forward args and kwargs to ``param_dtype``. This is to ensure 2025-09-07T07:36:50.7583831Z that parameter and input dtypes match for forward computation, as 2025-09-07T07:36:50.7584062Z required by many ops. This may need to be set to ``True`` when only 2025-09-07T07:36:50.7584281Z applying mixed precision to some but not all FSDP modules, in which 2025-09-07T07:36:50.7584499Z case a mixed-precision FSDP submodule needs to recast its inputs. 2025-09-07T07:36:50.7584595Z (Default: ``False``) 2025-09-07T07:36:50.7584820Z cast_root_forward_inputs (bool): If ``True``, then the root FSDP module 2025-09-07T07:36:50.7585011Z casts its forward args and kwargs to ``param_dtype``, overriding 2025-09-07T07:36:50.7585195Z the value of ``cast_forward_inputs``. For non-root FSDP modules, 2025-09-07T07:36:50.7585347Z this does not do anything. (Default: ``True``) 2025-09-07T07:36:50.7585558Z _module_classes_to_ignore: (Sequence[Type[nn.Module]]): This specifies 2025-09-07T07:36:50.7585747Z module classes to ignore for mixed precision when using an 2025-09-07T07:36:50.7585928Z ``auto_wrap_policy``: Modules of these classes will have FSDP 2025-09-07T07:36:50.7586169Z applied to them separately with mixed precision disabled (meaning 2025-09-07T07:36:50.7586370Z that the final FSDP construction would deviate from the specified 2025-09-07T07:36:50.7586556Z policy). If ``auto_wrap_policy`` is not specified, then this does 2025-09-07T07:36:50.7586759Z not do anything. This API is experimental and subject to change. 2025-09-07T07:36:50.7586862Z (Default: ``(_BatchNorm,)``) 2025-09-07T07:36:50.7586951Z 2025-09-07T07:36:50.7587118Z .. note:: This API is experimental and subject to change. 2025-09-07T07:36:50.7587194Z 2025-09-07T07:36:50.7587425Z .. note:: Only floating point tensors are cast to their specified dtypes. 2025-09-07T07:36:50.7587501Z 2025-09-07T07:36:50.7587690Z .. note:: In ``summon_full_params``, parameters are forced to full 2025-09-07T07:36:50.7587801Z precision, but buffers are not. 2025-09-07T07:36:50.7587880Z 2025-09-07T07:36:50.7588098Z .. note:: Layer norm and batch norm accumulate in ``float32`` even when 2025-09-07T07:36:50.7588302Z their inputs are in a low precision like ``float16`` or ``bfloat16``. 2025-09-07T07:36:50.7588598Z Disabling FSDP's mixed precision for those norm modules only means that 2025-09-07T07:36:50.7588804Z the affine parameters are kept in ``float32``. However, this incurs 2025-09-07T07:36:50.7589041Z separate all-gathers and reduce-scatters for those norm modules, which 2025-09-07T07:36:50.7589266Z may be inefficient, so if the workload permits, the user should prefer 2025-09-07T07:36:50.7589438Z to still apply mixed precision to those modules. 2025-09-07T07:36:50.7589530Z 2025-09-07T07:36:50.7589731Z .. note:: By default, if the user passes a model with any ``_BatchNorm`` 2025-09-07T07:36:50.7589933Z modules and specifies an ``auto_wrap_policy``, then the batch norm 2025-09-07T07:36:50.7590172Z modules will have FSDP applied to them separately with mixed precision 2025-09-07T07:36:50.7590370Z disabled. See the ``_module_classes_to_ignore`` argument. 2025-09-07T07:36:50.7590461Z 2025-09-07T07:36:50.7590670Z .. note:: ``MixedPrecision`` has ``cast_root_forward_inputs=True`` and 2025-09-07T07:36:50.7590885Z ``cast_forward_inputs=False`` by default. For the root FSDP instance, 2025-09-07T07:36:50.7591071Z its ``cast_root_forward_inputs`` takes precedence over its 2025-09-07T07:36:50.7591244Z ``cast_forward_inputs``. For non-root FSDP instances, their 2025-09-07T07:36:50.7591465Z ``cast_root_forward_inputs`` values are ignored. The default setting is 2025-09-07T07:36:50.7591689Z sufficient for the typical case where each FSDP instance has the same 2025-09-07T07:36:50.7591911Z ``MixedPrecision`` configuration and only needs to cast inputs to the 2025-09-07T07:36:50.7592145Z ``param_dtype`` at the beginning of the model's forward pass. 2025-09-07T07:36:50.7592224Z 2025-09-07T07:36:50.7592438Z .. note:: For nested FSDP instances with different ``MixedPrecision`` 2025-09-07T07:36:50.7592673Z configurations, we recommend setting individual ``cast_forward_inputs`` 2025-09-07T07:36:50.7592872Z values to configure casting inputs or not before each instance's 2025-09-07T07:36:50.7593077Z forward. In such a case, since the casts happen before each FSDP 2025-09-07T07:36:50.7593287Z instance's forward, a parent FSDP instance should have its non-FSDP 2025-09-07T07:36:50.7593528Z submodules run before its FSDP submodules to avoid the activation dtype 2025-09-07T07:36:50.7593733Z being changed due to a different ``MixedPrecision`` configuration. 2025-09-07T07:36:50.7593821Z 2025-09-07T07:36:50.7593909Z Example:: 2025-09-07T07:36:50.7593987Z 2025-09-07T07:36:50.7594129Z >>> # xdoctest: +SKIP("undefined variables") 2025-09-07T07:36:50.7594298Z >>> model = nn.Sequential(nn.Linear(3, 3), nn.Linear(3, 3)) 2025-09-07T07:36:50.7594519Z >>> model[1] = FSDP( 2025-09-07T07:36:50.7594629Z >>> model[1], 2025-09-07T07:36:50.7594932Z >>> mixed_precision=MixedPrecision(param_dtype=torch.float16, cast_forward_inputs=True), 2025-09-07T07:36:50.7595027Z >>> ) 2025-09-07T07:36:50.7595122Z >>> model = FSDP( 2025-09-07T07:36:50.7595206Z >>> model, 2025-09-07T07:36:50.7595524Z >>> mixed_precision=MixedPrecision(param_dtype=torch.bfloat16, cast_forward_inputs=True), 2025-09-07T07:36:50.7595604Z >>> ) 2025-09-07T07:36:50.7595691Z 2025-09-07T07:36:50.7595903Z The above shows a working example. On the other hand, if ``model[1]`` 2025-09-07T07:36:50.7596103Z were replaced with ``model[0]``, meaning that the submodule using 2025-09-07T07:36:50.7596338Z different ``MixedPrecision`` ran its forward first, then ``model[1]`` 2025-09-07T07:36:50.7596560Z would incorrectly see ``float16`` activations instead of ``bfloat16`` 2025-09-07T07:36:50.7596653Z ones. 2025-09-07T07:36:50.7596733Z 2025-09-07T07:36:50.7596808Z 2025-09-07T07:36:50.7597067Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.7597142Z 2025-09-07T07:36:50.7597249Z warnings.warn(msg) 2025-09-07T07:36:50.7597327Z 2025-09-07T07:36:50.7597536Z --- Parse Warning: 74 / 146 --- 2025-09-07T07:36:50.7598533Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=FullStateDictConfig in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/fsdp/api.py line=295. 2025-09-07T07:36:50.7598790Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.7598879Z 2025-09-07T07:36:50.7599087Z ``FullStateDictConfig`` is a config class meant to be used with 2025-09-07T07:36:50.7599284Z ``StateDictType.FULL_STATE_DICT``. We recommend enabling both 2025-09-07T07:36:50.7599529Z ``offload_to_cpu=True`` and ``rank0_only=True`` when saving full state 2025-09-07T07:36:50.7599746Z dicts to save GPU memory and CPU memory, respectively. This config class 2025-09-07T07:36:50.7599951Z is meant to be used via the :func:`state_dict_type` context manager as 2025-09-07T07:36:50.7600037Z follows: 2025-09-07T07:36:50.7600110Z 2025-09-07T07:36:50.7600246Z >>> # xdoctest: +SKIP("undefined variables") 2025-09-07T07:36:50.7600477Z >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP 2025-09-07T07:36:50.7600613Z >>> fsdp = FSDP(model, auto_wrap_policy=...) 2025-09-07T07:36:50.7600811Z >>> cfg = FullStateDictConfig(offload_to_cpu=True, rank0_only=True) 2025-09-07T07:36:50.7601024Z >>> with FSDP.state_dict_type(fsdp, StateDictType.FULL_STATE_DICT, cfg): 2025-09-07T07:36:50.7601169Z >>> state = fsdp.state_dict() 2025-09-07T07:36:50.7601378Z >>> # `state` will be empty on non rank 0 and contain CPU tensors on rank 0. 2025-09-07T07:36:50.7601623Z >>> # To reload checkpoint for inference, finetuning, transfer learning, etc: 2025-09-07T07:36:50.7601854Z >>> model = model_fn() # Initialize model in preparation for wrapping with FSDP 2025-09-07T07:36:50.7601953Z >>> if dist.get_rank() == 0: 2025-09-07T07:36:50.7602143Z >>> # Load checkpoint only on rank 0 to avoid memory redundancy 2025-09-07T07:36:50.7602284Z >>> state_dict = torch.load("my_checkpoint.pt") 2025-09-07T07:36:50.7602407Z >>> model.load_state_dict(state_dict) 2025-09-07T07:36:50.7602640Z >>> # All ranks initialize FSDP module as usual. `sync_module_states` argument 2025-09-07T07:36:50.7602873Z >>> # communicates loaded checkpoint states from rank 0 to rest of the world. 2025-09-07T07:36:50.7602979Z >>> fsdp = FSDP( 2025-09-07T07:36:50.7603058Z ... model, 2025-09-07T07:36:50.7603227Z ... device_id=torch.cuda.current_device(), 2025-09-07T07:36:50.7603332Z ... auto_wrap_policy=..., 2025-09-07T07:36:50.7603431Z ... sync_module_states=True, 2025-09-07T07:36:50.7603524Z ... ) 2025-09-07T07:36:50.7603732Z >>> # After this point, all ranks have FSDP model with loaded checkpoint. 2025-09-07T07:36:50.7603823Z 2025-09-07T07:36:50.7603907Z Attributes: 2025-09-07T07:36:50.7604097Z rank0_only (bool): If ``True``, then only rank 0 saves the full state 2025-09-07T07:36:50.7604307Z dict, and nonzero ranks save an empty dict. If ``False``, then all 2025-09-07T07:36:50.7604460Z ranks save the full state dict. (Default: ``False``) 2025-09-07T07:36:50.7604545Z 2025-09-07T07:36:50.7604795Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.7604872Z 2025-09-07T07:36:50.7604976Z warnings.warn(msg) 2025-09-07T07:36:50.7605050Z 2025-09-07T07:36:50.7605249Z --- Parse Warning: 75 / 146 --- 2025-09-07T07:36:50.7606436Z /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=634. 2025-09-07T07:36:50.7606690Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.7606942Z Set the ``state_dict_type`` of all the descendant FSDP modules of the target module. 2025-09-07T07:36:50.7607017Z 2025-09-07T07:36:50.7607314Z Also takes (optional) configuration for the model's and optimizer's state dict. 2025-09-07T07:36:50.7607516Z The target module does not have to be a FSDP module. If the target 2025-09-07T07:36:50.7607719Z module is a FSDP module, its ``state_dict_type`` will also be changed. 2025-09-07T07:36:50.7607811Z 2025-09-07T07:36:50.7608029Z .. note:: This API should be called for only the top-level (root) 2025-09-07T07:36:50.7608119Z module. 2025-09-07T07:36:50.7608198Z 2025-09-07T07:36:50.7608400Z .. note:: This API enables users to transparently use the conventional 2025-09-07T07:36:50.7608600Z ``state_dict`` API to take model checkpoints in cases where the 2025-09-07T07:36:50.7608801Z root FSDP module is wrapped by another ``nn.Module``. For example, 2025-09-07T07:36:50.7609017Z the following will ensure ``state_dict`` is called on all non-FSDP 2025-09-07T07:36:50.7609247Z instances, while dispatching into `sharded_state_dict` implementation 2025-09-07T07:36:50.7609344Z for FSDP: 2025-09-07T07:36:50.7609423Z 2025-09-07T07:36:50.7609510Z Example:: 2025-09-07T07:36:50.7609623Z 2025-09-07T07:36:50.7609751Z >>> # xdoctest: +SKIP("undefined variables") 2025-09-07T07:36:50.7609860Z >>> model = DDP(FSDP(...)) 2025-09-07T07:36:50.7609983Z >>> FSDP.set_state_dict_type( 2025-09-07T07:36:50.7610068Z >>> model, 2025-09-07T07:36:50.7610207Z >>> StateDictType.SHARDED_STATE_DICT, 2025-09-07T07:36:50.7610416Z >>> state_dict_config = ShardedStateDictConfig(offload_to_cpu=True), 2025-09-07T07:36:50.7610637Z >>> optim_state_dict_config = OptimStateDictConfig(offload_to_cpu=True), 2025-09-07T07:36:50.7610730Z >>> ) 2025-09-07T07:36:50.7610852Z >>> param_state_dict = model.state_dict() 2025-09-07T07:36:50.7611032Z >>> optim_state_dict = FSDP.optim_state_dict(model, optim) 2025-09-07T07:36:50.7611107Z 2025-09-07T07:36:50.7611185Z Args: 2025-09-07T07:36:50.7611316Z module (torch.nn.Module): Root module. 2025-09-07T07:36:50.7611545Z state_dict_type (StateDictType): the desired ``state_dict_type`` to set. 2025-09-07T07:36:50.7611815Z state_dict_config (Optional[StateDictConfig]): the configuration for the 2025-09-07T07:36:50.7611924Z target ``state_dict_type``. 2025-09-07T07:36:50.7612174Z optim_state_dict_config (Optional[OptimStateDictConfig]): the configuration 2025-09-07T07:36:50.7612296Z for the optimizer state dict. 2025-09-07T07:36:50.7612373Z 2025-09-07T07:36:50.7612468Z Returns: 2025-09-07T07:36:50.7612684Z A StateDictSettings that include the previous state_dict type and 2025-09-07T07:36:50.7612796Z configuration for the module. 2025-09-07T07:36:50.7612887Z 2025-09-07T07:36:50.7613135Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.7613227Z 2025-09-07T07:36:50.7613317Z warnings.warn(msg) 2025-09-07T07:36:50.7613394Z 2025-09-07T07:36:50.7613593Z --- Parse Warning: 76 / 146 --- 2025-09-07T07:36:50.7614765Z /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=792. 2025-09-07T07:36:50.7615041Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.7615283Z Set the ``state_dict_type`` of all the descendant FSDP modules of the target module. 2025-09-07T07:36:50.7615360Z 2025-09-07T07:36:50.7615715Z This context manager has the same functions as :meth:`set_state_dict_type`. Read the document of 2025-09-07T07:36:50.7615844Z :meth:`set_state_dict_type` for the detail. 2025-09-07T07:36:50.7615936Z 2025-09-07T07:36:50.7616022Z Example:: 2025-09-07T07:36:50.7616100Z 2025-09-07T07:36:50.7616238Z >>> # xdoctest: +SKIP("undefined variables") 2025-09-07T07:36:50.7616391Z >>> model = DDP(FSDP(...)) 2025-09-07T07:36:50.7616513Z >>> with FSDP.state_dict_type( 2025-09-07T07:36:50.7616599Z >>> model, 2025-09-07T07:36:50.7616727Z >>> StateDictType.SHARDED_STATE_DICT, 2025-09-07T07:36:50.7616817Z >>> ): 2025-09-07T07:36:50.7616934Z >>> checkpoint = model.state_dict() 2025-09-07T07:36:50.7617023Z 2025-09-07T07:36:50.7617103Z Args: 2025-09-07T07:36:50.7617224Z module (torch.nn.Module): Root module. 2025-09-07T07:36:50.7617461Z state_dict_type (StateDictType): the desired ``state_dict_type`` to set. 2025-09-07T07:36:50.7617690Z state_dict_config (Optional[StateDictConfig]): the model ``state_dict`` 2025-09-07T07:36:50.7617857Z configuration for the target ``state_dict_type``. 2025-09-07T07:36:50.7618110Z optim_state_dict_config (Optional[OptimStateDictConfig]): the optimizer 2025-09-07T07:36:50.7618306Z ``state_dict`` configuration for the target ``state_dict_type``. 2025-09-07T07:36:50.7618395Z 2025-09-07T07:36:50.7618645Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.7618734Z 2025-09-07T07:36:50.7618823Z warnings.warn(msg) 2025-09-07T07:36:50.7618900Z 2025-09-07T07:36:50.7619095Z --- Parse Warning: 77 / 146 --- 2025-09-07T07:36:50.7620288Z /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=1805. 2025-09-07T07:36:50.7620558Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.7620633Z 2025-09-07T07:36:50.7620866Z Transform the state-dict of an optimizer corresponding to a sharded model. 2025-09-07T07:36:50.7620981Z 2025-09-07T07:36:50.7621174Z The given state-dict can be transformed to one of three types: 2025-09-07T07:36:50.7621487Z 1) full optimizer state_dict, 2) sharded optimizer state_dict, 3) local optimizer state_dict. 2025-09-07T07:36:50.7621566Z 2025-09-07T07:36:50.7621796Z For full optimizer state_dict, all states are unflattened and not sharded. 2025-09-07T07:36:50.7622014Z Rank0 only and CPU only can be specified via :meth:`state_dict_type` to 2025-09-07T07:36:50.7622098Z avoid OOM. 2025-09-07T07:36:50.7622182Z 2025-09-07T07:36:50.7622416Z For sharded optimizer state_dict, all states are unflattened but sharded. 2025-09-07T07:36:50.7622616Z CPU only can be specified via :meth:`state_dict_type` to further save 2025-09-07T07:36:50.7622702Z memory. 2025-09-07T07:36:50.7622780Z 2025-09-07T07:36:50.7623000Z For local state_dict, no transformation will be performed. But a state 2025-09-07T07:36:50.7623236Z will be converted from nn.Tensor to ShardedTensor to represent its sharding 2025-09-07T07:36:50.7623345Z nature (this is not supported yet). 2025-09-07T07:36:50.7623426Z 2025-09-07T07:36:50.7623510Z Example:: 2025-09-07T07:36:50.7623595Z 2025-09-07T07:36:50.7623916Z >>> # xdoctest: +SKIP("undefined variables") 2025-09-07T07:36:50.7624150Z >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP 2025-09-07T07:36:50.7624318Z >>> from torch.distributed.fsdp import StateDictType 2025-09-07T07:36:50.7624494Z >>> from torch.distributed.fsdp import FullStateDictConfig 2025-09-07T07:36:50.7624772Z >>> from torch.distributed.fsdp import FullOptimStateDictConfig 2025-09-07T07:36:50.7624870Z >>> # Save a checkpoint 2025-09-07T07:36:50.7624966Z >>> model, optim = ... 2025-09-07T07:36:50.7625083Z >>> FSDP.set_state_dict_type( 2025-09-07T07:36:50.7625171Z >>> model, 2025-09-07T07:36:50.7625302Z >>> StateDictType.FULL_STATE_DICT, 2025-09-07T07:36:50.7625481Z >>> FullStateDictConfig(rank0_only=False), 2025-09-07T07:36:50.7625622Z >>> FullOptimStateDictConfig(rank0_only=False), 2025-09-07T07:36:50.7625715Z >>> ) 2025-09-07T07:36:50.7625823Z >>> state_dict = model.state_dict() 2025-09-07T07:36:50.7626003Z >>> optim_state_dict = FSDP.optim_state_dict(model, optim) 2025-09-07T07:36:50.7626144Z >>> save_a_checkpoint(state_dict, optim_state_dict) 2025-09-07T07:36:50.7626241Z >>> # Load a checkpoint 2025-09-07T07:36:50.7626353Z >>> model, optim = ... 2025-09-07T07:36:50.7626501Z >>> state_dict, optim_state_dict = load_a_checkpoint() 2025-09-07T07:36:50.7626606Z >>> FSDP.set_state_dict_type( 2025-09-07T07:36:50.7626704Z >>> model, 2025-09-07T07:36:50.7626819Z >>> StateDictType.FULL_STATE_DICT, 2025-09-07T07:36:50.7626995Z >>> FullStateDictConfig(rank0_only=False), 2025-09-07T07:36:50.7627138Z >>> FullOptimStateDictConfig(rank0_only=False), 2025-09-07T07:36:50.7627220Z >>> ) 2025-09-07T07:36:50.7627351Z >>> model.load_state_dict(state_dict) 2025-09-07T07:36:50.7627500Z >>> optim_state_dict = FSDP.optim_state_dict_to_load( 2025-09-07T07:36:50.7627624Z >>> model, optim, optim_state_dict 2025-09-07T07:36:50.7627705Z >>> ) 2025-09-07T07:36:50.7627828Z >>> optim.load_state_dict(optim_state_dict) 2025-09-07T07:36:50.7627916Z 2025-09-07T07:36:50.7627993Z Args: 2025-09-07T07:36:50.7628198Z model (torch.nn.Module): Root module (which may or may not be a 2025-09-07T07:36:50.7628397Z :class:`FullyShardedDataParallel` instance) whose parameters 2025-09-07T07:36:50.7628535Z were passed into the optimizer ``optim``. 2025-09-07T07:36:50.7628728Z optim (torch.optim.Optimizer): Optimizer for ``model`` 's 2025-09-07T07:36:50.7628821Z parameters. 2025-09-07T07:36:50.7629077Z optim_state_dict (Dict[str, Any]): the target optimizer state_dict to 2025-09-07T07:36:50.7629285Z transform. If the value is None, optim.state_dict() will be used. ( 2025-09-07T07:36:50.7629379Z Default: ``None``) 2025-09-07T07:36:50.7629625Z group (dist.ProcessGroup): Model's process group across which parameters 2025-09-07T07:36:50.7629808Z are sharded or ``None`` if using the default process group. ( 2025-09-07T07:36:50.7629912Z Default: ``None``) 2025-09-07T07:36:50.7629986Z 2025-09-07T07:36:50.7630067Z Returns: 2025-09-07T07:36:50.7630261Z Dict[str, Any]: A :class:`dict` containing the optimizer state for 2025-09-07T07:36:50.7630431Z ``model``. The sharding of the optimizer state is based on 2025-09-07T07:36:50.7630540Z ``state_dict_type``. 2025-09-07T07:36:50.7630612Z 2025-09-07T07:36:50.7630862Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.7630951Z 2025-09-07T07:36:50.7631044Z warnings.warn(msg) 2025-09-07T07:36:50.7631123Z 2025-09-07T07:36:50.7631332Z --- Parse Warning: 78 / 146 --- 2025-09-07T07:36:50.7632545Z /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=1903. 2025-09-07T07:36:50.7632814Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.7632889Z 2025-09-07T07:36:50.7633278Z Convert an optimizer state-dict so that it can be loaded into the optimizer associated with the FSDP model. 2025-09-07T07:36:50.7633355Z 2025-09-07T07:36:50.7633521Z Given a ``optim_state_dict`` that is transformed through 2025-09-07T07:36:50.7633743Z :meth:`optim_state_dict`, it gets converted to the flattened optimizer 2025-09-07T07:36:50.7633978Z state_dict that can be loaded to ``optim`` which is the optimizer for 2025-09-07T07:36:50.7634168Z ``model``. ``model`` must be sharded by FullyShardedDataParallel. 2025-09-07T07:36:50.7634241Z 2025-09-07T07:36:50.7634444Z >>> # xdoctest: +SKIP("undefined variables") 2025-09-07T07:36:50.7634694Z >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP 2025-09-07T07:36:50.7634845Z >>> from torch.distributed.fsdp import StateDictType 2025-09-07T07:36:50.7635033Z >>> from torch.distributed.fsdp import FullStateDictConfig 2025-09-07T07:36:50.7635228Z >>> from torch.distributed.fsdp import FullOptimStateDictConfig 2025-09-07T07:36:50.7635326Z >>> # Save a checkpoint 2025-09-07T07:36:50.7635436Z >>> model, optim = ... 2025-09-07T07:36:50.7635539Z >>> FSDP.set_state_dict_type( 2025-09-07T07:36:50.7635635Z >>> model, 2025-09-07T07:36:50.7635782Z >>> StateDictType.FULL_STATE_DICT, 2025-09-07T07:36:50.7635914Z >>> FullStateDictConfig(rank0_only=False), 2025-09-07T07:36:50.7636065Z >>> FullOptimStateDictConfig(rank0_only=False), 2025-09-07T07:36:50.7636141Z >>> ) 2025-09-07T07:36:50.7636259Z >>> state_dict = model.state_dict() 2025-09-07T07:36:50.7636374Z >>> original_osd = optim.state_dict() 2025-09-07T07:36:50.7636501Z >>> optim_state_dict = FSDP.optim_state_dict( 2025-09-07T07:36:50.7636595Z >>> model, 2025-09-07T07:36:50.7636674Z >>> optim, 2025-09-07T07:36:50.7636782Z >>> optim_state_dict=original_osd 2025-09-07T07:36:50.7636871Z >>> ) 2025-09-07T07:36:50.7637012Z >>> save_a_checkpoint(state_dict, optim_state_dict) 2025-09-07T07:36:50.7637116Z >>> # Load a checkpoint 2025-09-07T07:36:50.7637210Z >>> model, optim = ... 2025-09-07T07:36:50.7637360Z >>> state_dict, optim_state_dict = load_a_checkpoint() 2025-09-07T07:36:50.7637478Z >>> FSDP.set_state_dict_type( 2025-09-07T07:36:50.7637560Z >>> model, 2025-09-07T07:36:50.7637710Z >>> StateDictType.FULL_STATE_DICT, 2025-09-07T07:36:50.7637835Z >>> FullStateDictConfig(rank0_only=False), 2025-09-07T07:36:50.7637972Z >>> FullOptimStateDictConfig(rank0_only=False), 2025-09-07T07:36:50.7638065Z >>> ) 2025-09-07T07:36:50.7638174Z >>> model.load_state_dict(state_dict) 2025-09-07T07:36:50.7638330Z >>> optim_state_dict = FSDP.optim_state_dict_to_load( 2025-09-07T07:36:50.7638440Z >>> model, optim, optim_state_dict 2025-09-07T07:36:50.7638519Z >>> ) 2025-09-07T07:36:50.7638652Z >>> optim.load_state_dict(optim_state_dict) 2025-09-07T07:36:50.7638727Z 2025-09-07T07:36:50.7638821Z Args: 2025-09-07T07:36:50.7639013Z model (torch.nn.Module): Root module (which may or may not be a 2025-09-07T07:36:50.7639211Z :class:`FullyShardedDataParallel` instance) whose parameters 2025-09-07T07:36:50.7639353Z were passed into the optimizer ``optim``. 2025-09-07T07:36:50.7639534Z optim (torch.optim.Optimizer): Optimizer for ``model`` 's 2025-09-07T07:36:50.7639635Z parameters. 2025-09-07T07:36:50.7639843Z optim_state_dict (Dict[str, Any]): The optimizer states to be loaded. 2025-09-07T07:36:50.7640037Z is_named_optimizer (bool): Is this optimizer a NamedOptimizer or 2025-09-07T07:36:50.7640240Z KeyedOptimizer. Only set to True if ``optim`` is TorchRec's 2025-09-07T07:36:50.7640411Z KeyedOptimizer or torch.distributed's NamedOptimizer. 2025-09-07T07:36:50.7640611Z load_directly (bool): If this is set to True, this API will also 2025-09-07T07:36:50.7640832Z call optim.load_state_dict(result) before returning the result. 2025-09-07T07:36:50.7641049Z Otherwise, users are responsible to call ``optim.load_state_dict()`` 2025-09-07T07:36:50.7641159Z (Default: ``False``) 2025-09-07T07:36:50.7641396Z group (dist.ProcessGroup): Model's process group across which parameters 2025-09-07T07:36:50.7641609Z are sharded or ``None`` if using the default process group. ( 2025-09-07T07:36:50.7641701Z Default: ``None``) 2025-09-07T07:36:50.7641775Z 2025-09-07T07:36:50.7642033Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.7642110Z 2025-09-07T07:36:50.7642213Z warnings.warn(msg) 2025-09-07T07:36:50.7642288Z 2025-09-07T07:36:50.7642483Z --- Parse Warning: 79 / 146 --- 2025-09-07T07:36:50.7643505Z /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=129. 2025-09-07T07:36:50.7643762Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.7643876Z 2025-09-07T07:36:50.7644097Z RemoteModule instance can only be created after RPC initialization. 2025-09-07T07:36:50.7644173Z 2025-09-07T07:36:50.7644377Z It creates a user-specified module on a specified remote node. 2025-09-07T07:36:50.7644606Z It behaves like a regular ``nn.Module`` except that the ``forward`` method is 2025-09-07T07:36:50.7644721Z executed on the remote node. 2025-09-07T07:36:50.7644952Z It takes care of autograd recording to ensure the backward pass propagates 2025-09-07T07:36:50.7645104Z gradients back to the corresponding remote module. 2025-09-07T07:36:50.7645479Z It can be shared across processors using `RPC framework `__, 2025-09-07T07:36:50.7645672Z without incurring any overheads of copying the actual module, 2025-09-07T07:36:50.7645877Z which is equivalent to an :class:`~torch.distributed.rpc.RRef` 2025-09-07T07:36:50.7645983Z pointing to the remote module. 2025-09-07T07:36:50.7646059Z 2025-09-07T07:36:50.7646290Z The arguments of ``forward_async`` and ``forward`` are the same as 2025-09-07T07:36:50.7646487Z the ``forward`` method of the module returned by the ``module_cls``. 2025-09-07T07:36:50.7646581Z 2025-09-07T07:36:50.7646884Z Apart from ``forward_async`` and ``forward``, no other methods are supported from nn.Module for now. 2025-09-07T07:36:50.7646960Z 2025-09-07T07:36:50.7647217Z Particularly, to create a hybrid model, typically the local modules should be 2025-09-07T07:36:50.7647587Z created outside of remote modules, rather than as submodules of any remote module (by calling ``add_module``). 2025-09-07T07:36:50.7647685Z Hybrid Example: 2025-09-07T07:36:50.7647801Z >>> class HybridModel(nn.Module): 2025-09-07T07:36:50.7647910Z >>> def __init__(self) -> None: 2025-09-07T07:36:50.7648028Z >>> nn.Module.__init__(self) 2025-09-07T07:36:50.7648167Z >>> self.remote_embedding = RemoteModule(...) 2025-09-07T07:36:50.7648307Z >>> self.local_linear = nn.Linear(...) 2025-09-07T07:36:50.7648383Z 2025-09-07T07:36:50.7648578Z For example, if ``module_cls`` returns an instance of ``nn.Linear``, 2025-09-07T07:36:50.7648839Z that has ``forward`` method signature, ``def forward(input: Tensor) -> Tensor:``, 2025-09-07T07:36:50.7649041Z the generated ``RemoteModule`` will have 2 methods in signature of 2025-09-07T07:36:50.7649178Z ``def forward(input: Tensor) -> Tensor:`` and 2025-09-07T07:36:50.7649341Z ``def forward_async(input: Tensor) -> Future[Tensor]:``. 2025-09-07T07:36:50.7649418Z 2025-09-07T07:36:50.7649517Z .. note:: 2025-09-07T07:36:50.7649681Z If the remote module is placed on a cuda device, 2025-09-07T07:36:50.7649926Z any input CPU tensors will be automatically moved to the same cuda device, 2025-09-07T07:36:50.7650322Z and GPU tensors are returned over the wire according to the device map of the remote worker on TensorPipe RPC backend. 2025-09-07T07:36:50.7650423Z 2025-09-07T07:36:50.7650516Z Args: 2025-09-07T07:36:50.7650812Z remote_device (str): Device on the destination worker where we'd like to place this module. 2025-09-07T07:36:50.7651110Z The device can be a local device or a remote device specified by one of the following remote 2025-09-07T07:36:50.7651196Z formats: 2025-09-07T07:36:50.7651273Z 2025-09-07T07:36:50.7651421Z 1. "rank:/" (ex: "rank:0/cuda:0"). 2025-09-07T07:36:50.7651566Z 2. "/" (ex: "trainer0/cuda:0"). 2025-09-07T07:36:50.7651659Z 2025-09-07T07:36:50.7651904Z In addition, the device field can be optional and the default value is "cpu". 2025-09-07T07:36:50.7652017Z module_cls (nn.Module): For example, 2025-09-07T07:36:50.7652136Z >>> class MyModule(nn.Module): 2025-09-07T07:36:50.7652260Z >>> def forward(input): 2025-09-07T07:36:50.7652369Z >>> return input + 1 2025-09-07T07:36:50.7652453Z >>> 2025-09-07T07:36:50.7652548Z >>> module_cls = MyModule 2025-09-07T07:36:50.7652754Z args (Sequence, optional): args to be passed to ``module_cls``. 2025-09-07T07:36:50.7652941Z kwargs (Dict, optional): kwargs to be passed to ``module_cls``. 2025-09-07T07:36:50.7653224Z _module_interface_cls (type, optional): The TorchScript interface type for the module 2025-09-07T07:36:50.7653455Z to be created. The type object should be decorated by @torch.jit.interface. 2025-09-07T07:36:50.7653667Z If not provided, the generated RemoteModule is not torchscript-able. 2025-09-07T07:36:50.7653914Z Warning, this is an experimental API and susceptible to frequent changes. 2025-09-07T07:36:50.7653991Z 2025-09-07T07:36:50.7654084Z Returns: 2025-09-07T07:36:50.7654332Z A remote module instance which wraps the :class:`~nn.Module` created by the 2025-09-07T07:36:50.7654587Z user-provided ``module_cls``, it has a blocking ``forward`` method and an 2025-09-07T07:36:50.7654871Z asynchronous ``forward_async`` method that returns a future of the ``forward`` call 2025-09-07T07:36:50.7655013Z on the user-provided module on the remote side. 2025-09-07T07:36:50.7655104Z 2025-09-07T07:36:50.7655193Z Example:: 2025-09-07T07:36:50.7655350Z Run the following code in two different processes: 2025-09-07T07:36:50.7655443Z 2025-09-07T07:36:50.7655558Z >>> # xdoctest: +SKIP("distributed") 2025-09-07T07:36:50.7655666Z >>> # On worker 0: 2025-09-07T07:36:50.7655759Z >>> import torch 2025-09-07T07:36:50.7655886Z >>> import torch.distributed.rpc as rpc 2025-09-07T07:36:50.7656006Z >>> from torch import nn, Tensor 2025-09-07T07:36:50.7656222Z >>> from torch.distributed.nn.api.remote_module import RemoteModule 2025-09-07T07:36:50.7656319Z >>> 2025-09-07T07:36:50.7656458Z >>> rpc.init_rpc("worker0", rank=0, world_size=2) 2025-09-07T07:36:50.7656582Z >>> remote_linear_module = RemoteModule( 2025-09-07T07:36:50.7656720Z >>> "worker1/cpu", nn.Linear, args=(20, 30), 2025-09-07T07:36:50.7656803Z >>> ) 2025-09-07T07:36:50.7656911Z >>> input = torch.randn(128, 20) 2025-09-07T07:36:50.7657083Z >>> ret_fut = remote_linear_module.forward_async(input) 2025-09-07T07:36:50.7657183Z >>> ret = ret_fut.wait() 2025-09-07T07:36:50.7657292Z >>> rpc.shutdown() 2025-09-07T07:36:50.7657374Z 2025-09-07T07:36:50.7657463Z >>> # On worker 1: 2025-09-07T07:36:50.7657566Z >>> import torch 2025-09-07T07:36:50.7657764Z >>> import torch.distributed.rpc as rpc 2025-09-07T07:36:50.7657858Z >>> 2025-09-07T07:36:50.7657999Z >>> rpc.init_rpc("worker1", rank=1, world_size=2) 2025-09-07T07:36:50.7658090Z >>> rpc.shutdown() 2025-09-07T07:36:50.7658181Z 2025-09-07T07:36:50.7658432Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.7658550Z 2025-09-07T07:36:50.7658644Z warnings.warn(msg) 2025-09-07T07:36:50.7658722Z 2025-09-07T07:36:50.7658931Z --- Parse Warning: 80 / 146 --- 2025-09-07T07:36:50.7660002Z /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=506. 2025-09-07T07:36:50.7660272Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.7660352Z 2025-09-07T07:36:50.7660673Z Besides the constructor, a RemoteModule instance can also be initialized given a module RRef. 2025-09-07T07:36:50.7660762Z 2025-09-07T07:36:50.7661083Z This alternate initialization method can be particularly useful if we want to create multiple 2025-09-07T07:36:50.7661437Z RemoteModule instances that share the same underlying module and reduce memory consumption. 2025-09-07T07:36:50.7661519Z 2025-09-07T07:36:50.7661792Z Moreover, this also provides a workaround for passing script RemoteModule over RPC, 2025-09-07T07:36:50.7661981Z which is not supported. The recommended way is as follows: 2025-09-07T07:36:50.7662057Z 2025-09-07T07:36:50.7662185Z 1. the sender creates a RemoteModule; 2025-09-07T07:36:50.7662324Z 2. the sender sends its ``module_rref`` over RPC; 2025-09-07T07:36:50.7662657Z 3. the receiver calls this method to initialize another RemoteModule using the same ``module_rref``. 2025-09-07T07:36:50.7662746Z 2025-09-07T07:36:50.7662832Z Example:: 2025-09-07T07:36:50.7662994Z Run the following code in two different processes: 2025-09-07T07:36:50.7663071Z 2025-09-07T07:36:50.7663183Z >>> # xdoctest: +SKIP("distributed") 2025-09-07T07:36:50.7663283Z >>> # On worker 0: 2025-09-07T07:36:50.7663371Z >>> import torch 2025-09-07T07:36:50.7663517Z >>> import torch.distributed.rpc as rpc 2025-09-07T07:36:50.7663636Z >>> from torch import nn, Tensor 2025-09-07T07:36:50.7663848Z >>> from torch.distributed.nn.api.remote_module import RemoteModule 2025-09-07T07:36:50.7663937Z >>> 2025-09-07T07:36:50.7664073Z >>> rpc.init_rpc("worker0", rank=0, world_size=2) 2025-09-07T07:36:50.7664181Z >>> remote_module = RemoteModule( 2025-09-07T07:36:50.7664312Z >>> "worker1/cpu", nn.Linear, args=(20, 30), 2025-09-07T07:36:50.7664390Z >>> ) 2025-09-07T07:36:50.7664481Z >>> 2025-09-07T07:36:50.7664590Z >>> remote_module1 = rpc.rpc_sync( 2025-09-07T07:36:50.7664686Z >>> "worker1/cpu", 2025-09-07T07:36:50.7664815Z >>> RemoteModule.init_from_module_rref, 2025-09-07T07:36:50.7664969Z >>> ("worker1/cpu", remote_module1.get_module_rref()), 2025-09-07T07:36:50.7665058Z >>> ) 2025-09-07T07:36:50.7665151Z >>> rpc.shutdown() 2025-09-07T07:36:50.7665229Z 2025-09-07T07:36:50.7665327Z >>> # On worker 1: 2025-09-07T07:36:50.7665417Z >>> import torch 2025-09-07T07:36:50.7665547Z >>> import torch.distributed.rpc as rpc 2025-09-07T07:36:50.7665626Z >>> 2025-09-07T07:36:50.7665760Z >>> rpc.init_rpc("worker1", rank=1, world_size=2) 2025-09-07T07:36:50.7665863Z >>> rpc.shutdown() 2025-09-07T07:36:50.7665940Z 2025-09-07T07:36:50.7666022Z Args: 2025-09-07T07:36:50.7666324Z remote_device (str): Device on the destination worker where we'd like to place this module. 2025-09-07T07:36:50.7666637Z The device can be a local device or a remote device specified by one of the following remote 2025-09-07T07:36:50.7666735Z formats: 2025-09-07T07:36:50.7666813Z 2025-09-07T07:36:50.7666948Z 1. "rank:/" (ex: "rank:0/cuda:0"). 2025-09-07T07:36:50.7667111Z 2. "/" (ex: "trainer0/cuda:0"). 2025-09-07T07:36:50.7667214Z 2025-09-07T07:36:50.7667467Z In addition, the device field can be optional and the default value is "cpu". 2025-09-07T07:36:50.7667713Z module_rref (RRef[nn.Module]): The module reference shared by both the caller and 2025-09-07T07:36:50.7667816Z the created remote module. 2025-09-07T07:36:50.7668104Z _module_interface_cls (type, optional): The TorchScript interface type for the module 2025-09-07T07:36:50.7668335Z to be created. The type object should be decorated by @torch.jit.interface. 2025-09-07T07:36:50.7668562Z If not provided, the generated RemoteModule is not torchscript-able. 2025-09-07T07:36:50.7668796Z Warning, this is an experimental API and susceptible to frequent changes. 2025-09-07T07:36:50.7668872Z 2025-09-07T07:36:50.7668969Z Returns: 2025-09-07T07:36:50.7669209Z A remote module instance which wraps the :class:`~nn.Module` created by the 2025-09-07T07:36:50.7669479Z user-provided ``module_rref``, it has a blocking ``forward`` method and an 2025-09-07T07:36:50.7669747Z asynchronous ``forward_async`` method that returns a future of the ``forward`` call 2025-09-07T07:36:50.7669898Z on the user-provided module on the remote side. 2025-09-07T07:36:50.7669978Z 2025-09-07T07:36:50.7670232Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.7670318Z 2025-09-07T07:36:50.7670412Z warnings.warn(msg) 2025-09-07T07:36:50.7670488Z 2025-09-07T07:36:50.7670691Z --- Parse Warning: 81 / 146 --- 2025-09-07T07:36:50.7671680Z /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=598. 2025-09-07T07:36:50.7671953Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.7672034Z 2025-09-07T07:36:50.7672303Z A RemoteModule instance can only be created after RPC initialization. 2025-09-07T07:36:50.7672381Z 2025-09-07T07:36:50.7672573Z It creates a user-specified module on a specified remote node. 2025-09-07T07:36:50.7672818Z It behaves like a regular ``nn.Module`` except that the ``forward`` method is 2025-09-07T07:36:50.7672924Z executed on the remote node. 2025-09-07T07:36:50.7673169Z It takes care of autograd recording to ensure the backward pass propagates 2025-09-07T07:36:50.7673325Z gradients back to the corresponding remote module. 2025-09-07T07:36:50.7673403Z 2025-09-07T07:36:50.7673629Z It generates two methods ``forward_async`` and ``forward`` based on the 2025-09-07T07:36:50.7673839Z signature of the ``forward`` method of ``module_cls``. ``forward_async`` 2025-09-07T07:36:50.7674102Z runs asynchronously and returns a Future. The arguments of ``forward_async`` 2025-09-07T07:36:50.7674299Z and ``forward`` are the same as the ``forward`` method of the module 2025-09-07T07:36:50.7674489Z returned by the ``module_cls``. 2025-09-07T07:36:50.7674580Z 2025-09-07T07:36:50.7674775Z For example, if ``module_cls`` returns an instance of ``nn.Linear``, 2025-09-07T07:36:50.7675031Z that has ``forward`` method signature: ``def forward(input: Tensor) -> Tensor:``, 2025-09-07T07:36:50.7675255Z the generated ``RemoteModule`` will have 2 methods with the signatures: 2025-09-07T07:36:50.7675333Z 2025-09-07T07:36:50.7675468Z | ``def forward(input: Tensor) -> Tensor:`` 2025-09-07T07:36:50.7675661Z | ``def forward_async(input: Tensor) -> Future[Tensor]:`` 2025-09-07T07:36:50.7675752Z 2025-09-07T07:36:50.7675833Z Args: 2025-09-07T07:36:50.7676128Z remote_device (str): Device on the destination worker where we'd like to place this module. 2025-09-07T07:36:50.7676481Z The format should be "/", where the device field can be parsed as torch.device type. 2025-09-07T07:36:50.7676649Z E.g., "trainer0/cpu", "trainer0", "ps0/cuda:0". 2025-09-07T07:36:50.7676900Z In addition, the device field can be optional and the default value is "cpu". 2025-09-07T07:36:50.7677145Z module_cls (nn.Module): Class for the module to be created remotely. For example, 2025-09-07T07:36:50.7677222Z 2025-09-07T07:36:50.7677340Z >>> class MyModule(nn.Module): 2025-09-07T07:36:50.7677438Z >>> def forward(input): 2025-09-07T07:36:50.7677548Z >>> return input + 1 2025-09-07T07:36:50.7677627Z >>> 2025-09-07T07:36:50.7677729Z >>> module_cls = MyModule 2025-09-07T07:36:50.7677815Z 2025-09-07T07:36:50.7678011Z args (Sequence, optional): args to be passed to ``module_cls``. 2025-09-07T07:36:50.7678224Z kwargs (Dict, optional): kwargs to be passed to ``module_cls``. 2025-09-07T07:36:50.7678316Z 2025-09-07T07:36:50.7678400Z Returns: 2025-09-07T07:36:50.7678656Z A remote module instance which wraps the :class:`~nn.Module` created by the 2025-09-07T07:36:50.7678881Z user-provided ``module_cls``, it has a blocking ``forward`` method and an 2025-09-07T07:36:50.7679154Z asynchronous ``forward_async`` method that returns a future of the ``forward`` call 2025-09-07T07:36:50.7679308Z on the user-provided module on the remote side. 2025-09-07T07:36:50.7679384Z 2025-09-07T07:36:50.7679482Z Example:: 2025-09-07T07:36:50.7679633Z Run the following code in two different processes: 2025-09-07T07:36:50.7679713Z 2025-09-07T07:36:50.7679836Z >>> # xdoctest: +SKIP("distributed") 2025-09-07T07:36:50.7679927Z >>> # On worker 0: 2025-09-07T07:36:50.7680023Z >>> import torch 2025-09-07T07:36:50.7680146Z >>> import torch.distributed.rpc as rpc 2025-09-07T07:36:50.7680256Z >>> from torch import nn, Tensor 2025-09-07T07:36:50.7680509Z >>> from torch.distributed.nn.api.remote_module import RemoteModule 2025-09-07T07:36:50.7680591Z >>> 2025-09-07T07:36:50.7680738Z >>> rpc.init_rpc("worker0", rank=0, world_size=2) 2025-09-07T07:36:50.7680857Z >>> remote_linear_module = RemoteModule( 2025-09-07T07:36:50.7680983Z >>> "worker1/cpu", nn.Linear, args=(20, 30), 2025-09-07T07:36:50.7681077Z >>> ) 2025-09-07T07:36:50.7681180Z >>> input = torch.randn(128, 20) 2025-09-07T07:36:50.7681340Z >>> ret_fut = remote_linear_module.forward_async(input) 2025-09-07T07:36:50.7681436Z >>> ret = ret_fut.wait() 2025-09-07T07:36:50.7681530Z >>> rpc.shutdown() 2025-09-07T07:36:50.7681617Z 2025-09-07T07:36:50.7681703Z >>> # On worker 1: 2025-09-07T07:36:50.7681788Z >>> import torch 2025-09-07T07:36:50.7681920Z >>> import torch.distributed.rpc as rpc 2025-09-07T07:36:50.7682004Z >>> 2025-09-07T07:36:50.7682150Z >>> rpc.init_rpc("worker1", rank=1, world_size=2) 2025-09-07T07:36:50.7682247Z >>> rpc.shutdown() 2025-09-07T07:36:50.7682323Z 2025-09-07T07:36:50.7682524Z Furthermore, a more practical example that is combined with 2025-09-07T07:36:50.7683000Z `DistributedDataParallel `__ (DDP) 2025-09-07T07:36:50.7683336Z can be found in this `tutorial `__. 2025-09-07T07:36:50.7683416Z 2025-09-07T07:36:50.7683665Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.7683758Z 2025-09-07T07:36:50.7683874Z warnings.warn(msg) 2025-09-07T07:36:50.7683961Z 2025-09-07T07:36:50.7684162Z --- Parse Warning: 82 / 146 --- 2025-09-07T07:36:50.7685171Z /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=129. 2025-09-07T07:36:50.7685473Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.7685553Z 2025-09-07T07:36:50.7685808Z DistributedOptimizer takes remote references to parameters scattered 2025-09-07T07:36:50.7686047Z across workers and applies the given optimizer locally for each parameter. 2025-09-07T07:36:50.7686126Z 2025-09-07T07:36:50.7686374Z This class uses :meth:`~torch.distributed.autograd.get_gradients` in order 2025-09-07T07:36:50.7686526Z to retrieve the gradients for specific parameters. 2025-09-07T07:36:50.7686615Z 2025-09-07T07:36:50.7686712Z Concurrent calls to 2025-09-07T07:36:50.7686916Z :meth:`~torch.distributed.optim.DistributedOptimizer.step`, 2025-09-07T07:36:50.7687070Z either from the same or different clients, will 2025-09-07T07:36:50.7687318Z be serialized on each worker -- as each worker's optimizer can only work 2025-09-07T07:36:50.7687537Z on one set of gradients at a time. However, there is no guarantee that 2025-09-07T07:36:50.7687780Z the full forward-backward-optimizer sequence will execute for one client 2025-09-07T07:36:50.7687995Z at a time. This means that the gradients being applied may not correspond 2025-09-07T07:36:50.7688231Z to the latest forward pass executed on a given worker. Also, there is no 2025-09-07T07:36:50.7688346Z guaranteed ordering across workers. 2025-09-07T07:36:50.7688443Z 2025-09-07T07:36:50.7688702Z `DistributedOptimizer` creates the local optimizer with TorchScript enabled 2025-09-07T07:36:50.7688931Z by default, so that optimizer updates are not blocked by the Python Global 2025-09-07T07:36:50.7689185Z Interpreter Lock (GIL) in the case of multithreaded training (e.g. Distributed 2025-09-07T07:36:50.7689422Z Model Parallel). This feature is currently enabled for most optimizers. You 2025-09-07T07:36:50.7689719Z can also follow `the recipe`__ in PyTorch tutorials to enable TorchScript support 2025-09-07T07:36:50.7689831Z for your own custom optimizers. 2025-09-07T07:36:50.7689912Z 2025-09-07T07:36:50.7690002Z Args: 2025-09-07T07:36:50.7690196Z optimizer_class (optim.Optimizer): the class of optimizer to 2025-09-07T07:36:50.7690311Z instantiate on each worker. 2025-09-07T07:36:50.7690517Z params_rref (list[RRef]): list of RRefs to local or remote parameters 2025-09-07T07:36:50.7690604Z to optimize. 2025-09-07T07:36:50.7690824Z args: arguments to pass to the optimizer constructor on each worker. 2025-09-07T07:36:50.7691043Z kwargs: arguments to pass to the optimizer constructor on each worker. 2025-09-07T07:36:50.7691135Z 2025-09-07T07:36:50.7691225Z Example:: 2025-09-07T07:36:50.7691339Z >>> # xdoctest: +SKIP("distributed") 2025-09-07T07:36:50.7691515Z >>> import torch.distributed.autograd as dist_autograd 2025-09-07T07:36:50.7691637Z >>> import torch.distributed.rpc as rpc 2025-09-07T07:36:50.7691754Z >>> from torch import optim 2025-09-07T07:36:50.7691936Z >>> from torch.distributed.optim import DistributedOptimizer 2025-09-07T07:36:50.7692016Z >>> 2025-09-07T07:36:50.7692158Z >>> with dist_autograd.context() as context_id: 2025-09-07T07:36:50.7692253Z >>> # Forward pass. 2025-09-07T07:36:50.7692459Z >>> rref1 = rpc.remote("worker1", torch.add, args=(torch.ones(2), 3)) 2025-09-07T07:36:50.7692650Z >>> rref2 = rpc.remote("worker1", torch.add, args=(torch.ones(2), 1)) 2025-09-07T07:36:50.7692773Z >>> loss = rref1.to_here() + rref2.to_here() 2025-09-07T07:36:50.7692885Z >>> 2025-09-07T07:36:50.7692981Z >>> # Backward pass. 2025-09-07T07:36:50.7693136Z >>> dist_autograd.backward(context_id, [loss.sum()]) 2025-09-07T07:36:50.7693220Z >>> 2025-09-07T07:36:50.7693311Z >>> # Optimizer. 2025-09-07T07:36:50.7693446Z >>> dist_optim = DistributedOptimizer( 2025-09-07T07:36:50.7693564Z >>> optim.SGD, 2025-09-07T07:36:50.7693669Z >>> [rref1, rref2], 2025-09-07T07:36:50.7693755Z >>> lr=0.05, 2025-09-07T07:36:50.7693836Z >>> ) 2025-09-07T07:36:50.7693957Z >>> dist_optim.step(context_id) 2025-09-07T07:36:50.7694033Z 2025-09-07T07:36:50.7694185Z __ https://github.com/pytorch/tutorials/pull/1465 2025-09-07T07:36:50.7694273Z 2025-09-07T07:36:50.7694522Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.7694611Z 2025-09-07T07:36:50.7694708Z warnings.warn(msg) 2025-09-07T07:36:50.7694785Z 2025-09-07T07:36:50.7694992Z --- Parse Warning: 83 / 146 --- 2025-09-07T07:36:50.7696071Z /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. 2025-09-07T07:36:50.7696376Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.7696455Z 2025-09-07T07:36:50.7696845Z Wraps an arbitrary :class:`torch.optim.Optimizer` and runs `post-local SGD `_, 2025-09-07T07:36:50.7697009Z This optimizer runs local optimizer at every step. 2025-09-07T07:36:50.7697344Z After the warm-up stage, it averages parameters periodically after the local optimizer is applied. 2025-09-07T07:36:50.7697434Z 2025-09-07T07:36:50.7697518Z Args: 2025-09-07T07:36:50.7697620Z optim: The local optimizer. 2025-09-07T07:36:50.7697846Z averager: A model averager instance to run post-localSGD algorithm. 2025-09-07T07:36:50.7697925Z 2025-09-07T07:36:50.7698019Z Example:: 2025-09-07T07:36:50.7698098Z 2025-09-07T07:36:50.7698226Z >>> # xdoctest: +SKIP("undefined variables") 2025-09-07T07:36:50.7698327Z >>> import torch 2025-09-07T07:36:50.7698510Z >>> import torch.distributed as dist 2025-09-07T07:36:50.7698792Z >>> import torch.distributed.algorithms.model_averaging.averagers as averagers 2025-09-07T07:36:50.7698893Z >>> import torch.nn as nn 2025-09-07T07:36:50.7699084Z >>> from torch.distributed.optim import PostLocalSGDOptimizer 2025-09-07T07:36:50.7699364Z >>> from torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook import ( 2025-09-07T07:36:50.7699464Z >>> PostLocalSGDState, 2025-09-07T07:36:50.7699574Z >>> post_localSGD_hook, 2025-09-07T07:36:50.7699654Z >>> ) 2025-09-07T07:36:50.7699736Z >>> 2025-09-07T07:36:50.7699897Z >>> model = nn.parallel.DistributedDataParallel( 2025-09-07T07:36:50.7700036Z >>> module, device_ids=[rank], output_device=rank 2025-09-07T07:36:50.7700130Z >>> ) 2025-09-07T07:36:50.7700208Z >>> 2025-09-07T07:36:50.7700352Z >>> # Register a post-localSGD communication hook. 2025-09-07T07:36:50.7700663Z >>> state = PostLocalSGDState(process_group=None, subgroup=None, start_localSGD_iter=100) 2025-09-07T07:36:50.7700825Z >>> model.register_comm_hook(state, post_localSGD_hook) 2025-09-07T07:36:50.7700915Z >>> 2025-09-07T07:36:50.7701117Z >>> # Create a post-localSGD optimizer that wraps a local optimizer. 2025-09-07T07:36:50.7701366Z >>> # Note that ``warmup_steps`` used in ``PostLocalSGDOptimizer`` must be the same as 2025-09-07T07:36:50.7701543Z >>> # ``start_localSGD_iter`` used in ``PostLocalSGDState``. 2025-09-07T07:36:50.7701776Z >>> local_optim = torch.optim.SGD(params=model.parameters(), lr=0.01) 2025-09-07T07:36:50.7701902Z >>> opt = PostLocalSGDOptimizer( 2025-09-07T07:36:50.7702000Z >>> optim=local_optim, 2025-09-07T07:36:50.7702243Z >>> averager=averagers.PeriodicModelAverager(period=4, warmup_steps=100) 2025-09-07T07:36:50.7702337Z >>> ) 2025-09-07T07:36:50.7702439Z >>> 2025-09-07T07:36:50.7702674Z >>> # In the first 100 steps, DDP runs global gradient averaging at every step. 2025-09-07T07:36:50.7702973Z >>> # After 100 steps, DDP runs gradient averaging within each subgroup (intra-node by default), 2025-09-07T07:36:50.7703345Z >>> # and post-localSGD optimizer runs global model averaging every 4 steps after applying the local optimizer. 2025-09-07T07:36:50.7703459Z >>> for step in range(0, 200): 2025-09-07T07:36:50.7703552Z >>> opt.zero_grad() 2025-09-07T07:36:50.7703674Z >>> loss = loss_fn(output, labels) 2025-09-07T07:36:50.7703767Z >>> loss.backward() 2025-09-07T07:36:50.7703858Z >>> opt.step() 2025-09-07T07:36:50.7703947Z 2025-09-07T07:36:50.7704200Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.7704303Z 2025-09-07T07:36:50.7704408Z warnings.warn(msg) 2025-09-07T07:36:50.7704484Z 2025-09-07T07:36:50.7704686Z --- Parse Warning: 84 / 146 --- 2025-09-07T07:36:50.7705790Z /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=284. 2025-09-07T07:36:50.7706060Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.7706136Z 2025-09-07T07:36:50.7706540Z Wrap an arbitrary :class:`optim.Optimizer ` and shards its states across ranks in the group. 2025-09-07T07:36:50.7706627Z 2025-09-07T07:36:50.7706871Z The sharing is done as described by `ZeRO `_. 2025-09-07T07:36:50.7706959Z 2025-09-07T07:36:50.7707106Z The local optimizer instance in each rank is only 2025-09-07T07:36:50.7707350Z responsible for updating approximately ``1 / world_size`` parameters and 2025-09-07T07:36:50.7707587Z hence only needs to keep ``1 / world_size`` optimizer states. After 2025-09-07T07:36:50.7707827Z parameters are updated locally, each rank will broadcast its parameters to 2025-09-07T07:36:50.7708020Z all other peers to keep all model replicas in the same state. 2025-09-07T07:36:50.7708211Z ``ZeroRedundancyOptimizer`` can be used in conjunction with 2025-09-07T07:36:50.7708465Z :class:`torch.nn.parallel.DistributedDataParallel` to reduce per-rank peak 2025-09-07T07:36:50.7708571Z memory consumption. 2025-09-07T07:36:50.7708647Z 2025-09-07T07:36:50.7708915Z ``ZeroRedundancyOptimizer`` uses a sorted-greedy algorithm to pack a number 2025-09-07T07:36:50.7709144Z of parameters at each rank. Each parameter belongs to a single rank and is 2025-09-07T07:36:50.7709383Z not divided among ranks. The partition is arbitrary and might not match the 2025-09-07T07:36:50.7709523Z the parameter registration or usage order. 2025-09-07T07:36:50.7709602Z 2025-09-07T07:36:50.7709698Z Arguments: 2025-09-07T07:36:50.7709890Z params (``Iterable``): an ``Iterable`` of :class:`torch.Tensor` s 2025-09-07T07:36:50.7710073Z or :class:`dict` s giving all parameters, which will be sharded 2025-09-07T07:36:50.7710174Z across ranks. 2025-09-07T07:36:50.7710250Z 2025-09-07T07:36:50.7710335Z Keyword Args: 2025-09-07T07:36:50.7710563Z optimizer_class (:class:`torch.nn.Optimizer`): the class of the local 2025-09-07T07:36:50.7710651Z optimizer. 2025-09-07T07:36:50.7710867Z process_group (``ProcessGroup``, optional): ``torch.distributed`` 2025-09-07T07:36:50.7711083Z ``ProcessGroup`` (default: ``dist.group.WORLD`` initialized by 2025-09-07T07:36:50.7711240Z :meth:`torch.distributed.init_process_group`). 2025-09-07T07:36:50.7711465Z parameters_as_bucket_view (bool, optional): if ``True``, parameters are 2025-09-07T07:36:50.7711676Z packed into buckets to speed up communication, and ``param.data`` 2025-09-07T07:36:50.7711913Z fields point to bucket views at different offsets; if ``False``, 2025-09-07T07:36:50.7712119Z each individual parameter is communicated separately, and each 2025-09-07T07:36:50.7712282Z ``params.data`` stays intact (default: ``False``). 2025-09-07T07:36:50.7712473Z overlap_with_ddp (bool, optional): if ``True``, :meth:`step` is 2025-09-07T07:36:50.7712673Z overlapped with :class:`DistributedDataParallel` 's gradient 2025-09-07T07:36:50.7712898Z synchronization; this requires (1) either a functional optimizer 2025-09-07T07:36:50.7713080Z for the ``optimizer_class`` argument or one with a functional 2025-09-07T07:36:50.7713265Z equivalent and (2) registering a DDP communication hook 2025-09-07T07:36:50.7713464Z constructed from one of the functions in ``ddp_zero_hook.py``; 2025-09-07T07:36:50.7713655Z parameters are packed into buckets matching those in 2025-09-07T07:36:50.7713820Z :class:`DistributedDataParallel`, meaning that the 2025-09-07T07:36:50.7713965Z ``parameters_as_bucket_view`` argument is ignored. 2025-09-07T07:36:50.7714157Z If ``False``, :meth:`step` runs disjointly after the backward pass 2025-09-07T07:36:50.7714249Z (per normal). 2025-09-07T07:36:50.7714428Z (default: ``False``) 2025-09-07T07:36:50.7714654Z **defaults: any trailing arguments, which are forwarded to the local 2025-09-07T07:36:50.7714741Z optimizer. 2025-09-07T07:36:50.7714830Z 2025-09-07T07:36:50.7714919Z Example:: 2025-09-07T07:36:50.7714995Z 2025-09-07T07:36:50.7715102Z >>> # xdoctest: +SKIP 2025-09-07T07:36:50.7715200Z >>> import torch.nn as nn 2025-09-07T07:36:50.7715410Z >>> from torch.distributed.optim import ZeroRedundancyOptimizer 2025-09-07T07:36:50.7715617Z >>> from torch.nn.parallel import DistributedDataParallel as DDP 2025-09-07T07:36:50.7715875Z >>> model = nn.Sequential(*[nn.Linear(2000, 2000).to(rank) for _ in range(20)]) 2025-09-07T07:36:50.7716005Z >>> ddp = DDP(model, device_ids=[rank]) 2025-09-07T07:36:50.7716124Z >>> opt = ZeroRedundancyOptimizer( 2025-09-07T07:36:50.7716238Z >>> ddp.parameters(), 2025-09-07T07:36:50.7716359Z >>> optimizer_class=torch.optim.Adam, 2025-09-07T07:36:50.7716447Z >>> lr=0.01 2025-09-07T07:36:50.7716541Z >>> ) 2025-09-07T07:36:50.7716652Z >>> ddp(inputs).sum().backward() 2025-09-07T07:36:50.7716742Z >>> opt.step() 2025-09-07T07:36:50.7716836Z 2025-09-07T07:36:50.7716925Z .. warning:: 2025-09-07T07:36:50.7717150Z Currently, ``ZeroRedundancyOptimizer`` requires that all of the 2025-09-07T07:36:50.7717295Z passed-in parameters are the same dense type. 2025-09-07T07:36:50.7717377Z 2025-09-07T07:36:50.7717477Z .. warning:: 2025-09-07T07:36:50.7717688Z If you pass ``overlap_with_ddp=True``, be wary of the following: Given 2025-09-07T07:36:50.7717903Z the way that overlapping :class:`DistributedDataParallel` with 2025-09-07T07:36:50.7718137Z :class:`ZeroRedundancyOptimizer` is currently implemented, the first 2025-09-07T07:36:50.7718349Z two or three training iterations do not perform parameter updates in 2025-09-07T07:36:50.7718554Z the optimizer step, depending on if ``static_graph=False`` or 2025-09-07T07:36:50.7718735Z ``static_graph=True``, respectively. This is because it needs 2025-09-07T07:36:50.7718935Z information about the gradient bucketing strategy used by 2025-09-07T07:36:50.7719179Z :class:`DistributedDataParallel`, which is not finalized until the 2025-09-07T07:36:50.7719378Z second forward pass if ``static_graph=False`` or until the third 2025-09-07T07:36:50.7719601Z forward pass if ``static_graph=True``. To adjust for this, one option 2025-09-07T07:36:50.7719711Z is to prepend dummy inputs. 2025-09-07T07:36:50.7719831Z 2025-09-07T07:36:50.7720088Z .. warning:: ZeroRedundancyOptimizer is experimental and subject to change. 2025-09-07T07:36:50.7720166Z 2025-09-07T07:36:50.7720431Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.7720511Z 2025-09-07T07:36:50.7720621Z warnings.warn(msg) 2025-09-07T07:36:50.7720698Z 2025-09-07T07:36:50.7720903Z --- Parse Warning: 85 / 146 --- 2025-09-07T07:36:50.7721911Z /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=29. 2025-09-07T07:36:50.7722174Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.7722307Z 2025-09-07T07:36:50.7722536Z Custom reducer class that can be used to specify a custom operation that 2025-09-07T07:36:50.7722713Z reduces losses of multiple microbatches into one value. 2025-09-07T07:36:50.7722800Z 2025-09-07T07:36:50.7722883Z Example: 2025-09-07T07:36:50.7722997Z >>> # xdoctest: +SKIP 2025-09-07T07:36:50.7723105Z >>> sum_reducer = _CustomReducer( 2025-09-07T07:36:50.7723201Z >>> torch.tensor(0.0), 2025-09-07T07:36:50.7723305Z >>> lambda a, b: a + b 2025-09-07T07:36:50.7723382Z >>> ) 2025-09-07T07:36:50.7723471Z 2025-09-07T07:36:50.7723885Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.7723963Z 2025-09-07T07:36:50.7724072Z warnings.warn(msg) 2025-09-07T07:36:50.7724151Z 2025-09-07T07:36:50.7724340Z --- Parse Warning: 86 / 146 --- 2025-09-07T07:36:50.7725279Z /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. 2025-09-07T07:36:50.7725615Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.7725704Z 2025-09-07T07:36:50.7725941Z A decorator for a function indicating that the return value of the function 2025-09-07T07:36:50.7726161Z is guaranteed to be a :class:`~torch.futures.Future` object and this 2025-09-07T07:36:50.7726402Z function can run asynchronously on the RPC callee. More specifically, the 2025-09-07T07:36:50.7726635Z callee extracts the :class:`~torch.futures.Future` returned by the wrapped 2025-09-07T07:36:50.7726881Z function and installs subsequent processing steps as a callback to that 2025-09-07T07:36:50.7727115Z :class:`~torch.futures.Future`. The installed callback will read the value 2025-09-07T07:36:50.7727328Z from the :class:`~torch.futures.Future` when completed and send the 2025-09-07T07:36:50.7727509Z value back as the RPC response. That also means the returned 2025-09-07T07:36:50.7727744Z :class:`~torch.futures.Future` only exists on the callee side and is never 2025-09-07T07:36:50.7727975Z sent through RPC. This decorator is useful when the wrapped function's 2025-09-07T07:36:50.7728170Z (``fn``) execution needs to pause and resume due to, e.g., containing 2025-09-07T07:36:50.7728403Z :meth:`~torch.distributed.rpc.rpc_async` or waiting for other signals. 2025-09-07T07:36:50.7728484Z 2025-09-07T07:36:50.7728695Z .. note:: To enable asynchronous execution, applications must pass the 2025-09-07T07:36:50.7728935Z function object returned by this decorator to RPC APIs. If RPC detected 2025-09-07T07:36:50.7729192Z attributes installed by this decorator, it knows that this function 2025-09-07T07:36:50.7729388Z returns a ``Future`` object and will handle that accordingly. 2025-09-07T07:36:50.7729603Z However, this does not mean this decorator has to be outmost one when 2025-09-07T07:36:50.7729846Z defining a function. For example, when combined with ``@staticmethod`` 2025-09-07T07:36:50.7730093Z or ``@classmethod``, ``@rpc.functions.async_execution`` needs to be the 2025-09-07T07:36:50.7730310Z inner decorator to allow the target function be recognized as a static 2025-09-07T07:36:50.7730545Z or class function. This target function can still execute asynchronously 2025-09-07T07:36:50.7730766Z because, when accessed, the static or class method preserves attributes 2025-09-07T07:36:50.7730926Z installed by ``@rpc.functions.async_execution``. 2025-09-07T07:36:50.7731004Z 2025-09-07T07:36:50.7731080Z 2025-09-07T07:36:50.7731175Z Example:: 2025-09-07T07:36:50.7731376Z The returned :class:`~torch.futures.Future` object can come from 2025-09-07T07:36:50.7731516Z :meth:`~torch.distributed.rpc.rpc_async`, 2025-09-07T07:36:50.7731740Z :meth:`~torch.futures.Future.then`, or :class:`~torch.futures.Future` 2025-09-07T07:36:50.7731948Z constructor. The example below shows directly using the 2025-09-07T07:36:50.7732092Z :class:`~torch.futures.Future` returned by 2025-09-07T07:36:50.7732211Z :meth:`~torch.futures.Future.then`. 2025-09-07T07:36:50.7732301Z 2025-09-07T07:36:50.7732418Z >>> from torch.distributed import rpc 2025-09-07T07:36:50.7732499Z >>> 2025-09-07T07:36:50.7732625Z >>> # omitting setup and shutdown RPC 2025-09-07T07:36:50.7732704Z >>> 2025-09-07T07:36:50.7732795Z >>> # On all workers 2025-09-07T07:36:50.7732922Z >>> @rpc.functions.async_execution 2025-09-07T07:36:50.7733038Z >>> def async_add_chained(to, x, y, z): 2025-09-07T07:36:50.7733248Z >>> # This function runs on "worker1" and returns immediately when 2025-09-07T07:36:50.7733438Z >>> # the callback is installed through the `then(cb)` API. In the 2025-09-07T07:36:50.7733622Z >>> # mean time, the `rpc_async` to "worker2" can run concurrently. 2025-09-07T07:36:50.7733822Z >>> # When the return value of that `rpc_async` arrives at 2025-09-07T07:36:50.7734008Z >>> # "worker1", "worker1" will run the lambda function accordingly 2025-09-07T07:36:50.7734204Z >>> # and set the value for the previously returned `Future`, which 2025-09-07T07:36:50.7734385Z >>> # will then trigger RPC to send the result back to "worker0". 2025-09-07T07:36:50.7734561Z >>> return rpc.rpc_async(to, torch.add, args=(x, y)).then( 2025-09-07T07:36:50.7734670Z >>> lambda fut: fut.wait() + z 2025-09-07T07:36:50.7734751Z >>> ) 2025-09-07T07:36:50.7734842Z >>> 2025-09-07T07:36:50.7734932Z >>> # On worker0 2025-09-07T07:36:50.7735027Z >>> # xdoctest: +SKIP 2025-09-07T07:36:50.7735134Z >>> ret = rpc.rpc_sync( 2025-09-07T07:36:50.7735222Z >>> "worker1", 2025-09-07T07:36:50.7735331Z >>> async_add_chained, 2025-09-07T07:36:50.7735452Z >>> args=("worker2", torch.ones(2), 1, 1) 2025-09-07T07:36:50.7735536Z >>> ) 2025-09-07T07:36:50.7735664Z >>> print(ret) # prints tensor([3., 3.]) 2025-09-07T07:36:50.7735741Z 2025-09-07T07:36:50.7735975Z When combined with TorchScript decorators, this decorator must be the 2025-09-07T07:36:50.7736062Z outmost one. 2025-09-07T07:36:50.7736137Z 2025-09-07T07:36:50.7736255Z >>> from torch import Tensor 2025-09-07T07:36:50.7736369Z >>> from torch.futures import Future 2025-09-07T07:36:50.7736500Z >>> from torch.distributed import rpc 2025-09-07T07:36:50.7736579Z >>> 2025-09-07T07:36:50.7736692Z >>> # omitting setup and shutdown RPC 2025-09-07T07:36:50.7736810Z >>> 2025-09-07T07:36:50.7736901Z >>> # On all workers 2025-09-07T07:36:50.7736995Z >>> @torch.jit.script 2025-09-07T07:36:50.7737150Z >>> def script_add(x: Tensor, y: Tensor) -> Tensor: 2025-09-07T07:36:50.7737243Z >>> return x + y 2025-09-07T07:36:50.7737361Z >>> 2025-09-07T07:36:50.7737477Z >>> @rpc.functions.async_execution 2025-09-07T07:36:50.7737572Z >>> @torch.jit.script 2025-09-07T07:36:50.7737769Z >>> def async_add(to: str, x: Tensor, y: Tensor) -> Future[Tensor]: 2025-09-07T07:36:50.7737907Z >>> return rpc.rpc_async(to, script_add, (x, y)) 2025-09-07T07:36:50.7737998Z >>> 2025-09-07T07:36:50.7738084Z >>> # On worker0 2025-09-07T07:36:50.7738180Z >>> ret = rpc.rpc_sync( 2025-09-07T07:36:50.7738279Z >>> "worker1", 2025-09-07T07:36:50.7738364Z >>> async_add, 2025-09-07T07:36:50.7738491Z >>> args=("worker2", torch.ones(2), 1) 2025-09-07T07:36:50.7738574Z >>> ) 2025-09-07T07:36:50.7738691Z >>> print(ret) # prints tensor([2., 2.]) 2025-09-07T07:36:50.7738779Z 2025-09-07T07:36:50.7738998Z When combined with static or class method, this decorator must be the 2025-09-07T07:36:50.7739107Z inner one. 2025-09-07T07:36:50.7739198Z 2025-09-07T07:36:50.7739318Z >>> from torch.distributed import rpc 2025-09-07T07:36:50.7739410Z >>> 2025-09-07T07:36:50.7739523Z >>> # omitting setup and shutdown RPC 2025-09-07T07:36:50.7739602Z >>> 2025-09-07T07:36:50.7739704Z >>> # On all workers 2025-09-07T07:36:50.7739815Z >>> class AsyncExecutionClass: 2025-09-07T07:36:50.7739903Z >>> 2025-09-07T07:36:50.7739993Z >>> @staticmethod 2025-09-07T07:36:50.7740106Z >>> @rpc.functions.async_execution 2025-09-07T07:36:50.7740231Z >>> def static_async_add(to, x, y, z): 2025-09-07T07:36:50.7740403Z >>> return rpc.rpc_async(to, torch.add, args=(x, y)).then( 2025-09-07T07:36:50.7740531Z >>> lambda fut: fut.wait() + z 2025-09-07T07:36:50.7740613Z >>> ) 2025-09-07T07:36:50.7740696Z >>> 2025-09-07T07:36:50.7740803Z >>> @classmethod 2025-09-07T07:36:50.7740920Z >>> @rpc.functions.async_execution 2025-09-07T07:36:50.7741068Z >>> def class_async_add(cls, to, x, y, z): 2025-09-07T07:36:50.7741207Z >>> ret_fut = torch.futures.Future() 2025-09-07T07:36:50.7741352Z >>> rpc.rpc_async(to, torch.add, args=(x, y)).then( 2025-09-07T07:36:50.7741512Z >>> lambda fut: ret_fut.set_result(fut.wait() + z) 2025-09-07T07:36:50.7741647Z >>> ) 2025-09-07T07:36:50.7741743Z >>> return ret_fut 2025-09-07T07:36:50.7741832Z >>> 2025-09-07T07:36:50.7741945Z >>> @rpc.functions.async_execution 2025-09-07T07:36:50.7742079Z >>> def bound_async_add(self, to, x, y, z): 2025-09-07T07:36:50.7742289Z >>> return rpc.rpc_async(to, torch.add, args=(x, y)).then( 2025-09-07T07:36:50.7742400Z >>> lambda fut: fut.wait() + z 2025-09-07T07:36:50.7742495Z >>> ) 2025-09-07T07:36:50.7742576Z >>> 2025-09-07T07:36:50.7742674Z >>> # On worker0 2025-09-07T07:36:50.7742774Z >>> ret = rpc.rpc_sync( 2025-09-07T07:36:50.7742863Z >>> "worker1", 2025-09-07T07:36:50.7743007Z >>> AsyncExecutionClass.static_async_add, 2025-09-07T07:36:50.7743124Z >>> args=("worker2", torch.ones(2), 1, 2) 2025-09-07T07:36:50.7743213Z >>> ) 2025-09-07T07:36:50.7743329Z >>> print(ret) # prints tensor([4., 4.]) 2025-09-07T07:36:50.7743409Z >>> 2025-09-07T07:36:50.7743517Z >>> ret = rpc.rpc_sync( 2025-09-07T07:36:50.7743605Z >>> "worker1", 2025-09-07T07:36:50.7743751Z >>> AsyncExecutionClass.class_async_add, 2025-09-07T07:36:50.7743894Z >>> args=("worker2", torch.ones(2), 1, 2) 2025-09-07T07:36:50.7743975Z >>> ) 2025-09-07T07:36:50.7744106Z >>> print(ret) # prints tensor([4., 4.]) 2025-09-07T07:36:50.7744184Z 2025-09-07T07:36:50.7744348Z This decorator also works with RRef helpers, i.e., . 2025-09-07T07:36:50.7744503Z :meth:`torch.distributed.rpc.RRef.rpc_sync`, 2025-09-07T07:36:50.7744686Z :meth:`torch.distributed.rpc.RRef.rpc_async`, and 2025-09-07T07:36:50.7744837Z :meth:`torch.distributed.rpc.RRef.remote`. 2025-09-07T07:36:50.7744916Z 2025-09-07T07:36:50.7745036Z >>> from torch.distributed import rpc 2025-09-07T07:36:50.7745133Z >>> 2025-09-07T07:36:50.7745268Z >>> # reuse the AsyncExecutionClass class above 2025-09-07T07:36:50.7745432Z >>> rref = rpc.remote("worker1", AsyncExecutionClass) 2025-09-07T07:36:50.7745644Z >>> ret = rref.rpc_sync().static_async_add("worker2", torch.ones(2), 1, 2) 2025-09-07T07:36:50.7745762Z >>> print(ret) # prints tensor([4., 4.]) 2025-09-07T07:36:50.7745856Z >>> 2025-09-07T07:36:50.7746007Z >>> rref = rpc.remote("worker1", AsyncExecutionClass) 2025-09-07T07:36:50.7746261Z >>> ret = rref.rpc_async().static_async_add("worker2", torch.ones(2), 1, 2).wait() 2025-09-07T07:36:50.7746406Z >>> print(ret) # prints tensor([4., 4.]) 2025-09-07T07:36:50.7746490Z >>> 2025-09-07T07:36:50.7746654Z >>> rref = rpc.remote("worker1", AsyncExecutionClass) 2025-09-07T07:36:50.7746888Z >>> ret = rref.remote().static_async_add("worker2", torch.ones(2), 1, 2).to_here() 2025-09-07T07:36:50.7747020Z >>> print(ret) # prints tensor([4., 4.]) 2025-09-07T07:36:50.7747099Z 2025-09-07T07:36:50.7747352Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.7747443Z 2025-09-07T07:36:50.7747540Z warnings.warn(msg) 2025-09-07T07:36:50.7747629Z 2025-09-07T07:36:50.7747835Z --- Parse Warning: 87 / 146 --- 2025-09-07T07:36:50.7748915Z /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=113. 2025-09-07T07:36:50.7749219Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.7749299Z 2025-09-07T07:36:50.7749513Z Set device mapping between each RPC caller and callee pair. This 2025-09-07T07:36:50.7749692Z function can be called multiple times to incrementally add 2025-09-07T07:36:50.7749803Z device placement configurations. 2025-09-07T07:36:50.7749890Z 2025-09-07T07:36:50.7749972Z Args: 2025-09-07T07:36:50.7750079Z to (str): Callee name. 2025-09-07T07:36:50.7750271Z device_map (Dict of int, str, or torch.device): Device placement 2025-09-07T07:36:50.7750449Z mappings from this worker to the callee. This map must be 2025-09-07T07:36:50.7750551Z invertible. 2025-09-07T07:36:50.7750628Z 2025-09-07T07:36:50.7750720Z Example: 2025-09-07T07:36:50.7750832Z >>> # xdoctest: +SKIP("distributed") 2025-09-07T07:36:50.7750925Z >>> # both workers 2025-09-07T07:36:50.7751026Z >>> def add(x, y): 2025-09-07T07:36:50.7751163Z >>> print(x) # tensor([1., 1.], device='cuda:1') 2025-09-07T07:36:50.7751267Z >>> return x + y, (x + y).to(2) 2025-09-07T07:36:50.7751357Z >>> 2025-09-07T07:36:50.7751444Z >>> # on worker 0 2025-09-07T07:36:50.7751586Z >>> options = TensorPipeRpcBackendOptions( 2025-09-07T07:36:50.7751688Z >>> num_worker_threads=8, 2025-09-07T07:36:50.7751800Z >>> device_maps={"worker1": {0: 1}} 2025-09-07T07:36:50.7751939Z >>> # maps worker0's cuda:0 to worker1's cuda:1 2025-09-07T07:36:50.7752019Z >>> ) 2025-09-07T07:36:50.7752157Z >>> options.set_device_map("worker1", {1: 2}) 2025-09-07T07:36:50.7752308Z >>> # maps worker0's cuda:1 to worker1's cuda:2 2025-09-07T07:36:50.7752388Z >>> 2025-09-07T07:36:50.7752489Z >>> rpc.init_rpc( 2025-09-07T07:36:50.7752578Z >>> "worker0", 2025-09-07T07:36:50.7752675Z >>> rank=0, 2025-09-07T07:36:50.7752765Z >>> world_size=2, 2025-09-07T07:36:50.7752947Z >>> backend=rpc.BackendType.TENSORPIPE, 2025-09-07T07:36:50.7753070Z >>> rpc_backend_options=options 2025-09-07T07:36:50.7753149Z >>> ) 2025-09-07T07:36:50.7753240Z >>> 2025-09-07T07:36:50.7753334Z >>> x = torch.ones(2) 2025-09-07T07:36:50.7753493Z >>> rets = rpc.rpc_sync("worker1", add, args=(x.to(0), 1)) 2025-09-07T07:36:50.7753690Z >>> # The first argument will be moved to cuda:1 on worker1. When 2025-09-07T07:36:50.7753872Z >>> # sending the return value back, it will follow the invert of 2025-09-07T07:36:50.7754059Z >>> # the device map, and hence will be moved back to cuda:0 and 2025-09-07T07:36:50.7754152Z >>> # cuda:1 on worker0 2025-09-07T07:36:50.7754296Z >>> print(rets[0]) # tensor([2., 2.], device='cuda:0') 2025-09-07T07:36:50.7754537Z >>> print(rets[1]) # tensor([2., 2.], device='cuda:1') 2025-09-07T07:36:50.7754652Z 2025-09-07T07:36:50.7754908Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.7755001Z 2025-09-07T07:36:50.7755096Z warnings.warn(msg) 2025-09-07T07:36:50.7755185Z 2025-09-07T07:36:50.7755387Z --- Parse Warning: 88 / 146 --- 2025-09-07T07:36:50.7756501Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=_server_process_global_profile in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/rpc/server_process_global_profiler.py line=19. 2025-09-07T07:36:50.7756774Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.7756854Z 2025-09-07T07:36:50.7757063Z It has the same API as ``torch.autograd.profiler.profile`` class, 2025-09-07T07:36:50.7757339Z except that it enables profiling on all threads running RPC server request callbacks. 2025-09-07T07:36:50.7757433Z 2025-09-07T07:36:50.7757741Z Context manager that manages autograd profiler state and holds a summary of results. 2025-09-07T07:36:50.7757976Z Under the hood it just records events of functions being executed in C++ and 2025-09-07T07:36:50.7758219Z exposes those events to Python. You can wrap any code into it and it will 2025-09-07T07:36:50.7758345Z only report runtime of PyTorch functions. 2025-09-07T07:36:50.7758629Z Note: profiler is thread local and is automatically propagated into the async tasks 2025-09-07T07:36:50.7758708Z 2025-09-07T07:36:50.7758788Z Args: 2025-09-07T07:36:50.7759067Z enabled (bool, optional): Setting this to False makes this context manager a no-op. 2025-09-07T07:36:50.7759165Z Default: ``True``. 2025-09-07T07:36:50.7759255Z 2025-09-07T07:36:50.7759535Z use_cuda (bool, optional): Enables timing of CUDA events as well using the cudaEvent API. 2025-09-07T07:36:50.7759732Z Adds approximately 4us of overhead to each tensor operation. 2025-09-07T07:36:50.7759843Z Default: ``False`` 2025-09-07T07:36:50.7759922Z 2025-09-07T07:36:50.7760158Z record_shapes (bool, optional): If shapes recording is set, information 2025-09-07T07:36:50.7760385Z about input dimensions will be collected. This allows one to see which 2025-09-07T07:36:50.7760596Z dimensions have been used under the hood and further group by them 2025-09-07T07:36:50.7760824Z using prof.key_averages(group_by_input_shape=True). Please note that 2025-09-07T07:36:50.7761045Z shape recording might skew your profiling data. It is recommended to 2025-09-07T07:36:50.7761313Z use separate runs with and without shape recording to validate the timing. 2025-09-07T07:36:50.7761536Z Most likely the skew will be negligible for bottom most events (in a case 2025-09-07T07:36:50.7761744Z of nested function calls). But for higher level functions the total 2025-09-07T07:36:50.7761960Z self cpu time might be artificially increased because of the shape 2025-09-07T07:36:50.7762077Z collection. 2025-09-07T07:36:50.7762169Z 2025-09-07T07:36:50.7762434Z profile_memory (bool, optional): Whether to report memory usage, default: ``False`` 2025-09-07T07:36:50.7762511Z 2025-09-07T07:36:50.7762613Z .. warning:: 2025-09-07T07:36:50.7762813Z Enabling memory profiling incurs additional profiler overhead 2025-09-07T07:36:50.7762903Z 2025-09-07T07:36:50.7762986Z .. warning:: 2025-09-07T07:36:50.7763286Z Due to some CUDA multiprocessing limitations (see :ref:`multiprocessing-cuda-note`), 2025-09-07T07:36:50.7763494Z one cannot use the profiler with ``use_cuda = True`` to benchmark 2025-09-07T07:36:50.7763779Z DataLoaders with ``num_workers > 0``. If you wish to benchmark data loading, 2025-09-07T07:36:50.7763951Z please use ``use_cuda = False`` or ``num_workers = 0``. 2025-09-07T07:36:50.7764055Z 2025-09-07T07:36:50.7764138Z Example: 2025-09-07T07:36:50.7764252Z >>> # xdoctest: +SKIP 2025-09-07T07:36:50.7764346Z >>> # On worker 0: 2025-09-07T07:36:50.7764436Z >>> import torch 2025-09-07T07:36:50.7764575Z >>> import torch.distributed.rpc as rpc 2025-09-07T07:36:50.7764714Z >>> rpc.init_rpc("worker0", rank=0, world_size=2) 2025-09-07T07:36:50.7764910Z >>> x, y = torch.tensor(1), torch.tensor(2) 2025-09-07T07:36:50.7765022Z >>> outer_profile_rref = rpc.remote( 2025-09-07T07:36:50.7765183Z ... dst_worker_name, rpc._server_process_global_profile 2025-09-07T07:36:50.7765274Z ... ) 2025-09-07T07:36:50.7765400Z >>> outer_profile_rref.rpc_sync().__enter__() 2025-09-07T07:36:50.7765560Z >>> rpc.rpc_sync(dst_worker_name, torch.add, (x, y)) 2025-09-07T07:36:50.7765674Z >>> inner_profile_rref = rpc.remote( 2025-09-07T07:36:50.7765830Z ... dst_worker_name, rpc._server_process_global_profile 2025-09-07T07:36:50.7765923Z ... ) 2025-09-07T07:36:50.7766075Z >>> inner_profile_rref.rpc_sync().__enter__() 2025-09-07T07:36:50.7766231Z >>> rpc.rpc_sync(dst_worker_name, torch.sub, (x, y)) 2025-09-07T07:36:50.7766400Z >>> inner_profile_rref.rpc_sync().__exit__(None, None, None) 2025-09-07T07:36:50.7766567Z >>> outer_profile_rref.rpc_sync().__exit__(None, None, None) 2025-09-07T07:36:50.7766733Z >>> print(inner_profile_rref.rpc_sync().key_averages()) 2025-09-07T07:36:50.7766969Z --------- --------------- --------------- --------------- --------------- --------------- --------------- 2025-09-07T07:36:50.7767294Z Name Self CPU total % Self CPU total CPU total % CPU total CPU time avg Number of Calls 2025-09-07T07:36:50.7767521Z --------- --------------- --------------- --------------- --------------- --------------- --------------- 2025-09-07T07:36:50.7767724Z sub 85.06% 76.275us 100.00% 89.667us 89.667us 1 2025-09-07T07:36:50.7767918Z empty 14.94% 13.392us 14.94% 13.392us 13.392us 1 2025-09-07T07:36:50.7768142Z --------- --------------- --------------- --------------- --------------- --------------- --------------- 2025-09-07T07:36:50.7768260Z Self CPU time total: 89.667us 2025-09-07T07:36:50.7768418Z >>> print(outer_profile_rref.rpc_sync().key_averages()) 2025-09-07T07:36:50.7768651Z --------- --------------- --------------- --------------- --------------- --------------- --------------- 2025-09-07T07:36:50.7768984Z Name Self CPU total % Self CPU total CPU total % CPU total CPU time avg Number of Calls 2025-09-07T07:36:50.7769217Z --------- --------------- --------------- --------------- --------------- --------------- --------------- 2025-09-07T07:36:50.7769400Z sub 35.65% 76.275us 41.91% 89.667us 89.667us 1 2025-09-07T07:36:50.7769613Z empty 12.67% 27.101us 12.67% 27.101us 13.551us 2 2025-09-07T07:36:50.7769806Z add 51.68% 110.550us 58.09% 124.259us 124.259us 1 2025-09-07T07:36:50.7770029Z --------- --------------- --------------- --------------- --------------- --------------- --------------- 2025-09-07T07:36:50.7770150Z Self CPU time total: 213.926us 2025-09-07T07:36:50.7770246Z >>> rpc.shutdown() 2025-09-07T07:36:50.7770324Z 2025-09-07T07:36:50.7770424Z >>> # On worker 1: 2025-09-07T07:36:50.7770550Z >>> import torch.distributed.rpc as rpc 2025-09-07T07:36:50.7770699Z >>> rpc.init_rpc("worker1", rank=1, world_size=2) 2025-09-07T07:36:50.7770856Z >>> # wait for worker 0 to finish work, and then shutdown. 2025-09-07T07:36:50.7770975Z >>> rpc.shutdown() 2025-09-07T07:36:50.7771062Z 2025-09-07T07:36:50.7771315Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.7771405Z 2025-09-07T07:36:50.7771500Z warnings.warn(msg) 2025-09-07T07:36:50.7771576Z 2025-09-07T07:36:50.7771782Z --- Parse Warning: 89 / 146 --- 2025-09-07T07:36:50.7772786Z /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=35. 2025-09-07T07:36:50.7773058Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.7773134Z 2025-09-07T07:36:50.7773400Z :meth:`local_map` is an experimental API that allows users to pass :class:`DTensor` s 2025-09-07T07:36:50.7773688Z to a function that is written to be applied on ``torch.Tensor`` s. It is done by extracting 2025-09-07T07:36:50.7773955Z the local components of :class:`DTensor`, call the function, and wrap the outputs to 2025-09-07T07:36:50.7774157Z :class:`DTensor` according to the ``out_placements``. 2025-09-07T07:36:50.7774242Z 2025-09-07T07:36:50.7774322Z Args: 2025-09-07T07:36:50.7774540Z func (Callable): the function to be applied on each local shard of 2025-09-07T07:36:50.7774638Z :class:`DTensor` s. 2025-09-07T07:36:50.7774875Z out_placements (Union[`PlacementType`, Tuple[`PlacementType`, ...]]): 2025-09-07T07:36:50.7775129Z the desired placements of the :class:`DTensor` s in ``func``'s flattened output. 2025-09-07T07:36:50.7775370Z If the flattened ``output`` is a single value, the ``out_placements`` should be 2025-09-07T07:36:50.7775626Z of type `PlacementType`. Otherwise if the flattened ``output`` has multiple 2025-09-07T07:36:50.7775872Z values, the ``out_placements`` should be a tuple of `PlacementType` values 1:1 2025-09-07T07:36:50.7776007Z mapping to the flattened ``output``. 2025-09-07T07:36:50.7776213Z Besides, for :class:`Tensor` output, we use `PlacementType` as its 2025-09-07T07:36:50.7776484Z placements (a `Tuple[Placement]` value). For non-Tensor output, the `PlacementType` 2025-09-07T07:36:50.7776592Z should be `None`. 2025-09-07T07:36:50.7776828Z Note that the only exception is when no :class:`DTensor` argument is passed 2025-09-07T07:36:50.7777063Z in. In this case, even if `out_placements` is not `None`, the result function 2025-09-07T07:36:50.7777318Z should ignore the desired placements because the function is not running with 2025-09-07T07:36:50.7777454Z :class:`DTensor` s. 2025-09-07T07:36:50.7777621Z in_placements (Tuple[`PlacementType`, ...], optional): 2025-09-07T07:36:50.7777896Z the required placements of the :class:`DTensor` s in the flattened inputs of ``func``. 2025-09-07T07:36:50.7778147Z If ``in_placements`` is specified, :meth:`local_map` would examine whether the 2025-09-07T07:36:50.7778402Z placements of each :class:`DTensor` argument is the same as the required 2025-09-07T07:36:50.7778595Z placements or not. If the placements are not the same and 2025-09-07T07:36:50.7778839Z ``redistribute_inputs`` is ``False``, an exception will be raised. Otherwise if 2025-09-07T07:36:50.7779080Z ``redistribute_inputs`` is ``True``, the argument will be first redistributed to 2025-09-07T07:36:50.7779352Z the required sharding placements before passing its local tensor to ``func``. 2025-09-07T07:36:50.7779579Z The only exception is when required placements are not ``None`` and the 2025-09-07T07:36:50.7779835Z argument is a :class:`torch.Tensor`. In this case, the placements examination 2025-09-07T07:36:50.7780050Z will be skipped and the argument will be directly passed to ``func``. 2025-09-07T07:36:50.7780298Z If ``in_placements`` is ``None``, no placements examination will be performed. 2025-09-07T07:36:50.7780409Z Default: None 2025-09-07T07:36:50.7780592Z in_grad_placements (Tuple[`PlacementType`, ...], optional): 2025-09-07T07:36:50.7780810Z the placements hint of the :class:`DTensor` s gradient corresponds 2025-09-07T07:36:50.7781018Z to the flattened input DTensor. This argument is the hint that user 2025-09-07T07:36:50.7781216Z can give to :meth:`to_local` in case the gradient layout of the 2025-09-07T07:36:50.7781430Z local tensor input does not match its :class:`DTensor` input layout. 2025-09-07T07:36:50.7781629Z If not specified, we will assume the gradient layout of the local 2025-09-07T07:36:50.7781855Z tensor input remains the same as the original :class:`DTensor` input 2025-09-07T07:36:50.7782018Z and use that for gradient computation. Default: None. 2025-09-07T07:36:50.7782171Z device_mesh (:class:`DeviceMesh`, optional): 2025-09-07T07:36:50.7782419Z the device mesh that the output :class:`DTensor` s are placed on. If not 2025-09-07T07:36:50.7782678Z specified, this will be inferred from the first input :class:`DTensor`'s device 2025-09-07T07:36:50.7782788Z mesh. Default: None. 2025-09-07T07:36:50.7782869Z 2025-09-07T07:36:50.7782966Z Keyword Args: 2025-09-07T07:36:50.7783085Z redistribute_inputs (bool, optional): 2025-09-07T07:36:50.7783338Z the bool value indicating whether to reshard the input :class:`DTensor` s when 2025-09-07T07:36:50.7783592Z their placements are different from the required input placements. If this 2025-09-07T07:36:50.7783816Z value is ``False`` and some :class:`DTensor` input has a different placement, 2025-09-07T07:36:50.7783964Z an exception will be raised. Default: False. 2025-09-07T07:36:50.7784043Z 2025-09-07T07:36:50.7784124Z Returns: 2025-09-07T07:36:50.7784389Z A ``Callable`` that applies ``func`` to each local shard of the input :class:`DTensor` 2025-09-07T07:36:50.7784632Z and returns a :class:`DTensor` constructed from the return value of ``func``. 2025-09-07T07:36:50.7784719Z 2025-09-07T07:36:50.7784796Z Raises: 2025-09-07T07:36:50.7785032Z AssertionError: For any non-DTensor output, we require its corresponding 2025-09-07T07:36:50.7785299Z output placement in ``out_placements`` be None. An AssertionError will be raised 2025-09-07T07:36:50.7785399Z if this is not the case. 2025-09-07T07:36:50.7785486Z 2025-09-07T07:36:50.7785744Z ValueError: If ``redistribute_inputs=False`` but the input :class:`DTensor` needs 2025-09-07T07:36:50.7785930Z a redistribution according to ``in_placements``. 2025-09-07T07:36:50.7786024Z 2025-09-07T07:36:50.7786110Z Example: 2025-09-07T07:36:50.7786223Z >>> # xdoctest: +SKIP("distributed") 2025-09-07T07:36:50.7786368Z >>> def mm_allreduce_forward(device_mesh, W, X): 2025-09-07T07:36:50.7786516Z >>> partial_sum_tensor = torch.mm(W, X) 2025-09-07T07:36:50.7786768Z >>> reduced_tensor = funcol.all_reduce(partial_sum_tensor, "sum", device_mesh) 2025-09-07T07:36:50.7786871Z >>> return reduced_tensor 2025-09-07T07:36:50.7786958Z >>> 2025-09-07T07:36:50.7787098Z >>> W = torch.randn(12, 8, requires_grad=False) 2025-09-07T07:36:50.7787222Z >>> X = torch.randn(8, 16, requires_grad=False) 2025-09-07T07:36:50.7787332Z >>> Y = torch.mm(W, X) 2025-09-07T07:36:50.7787517Z >>> row_wise = [Shard(0)] # row-wise sharding placements on 1-d mesh 2025-09-07T07:36:50.7787700Z >>> col_wise = [Shard(1)] # col-wise sharding placements on 1-d mesh 2025-09-07T07:36:50.7787791Z >>> 2025-09-07T07:36:50.7788062Z >>> # local_mm_allreduce_forward is the function wrapped with DTensor/Tensor conversion 2025-09-07T07:36:50.7788221Z >>> local_mm_allreduce_forward = local_map( 2025-09-07T07:36:50.7788327Z >>> mm_allreduce_forward, 2025-09-07T07:36:50.7788442Z >>> out_placements=[Replicate()], 2025-09-07T07:36:50.7788571Z >>> in_placements=[col_wise, row_wise], 2025-09-07T07:36:50.7788675Z >>> device_mesh=device_mesh, 2025-09-07T07:36:50.7788765Z >>> ) 2025-09-07T07:36:50.7788845Z >>> 2025-09-07T07:36:50.7788945Z >>> W_dt = distribute_tensor( 2025-09-07T07:36:50.7789057Z ... W, device_mesh, (col_wise) 2025-09-07T07:36:50.7789168Z ... ) # col-wisely sharded W tensor 2025-09-07T07:36:50.7789280Z >>> X_dt = distribute_tensor( 2025-09-07T07:36:50.7789384Z ... X, device_mesh, (row_wise) 2025-09-07T07:36:50.7789489Z ... ) # row-wisely sharded X tensor 2025-09-07T07:36:50.7789611Z >>> Y_dt = local_mm_allreduce_forward( 2025-09-07T07:36:50.7789710Z ... device_mesh, W_dt, X_dt 2025-09-07T07:36:50.7789868Z ... ) # apply local_mm_allreduce_forward to DTensors 2025-09-07T07:36:50.7789946Z 2025-09-07T07:36:50.7790199Z .. note:: This API is currently experimental and subject to change 2025-09-07T07:36:50.7790290Z 2025-09-07T07:36:50.7790541Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.7790631Z 2025-09-07T07:36:50.7790727Z warnings.warn(msg) 2025-09-07T07:36:50.7790804Z 2025-09-07T07:36:50.7791045Z --- Parse Warning: 90 / 146 --- 2025-09-07T07:36:50.7792135Z /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=25. 2025-09-07T07:36:50.7792409Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.7792488Z 2025-09-07T07:36:50.7792766Z :meth:`register_sharding` is an experimental API that allows users to register sharding 2025-09-07T07:36:50.7793024Z strategies for an operator when the tensor inputs and outputs are DTensor. 2025-09-07T07:36:50.7793277Z It can be useful when: (1) there doesn't exist a default sharding strategy for ``op``, 2025-09-07T07:36:50.7793528Z e.g. when ``op`` is a custom operator that is not supported by :class:`DTensor`; (2) 2025-09-07T07:36:50.7793804Z when users would like to overwrite default sharding strategies of existing operators. 2025-09-07T07:36:50.7793882Z 2025-09-07T07:36:50.7793972Z Args: 2025-09-07T07:36:50.7794100Z op (Union[OpOverload, List[OpOverload]]): 2025-09-07T07:36:50.7794327Z An op or a list of ops to register the customized sharding function. 2025-09-07T07:36:50.7794488Z 2025-09-07T07:36:50.7794574Z Returns: 2025-09-07T07:36:50.7794854Z A function decorator which can be used to wrap a function that defines the sharding 2025-09-07T07:36:50.7795130Z strategy for the operator specified in ``op``. The defined sharding strategy will be 2025-09-07T07:36:50.7795447Z registered to DTensor and will override the default sharding strategy if DTensor has 2025-09-07T07:36:50.7795749Z already implemented the operator. The customized sharding function takes the same inputs 2025-09-07T07:36:50.7795988Z as the original op (except that if an arg is a :class:`torch.Tensor`, it will be 2025-09-07T07:36:50.7796272Z replaced by a tensor-like object that DTensor uses internally). The function should 2025-09-07T07:36:50.7796541Z return a sequence of 2-tuples, each specifying acceptable output placements and its 2025-09-07T07:36:50.7796667Z corresponding input placements. 2025-09-07T07:36:50.7796744Z 2025-09-07T07:36:50.7796827Z Example: 2025-09-07T07:36:50.7796953Z >>> # xdoctest: +SKIP("distributed") 2025-09-07T07:36:50.7797085Z >>> @register_sharding(aten._softmax.default) 2025-09-07T07:36:50.7797278Z >>> def custom_softmax_sharding(x, dim, half_to_float): 2025-09-07T07:36:50.7797420Z >>> softmax_dim = dim if dim >= 0 else dim + x.ndim 2025-09-07T07:36:50.7797530Z >>> acceptable_shardings = [] 2025-09-07T07:36:50.7797622Z >>> 2025-09-07T07:36:50.7797796Z >>> all_replicate = ([Replicate()], [Replicate(), None, None]) 2025-09-07T07:36:50.7797948Z >>> acceptable_shardings.append(all_replicate) 2025-09-07T07:36:50.7798028Z >>> 2025-09-07T07:36:50.7798144Z >>> for sharding_dim in range(x.ndim): 2025-09-07T07:36:50.7798270Z >>> if sharding_dim != softmax_dim: 2025-09-07T07:36:50.7798367Z >>> all_sharded = ( 2025-09-07T07:36:50.7798487Z >>> [Shard(sharding_dim)], 2025-09-07T07:36:50.7798609Z >>> [Shard(sharding_dim), None, None], 2025-09-07T07:36:50.7798693Z >>> ) 2025-09-07T07:36:50.7798840Z >>> acceptable_shardings.append(all_sharded) 2025-09-07T07:36:50.7798918Z >>> 2025-09-07T07:36:50.7799060Z >>> return acceptable_shardings 2025-09-07T07:36:50.7799137Z 2025-09-07T07:36:50.7799330Z .. note:: This API is currently experimental and subject to change 2025-09-07T07:36:50.7799416Z 2025-09-07T07:36:50.7799667Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.7799756Z 2025-09-07T07:36:50.7799852Z warnings.warn(msg) 2025-09-07T07:36:50.7799928Z 2025-09-07T07:36:50.7800143Z --- Parse Warning: 91 / 146 --- 2025-09-07T07:36:50.7801174Z /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=428. 2025-09-07T07:36:50.7801447Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.7801526Z 2025-09-07T07:36:50.7801913Z Configure the nn.Module's inputs to convert the input tensors of the nn.Module to DTensors at runtime according to 2025-09-07T07:36:50.7802244Z ``input_layouts``, and perform layout redistribution according to the ``desired_input_layouts``. 2025-09-07T07:36:50.7802320Z 2025-09-07T07:36:50.7802419Z Keyword Args: 2025-09-07T07:36:50.7802619Z input_layouts (Union[Placement, Tuple[Optional[Placement]]]): 2025-09-07T07:36:50.7802947Z The DTensor layouts of input tensors for the nn.Module, this is used to convert the input tensors to 2025-09-07T07:36:50.7803353Z DTensors. If some inputs are not torch.Tensor or no need to convert to DTensors, ``None`` need to be specified 2025-09-07T07:36:50.7803471Z as a placeholder. default: None. 2025-09-07T07:36:50.7803713Z desired_input_layouts (Union[Placement, Tuple[Optional[Placement]]]): 2025-09-07T07:36:50.7804092Z The desired DTensor layout of input tensors for the nn.Module, this is used to ensure the inputs of the nn.Module 2025-09-07T07:36:50.7804516Z have the desired DTensor layouts. This argument needs to have the same length with ``input_layouts``. default: None. 2025-09-07T07:36:50.7804657Z input_kwarg_layouts (Dict[str, Placement]): 2025-09-07T07:36:50.7805036Z The DTensor layouts of input kwargs for the nn.Module, this is used to convert the input kwarg tensors to DTensors. 2025-09-07T07:36:50.7805140Z default: None 2025-09-07T07:36:50.7805297Z desired_input_kwarg_layouts: (Dict[str, Placement]): 2025-09-07T07:36:50.7805680Z The desired DTensor layout of input kwargs for the nn.Module, this is used to ensure the inputs of the nn.Module 2025-09-07T07:36:50.7805825Z have the desired DTensor layouts. default: None. 2025-09-07T07:36:50.7805938Z use_local_output (bool, optional): 2025-09-07T07:36:50.7810546Z Whether to use local :class:`torch.Tensor` instead of :class:`DTensor` for the module inputs, default: False. 2025-09-07T07:36:50.7810688Z Returns: 2025-09-07T07:36:50.7811015Z A :class:`ParallelStyle` object that prepares the sharding layouts of the nn.Module's inputs. 2025-09-07T07:36:50.7811109Z 2025-09-07T07:36:50.7811200Z Example:: 2025-09-07T07:36:50.7811319Z >>> # xdoctest: +SKIP(failing) 2025-09-07T07:36:50.7811632Z >>> from torch.distributed.tensor.parallel import parallelize_module, PrepareModuleInput 2025-09-07T07:36:50.7811828Z >>> from torch.distributed.device_mesh import init_device_mesh 2025-09-07T07:36:50.7811925Z >>> ... 2025-09-07T07:36:50.7812233Z >>> block = TransformerBlock(...) # block is a nn.Module that contains an "attn" Attention submodule 2025-09-07T07:36:50.7812373Z >>> tp_mesh = init_device_mesh("cuda", (8,)) 2025-09-07T07:36:50.7812454Z >>> 2025-09-07T07:36:50.7812789Z >>> # According to the style specified below, the first input of attn will be annotated to Sharded DTensor 2025-09-07T07:36:50.7813025Z >>> # and then redistributed to Replicated DTensor. 2025-09-07T07:36:50.7813129Z >>> parallelize_module( 2025-09-07T07:36:50.7813276Z >>> block, # this can be a submodule or module 2025-09-07T07:36:50.7813367Z >>> tp_mesh, 2025-09-07T07:36:50.7813469Z >>> parallelize_plan={ 2025-09-07T07:36:50.7813604Z >>> "attn": PrepareModuleInput( 2025-09-07T07:36:50.7813741Z >>> input_layouts=(Shard(0), None, None, ...), 2025-09-07T07:36:50.7813921Z >>> desired_input_layouts=(Replicate(), None, None, ...) 2025-09-07T07:36:50.7814007Z >>> ), 2025-09-07T07:36:50.7814088Z >>> } 2025-09-07T07:36:50.7814189Z >>> ) 2025-09-07T07:36:50.7814268Z 2025-09-07T07:36:50.7814535Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.7814615Z 2025-09-07T07:36:50.7814712Z warnings.warn(msg) 2025-09-07T07:36:50.7814802Z 2025-09-07T07:36:50.7815036Z --- Parse Warning: 92 / 146 --- 2025-09-07T07:36:50.7816089Z /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=597. 2025-09-07T07:36:50.7816352Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.7816430Z 2025-09-07T07:36:50.7816837Z Configure the nn.Module's outputs to convert the output tensors of the nn.Module to DTensors at runtime according to 2025-09-07T07:36:50.7817206Z ``output_layouts``, and perform layout redistribution according to the ``desired_output_layouts``. 2025-09-07T07:36:50.7817297Z 2025-09-07T07:36:50.7817384Z Keyword Args: 2025-09-07T07:36:50.7817552Z output_layouts (Union[Placement, Tuple[Placement]]): 2025-09-07T07:36:50.7817905Z The DTensor layouts of output tensors for the nn.Module, this is used to convert the output tensors to 2025-09-07T07:36:50.7818315Z DTensors if they are :class:`torch.Tensor`. If some outputs are not torch.Tensor or no need to convert to DTensors, 2025-09-07T07:36:50.7818471Z ``None`` need to be specified as a placeholder. 2025-09-07T07:36:50.7818668Z desired_output_layouts (Union[Placement, Tuple[Placement]]): 2025-09-07T07:36:50.7819068Z The desired DTensor layouts of output tensors for the nn.Module, this is used to ensure the outputs of the nn.Module 2025-09-07T07:36:50.7819184Z have the desired DTensor layouts. 2025-09-07T07:36:50.7819298Z use_local_output (bool, optional): 2025-09-07T07:36:50.7819671Z Whether to use local :class:`torch.Tensor` instead of :class:`DTensor` for the module outputs, default: True. 2025-09-07T07:36:50.7819781Z Returns: 2025-09-07T07:36:50.7820085Z A ParallelStyle object that prepares the sharding layouts of the nn.Module's outputs. 2025-09-07T07:36:50.7820167Z 2025-09-07T07:36:50.7820253Z Example:: 2025-09-07T07:36:50.7820371Z >>> # xdoctest: +SKIP(failing) 2025-09-07T07:36:50.7820680Z >>> from torch.distributed.tensor.parallel import parallelize_module, PrepareModuleOutput 2025-09-07T07:36:50.7820880Z >>> from torch.distributed.device_mesh import init_device_mesh 2025-09-07T07:36:50.7820960Z >>> ... 2025-09-07T07:36:50.7821260Z >>> block = TransformerBlock(...) # block is a nn.Module that contains an "attn" Attention submodule 2025-09-07T07:36:50.7821403Z >>> tp_mesh = init_device_mesh("cuda", (8,)) 2025-09-07T07:36:50.7821482Z >>> 2025-09-07T07:36:50.7821897Z >>> # According to the style specified below, the output of the TransformerBlock will be converted to Replicated DTensor 2025-09-07T07:36:50.7822031Z >>> # and then redistributed to Sharded DTensor. 2025-09-07T07:36:50.7822135Z >>> parallelize_module( 2025-09-07T07:36:50.7822323Z >>> block, # this can be a submodule or module 2025-09-07T07:36:50.7822412Z >>> tp_mesh, 2025-09-07T07:36:50.7822560Z >>> parallelize_plan = PrepareModuleOutput( 2025-09-07T07:36:50.7822676Z >>> output_layouts=Replicate(), 2025-09-07T07:36:50.7822793Z >>> desired_output_layouts=Shard(0) 2025-09-07T07:36:50.7822888Z >>> ) 2025-09-07T07:36:50.7822967Z >>> ) 2025-09-07T07:36:50.7823045Z 2025-09-07T07:36:50.7823307Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.7823387Z 2025-09-07T07:36:50.7823494Z warnings.warn(msg) 2025-09-07T07:36:50.7823573Z 2025-09-07T07:36:50.7823949Z --- Parse Warning: 93 / 146 --- 2025-09-07T07:36:50.7825027Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=PrepareModuleInputOutput in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/parallel/style.py line=705. 2025-09-07T07:36:50.7825291Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.7825382Z 2025-09-07T07:36:50.7825817Z Configure the nn.Module's inputs (and outputs) to convert the input tensors (and output tensors, respectively) of the nn.Module 2025-09-07T07:36:50.7826252Z to DTensors at runtime according to ``input_layouts`` (and output_layouts, respectively), and perform layout redistribution 2025-09-07T07:36:50.7826629Z according to the ``desired_input_layouts`` (and ``desired_output_layouts``, respectively). This is a combination of 2025-09-07T07:36:50.7826913Z :class:`PrepareModuleInput` and :class:`PrepareModuleOutput`. 2025-09-07T07:36:50.7827009Z 2025-09-07T07:36:50.7827096Z Keyword Args: 2025-09-07T07:36:50.7827306Z input_layouts (Union[Placement, Tuple[Optional[Placement]]]): 2025-09-07T07:36:50.7827646Z The DTensor layouts of input tensors for the nn.Module, this is used to convert the input tensors to 2025-09-07T07:36:50.7828052Z DTensors. If some inputs are not torch.Tensor or no need to convert to DTensors, ``None`` need to be specified 2025-09-07T07:36:50.7828184Z as a placeholder. default: None. 2025-09-07T07:36:50.7828414Z desired_input_layouts (Union[Placement, Tuple[Optional[Placement]]]): 2025-09-07T07:36:50.7828802Z The desired DTensor layout of input tensors for the nn.Module, this is used to ensure the inputs of the nn.Module 2025-09-07T07:36:50.7829194Z have the desired DTensor layouts. This argument needs to have the same length with ``input_layouts``. default: None. 2025-09-07T07:36:50.7829339Z input_kwarg_layouts (Dict[str, Placement]): 2025-09-07T07:36:50.7829712Z The DTensor layouts of input kwargs for the nn.Module, this is used to convert the input kwarg tensors to DTensors. 2025-09-07T07:36:50.7829845Z default: None 2025-09-07T07:36:50.7830019Z desired_input_kwarg_layouts: (Dict[str, Placement]): 2025-09-07T07:36:50.7830386Z The desired DTensor layout of input kwargs for the nn.Module, this is used to ensure the inputs of the nn.Module 2025-09-07T07:36:50.7830542Z have the desired DTensor layouts. default: None. 2025-09-07T07:36:50.7830655Z use_local_input (bool, optional): 2025-09-07T07:36:50.7831015Z Whether to use local :class:`torch.Tensor` instead of :class:`DTensor` for the module inputs, default: False. 2025-09-07T07:36:50.7831190Z output_layouts (Union[Placement, Tuple[Placement]]): 2025-09-07T07:36:50.7831527Z The DTensor layouts of output tensors for the nn.Module, this is used to convert the output tensors to 2025-09-07T07:36:50.7831920Z DTensors if they are :class:`torch.Tensor`. If some outputs are not torch.Tensor or no need to convert to DTensors, 2025-09-07T07:36:50.7832062Z ``None`` need to be specified as a placeholder. 2025-09-07T07:36:50.7832292Z desired_output_layouts (Union[Placement, Tuple[Placement]]): 2025-09-07T07:36:50.7832692Z The desired DTensor layouts of output tensors for the nn.Module, this is used to ensure the outputs of the nn.Module 2025-09-07T07:36:50.7832808Z have the desired DTensor layouts. 2025-09-07T07:36:50.7832932Z use_local_output (bool, optional): 2025-09-07T07:36:50.7833292Z Whether to use local :class:`torch.Tensor` instead of :class:`DTensor` for the module outputs, default: True. 2025-09-07T07:36:50.7833385Z Returns: 2025-09-07T07:36:50.7833751Z A :class:`ParallelStyle` object that prepares the sharding layouts of the nn.Module's inputs and outputs. 2025-09-07T07:36:50.7833833Z 2025-09-07T07:36:50.7833937Z Example:: 2025-09-07T07:36:50.7834044Z >>> # xdoctest: +SKIP(failing) 2025-09-07T07:36:50.7834446Z >>> from torch.distributed.tensor.parallel import parallelize_module, PrepareModuleInputOutput 2025-09-07T07:36:50.7834658Z >>> from torch.distributed.device_mesh import init_device_mesh 2025-09-07T07:36:50.7834739Z >>> ... 2025-09-07T07:36:50.7835057Z >>> block = TransformerBlock(...) # block is a nn.Module that contains an "attn" Attention submodule 2025-09-07T07:36:50.7835183Z >>> tp_mesh = init_device_mesh("cuda", (8,)) 2025-09-07T07:36:50.7835277Z >>> 2025-09-07T07:36:50.7835616Z >>> # According to the style specified below, the first input of attn will be annotated as Sharded DTensor 2025-09-07T07:36:50.7835972Z >>> # and then redistributed to Replicated DTensor, and the output of the TransformerBlock will be annotated 2025-09-07T07:36:50.7836224Z >>> # as Replicated DTensor and then redistributed to Sharded DTensor. 2025-09-07T07:36:50.7836327Z >>> parallelize_module( 2025-09-07T07:36:50.7836474Z >>> block, # this can be a submodule or module 2025-09-07T07:36:50.7836566Z >>> tp_mesh, 2025-09-07T07:36:50.7836694Z >>> parallelize_plan={ 2025-09-07T07:36:50.7836838Z >>> "attn": PrepareModuleInputOutput( 2025-09-07T07:36:50.7836976Z >>> input_layouts=(Shard(0), None, None, ...), 2025-09-07T07:36:50.7837155Z >>> desired_input_layouts=(Replicate(), None, None, ...), 2025-09-07T07:36:50.7837271Z >>> output_layouts=Replicate(), 2025-09-07T07:36:50.7837395Z >>> desired_output_layouts=Shard(0), 2025-09-07T07:36:50.7837492Z >>> ), 2025-09-07T07:36:50.7837573Z >>> } 2025-09-07T07:36:50.7837668Z >>> ) 2025-09-07T07:36:50.7837747Z 2025-09-07T07:36:50.7838002Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.7838097Z 2025-09-07T07:36:50.7838194Z warnings.warn(msg) 2025-09-07T07:36:50.7838271Z 2025-09-07T07:36:50.7838513Z --- Parse Warning: 94 / 146 --- 2025-09-07T07:36:50.7839615Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=LowRankMultivariateNormal in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/lowrank_multivariate_normal.py line=56. 2025-09-07T07:36:50.7839889Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.7839966Z 2025-09-07T07:36:50.7840283Z Creates a multivariate normal distribution with covariance matrix having a low-rank form 2025-09-07T07:36:50.7840471Z parameterized by :attr:`cov_factor` and :attr:`cov_diag`:: 2025-09-07T07:36:50.7840550Z 2025-09-07T07:36:50.7840736Z covariance_matrix = cov_factor @ cov_factor.T + cov_diag 2025-09-07T07:36:50.7840815Z 2025-09-07T07:36:50.7840898Z Example: 2025-09-07T07:36:50.7841051Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_LAPACK) 2025-09-07T07:36:50.7841190Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2025-09-07T07:36:50.7841316Z >>> m = LowRankMultivariateNormal( 2025-09-07T07:36:50.7841523Z ... torch.zeros(2), torch.tensor([[1.0], [0.0]]), torch.ones(2) 2025-09-07T07:36:50.7841604Z ... ) 2025-09-07T07:36:50.7841905Z >>> m.sample() # normally distributed with mean=`[0,0]`, cov_factor=`[[1],[0]]`, cov_diag=`[1,1]` 2025-09-07T07:36:50.7842006Z tensor([-0.2102, -0.5429]) 2025-09-07T07:36:50.7842096Z 2025-09-07T07:36:50.7842176Z Args: 2025-09-07T07:36:50.7842410Z loc (Tensor): mean of the distribution with shape `batch_shape + event_shape` 2025-09-07T07:36:50.7842673Z cov_factor (Tensor): factor part of low-rank form of covariance matrix with shape 2025-09-07T07:36:50.7842792Z `batch_shape + event_shape + (rank,)` 2025-09-07T07:36:50.7843058Z cov_diag (Tensor): diagonal part of low-rank form of covariance matrix with shape 2025-09-07T07:36:50.7843166Z `batch_shape + event_shape` 2025-09-07T07:36:50.7843245Z 2025-09-07T07:36:50.7843341Z Note: 2025-09-07T07:36:50.7843613Z The computation for determinant and inverse of covariance matrix is avoided when 2025-09-07T07:36:50.7843861Z `cov_factor.shape[1] << cov_factor.shape[0]` thanks to `Woodbury matrix identity 2025-09-07T07:36:50.7844109Z `_ and 2025-09-07T07:36:50.7844401Z `matrix determinant lemma `_. 2025-09-07T07:36:50.7844663Z Thanks to these formulas, we just need to compute the determinant and inverse of 2025-09-07T07:36:50.7844787Z the small size "capacitance" matrix:: 2025-09-07T07:36:50.7844875Z 2025-09-07T07:36:50.7845082Z capacitance = I + cov_factor.T @ inv(cov_diag) @ cov_factor 2025-09-07T07:36:50.7845164Z 2025-09-07T07:36:50.7845432Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.7845514Z 2025-09-07T07:36:50.7845623Z warnings.warn(msg) 2025-09-07T07:36:50.7845726Z 2025-09-07T07:36:50.7845914Z --- Parse Warning: 95 / 146 --- 2025-09-07T07:36:50.7846935Z /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=15. 2025-09-07T07:36:50.7847195Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.7847285Z 2025-09-07T07:36:50.7847506Z The `MixtureSameFamily` distribution implements a (batch of) mixture 2025-09-07T07:36:50.7847760Z distribution where all component are from different parameterizations of 2025-09-07T07:36:50.7847974Z the same distribution type. It is parameterized by a `Categorical` 2025-09-07T07:36:50.7848172Z "selecting distribution" (over `k` component) and a component 2025-09-07T07:36:50.7848412Z distribution, i.e., a `Distribution` with a rightmost batch shape 2025-09-07T07:36:50.7848574Z (equal to `[k]`) which indexes each (batch of) component. 2025-09-07T07:36:50.7848664Z 2025-09-07T07:36:50.7848754Z Examples:: 2025-09-07T07:36:50.7848834Z 2025-09-07T07:36:50.7848964Z >>> # xdoctest: +SKIP("undefined vars") 2025-09-07T07:36:50.7849164Z >>> # Construct Gaussian Mixture Model in 1D consisting of 5 equally 2025-09-07T07:36:50.7849279Z >>> # weighted normal distributions 2025-09-07T07:36:50.7849407Z >>> mix = D.Categorical(torch.ones(5,)) 2025-09-07T07:36:50.7849559Z >>> comp = D.Normal(torch.randn(5,), torch.rand(5,)) 2025-09-07T07:36:50.7849693Z >>> gmm = MixtureSameFamily(mix, comp) 2025-09-07T07:36:50.7849773Z 2025-09-07T07:36:50.7849970Z >>> # Construct Gaussian Mixture Model in 2D consisting of 5 equally 2025-09-07T07:36:50.7850111Z >>> # weighted bivariate normal distributions 2025-09-07T07:36:50.7850229Z >>> mix = D.Categorical(torch.ones(5,)) 2025-09-07T07:36:50.7850378Z >>> comp = D.Independent(D.Normal( 2025-09-07T07:36:50.7850511Z ... torch.randn(5,2), torch.rand(5,2)), 1) 2025-09-07T07:36:50.7850630Z >>> gmm = MixtureSameFamily(mix, comp) 2025-09-07T07:36:50.7850719Z 2025-09-07T07:36:50.7850898Z >>> # Construct a batch of 3 Gaussian Mixture Models in 2D each 2025-09-07T07:36:50.7851108Z >>> # consisting of 5 random weighted bivariate normal distributions 2025-09-07T07:36:50.7851230Z >>> mix = D.Categorical(torch.rand(3,5)) 2025-09-07T07:36:50.7851343Z >>> comp = D.Independent(D.Normal( 2025-09-07T07:36:50.7851489Z ... torch.randn(3,5,2), torch.rand(3,5,2)), 1) 2025-09-07T07:36:50.7851607Z >>> gmm = MixtureSameFamily(mix, comp) 2025-09-07T07:36:50.7851698Z 2025-09-07T07:36:50.7851777Z Args: 2025-09-07T07:36:50.7851980Z mixture_distribution: `torch.distributions.Categorical`-like 2025-09-07T07:36:50.7852177Z instance. Manages the probability of selecting component. 2025-09-07T07:36:50.7852347Z The number of categories must match the rightmost batch 2025-09-07T07:36:50.7852545Z dimension of the `component_distribution`. Must have either 2025-09-07T07:36:50.7852685Z scalar `batch_shape` or `batch_shape` matching 2025-09-07T07:36:50.7852822Z `component_distribution.batch_shape[:-1]` 2025-09-07T07:36:50.7853051Z component_distribution: `torch.distributions.Distribution`-like 2025-09-07T07:36:50.7853228Z instance. Right-most batch dimension indexes component. 2025-09-07T07:36:50.7853321Z 2025-09-07T07:36:50.7853596Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.7853675Z 2025-09-07T07:36:50.7853784Z warnings.warn(msg) 2025-09-07T07:36:50.7853859Z 2025-09-07T07:36:50.7854060Z --- Parse Warning: 96 / 146 --- 2025-09-07T07:36:50.7855056Z /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=120. 2025-09-07T07:36:50.7855344Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.7855430Z 2025-09-07T07:36:50.7855611Z Creates a RelaxedBernoulli distribution, parametrized by 2025-09-07T07:36:50.7855810Z :attr:`temperature`, and either :attr:`probs` or :attr:`logits` 2025-09-07T07:36:50.7856024Z (but not both). This is a relaxed version of the `Bernoulli` distribution, 2025-09-07T07:36:50.7856206Z so the values are in (0, 1), and has reparametrizable samples. 2025-09-07T07:36:50.7856293Z 2025-09-07T07:36:50.7856379Z Example:: 2025-09-07T07:36:50.7856468Z 2025-09-07T07:36:50.7856606Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2025-09-07T07:36:50.7856794Z >>> m = RelaxedBernoulli(torch.tensor([2.2]), 2025-09-07T07:36:50.7856931Z ... torch.tensor([0.1, 0.2, 0.3, 0.99])) 2025-09-07T07:36:50.7857016Z >>> m.sample() 2025-09-07T07:36:50.7857148Z tensor([ 0.2951, 0.3442, 0.8918, 0.9021]) 2025-09-07T07:36:50.7857226Z 2025-09-07T07:36:50.7857306Z Args: 2025-09-07T07:36:50.7857458Z temperature (Tensor): relaxation temperature 2025-09-07T07:36:50.7857626Z probs (Number, Tensor): the probability of sampling `1` 2025-09-07T07:36:50.7857797Z logits (Number, Tensor): the log-odds of sampling `1` 2025-09-07T07:36:50.7857875Z 2025-09-07T07:36:50.7858124Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.7858218Z 2025-09-07T07:36:50.7858314Z warnings.warn(msg) 2025-09-07T07:36:50.7858391Z 2025-09-07T07:36:50.7858591Z --- Parse Warning: 97 / 146 --- 2025-09-07T07:36:50.7859665Z /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=109. 2025-09-07T07:36:50.7859938Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.7860018Z 2025-09-07T07:36:50.7860243Z Creates a RelaxedOneHotCategorical distribution parametrized by 2025-09-07T07:36:50.7860438Z :attr:`temperature`, and either :attr:`probs` or :attr:`logits`. 2025-09-07T07:36:50.7860675Z This is a relaxed version of the :class:`OneHotCategorical` distribution, so 2025-09-07T07:36:50.7860852Z its samples are on simplex, and are reparametrizable. 2025-09-07T07:36:50.7860931Z 2025-09-07T07:36:50.7861029Z Example:: 2025-09-07T07:36:50.7861107Z 2025-09-07T07:36:50.7861242Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2025-09-07T07:36:50.7861410Z >>> m = RelaxedOneHotCategorical(torch.tensor([2.2]), 2025-09-07T07:36:50.7861536Z ... torch.tensor([0.1, 0.2, 0.3, 0.4])) 2025-09-07T07:36:50.7861623Z >>> m.sample() 2025-09-07T07:36:50.7861754Z tensor([ 0.1294, 0.2324, 0.3859, 0.2523]) 2025-09-07T07:36:50.7861835Z 2025-09-07T07:36:50.7861926Z Args: 2025-09-07T07:36:50.7862064Z temperature (Tensor): relaxation temperature 2025-09-07T07:36:50.7862180Z probs (Tensor): event probabilities 2025-09-07T07:36:50.7862375Z logits (Tensor): unnormalized log probability for each event 2025-09-07T07:36:50.7862454Z 2025-09-07T07:36:50.7862714Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.7862816Z 2025-09-07T07:36:50.7862912Z warnings.warn(msg) 2025-09-07T07:36:50.7863001Z 2025-09-07T07:36:50.7863182Z --- Parse Warning: 98 / 146 --- 2025-09-07T07:36:50.7864217Z /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. 2025-09-07T07:36:50.7864511Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.7864701Z Return a new dict with new, potentially nested, key value pair 2025-09-07T07:36:50.7864790Z 2025-09-07T07:36:50.7864881Z >>> purchase = { 2025-09-07T07:36:50.7864985Z ... "name": "Alice", 2025-09-07T07:36:50.7865158Z ... "order": {"items": ["Apple", "Orange"], "costs": [0.50, 1.25]}, 2025-09-07T07:36:50.7865274Z ... "credit card": "5555-1234-1234-1234", 2025-09-07T07:36:50.7865367Z ... } 2025-09-07T07:36:50.7865570Z >>> assoc_in(purchase, ["order", "costs"], [0.25, 1.00]) # doctest: +SKIP 2025-09-07T07:36:50.7865685Z {'credit card': '5555-1234-1234-1234', 2025-09-07T07:36:50.7865801Z 'name': 'Alice', 2025-09-07T07:36:50.7865966Z 'order': {'costs': [0.25, 1.00], 'items': ['Apple', 'Orange']}} 2025-09-07T07:36:50.7866057Z 2025-09-07T07:36:50.7866308Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.7866395Z 2025-09-07T07:36:50.7866488Z warnings.warn(msg) 2025-09-07T07:36:50.7866567Z 2025-09-07T07:36:50.7866757Z --- Parse Warning: 99 / 146 --- 2025-09-07T07:36:50.7867779Z /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. 2025-09-07T07:36:50.7868052Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.7868199Z Update value in a (potentially) nested dictionary 2025-09-07T07:36:50.7868282Z 2025-09-07T07:36:50.7868376Z inputs: 2025-09-07T07:36:50.7868491Z d - dictionary on which to operate 2025-09-07T07:36:50.7868748Z keys - list or tuple giving the location of the value to be changed in d 2025-09-07T07:36:50.7868872Z func - function to operate on that value 2025-09-07T07:36:50.7868951Z 2025-09-07T07:36:50.7869146Z If keys == [k0,..,kX] and d[k0]..[kX] == v, update_in returns a copy of the 2025-09-07T07:36:50.7869377Z original dictionary with v replaced by func(v), but does not mutate the 2025-09-07T07:36:50.7869495Z original dictionary. 2025-09-07T07:36:50.7869571Z 2025-09-07T07:36:50.7869775Z If k0 is not a key in d, update_in creates nested dictionaries to the depth 2025-09-07T07:36:50.7870001Z specified by the keys, with the innermost value set to func(default). 2025-09-07T07:36:50.7870078Z 2025-09-07T07:36:50.7870186Z >>> inc = lambda x: x + 1 2025-09-07T07:36:50.7870290Z >>> update_in({"a": 0}, ["a"], inc) 2025-09-07T07:36:50.7870373Z {'a': 1} 2025-09-07T07:36:50.7870460Z 2025-09-07T07:36:50.7870554Z >>> transaction = { 2025-09-07T07:36:50.7870659Z ... "name": "Alice", 2025-09-07T07:36:50.7870845Z ... "purchase": {"items": ["Apple", "Orange"], "costs": [0.50, 1.25]}, 2025-09-07T07:36:50.7870959Z ... "credit card": "5555-1234-1234-1234", 2025-09-07T07:36:50.7871054Z ... } 2025-09-07T07:36:50.7871266Z >>> update_in(transaction, ["purchase", "costs"], sum) # doctest: +SKIP 2025-09-07T07:36:50.7871418Z {'credit card': '5555-1234-1234-1234', 2025-09-07T07:36:50.7871506Z 'name': 'Alice', 2025-09-07T07:36:50.7871669Z 'purchase': {'costs': 1.75, 'items': ['Apple', 'Orange']}} 2025-09-07T07:36:50.7871785Z 2025-09-07T07:36:50.7871903Z >>> # updating a value when k0 is not in d 2025-09-07T07:36:50.7872043Z >>> update_in({}, [1, 2, 3], str, default="bar") 2025-09-07T07:36:50.7872131Z {1: {2: {3: 'bar'}}} 2025-09-07T07:36:50.7872244Z >>> update_in({1: "foo"}, [2, 3, 4], inc, 0) 2025-09-07T07:36:50.7872375Z {1: 'foo', 2: {3: {4: 1}}} 2025-09-07T07:36:50.7872455Z 2025-09-07T07:36:50.7872704Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.7872792Z 2025-09-07T07:36:50.7872886Z warnings.warn(msg) 2025-09-07T07:36:50.7872976Z 2025-09-07T07:36:50.7873158Z --- Parse Warning: 100 / 146 --- 2025-09-07T07:36:50.7874165Z /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. 2025-09-07T07:36:50.7874518Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.7874686Z Returns coll[i0][i1]...[iX] where [i0, i1, ..., iX]==keys. 2025-09-07T07:36:50.7874813Z 2025-09-07T07:36:50.7874988Z If coll[i0][i1]...[iX] cannot be found, returns ``default``, unless 2025-09-07T07:36:50.7875204Z ``no_default`` is specified, then it raises KeyError or IndexError. 2025-09-07T07:36:50.7875283Z 2025-09-07T07:36:50.7875486Z ``get_in`` is a generalization of ``operator.getitem`` for nested data 2025-09-07T07:36:50.7875631Z structures such as dictionaries and lists. 2025-09-07T07:36:50.7875707Z 2025-09-07T07:36:50.7875796Z >>> transaction = { 2025-09-07T07:36:50.7875900Z ... "name": "Alice", 2025-09-07T07:36:50.7876087Z ... "purchase": {"items": ["Apple", "Orange"], "costs": [0.50, 1.25]}, 2025-09-07T07:36:50.7876216Z ... "credit card": "5555-1234-1234-1234", 2025-09-07T07:36:50.7876299Z ... } 2025-09-07T07:36:50.7876431Z >>> get_in(["purchase", "items", 0], transaction) 2025-09-07T07:36:50.7876523Z 'Apple' 2025-09-07T07:36:50.7876628Z >>> get_in(["name"], transaction) 2025-09-07T07:36:50.7876726Z 'Alice' 2025-09-07T07:36:50.7876852Z >>> get_in(["purchase", "total"], transaction) 2025-09-07T07:36:50.7877029Z >>> get_in(["purchase", "items", "apple"], transaction) 2025-09-07T07:36:50.7877178Z >>> get_in(["purchase", "items", 10], transaction) 2025-09-07T07:36:50.7877311Z >>> get_in(["purchase", "total"], transaction, 0) 2025-09-07T07:36:50.7877404Z 0 2025-09-07T07:36:50.7877516Z >>> get_in(["y"], {}, no_default=True) 2025-09-07T07:36:50.7877626Z Traceback (most recent call last): 2025-09-07T07:36:50.7877718Z ... 2025-09-07T07:36:50.7877859Z KeyError: 'y' 2025-09-07T07:36:50.7877936Z 2025-09-07T07:36:50.7878028Z See Also: 2025-09-07T07:36:50.7878122Z itertoolz.get 2025-09-07T07:36:50.7878230Z operator.getitem 2025-09-07T07:36:50.7878310Z 2025-09-07T07:36:50.7878565Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.7878656Z 2025-09-07T07:36:50.7878750Z warnings.warn(msg) 2025-09-07T07:36:50.7878840Z 2025-09-07T07:36:50.7879033Z --- Parse Warning: 101 / 146 --- 2025-09-07T07:36:50.7880057Z /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. 2025-09-07T07:36:50.7880328Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.7880442Z Group a collection by a key function 2025-09-07T07:36:50.7880536Z 2025-09-07T07:36:50.7880696Z >>> names = ["Alice", "Bob", "Charlie", "Dan", "Edith", "Frank"] 2025-09-07T07:36:50.7880842Z >>> groupby(len, names) # doctest: +SKIP 2025-09-07T07:36:50.7881012Z {3: ['Bob', 'Dan'], 5: ['Alice', 'Edith', 'Frank'], 7: ['Charlie']} 2025-09-07T07:36:50.7881094Z 2025-09-07T07:36:50.7881210Z >>> iseven = lambda x: x % 2 == 0 2025-09-07T07:36:50.7881378Z >>> groupby(iseven, [1, 2, 3, 4, 5, 6, 7, 8]) # doctest: +SKIP 2025-09-07T07:36:50.7881511Z {False: [1, 3, 5, 7], True: [2, 4, 6, 8]} 2025-09-07T07:36:50.7881600Z 2025-09-07T07:36:50.7881743Z Non-callable keys imply grouping on a member. 2025-09-07T07:36:50.7881831Z 2025-09-07T07:36:50.7881915Z >>> groupby( 2025-09-07T07:36:50.7882003Z ... "gender", 2025-09-07T07:36:50.7882094Z ... [ 2025-09-07T07:36:50.7882213Z ... {"name": "Alice", "gender": "F"}, 2025-09-07T07:36:50.7882334Z ... {"name": "Bob", "gender": "M"}, 2025-09-07T07:36:50.7882462Z ... {"name": "Charlie", "gender": "M"}, 2025-09-07T07:36:50.7882548Z ... ], 2025-09-07T07:36:50.7882655Z ... ) # doctest:+SKIP 2025-09-07T07:36:50.7882774Z {'F': [{'gender': 'F', 'name': 'Alice'}], 2025-09-07T07:36:50.7882907Z 'M': [{'gender': 'M', 'name': 'Bob'}, 2025-09-07T07:36:50.7883022Z {'gender': 'M', 'name': 'Charlie'}]} 2025-09-07T07:36:50.7883102Z 2025-09-07T07:36:50.7883254Z Not to be confused with ``itertools.groupby`` 2025-09-07T07:36:50.7883332Z 2025-09-07T07:36:50.7883418Z See Also: 2025-09-07T07:36:50.7883508Z countby 2025-09-07T07:36:50.7883588Z 2025-09-07T07:36:50.7883851Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.7883927Z 2025-09-07T07:36:50.7884021Z warnings.warn(msg) 2025-09-07T07:36:50.7884107Z 2025-09-07T07:36:50.7884289Z --- Parse Warning: 102 / 146 --- 2025-09-07T07:36:50.7885147Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=calculate_gain in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py line=142. 2025-09-07T07:36:50.7885404Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.7885655Z Return the recommended gain value for the given nonlinearity function. 2025-09-07T07:36:50.7885742Z 2025-09-07T07:36:50.7885845Z The values are as follows: 2025-09-07T07:36:50.7885929Z 2025-09-07T07:36:50.7886040Z ================= ==================================================== 2025-09-07T07:36:50.7886140Z nonlinearity gain 2025-09-07T07:36:50.7886258Z ================= ==================================================== 2025-09-07T07:36:50.7886359Z Linear / Identity :math:`1` 2025-09-07T07:36:50.7886463Z Conv{1,2,3}D :math:`1` 2025-09-07T07:36:50.7886556Z Sigmoid :math:`1` 2025-09-07T07:36:50.7886665Z Tanh :math:`\frac{5}{3}` 2025-09-07T07:36:50.7886778Z ReLU :math:`\sqrt{2}` 2025-09-07T07:36:50.7886968Z Leaky Relu :math:`\sqrt{\frac{2}{1 + \text{negative\_slope}^2}}` 2025-09-07T07:36:50.7887085Z SELU :math:`\frac{3}{4}` 2025-09-07T07:36:50.7887196Z ================= ==================================================== 2025-09-07T07:36:50.7887278Z 2025-09-07T07:36:50.7887374Z .. warning:: 2025-09-07T07:36:50.7887558Z In order to implement `Self-Normalizing Neural Networks`_ , 2025-09-07T07:36:50.7887806Z you should use ``nonlinearity='linear'`` instead of ``nonlinearity='selu'``. 2025-09-07T07:36:50.7887970Z This gives the initial weights a variance of ``1 / N``, 2025-09-07T07:36:50.7888191Z which is necessary to induce a stable fixed point in the forward pass. 2025-09-07T07:36:50.7888418Z In contrast, the default gain for ``SELU`` sacrifices the normalization 2025-09-07T07:36:50.7888627Z effect for more stable gradient flow in rectangular layers. 2025-09-07T07:36:50.7888713Z 2025-09-07T07:36:50.7888794Z Args: 2025-09-07T07:36:50.7888992Z nonlinearity: the non-linear function (`nn.functional` name) 2025-09-07T07:36:50.7889166Z param: optional parameter for the non-linear function 2025-09-07T07:36:50.7889281Z 2025-09-07T07:36:50.7889367Z Examples: 2025-09-07T07:36:50.7889490Z >>> gain = nn.init.calculate_gain( 2025-09-07T07:36:50.7889589Z ... "leaky_relu", 0.2 2025-09-07T07:36:50.7889720Z ... ) # leaky_relu with negative_slope=0.2 2025-09-07T07:36:50.7889797Z 2025-09-07T07:36:50.7890276Z .. _Self-Normalizing Neural Networks: https://papers.nips.cc/paper/2017/hash/5d44ee6f2c3f71b73125876103c8f6c4-Abstract.html 2025-09-07T07:36:50.7890365Z 2025-09-07T07:36:50.7890616Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.7890701Z 2025-09-07T07:36:50.7890797Z warnings.warn(msg) 2025-09-07T07:36:50.7890871Z 2025-09-07T07:36:50.7891063Z --- Parse Warning: 103 / 146 --- 2025-09-07T07:36:50.7892003Z /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=603. 2025-09-07T07:36:50.7892270Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.7892445Z Applies Batch Normalization over a N-Dimensional input. 2025-09-07T07:36:50.7892524Z 2025-09-07T07:36:50.7892875Z The N-D input is a mini-batch of [N-2]D inputs with additional channel dimension) as described in the paper 2025-09-07T07:36:50.7893103Z `Batch Normalization: Accelerating Deep Network Training by Reducing 2025-09-07T07:36:50.7893316Z Internal Covariate Shift `__ . 2025-09-07T07:36:50.7893394Z 2025-09-07T07:36:50.7893480Z .. math:: 2025-09-07T07:36:50.7893564Z 2025-09-07T07:36:50.7893778Z y = \frac{x - \mathrm{E}[x]}{ \sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta 2025-09-07T07:36:50.7893863Z 2025-09-07T07:36:50.7894120Z The mean and standard-deviation are calculated per-dimension over all 2025-09-07T07:36:50.7894350Z mini-batches of the same process groups. :math:`\gamma` and :math:`\beta` 2025-09-07T07:36:50.7894603Z are learnable parameter vectors of size `C` (where `C` is the input size). 2025-09-07T07:36:50.7894779Z By default, the elements of :math:`\gamma` are sampled from 2025-09-07T07:36:50.7894986Z :math:`\mathcal{U}(0, 1)` and the elements of :math:`\beta` are set to 0. 2025-09-07T07:36:50.7895243Z The standard-deviation is calculated via the biased estimator, equivalent to 2025-09-07T07:36:50.7895366Z `torch.var(input, unbiased=False)`. 2025-09-07T07:36:50.7895446Z 2025-09-07T07:36:50.7895678Z Also by default, during training this layer keeps running estimates of its 2025-09-07T07:36:50.7895911Z computed mean and variance, which are then used for normalization during 2025-09-07T07:36:50.7896151Z evaluation. The running estimates are kept with a default :attr:`momentum` 2025-09-07T07:36:50.7896246Z of 0.1. 2025-09-07T07:36:50.7896320Z 2025-09-07T07:36:50.7896543Z If :attr:`track_running_stats` is set to ``False``, this layer then does not 2025-09-07T07:36:50.7896769Z keep running estimates, and batch statistics are instead used during 2025-09-07T07:36:50.7896869Z evaluation time as well. 2025-09-07T07:36:50.7896948Z 2025-09-07T07:36:50.7897038Z .. note:: 2025-09-07T07:36:50.7897257Z This :attr:`momentum` argument is different from one used in optimizer 2025-09-07T07:36:50.7897516Z classes and the conventional notion of momentum. Mathematically, the 2025-09-07T07:36:50.7897647Z update rule for running statistics here is 2025-09-07T07:36:50.7897910Z :math:`\hat{x}_\text{new} = (1 - \text{momentum}) \times \hat{x} + \text{momentum} \times x_t`, 2025-09-07T07:36:50.7898122Z where :math:`\hat{x}` is the estimated statistic and :math:`x_t` is the 2025-09-07T07:36:50.7898256Z new observed value. 2025-09-07T07:36:50.7898342Z 2025-09-07T07:36:50.7898638Z Because the Batch Normalization is done for each channel in the ``C`` dimension, computing 2025-09-07T07:36:50.7898891Z statistics on ``(N, +)`` slices, it's common terminology to call this Volumetric Batch 2025-09-07T07:36:50.7899065Z Normalization or Spatio-temporal Batch Normalization. 2025-09-07T07:36:50.7899142Z 2025-09-07T07:36:50.7899296Z Currently :class:`SyncBatchNorm` only supports 2025-09-07T07:36:50.7899574Z :class:`~torch.nn.DistributedDataParallel` (DDP) with single GPU per process. Use 2025-09-07T07:36:50.7899792Z :meth:`torch.nn.SyncBatchNorm.convert_sync_batchnorm()` to convert 2025-09-07T07:36:50.7899992Z :attr:`BatchNorm*D` layer to :class:`SyncBatchNorm` before wrapping 2025-09-07T07:36:50.7900115Z Network with DDP. 2025-09-07T07:36:50.7900202Z 2025-09-07T07:36:50.7900286Z Args: 2025-09-07T07:36:50.7900452Z num_features: :math:`C` from an expected input of size 2025-09-07T07:36:50.7900553Z :math:`(N, C, +)` 2025-09-07T07:36:50.7900737Z eps: a value added to the denominator for numerical stability. 2025-09-07T07:36:50.7900841Z Default: ``1e-5`` 2025-09-07T07:36:50.7901029Z momentum: the value used for the running_mean and running_var 2025-09-07T07:36:50.7901235Z computation. Can be set to ``None`` for cumulative moving average 2025-09-07T07:36:50.7901365Z (i.e. simple average). Default: 0.1 2025-09-07T07:36:50.7901565Z affine: a boolean value that when set to ``True``, this module has 2025-09-07T07:36:50.7901722Z learnable affine parameters. Default: ``True`` 2025-09-07T07:36:50.7901931Z track_running_stats: a boolean value that when set to ``True``, this 2025-09-07T07:36:50.7902176Z module tracks the running mean and variance, and when set to ``False``, 2025-09-07T07:36:50.7902421Z this module does not track such statistics, and initializes statistics 2025-09-07T07:36:50.7902621Z buffers :attr:`running_mean` and :attr:`running_var` as ``None``. 2025-09-07T07:36:50.7902858Z When these buffers are ``None``, this module always uses batch statistics. 2025-09-07T07:36:50.7903004Z in both training and eval modes. Default: ``True`` 2025-09-07T07:36:50.7903259Z process_group: synchronization of stats happen within each process group 2025-09-07T07:36:50.7903478Z individually. Default behavior is synchronization across the whole 2025-09-07T07:36:50.7903564Z world 2025-09-07T07:36:50.7903647Z 2025-09-07T07:36:50.7903727Z Shape: 2025-09-07T07:36:50.7903846Z - Input: :math:`(N, C, +)` 2025-09-07T07:36:50.7903990Z - Output: :math:`(N, C, +)` (same shape as input) 2025-09-07T07:36:50.7904067Z 2025-09-07T07:36:50.7904164Z .. note:: 2025-09-07T07:36:50.7904403Z Synchronization of batchnorm statistics occurs only while training, i.e. 2025-09-07T07:36:50.7904610Z synchronization is disabled when ``model.eval()`` is set or if 2025-09-07T07:36:50.7904734Z ``self.training`` is otherwise ``False``. 2025-09-07T07:36:50.7904809Z 2025-09-07T07:36:50.7904909Z Examples:: 2025-09-07T07:36:50.7904982Z 2025-09-07T07:36:50.7905078Z >>> # xdoctest: +SKIP 2025-09-07T07:36:50.7905200Z >>> # With Learnable Parameters 2025-09-07T07:36:50.7905305Z >>> m = nn.SyncBatchNorm(100) 2025-09-07T07:36:50.7905464Z >>> # creating process group (optional) 2025-09-07T07:36:50.7905601Z >>> # ranks is a list of int identifying rank ids. 2025-09-07T07:36:50.7905699Z >>> ranks = list(range(8)) 2025-09-07T07:36:50.7905817Z >>> r1, r2 = ranks[:4], ranks[4:] 2025-09-07T07:36:50.7905962Z >>> # Note: every rank calls into new_group for every 2025-09-07T07:36:50.7906144Z >>> # process group created, even if that rank is not 2025-09-07T07:36:50.7906240Z >>> # part of the group. 2025-09-07T07:36:50.7906483Z >>> process_groups = [torch.distributed.new_group(pids) for pids in [r1, r2]] 2025-09-07T07:36:50.7906693Z >>> process_group = process_groups[0 if dist.get_rank() <= 3 else 1] 2025-09-07T07:36:50.7906807Z >>> # Without Learnable Parameters 2025-09-07T07:36:50.7907017Z >>> m = nn.BatchNorm3d(100, affine=False, process_group=process_group) 2025-09-07T07:36:50.7907134Z >>> input = torch.randn(20, 100, 35, 45, 10) 2025-09-07T07:36:50.7907230Z >>> output = m(input) 2025-09-07T07:36:50.7907320Z 2025-09-07T07:36:50.7907429Z >>> # network is nn.BatchNorm layer 2025-09-07T07:36:50.7907738Z >>> sync_bn_network = nn.SyncBatchNorm.convert_sync_batchnorm(network, process_group) 2025-09-07T07:36:50.7907900Z >>> # only single gpu per process is currently supported 2025-09-07T07:36:50.7908110Z >>> ddp_sync_bn_network = torch.nn.parallel.DistributedDataParallel( 2025-09-07T07:36:50.7908236Z >>> sync_bn_network, 2025-09-07T07:36:50.7908363Z >>> device_ids=[args.local_rank], 2025-09-07T07:36:50.7908503Z >>> output_device=args.local_rank) 2025-09-07T07:36:50.7908579Z 2025-09-07T07:36:50.7908830Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.7908923Z 2025-09-07T07:36:50.7909020Z warnings.warn(msg) 2025-09-07T07:36:50.7909111Z 2025-09-07T07:36:50.7909348Z --- Parse Warning: 104 / 146 --- 2025-09-07T07:36:50.7910397Z /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=830. 2025-09-07T07:36:50.7910676Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.7910982Z Converts all :attr:`BatchNorm*D` layers in the model to :class:`torch.nn.SyncBatchNorm` layers. 2025-09-07T07:36:50.7911071Z 2025-09-07T07:36:50.7911151Z Args: 2025-09-07T07:36:50.7911403Z module (nn.Module): module containing one or more :attr:`BatchNorm*D` layers 2025-09-07T07:36:50.7911618Z process_group (optional): process group to scope synchronization, 2025-09-07T07:36:50.7911725Z default is the whole world 2025-09-07T07:36:50.7911815Z 2025-09-07T07:36:50.7911897Z Returns: 2025-09-07T07:36:50.7912154Z The original :attr:`module` with the converted :class:`torch.nn.SyncBatchNorm` 2025-09-07T07:36:50.7912366Z layers. If the original :attr:`module` is a :attr:`BatchNorm*D` layer, 2025-09-07T07:36:50.7912576Z a new :class:`torch.nn.SyncBatchNorm` layer object will be returned 2025-09-07T07:36:50.7912675Z instead. 2025-09-07T07:36:50.7912752Z 2025-09-07T07:36:50.7912852Z Example:: 2025-09-07T07:36:50.7912930Z 2025-09-07T07:36:50.7913046Z >>> # Network with nn.BatchNorm layer 2025-09-07T07:36:50.7913192Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2025-09-07T07:36:50.7913307Z >>> module = torch.nn.Sequential( 2025-09-07T07:36:50.7913422Z >>> torch.nn.Linear(20, 100), 2025-09-07T07:36:50.7913578Z >>> torch.nn.BatchNorm1d(100), 2025-09-07T07:36:50.7913670Z >>> ).cuda() 2025-09-07T07:36:50.7913804Z >>> # creating process group (optional) 2025-09-07T07:36:50.7913944Z >>> # ranks is a list of int identifying rank ids. 2025-09-07T07:36:50.7914074Z >>> ranks = list(range(8)) 2025-09-07T07:36:50.7914196Z >>> r1, r2 = ranks[:4], ranks[4:] 2025-09-07T07:36:50.7914412Z >>> # Note: every rank calls into new_group for every 2025-09-07T07:36:50.7914581Z >>> # process group created, even if that rank is not 2025-09-07T07:36:50.7914684Z >>> # part of the group. 2025-09-07T07:36:50.7914802Z >>> # xdoctest: +SKIP("distributed") 2025-09-07T07:36:50.7915066Z >>> process_groups = [torch.distributed.new_group(pids) for pids in [r1, r2]] 2025-09-07T07:36:50.7915268Z >>> process_group = process_groups[0 if dist.get_rank() <= 3 else 1] 2025-09-07T07:36:50.7915576Z >>> sync_bn_module = torch.nn.SyncBatchNorm.convert_sync_batchnorm(module, process_group) 2025-09-07T07:36:50.7915652Z 2025-09-07T07:36:50.7915759Z 2025-09-07T07:36:50.7916020Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.7916105Z 2025-09-07T07:36:50.7916213Z warnings.warn(msg) 2025-09-07T07:36:50.7916290Z 2025-09-07T07:36:50.7916488Z --- Parse Warning: 105 / 146 --- 2025-09-07T07:36:50.7917374Z /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=66. 2025-09-07T07:36:50.7917629Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.7917720Z 2025-09-07T07:36:50.7918030Z Unflattens a tensor dim expanding it to a desired shape. For use with :class:`~nn.Sequential`. 2025-09-07T07:36:50.7918116Z 2025-09-07T07:36:50.7918385Z * :attr:`dim` specifies the dimension of the input tensor to be unflattened, and it can 2025-09-07T07:36:50.7918606Z be either `int` or `str` when `Tensor` or `NamedTensor` is used, respectively. 2025-09-07T07:36:50.7918696Z 2025-09-07T07:36:50.7919035Z * :attr:`unflattened_size` is the new shape of the unflattened dimension of the tensor and it can be 2025-09-07T07:36:50.7919296Z a `tuple` of ints or a `list` of ints or `torch.Size` for `Tensor` input; a `NamedShape` 2025-09-07T07:36:50.7919462Z (tuple of `(name, size)` tuples) for `NamedTensor` input. 2025-09-07T07:36:50.7919540Z 2025-09-07T07:36:50.7919630Z Shape: 2025-09-07T07:36:50.7919845Z - Input: :math:`(*, S_{\text{dim}}, *)`, where :math:`S_{\text{dim}}` is the size at 2025-09-07T07:36:50.7920100Z dimension :attr:`dim` and :math:`*` means any number of dimensions including none. 2025-09-07T07:36:50.7920321Z - Output: :math:`(*, U_1, ..., U_n, *)`, where :math:`U` = :attr:`unflattened_size` and 2025-09-07T07:36:50.7920448Z :math:`\prod_{i=1}^n U_i = S_{\text{dim}}`. 2025-09-07T07:36:50.7920532Z 2025-09-07T07:36:50.7920611Z Args: 2025-09-07T07:36:50.7920763Z dim (Union[int, str]): Dimension to be unflattened 2025-09-07T07:36:50.7921115Z unflattened_size (Union[torch.Size, Tuple, List, NamedShape]): New shape of the unflattened dimension 2025-09-07T07:36:50.7921193Z 2025-09-07T07:36:50.7921282Z Examples: 2025-09-07T07:36:50.7921388Z >>> input = torch.randn(2, 50) 2025-09-07T07:36:50.7921481Z >>> # With tuple of ints 2025-09-07T07:36:50.7921587Z >>> m = nn.Sequential( 2025-09-07T07:36:50.7921682Z >>> nn.Linear(50, 50), 2025-09-07T07:36:50.7921793Z >>> nn.Unflatten(1, (2, 5, 5)) 2025-09-07T07:36:50.7921872Z >>> ) 2025-09-07T07:36:50.7921965Z >>> output = m(input) 2025-09-07T07:36:50.7922105Z >>> output.size() 2025-09-07T07:36:50.7922201Z torch.Size([2, 2, 5, 5]) 2025-09-07T07:36:50.7922301Z >>> # With torch.Size 2025-09-07T07:36:50.7922397Z >>> m = nn.Sequential( 2025-09-07T07:36:50.7922491Z >>> nn.Linear(50, 50), 2025-09-07T07:36:50.7922623Z >>> nn.Unflatten(1, torch.Size([2, 5, 5])) 2025-09-07T07:36:50.7922729Z >>> ) 2025-09-07T07:36:50.7922831Z >>> output = m(input) 2025-09-07T07:36:50.7922924Z >>> output.size() 2025-09-07T07:36:50.7923023Z torch.Size([2, 2, 5, 5]) 2025-09-07T07:36:50.7923152Z >>> # With namedshape (tuple of tuples) 2025-09-07T07:36:50.7923299Z >>> input = torch.randn(2, 50, names=("N", "features")) 2025-09-07T07:36:50.7923513Z >>> unflatten = nn.Unflatten("features", (("C", 2), ("H", 5), ("W", 5))) 2025-09-07T07:36:50.7923933Z >>> output = unflatten(input) 2025-09-07T07:36:50.7924028Z >>> output.size() 2025-09-07T07:36:50.7924139Z torch.Size([2, 2, 5, 5]) 2025-09-07T07:36:50.7924217Z 2025-09-07T07:36:50.7924467Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.7924558Z 2025-09-07T07:36:50.7924733Z warnings.warn(msg) 2025-09-07T07:36:50.7924824Z 2025-09-07T07:36:50.7925025Z --- Parse Warning: 106 / 146 --- 2025-09-07T07:36:50.7926025Z /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=1798. 2025-09-07T07:36:50.7926295Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.7926488Z Creates a criterion that measures the triplet loss given input 2025-09-07T07:36:50.7926691Z tensors :math:`a`, :math:`p`, and :math:`n` (representing anchor, 2025-09-07T07:36:50.7926901Z positive, and negative examples, respectively), and a nonnegative, 2025-09-07T07:36:50.7927152Z real-valued function ("distance function") used to compute the relationship 2025-09-07T07:36:50.7927371Z between the anchor and positive example ("positive distance") and the 2025-09-07T07:36:50.7927517Z anchor and negative example ("negative distance"). 2025-09-07T07:36:50.7927608Z 2025-09-07T07:36:50.7927851Z The unreduced loss (i.e., with :attr:`reduction` set to ``'none'``) 2025-09-07T07:36:50.7927961Z can be described as: 2025-09-07T07:36:50.7928037Z 2025-09-07T07:36:50.7928120Z .. math:: 2025-09-07T07:36:50.7928264Z \ell(a, p, n) = L = \{l_1,\dots,l_N\}^\top, \quad 2025-09-07T07:36:50.7928412Z l_i = \max \{d(a_i, p_i) - d(a_i, n_i) + {\rm margin}, 0\} 2025-09-07T07:36:50.7928485Z 2025-09-07T07:36:50.7928741Z where :math:`N` is the batch size; :math:`d` is a nonnegative, real-valued function 2025-09-07T07:36:50.7929028Z quantifying the closeness of two tensors, referred to as the :attr:`distance_function`; 2025-09-07T07:36:50.7929273Z and :math:`margin` is a nonnegative margin representing the minimum difference 2025-09-07T07:36:50.7929516Z between the positive and negative distances that is required for the loss to 2025-09-07T07:36:50.7929754Z be 0. The input tensors have :math:`N` elements each and can be of any shape 2025-09-07T07:36:50.7929875Z that the distance function can handle. 2025-09-07T07:36:50.7929951Z 2025-09-07T07:36:50.7930077Z If :attr:`reduction` is not ``'none'`` 2025-09-07T07:36:50.7930179Z (default ``'mean'``), then: 2025-09-07T07:36:50.7930255Z 2025-09-07T07:36:50.7930389Z .. math:: 2025-09-07T07:36:50.7930472Z \ell(x, y) = 2025-09-07T07:36:50.7930570Z \begin{cases} 2025-09-07T07:36:50.7930765Z \operatorname{mean}(L), & \text{if reduction} = \text{`mean';}\\ 2025-09-07T07:36:50.7930981Z \operatorname{sum}(L), & \text{if reduction} = \text{`sum'.} 2025-09-07T07:36:50.7931076Z \end{cases} 2025-09-07T07:36:50.7931148Z 2025-09-07T07:36:50.7931385Z See also :class:`~torch.nn.TripletMarginLoss`, which computes the triplet 2025-09-07T07:36:50.7931630Z loss for input tensors using the :math:`l_p` distance as the distance function. 2025-09-07T07:36:50.7931742Z 2025-09-07T07:36:50.7931832Z Args: 2025-09-07T07:36:50.7932099Z distance_function (Callable, optional): A nonnegative, real-valued function that 2025-09-07T07:36:50.7932292Z quantifies the closeness of two tensors. If not specified, 2025-09-07T07:36:50.7932456Z `nn.PairwiseDistance` will be used. Default: ``None`` 2025-09-07T07:36:50.7932719Z margin (float, optional): A nonnegative margin representing the minimum difference 2025-09-07T07:36:50.7932995Z between the positive and negative distances required for the loss to be 0. Larger 2025-09-07T07:36:50.7933270Z margins penalize cases where the negative examples are not distant enough from the 2025-09-07T07:36:50.7933454Z anchors, relative to the positives. Default: :math:`1`. 2025-09-07T07:36:50.7933747Z swap (bool, optional): Whether to use the distance swap described in the paper 2025-09-07T07:36:50.7934016Z `Learning shallow convolutional feature descriptors with triplet losses` by 2025-09-07T07:36:50.7934250Z V. Balntas, E. Riba et al. If True, and if the positive example is closer to the 2025-09-07T07:36:50.7934514Z negative example than the anchor is, swaps the positive example and the anchor in 2025-09-07T07:36:50.7934659Z the loss computation. Default: ``False``. 2025-09-07T07:36:50.7934931Z reduction (str, optional): Specifies the (optional) reduction to apply to the output: 2025-09-07T07:36:50.7935123Z ``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: no reduction will be applied, 2025-09-07T07:36:50.7935300Z ``'mean'``: the sum of the output will be divided by the number of 2025-09-07T07:36:50.7935532Z elements in the output, ``'sum'``: the output will be summed. Default: ``'mean'`` 2025-09-07T07:36:50.7935625Z 2025-09-07T07:36:50.7935703Z 2025-09-07T07:36:50.7935822Z Shape: 2025-09-07T07:36:50.7936061Z - Input: :math:`(N, *)` where :math:`*` represents any number of additional dimensions 2025-09-07T07:36:50.7936186Z as supported by the distance function. 2025-09-07T07:36:50.7936452Z - Output: A Tensor of shape :math:`(N)` if :attr:`reduction` is ``'none'``, or a scalar 2025-09-07T07:36:50.7936539Z otherwise. 2025-09-07T07:36:50.7936626Z 2025-09-07T07:36:50.7936708Z Examples: 2025-09-07T07:36:50.7936781Z 2025-09-07T07:36:50.7936890Z >>> # Initialize embeddings 2025-09-07T07:36:50.7937003Z >>> embedding = nn.Embedding(1000, 128) 2025-09-07T07:36:50.7937137Z >>> anchor_ids = torch.randint(0, 1000, (1,)) 2025-09-07T07:36:50.7937263Z >>> positive_ids = torch.randint(0, 1000, (1,)) 2025-09-07T07:36:50.7937384Z >>> negative_ids = torch.randint(0, 1000, (1,)) 2025-09-07T07:36:50.7937504Z >>> anchor = embedding(anchor_ids) 2025-09-07T07:36:50.7937622Z >>> positive = embedding(positive_ids) 2025-09-07T07:36:50.7937744Z >>> negative = embedding(negative_ids) 2025-09-07T07:36:50.7937824Z >>> 2025-09-07T07:36:50.7937927Z >>> # Built-in Distance Function 2025-09-07T07:36:50.7938031Z >>> triplet_loss = \ 2025-09-07T07:36:50.7938304Z >>> nn.TripletMarginWithDistanceLoss(distance_function=nn.PairwiseDistance()) 2025-09-07T07:36:50.7938475Z >>> output = triplet_loss(anchor, positive, negative) 2025-09-07T07:36:50.7938571Z >>> output.backward() 2025-09-07T07:36:50.7938650Z >>> 2025-09-07T07:36:50.7938766Z >>> # Custom Distance Function 2025-09-07T07:36:50.7938892Z >>> def l_infinity(x1, x2): 2025-09-07T07:36:50.7939046Z >>> return torch.max(torch.abs(x1 - x2), dim=1).values 2025-09-07T07:36:50.7939142Z >>> 2025-09-07T07:36:50.7939332Z >>> # xdoctest: +SKIP("FIXME: Would call backwards a second time") 2025-09-07T07:36:50.7939467Z >>> triplet_loss = ( 2025-09-07T07:36:50.7939748Z >>> nn.TripletMarginWithDistanceLoss(distance_function=l_infinity, margin=1.5)) 2025-09-07T07:36:50.7939901Z >>> output = triplet_loss(anchor, positive, negative) 2025-09-07T07:36:50.7940015Z >>> output.backward() 2025-09-07T07:36:50.7940096Z >>> 2025-09-07T07:36:50.7940233Z >>> # Custom Distance Function (Lambda) 2025-09-07T07:36:50.7940328Z >>> triplet_loss = ( 2025-09-07T07:36:50.7940458Z >>> nn.TripletMarginWithDistanceLoss( 2025-09-07T07:36:50.7940689Z >>> distance_function=lambda x, y: 1.0 - F.cosine_similarity(x, y))) 2025-09-07T07:36:50.7940843Z >>> output = triplet_loss(anchor, positive, negative) 2025-09-07T07:36:50.7940959Z >>> output.backward() 2025-09-07T07:36:50.7941040Z 2025-09-07T07:36:50.7941125Z Reference: 2025-09-07T07:36:50.7941473Z V. Balntas, et al.: Learning shallow convolutional feature descriptors with triplet losses: 2025-09-07T07:36:50.7941695Z https://bmva-archive.org.uk/bmvc/2016/papers/paper119/index.html 2025-09-07T07:36:50.7941790Z 2025-09-07T07:36:50.7942046Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 17)) 2025-09-07T07:36:50.7942125Z 2025-09-07T07:36:50.7942235Z warnings.warn(msg) 2025-09-07T07:36:50.7942311Z 2025-09-07T07:36:50.7942523Z --- Parse Warning: 107 / 146 --- 2025-09-07T07:36:50.7943391Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=CTCLoss in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py line=1933. 2025-09-07T07:36:50.7943652Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.7943805Z The Connectionist Temporal Classification loss. 2025-09-07T07:36:50.7943882Z 2025-09-07T07:36:50.7944303Z Calculates loss between a continuous (unsegmented) time series and a target sequence. CTCLoss sums over the 2025-09-07T07:36:50.7944656Z probability of possible alignments of input to target, producing a loss value which is differentiable 2025-09-07T07:36:50.7944992Z with respect to each input node. The alignment of input to target is assumed to be "many-to-one", which 2025-09-07T07:36:50.7945303Z limits the length of the target sequence such that it must be :math:`\leq` the input length. 2025-09-07T07:36:50.7945380Z 2025-09-07T07:36:50.7945477Z Args: 2025-09-07T07:36:50.7945639Z blank (int, optional): blank label. Default :math:`0`. 2025-09-07T07:36:50.7945880Z reduction (str, optional): Specifies the reduction to apply to the output: 2025-09-07T07:36:50.7946075Z ``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: no reduction will be applied, 2025-09-07T07:36:50.7946276Z ``'mean'``: the output losses will be divided by the target lengths and 2025-09-07T07:36:50.7946535Z then the mean over the batch is taken, ``'sum'``: the output losses will be summed. 2025-09-07T07:36:50.7946629Z Default: ``'mean'`` 2025-09-07T07:36:50.7946757Z zero_infinity (bool, optional): 2025-09-07T07:36:50.7946952Z Whether to zero infinite losses and the associated gradients. 2025-09-07T07:36:50.7947080Z Default: ``False`` 2025-09-07T07:36:50.7947276Z Infinite losses mainly occur when the inputs are too short 2025-09-07T07:36:50.7947384Z to be aligned to the targets. 2025-09-07T07:36:50.7947474Z 2025-09-07T07:36:50.7947555Z Shape: 2025-09-07T07:36:50.7947769Z - Log_probs: Tensor of size :math:`(T, N, C)` or :math:`(T, C)`, 2025-09-07T07:36:50.7947901Z where :math:`T = \text{input length}`, 2025-09-07T07:36:50.7948016Z :math:`N = \text{batch size}`, and 2025-09-07T07:36:50.7948170Z :math:`C = \text{number of classes (including blank)}`. 2025-09-07T07:36:50.7948433Z The logarithmized probabilities of the outputs (e.g. obtained with 2025-09-07T07:36:50.7948563Z :func:`torch.nn.functional.log_softmax`). 2025-09-07T07:36:50.7948702Z - Targets: Tensor of size :math:`(N, S)` or 2025-09-07T07:36:50.7948858Z :math:`(\operatorname{sum}(\text{target\_lengths}))`, 2025-09-07T07:36:50.7948994Z where :math:`N = \text{batch size}` and 2025-09-07T07:36:50.7949153Z :math:`S = \text{max target length, if shape is } (N, S)`. 2025-09-07T07:36:50.7949349Z It represents the target sequences. Each element in the target 2025-09-07T07:36:50.7949601Z sequence is a class index. And the target index cannot be blank (default=0). 2025-09-07T07:36:50.7949753Z In the :math:`(N, S)` form, targets are padded to the 2025-09-07T07:36:50.7949929Z length of the longest sequence, and stacked. 2025-09-07T07:36:50.7950122Z In the :math:`(\operatorname{sum}(\text{target\_lengths}))` form, 2025-09-07T07:36:50.7950252Z the targets are assumed to be un-padded and 2025-09-07T07:36:50.7950381Z concatenated within 1 dimension. 2025-09-07T07:36:50.7950587Z - Input_lengths: Tuple or tensor of size :math:`(N)` or :math:`()`, 2025-09-07T07:36:50.7950795Z where :math:`N = \text{batch size}`. It represents the lengths of the 2025-09-07T07:36:50.7950998Z inputs (must each be :math:`\leq T`). And the lengths are specified 2025-09-07T07:36:50.7951224Z for each sequence to achieve masking under the assumption that sequences 2025-09-07T07:36:50.7951346Z are padded to equal lengths. 2025-09-07T07:36:50.7951552Z - Target_lengths: Tuple or tensor of size :math:`(N)` or :math:`()`, 2025-09-07T07:36:50.7951775Z where :math:`N = \text{batch size}`. It represents lengths of the targets. 2025-09-07T07:36:50.7952033Z Lengths are specified for each sequence to achieve masking under the 2025-09-07T07:36:50.7952279Z assumption that sequences are padded to equal lengths. If target shape is 2025-09-07T07:36:50.7952461Z :math:`(N,S)`, target_lengths are effectively the stop index 2025-09-07T07:36:50.7952713Z :math:`s_n` for each target sequence, such that ``target_n = targets[n,0:s_n]`` for 2025-09-07T07:36:50.7952899Z each target in a batch. Lengths must each be :math:`\leq S` 2025-09-07T07:36:50.7953137Z If the targets are given as a 1d tensor that is the concatenation of individual 2025-09-07T07:36:50.7953383Z targets, the target_lengths must add up to the total length of the tensor. 2025-09-07T07:36:50.7953569Z - Output: scalar if :attr:`reduction` is ``'mean'`` (default) or 2025-09-07T07:36:50.7953797Z ``'sum'``. If :attr:`reduction` is ``'none'``, then :math:`(N)` if input is batched or 2025-09-07T07:36:50.7954005Z :math:`()` if input is unbatched, where :math:`N = \text{batch size}`. 2025-09-07T07:36:50.7954082Z 2025-09-07T07:36:50.7954177Z Examples: 2025-09-07T07:36:50.7954255Z 2025-09-07T07:36:50.7954453Z >>> # Target are to be padded 2025-09-07T07:36:50.7954584Z >>> T = 50 # Input sequence length 2025-09-07T07:36:50.7954719Z >>> C = 20 # Number of classes (including blank) 2025-09-07T07:36:50.7954826Z >>> N = 16 # Batch size 2025-09-07T07:36:50.7955054Z >>> S = 30 # Target sequence length of longest target in batch (padding length) 2025-09-07T07:36:50.7955272Z >>> S_min = 10 # Minimum target length, for demonstration purposes 2025-09-07T07:36:50.7955367Z >>> 2025-09-07T07:36:50.7955566Z >>> # Initialize random batch of input vectors, for *size = (T,N,C) 2025-09-07T07:36:50.7955784Z >>> input = torch.randn(T, N, C).log_softmax(2).detach().requires_grad_() 2025-09-07T07:36:50.7955891Z >>> 2025-09-07T07:36:50.7956083Z >>> # Initialize random batch of targets (0 = blank, 1:C = classes) 2025-09-07T07:36:50.7956309Z >>> target = torch.randint(low=1, high=C, size=(N, S), dtype=torch.long) 2025-09-07T07:36:50.7956391Z >>> 2025-09-07T07:36:50.7956620Z >>> input_lengths = torch.full(size=(N,), fill_value=T, dtype=torch.long) 2025-09-07T07:36:50.7956736Z >>> target_lengths = torch.randint( 2025-09-07T07:36:50.7956828Z ... low=S_min, 2025-09-07T07:36:50.7956927Z ... high=S, 2025-09-07T07:36:50.7957015Z ... size=(N,), 2025-09-07T07:36:50.7957120Z ... dtype=torch.long, 2025-09-07T07:36:50.7957210Z ... ) 2025-09-07T07:36:50.7957313Z >>> ctc_loss = nn.CTCLoss() 2025-09-07T07:36:50.7957509Z >>> loss = ctc_loss(input, target, input_lengths, target_lengths) 2025-09-07T07:36:50.7957630Z >>> loss.backward() 2025-09-07T07:36:50.7957714Z >>> 2025-09-07T07:36:50.7957804Z >>> 2025-09-07T07:36:50.7957916Z >>> # Target are to be un-padded 2025-09-07T07:36:50.7958034Z >>> T = 50 # Input sequence length 2025-09-07T07:36:50.7958165Z >>> C = 20 # Number of classes (including blank) 2025-09-07T07:36:50.7958259Z >>> N = 16 # Batch size 2025-09-07T07:36:50.7958348Z >>> 2025-09-07T07:36:50.7958544Z >>> # Initialize random batch of input vectors, for *size = (T,N,C) 2025-09-07T07:36:50.7958759Z >>> input = torch.randn(T, N, C).log_softmax(2).detach().requires_grad_() 2025-09-07T07:36:50.7958975Z >>> input_lengths = torch.full(size=(N,), fill_value=T, dtype=torch.long) 2025-09-07T07:36:50.7959055Z >>> 2025-09-07T07:36:50.7959281Z >>> # Initialize random batch of targets (0 = blank, 1:C = classes) 2025-09-07T07:36:50.7959516Z >>> target_lengths = torch.randint(low=1, high=T, size=(N,), dtype=torch.long) 2025-09-07T07:36:50.7959659Z >>> target = torch.randint( 2025-09-07T07:36:50.7959745Z ... low=1, 2025-09-07T07:36:50.7959832Z ... high=C, 2025-09-07T07:36:50.7959956Z ... size=(sum(target_lengths),), 2025-09-07T07:36:50.7960056Z ... dtype=torch.long, 2025-09-07T07:36:50.7960147Z ... ) 2025-09-07T07:36:50.7960246Z >>> ctc_loss = nn.CTCLoss() 2025-09-07T07:36:50.7960426Z >>> loss = ctc_loss(input, target, input_lengths, target_lengths) 2025-09-07T07:36:50.7960532Z >>> loss.backward() 2025-09-07T07:36:50.7960613Z >>> 2025-09-07T07:36:50.7960705Z >>> 2025-09-07T07:36:50.7960887Z >>> # Target are to be un-padded and unbatched (effectively N=1) 2025-09-07T07:36:50.7960996Z >>> T = 50 # Input sequence length 2025-09-07T07:36:50.7961139Z >>> C = 20 # Number of classes (including blank) 2025-09-07T07:36:50.7961221Z >>> 2025-09-07T07:36:50.7961422Z >>> # Initialize random batch of input vectors, for *size = (T,C) 2025-09-07T07:36:50.7961555Z >>> # xdoctest: +SKIP("FIXME: error in doctest") 2025-09-07T07:36:50.7961753Z >>> input = torch.randn(T, C).log_softmax(1).detach().requires_grad_() 2025-09-07T07:36:50.7961913Z >>> input_lengths = torch.tensor(T, dtype=torch.long) 2025-09-07T07:36:50.7961992Z >>> 2025-09-07T07:36:50.7962194Z >>> # Initialize random batch of targets (0 = blank, 1:C = classes) 2025-09-07T07:36:50.7962456Z >>> target_lengths = torch.randint(low=1, high=T, size=(), dtype=torch.long) 2025-09-07T07:36:50.7962561Z >>> target = torch.randint( 2025-09-07T07:36:50.7962658Z ... low=1, 2025-09-07T07:36:50.7962743Z ... high=C, 2025-09-07T07:36:50.7962863Z ... size=(target_lengths,), 2025-09-07T07:36:50.7962962Z ... dtype=torch.long, 2025-09-07T07:36:50.7963070Z ... ) 2025-09-07T07:36:50.7963183Z >>> ctc_loss = nn.CTCLoss() 2025-09-07T07:36:50.7963363Z >>> loss = ctc_loss(input, target, input_lengths, target_lengths) 2025-09-07T07:36:50.7963471Z >>> loss.backward() 2025-09-07T07:36:50.7963549Z 2025-09-07T07:36:50.7963635Z Reference: 2025-09-07T07:36:50.7963819Z A. Graves et al.: Connectionist Temporal Classification: 2025-09-07T07:36:50.7964049Z Labelling Unsegmented Sequence Data with Recurrent Neural Networks: 2025-09-07T07:36:50.7964210Z https://www.cs.toronto.edu/~graves/icml_2006.pdf 2025-09-07T07:36:50.7964301Z 2025-09-07T07:36:50.7964385Z Note: 2025-09-07T07:36:50.7964633Z In order to use CuDNN, the following must be satisfied: :attr:`targets` must be 2025-09-07T07:36:50.7964911Z in concatenated format, all :attr:`input_lengths` must be `T`. :math:`blank=0`, 2025-09-07T07:36:50.7965133Z :attr:`target_lengths` :math:`\leq 256`, the integer arguments must be of 2025-09-07T07:36:50.7965250Z dtype :attr:`torch.int32`. 2025-09-07T07:36:50.7965328Z 2025-09-07T07:36:50.7965609Z The regular implementation uses the (more common in PyTorch) `torch.long` dtype. 2025-09-07T07:36:50.7965685Z 2025-09-07T07:36:50.7965762Z 2025-09-07T07:36:50.7965853Z Note: 2025-09-07T07:36:50.7966095Z In some circumstances when using the CUDA backend with CuDNN, this operator 2025-09-07T07:36:50.7966357Z may select a nondeterministic algorithm to increase performance. If this is 2025-09-07T07:36:50.7966596Z undesirable, you can try to make the operation deterministic (potentially at 2025-09-07T07:36:50.7966827Z a performance cost) by setting ``torch.backends.cudnn.deterministic = 2025-09-07T07:36:50.7966925Z True``. 2025-09-07T07:36:50.7967119Z Please see the notes on :doc:`/notes/randomness` for background. 2025-09-07T07:36:50.7967244Z 2025-09-07T07:36:50.7967497Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.7967573Z 2025-09-07T07:36:50.7967678Z warnings.warn(msg) 2025-09-07T07:36:50.7967755Z 2025-09-07T07:36:50.7967986Z --- Parse Warning: 108 / 146 --- 2025-09-07T07:36:50.7968886Z /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=410. 2025-09-07T07:36:50.7969150Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.7969317Z Computes a partial inverse of :class:`MaxPool2d`. 2025-09-07T07:36:50.7969394Z 2025-09-07T07:36:50.7969662Z :class:`MaxPool2d` is not fully invertible, since the non-maximal values are lost. 2025-09-07T07:36:50.7969742Z 2025-09-07T07:36:50.7969971Z :class:`MaxUnpool2d` takes in as input the output of :class:`MaxPool2d` 2025-09-07T07:36:50.7970223Z including the indices of the maximal values and computes a partial inverse 2025-09-07T07:36:50.7970369Z in which all non-maximal values are set to zero. 2025-09-07T07:36:50.7970459Z 2025-09-07T07:36:50.7970540Z Note: 2025-09-07T07:36:50.7970852Z This operation may behave nondeterministically when the input indices has repeat values. 2025-09-07T07:36:50.7971245Z See https://github.com/pytorch/pytorch/issues/80827 and :doc:`/notes/randomness` for more information. 2025-09-07T07:36:50.7971326Z 2025-09-07T07:36:50.7971599Z .. note:: :class:`MaxPool2d` can map several input sizes to the same output 2025-09-07T07:36:50.7971770Z sizes. Hence, the inversion process can get ambiguous. 2025-09-07T07:36:50.7971958Z To accommodate this, you can provide the needed output size 2025-09-07T07:36:50.7972203Z as an additional argument :attr:`output_size` in the forward call. 2025-09-07T07:36:50.7972323Z See the Inputs and Example below. 2025-09-07T07:36:50.7972413Z 2025-09-07T07:36:50.7972495Z Args: 2025-09-07T07:36:50.7972674Z kernel_size (int or tuple): Size of the max pooling window. 2025-09-07T07:36:50.7972856Z stride (int or tuple): Stride of the max pooling window. 2025-09-07T07:36:50.7972991Z It is set to :attr:`kernel_size` by default. 2025-09-07T07:36:50.7973184Z padding (int or tuple): Padding that was added to the input 2025-09-07T07:36:50.7973265Z 2025-09-07T07:36:50.7973352Z Inputs: 2025-09-07T07:36:50.7973520Z - `input`: the input Tensor to invert 2025-09-07T07:36:50.7973723Z - `indices`: the indices given out by :class:`~torch.nn.MaxPool2d` 2025-09-07T07:36:50.7973911Z - `output_size` (optional): the targeted output size 2025-09-07T07:36:50.7973990Z 2025-09-07T07:36:50.7974072Z Shape: 2025-09-07T07:36:50.7974268Z - Input: :math:`(N, C, H_{in}, W_{in})` or :math:`(C, H_{in}, W_{in})`. 2025-09-07T07:36:50.7974474Z - Output: :math:`(N, C, H_{out}, W_{out})` or :math:`(C, H_{out}, W_{out})`, where 2025-09-07T07:36:50.7974561Z 2025-09-07T07:36:50.7974648Z .. math:: 2025-09-07T07:36:50.7974915Z H_{out} = (H_{in} - 1) \times \text{stride[0]} - 2 \times \text{padding[0]} + \text{kernel\_size[0]} 2025-09-07T07:36:50.7975003Z 2025-09-07T07:36:50.7975088Z .. math:: 2025-09-07T07:36:50.7975355Z W_{out} = (W_{in} - 1) \times \text{stride[1]} - 2 \times \text{padding[1]} + \text{kernel\_size[1]} 2025-09-07T07:36:50.7975433Z 2025-09-07T07:36:50.7975595Z or as given by :attr:`output_size` in the call operator 2025-09-07T07:36:50.7975683Z 2025-09-07T07:36:50.7975771Z Example:: 2025-09-07T07:36:50.7975859Z 2025-09-07T07:36:50.7976042Z >>> pool = nn.MaxPool2d(2, stride=2, return_indices=True) 2025-09-07T07:36:50.7976166Z >>> unpool = nn.MaxUnpool2d(2, stride=2) 2025-09-07T07:36:50.7976304Z >>> input = torch.tensor([[[[ 1., 2., 3., 4.], 2025-09-07T07:36:50.7976410Z [ 5., 6., 7., 8.], 2025-09-07T07:36:50.7976525Z [ 9., 10., 11., 12.], 2025-09-07T07:36:50.7976632Z [13., 14., 15., 16.]]]]) 2025-09-07T07:36:50.7976746Z >>> output, indices = pool(input) 2025-09-07T07:36:50.7976856Z >>> unpool(output, indices) 2025-09-07T07:36:50.7976961Z tensor([[[[ 0., 0., 0., 0.], 2025-09-07T07:36:50.7977068Z [ 0., 6., 0., 8.], 2025-09-07T07:36:50.7977163Z [ 0., 0., 0., 0.], 2025-09-07T07:36:50.7977264Z [ 0., 14., 0., 16.]]]]) 2025-09-07T07:36:50.7977487Z >>> # Now using output_size to resolve an ambiguous size for the inverse 2025-09-07T07:36:50.7977627Z >>> input = torch.tensor([[[[ 1., 2., 3., 4., 5.], 2025-09-07T07:36:50.7977746Z [ 6., 7., 8., 9., 10.], 2025-09-07T07:36:50.7977853Z [11., 12., 13., 14., 15.], 2025-09-07T07:36:50.7977964Z [16., 17., 18., 19., 20.]]]]) 2025-09-07T07:36:50.7978088Z >>> output, indices = pool(input) 2025-09-07T07:36:50.7978256Z >>> # This call will not work without specifying output_size 2025-09-07T07:36:50.7978445Z >>> unpool(output, indices, output_size=input.size()) 2025-09-07T07:36:50.7978550Z tensor([[[[ 0., 0., 0., 0., 0.], 2025-09-07T07:36:50.7978652Z [ 0., 7., 0., 9., 0.], 2025-09-07T07:36:50.7978764Z [ 0., 0., 0., 0., 0.], 2025-09-07T07:36:50.7978864Z [ 0., 17., 0., 19., 0.]]]]) 2025-09-07T07:36:50.7978984Z 2025-09-07T07:36:50.7979061Z 2025-09-07T07:36:50.7979141Z 2025-09-07T07:36:50.7979406Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.7979483Z 2025-09-07T07:36:50.7979579Z warnings.warn(msg) 2025-09-07T07:36:50.7979668Z 2025-09-07T07:36:50.7979866Z --- Parse Warning: 109 / 146 --- 2025-09-07T07:36:50.7980780Z /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=272. 2025-09-07T07:36:50.7981043Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.7981370Z Compute sums or means of 'bags' of embeddings, without instantiating the intermediate embeddings. 2025-09-07T07:36:50.7981490Z 2025-09-07T07:36:50.7981812Z For bags of constant length, no :attr:`per_sample_weights`, no indices equal to :attr:`padding_idx`, 2025-09-07T07:36:50.7981933Z and with 2D inputs, this class 2025-09-07T07:36:50.7982010Z 2025-09-07T07:36:50.7982323Z * with ``mode="sum"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.sum(dim=1)``, 2025-09-07T07:36:50.7982636Z * with ``mode="mean"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.mean(dim=1)``, 2025-09-07T07:36:50.7982932Z * with ``mode="max"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.max(dim=1)``. 2025-09-07T07:36:50.7983020Z 2025-09-07T07:36:50.7983377Z However, :class:`~torch.nn.EmbeddingBag` is much more time and memory efficient than using a chain of these 2025-09-07T07:36:50.7983475Z operations. 2025-09-07T07:36:50.7983552Z 2025-09-07T07:36:50.7983812Z EmbeddingBag also supports per-sample weights as an argument to the forward 2025-09-07T07:36:50.7984083Z pass. This scales the output of the Embedding before performing a weighted 2025-09-07T07:36:50.7984333Z reduction as specified by ``mode``. If :attr:`per_sample_weights` is passed, the 2025-09-07T07:36:50.7984578Z only supported ``mode`` is ``"sum"``, which computes a weighted sum according to 2025-09-07T07:36:50.7984681Z :attr:`per_sample_weights`. 2025-09-07T07:36:50.7984758Z 2025-09-07T07:36:50.7984851Z Args: 2025-09-07T07:36:50.7985036Z num_embeddings (int): size of the dictionary of embeddings 2025-09-07T07:36:50.7985210Z embedding_dim (int): the size of each embedding vector 2025-09-07T07:36:50.7985526Z max_norm (float, optional): If given, each embedding vector with norm larger than :attr:`max_norm` 2025-09-07T07:36:50.7985677Z is renormalized to have norm :attr:`max_norm`. 2025-09-07T07:36:50.7986029Z norm_type (float, optional): The p of the p-norm to compute for the :attr:`max_norm` option. Default ``2``. 2025-09-07T07:36:50.7986362Z scale_grad_by_freq (bool, optional): if given, this will scale gradients by the inverse of frequency of 2025-09-07T07:36:50.7986533Z the words in the mini-batch. Default ``False``. 2025-09-07T07:36:50.7986714Z Note: this option is not supported when ``mode="max"``. 2025-09-07T07:36:50.7986975Z mode (str, optional): ``"sum"``, ``"mean"`` or ``"max"``. Specifies the way to reduce the bag. 2025-09-07T07:36:50.7987215Z ``"sum"`` computes the weighted sum, taking :attr:`per_sample_weights` 2025-09-07T07:36:50.7987433Z into consideration. ``"mean"`` computes the average of the values 2025-09-07T07:36:50.7987618Z in the bag, ``"max"`` computes the max value over each bag. 2025-09-07T07:36:50.7987733Z Default: ``"mean"`` 2025-09-07T07:36:50.7988087Z sparse (bool, optional): if ``True``, gradient w.r.t. :attr:`weight` matrix will be a sparse tensor. See 2025-09-07T07:36:50.7988339Z Notes for more details regarding sparse gradients. Note: this option is not 2025-09-07T07:36:50.7988463Z supported when ``mode="max"``. 2025-09-07T07:36:50.7988843Z include_last_offset (bool, optional): if ``True``, :attr:`offsets` has one additional element, where the last element 2025-09-07T07:36:50.7989064Z is equivalent to the size of `indices`. This matches the CSR format. 2025-09-07T07:36:50.7989403Z padding_idx (int, optional): If specified, the entries at :attr:`padding_idx` do not contribute to the 2025-09-07T07:36:50.7989670Z gradient; therefore, the embedding vector at :attr:`padding_idx` is not updated 2025-09-07T07:36:50.7989950Z during training, i.e. it remains as a fixed "pad". For a newly constructed 2025-09-07T07:36:50.7990214Z EmbeddingBag, the embedding vector at :attr:`padding_idx` will default to all 2025-09-07T07:36:50.7990456Z zeros, but can be updated to another value to be used as the padding vector. 2025-09-07T07:36:50.7990713Z Note that the embedding vector at :attr:`padding_idx` is excluded from the 2025-09-07T07:36:50.7990817Z reduction. 2025-09-07T07:36:50.7990911Z 2025-09-07T07:36:50.7990999Z Attributes: 2025-09-07T07:36:50.7991326Z weight (Tensor): the learnable weights of the module of shape `(num_embeddings, embedding_dim)` 2025-09-07T07:36:50.7991465Z initialized from :math:`\mathcal{N}(0, 1)`. 2025-09-07T07:36:50.7991541Z 2025-09-07T07:36:50.7991645Z Examples:: 2025-09-07T07:36:50.7991746Z 2025-09-07T07:36:50.7991911Z >>> # an EmbeddingBag module containing 10 tensors of size 3 2025-09-07T07:36:50.7992078Z >>> embedding_sum = nn.EmbeddingBag(10, 3, mode='sum') 2025-09-07T07:36:50.7992202Z >>> # a batch of 2 samples of 4 indices each 2025-09-07T07:36:50.7992396Z >>> input = torch.tensor([1, 2, 4, 5, 4, 3, 2, 9], dtype=torch.long) 2025-09-07T07:36:50.7992541Z >>> offsets = torch.tensor([0, 4], dtype=torch.long) 2025-09-07T07:36:50.7992689Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2025-09-07T07:36:50.7992804Z >>> embedding_sum(input, offsets) 2025-09-07T07:36:50.7992910Z tensor([[-0.8861, -5.4350, -0.0523], 2025-09-07T07:36:50.7993022Z [ 1.1306, -2.5798, -1.0044]]) 2025-09-07T07:36:50.7993101Z 2025-09-07T07:36:50.7993211Z >>> # Example with padding_idx 2025-09-07T07:36:50.7993428Z >>> embedding_sum = nn.EmbeddingBag(10, 3, mode='sum', padding_idx=2) 2025-09-07T07:36:50.7993610Z >>> input = torch.tensor([2, 2, 2, 2, 4, 3, 2, 9], dtype=torch.long) 2025-09-07T07:36:50.7993767Z >>> offsets = torch.tensor([0, 4], dtype=torch.long) 2025-09-07T07:36:50.7993876Z >>> embedding_sum(input, offsets) 2025-09-07T07:36:50.7993978Z tensor([[ 0.0000, 0.0000, 0.0000], 2025-09-07T07:36:50.7994108Z [-0.7082, 3.2145, -2.6251]]) 2025-09-07T07:36:50.7994186Z 2025-09-07T07:36:50.7994456Z >>> # An EmbeddingBag can be loaded from an Embedding like so 2025-09-07T07:36:50.7994632Z >>> embedding = nn.Embedding(10, 3, padding_idx=2) 2025-09-07T07:36:50.7994788Z >>> embedding_sum = nn.EmbeddingBag.from_pretrained( 2025-09-07T07:36:50.7994906Z embedding.weight, 2025-09-07T07:36:50.7995036Z padding_idx=embedding.padding_idx, 2025-09-07T07:36:50.7995140Z mode='sum') 2025-09-07T07:36:50.7995249Z 2025-09-07T07:36:50.7995501Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.7995589Z 2025-09-07T07:36:50.7995686Z warnings.warn(msg) 2025-09-07T07:36:50.7995781Z 2025-09-07T07:36:50.7995994Z --- Parse Warning: 110 / 146 --- 2025-09-07T07:36:50.7996951Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=Transformer.forward in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py line=186. 2025-09-07T07:36:50.7997232Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.7997381Z Take in and process masked source/target sequences. 2025-09-07T07:36:50.7997474Z 2025-09-07T07:36:50.7997562Z .. note:: 2025-09-07T07:36:50.7997668Z 2025-09-07T07:36:50.7998074Z If a boolean tensor is provided for any of the [src/tgt/memory]_mask arguments, positions with a ``True`` value are 2025-09-07T07:36:50.7998217Z not allowed to participate in the attention, 2025-09-07T07:36:50.7998418Z which is the opposite of the definition for :attr:`attn_mask` 2025-09-07T07:36:50.7998601Z in :func:`torch.nn.functional.scaled_dot_product_attention`. 2025-09-07T07:36:50.7998678Z 2025-09-07T07:36:50.7998772Z Args: 2025-09-07T07:36:50.7998906Z src: the sequence to the encoder (required). 2025-09-07T07:36:50.7999049Z tgt: the sequence to the decoder (required). 2025-09-07T07:36:50.7999230Z src_mask: the additive mask for the src sequence (optional). 2025-09-07T07:36:50.7999411Z tgt_mask: the additive mask for the tgt sequence (optional). 2025-09-07T07:36:50.7999629Z memory_mask: the additive mask for the encoder output (optional). 2025-09-07T07:36:50.7999882Z src_key_padding_mask: the Tensor mask for src keys per batch (optional). 2025-09-07T07:36:50.8000117Z tgt_key_padding_mask: the Tensor mask for tgt keys per batch (optional). 2025-09-07T07:36:50.8000363Z memory_key_padding_mask: the Tensor mask for memory keys per batch (optional). 2025-09-07T07:36:50.8000593Z src_is_causal: If specified, applies a causal mask as ``src_mask``. 2025-09-07T07:36:50.8000739Z Default: ``None``; try to detect a causal mask. 2025-09-07T07:36:50.8000831Z Warning: 2025-09-07T07:36:50.8001001Z ``src_is_causal`` provides a hint that ``src_mask`` is 2025-09-07T07:36:50.8001179Z the causal mask. Providing incorrect hints can result in 2025-09-07T07:36:50.8001356Z incorrect execution, including forward and backward 2025-09-07T07:36:50.8001458Z compatibility. 2025-09-07T07:36:50.8001665Z tgt_is_causal: If specified, applies a causal mask as ``tgt_mask``. 2025-09-07T07:36:50.8001827Z Default: ``None``; try to detect a causal mask. 2025-09-07T07:36:50.8001918Z Warning: 2025-09-07T07:36:50.8002088Z ``tgt_is_causal`` provides a hint that ``tgt_mask`` is 2025-09-07T07:36:50.8002263Z the causal mask. Providing incorrect hints can result in 2025-09-07T07:36:50.8002424Z incorrect execution, including forward and backward 2025-09-07T07:36:50.8002538Z compatibility. 2025-09-07T07:36:50.8002715Z memory_is_causal: If specified, applies a causal mask as 2025-09-07T07:36:50.8002851Z ``memory_mask``. 2025-09-07T07:36:50.8002953Z Default: ``False``. 2025-09-07T07:36:50.8003043Z Warning: 2025-09-07T07:36:50.8003193Z ``memory_is_causal`` provides a hint that 2025-09-07T07:36:50.8003414Z ``memory_mask`` is the causal mask. Providing incorrect 2025-09-07T07:36:50.8003611Z hints can result in incorrect execution, including 2025-09-07T07:36:50.8003740Z forward and backward compatibility. 2025-09-07T07:36:50.8003819Z 2025-09-07T07:36:50.8003921Z Shape: 2025-09-07T07:36:50.8004171Z - src: :math:`(S, E)` for unbatched input, :math:`(S, N, E)` if `batch_first=False` or 2025-09-07T07:36:50.8004304Z `(N, S, E)` if `batch_first=True`. 2025-09-07T07:36:50.8004542Z - tgt: :math:`(T, E)` for unbatched input, :math:`(T, N, E)` if `batch_first=False` or 2025-09-07T07:36:50.8004660Z `(N, T, E)` if `batch_first=True`. 2025-09-07T07:36:50.8004866Z - src_mask: :math:`(S, S)` or :math:`(N\cdot\text{num\_heads}, S, S)`. 2025-09-07T07:36:50.8005052Z - tgt_mask: :math:`(T, T)` or :math:`(N\cdot\text{num\_heads}, T, T)`. 2025-09-07T07:36:50.8005206Z - memory_mask: :math:`(T, S)`. 2025-09-07T07:36:50.8005453Z - src_key_padding_mask: :math:`(S)` for unbatched input otherwise :math:`(N, S)`. 2025-09-07T07:36:50.8005692Z - tgt_key_padding_mask: :math:`(T)` for unbatched input otherwise :math:`(N, T)`. 2025-09-07T07:36:50.8005959Z - memory_key_padding_mask: :math:`(S)` for unbatched input otherwise :math:`(N, S)`. 2025-09-07T07:36:50.8006041Z 2025-09-07T07:36:50.8006370Z Note: [src/tgt/memory]_mask ensures that position :math:`i` is allowed to attend the unmasked 2025-09-07T07:36:50.8006576Z positions. If a BoolTensor is provided, positions with ``True`` 2025-09-07T07:36:50.8006840Z are not allowed to attend while ``False`` values will be unchanged. If a FloatTensor 2025-09-07T07:36:50.8007025Z is provided, it will be added to the attention weight. 2025-09-07T07:36:50.8007330Z [src/tgt/memory]_key_padding_mask provides specified elements in the key to be ignored by 2025-09-07T07:36:50.8007587Z the attention. If a BoolTensor is provided, the positions with the 2025-09-07T07:36:50.8007900Z value of ``True`` will be ignored while the position with the value of ``False`` will be unchanged. 2025-09-07T07:36:50.8007993Z 2025-09-07T07:36:50.8008251Z - output: :math:`(T, E)` for unbatched input, :math:`(T, N, E)` if `batch_first=False` or 2025-09-07T07:36:50.8008365Z `(N, T, E)` if `batch_first=True`. 2025-09-07T07:36:50.8008452Z 2025-09-07T07:36:50.8008689Z Note: Due to the multi-head attention architecture in the transformer model, 2025-09-07T07:36:50.8008938Z the output sequence length of a transformer is same as the input sequence 2025-09-07T07:36:50.8009056Z (i.e. target) length of the decoder. 2025-09-07T07:36:50.8009135Z 2025-09-07T07:36:50.8009472Z where :math:`S` is the source sequence length, :math:`T` is the target sequence length, :math:`N` is the 2025-09-07T07:36:50.8009606Z batch size, :math:`E` is the feature number 2025-09-07T07:36:50.8009694Z 2025-09-07T07:36:50.8009781Z Examples: 2025-09-07T07:36:50.8009882Z >>> # xdoctest: +SKIP 2025-09-07T07:36:50.8010006Z >>> output = transformer_model( 2025-09-07T07:36:50.8010150Z ... src, tgt, src_mask=src_mask, tgt_mask=tgt_mask 2025-09-07T07:36:50.8010242Z ... ) 2025-09-07T07:36:50.8010320Z 2025-09-07T07:36:50.8010572Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.8010687Z 2025-09-07T07:36:50.8010783Z warnings.warn(msg) 2025-09-07T07:36:50.8010861Z 2025-09-07T07:36:50.8011076Z --- Parse Warning: 111 / 146 --- 2025-09-07T07:36:50.8012101Z /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=1766. 2025-09-07T07:36:50.8012417Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.8012497Z 2025-09-07T07:36:50.8012743Z Context manager for training with uneven inputs across processes in DDP. 2025-09-07T07:36:50.8012822Z 2025-09-07T07:36:50.8013044Z This context manager will keep track of already-joined DDP processes, 2025-09-07T07:36:50.8013260Z and "shadow" the forward and backward passes by inserting collective 2025-09-07T07:36:50.8013492Z communication operations to match with the ones created by non-joined 2025-09-07T07:36:50.8013733Z DDP processes. This will ensure each collective call has a corresponding 2025-09-07T07:36:50.8013952Z call by already-joined DDP processes, preventing hangs or errors that 2025-09-07T07:36:50.8014178Z would otherwise happen when training with uneven inputs across 2025-09-07T07:36:50.8014428Z processes. Alternatively, if the flag ``throw_on_early_termination`` is 2025-09-07T07:36:50.8014634Z specified to be ``True``, all trainers will throw an error once one rank 2025-09-07T07:36:50.8014839Z runs out of inputs, allowing these errors to be caught and handled 2025-09-07T07:36:50.8014997Z according to application logic. 2025-09-07T07:36:50.8015075Z 2025-09-07T07:36:50.8015297Z Once all DDP processes have joined, the context manager will broadcast 2025-09-07T07:36:50.8015519Z the model corresponding to the last joined process to all processes to 2025-09-07T07:36:50.8015679Z ensure the model is the same across all processes 2025-09-07T07:36:50.8015783Z (which is guaranteed by DDP). 2025-09-07T07:36:50.8015862Z 2025-09-07T07:36:50.8016072Z To use this to enable training with uneven inputs across processes, 2025-09-07T07:36:50.8016294Z simply wrap this context manager around your training loop. No further 2025-09-07T07:36:50.8016502Z modifications to the model or data loading is required. 2025-09-07T07:36:50.8016581Z 2025-09-07T07:36:50.8016670Z .. warning:: 2025-09-07T07:36:50.8016885Z If the model or training loop this context manager is wrapped around 2025-09-07T07:36:50.8017065Z has additional distributed collective operations, such as 2025-09-07T07:36:50.8017263Z ``SyncBatchNorm`` in the model's forward pass, then the flag 2025-09-07T07:36:50.8017465Z ``throw_on_early_termination`` must be enabled. This is because this 2025-09-07T07:36:50.8017673Z context manager is not aware of non-DDP collective communication. 2025-09-07T07:36:50.8017857Z This flag will cause all ranks to throw when any one rank 2025-09-07T07:36:50.8018066Z exhausts inputs, allowing these errors to be caught and recovered 2025-09-07T07:36:50.8018179Z from across all ranks. 2025-09-07T07:36:50.8018261Z 2025-09-07T07:36:50.8018343Z Args: 2025-09-07T07:36:50.8018540Z divide_by_initial_world_size (bool): If ``True``, will divide 2025-09-07T07:36:50.8018744Z gradients by the initial ``world_size`` DDP training was launched 2025-09-07T07:36:50.8018920Z with. If ``False``, will compute the effective world size 2025-09-07T07:36:50.8019100Z (number of ranks that have not depleted their inputs yet) and 2025-09-07T07:36:50.8019245Z divide gradients by that during allreduce. Set 2025-09-07T07:36:50.8019433Z ``divide_by_initial_world_size=True`` to ensure every input 2025-09-07T07:36:50.8019636Z sample including the uneven inputs have equal weight in terms of 2025-09-07T07:36:50.8019842Z how much they contribute to the global gradient. This is 2025-09-07T07:36:50.8020013Z achieved by always dividing the gradient by the initial 2025-09-07T07:36:50.8020193Z ``world_size`` even when we encounter uneven inputs. If you set 2025-09-07T07:36:50.8020372Z this to ``False``, we divide the gradient by the remaining 2025-09-07T07:36:50.8020589Z number of nodes. This ensures parity with training on a smaller 2025-09-07T07:36:50.8020781Z ``world_size`` although it also means the uneven inputs would 2025-09-07T07:36:50.8020970Z contribute more towards the global gradient. Typically, you 2025-09-07T07:36:50.8021158Z would want to set this to ``True`` for cases where the last few 2025-09-07T07:36:50.8021361Z inputs of your training job are uneven. In extreme cases, where 2025-09-07T07:36:50.8021542Z there is a large discrepancy in the number of inputs, setting 2025-09-07T07:36:50.8021698Z this to ``False`` might provide better results. 2025-09-07T07:36:50.8021905Z enable (bool): Whether to enable uneven input detection or not. Pass 2025-09-07T07:36:50.8022072Z in ``enable=False`` to disable in cases where you know that 2025-09-07T07:36:50.8022290Z inputs are even across participating processes. Default is 2025-09-07T07:36:50.8022377Z ``True``. 2025-09-07T07:36:50.8022569Z throw_on_early_termination (bool): Whether to throw an error 2025-09-07T07:36:50.8022741Z or continue training when at least one rank has exhausted 2025-09-07T07:36:50.8022922Z inputs. If ``True``, will throw upon the first rank reaching end 2025-09-07T07:36:50.8023100Z of data. If ``False``, will continue training with a smaller 2025-09-07T07:36:50.8023290Z effective world size until all ranks are joined. Note that if 2025-09-07T07:36:50.8023422Z this flag is specified, then the flag 2025-09-07T07:36:50.8023593Z ``divide_by_initial_world_size`` would be ignored. Default 2025-09-07T07:36:50.8023886Z is ``False``. 2025-09-07T07:36:50.8023966Z 2025-09-07T07:36:50.8024042Z 2025-09-07T07:36:50.8024145Z Example:: 2025-09-07T07:36:50.8024223Z 2025-09-07T07:36:50.8024337Z >>> # xdoctest: +SKIP("Distributed") 2025-09-07T07:36:50.8024519Z >>> import torch 2025-09-07T07:36:50.8024637Z >>> import torch.distributed as dist 2025-09-07T07:36:50.8024736Z >>> import os 2025-09-07T07:36:50.8024855Z >>> import torch.multiprocessing as mp 2025-09-07T07:36:50.8024955Z >>> import torch.nn as nn 2025-09-07T07:36:50.8025069Z >>> # On each spawned worker 2025-09-07T07:36:50.8025163Z >>> def worker(rank): 2025-09-07T07:36:50.8025349Z >>> dist.init_process_group("nccl", rank=rank, world_size=2) 2025-09-07T07:36:50.8025460Z >>> torch.cuda.set_device(rank) 2025-09-07T07:36:50.8025592Z >>> model = nn.Linear(1, 1, bias=False).to(rank) 2025-09-07T07:36:50.8025775Z >>> model = torch.nn.parallel.DistributedDataParallel( 2025-09-07T07:36:50.8025914Z >>> model, device_ids=[rank], output_device=rank 2025-09-07T07:36:50.8026014Z >>> ) 2025-09-07T07:36:50.8026141Z >>> # Rank 1 gets one more input than rank 0. 2025-09-07T07:36:50.8026331Z >>> inputs = [torch.tensor([1]).float() for _ in range(10 + rank)] 2025-09-07T07:36:50.8026441Z >>> with model.join(): 2025-09-07T07:36:50.8026538Z >>> for _ in range(5): 2025-09-07T07:36:50.8026650Z >>> for inp in inputs: 2025-09-07T07:36:50.8026764Z >>> loss = model(inp).sum() 2025-09-07T07:36:50.8026866Z >>> loss.backward() 2025-09-07T07:36:50.8027064Z >>> # Without the join() API, the below synchronization will hang 2025-09-07T07:36:50.8027205Z >>> # blocking for rank 1's allreduce to complete. 2025-09-07T07:36:50.8027390Z >>> torch.cuda.synchronize(device=rank) 2025-09-07T07:36:50.8027470Z 2025-09-07T07:36:50.8027724Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.8027820Z 2025-09-07T07:36:50.8027916Z warnings.warn(msg) 2025-09-07T07:36:50.8028065Z 2025-09-07T07:36:50.8028297Z --- Parse Warning: 112 / 146 --- 2025-09-07T07:36:50.8029388Z /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=2057. 2025-09-07T07:36:50.8029660Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.8029740Z 2025-09-07T07:36:50.8030057Z Register an optimizer in DDP to optimize parameter immediately after its gradient reduction. 2025-09-07T07:36:50.8030137Z 2025-09-07T07:36:50.8030343Z Registers an optimizer with DDP such that the optimization for a 2025-09-07T07:36:50.8030562Z parameter will run immediately when that parameter's gradient is 2025-09-07T07:36:50.8030803Z finished with reduction, instead of waiting for all parameters' 2025-09-07T07:36:50.8031033Z gradients to finish reduction. This can result in a training speedup 2025-09-07T07:36:50.8031248Z depending on your workload since the optimizer can run while gradient 2025-09-07T07:36:50.8031474Z reduction for other parameters are still ongoing. In addition, this has 2025-09-07T07:36:50.8031707Z the potential to reduce peak memory consumption during training, as it 2025-09-07T07:36:50.8031903Z only needs to load the per-parameter optimizer states of a single 2025-09-07T07:36:50.8032124Z parameter at a time, instead of loading all per-parameter optimizer 2025-09-07T07:36:50.8032213Z states at once. 2025-09-07T07:36:50.8032292Z 2025-09-07T07:36:50.8032386Z Args: 2025-09-07T07:36:50.8032581Z optim (Type): a ``torch.optim.Optimizer`` class to be registered 2025-09-07T07:36:50.8032695Z as a fused optimizer. 2025-09-07T07:36:50.8032864Z *args (Sequence[Any]): Arguments to forward to `optim`. 2025-09-07T07:36:50.8033100Z optim_params (Optional[Iterable[torch.Tensor]]): Set of parameters 2025-09-07T07:36:50.8033337Z to optimize, similar to `params` argument of traditional `torch.optim` 2025-09-07T07:36:50.8033538Z Optimizers. If this is omitted, all DDP model parameters will be 2025-09-07T07:36:50.8033638Z optimized. 2025-09-07T07:36:50.8033835Z **kwargs: (Dict[str, Any]): Keyword arguments to forward to `optim`. 2025-09-07T07:36:50.8033915Z 2025-09-07T07:36:50.8034020Z .. warning :: 2025-09-07T07:36:50.8034229Z _register_fused_optim should only be called once on a DDP instance, 2025-09-07T07:36:50.8034530Z and registering multiple fused optimizers for the same DDP model 2025-09-07T07:36:50.8034661Z is not currently supported. Please ping 2025-09-07T07:36:50.8034891Z https://github.com/pytorch/pytorch/issues/71595 if this is necessary 2025-09-07T07:36:50.8035003Z for your use case. 2025-09-07T07:36:50.8035081Z 2025-09-07T07:36:50.8035187Z .. warning :: 2025-09-07T07:36:50.8035383Z _register_fused_optim and register_comm_hook currently do not 2025-09-07T07:36:50.8035595Z compose together, meaning that custom DDP communication hooks are 2025-09-07T07:36:50.8035776Z not supported with overlapped optimizers. Please ping 2025-09-07T07:36:50.8036000Z https://github.com/pytorch/pytorch/issues/71595 if this is necessary 2025-09-07T07:36:50.8036109Z for your use case. 2025-09-07T07:36:50.8036191Z 2025-09-07T07:36:50.8036279Z .. warning :: 2025-09-07T07:36:50.8036515Z Gradient accumulation and DDP `no_sync` are currently not supported 2025-09-07T07:36:50.8036670Z with overlapped optimizer. Please ping 2025-09-07T07:36:50.8036892Z https://github.com/pytorch/pytorch/issues/71595 if this is necessary 2025-09-07T07:36:50.8037001Z for your use case. 2025-09-07T07:36:50.8037082Z 2025-09-07T07:36:50.8037182Z Example:: 2025-09-07T07:36:50.8037261Z 2025-09-07T07:36:50.8037394Z >>> # xdoctest: +SKIP("No rendezvous handler") 2025-09-07T07:36:50.8037725Z >>> torch.distributed.init_process_group(backend='nccl', world_size=4, init_method='...') 2025-09-07T07:36:50.8037915Z >>> net = torch.nn.parallel.DistributedDataParallel(model, pg) 2025-09-07T07:36:50.8038016Z >>> lr = 1e-2 2025-09-07T07:36:50.8038108Z >>> betas = (0.9, 0.99) 2025-09-07T07:36:50.8038195Z >>> eps = 1e-6 2025-09-07T07:36:50.8038431Z >>> net._register_fused_optim(torch.optim.Adam, lr, betas=betas, eps=eps) 2025-09-07T07:36:50.8038549Z >>> # Example with subset of parameters 2025-09-07T07:36:50.8038700Z >>> params_to_opt = [list(net.parameters())[0]] 2025-09-07T07:36:50.8038806Z >>> net._register_fused_optim( 2025-09-07T07:36:50.8039034Z ... torch.optim.Adam, lr, optim_params=params_to_opt, betas=betas, eps=eps 2025-09-07T07:36:50.8039153Z ... ) 2025-09-07T07:36:50.8039231Z 2025-09-07T07:36:50.8039495Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.8039573Z 2025-09-07T07:36:50.8039667Z warnings.warn(msg) 2025-09-07T07:36:50.8039756Z 2025-09-07T07:36:50.8039962Z --- Parse Warning: 113 / 146 --- 2025-09-07T07:36:50.8040980Z /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=14. 2025-09-07T07:36:50.8041241Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.8041457Z Convert ``memory_format`` of ``nn.Conv2d.weight`` to ``memory_format``. 2025-09-07T07:36:50.8041547Z 2025-09-07T07:36:50.8041818Z The conversion recursively applies to nested ``nn.Module``, including ``module``. 2025-09-07T07:36:50.8042102Z Note that it only changes the memory_format, but not the semantics of each dimensions. 2025-09-07T07:36:50.8042392Z This function is used to facilitate the computation to adopt NHWC kernels, which 2025-09-07T07:36:50.8042699Z provides considerable speed up for fp16 data on CUDA devices with compute capability >= 7.0 2025-09-07T07:36:50.8042789Z 2025-09-07T07:36:50.8042873Z .. note:: 2025-09-07T07:36:50.8043113Z Calling ``model.to(memory_format=torch.channels_last)`` is more aggressive 2025-09-07T07:36:50.8043333Z than the utility function ``convert_conv2d_weight_memory_format``. Any 2025-09-07T07:36:50.8043543Z layer with 4d weight will be affected by ``model.to``, which does not 2025-09-07T07:36:50.8043782Z necessarily benefit from conversion to specified ``memory_format``. 2025-09-07T07:36:50.8044002Z One place we are confident in is that NHWC(channels_last) conversion for 2025-09-07T07:36:50.8044232Z convolution in cuDNN, as it is beneficial to run convolution in NHWC, 2025-09-07T07:36:50.8044435Z even in cases where we have to apply permutation to input tensors. 2025-09-07T07:36:50.8044525Z 2025-09-07T07:36:50.8044748Z Hence our strategy here is to convert only the weight of convolution to 2025-09-07T07:36:50.8044863Z channels_last. This ensures that; 2025-09-07T07:36:50.8045091Z 1. Fast convolution kernels will be used, the benefit of which could 2025-09-07T07:36:50.8045321Z outweigh overhead of permutation (if input is not in the same format). 2025-09-07T07:36:50.8045567Z 2. No unnecessary permutations are applied on layers that do not benefit 2025-09-07T07:36:50.8045719Z from memory_format conversion. 2025-09-07T07:36:50.8045799Z 2025-09-07T07:36:50.8046036Z The optimal case is that, layers between convolution layers are channels 2025-09-07T07:36:50.8046277Z last compatible. Input tensor would be permuted to channels last when it 2025-09-07T07:36:50.8046520Z encounters the first convolution layer and stay in that memory format. 2025-09-07T07:36:50.8046780Z Hence following convolutions will not need to permute its input tensor. 2025-09-07T07:36:50.8046860Z 2025-09-07T07:36:50.8047088Z In case where a channels last incompatible layer is between convolution 2025-09-07T07:36:50.8047297Z layers, we need to permute the input tensor back to contiguous format 2025-09-07T07:36:50.8047527Z for that layer. The input tensor will go through the remaining layers in 2025-09-07T07:36:50.8047749Z contiguous format and be permuted to channels last when it encounters 2025-09-07T07:36:50.8047957Z another convolution layer. There's no point in propagating that 2025-09-07T07:36:50.8048183Z permutation to an earlier layer, as most layers are quite agnostic to 2025-09-07T07:36:50.8048304Z ``memory_format``. 2025-09-07T07:36:50.8048392Z 2025-09-07T07:36:50.8048622Z This claim might change when PyTorch supports fusion of permutation, as 2025-09-07T07:36:50.8048841Z there might have been a better spot to fuse the permutation other than 2025-09-07T07:36:50.8048969Z immediately before a convolution. 2025-09-07T07:36:50.8049046Z 2025-09-07T07:36:50.8049139Z Args: 2025-09-07T07:36:50.8049349Z module (nn.Module): ``nn.Conv2d`` & ``nn.ConvTranspose2d`` or container 2025-09-07T07:36:50.8049450Z ``nn.Module`` 2025-09-07T07:36:50.8049611Z memory_format: user specified ``memory_format``, 2025-09-07T07:36:50.8049790Z e.g. ``torch.channels_last`` or ``torch.contiguous_format`` 2025-09-07T07:36:50.8049881Z 2025-09-07T07:36:50.8049963Z Returns: 2025-09-07T07:36:50.8050103Z The original module with updated ``nn.Conv2d`` 2025-09-07T07:36:50.8050194Z 2025-09-07T07:36:50.8050277Z Example: 2025-09-07T07:36:50.8050426Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2025-09-07T07:36:50.8050606Z >>> # xdoctest: +REQUIRES(env:CUBLAS_WORKSPACE_CONFIG) 2025-09-07T07:36:50.8050710Z >>> input = torch.randint( 2025-09-07T07:36:50.8050867Z ... 1, 10, (2, 8, 4, 4), dtype=torch.float16, device="cuda" 2025-09-07T07:36:50.8050948Z ... ) 2025-09-07T07:36:50.8051064Z >>> model = nn.Sequential( 2025-09-07T07:36:50.8051177Z >>> nn.Conv2d(8, 4, 3)).cuda().half() 2025-09-07T07:36:50.8051278Z >>> # This is identical to: 2025-09-07T07:36:50.8051533Z >>> # nn.utils.convert_conv2d_weight_memory_format(model, torch.channels_last) 2025-09-07T07:36:50.8051694Z >>> model = nn.utils.convert_conv2d_weight_memory_format( 2025-09-07T07:36:50.8051817Z ... model, torch.channels_last 2025-09-07T07:36:50.8051896Z ... ) 2025-09-07T07:36:50.8051994Z >>> out = model(input) 2025-09-07T07:36:50.8052085Z 2025-09-07T07:36:50.8052341Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.8052417Z 2025-09-07T07:36:50.8052524Z warnings.warn(msg) 2025-09-07T07:36:50.8052599Z 2025-09-07T07:36:50.8052804Z --- Parse Warning: 114 / 146 --- 2025-09-07T07:36:50.8053808Z /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=93. 2025-09-07T07:36:50.8054079Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.8054314Z Convert ``memory_format`` of ``nn.Conv3d.weight`` to ``memory_format`` 2025-09-07T07:36:50.8054586Z The conversion recursively applies to nested ``nn.Module``, including ``module``. 2025-09-07T07:36:50.8054876Z Note that it only changes the memory_format, but not the semantics of each dimensions. 2025-09-07T07:36:50.8055157Z This function is used to facilitate the computation to adopt NHWC kernels, which 2025-09-07T07:36:50.8055477Z provides considerable speed up for fp16 data on CUDA devices with compute capability >= 7.0 2025-09-07T07:36:50.8055555Z 2025-09-07T07:36:50.8055640Z .. note:: 2025-09-07T07:36:50.8055895Z Calling ``model.to(memory_format=torch.channels_last_3d)`` is more aggressive 2025-09-07T07:36:50.8056113Z than the utility function ``convert_conv3d_weight_memory_format``. Any 2025-09-07T07:36:50.8056334Z layer with 4d weight will be affected by ``model.to``, which does not 2025-09-07T07:36:50.8056557Z necessarily benefit from conversion to specified ``memory_format``. 2025-09-07T07:36:50.8056789Z One place we are confident in is that NDHWC(channels_last_3d) conversion for 2025-09-07T07:36:50.8057045Z convolution in cuDNN, as it is beneficial to run convolution in NDHWC, 2025-09-07T07:36:50.8057249Z even in cases where we have to apply permutation to input tensors. 2025-09-07T07:36:50.8057339Z 2025-09-07T07:36:50.8057563Z Hence our strategy here is to convert only the weight of convolution to 2025-09-07T07:36:50.8057681Z channels_last_3d. This ensures that; 2025-09-07T07:36:50.8057906Z 1. Fast convolution kernels will be used, the benefit of which could 2025-09-07T07:36:50.8058136Z outweigh overhead of permutation (if input is not in the same format). 2025-09-07T07:36:50.8058376Z 2. No unnecessary permutations are applied on layers that do not benefit 2025-09-07T07:36:50.8058489Z from memory_format conversion. 2025-09-07T07:36:50.8058570Z 2025-09-07T07:36:50.8058804Z The optimal case is that, layers between convolution layers are channels 2025-09-07T07:36:50.8059039Z last compatible. Input tensor would be permuted to channels last when it 2025-09-07T07:36:50.8059304Z encounters the first convolution layer and stay in that memory format. 2025-09-07T07:36:50.8059540Z Hence following convolutions will not need to permute its input tensor. 2025-09-07T07:36:50.8059619Z 2025-09-07T07:36:50.8059851Z In case where a channels last incompatible layer is between convolution 2025-09-07T07:36:50.8060060Z layers, we need to permute the input tensor back to contiguous format 2025-09-07T07:36:50.8060294Z for that layer. The input tensor will go through the remaining layers in 2025-09-07T07:36:50.8060517Z contiguous format and be permuted to channels last when it encounters 2025-09-07T07:36:50.8060736Z another convolution layer. There's no point in propagating that 2025-09-07T07:36:50.8060952Z permutation to an earlier layer, as most layers are quite agnostic to 2025-09-07T07:36:50.8061049Z ``memory_format``. 2025-09-07T07:36:50.8061139Z 2025-09-07T07:36:50.8061372Z This claim might change when PyTorch supports fusion of permutation, as 2025-09-07T07:36:50.8061602Z there might have been a better spot to fuse the permutation other than 2025-09-07T07:36:50.8061722Z immediately before a convolution. 2025-09-07T07:36:50.8061798Z 2025-09-07T07:36:50.8061892Z Args: 2025-09-07T07:36:50.8062105Z module (nn.Module): ``nn.Conv3d`` & ``nn.ConvTranspose3d`` or container 2025-09-07T07:36:50.8062223Z ``nn.Module`` 2025-09-07T07:36:50.8062371Z memory_format: user specified ``memory_format``, 2025-09-07T07:36:50.8062571Z e.g. ``torch.channels_last`` or ``torch.contiguous_format`` 2025-09-07T07:36:50.8062663Z 2025-09-07T07:36:50.8062748Z Returns: 2025-09-07T07:36:50.8062887Z The original module with updated ``nn.Conv3d`` 2025-09-07T07:36:50.8062979Z 2025-09-07T07:36:50.8063062Z Example: 2025-09-07T07:36:50.8063210Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2025-09-07T07:36:50.8063420Z >>> # xdoctest: +REQUIRES(env:CUBLAS_WORKSPACE_CONFIG) 2025-09-07T07:36:50.8063524Z >>> input = torch.randint( 2025-09-07T07:36:50.8063683Z ... 1, 10, (2, 8, 4, 4, 4), dtype=torch.float16, device="cuda" 2025-09-07T07:36:50.8063762Z ... ) 2025-09-07T07:36:50.8063874Z >>> model = nn.Sequential( 2025-09-07T07:36:50.8063985Z >>> nn.Conv3d(8, 4, 3)).cuda().half() 2025-09-07T07:36:50.8064086Z >>> # This is identical to: 2025-09-07T07:36:50.8064352Z >>> # nn.utils.convert_conv3d_weight_memory_format(model, torch.channels_last_3d) 2025-09-07T07:36:50.8064516Z >>> model = nn.utils.convert_conv3d_weight_memory_format( 2025-09-07T07:36:50.8064644Z ... model, torch.channels_last_3d 2025-09-07T07:36:50.8064752Z ... ) 2025-09-07T07:36:50.8064856Z >>> out = model(input) 2025-09-07T07:36:50.8064950Z 2025-09-07T07:36:50.8065203Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.8065295Z 2025-09-07T07:36:50.8065390Z warnings.warn(msg) 2025-09-07T07:36:50.8065468Z 2025-09-07T07:36:50.8065679Z --- Parse Warning: 115 / 146 --- 2025-09-07T07:36:50.8066659Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=register_parametrization in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/parametrize.py line=424. 2025-09-07T07:36:50.8066935Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.8067091Z Register a parametrization to a tensor in a module. 2025-09-07T07:36:50.8067169Z 2025-09-07T07:36:50.8067459Z Assume that ``tensor_name="weight"`` for simplicity. When accessing ``module.weight``, 2025-09-07T07:36:50.8067768Z the module will return the parametrized version ``parametrization(module.weight)``. 2025-09-07T07:36:50.8068040Z If the original tensor requires a gradient, the backward pass will differentiate 2025-09-07T07:36:50.8068328Z through :attr:`parametrization`, and the optimizer will update the tensor accordingly. 2025-09-07T07:36:50.8068408Z 2025-09-07T07:36:50.8068729Z The first time that a module registers a parametrization, this function will add an attribute 2025-09-07T07:36:50.8068970Z ``parametrizations`` to the module of type :class:`~ParametrizationList`. 2025-09-07T07:36:50.8069064Z 2025-09-07T07:36:50.8069319Z The list of parametrizations on the tensor ``weight`` will be accessible under 2025-09-07T07:36:50.8069446Z ``module.parametrizations.weight``. 2025-09-07T07:36:50.8069540Z 2025-09-07T07:36:50.8069675Z The original tensor will be accessible under 2025-09-07T07:36:50.8069839Z ``module.parametrizations.weight.original``. 2025-09-07T07:36:50.8069923Z 2025-09-07T07:36:50.8070189Z Parametrizations may be concatenated by registering several parametrizations 2025-09-07T07:36:50.8070303Z on the same attribute. 2025-09-07T07:36:50.8070385Z 2025-09-07T07:36:50.8070639Z The training mode of a registered parametrization is updated on registration 2025-09-07T07:36:50.8070773Z to match the training mode of the host module 2025-09-07T07:36:50.8070853Z 2025-09-07T07:36:50.8071171Z Parametrized parameters and buffers have an inbuilt caching system that can be activated 2025-09-07T07:36:50.8071296Z using the context manager :func:`cached`. 2025-09-07T07:36:50.8071387Z 2025-09-07T07:36:50.8071652Z A :attr:`parametrization` may optionally implement a method with signature 2025-09-07T07:36:50.8071728Z 2025-09-07T07:36:50.8071842Z .. code-block:: python 2025-09-07T07:36:50.8071927Z 2025-09-07T07:36:50.8072155Z def right_inverse(self, X: Tensor) -> Union[Tensor, Sequence[Tensor]] 2025-09-07T07:36:50.8072258Z 2025-09-07T07:36:50.8072518Z This method is called on the unparametrized tensor when the first parametrization 2025-09-07T07:36:50.8072735Z is registered to compute the initial value of the original tensor. 2025-09-07T07:36:50.8073043Z If this method is not implemented, the original tensor will be just the unparametrized tensor. 2025-09-07T07:36:50.8073134Z 2025-09-07T07:36:50.8073441Z If all the parametrizations registered on a tensor implement `right_inverse` it is possible 2025-09-07T07:36:50.8073725Z to initialize a parametrized tensor by assigning to it, as shown in the example below. 2025-09-07T07:36:50.8073812Z 2025-09-07T07:36:50.8074034Z It is possible for the first parametrization to depend on several inputs. 2025-09-07T07:36:50.8074284Z This may be implemented returning a tuple of tensors from ``right_inverse`` 2025-09-07T07:36:50.8074622Z (see the example implementation of a ``RankOne`` parametrization below). 2025-09-07T07:36:50.8074705Z 2025-09-07T07:36:50.8075050Z In this case, the unconstrained tensors are also located under ``module.parametrizations.weight`` 2025-09-07T07:36:50.8075178Z with names ``original0``, ``original1``,... 2025-09-07T07:36:50.8075267Z 2025-09-07T07:36:50.8075350Z .. note:: 2025-09-07T07:36:50.8075425Z 2025-09-07T07:36:50.8075703Z If unsafe=False (default) both the forward and right_inverse methods will be called 2025-09-07T07:36:50.8075849Z once to perform a number of consistency checks. 2025-09-07T07:36:50.8076125Z If unsafe=True, then right_inverse will be called if the tensor is not parametrized, 2025-09-07T07:36:50.8076251Z and nothing will be called otherwise. 2025-09-07T07:36:50.8076328Z 2025-09-07T07:36:50.8076422Z .. note:: 2025-09-07T07:36:50.8076504Z 2025-09-07T07:36:50.8076715Z In most situations, ``right_inverse`` will be a function such that 2025-09-07T07:36:50.8076876Z ``forward(right_inverse(X)) == X`` (see 2025-09-07T07:36:50.8077158Z `right inverse `_). 2025-09-07T07:36:50.8077423Z Sometimes, when the parametrization is not surjective, it may be reasonable 2025-09-07T07:36:50.8077515Z to relax this. 2025-09-07T07:36:50.8077606Z 2025-09-07T07:36:50.8077694Z .. warning:: 2025-09-07T07:36:50.8077771Z 2025-09-07T07:36:50.8078056Z If a parametrization depends on several inputs, :func:`~register_parametrization` 2025-09-07T07:36:50.8078316Z will register a number of new parameters. If such parametrization is registered 2025-09-07T07:36:50.8078599Z after the optimizer is created, these new parameters will need to be added manually 2025-09-07T07:36:50.8078794Z to the optimizer. See :meth:`torch.Optimizer.add_param_group`. 2025-09-07T07:36:50.8078873Z 2025-09-07T07:36:50.8078966Z Args: 2025-09-07T07:36:50.8079182Z module (nn.Module): module on which to register the parametrization 2025-09-07T07:36:50.8079407Z tensor_name (str): name of the parameter or buffer on which to register 2025-09-07T07:36:50.8079508Z the parametrization 2025-09-07T07:36:50.8079710Z parametrization (nn.Module): the parametrization to register 2025-09-07T07:36:50.8079808Z Keyword args: 2025-09-07T07:36:50.8080022Z unsafe (bool): a boolean flag that denotes whether the parametrization 2025-09-07T07:36:50.8080221Z may change the dtype and shape of the tensor. Default: `False` 2025-09-07T07:36:50.8080511Z Warning: the parametrization is not checked for consistency upon registration. 2025-09-07T07:36:50.8080630Z Enable this flag at your own risk. 2025-09-07T07:36:50.8080720Z 2025-09-07T07:36:50.8080800Z Raises: 2025-09-07T07:36:50.8081099Z ValueError: if the module does not have a parameter or a buffer named :attr:`tensor_name` 2025-09-07T07:36:50.8081202Z 2025-09-07T07:36:50.8081287Z Examples: 2025-09-07T07:36:50.8081445Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_LAPACK) 2025-09-07T07:36:50.8081540Z >>> import torch 2025-09-07T07:36:50.8081645Z >>> import torch.nn as nn 2025-09-07T07:36:50.8081793Z >>> import torch.nn.utils.parametrize as P 2025-09-07T07:36:50.8081874Z >>> 2025-09-07T07:36:50.8081993Z >>> class Symmetric(nn.Module): 2025-09-07T07:36:50.8082094Z >>> def forward(self, X): 2025-09-07T07:36:50.8082274Z >>> return X.triu() + X.triu(1).T # Return a symmetric matrix 2025-09-07T07:36:50.8082366Z >>> 2025-09-07T07:36:50.8082477Z >>> def right_inverse(self, A): 2025-09-07T07:36:50.8082609Z >>> return A.triu() 2025-09-07T07:36:50.8082689Z >>> 2025-09-07T07:36:50.8082787Z >>> m = nn.Linear(5, 5) 2025-09-07T07:36:50.8082965Z >>> P.register_parametrization(m, "weight", Symmetric()) 2025-09-07T07:36:50.8083202Z >>> print(torch.allclose(m.weight, m.weight.T)) # m.weight is now symmetric 2025-09-07T07:36:50.8083293Z True 2025-09-07T07:36:50.8083389Z >>> A = torch.rand(5, 5) 2025-09-07T07:36:50.8083499Z >>> A = A + A.T # A is now symmetric 2025-09-07T07:36:50.8083703Z >>> m.weight = A # Initialize the weight to be the symmetric matrix A 2025-09-07T07:36:50.8083824Z >>> print(torch.allclose(m.weight, A)) 2025-09-07T07:36:50.8083916Z True 2025-09-07T07:36:50.8083995Z 2025-09-07T07:36:50.8084100Z >>> class RankOne(nn.Module): 2025-09-07T07:36:50.8084218Z >>> def forward(self, x, y): 2025-09-07T07:36:50.8084356Z >>> # Form a rank 1 matrix multiplying two vectors 2025-09-07T07:36:50.8084499Z >>> return x.unsqueeze(-1) @ y.unsqueeze(-2) 2025-09-07T07:36:50.8084605Z >>> 2025-09-07T07:36:50.8084716Z >>> def right_inverse(self, Z): 2025-09-07T07:36:50.8084846Z >>> # Project Z onto the rank 1 matrices 2025-09-07T07:36:50.8084997Z >>> U, S, Vh = torch.linalg.svd(Z, full_matrices=False) 2025-09-07T07:36:50.8085124Z >>> # Return rescaled singular vectors 2025-09-07T07:36:50.8085241Z >>> s0_sqrt = S[0].sqrt().unsqueeze(-1) 2025-09-07T07:36:50.8085387Z >>> return U[..., :, 0] * s0_sqrt, Vh[..., 0, :] * s0_sqrt 2025-09-07T07:36:50.8085480Z >>> 2025-09-07T07:36:50.8085626Z >>> linear_rank_one = P.register_parametrization( 2025-09-07T07:36:50.8085760Z ... nn.Linear(4, 4), "weight", RankOne() 2025-09-07T07:36:50.8085840Z ... ) 2025-09-07T07:36:50.8086044Z >>> print(torch.linalg.matrix_rank(linear_rank_one.weight).item()) 2025-09-07T07:36:50.8086138Z 1 2025-09-07T07:36:50.8086214Z 2025-09-07T07:36:50.8086294Z 2025-09-07T07:36:50.8086555Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.8086632Z 2025-09-07T07:36:50.8086738Z warnings.warn(msg) 2025-09-07T07:36:50.8086818Z 2025-09-07T07:36:50.8087030Z --- Parse Warning: 116 / 146 --- 2025-09-07T07:36:50.8087923Z /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=979. 2025-09-07T07:36:50.8088209Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.8088527Z Prune tensor by removing channels with the lowest L\ ``n``-norm along the specified dimension. 2025-09-07T07:36:50.8088606Z 2025-09-07T07:36:50.8088854Z Prunes tensor corresponding to parameter called ``name`` in ``module`` 2025-09-07T07:36:50.8089098Z by removing the specified ``amount`` of (currently unpruned) channels 2025-09-07T07:36:50.8089273Z along the specified ``dim`` with the lowest L\ ``n``-norm. 2025-09-07T07:36:50.8089474Z Modifies module in place (and also return the modified module) 2025-09-07T07:36:50.8089556Z by: 2025-09-07T07:36:50.8089634Z 2025-09-07T07:36:50.8089847Z 1) adding a named buffer called ``name+'_mask'`` corresponding to the 2025-09-07T07:36:50.8090066Z binary mask applied to the parameter ``name`` by the pruning method. 2025-09-07T07:36:50.8090283Z 2) replacing the parameter ``name`` by its pruned version, while the 2025-09-07T07:36:50.8090489Z original (unpruned) parameter is stored in a new parameter named 2025-09-07T07:36:50.8090594Z ``name+'_orig'``. 2025-09-07T07:36:50.8090671Z 2025-09-07T07:36:50.8090778Z Args: 2025-09-07T07:36:50.8090965Z module (nn.Module): module containing the tensor to prune 2025-09-07T07:36:50.8091148Z name (str): parameter name within ``module`` on which pruning 2025-09-07T07:36:50.8091241Z will act. 2025-09-07T07:36:50.8091421Z amount (int or float): quantity of parameters to prune. 2025-09-07T07:36:50.8091596Z If ``float``, should be between 0.0 and 1.0 and represent the 2025-09-07T07:36:50.8091812Z fraction of parameters to prune. If ``int``, it represents the 2025-09-07T07:36:50.8091942Z absolute number of parameters to prune. 2025-09-07T07:36:50.8092143Z n (int, float, inf, -inf, 'fro', 'nuc'): See documentation of valid 2025-09-07T07:36:50.8092299Z entries for argument ``p`` in :func:`torch.norm`. 2025-09-07T07:36:50.8092495Z dim (int): index of the dim along which we define channels to prune. 2025-09-07T07:36:50.8092738Z importance_scores (torch.Tensor): tensor of importance scores (of same 2025-09-07T07:36:50.8092951Z shape as module parameter) used to compute mask for pruning. 2025-09-07T07:36:50.8093185Z The values in this tensor indicate the importance of the corresponding 2025-09-07T07:36:50.8093315Z elements in the parameter being pruned. 2025-09-07T07:36:50.8093536Z If unspecified or None, the module parameter will be used in its place. 2025-09-07T07:36:50.8093626Z 2025-09-07T07:36:50.8093707Z Returns: 2025-09-07T07:36:50.8093926Z module (nn.Module): modified (i.e. pruned) version of the input module 2025-09-07T07:36:50.8097984Z 2025-09-07T07:36:50.8098087Z Examples: 2025-09-07T07:36:50.8098236Z >>> from torch.nn.utils import prune 2025-09-07T07:36:50.8098343Z >>> m = prune.ln_structured( 2025-09-07T07:36:50.8098537Z ... nn.Conv2d(5, 3, 2), "weight", amount=0.3, dim=1, n=float("-inf") 2025-09-07T07:36:50.8098636Z ... ) 2025-09-07T07:36:50.8098717Z 2025-09-07T07:36:50.8098995Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.8099073Z 2025-09-07T07:36:50.8099171Z warnings.warn(msg) 2025-09-07T07:36:50.8099262Z 2025-09-07T07:36:50.8099498Z --- Parse Warning: 117 / 146 --- 2025-09-07T07:36:50.8100436Z /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=1026. 2025-09-07T07:36:50.8100699Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.8100850Z 2025-09-07T07:36:50.8101295Z Globally prunes tensors corresponding to all parameters in ``parameters`` by applying the specified ``pruning_method``. 2025-09-07T07:36:50.8101376Z 2025-09-07T07:36:50.8101498Z Modifies modules in place by: 2025-09-07T07:36:50.8101576Z 2025-09-07T07:36:50.8101825Z 1) adding a named buffer called ``name+'_mask'`` corresponding to the 2025-09-07T07:36:50.8102055Z binary mask applied to the parameter ``name`` by the pruning method. 2025-09-07T07:36:50.8102263Z 2) replacing the parameter ``name`` by its pruned version, while the 2025-09-07T07:36:50.8102480Z original (unpruned) parameter is stored in a new parameter named 2025-09-07T07:36:50.8102572Z ``name+'_orig'``. 2025-09-07T07:36:50.8102651Z 2025-09-07T07:36:50.8102747Z Args: 2025-09-07T07:36:50.8102945Z parameters (Iterable of (module, name) tuples): parameters of 2025-09-07T07:36:50.8103144Z the model to prune in a global fashion, i.e. by aggregating all 2025-09-07T07:36:50.8103349Z weights prior to deciding which ones to prune. module must be of 2025-09-07T07:36:50.8103500Z type :class:`nn.Module`, and name must be a string. 2025-09-07T07:36:50.8103762Z pruning_method (function): a valid pruning function from this module, 2025-09-07T07:36:50.8103938Z or a custom one implemented by the user that satisfies the 2025-09-07T07:36:50.8104176Z implementation guidelines and has ``PRUNING_TYPE='unstructured'``. 2025-09-07T07:36:50.8104404Z importance_scores (dict): a dictionary mapping (module, name) tuples to 2025-09-07T07:36:50.8104623Z the corresponding parameter's importance scores tensor. The tensor 2025-09-07T07:36:50.8104841Z should be the same shape as the parameter, and is used for computing 2025-09-07T07:36:50.8104942Z mask for pruning. 2025-09-07T07:36:50.8105165Z If unspecified or None, the parameter will be used in place of its 2025-09-07T07:36:50.8105264Z importance scores. 2025-09-07T07:36:50.8105392Z kwargs: other keyword arguments such as: 2025-09-07T07:36:50.8105597Z amount (int or float): quantity of parameters to prune across the 2025-09-07T07:36:50.8105702Z specified parameters. 2025-09-07T07:36:50.8105920Z If ``float``, should be between 0.0 and 1.0 and represent the 2025-09-07T07:36:50.8106115Z fraction of parameters to prune. If ``int``, it represents the 2025-09-07T07:36:50.8106240Z absolute number of parameters to prune. 2025-09-07T07:36:50.8106325Z 2025-09-07T07:36:50.8106404Z Raises: 2025-09-07T07:36:50.8106561Z TypeError: if ``PRUNING_TYPE != 'unstructured'`` 2025-09-07T07:36:50.8106640Z 2025-09-07T07:36:50.8106719Z Note: 2025-09-07T07:36:50.8106945Z Since global structured pruning doesn't make much sense unless the 2025-09-07T07:36:50.8107143Z norm is normalized by the size of the parameter, we now limit the 2025-09-07T07:36:50.8107302Z scope of global pruning to unstructured methods. 2025-09-07T07:36:50.8107380Z 2025-09-07T07:36:50.8107462Z Examples: 2025-09-07T07:36:50.8107593Z >>> from torch.nn.utils import prune 2025-09-07T07:36:50.8107713Z >>> from collections import OrderedDict 2025-09-07T07:36:50.8107827Z >>> net = nn.Sequential( 2025-09-07T07:36:50.8107920Z ... OrderedDict( 2025-09-07T07:36:50.8108003Z ... [ 2025-09-07T07:36:50.8108133Z ... ("first", nn.Linear(10, 4)), 2025-09-07T07:36:50.8108250Z ... ("second", nn.Linear(4, 1)), 2025-09-07T07:36:50.8108331Z ... ] 2025-09-07T07:36:50.8108423Z ... ) 2025-09-07T07:36:50.8108503Z ... ) 2025-09-07T07:36:50.8108620Z >>> parameters_to_prune = ( 2025-09-07T07:36:50.8108720Z ... (net.first, "weight"), 2025-09-07T07:36:50.8108817Z ... (net.second, "weight"), 2025-09-07T07:36:50.8108935Z ... ) 2025-09-07T07:36:50.8109044Z >>> prune.global_unstructured( 2025-09-07T07:36:50.8109156Z ... parameters_to_prune, 2025-09-07T07:36:50.8109287Z ... pruning_method=prune.L1Unstructured, 2025-09-07T07:36:50.8109378Z ... amount=10, 2025-09-07T07:36:50.8109496Z ... ) 2025-09-07T07:36:50.8109718Z >>> print(sum(torch.nn.utils.parameters_to_vector(net.buffers()) == 0)) 2025-09-07T07:36:50.8109818Z tensor(10) 2025-09-07T07:36:50.8109898Z 2025-09-07T07:36:50.8109977Z 2025-09-07T07:36:50.8110245Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.8110323Z 2025-09-07T07:36:50.8110418Z warnings.warn(msg) 2025-09-07T07:36:50.8110510Z 2025-09-07T07:36:50.8110710Z --- Parse Warning: 118 / 146 --- 2025-09-07T07:36:50.8111613Z /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=1149. 2025-09-07T07:36:50.8111873Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.8112324Z Prune tensor corresponding to parameter called ``name`` in ``module`` by applying the pre-computed mask in ``mask``. 2025-09-07T07:36:50.8112405Z 2025-09-07T07:36:50.8112613Z Modifies module in place (and also return the modified module) by: 2025-09-07T07:36:50.8112700Z 2025-09-07T07:36:50.8112906Z 1) adding a named buffer called ``name+'_mask'`` corresponding to the 2025-09-07T07:36:50.8113135Z binary mask applied to the parameter ``name`` by the pruning method. 2025-09-07T07:36:50.8113344Z 2) replacing the parameter ``name`` by its pruned version, while the 2025-09-07T07:36:50.8113552Z original (unpruned) parameter is stored in a new parameter named 2025-09-07T07:36:50.8113661Z ``name+'_orig'``. 2025-09-07T07:36:50.8113740Z 2025-09-07T07:36:50.8113822Z Args: 2025-09-07T07:36:50.8114009Z module (nn.Module): module containing the tensor to prune 2025-09-07T07:36:50.8114190Z name (str): parameter name within ``module`` on which pruning 2025-09-07T07:36:50.8114291Z will act. 2025-09-07T07:36:50.8114593Z mask (Tensor): binary mask to be applied to the parameter. 2025-09-07T07:36:50.8114675Z 2025-09-07T07:36:50.8114772Z Returns: 2025-09-07T07:36:50.8114990Z module (nn.Module): modified (i.e. pruned) version of the input module 2025-09-07T07:36:50.8115078Z 2025-09-07T07:36:50.8115164Z Examples: 2025-09-07T07:36:50.8115286Z >>> from torch.nn.utils import prune 2025-09-07T07:36:50.8115406Z >>> m = prune.custom_from_mask( 2025-09-07T07:36:50.8115573Z ... nn.Linear(5, 3), name="bias", mask=torch.tensor([0, 1, 0]) 2025-09-07T07:36:50.8115665Z ... ) 2025-09-07T07:36:50.8115765Z >>> print(m.bias_mask) 2025-09-07T07:36:50.8115859Z tensor([0., 1., 0.]) 2025-09-07T07:36:50.8115948Z 2025-09-07T07:36:50.8116027Z 2025-09-07T07:36:50.8116293Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.8116373Z 2025-09-07T07:36:50.8116470Z warnings.warn(msg) 2025-09-07T07:36:50.8116562Z 2025-09-07T07:36:50.8116761Z --- Parse Warning: 119 / 146 --- 2025-09-07T07:36:50.8117665Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=pad_packed_sequence in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/rnn.py line=350. 2025-09-07T07:36:50.8117924Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.8118068Z Pad a packed batch of variable length sequences. 2025-09-07T07:36:50.8118158Z 2025-09-07T07:36:50.8118358Z It is an inverse operation to :func:`pack_padded_sequence`. 2025-09-07T07:36:50.8118449Z 2025-09-07T07:36:50.8118732Z The returned Tensor's data will be of size ``T x B x *`` (if :attr:`batch_first` is ``False``) 2025-09-07T07:36:50.8118975Z or ``B x T x *`` (if :attr:`batch_first` is ``True``) , where ``T`` is the length of the longest 2025-09-07T07:36:50.8119128Z sequence and ``B`` is the batch size. 2025-09-07T07:36:50.8119206Z 2025-09-07T07:36:50.8119307Z Example: 2025-09-07T07:36:50.8119547Z >>> from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence 2025-09-07T07:36:50.8119693Z >>> seq = torch.tensor([[1, 2, 0], [3, 0, 0], [4, 5, 6]]) 2025-09-07T07:36:50.8119800Z >>> lens = [2, 1, 3] 2025-09-07T07:36:50.8119917Z >>> packed = pack_padded_sequence( 2025-09-07T07:36:50.8120080Z ... seq, lens, batch_first=True, enforce_sorted=False 2025-09-07T07:36:50.8120159Z ... ) 2025-09-07T07:36:50.8120248Z >>> packed 2025-09-07T07:36:50.8120502Z PackedSequence(data=tensor([4, 1, 3, 5, 2, 6]), batch_sizes=tensor([3, 2, 1]), 2025-09-07T07:36:50.8120740Z sorted_indices=tensor([2, 0, 1]), unsorted_indices=tensor([1, 2, 0])) 2025-09-07T07:36:50.8120999Z >>> seq_unpacked, lens_unpacked = pad_packed_sequence(packed, batch_first=True) 2025-09-07T07:36:50.8121091Z >>> seq_unpacked 2025-09-07T07:36:50.8121184Z tensor([[1, 2, 0], 2025-09-07T07:36:50.8121281Z [3, 0, 0], 2025-09-07T07:36:50.8121367Z [4, 5, 6]]) 2025-09-07T07:36:50.8121469Z >>> lens_unpacked 2025-09-07T07:36:50.8121557Z tensor([2, 1, 3]) 2025-09-07T07:36:50.8121636Z 2025-09-07T07:36:50.8121736Z .. note:: 2025-09-07T07:36:50.8121881Z :attr:`total_length` is useful to implement the 2025-09-07T07:36:50.8122106Z ``pack sequence -> recurrent network -> unpack sequence`` pattern in a 2025-09-07T07:36:50.8122331Z :class:`~torch.nn.Module` wrapped in :class:`~torch.nn.DataParallel`. 2025-09-07T07:36:50.8122561Z See :ref:`this FAQ section ` for 2025-09-07T07:36:50.8122660Z details. 2025-09-07T07:36:50.8122740Z 2025-09-07T07:36:50.8122848Z Args: 2025-09-07T07:36:50.8122990Z sequence (PackedSequence): batch to pad 2025-09-07T07:36:50.8123224Z batch_first (bool, optional): if ``True``, the output will be in ``B x T x *`` 2025-09-07T07:36:50.8123349Z format, ``T x B x *`` otherwise. 2025-09-07T07:36:50.8123536Z padding_value (float, optional): values for padded elements. 2025-09-07T07:36:50.8123985Z total_length (int, optional): if not ``None``, the output will be padded to 2025-09-07T07:36:50.8124239Z have length :attr:`total_length`. This method will throw :class:`ValueError` 2025-09-07T07:36:50.8124426Z if :attr:`total_length` is less than the max sequence length in 2025-09-07T07:36:50.8124541Z :attr:`sequence`. 2025-09-07T07:36:50.8124621Z 2025-09-07T07:36:50.8124706Z Returns: 2025-09-07T07:36:50.8124907Z Tuple of Tensor containing the padded sequence, and a Tensor 2025-09-07T07:36:50.8125106Z containing the list of lengths of each sequence in the batch. 2025-09-07T07:36:50.8125343Z Batch elements will be re-ordered as they were ordered originally when 2025-09-07T07:36:50.8125559Z the batch was passed to ``pack_padded_sequence`` or ``pack_sequence``. 2025-09-07T07:36:50.8125640Z 2025-09-07T07:36:50.8125906Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.8125985Z 2025-09-07T07:36:50.8126093Z warnings.warn(msg) 2025-09-07T07:36:50.8126172Z 2025-09-07T07:36:50.8126373Z --- Parse Warning: 120 / 146 --- 2025-09-07T07:36:50.8127368Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=SequentialLR in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py line=811. 2025-09-07T07:36:50.8127626Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.8128019Z Contains a list of schedulers expected to be called sequentially during the optimization process. 2025-09-07T07:36:50.8128099Z 2025-09-07T07:36:50.8128484Z Specifically, the schedulers will be called according to the milestone points, which should provide exact 2025-09-07T07:36:50.8128704Z intervals by which each scheduler should be called at a given epoch. 2025-09-07T07:36:50.8128808Z 2025-09-07T07:36:50.8128905Z Args: 2025-09-07T07:36:50.8129040Z optimizer (Optimizer): Wrapped optimizer. 2025-09-07T07:36:50.8129183Z schedulers (list): List of chained schedulers. 2025-09-07T07:36:50.8129409Z milestones (list): List of integers that reflects milestone points. 2025-09-07T07:36:50.8129572Z last_epoch (int): The index of last epoch. Default: -1. 2025-09-07T07:36:50.8129702Z 2025-09-07T07:36:50.8129786Z Example: 2025-09-07T07:36:50.8129886Z >>> # xdoctest: +SKIP 2025-09-07T07:36:50.8130055Z >>> # Assuming optimizer uses lr = 0.05 for all groups 2025-09-07T07:36:50.8130159Z >>> # lr = 0.005 if epoch == 0 2025-09-07T07:36:50.8130276Z >>> # lr = 0.005 if epoch == 1 2025-09-07T07:36:50.8130378Z >>> # lr = 0.005 if epoch == 2 2025-09-07T07:36:50.8130460Z >>> # ... 2025-09-07T07:36:50.8130579Z >>> # lr = 0.05 if epoch == 20 2025-09-07T07:36:50.8130683Z >>> # lr = 0.045 if epoch == 21 2025-09-07T07:36:50.8130798Z >>> # lr = 0.0405 if epoch == 22 2025-09-07T07:36:50.8131000Z >>> scheduler1 = ConstantLR(optimizer, factor=0.1, total_iters=20) 2025-09-07T07:36:50.8131157Z >>> scheduler2 = ExponentialLR(optimizer, gamma=0.9) 2025-09-07T07:36:50.8131279Z >>> scheduler = SequentialLR( 2025-09-07T07:36:50.8131373Z ... optimizer, 2025-09-07T07:36:50.8131521Z ... schedulers=[scheduler1, scheduler2], 2025-09-07T07:36:50.8131671Z ... milestones=[20], 2025-09-07T07:36:50.8131753Z ... ) 2025-09-07T07:36:50.8131870Z >>> for epoch in range(100): 2025-09-07T07:36:50.8131962Z >>> train(...) 2025-09-07T07:36:50.8132067Z >>> validate(...) 2025-09-07T07:36:50.8132165Z >>> scheduler.step() 2025-09-07T07:36:50.8132244Z 2025-09-07T07:36:50.8132434Z .. image:: ../scripts/lr_scheduler_images/SequentialLR.png 2025-09-07T07:36:50.8132512Z 2025-09-07T07:36:50.8132774Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.8132854Z 2025-09-07T07:36:50.8132949Z warnings.warn(msg) 2025-09-07T07:36:50.8133042Z 2025-09-07T07:36:50.8133233Z --- Parse Warning: 121 / 146 --- 2025-09-07T07:36:50.8134188Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ReduceLROnPlateau in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py line=1236. 2025-09-07T07:36:50.8134434Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-09-07T07:36:50.8134614Z Reduce learning rate when a metric has stopped improving. 2025-09-07T07:36:50.8134709Z 2025-09-07T07:36:50.8134908Z Models often benefit from reducing the learning rate by a factor 2025-09-07T07:36:50.8135110Z of 2-10 once learning stagnates. This scheduler reads a metrics 2025-09-07T07:36:50.8135302Z quantity and if no improvement is seen for a 'patience' number 2025-09-07T07:36:50.8135472Z of epochs, the learning rate is reduced. 2025-09-07T07:36:50.8135562Z 2025-09-07T07:36:50.8135646Z Args: 2025-09-07T07:36:50.8135789Z optimizer (Optimizer): Wrapped optimizer. 2025-09-07T07:36:50.8135946Z mode (str): One of `min`, `max`. In `min` mode, lr will 2025-09-07T07:36:50.8136102Z be reduced when the quantity monitored has stopped 2025-09-07T07:36:50.8136597Z decreasing; in `max` mode it will be reduced when the 2025-09-07T07:36:50.8136786Z quantity monitored has stopped increasing. Default: 'min'. 2025-09-07T07:36:50.8136968Z factor (float): Factor by which the learning rate will be 2025-09-07T07:36:50.8137103Z reduced. new_lr = lr * factor. Default: 0.1. 2025-09-07T07:36:50.8137323Z patience (int): The number of allowed epochs with no improvement after 2025-09-07T07:36:50.8137462Z which the learning rate will be reduced. 2025-09-07T07:36:50.8137688Z For example, consider the case of having no patience (`patience = 0`). 2025-09-07T07:36:50.8138066Z In the first epoch, a baseline is established and is always considered good as there's no previous baseline. 2025-09-07T07:36:50.8138298Z In the second epoch, if the performance is worse than the baseline, 2025-09-07T07:36:50.8138448Z we have what is considered an intolerable epoch. 2025-09-07T07:36:50.8138725Z Since the count of intolerable epochs (1) is greater than the patience level (0), 2025-09-07T07:36:50.8138894Z the learning rate is reduced at the end of this epoch. 2025-09-07T07:36:50.8139227Z From the third epoch onwards, the learning rate continues to be reduced at the end of each epoch 2025-09-07T07:36:50.8139542Z if the performance is worse than the baseline. If the performance improves or remains the same, 2025-09-07T07:36:50.8139672Z the learning rate is not adjusted. 2025-09-07T07:36:50.8139998Z Default: 10. 2025-09-07T07:36:50.8140198Z threshold (float): Threshold for measuring the new optimum, 2025-09-07T07:36:50.8140370Z to only focus on significant changes. Default: 1e-4. 2025-09-07T07:36:50.8140542Z threshold_mode (str): One of `rel`, `abs`. In `rel` mode, 2025-09-07T07:36:50.8140752Z dynamic_threshold = best * ( 1 + threshold ) in 'max' 2025-09-07T07:36:50.8140893Z mode or best * ( 1 - threshold ) in `min` mode. 2025-09-07T07:36:50.8141046Z In `abs` mode, dynamic_threshold = best + threshold in 2025-09-07T07:36:50.8141236Z `max` mode or best - threshold in `min` mode. Default: 'rel'. 2025-09-07T07:36:50.8141405Z cooldown (int): Number of epochs to wait before resuming 2025-09-07T07:36:50.8141593Z normal operation after lr has been reduced. Default: 0. 2025-09-07T07:36:50.8141755Z min_lr (float or list): A scalar or a list of scalars. A 2025-09-07T07:36:50.8141918Z lower bound on the learning rate of all param groups 2025-09-07T07:36:50.8142063Z or each group respectively. Default: 0. 2025-09-07T07:36:50.8142238Z eps (float): Minimal decay applied to lr. If the difference 2025-09-07T07:36:50.8142430Z between new and old lr is smaller than eps, the update is 2025-09-07T07:36:50.8142535Z ignored. Default: 1e-8. 2025-09-07T07:36:50.8142615Z 2025-09-07T07:36:50.8142712Z Example: 2025-09-07T07:36:50.8142810Z >>> # xdoctest: +SKIP 2025-09-07T07:36:50.8143050Z >>> optimizer = torch.optim.SGD(model.parameters(), lr=0.1, momentum=0.9) 2025-09-07T07:36:50.8143207Z >>> scheduler = ReduceLROnPlateau(optimizer, "min") 2025-09-07T07:36:50.8143310Z >>> for epoch in range(10): 2025-09-07T07:36:50.8143415Z >>> train(...) 2025-09-07T07:36:50.8143523Z >>> val_loss = validate(...) 2025-09-07T07:36:50.8143716Z >>> # Note that step should be called after validate() 2025-09-07T07:36:50.8143828Z >>> scheduler.step(val_loss) 2025-09-07T07:36:50.8143907Z 2025-09-07T07:36:50.8144112Z .. image:: ../scripts/lr_scheduler_images/ReduceLROnPlateau.png 2025-09-07T07:36:50.8144234Z 2025-09-07T07:36:50.8144662Z Original Error: IndentationError('unexpected indent', ('', 8, 4, ' scheduler.step(val_loss)\n', 8, -1)) 2025-09-07T07:36:50.8144739Z 2025-09-07T07:36:50.8144843Z scheduler.step(val_loss) 2025-09-07T07:36:50.8144933Z ^ 2025-09-07T07:36:50.8145027Z warnings.warn(msg) 2025-09-07T07:36:50.8145105Z 2025-09-07T07:36:50.8145346Z --- Parse Warning: 122 / 146 --- 2025-09-07T07:36:50.8146231Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=CyclicLR in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py line=1433. 2025-09-07T07:36:50.8146506Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.8146831Z Sets the learning rate of each parameter group according to cyclical learning rate policy (CLR). 2025-09-07T07:36:50.8146949Z 2025-09-07T07:36:50.8147236Z The policy cycles the learning rate between two boundaries with a constant frequency, 2025-09-07T07:36:50.8147490Z as detailed in the paper `Cyclical Learning Rates for Training Neural Networks`_. 2025-09-07T07:36:50.8147735Z The distance between the two boundaries can be scaled on a per-iteration 2025-09-07T07:36:50.8147831Z or per-cycle basis. 2025-09-07T07:36:50.8147925Z 2025-09-07T07:36:50.8148167Z Cyclical learning rate policy changes the learning rate after every batch. 2025-09-07T07:36:50.8148361Z `step` should be called after a batch has been used for training. 2025-09-07T07:36:50.8148454Z 2025-09-07T07:36:50.8148659Z This class has three built-in policies, as put forth in the paper: 2025-09-07T07:36:50.8148750Z 2025-09-07T07:36:50.8148952Z * "triangular": A basic triangular cycle without amplitude scaling. 2025-09-07T07:36:50.8149253Z * "triangular2": A basic triangular cycle that scales initial amplitude by half each cycle. 2025-09-07T07:36:50.8149616Z * "exp_range": A cycle that scales initial amplitude by :math:`\text{gamma}^{\text{cycle iterations}}` 2025-09-07T07:36:50.8149718Z at each cycle iteration. 2025-09-07T07:36:50.8149808Z 2025-09-07T07:36:50.8150046Z This implementation was adapted from the github repo: `bckenstler/CLR`_ 2025-09-07T07:36:50.8150124Z 2025-09-07T07:36:50.8150219Z Args: 2025-09-07T07:36:50.8150355Z optimizer (Optimizer): Wrapped optimizer. 2025-09-07T07:36:50.8150542Z base_lr (float or list): Initial learning rate which is the 2025-09-07T07:36:50.8150710Z lower boundary in the cycle for each parameter group. 2025-09-07T07:36:50.8150901Z max_lr (float or list): Upper learning rate boundaries in the cycle 2025-09-07T07:36:50.8151048Z for each parameter group. Functionally, 2025-09-07T07:36:50.8151203Z it defines the cycle amplitude (max_lr - base_lr). 2025-09-07T07:36:50.8151349Z The lr at any cycle is the sum of base_lr 2025-09-07T07:36:50.8151485Z and some scaling of the amplitude; therefore 2025-09-07T07:36:50.8151629Z max_lr may not actually be reached depending on 2025-09-07T07:36:50.8151738Z scaling function. 2025-09-07T07:36:50.8151903Z step_size_up (int): Number of training iterations in the 2025-09-07T07:36:50.8152044Z increasing half of a cycle. Default: 2000 2025-09-07T07:36:50.8152216Z step_size_down (int): Number of training iterations in the 2025-09-07T07:36:50.8152407Z decreasing half of a cycle. If step_size_down is None, 2025-09-07T07:36:50.8152545Z it is set to step_size_up. Default: None 2025-09-07T07:36:50.8152707Z mode (str): One of {triangular, triangular2, exp_range}. 2025-09-07T07:36:50.8152864Z Values correspond to policies detailed above. 2025-09-07T07:36:50.8153039Z If scale_fn is not None, this argument is ignored. 2025-09-07T07:36:50.8153143Z Default: 'triangular' 2025-09-07T07:36:50.8153320Z gamma (float): Constant in 'exp_range' scaling function: 2025-09-07T07:36:50.8153425Z gamma**(cycle iterations) 2025-09-07T07:36:50.8153531Z Default: 1.0 2025-09-07T07:36:50.8153717Z scale_fn (function): Custom scaling policy defined by a single 2025-09-07T07:36:50.8153831Z argument lambda function, where 2025-09-07T07:36:50.8153957Z 0 <= scale_fn(x) <= 1 for all x >= 0. 2025-09-07T07:36:50.8154077Z If specified, then 'mode' is ignored. 2025-09-07T07:36:50.8154182Z Default: None 2025-09-07T07:36:50.8154308Z scale_mode (str): {'cycle', 'iterations'}. 2025-09-07T07:36:50.8154539Z Defines whether scale_fn is evaluated on 2025-09-07T07:36:50.8154722Z cycle number or cycle iterations (training 2025-09-07T07:36:50.8154846Z iterations since start of cycle). 2025-09-07T07:36:50.8154953Z Default: 'cycle' 2025-09-07T07:36:50.8155147Z cycle_momentum (bool): If ``True``, momentum is cycled inversely 2025-09-07T07:36:50.8155329Z to learning rate between 'base_momentum' and 'max_momentum'. 2025-09-07T07:36:50.8155434Z Default: True 2025-09-07T07:36:50.8155647Z base_momentum (float or list): Lower momentum boundaries in the cycle 2025-09-07T07:36:50.8155863Z for each parameter group. Note that momentum is cycled inversely 2025-09-07T07:36:50.8156025Z to learning rate; at the peak of a cycle, momentum is 2025-09-07T07:36:50.8156163Z 'base_momentum' and learning rate is 'max_lr'. 2025-09-07T07:36:50.8156266Z Default: 0.8 2025-09-07T07:36:50.8156476Z max_momentum (float or list): Upper momentum boundaries in the cycle 2025-09-07T07:36:50.8156642Z for each parameter group. Functionally, 2025-09-07T07:36:50.8156837Z it defines the cycle amplitude (max_momentum - base_momentum). 2025-09-07T07:36:50.8157016Z The momentum at any cycle is the difference of max_momentum 2025-09-07T07:36:50.8157163Z and some scaling of the amplitude; therefore 2025-09-07T07:36:50.8157330Z base_momentum may not actually be reached depending on 2025-09-07T07:36:50.8157518Z scaling function. Note that momentum is cycled inversely 2025-09-07T07:36:50.8157728Z to learning rate; at the start of a cycle, momentum is 'max_momentum' 2025-09-07T07:36:50.8157857Z and learning rate is 'base_lr' 2025-09-07T07:36:50.8157947Z Default: 0.9 2025-09-07T07:36:50.8158170Z last_epoch (int): The index of the last batch. This parameter is used when 2025-09-07T07:36:50.8158402Z resuming a training job. Since `step()` should be invoked after each 2025-09-07T07:36:50.8158617Z batch instead of after each epoch, this number represents the total 2025-09-07T07:36:50.8158847Z number of *batches* computed, not the total number of epochs computed. 2025-09-07T07:36:50.8159039Z When last_epoch=-1, the schedule is started from the beginning. 2025-09-07T07:36:50.8159131Z Default: -1 2025-09-07T07:36:50.8159222Z 2025-09-07T07:36:50.8159307Z Example: 2025-09-07T07:36:50.8159417Z >>> # xdoctest: +SKIP 2025-09-07T07:36:50.8159643Z >>> optimizer = torch.optim.SGD(model.parameters(), lr=0.1, momentum=0.9) 2025-09-07T07:36:50.8159819Z >>> scheduler = torch.optim.lr_scheduler.CyclicLR( 2025-09-07T07:36:50.8159932Z ... optimizer, 2025-09-07T07:36:50.8160023Z ... base_lr=0.01, 2025-09-07T07:36:50.8160113Z ... max_lr=0.1, 2025-09-07T07:36:50.8160221Z ... step_size_up=10, 2025-09-07T07:36:50.8160325Z ... ) 2025-09-07T07:36:50.8160485Z >>> data_loader = torch.utils.data.DataLoader(...) 2025-09-07T07:36:50.8160585Z >>> for epoch in range(10): 2025-09-07T07:36:50.8160691Z >>> for batch in data_loader: 2025-09-07T07:36:50.8160802Z >>> train_batch(...) 2025-09-07T07:36:50.8160902Z >>> scheduler.step() 2025-09-07T07:36:50.8160994Z 2025-09-07T07:36:50.8161147Z .. image:: ../scripts/lr_scheduler_images/CyclicLR.png 2025-09-07T07:36:50.8161225Z 2025-09-07T07:36:50.8161548Z .. _Cyclical Learning Rates for Training Neural Networks: https://arxiv.org/abs/1506.01186 2025-09-07T07:36:50.8161714Z .. _bckenstler/CLR: https://github.com/bckenstler/CLR 2025-09-07T07:36:50.8161808Z 2025-09-07T07:36:50.8162058Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.8162163Z 2025-09-07T07:36:50.8162271Z warnings.warn(msg) 2025-09-07T07:36:50.8162348Z 2025-09-07T07:36:50.8162572Z --- Parse Warning: 123 / 146 --- 2025-09-07T07:36:50.8163564Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=CosineAnnealingWarmRestarts in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py line=1725. 2025-09-07T07:36:50.8163824Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.8164087Z Set the learning rate of each parameter group using a cosine annealing schedule. 2025-09-07T07:36:50.8164163Z 2025-09-07T07:36:50.8164354Z The :math:`\eta_{max}` is set to the initial lr, :math:`T_{cur}` 2025-09-07T07:36:50.8164584Z is the number of epochs since the last restart and :math:`T_{i}` is the number 2025-09-07T07:36:50.8164717Z of epochs between two warm restarts in SGDR: 2025-09-07T07:36:50.8164809Z 2025-09-07T07:36:50.8164894Z .. math:: 2025-09-07T07:36:50.8165113Z \eta_t = \eta_{min} + \frac{1}{2}(\eta_{max} - \eta_{min})\left(1 + 2025-09-07T07:36:50.8165248Z \cos\left(\frac{T_{cur}}{T_{i}}\pi\right)\right) 2025-09-07T07:36:50.8165324Z 2025-09-07T07:36:50.8165504Z When :math:`T_{cur}=T_{i}`, set :math:`\eta_t = \eta_{min}`. 2025-09-07T07:36:50.8165686Z When :math:`T_{cur}=0` after restart, set :math:`\eta_t=\eta_{max}`. 2025-09-07T07:36:50.8165777Z 2025-09-07T07:36:50.8165875Z It has been proposed in 2025-09-07T07:36:50.8166044Z `SGDR: Stochastic Gradient Descent with Warm Restarts`_. 2025-09-07T07:36:50.8166135Z 2025-09-07T07:36:50.8166217Z Args: 2025-09-07T07:36:50.8166366Z optimizer (Optimizer): Wrapped optimizer. 2025-09-07T07:36:50.8166518Z T_0 (int): Number of iterations until the first restart. 2025-09-07T07:36:50.8166803Z T_mult (int, optional): A factor by which :math:`T_{i}` increases after a restart. Default: 1. 2025-09-07T07:36:50.8167000Z eta_min (float, optional): Minimum learning rate. Default: 0. 2025-09-07T07:36:50.8167210Z last_epoch (int, optional): The index of the last epoch. Default: -1. 2025-09-07T07:36:50.8167296Z 2025-09-07T07:36:50.8167459Z .. _SGDR\: Stochastic Gradient Descent with Warm Restarts: 2025-09-07T07:36:50.8167574Z https://arxiv.org/abs/1608.03983 2025-09-07T07:36:50.8167659Z 2025-09-07T07:36:50.8167741Z Example: 2025-09-07T07:36:50.8167837Z >>> # xdoctest: +SKIP 2025-09-07T07:36:50.8168024Z >>> optimizer = torch.optim.SGD(model.parameters(), lr=0.05) 2025-09-07T07:36:50.8168273Z >>> scheduler = torch.optim.lr_scheduler.CosineAnnealingWarmRestarts( 2025-09-07T07:36:50.8168381Z ... optimizer, T_0=20 2025-09-07T07:36:50.8168460Z ... ) 2025-09-07T07:36:50.8168565Z >>> for epoch in range(100): 2025-09-07T07:36:50.8168662Z >>> train(...) 2025-09-07T07:36:50.8168791Z >>> validate(...) 2025-09-07T07:36:50.8168894Z >>> scheduler.step() 2025-09-07T07:36:50.8168967Z 2025-09-07T07:36:50.8169202Z .. image:: ../scripts/lr_scheduler_images/CosineAnnealingWarmRestarts.png 2025-09-07T07:36:50.8169288Z 2025-09-07T07:36:50.8169536Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.8169628Z 2025-09-07T07:36:50.8169719Z warnings.warn(msg) 2025-09-07T07:36:50.8169794Z 2025-09-07T07:36:50.8170000Z --- Parse Warning: 124 / 146 --- 2025-09-07T07:36:50.8170895Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=OneCycleLR in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py line=1875. 2025-09-07T07:36:50.8171165Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.8171499Z Sets the learning rate of each parameter group according to the 1cycle learning rate policy. 2025-09-07T07:36:50.8171579Z 2025-09-07T07:36:50.8171882Z The 1cycle policy anneals the learning rate from an initial learning rate to some maximum 2025-09-07T07:36:50.8172172Z learning rate and then from that maximum learning rate to some minimum learning rate much 2025-09-07T07:36:50.8172297Z lower than the initial learning rate. 2025-09-07T07:36:50.8172518Z This policy was initially described in the paper `Super-Convergence: 2025-09-07T07:36:50.8172740Z Very Fast Training of Neural Networks Using Large Learning Rates`_. 2025-09-07T07:36:50.8172815Z 2025-09-07T07:36:50.8173063Z The 1cycle learning rate policy changes the learning rate after every batch. 2025-09-07T07:36:50.8173265Z `step` should be called after a batch has been used for training. 2025-09-07T07:36:50.8173342Z 2025-09-07T07:36:50.8173453Z This scheduler is not chainable. 2025-09-07T07:36:50.8173540Z 2025-09-07T07:36:50.8173802Z Note also that the total number of steps in the cycle can be determined in one 2025-09-07T07:36:50.8173946Z of two ways (listed in order of precedence): 2025-09-07T07:36:50.8174064Z 2025-09-07T07:36:50.8174206Z #. A value for total_steps is explicitly provided. 2025-09-07T07:36:50.8174397Z #. A number of epochs (epochs) and a number of steps per epoch 2025-09-07T07:36:50.8174508Z (steps_per_epoch) are provided. 2025-09-07T07:36:50.8174671Z In this case, the number of total steps is inferred by 2025-09-07T07:36:50.8174792Z total_steps = epochs * steps_per_epoch 2025-09-07T07:36:50.8174870Z 2025-09-07T07:36:50.8175113Z You must either provide a value for total_steps or provide a value for both 2025-09-07T07:36:50.8175215Z epochs and steps_per_epoch. 2025-09-07T07:36:50.8175302Z 2025-09-07T07:36:50.8175604Z The default behaviour of this scheduler follows the fastai implementation of 1cycle, which 2025-09-07T07:36:50.8175900Z claims that "unpublished work has shown even better results by using only two phases". To 2025-09-07T07:36:50.8176150Z mimic the behaviour of the original paper instead, set ``three_phase=True``. 2025-09-07T07:36:50.8176229Z 2025-09-07T07:36:50.8176319Z Args: 2025-09-07T07:36:50.8176453Z optimizer (Optimizer): Wrapped optimizer. 2025-09-07T07:36:50.8176649Z max_lr (float or list): Upper learning rate boundaries in the cycle 2025-09-07T07:36:50.8176769Z for each parameter group. 2025-09-07T07:36:50.8177005Z total_steps (int): The total number of steps in the cycle. Note that 2025-09-07T07:36:50.8177221Z if a value is not provided here, then it must be inferred by providing 2025-09-07T07:36:50.8177350Z a value for epochs and steps_per_epoch. 2025-09-07T07:36:50.8177444Z Default: None 2025-09-07T07:36:50.8177653Z epochs (int): The number of epochs to train for. This is used along 2025-09-07T07:36:50.8177920Z with steps_per_epoch in order to infer the total number of steps in the cycle 2025-09-07T07:36:50.8178063Z if a value for total_steps is not provided. 2025-09-07T07:36:50.8178153Z Default: None 2025-09-07T07:36:50.8178375Z steps_per_epoch (int): The number of steps per epoch to train for. This is 2025-09-07T07:36:50.8178611Z used along with epochs in order to infer the total number of steps in the 2025-09-07T07:36:50.8178758Z cycle if a value for total_steps is not provided. 2025-09-07T07:36:50.8178859Z Default: None 2025-09-07T07:36:50.8179083Z pct_start (float): The percentage of the cycle (in number of steps) spent 2025-09-07T07:36:50.8179209Z increasing the learning rate. 2025-09-07T07:36:50.8179323Z Default: 0.3 2025-09-07T07:36:50.8179446Z anneal_strategy (str): {'cos', 'linear'} 2025-09-07T07:36:50.8179701Z Specifies the annealing strategy: "cos" for cosine annealing, "linear" for 2025-09-07T07:36:50.8179797Z linear annealing. 2025-09-07T07:36:50.8179890Z Default: 'cos' 2025-09-07T07:36:50.8180094Z cycle_momentum (bool): If ``True``, momentum is cycled inversely 2025-09-07T07:36:50.8180275Z to learning rate between 'base_momentum' and 'max_momentum'. 2025-09-07T07:36:50.8180378Z Default: True 2025-09-07T07:36:50.8180591Z base_momentum (float or list): Lower momentum boundaries in the cycle 2025-09-07T07:36:50.8180805Z for each parameter group. Note that momentum is cycled inversely 2025-09-07T07:36:50.8180964Z to learning rate; at the peak of a cycle, momentum is 2025-09-07T07:36:50.8181103Z 'base_momentum' and learning rate is 'max_lr'. 2025-09-07T07:36:50.8181203Z Default: 0.85 2025-09-07T07:36:50.8181443Z max_momentum (float or list): Upper momentum boundaries in the cycle 2025-09-07T07:36:50.8181583Z for each parameter group. Functionally, 2025-09-07T07:36:50.8181773Z it defines the cycle amplitude (max_momentum - base_momentum). 2025-09-07T07:36:50.8181895Z Note that momentum is cycled inversely 2025-09-07T07:36:50.8182110Z to learning rate; at the start of a cycle, momentum is 'max_momentum' 2025-09-07T07:36:50.8182220Z and learning rate is 'base_lr' 2025-09-07T07:36:50.8182318Z Default: 0.95 2025-09-07T07:36:50.8182497Z div_factor (float): Determines the initial learning rate via 2025-09-07T07:36:50.8182608Z initial_lr = max_lr/div_factor 2025-09-07T07:36:50.8182704Z Default: 25 2025-09-07T07:36:50.8182907Z final_div_factor (float): Determines the minimum learning rate via 2025-09-07T07:36:50.8183038Z min_lr = initial_lr/final_div_factor 2025-09-07T07:36:50.8183130Z Default: 1e4 2025-09-07T07:36:50.8183370Z three_phase (bool): If ``True``, use a third phase of the schedule to annihilate the 2025-09-07T07:36:50.8183631Z learning rate according to 'final_div_factor' instead of modifying the second 2025-09-07T07:36:50.8183868Z phase (the first two phases will be symmetrical about the step indicated by 2025-09-07T07:36:50.8183969Z 'pct_start'). 2025-09-07T07:36:50.8184181Z last_epoch (int): The index of the last batch. This parameter is used when 2025-09-07T07:36:50.8184416Z resuming a training job. Since `step()` should be invoked after each 2025-09-07T07:36:50.8184638Z batch instead of after each epoch, this number represents the total 2025-09-07T07:36:50.8184856Z number of *batches* computed, not the total number of epochs computed. 2025-09-07T07:36:50.8185060Z When last_epoch=-1, the schedule is started from the beginning. 2025-09-07T07:36:50.8185181Z Default: -1 2025-09-07T07:36:50.8185257Z 2025-09-07T07:36:50.8185349Z Example: 2025-09-07T07:36:50.8185444Z >>> # xdoctest: +SKIP 2025-09-07T07:36:50.8185608Z >>> data_loader = torch.utils.data.DataLoader(...) 2025-09-07T07:36:50.8185836Z >>> optimizer = torch.optim.SGD(model.parameters(), lr=1e-4, momentum=0.9) 2025-09-07T07:36:50.8185992Z >>> scheduler = torch.optim.lr_scheduler.OneCycleLR( 2025-09-07T07:36:50.8186208Z ... optimizer, max_lr=0.01, steps_per_epoch=len(data_loader), epochs=10 2025-09-07T07:36:50.8186289Z ... ) 2025-09-07T07:36:50.8186401Z >>> for epoch in range(10): 2025-09-07T07:36:50.8186507Z >>> for batch in data_loader: 2025-09-07T07:36:50.8186630Z >>> train_batch(...) 2025-09-07T07:36:50.8186743Z >>> optimizer.step() 2025-09-07T07:36:50.8186846Z >>> scheduler.step() 2025-09-07T07:36:50.8186938Z 2025-09-07T07:36:50.8187103Z .. image:: ../scripts/lr_scheduler_images/OneCycleLR.png 2025-09-07T07:36:50.8187179Z 2025-09-07T07:36:50.8187476Z .. _Super-Convergence\: Very Fast Training of Neural Networks Using Large Learning Rates: 2025-09-07T07:36:50.8187590Z https://arxiv.org/abs/1708.07120 2025-09-07T07:36:50.8187679Z 2025-09-07T07:36:50.8187927Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.8188006Z 2025-09-07T07:36:50.8188106Z warnings.warn(msg) 2025-09-07T07:36:50.8188182Z 2025-09-07T07:36:50.8188382Z --- Parse Warning: 125 / 146 --- 2025-09-07T07:36:50.8189324Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=Optimizer.load_state_dict in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/optimizer.py line=868. 2025-09-07T07:36:50.8189617Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.8189728Z Load the optimizer state. 2025-09-07T07:36:50.8189804Z 2025-09-07T07:36:50.8189896Z Args: 2025-09-07T07:36:50.8190091Z state_dict (dict): optimizer state. Should be an object returned 2025-09-07T07:36:50.8190212Z from a call to :meth:`state_dict`. 2025-09-07T07:36:50.8190296Z 2025-09-07T07:36:50.8190383Z .. warning:: 2025-09-07T07:36:50.8190719Z Make sure this method is called after initializing :class:`torch.optim.lr_scheduler.LRScheduler`, 2025-09-07T07:36:50.8190939Z as calling it beforehand will overwrite the loaded learning rates. 2025-09-07T07:36:50.8191019Z 2025-09-07T07:36:50.8191114Z .. note:: 2025-09-07T07:36:50.8191405Z The names of the parameters (if they exist under the "param_names" key of each param group 2025-09-07T07:36:50.8191593Z in :meth:`state_dict`) will not affect the loading process. 2025-09-07T07:36:50.8191918Z To use the parameters' names for custom cases (such as when the parameters in the loaded state dict 2025-09-07T07:36:50.8192074Z differ from those initialized in the optimizer), 2025-09-07T07:36:50.8192393Z a custom ``register_load_state_dict_pre_hook`` should be implemented to adapt the loaded dict 2025-09-07T07:36:50.8192484Z accordingly. 2025-09-07T07:36:50.8192785Z If ``param_names`` exist in loaded state dict ``param_groups`` they will be saved and override 2025-09-07T07:36:50.8193123Z the current names, if present, in the optimizer state. If they do not exist in loaded state dict, 2025-09-07T07:36:50.8193285Z the optimizer ``param_names`` will remain unchanged. 2025-09-07T07:36:50.8193376Z 2025-09-07T07:36:50.8193460Z Example: 2025-09-07T07:36:50.8193602Z >>> # xdoctest: +SKIP 2025-09-07T07:36:50.8193717Z >>> model = torch.nn.Linear(10, 10) 2025-09-07T07:36:50.8193882Z >>> optim = torch.optim.SGD(model.parameters(), lr=3e-4) 2025-09-07T07:36:50.8194047Z >>> scheduler1 = torch.optim.lr_scheduler.LinearLR( 2025-09-07T07:36:50.8194136Z ... optim, 2025-09-07T07:36:50.8194249Z ... start_factor=0.1, 2025-09-07T07:36:50.8194428Z ... end_factor=1, 2025-09-07T07:36:50.8194527Z ... total_iters=20, 2025-09-07T07:36:50.8194660Z ... ) 2025-09-07T07:36:50.8194850Z >>> scheduler2 = torch.optim.lr_scheduler.CosineAnnealingLR( 2025-09-07T07:36:50.8194952Z ... optim, 2025-09-07T07:36:50.8195042Z ... T_max=80, 2025-09-07T07:36:50.8195167Z ... eta_min=3e-5, 2025-09-07T07:36:50.8195262Z ... ) 2025-09-07T07:36:50.8195406Z >>> lr = torch.optim.lr_scheduler.SequentialLR( 2025-09-07T07:36:50.8195504Z ... optim, 2025-09-07T07:36:50.8195633Z ... schedulers=[scheduler1, scheduler2], 2025-09-07T07:36:50.8195734Z ... milestones=[20], 2025-09-07T07:36:50.8195820Z ... ) 2025-09-07T07:36:50.8195965Z >>> lr.load_state_dict(torch.load("./save_seq.pt")) 2025-09-07T07:36:50.8196168Z >>> # now load the optimizer checkpoint after loading the LRScheduler 2025-09-07T07:36:50.8196339Z >>> optim.load_state_dict(torch.load("./save_optim.pt")) 2025-09-07T07:36:50.8196419Z 2025-09-07T07:36:50.8196511Z 2025-09-07T07:36:50.8196760Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.8196842Z 2025-09-07T07:36:50.8196948Z warnings.warn(msg) 2025-09-07T07:36:50.8197024Z 2025-09-07T07:36:50.8197237Z --- Parse Warning: 126 / 146 --- 2025-09-07T07:36:50.8198158Z /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=120. 2025-09-07T07:36:50.8198419Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.8198787Z Implements averaged model for Stochastic Weight Averaging (SWA) and Exponential Moving Average (EMA). 2025-09-07T07:36:50.8198864Z 2025-09-07T07:36:50.8199116Z Stochastic Weight Averaging was proposed in `Averaging Weights Leads to 2025-09-07T07:36:50.8199325Z Wider Optima and Better Generalization`_ by Pavel Izmailov, Dmitrii 2025-09-07T07:36:50.8199551Z Podoprikhin, Timur Garipov, Dmitry Vetrov and Andrew Gordon Wilson 2025-09-07T07:36:50.8199633Z (UAI 2018). 2025-09-07T07:36:50.8199706Z 2025-09-07T07:36:50.8199926Z Exponential Moving Average is a variation of `Polyak averaging`_, 2025-09-07T07:36:50.8200163Z but using exponential weights instead of equal weights across iterations. 2025-09-07T07:36:50.8200250Z 2025-09-07T07:36:50.8200480Z AveragedModel class creates a copy of the provided module :attr:`model` 2025-09-07T07:36:50.8200698Z on the device :attr:`device` and allows to compute running averages of the 2025-09-07T07:36:50.8200816Z parameters of the :attr:`model`. 2025-09-07T07:36:50.8200895Z 2025-09-07T07:36:50.8200975Z Args: 2025-09-07T07:36:50.8201135Z model (torch.nn.Module): model to use with SWA/EMA 2025-09-07T07:36:50.8201428Z device (torch.device, optional): if provided, the averaged model will be 2025-09-07T07:36:50.8201545Z stored on the :attr:`device` 2025-09-07T07:36:50.8201745Z avg_fn (function, optional): the averaging function used to update 2025-09-07T07:36:50.8201946Z parameters; the function must take in the current value of the 2025-09-07T07:36:50.8202200Z :class:`AveragedModel` parameter, the current value of :attr:`model` 2025-09-07T07:36:50.8202391Z parameter, and the number of models already averaged; if None, 2025-09-07T07:36:50.8202556Z an equally weighted average is used (default: None) 2025-09-07T07:36:50.8202783Z multi_avg_fn (function, optional): the averaging function used to update 2025-09-07T07:36:50.8203024Z parameters inplace; the function must take in the current values of the 2025-09-07T07:36:50.8203284Z :class:`AveragedModel` parameters as a list, the current values of :attr:`model` 2025-09-07T07:36:50.8203508Z parameters as a list, and the number of models already averaged; if None, 2025-09-07T07:36:50.8203672Z an equally weighted average is used (default: None) 2025-09-07T07:36:50.8203897Z use_buffers (bool): if ``True``, it will compute running averages for 2025-09-07T07:36:50.8204134Z both the parameters and the buffers of the model. (default: ``False``) 2025-09-07T07:36:50.8204208Z 2025-09-07T07:36:50.8204291Z Example: 2025-09-07T07:36:50.8204425Z >>> # xdoctest: +SKIP("undefined variables") 2025-09-07T07:36:50.8204550Z >>> loader, optimizer, model, loss_fn = ... 2025-09-07T07:36:50.8204724Z >>> swa_model = torch.optim.swa_utils.AveragedModel(model) 2025-09-07T07:36:50.8204941Z >>> scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, 2025-09-07T07:36:50.8205050Z >>> T_max=300) 2025-09-07T07:36:50.8205151Z >>> swa_start = 160 2025-09-07T07:36:50.8205286Z >>> swa_scheduler = SWALR(optimizer, swa_lr=0.05) 2025-09-07T07:36:50.8205390Z >>> for i in range(300): 2025-09-07T07:36:50.8205504Z >>> for input, target in loader: 2025-09-07T07:36:50.8205613Z >>> optimizer.zero_grad() 2025-09-07T07:36:50.8205794Z >>> loss_fn(model(input), target).backward() 2025-09-07T07:36:50.8205900Z >>> optimizer.step() 2025-09-07T07:36:50.8206007Z >>> if i > swa_start: 2025-09-07T07:36:50.8206132Z >>> swa_model.update_parameters(model) 2025-09-07T07:36:50.8206243Z >>> swa_scheduler.step() 2025-09-07T07:36:50.8206337Z >>> else: 2025-09-07T07:36:50.8206450Z >>> scheduler.step() 2025-09-07T07:36:50.8206543Z >>> 2025-09-07T07:36:50.8206692Z >>> # Update bn statistics for the swa_model at the end 2025-09-07T07:36:50.8206852Z >>> torch.optim.swa_utils.update_bn(loader, swa_model) 2025-09-07T07:36:50.8206939Z 2025-09-07T07:36:50.8207236Z You can also use custom averaging functions with the `avg_fn` or `multi_avg_fn` parameters. 2025-09-07T07:36:50.8207440Z If no averaging function is provided, the default is to compute 2025-09-07T07:36:50.8207588Z equally-weighted average of the weights (SWA). 2025-09-07T07:36:50.8207667Z 2025-09-07T07:36:50.8207766Z Example: 2025-09-07T07:36:50.8207889Z >>> # xdoctest: +SKIP("undefined variables") 2025-09-07T07:36:50.8208103Z >>> # Compute exponential moving averages of the weights and buffers 2025-09-07T07:36:50.8208267Z >>> ema_model = torch.optim.swa_utils.AveragedModel(model, 2025-09-07T07:36:50.8208479Z >>> torch.optim.swa_utils.get_ema_multi_avg_fn(0.9), use_buffers=True) 2025-09-07T07:36:50.8208565Z 2025-09-07T07:36:50.8208649Z .. note:: 2025-09-07T07:36:50.8208901Z When using SWA/EMA with models containing Batch Normalization you may 2025-09-07T07:36:50.8209103Z need to update the activation statistics for Batch Normalization. 2025-09-07T07:36:50.8209333Z This can be done either by using the :meth:`torch.optim.swa_utils.update_bn` 2025-09-07T07:36:50.8209591Z or by setting :attr:`use_buffers` to `True`. The first approach updates the 2025-09-07T07:36:50.8209827Z statistics in a post-training step by passing data through the model. The 2025-09-07T07:36:50.8210070Z second does it during the parameter update phase by averaging all buffers. 2025-09-07T07:36:50.8210308Z Empirical evidence has shown that updating the statistics in normalization 2025-09-07T07:36:50.8210530Z layers increases accuracy, but you may wish to empirically test which 2025-09-07T07:36:50.8210690Z approach yields the best results in your problem. 2025-09-07T07:36:50.8210768Z 2025-09-07T07:36:50.8210865Z .. note:: 2025-09-07T07:36:50.8211112Z :attr:`avg_fn` and `multi_avg_fn` are not saved in the :meth:`state_dict` of the model. 2025-09-07T07:36:50.8211212Z 2025-09-07T07:36:50.8211305Z .. note:: 2025-09-07T07:36:50.8211498Z When :meth:`update_parameters` is called for the first time (i.e. 2025-09-07T07:36:50.8211693Z :attr:`n_averaged` is `0`) the parameters of `model` are copied 2025-09-07T07:36:50.8211889Z to the parameters of :class:`AveragedModel`. For every subsequent 2025-09-07T07:36:50.8212072Z call of :meth:`update_parameters` the function `avg_fn` is used 2025-09-07T07:36:50.8212184Z to update the parameters. 2025-09-07T07:36:50.8212260Z 2025-09-07T07:36:50.8212489Z .. _Averaging Weights Leads to Wider Optima and Better Generalization: 2025-09-07T07:36:50.8212607Z https://arxiv.org/abs/1803.05407 2025-09-07T07:36:50.8212849Z .. _There Are Many Consistent Explanations of Unlabeled Data: Why You Should 2025-09-07T07:36:50.8212943Z Average: 2025-09-07T07:36:50.8213053Z https://arxiv.org/abs/1806.05594 2025-09-07T07:36:50.8213259Z .. _SWALP: Stochastic Weight Averaging in Low-Precision Training: 2025-09-07T07:36:50.8213373Z https://arxiv.org/abs/1904.11943 2025-09-07T07:36:50.8213625Z .. _Stochastic Weight Averaging in Parallel: Large-Batch Training That 2025-09-07T07:36:50.8213735Z Generalizes Well: 2025-09-07T07:36:50.8213844Z https://arxiv.org/abs/2001.02312 2025-09-07T07:36:50.8213948Z .. _Polyak averaging: 2025-09-07T07:36:50.8214111Z https://paperswithcode.com/method/polyak-averaging 2025-09-07T07:36:50.8214198Z 2025-09-07T07:36:50.8214451Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.8214528Z 2025-09-07T07:36:50.8214630Z warnings.warn(msg) 2025-09-07T07:36:50.8214706Z 2025-09-07T07:36:50.8214908Z --- Parse Warning: 127 / 146 --- 2025-09-07T07:36:50.8215764Z /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=375. 2025-09-07T07:36:50.8216031Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.8216252Z Anneals the learning rate in each parameter group to a fixed value. 2025-09-07T07:36:50.8216328Z 2025-09-07T07:36:50.8216559Z This learning rate scheduler is meant to be used with Stochastic Weight 2025-09-07T07:36:50.8216774Z Averaging (SWA) method (see `torch.optim.swa_utils.AveragedModel`). 2025-09-07T07:36:50.8216852Z 2025-09-07T07:36:50.8216941Z Args: 2025-09-07T07:36:50.8217109Z optimizer (torch.optim.Optimizer): wrapped optimizer 2025-09-07T07:36:50.8217339Z swa_lrs (float or list): the learning rate value for all param groups 2025-09-07T07:36:50.8217477Z together or separately for each group. 2025-09-07T07:36:50.8217675Z annealing_epochs (int): number of epochs in the annealing phase 2025-09-07T07:36:50.8217778Z (default: 10) 2025-09-07T07:36:50.8217992Z annealing_strategy (str): "cos" or "linear"; specifies the annealing 2025-09-07T07:36:50.8218226Z strategy: "cos" for cosine annealing, "linear" for linear annealing 2025-09-07T07:36:50.8218329Z (default: "cos") 2025-09-07T07:36:50.8218505Z last_epoch (int): the index of the last epoch (default: -1) 2025-09-07T07:36:50.8218592Z 2025-09-07T07:36:50.8218767Z The :class:`SWALR` scheduler can be used together with other 2025-09-07T07:36:50.8218982Z schedulers to switch to a constant learning rate late in the training 2025-09-07T07:36:50.8219094Z as in the example below. 2025-09-07T07:36:50.8219171Z 2025-09-07T07:36:50.8219266Z Example: 2025-09-07T07:36:50.8219392Z >>> # xdoctest: +SKIP("Undefined variables") 2025-09-07T07:36:50.8219504Z >>> loader, optimizer, model = ... 2025-09-07T07:36:50.8219649Z >>> lr_lambda = lambda epoch: 0.9 2025-09-07T07:36:50.8219869Z >>> scheduler = torch.optim.lr_scheduler.MultiplicativeLR(optimizer, 2025-09-07T07:36:50.8219985Z >>> lr_lambda=lr_lambda) 2025-09-07T07:36:50.8220150Z >>> swa_scheduler = torch.optim.swa_utils.SWALR(optimizer, 2025-09-07T07:36:50.8220324Z >>> anneal_strategy="linear", anneal_epochs=20, swa_lr=0.05) 2025-09-07T07:36:50.8220432Z >>> swa_start = 160 2025-09-07T07:36:50.8220527Z >>> for i in range(300): 2025-09-07T07:36:50.8220658Z >>> for input, target in loader: 2025-09-07T07:36:50.8220768Z >>> optimizer.zero_grad() 2025-09-07T07:36:50.8220904Z >>> loss_fn(model(input), target).backward() 2025-09-07T07:36:50.8221020Z >>> optimizer.step() 2025-09-07T07:36:50.8221119Z >>> if i > swa_start: 2025-09-07T07:36:50.8221241Z >>> swa_scheduler.step() 2025-09-07T07:36:50.8221331Z >>> else: 2025-09-07T07:36:50.8221434Z >>> scheduler.step() 2025-09-07T07:36:50.8221555Z 2025-09-07T07:36:50.8221777Z .. _Averaging Weights Leads to Wider Optima and Better Generalization: 2025-09-07T07:36:50.8221893Z https://arxiv.org/abs/1803.05407 2025-09-07T07:36:50.8221985Z 2025-09-07T07:36:50.8222235Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.8222327Z 2025-09-07T07:36:50.8222425Z warnings.warn(msg) 2025-09-07T07:36:50.8222505Z 2025-09-07T07:36:50.8222713Z --- Parse Warning: 128 / 146 --- 2025-09-07T07:36:50.8223804Z /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=1331. 2025-09-07T07:36:50.8224078Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.8224228Z Asserts that ``actual`` and ``expected`` are close. 2025-09-07T07:36:50.8224312Z 2025-09-07T07:36:50.8224691Z If ``actual`` and ``expected`` are strided, non-quantized, real-valued, and finite, they are considered close if 2025-09-07T07:36:50.8224770Z 2025-09-07T07:36:50.8224872Z .. math:: 2025-09-07T07:36:50.8224952Z 2025-09-07T07:36:50.8225315Z \lvert \text{actual} - \text{expected} \rvert \le \texttt{atol} + \texttt{rtol} \cdot \lvert \text{expected} \rvert 2025-09-07T07:36:50.8225408Z 2025-09-07T07:36:50.8225755Z Non-finite values (``-inf`` and ``inf``) are only considered close if and only if they are equal. ``NaN``'s are 2025-09-07T07:36:50.8226049Z only considered equal to each other if ``equal_nan`` is ``True``. 2025-09-07T07:36:50.8226128Z 2025-09-07T07:36:50.8226325Z In addition, they are only considered close if they have the same 2025-09-07T07:36:50.8226418Z 2025-09-07T07:36:50.8226609Z - :attr:`~torch.Tensor.device` (if ``check_device`` is ``True``), 2025-09-07T07:36:50.8226798Z - ``dtype`` (if ``check_dtype`` is ``True``), 2025-09-07T07:36:50.8226940Z - ``layout`` (if ``check_layout`` is ``True``), and 2025-09-07T07:36:50.8227061Z - stride (if ``check_stride`` is ``True``). 2025-09-07T07:36:50.8227153Z 2025-09-07T07:36:50.8227451Z If either ``actual`` or ``expected`` is a meta tensor, only the attribute checks will be performed. 2025-09-07T07:36:50.8227546Z 2025-09-07T07:36:50.8227902Z If ``actual`` and ``expected`` are sparse (either having COO, CSR, CSC, BSR, or BSC layout), their strided members are 2025-09-07T07:36:50.8228279Z checked individually. Indices, namely ``indices`` for COO, ``crow_indices`` and ``col_indices`` for CSR and BSR, 2025-09-07T07:36:50.8228513Z or ``ccol_indices`` and ``row_indices`` for CSC and BSC layouts, respectively, 2025-09-07T07:36:50.8228932Z are always checked for equality whereas the values are checked for closeness according to the definition above. 2025-09-07T07:36:50.8229024Z 2025-09-07T07:36:50.8229302Z If ``actual`` and ``expected`` are quantized, they are considered close if they have the same 2025-09-07T07:36:50.8229667Z :meth:`~torch.Tensor.qscheme` and the result of :meth:`~torch.Tensor.dequantize` is close according to the 2025-09-07T07:36:50.8229764Z definition above. 2025-09-07T07:36:50.8229840Z 2025-09-07T07:36:50.8230152Z ``actual`` and ``expected`` can be :class:`~torch.Tensor`'s or any tensor-or-scalar-likes from which 2025-09-07T07:36:50.8230527Z :class:`torch.Tensor`'s can be constructed with :func:`torch.as_tensor`. Except for Python scalars the input types 2025-09-07T07:36:50.8230898Z have to be directly related. In addition, ``actual`` and ``expected`` can be :class:`~collections.abc.Sequence`'s 2025-09-07T07:36:50.8231275Z or :class:`~collections.abc.Mapping`'s in which case they are considered close if their structure matches and all 2025-09-07T07:36:50.8231537Z their elements are considered close according to the above definition. 2025-09-07T07:36:50.8231626Z 2025-09-07T07:36:50.8231711Z .. note:: 2025-09-07T07:36:50.8231800Z 2025-09-07T07:36:50.8232132Z Python scalars are an exception to the type relation requirement, because their :func:`type`, i.e. 2025-09-07T07:36:50.8232452Z :class:`int`, :class:`float`, and :class:`complex`, is equivalent to the ``dtype`` of a tensor-like. Thus, 2025-09-07T07:36:50.8232734Z Python scalars of different types can be checked, but require ``check_dtype=False``. 2025-09-07T07:36:50.8232813Z 2025-09-07T07:36:50.8232907Z Args: 2025-09-07T07:36:50.8233013Z actual (Any): Actual input. 2025-09-07T07:36:50.8233126Z expected (Any): Expected input. 2025-09-07T07:36:50.8233496Z allow_subclasses (bool): If ``True`` (default) and except for Python scalars, inputs of directly related types 2025-09-07T07:36:50.8233658Z are allowed. Otherwise type equality is required. 2025-09-07T07:36:50.8234030Z rtol (Optional[float]): Relative tolerance. If specified ``atol`` must also be specified. If omitted, default 2025-09-07T07:36:50.8234291Z values based on the :attr:`~torch.Tensor.dtype` are selected with the below table. 2025-09-07T07:36:50.8234725Z atol (Optional[float]): Absolute tolerance. If specified ``rtol`` must also be specified. If omitted, default 2025-09-07T07:36:50.8234983Z values based on the :attr:`~torch.Tensor.dtype` are selected with the below table. 2025-09-07T07:36:50.8235257Z equal_nan (Union[bool, str]): If ``True``, two ``NaN`` values will be considered equal. 2025-09-07T07:36:50.8235557Z check_device (bool): If ``True`` (default), asserts that corresponding tensors are on the same 2025-09-07T07:36:50.8235805Z :attr:`~torch.Tensor.device`. If this check is disabled, tensors on different 2025-09-07T07:36:50.8236075Z :attr:`~torch.Tensor.device`'s are moved to the CPU before being compared. 2025-09-07T07:36:50.8236423Z check_dtype (bool): If ``True`` (default), asserts that corresponding tensors have the same ``dtype``. If this 2025-09-07T07:36:50.8236766Z check is disabled, tensors with different ``dtype``'s are promoted to a common ``dtype`` (according to 2025-09-07T07:36:50.8236935Z :func:`torch.promote_types`) before being compared. 2025-09-07T07:36:50.8237291Z check_layout (bool): If ``True`` (default), asserts that corresponding tensors have the same ``layout``. If this 2025-09-07T07:36:50.8237635Z check is disabled, tensors with different ``layout``'s are converted to strided tensors before being 2025-09-07T07:36:50.8237725Z compared. 2025-09-07T07:36:50.8238097Z check_stride (bool): If ``True`` and corresponding tensors are strided, asserts that they have the same stride. 2025-09-07T07:36:50.8238493Z msg (Optional[Union[str, Callable[[str], str]]]): Optional error message to use in case a failure occurs during 2025-09-07T07:36:50.8238846Z the comparison. Can also passed as callable in which case it will be called with the generated message and 2025-09-07T07:36:50.8238971Z should return the new message. 2025-09-07T07:36:50.8239048Z 2025-09-07T07:36:50.8239141Z Raises: 2025-09-07T07:36:50.8239370Z ValueError: If no :class:`torch.Tensor` can be constructed from an input. 2025-09-07T07:36:50.8239534Z ValueError: If only ``rtol`` or ``atol`` is specified. 2025-09-07T07:36:50.8239866Z AssertionError: If corresponding inputs are not Python scalars and are not directly related. 2025-09-07T07:36:50.8240226Z AssertionError: If ``allow_subclasses`` is ``False``, but corresponding inputs are not Python scalars and have 2025-09-07T07:36:50.8240335Z different types. 2025-09-07T07:36:50.8240722Z AssertionError: If the inputs are :class:`~collections.abc.Sequence`'s, but their length does not match. 2025-09-07T07:36:50.8241097Z AssertionError: If the inputs are :class:`~collections.abc.Mapping`'s, but their set of keys do not match. 2025-09-07T07:36:50.8241413Z AssertionError: If corresponding tensors do not have the same :attr:`~torch.Tensor.shape`. 2025-09-07T07:36:50.8241706Z AssertionError: If ``check_layout`` is ``True``, but corresponding tensors do not have the same 2025-09-07T07:36:50.8241835Z :attr:`~torch.Tensor.layout`. 2025-09-07T07:36:50.8242054Z AssertionError: If only one of corresponding tensors is quantized. 2025-09-07T07:36:50.8242449Z AssertionError: If corresponding tensors are quantized, but have different :meth:`~torch.Tensor.qscheme`'s. 2025-09-07T07:36:50.8242739Z AssertionError: If ``check_device`` is ``True``, but corresponding tensors are not on the same 2025-09-07T07:36:50.8242857Z :attr:`~torch.Tensor.device`. 2025-09-07T07:36:50.8243197Z AssertionError: If ``check_dtype`` is ``True``, but corresponding tensors do not have the same ``dtype``. 2025-09-07T07:36:50.8243552Z AssertionError: If ``check_stride`` is ``True``, but corresponding strided tensors do not have the same stride. 2025-09-07T07:36:50.8243924Z AssertionError: If the values of corresponding tensors are not close according to the definition above. 2025-09-07T07:36:50.8244001Z 2025-09-07T07:36:50.8244376Z The following table displays the default ``rtol`` and ``atol`` for different ``dtype``'s. In case of mismatching 2025-09-07T07:36:50.8244546Z ``dtype``'s, the maximum of both tolerances is used. 2025-09-07T07:36:50.8244624Z 2025-09-07T07:36:50.8244764Z +---------------------------+------------+----------+ 2025-09-07T07:36:50.8244889Z | ``dtype`` | ``rtol`` | ``atol`` | 2025-09-07T07:36:50.8245031Z +===========================+============+==========+ 2025-09-07T07:36:50.8245166Z | :attr:`~torch.float16` | ``1e-3`` | ``1e-5`` | 2025-09-07T07:36:50.8245287Z +---------------------------+------------+----------+ 2025-09-07T07:36:50.8245433Z | :attr:`~torch.bfloat16` | ``1.6e-2`` | ``1e-5`` | 2025-09-07T07:36:50.8245549Z +---------------------------+------------+----------+ 2025-09-07T07:36:50.8245691Z | :attr:`~torch.float32` | ``1.3e-6`` | ``1e-5`` | 2025-09-07T07:36:50.8245808Z +---------------------------+------------+----------+ 2025-09-07T07:36:50.8245940Z | :attr:`~torch.float64` | ``1e-7`` | ``1e-7`` | 2025-09-07T07:36:50.8246069Z +---------------------------+------------+----------+ 2025-09-07T07:36:50.8246200Z | :attr:`~torch.complex32` | ``1e-3`` | ``1e-5`` | 2025-09-07T07:36:50.8246355Z +---------------------------+------------+----------+ 2025-09-07T07:36:50.8246490Z | :attr:`~torch.complex64` | ``1.3e-6`` | ``1e-5`` | 2025-09-07T07:36:50.8246608Z +---------------------------+------------+----------+ 2025-09-07T07:36:50.8246752Z | :attr:`~torch.complex128` | ``1e-7`` | ``1e-7`` | 2025-09-07T07:36:50.8246872Z +---------------------------+------------+----------+ 2025-09-07T07:36:50.8247013Z | :attr:`~torch.quint8` | ``1.3e-6`` | ``1e-5`` | 2025-09-07T07:36:50.8247131Z +---------------------------+------------+----------+ 2025-09-07T07:36:50.8247260Z | :attr:`~torch.quint2x4` | ``1.3e-6`` | ``1e-5`` | 2025-09-07T07:36:50.8247392Z +---------------------------+------------+----------+ 2025-09-07T07:36:50.8247527Z | :attr:`~torch.quint4x2` | ``1.3e-6`` | ``1e-5`` | 2025-09-07T07:36:50.8247657Z +---------------------------+------------+----------+ 2025-09-07T07:36:50.8247786Z | :attr:`~torch.qint8` | ``1.3e-6`` | ``1e-5`` | 2025-09-07T07:36:50.8247905Z +---------------------------+------------+----------+ 2025-09-07T07:36:50.8248071Z | :attr:`~torch.qint32` | ``1.3e-6`` | ``1e-5`` | 2025-09-07T07:36:50.8248194Z +---------------------------+------------+----------+ 2025-09-07T07:36:50.8248323Z | other | ``0.0`` | ``0.0`` | 2025-09-07T07:36:50.8248440Z +---------------------------+------------+----------+ 2025-09-07T07:36:50.8248517Z 2025-09-07T07:36:50.8248615Z .. note:: 2025-09-07T07:36:50.8248693Z 2025-09-07T07:36:50.8249085Z :func:`~torch.testing.assert_close` is highly configurable with strict default settings. Users are encouraged 2025-09-07T07:36:50.8249436Z to :func:`~functools.partial` it to fit their use case. For example, if an equality check is needed, one might 2025-09-07T07:36:50.8249758Z define an ``assert_equal`` that uses zero tolerances for every ``dtype`` by default: 2025-09-07T07:36:50.8249847Z 2025-09-07T07:36:50.8249943Z >>> import functools 2025-09-07T07:36:50.8250215Z >>> assert_equal = functools.partial(torch.testing.assert_close, rtol=0, atol=0) 2025-09-07T07:36:50.8250321Z >>> assert_equal(1e-9, 1e-10) 2025-09-07T07:36:50.8250435Z Traceback (most recent call last): 2025-09-07T07:36:50.8250531Z ... 2025-09-07T07:36:50.8250658Z AssertionError: Scalars are not equal! 2025-09-07T07:36:50.8250760Z 2025-09-07T07:36:50.8250866Z Expected 1e-10 but got 1e-09. 2025-09-07T07:36:50.8250991Z Absolute difference: 9.000000000000001e-10 2025-09-07T07:36:50.8251103Z Relative difference: 9.0 2025-09-07T07:36:50.8251209Z 2025-09-07T07:36:50.8251307Z Examples: 2025-09-07T07:36:50.8251418Z >>> # tensor to tensor comparison 2025-09-07T07:36:50.8251547Z >>> expected = torch.tensor([1e0, 1e-1, 1e-2]) 2025-09-07T07:36:50.8251692Z >>> actual = torch.acos(torch.cos(expected)) 2025-09-07T07:36:50.8251860Z >>> torch.testing.assert_close(actual, expected) 2025-09-07T07:36:50.8251950Z 2025-09-07T07:36:50.8252061Z >>> # scalar to scalar comparison 2025-09-07T07:36:50.8252150Z >>> import math 2025-09-07T07:36:50.8252266Z >>> expected = math.sqrt(2.0) 2025-09-07T07:36:50.8252369Z >>> actual = 2.0 / math.sqrt(2.0) 2025-09-07T07:36:50.8252512Z >>> torch.testing.assert_close(actual, expected) 2025-09-07T07:36:50.8252602Z 2025-09-07T07:36:50.8252724Z >>> # numpy array to numpy array comparison 2025-09-07T07:36:50.8252835Z >>> import numpy as np 2025-09-07T07:36:50.8252957Z >>> expected = np.array([1e0, 1e-1, 1e-2]) 2025-09-07T07:36:50.8253077Z >>> actual = np.arccos(np.cos(expected)) 2025-09-07T07:36:50.8253227Z >>> torch.testing.assert_close(actual, expected) 2025-09-07T07:36:50.8253329Z 2025-09-07T07:36:50.8253453Z >>> # sequence to sequence comparison 2025-09-07T07:36:50.8253554Z >>> import numpy as np 2025-09-07T07:36:50.8253803Z >>> # The types of the sequences do not have to match. They only have to have the same 2025-09-07T07:36:50.8253943Z >>> # length and their elements have to match. 2025-09-07T07:36:50.8254097Z >>> expected = [torch.tensor([1.0]), 2.0, np.array(3.0)] 2025-09-07T07:36:50.8254213Z >>> actual = tuple(expected) 2025-09-07T07:36:50.8254352Z >>> torch.testing.assert_close(actual, expected) 2025-09-07T07:36:50.8254429Z 2025-09-07T07:36:50.8254557Z >>> # mapping to mapping comparison 2025-09-07T07:36:50.8254679Z >>> from collections import OrderedDict 2025-09-07T07:36:50.8254789Z >>> import numpy as np 2025-09-07T07:36:50.8254892Z >>> foo = torch.tensor(1.0) 2025-09-07T07:36:50.8254981Z >>> bar = 2.0 2025-09-07T07:36:50.8255092Z >>> baz = np.array(3.0) 2025-09-07T07:36:50.8255367Z >>> # The types and a possible ordering of mappings do not have to match. They only 2025-09-07T07:36:50.8255582Z >>> # have to have the same set of keys and their elements have to match. 2025-09-07T07:36:50.8255787Z >>> expected = OrderedDict([("foo", foo), ("bar", bar), ("baz", baz)]) 2025-09-07T07:36:50.8255917Z >>> actual = {"baz": baz, "bar": bar, "foo": foo} 2025-09-07T07:36:50.8256071Z >>> torch.testing.assert_close(actual, expected) 2025-09-07T07:36:50.8256149Z 2025-09-07T07:36:50.8256284Z >>> expected = torch.tensor([1.0, 2.0, 3.0]) 2025-09-07T07:36:50.8256393Z >>> actual = expected.clone() 2025-09-07T07:36:50.8256563Z >>> # By default, directly related instances can be compared 2025-09-07T07:36:50.8256793Z >>> torch.testing.assert_close(torch.nn.Parameter(actual), expected) 2025-09-07T07:36:50.8256982Z >>> # This check can be made more strict with allow_subclasses=False 2025-09-07T07:36:50.8257114Z >>> torch.testing.assert_close( 2025-09-07T07:36:50.8257313Z ... torch.nn.Parameter(actual), expected, allow_subclasses=False 2025-09-07T07:36:50.8257392Z ... ) 2025-09-07T07:36:50.8257524Z Traceback (most recent call last): 2025-09-07T07:36:50.8257605Z ... 2025-09-07T07:36:50.8257821Z TypeError: No comparison pair was able to handle inputs of type 2025-09-07T07:36:50.8258034Z and . 2025-09-07T07:36:50.8258255Z >>> # If the inputs are not directly related, they are never considered close 2025-09-07T07:36:50.8258462Z >>> torch.testing.assert_close(actual.numpy(), expected) 2025-09-07T07:36:50.8258577Z Traceback (most recent call last): 2025-09-07T07:36:50.8258672Z ... 2025-09-07T07:36:50.8258963Z TypeError: No comparison pair was able to handle inputs of type 2025-09-07T07:36:50.8259097Z and . 2025-09-07T07:36:50.8259374Z >>> # Exceptions to these rules are Python scalars. They can be checked regardless of 2025-09-07T07:36:50.8259494Z >>> # their type if check_dtype=False. 2025-09-07T07:36:50.8259673Z >>> torch.testing.assert_close(1.0, 1, check_dtype=False) 2025-09-07T07:36:50.8259755Z 2025-09-07T07:36:50.8259860Z >>> # NaN != NaN by default. 2025-09-07T07:36:50.8259998Z >>> expected = torch.tensor(float("Nan")) 2025-09-07T07:36:50.8260107Z >>> actual = expected.clone() 2025-09-07T07:36:50.8260260Z >>> torch.testing.assert_close(actual, expected) 2025-09-07T07:36:50.8260375Z Traceback (most recent call last): 2025-09-07T07:36:50.8260454Z ... 2025-09-07T07:36:50.8260592Z AssertionError: Scalars are not close! 2025-09-07T07:36:50.8260707Z 2025-09-07T07:36:50.8260823Z Expected nan but got nan. 2025-09-07T07:36:50.8260968Z Absolute difference: nan (up to 1e-05 allowed) 2025-09-07T07:36:50.8261111Z Relative difference: nan (up to 1.3e-06 allowed) 2025-09-07T07:36:50.8261322Z >>> torch.testing.assert_close(actual, expected, equal_nan=True) 2025-09-07T07:36:50.8261400Z 2025-09-07T07:36:50.8261532Z >>> expected = torch.tensor([1.0, 2.0, 3.0]) 2025-09-07T07:36:50.8261650Z >>> actual = torch.tensor([1.0, 4.0, 5.0]) 2025-09-07T07:36:50.8261792Z >>> # The default error message can be overwritten. 2025-09-07T07:36:50.8261913Z >>> torch.testing.assert_close( 2025-09-07T07:36:50.8262094Z ... actual, expected, msg="Argh, the tensors are not close!" 2025-09-07T07:36:50.8262184Z ... ) 2025-09-07T07:36:50.8262298Z Traceback (most recent call last): 2025-09-07T07:36:50.8262382Z ... 2025-09-07T07:36:50.8262540Z AssertionError: Argh, the tensors are not close! 2025-09-07T07:36:50.8262786Z >>> # If msg is a callable, it can be used to augment the generated message with 2025-09-07T07:36:50.8262894Z >>> # extra information 2025-09-07T07:36:50.8263004Z >>> torch.testing.assert_close( 2025-09-07T07:36:50.8263198Z ... actual, expected, msg=lambda msg: f"Header\n\n{msg}\n\nFooter" 2025-09-07T07:36:50.8263294Z ... ) 2025-09-07T07:36:50.8263409Z Traceback (most recent call last): 2025-09-07T07:36:50.8263487Z ... 2025-09-07T07:36:50.8263602Z AssertionError: Header 2025-09-07T07:36:50.8263693Z 2025-09-07T07:36:50.8263810Z Tensor-likes are not close! 2025-09-07T07:36:50.8263896Z 2025-09-07T07:36:50.8264006Z Mismatched elements: 2 / 3 (66.7%) 2025-09-07T07:36:50.8264242Z Greatest absolute difference: 2.0 at index (1,) (up to 1e-05 allowed) 2025-09-07T07:36:50.8264473Z Greatest relative difference: 1.0 at index (1,) (up to 1.3e-06 allowed) 2025-09-07T07:36:50.8264575Z 2025-09-07T07:36:50.8264660Z Footer 2025-09-07T07:36:50.8264740Z 2025-09-07T07:36:50.8264998Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.8265078Z 2025-09-07T07:36:50.8265183Z warnings.warn(msg) 2025-09-07T07:36:50.8265261Z 2025-09-07T07:36:50.8265483Z --- Parse Warning: 129 / 146 --- 2025-09-07T07:36:50.8266450Z /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=134. 2025-09-07T07:36:50.8266710Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.8266865Z Register a container-like type as pytree node. 2025-09-07T07:36:50.8266943Z 2025-09-07T07:36:50.8267059Z Args: 2025-09-07T07:36:50.8267254Z cls (type): A Python type to treat as an internal pytree node. 2025-09-07T07:36:50.8267521Z flatten_fn (callable): A function to be used during flattening, taking an instance of 2025-09-07T07:36:50.8267778Z ``cls`` and returning a pair, with (1) an iterable for the children to be flattened 2025-09-07T07:36:50.8268069Z recursively, and (2) some hashable auxiliary data to be stored in the treespec and to be 2025-09-07T07:36:50.8268185Z passed to the ``unflatten_fn``. 2025-09-07T07:36:50.8268475Z unflatten_fn (callable): A function taking two arguments: the auxiliary data that was 2025-09-07T07:36:50.8268732Z returned by ``flatten_fn`` and stored in the treespec, and the unflattened children. 2025-09-07T07:36:50.8268896Z The function should return an instance of ``cls``. 2025-09-07T07:36:50.8269189Z serialized_type_name (str, optional): A keyword argument used to specify the fully 2025-09-07T07:36:50.8269363Z qualified name used when serializing the tree spec. 2025-09-07T07:36:50.8269668Z to_dumpable_context (callable, optional): An optional keyword argument to custom specify how 2025-09-07T07:36:50.8269941Z to convert the context of the pytree to a custom json dumpable representation. This is 2025-09-07T07:36:50.8270225Z used for json serialization, which is being used in :mod:`torch.export` right now. 2025-09-07T07:36:50.8270526Z from_dumpable_context (callable, optional): An optional keyword argument to custom specify 2025-09-07T07:36:50.8270797Z how to convert the custom json dumpable representation of the context back to the 2025-09-07T07:36:50.8271053Z original context. This is used for json deserialization, which is being used in 2025-09-07T07:36:50.8271171Z :mod:`torch.export` right now. 2025-09-07T07:36:50.8271261Z 2025-09-07T07:36:50.8271379Z Example:: 2025-09-07T07:36:50.8271469Z 2025-09-07T07:36:50.8271566Z >>> # xdoctest: +SKIP 2025-09-07T07:36:50.8271704Z >>> # Registry a Python type with lambda functions 2025-09-07T07:36:50.8271819Z >>> register_pytree_node( 2025-09-07T07:36:50.8271903Z ... set, 2025-09-07T07:36:50.8272036Z ... lambda s: (sorted(s), None, None), 2025-09-07T07:36:50.8272158Z ... lambda children, _: set(children), 2025-09-07T07:36:50.8272240Z ... ) 2025-09-07T07:36:50.8272331Z 2025-09-07T07:36:50.8272584Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.8272674Z 2025-09-07T07:36:50.8272767Z warnings.warn(msg) 2025-09-07T07:36:50.8272844Z 2025-09-07T07:36:50.8273051Z --- Parse Warning: 130 / 146 --- 2025-09-07T07:36:50.8274031Z /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=1226. 2025-09-07T07:36:50.8274302Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.8274455Z 2025-09-07T07:36:50.8274671Z Context passed to policy function during selective checkpointing. 2025-09-07T07:36:50.8274762Z 2025-09-07T07:36:50.8274984Z This class is used to pass relevant metadata to the policy function during 2025-09-07T07:36:50.8275258Z selective checkpointing. The metadata includes whether the current invocation 2025-09-07T07:36:50.8275450Z of the policy function is during recomputation or not. 2025-09-07T07:36:50.8275526Z 2025-09-07T07:36:50.8275623Z Example: 2025-09-07T07:36:50.8275723Z >>> # xdoctest: +SKIP(stub) 2025-09-07T07:36:50.8275816Z >>> 2025-09-07T07:36:50.8275941Z >>> def policy_fn(ctx, op, *args, **kwargs): 2025-09-07T07:36:50.8276073Z >>> print(ctx.is_recompute) 2025-09-07T07:36:50.8276162Z >>> 2025-09-07T07:36:50.8276431Z >>> context_fn = functools.partial(create_selective_checkpoint_contexts, policy_fn) 2025-09-07T07:36:50.8276509Z >>> 2025-09-07T07:36:50.8276661Z >>> out = torch.utils.checkpoint.checkpoint( 2025-09-07T07:36:50.8276748Z >>> fn, x, y, 2025-09-07T07:36:50.8276856Z >>> use_reentrant=False, 2025-09-07T07:36:50.8276959Z >>> context_fn=context_fn, 2025-09-07T07:36:50.8277037Z >>> ) 2025-09-07T07:36:50.8277123Z 2025-09-07T07:36:50.8277373Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.8277460Z 2025-09-07T07:36:50.8277553Z warnings.warn(msg) 2025-09-07T07:36:50.8277628Z 2025-09-07T07:36:50.8277833Z --- Parse Warning: 131 / 146 --- 2025-09-07T07:36:50.8278862Z /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=1366. 2025-09-07T07:36:50.8279134Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.8279209Z 2025-09-07T07:36:50.8279443Z Helper to avoid recomputing certain ops during activation checkpointing. 2025-09-07T07:36:50.8279532Z 2025-09-07T07:36:50.8279744Z Use this with `torch.utils.checkpoint.checkpoint` to control which 2025-09-07T07:36:50.8279915Z operations are recomputed during the backward pass. 2025-09-07T07:36:50.8279993Z 2025-09-07T07:36:50.8280074Z Args: 2025-09-07T07:36:50.8280203Z policy_fn_or_list (Callable or List): 2025-09-07T07:36:50.8280364Z - If a policy function is provided, it should accept a 2025-09-07T07:36:50.8280617Z :class:`SelectiveCheckpointContext`, the :class:`OpOverload`, args and 2025-09-07T07:36:50.8280853Z kwargs to the op, and return a :class:`CheckpointPolicy` enum value 2025-09-07T07:36:50.8281093Z indicating whether the execution of the op should be recomputed or not. 2025-09-07T07:36:50.8281355Z - If a list of operations is provided, it is equivalent to a policy 2025-09-07T07:36:50.8281536Z returning `CheckpointPolicy.MUST_SAVE` for the specified 2025-09-07T07:36:50.8281761Z operations and `CheckpointPolicy.PREFER_RECOMPUTE` for all other 2025-09-07T07:36:50.8281852Z operations. 2025-09-07T07:36:50.8282066Z allow_cache_entry_mutation (bool, optional): By default, an error is 2025-09-07T07:36:50.8282292Z raised if any tensors cached by selective activation checkpoint are 2025-09-07T07:36:50.8282498Z mutated in order to ensure correctness. If set to `True`, this check 2025-09-07T07:36:50.8282601Z is disabled. 2025-09-07T07:36:50.8282685Z Returns: 2025-09-07T07:36:50.8282800Z A tuple of two context managers. 2025-09-07T07:36:50.8282889Z 2025-09-07T07:36:50.8282969Z Example: 2025-09-07T07:36:50.8283082Z >>> # xdoctest: +REQUIRES(LINUX) 2025-09-07T07:36:50.8283177Z >>> import functools 2025-09-07T07:36:50.8283254Z >>> 2025-09-07T07:36:50.8283389Z >>> x = torch.rand(10, 10, requires_grad=True) 2025-09-07T07:36:50.8283508Z >>> y = torch.rand(10, 10, requires_grad=True) 2025-09-07T07:36:50.8283586Z >>> 2025-09-07T07:36:50.8283687Z >>> ops_to_save = [ 2025-09-07T07:36:50.8283799Z >>> torch.ops.aten.mm.default, 2025-09-07T07:36:50.8283893Z >>> ] 2025-09-07T07:36:50.8283971Z >>> 2025-09-07T07:36:50.8284122Z >>> def policy_fn(ctx, op, *args, **kwargs): 2025-09-07T07:36:50.8284234Z >>> if op in ops_to_save: 2025-09-07T07:36:50.8284358Z >>> return CheckpointPolicy.MUST_SAVE 2025-09-07T07:36:50.8284454Z >>> else: 2025-09-07T07:36:50.8284592Z >>> return CheckpointPolicy.PREFER_RECOMPUTE 2025-09-07T07:36:50.8284708Z >>> 2025-09-07T07:36:50.8284989Z >>> context_fn = functools.partial(create_selective_checkpoint_contexts, policy_fn) 2025-09-07T07:36:50.8285069Z >>> 2025-09-07T07:36:50.8285178Z >>> # or equivalently 2025-09-07T07:36:50.8285448Z >>> context_fn = functools.partial(create_selective_checkpoint_contexts, ops_to_save) 2025-09-07T07:36:50.8285527Z >>> 2025-09-07T07:36:50.8285632Z >>> def fn(x, y): 2025-09-07T07:36:50.8285831Z >>> return torch.sigmoid(torch.matmul(torch.matmul(x, y), y)) * y 2025-09-07T07:36:50.8285929Z >>> 2025-09-07T07:36:50.8286073Z >>> out = torch.utils.checkpoint.checkpoint( 2025-09-07T07:36:50.8286161Z >>> fn, x, y, 2025-09-07T07:36:50.8286276Z >>> use_reentrant=False, 2025-09-07T07:36:50.8286405Z >>> context_fn=context_fn, 2025-09-07T07:36:50.8286482Z >>> ) 2025-09-07T07:36:50.8286582Z 2025-09-07T07:36:50.8286837Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.8286929Z 2025-09-07T07:36:50.8287026Z warnings.warn(msg) 2025-09-07T07:36:50.8287106Z 2025-09-07T07:36:50.8287313Z --- Parse Warning: 132 / 146 --- 2025-09-07T07:36:50.8288227Z /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=1158. 2025-09-07T07:36:50.8288500Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.8288580Z 2025-09-07T07:36:50.8288724Z Create a :class:`setuptools.Extension` for C++. 2025-09-07T07:36:50.8288817Z 2025-09-07T07:36:50.8289056Z Convenience method that creates a :class:`setuptools.Extension` with the 2025-09-07T07:36:50.8289295Z bare minimum (but often sufficient) arguments to build a C++ extension. 2025-09-07T07:36:50.8289404Z 2025-09-07T07:36:50.8289613Z All arguments are forwarded to the :class:`setuptools.Extension` 2025-09-07T07:36:50.8289773Z constructor. Full list arguments can be found at 2025-09-07T07:36:50.8290099Z https://setuptools.pypa.io/en/latest/userguide/ext_modules.html#extension-api-reference 2025-09-07T07:36:50.8290192Z 2025-09-07T07:36:50.8290283Z .. warning:: 2025-09-07T07:36:50.8290509Z The PyTorch python API (as provided in libtorch_python) cannot be built 2025-09-07T07:36:50.8290731Z with the flag ``py_limited_api=True``. When this flag is passed, it is 2025-09-07T07:36:50.8290925Z the user's responsibility in their library to not use APIs from 2025-09-07T07:36:50.8291166Z libtorch_python (in particular pytorch/python bindings) and to only use 2025-09-07T07:36:50.8291382Z APIs from libtorch (aten objects, operators and the dispatcher). For 2025-09-07T07:36:50.8291600Z example, to give access to custom ops from python, the library should 2025-09-07T07:36:50.8291743Z register the ops through the dispatcher. 2025-09-07T07:36:50.8291821Z 2025-09-07T07:36:50.8292057Z Contrary to CPython setuptools, who does not define -DPy_LIMITED_API 2025-09-07T07:36:50.8292260Z as a compile flag when py_limited_api is specified as an option for 2025-09-07T07:36:50.8292465Z the "bdist_wheel" command in ``setup``, PyTorch does! We will specify 2025-09-07T07:36:50.8292687Z -DPy_LIMITED_API=min_supported_cpython to best enforce consistency, 2025-09-07T07:36:50.8292895Z safety, and sanity in order to encourage best practices. To target a 2025-09-07T07:36:50.8293143Z different version, set min_supported_cpython to the hexcode of the 2025-09-07T07:36:50.8293247Z CPython version of choice. 2025-09-07T07:36:50.8293326Z 2025-09-07T07:36:50.8293419Z Example: 2025-09-07T07:36:50.8293514Z >>> # xdoctest: +SKIP 2025-09-07T07:36:50.8293700Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2025-09-07T07:36:50.8293811Z >>> from setuptools import setup 2025-09-07T07:36:50.8294027Z >>> from torch.utils.cpp_extension import BuildExtension, CppExtension 2025-09-07T07:36:50.8294119Z >>> setup( 2025-09-07T07:36:50.8294216Z ... name='extension', 2025-09-07T07:36:50.8294318Z ... ext_modules=[ 2025-09-07T07:36:50.8294412Z ... CppExtension( 2025-09-07T07:36:50.8294514Z ... name='extension', 2025-09-07T07:36:50.8294645Z ... sources=['extension.cpp'], 2025-09-07T07:36:50.8294761Z ... extra_compile_args=['-g'], 2025-09-07T07:36:50.8294924Z ... extra_link_args=['-Wl,--no-as-needed', '-lm']) 2025-09-07T07:36:50.8295003Z ... ], 2025-09-07T07:36:50.8295093Z ... cmdclass={ 2025-09-07T07:36:50.8295239Z ... 'build_ext': BuildExtension 2025-09-07T07:36:50.8295322Z ... }) 2025-09-07T07:36:50.8295416Z 2025-09-07T07:36:50.8295665Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.8295743Z 2025-09-07T07:36:50.8295851Z warnings.warn(msg) 2025-09-07T07:36:50.8295927Z 2025-09-07T07:36:50.8296120Z --- Parse Warning: 133 / 146 --- 2025-09-07T07:36:50.8297050Z /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=1228. 2025-09-07T07:36:50.8297310Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.8297399Z 2025-09-07T07:36:50.8297555Z Create a :class:`setuptools.Extension` for CUDA/C++. 2025-09-07T07:36:50.8297643Z 2025-09-07T07:36:50.8297883Z Convenience method that creates a :class:`setuptools.Extension` with the 2025-09-07T07:36:50.8298108Z bare minimum (but often sufficient) arguments to build a CUDA/C++ 2025-09-07T07:36:50.8298353Z extension. This includes the CUDA include path, library path and runtime 2025-09-07T07:36:50.8298434Z library. 2025-09-07T07:36:50.8298522Z 2025-09-07T07:36:50.8298725Z All arguments are forwarded to the :class:`setuptools.Extension` 2025-09-07T07:36:50.8298872Z constructor. Full list arguments can be found at 2025-09-07T07:36:50.8299204Z https://setuptools.pypa.io/en/latest/userguide/ext_modules.html#extension-api-reference 2025-09-07T07:36:50.8299282Z 2025-09-07T07:36:50.8299369Z .. warning:: 2025-09-07T07:36:50.8299609Z The PyTorch python API (as provided in libtorch_python) cannot be built 2025-09-07T07:36:50.8299816Z with the flag ``py_limited_api=True``. When this flag is passed, it is 2025-09-07T07:36:50.8300020Z the user's responsibility in their library to not use APIs from 2025-09-07T07:36:50.8300252Z libtorch_python (in particular pytorch/python bindings) and to only use 2025-09-07T07:36:50.8300482Z APIs from libtorch (aten objects, operators and the dispatcher). For 2025-09-07T07:36:50.8300692Z example, to give access to custom ops from python, the library should 2025-09-07T07:36:50.8300818Z register the ops through the dispatcher. 2025-09-07T07:36:50.8300908Z 2025-09-07T07:36:50.8301126Z Contrary to CPython setuptools, who does not define -DPy_LIMITED_API 2025-09-07T07:36:50.8301341Z as a compile flag when py_limited_api is specified as an option for 2025-09-07T07:36:50.8301546Z the "bdist_wheel" command in ``setup``, PyTorch does! We will specify 2025-09-07T07:36:50.8301798Z -DPy_LIMITED_API=min_supported_cpython to best enforce consistency, 2025-09-07T07:36:50.8302022Z safety, and sanity in order to encourage best practices. To target a 2025-09-07T07:36:50.8302239Z different version, set min_supported_cpython to the hexcode of the 2025-09-07T07:36:50.8302353Z CPython version of choice. 2025-09-07T07:36:50.8302470Z 2025-09-07T07:36:50.8302554Z Example: 2025-09-07T07:36:50.8302660Z >>> # xdoctest: +SKIP 2025-09-07T07:36:50.8302807Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2025-09-07T07:36:50.8302932Z >>> from setuptools import setup 2025-09-07T07:36:50.8303151Z >>> from torch.utils.cpp_extension import BuildExtension, CUDAExtension 2025-09-07T07:36:50.8303235Z >>> setup( 2025-09-07T07:36:50.8303349Z ... name='cuda_extension', 2025-09-07T07:36:50.8303440Z ... ext_modules=[ 2025-09-07T07:36:50.8303538Z ... CUDAExtension( 2025-09-07T07:36:50.8303665Z ... name='cuda_extension', 2025-09-07T07:36:50.8303826Z ... sources=['extension.cpp', 'extension_kernel.cu'], 2025-09-07T07:36:50.8303963Z ... extra_compile_args={'cxx': ['-g'], 2025-09-07T07:36:50.8304105Z ... 'nvcc': ['-O2']}, 2025-09-07T07:36:50.8304262Z ... extra_link_args=['-Wl,--no-as-needed', '-lcuda']) 2025-09-07T07:36:50.8304356Z ... ], 2025-09-07T07:36:50.8304447Z ... cmdclass={ 2025-09-07T07:36:50.8304574Z ... 'build_ext': BuildExtension 2025-09-07T07:36:50.8304656Z ... }) 2025-09-07T07:36:50.8304733Z 2025-09-07T07:36:50.8304844Z Compute capabilities: 2025-09-07T07:36:50.8304921Z 2025-09-07T07:36:50.8305232Z By default the extension will be compiled to run on all archs of the cards visible during the 2025-09-07T07:36:50.8305525Z building process of the extension, plus PTX. If down the road a new card is installed the 2025-09-07T07:36:50.8305820Z extension may need to be recompiled. If a visible card has a compute capability (CC) that's 2025-09-07T07:36:50.8306132Z newer than the newest version for which your nvcc can build fully-compiled binaries, PyTorch 2025-09-07T07:36:50.8306445Z will make nvcc fall back to building kernels with the newest version of PTX your nvcc does 2025-09-07T07:36:50.8306580Z support (see below for details on PTX). 2025-09-07T07:36:50.8306655Z 2025-09-07T07:36:50.8306967Z You can override the default behavior using `TORCH_CUDA_ARCH_LIST` to explicitly specify which 2025-09-07T07:36:50.8307095Z CCs you want the extension to support: 2025-09-07T07:36:50.8307173Z 2025-09-07T07:36:50.8307369Z ``TORCH_CUDA_ARCH_LIST="6.1 8.6" python build_my_extension.py`` 2025-09-07T07:36:50.8307600Z ``TORCH_CUDA_ARCH_LIST="5.2 6.0 6.1 7.0 7.5 8.0 8.6+PTX" python build_my_extension.py`` 2025-09-07T07:36:50.8307677Z 2025-09-07T07:36:50.8308001Z The +PTX option causes extension kernel binaries to include PTX instructions for the specified 2025-09-07T07:36:50.8308315Z CC. PTX is an intermediate representation that allows kernels to runtime-compile for any CC >= 2025-09-07T07:36:50.8308629Z the specified CC (for example, 8.6+PTX generates PTX that can runtime-compile for any GPU with 2025-09-07T07:36:50.8308926Z CC >= 8.6). This improves your binary's forward compatibility. However, relying on older PTX to 2025-09-07T07:36:50.8309262Z provide forward compat by runtime-compiling for newer CCs can modestly reduce performance on 2025-09-07T07:36:50.8309533Z those newer CCs. If you know exact CC(s) of the GPUs you want to target, you're always better 2025-09-07T07:36:50.8309852Z off specifying them individually. For example, if you want your extension to run on 8.0 and 8.6, 2025-09-07T07:36:50.8310176Z "8.0+PTX" would work functionally because it includes PTX that can runtime-compile for 8.6, but 2025-09-07T07:36:50.8310297Z "8.0 8.6" would be better. 2025-09-07T07:36:50.8310386Z 2025-09-07T07:36:50.8310684Z Note that while it's possible to include all supported archs, the more archs get included the 2025-09-07T07:36:50.8310974Z slower the building process will be, as it will build a separate kernel image for each arch. 2025-09-07T07:36:50.8311088Z 2025-09-07T07:36:50.8311422Z Note that CUDA-11.5 nvcc will hit internal compiler error while parsing torch/extension.h on Windows. 2025-09-07T07:36:50.8311645Z To workaround the issue, move python binding logic to pure C++ file. 2025-09-07T07:36:50.8311724Z 2025-09-07T07:36:50.8311810Z Example use: 2025-09-07T07:36:50.8311921Z #include 2025-09-07T07:36:50.8312069Z at::Tensor SigmoidAlphaBlendForwardCuda(....) 2025-09-07T07:36:50.8312163Z 2025-09-07T07:36:50.8312249Z Instead of: 2025-09-07T07:36:50.8312356Z #include 2025-09-07T07:36:50.8312528Z torch::Tensor SigmoidAlphaBlendForwardCuda(...) 2025-09-07T07:36:50.8312606Z 2025-09-07T07:36:50.8312894Z Currently open issue for nvcc bug: https://github.com/pytorch/pytorch/issues/69460 2025-09-07T07:36:50.8313429Z Complete workaround code example: https://github.com/facebookresearch/pytorch3d/commit/cb170ac024a949f1f9614ffe6af1c38d972f7d48 2025-09-07T07:36:50.8313509Z 2025-09-07T07:36:50.8313632Z Relocatable device code linking: 2025-09-07T07:36:50.8313710Z 2025-09-07T07:36:50.8313999Z If you want to reference device symbols across compilation units (across object files), 2025-09-07T07:36:50.8314254Z the object files need to be built with `relocatable device code` (-rdc=true or -dc). 2025-09-07T07:36:50.8314707Z An exception to this rule is "dynamic parallelism" (nested kernel launches) which is not used a lot anymore. 2025-09-07T07:36:50.8315051Z `Relocatable device code` is less optimized so it needs to be used only on object files that need it. 2025-09-07T07:36:50.8315375Z Using `-dlto` (Device Link Time Optimization) at the device code compilation step and `dlink` step 2025-09-07T07:36:50.8315569Z helps reduce the protentional perf degradation of `-rdc`. 2025-09-07T07:36:50.8315736Z Note that it needs to be used at both steps to be useful. 2025-09-07T07:36:50.8315814Z 2025-09-07T07:36:50.8316231Z If you have `rdc` objects you need to have an extra `-dlink` (device linking) step before the CPU symbol linking step. 2025-09-07T07:36:50.8316403Z There is also a case where `-dlink` is used without `-rdc`: 2025-09-07T07:36:50.8316670Z when an extension is linked against a static lib containing rdc-compiled objects 2025-09-07T07:36:50.8316882Z like the [NVSHMEM library](https://developer.nvidia.com/nvshmem). 2025-09-07T07:36:50.8316961Z 2025-09-07T07:36:50.8317176Z Note: Ninja is required to build a CUDA Extension with RDC linking. 2025-09-07T07:36:50.8317252Z 2025-09-07T07:36:50.8317347Z Example: 2025-09-07T07:36:50.8317445Z >>> # xdoctest: +SKIP 2025-09-07T07:36:50.8317592Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2025-09-07T07:36:50.8317701Z >>> CUDAExtension( 2025-09-07T07:36:50.8317806Z ... name='cuda_extension', 2025-09-07T07:36:50.8317980Z ... sources=['extension.cpp', 'extension_kernel.cu'], 2025-09-07T07:36:50.8318077Z ... dlink=True, 2025-09-07T07:36:50.8318195Z ... dlink_libraries=["dlink_lib"], 2025-09-07T07:36:50.8318332Z ... extra_compile_args={'cxx': ['-g'], 2025-09-07T07:36:50.8318454Z ... 'nvcc': ['-O2', '-rdc=true']}) 2025-09-07T07:36:50.8318546Z 2025-09-07T07:36:50.8318798Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.8318877Z 2025-09-07T07:36:50.8318987Z warnings.warn(msg) 2025-09-07T07:36:50.8319065Z 2025-09-07T07:36:50.8319281Z --- Parse Warning: 134 / 146 --- 2025-09-07T07:36:50.8320251Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=SyclExtension in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/cpp_extension.py line=1420. 2025-09-07T07:36:50.8320514Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.8320635Z 2025-09-07T07:36:50.8320797Z Creates a :class:`setuptools.Extension` for SYCL/C++. 2025-09-07T07:36:50.8320888Z 2025-09-07T07:36:50.8321128Z Convenience method that creates a :class:`setuptools.Extension` with the 2025-09-07T07:36:50.8321324Z bare minimum (but often sufficient) arguments to build a SYCL/C++ 2025-09-07T07:36:50.8321428Z extension. 2025-09-07T07:36:50.8321508Z 2025-09-07T07:36:50.8321726Z All arguments are forwarded to the :class:`setuptools.Extension` 2025-09-07T07:36:50.8321813Z constructor. 2025-09-07T07:36:50.8321889Z 2025-09-07T07:36:50.8321989Z .. warning:: 2025-09-07T07:36:50.8322216Z The PyTorch python API (as provided in libtorch_python) cannot be built 2025-09-07T07:36:50.8322424Z with the flag ``py_limited_api=True``. When this flag is passed, it is 2025-09-07T07:36:50.8322660Z the user's responsibility in their library to not use APIs from 2025-09-07T07:36:50.8322894Z libtorch_python (in particular pytorch/python bindings) and to only use 2025-09-07T07:36:50.8323119Z APIs from libtorch (aten objects, operators and the dispatcher). For 2025-09-07T07:36:50.8323328Z example, to give access to custom ops from python, the library should 2025-09-07T07:36:50.8323469Z register the ops through the dispatcher. 2025-09-07T07:36:50.8323546Z 2025-09-07T07:36:50.8323925Z Contrary to CPython setuptools, who does not define -DPy_LIMITED_API 2025-09-07T07:36:50.8324143Z as a compile flag when py_limited_api is specified as an option for 2025-09-07T07:36:50.8324350Z the "bdist_wheel" command in ``setup``, PyTorch does! We will specify 2025-09-07T07:36:50.8324574Z -DPy_LIMITED_API=min_supported_cpython to best enforce consistency, 2025-09-07T07:36:50.8324784Z safety, and sanity in order to encourage best practices. To target a 2025-09-07T07:36:50.8324999Z different version, set min_supported_cpython to the hexcode of the 2025-09-07T07:36:50.8325187Z CPython version of choice. 2025-09-07T07:36:50.8325269Z 2025-09-07T07:36:50.8325354Z Example: 2025-09-07T07:36:50.8325464Z >>> # xdoctest: +SKIP 2025-09-07T07:36:50.8325612Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2025-09-07T07:36:50.8325846Z >>> from torch.utils.cpp_extension import BuildExtension, SyclExtension 2025-09-07T07:36:50.8325930Z >>> setup( 2025-09-07T07:36:50.8326032Z ... name='xpu_extension', 2025-09-07T07:36:50.8326136Z ... ext_modules=[ 2025-09-07T07:36:50.8326231Z ... SyclExtension( 2025-09-07T07:36:50.8326362Z ... name='xpu_extension', 2025-09-07T07:36:50.8326524Z ... sources=['extension.cpp', 'extension_kernel.cpp'], 2025-09-07T07:36:50.8326702Z ... extra_compile_args={'cxx': ['-g', '-std=c++20', '-fPIC']}) 2025-09-07T07:36:50.8326796Z ... ], 2025-09-07T07:36:50.8326891Z ... cmdclass={ 2025-09-07T07:36:50.8327016Z ... 'build_ext': BuildExtension 2025-09-07T07:36:50.8327096Z ... }) 2025-09-07T07:36:50.8327173Z 2025-09-07T07:36:50.8327481Z By default the extension will be compiled to run on all archs of the cards visible during the 2025-09-07T07:36:50.8327733Z building process of the extension. If down the road a new card is installed the 2025-09-07T07:36:50.8327998Z extension may need to be recompiled. You can override the default behavior using 2025-09-07T07:36:50.8328301Z `TORCH_XPU_ARCH_LIST` to explicitly specify which device architectures you want the extension 2025-09-07T07:36:50.8328423Z to support: 2025-09-07T07:36:50.8328516Z 2025-09-07T07:36:50.8328712Z ``TORCH_XPU_ARCH_LIST="pvc,xe-lpg" python build_my_extension.py`` 2025-09-07T07:36:50.8328803Z 2025-09-07T07:36:50.8329103Z Note that while it's possible to include all supported archs, the more archs get included the 2025-09-07T07:36:50.8329436Z slower the building process will be, as it will build a separate kernel image for each arch. 2025-09-07T07:36:50.8329528Z 2025-09-07T07:36:50.8329667Z Note: Ninja is required to build SyclExtension. 2025-09-07T07:36:50.8329755Z 2025-09-07T07:36:50.8330005Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.8330081Z 2025-09-07T07:36:50.8330188Z warnings.warn(msg) 2025-09-07T07:36:50.8330266Z 2025-09-07T07:36:50.8330483Z --- Parse Warning: 135 / 146 --- 2025-09-07T07:36:50.8331351Z /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=1597. 2025-09-07T07:36:50.8331611Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.8331744Z 2025-09-07T07:36:50.8331889Z Load a PyTorch C++ extension just-in-time (JIT). 2025-09-07T07:36:50.8331984Z 2025-09-07T07:36:50.8332190Z To load an extension, a Ninja build file is emitted, which is used to 2025-09-07T07:36:50.8332393Z compile the given sources into a dynamic library. This library is 2025-09-07T07:36:50.8332626Z subsequently loaded into the current Python process as a module and 2025-09-07T07:36:50.8332751Z returned from this function, ready for use. 2025-09-07T07:36:50.8332841Z 2025-09-07T07:36:50.8333045Z By default, the directory to which the build file is emitted and the 2025-09-07T07:36:50.8333277Z resulting library compiled to is ``/torch_extensions/``, where 2025-09-07T07:36:50.8333490Z ```` is the temporary folder on the current platform and ```` 2025-09-07T07:36:50.8333702Z the name of the extension. This location can be overridden in two ways. 2025-09-07T07:36:50.8333922Z First, if the ``TORCH_EXTENSIONS_DIR`` environment variable is set, it 2025-09-07T07:36:50.8334213Z replaces ``/torch_extensions`` and all extensions will be compiled 2025-09-07T07:36:50.8334433Z into subfolders of this directory. Second, if the ``build_directory`` 2025-09-07T07:36:50.8334674Z argument to this function is supplied, it overrides the entire path, i.e. 2025-09-07T07:36:50.8334838Z the library will be compiled into that folder directly. 2025-09-07T07:36:50.8334928Z 2025-09-07T07:36:50.8335135Z To compile the sources, the default system compiler (``c++``) is used, 2025-09-07T07:36:50.8335372Z which can be overridden by setting the ``CXX`` environment variable. To pass 2025-09-07T07:36:50.8335614Z additional arguments to the compilation process, ``extra_cflags`` or 2025-09-07T07:36:50.8335833Z ``extra_ldflags`` can be provided. For example, to compile your extension 2025-09-07T07:36:50.8336059Z with optimizations, pass ``extra_cflags=['-O3']``. You can also use 2025-09-07T07:36:50.8336214Z ``extra_cflags`` to pass further include directories. 2025-09-07T07:36:50.8336296Z 2025-09-07T07:36:50.8336544Z CUDA support with mixed compilation is provided. Simply pass CUDA source 2025-09-07T07:36:50.8336727Z files (``.cu`` or ``.cuh``) along with other sources. Such files will be 2025-09-07T07:36:50.8336978Z detected and compiled with nvcc rather than the C++ compiler. This includes 2025-09-07T07:36:50.8337189Z passing the CUDA lib64 directory as a library directory, and linking 2025-09-07T07:36:50.8337334Z ``cudart``. You can pass additional flags to nvcc via 2025-09-07T07:36:50.8337549Z ``extra_cuda_cflags``, just like with ``extra_cflags`` for C++. Various 2025-09-07T07:36:50.8337804Z heuristics for finding the CUDA install directory are used, which usually 2025-09-07T07:36:50.8338023Z work fine. If not, setting the ``CUDA_HOME`` environment variable is the 2025-09-07T07:36:50.8338111Z safest option. 2025-09-07T07:36:50.8338191Z 2025-09-07T07:36:50.8338435Z SYCL support with mixed compilation is provided. Simply pass SYCL source 2025-09-07T07:36:50.8338662Z files (``.sycl``) along with other sources. Such files will be detected 2025-09-07T07:36:50.8338888Z and compiled with SYCL compiler (such as Intel DPC++ Compiler) rather 2025-09-07T07:36:50.8339093Z than the C++ compiler. You can pass additional flags to SYCL compiler 2025-09-07T07:36:50.8339293Z via ``extra_sycl_cflags``, just like with ``extra_cflags`` for C++. 2025-09-07T07:36:50.8339496Z SYCL compiler is expected to be found via system PATH environment 2025-09-07T07:36:50.8339577Z variable. 2025-09-07T07:36:50.8339666Z 2025-09-07T07:36:50.8339745Z Args: 2025-09-07T07:36:50.8339953Z name: The name of the extension to build. This MUST be the same as the 2025-09-07T07:36:50.8340073Z name of the pybind11 module! 2025-09-07T07:36:50.8340271Z sources: A list of relative or absolute paths to C++ source files. 2025-09-07T07:36:50.8340528Z extra_cflags: optional list of compiler flags to forward to the build. 2025-09-07T07:36:50.8340746Z extra_cuda_cflags: optional list of compiler flags to forward to nvcc 2025-09-07T07:36:50.8340851Z when building CUDA sources. 2025-09-07T07:36:50.8341076Z extra_sycl_cflags: optional list of compiler flags to forward to SYCL 2025-09-07T07:36:50.8341196Z compiler when building SYCL sources. 2025-09-07T07:36:50.8341422Z extra_ldflags: optional list of linker flags to forward to the build. 2025-09-07T07:36:50.8341640Z extra_include_paths: optional list of include directories to forward 2025-09-07T07:36:50.8341742Z to the build. 2025-09-07T07:36:50.8341920Z build_directory: optional path to use as build workspace. 2025-09-07T07:36:50.8342096Z verbose: If ``True``, turns on verbose logging of load steps. 2025-09-07T07:36:50.8342328Z with_cuda: Determines whether CUDA headers and libraries are added to 2025-09-07T07:36:50.8342487Z the build. If set to ``None`` (default), this value is 2025-09-07T07:36:50.8342727Z automatically determined based on the existence of ``.cu`` or 2025-09-07T07:36:50.8342893Z ``.cuh`` in ``sources``. Set it to `True`` to force CUDA headers 2025-09-07T07:36:50.8343003Z and libraries to be included. 2025-09-07T07:36:50.8343231Z with_sycl: Determines whether SYCL headers and libraries are added to 2025-09-07T07:36:50.8343389Z the build. If set to ``None`` (default), this value is 2025-09-07T07:36:50.8343611Z automatically determined based on the existence of ``.sycl`` in 2025-09-07T07:36:50.8343811Z ``sources``. Set it to `True`` to force SYCL headers and 2025-09-07T07:36:50.8343919Z libraries to be included. 2025-09-07T07:36:50.8344137Z is_python_module: If ``True`` (default), imports the produced shared 2025-09-07T07:36:50.8344326Z library as a Python module. If ``False``, behavior depends on 2025-09-07T07:36:50.8344436Z ``is_standalone``. 2025-09-07T07:36:50.8344642Z is_standalone: If ``False`` (default) loads the constructed extension 2025-09-07T07:36:50.8344836Z into the process as a plain dynamic library. If ``True``, build a 2025-09-07T07:36:50.8344951Z standalone executable. 2025-09-07T07:36:50.8345029Z 2025-09-07T07:36:50.8345121Z Returns: 2025-09-07T07:36:50.8345233Z If ``is_python_module`` is ``True``: 2025-09-07T07:36:50.8345408Z Returns the loaded PyTorch extension as a Python module. 2025-09-07T07:36:50.8345502Z 2025-09-07T07:36:50.8345701Z If ``is_python_module`` is ``False`` and ``is_standalone`` is ``False``: 2025-09-07T07:36:50.8345946Z Returns nothing. (The shared library is loaded into the process as 2025-09-07T07:36:50.8346042Z a side effect.) 2025-09-07T07:36:50.8346122Z 2025-09-07T07:36:50.8346245Z If ``is_standalone`` is ``True``. 2025-09-07T07:36:50.8346443Z Return the path to the executable. (On Windows, TORCH_LIB_PATH is 2025-09-07T07:36:50.8346654Z added to the PATH environment variable as a side effect.) 2025-09-07T07:36:50.8346733Z 2025-09-07T07:36:50.8346816Z Example: 2025-09-07T07:36:50.8346925Z >>> # xdoctest: +SKIP 2025-09-07T07:36:50.8347060Z >>> from torch.utils.cpp_extension import load 2025-09-07T07:36:50.8347152Z >>> module = load( 2025-09-07T07:36:50.8347260Z ... name='extension', 2025-09-07T07:36:50.8347418Z ... sources=['extension.cpp', 'extension_kernel.cu'], 2025-09-07T07:36:50.8347529Z ... extra_cflags=['-O2'], 2025-09-07T07:36:50.8347620Z ... verbose=True) 2025-09-07T07:36:50.8347699Z 2025-09-07T07:36:50.8347961Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.8348040Z 2025-09-07T07:36:50.8348173Z warnings.warn(msg) 2025-09-07T07:36:50.8348251Z 2025-09-07T07:36:50.8348456Z --- Parse Warning: 136 / 146 --- 2025-09-07T07:36:50.8349376Z /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=1885. 2025-09-07T07:36:50.8349638Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.8349738Z 2025-09-07T07:36:50.8349947Z Load a PyTorch C++ extension just-in-time (JIT) from string sources. 2025-09-07T07:36:50.8350026Z 2025-09-07T07:36:50.8350277Z This function behaves exactly like :func:`load`, but takes its sources as 2025-09-07T07:36:50.8350510Z strings rather than filenames. These strings are stored to files in the 2025-09-07T07:36:50.8350731Z build directory, after which the behavior of :func:`load_inline` is 2025-09-07T07:36:50.8350835Z identical to :func:`load`. 2025-09-07T07:36:50.8350914Z 2025-09-07T07:36:50.8351011Z See `the 2025-09-07T07:36:50.8351369Z tests `_ 2025-09-07T07:36:50.8351507Z for good examples of using this function. 2025-09-07T07:36:50.8351586Z 2025-09-07T07:36:50.8351818Z Sources may omit two required parts of a typical non-inline C++ extension: 2025-09-07T07:36:50.8352072Z the necessary header includes, as well as the (pybind11) binding code. More 2025-09-07T07:36:50.8352311Z precisely, strings passed to ``cpp_sources`` are first concatenated into a 2025-09-07T07:36:50.8352506Z single ``.cpp`` file. This file is then prepended with ``#include 2025-09-07T07:36:50.8352603Z `` 2025-09-07T07:36:50.8352683Z 2025-09-07T07:36:50.8352923Z Furthermore, if the ``functions`` argument is supplied, bindings will be 2025-09-07T07:36:50.8353157Z automatically generated for each function specified. ``functions`` can 2025-09-07T07:36:50.8353389Z either be a list of function names, or a dictionary mapping from function 2025-09-07T07:36:50.8353613Z names to docstrings. If a list is given, the name of each function is used 2025-09-07T07:36:50.8353705Z as its docstring. 2025-09-07T07:36:50.8353800Z 2025-09-07T07:36:50.8354015Z The sources in ``cuda_sources`` are concatenated into a separate ``.cu`` 2025-09-07T07:36:50.8354209Z file and prepended with ``torch/types.h``, ``cuda.h`` and 2025-09-07T07:36:50.8354485Z ``cuda_runtime.h`` includes. The ``.cpp`` and ``.cu`` files are compiled 2025-09-07T07:36:50.8354709Z separately, but ultimately linked into a single library. Note that no 2025-09-07T07:36:50.8354983Z bindings are generated for functions in ``cuda_sources`` per se. To bind 2025-09-07T07:36:50.8355197Z to a CUDA kernel, you must create a C++ function that calls it, and either 2025-09-07T07:36:50.8355418Z declare or define this C++ function in one of the ``cpp_sources`` (and 2025-09-07T07:36:50.8355533Z include its name in ``functions``). 2025-09-07T07:36:50.8355636Z 2025-09-07T07:36:50.8355867Z The sources in ``sycl_sources`` are concatenated into a separate ``.sycl`` 2025-09-07T07:36:50.8356083Z file and prepended with ``torch/types.h``, ``sycl/sycl.hpp`` includes. 2025-09-07T07:36:50.8356292Z The ``.cpp`` and ``.sycl`` files are compiled separately, but ultimately 2025-09-07T07:36:50.8356507Z linked into a single library. Note that no bindings are generated for 2025-09-07T07:36:50.8356717Z functions in ``sycl_sources`` per se. To bind to a SYCL kernel, you must 2025-09-07T07:36:50.8356938Z create a C++ function that calls it, and either declare or define this 2025-09-07T07:36:50.8357126Z C++ function in one of the ``cpp_sources`` (and include its name 2025-09-07T07:36:50.8357233Z in ``functions``). 2025-09-07T07:36:50.8357312Z 2025-09-07T07:36:50.8357391Z 2025-09-07T07:36:50.8357505Z 2025-09-07T07:36:50.8357687Z See :func:`load` for a description of arguments omitted below. 2025-09-07T07:36:50.8357778Z 2025-09-07T07:36:50.8357859Z Args: 2025-09-07T07:36:50.8358070Z cpp_sources: A string, or list of strings, containing C++ source code. 2025-09-07T07:36:50.8358298Z cuda_sources: A string, or list of strings, containing CUDA source code. 2025-09-07T07:36:50.8358514Z sycl_sources: A string, or list of strings, containing SYCL source code. 2025-09-07T07:36:50.8358725Z functions: A list of function names for which to generate function 2025-09-07T07:36:50.8358931Z bindings. If a dictionary is given, it should map function names to 2025-09-07T07:36:50.8359112Z docstrings (which are otherwise just the function names). 2025-09-07T07:36:50.8359340Z with_cuda: Determines whether CUDA headers and libraries are added to 2025-09-07T07:36:50.8359495Z the build. If set to ``None`` (default), this value is 2025-09-07T07:36:50.8359710Z automatically determined based on whether ``cuda_sources`` is 2025-09-07T07:36:50.8359885Z provided. Set it to ``True`` to force CUDA headers 2025-09-07T07:36:50.8359993Z and libraries to be included. 2025-09-07T07:36:50.8360222Z with_sycl: Determines whether SYCL headers and libraries are added to 2025-09-07T07:36:50.8360377Z the build. If set to ``None`` (default), this value is 2025-09-07T07:36:50.8360589Z automatically determined based on whether ``sycl_sources`` is 2025-09-07T07:36:50.8360736Z provided. Set it to ``True`` to force SYCL headers 2025-09-07T07:36:50.8360842Z and libraries to be included. 2025-09-07T07:36:50.8361060Z with_pytorch_error_handling: Determines whether pytorch error and 2025-09-07T07:36:50.8361253Z warning macros are handled by pytorch instead of pybind. To do 2025-09-07T07:36:50.8361479Z this, each function ``foo`` is called via an intermediary ``_safe_foo`` 2025-09-07T07:36:50.8361677Z function. This redirection might cause issues in obscure cases 2025-09-07T07:36:50.8361862Z of cpp. This flag should be set to ``False`` when this redirect 2025-09-07T07:36:50.8361969Z causes issues. 2025-09-07T07:36:50.8362218Z no_implicit_headers: If ``True``, skips automatically adding headers, most notably 2025-09-07T07:36:50.8362458Z ``#include `` and ``#include `` lines. 2025-09-07T07:36:50.8362615Z Use this option to improve cold start times when you 2025-09-07T07:36:50.8362883Z already include the necessary headers in your source code. Default: ``False``. 2025-09-07T07:36:50.8362963Z 2025-09-07T07:36:50.8363046Z Example: 2025-09-07T07:36:50.8363228Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2025-09-07T07:36:50.8363387Z >>> from torch.utils.cpp_extension import load_inline 2025-09-07T07:36:50.8363478Z >>> source = """ 2025-09-07T07:36:50.8363632Z at::Tensor sin_add(at::Tensor x, at::Tensor y) { 2025-09-07T07:36:50.8363758Z return x.sin() + y.sin(); 2025-09-07T07:36:50.8363852Z } 2025-09-07T07:36:50.8363929Z """ 2025-09-07T07:36:50.8364066Z >>> module = load_inline(name='inline_extension', 2025-09-07T07:36:50.8364194Z ... cpp_sources=[source], 2025-09-07T07:36:50.8364308Z ... functions=['sin_add']) 2025-09-07T07:36:50.8364398Z 2025-09-07T07:36:50.8364480Z .. note:: 2025-09-07T07:36:50.8364715Z Since load_inline will just-in-time compile the source code, please ensure 2025-09-07T07:36:50.8364954Z that you have the right toolchains installed in the runtime. For example, 2025-09-07T07:36:50.8365170Z when loading C++, make sure a C++ compiler is available. If you're loading 2025-09-07T07:36:50.8365424Z a CUDA extension, you will need to additionally install the corresponding CUDA 2025-09-07T07:36:50.8365705Z toolkit (nvcc and any other dependencies your code has). Compiling toolchains 2025-09-07T07:36:50.8365946Z are not included when you install torch and must be additionally installed. 2025-09-07T07:36:50.8366037Z 2025-09-07T07:36:50.8366287Z During compiling, by default, the Ninja backend uses #CPUS + 2 workers to build 2025-09-07T07:36:50.8366515Z the extension. This may use up too many resources on some systems. One 2025-09-07T07:36:50.8366732Z can control the number of workers by setting the `MAX_JOBS` environment 2025-09-07T07:36:50.8366859Z variable to a non-negative number. 2025-09-07T07:36:50.8366938Z 2025-09-07T07:36:50.8367193Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.8367288Z 2025-09-07T07:36:50.8367385Z warnings.warn(msg) 2025-09-07T07:36:50.8367463Z 2025-09-07T07:36:50.8367694Z --- Parse Warning: 137 / 146 --- 2025-09-07T07:36:50.8368706Z /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. 2025-09-07T07:36:50.8368983Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.8369060Z 2025-09-07T07:36:50.8369366Z This class is a wrapper around a c++ component throughput_benchmark::ThroughputBenchmark. 2025-09-07T07:36:50.8369443Z 2025-09-07T07:36:50.8369737Z This wrapper on the throughput_benchmark::ThroughputBenchmark component is responsible 2025-09-07T07:36:50.8370006Z for executing a PyTorch module (nn.Module or ScriptModule) under an inference 2025-09-07T07:36:50.8370237Z server like load. It can emulate multiple calling threads to a single module 2025-09-07T07:36:50.8370491Z provided. In the future we plan to enhance this component to support inter and 2025-09-07T07:36:50.8370734Z intra-op parallelism as well as multiple models running in a single process. 2025-09-07T07:36:50.8370813Z 2025-09-07T07:36:50.8371078Z Please note that even though nn.Module is supported, it might incur an overhead 2025-09-07T07:36:50.8371295Z from the need to hold GIL every time we execute Python code or pass around 2025-09-07T07:36:50.8371542Z inputs as Python objects. As soon as you have a ScriptModule version of your 2025-09-07T07:36:50.8371775Z model for inference deployment it is better to switch to using it in this 2025-09-07T07:36:50.8371858Z benchmark. 2025-09-07T07:36:50.8371944Z 2025-09-07T07:36:50.8372028Z Example:: 2025-09-07T07:36:50.8372116Z 2025-09-07T07:36:50.8372232Z >>> # xdoctest: +SKIP("undefined vars") 2025-09-07T07:36:50.8372395Z >>> from torch.utils import ThroughputBenchmark 2025-09-07T07:36:50.8372532Z >>> bench = ThroughputBenchmark(my_module) 2025-09-07T07:36:50.8372690Z >>> # Pre-populate benchmark's data set with the inputs 2025-09-07T07:36:50.8372799Z >>> for input in inputs: 2025-09-07T07:36:50.8373042Z ... # Both args and kwargs work, same as any PyTorch Module / ScriptModule 2025-09-07T07:36:50.8373169Z ... bench.add_input(input[0], x2=input[1]) 2025-09-07T07:36:50.8373399Z >>> # Inputs supplied above are randomly used during the execution 2025-09-07T07:36:50.8373499Z >>> stats = bench.benchmark( 2025-09-07T07:36:50.8373613Z ... num_calling_threads=4, 2025-09-07T07:36:50.8373716Z ... num_warmup_iters = 100, 2025-09-07T07:36:50.8373808Z ... num_iters = 1000, 2025-09-07T07:36:50.8373899Z ... ) 2025-09-07T07:36:50.8374077Z >>> print("Avg latency (ms): {}".format(stats.latency_avg_ms)) 2025-09-07T07:36:50.8374257Z >>> print("Number of iterations: {}".format(stats.num_iters)) 2025-09-07T07:36:50.8374333Z 2025-09-07T07:36:50.8374582Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.8374694Z 2025-09-07T07:36:50.8374788Z warnings.warn(msg) 2025-09-07T07:36:50.8374867Z 2025-09-07T07:36:50.8375072Z --- Parse Warning: 138 / 146 --- 2025-09-07T07:36:50.8376018Z /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. 2025-09-07T07:36:50.8376289Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.8376486Z Sampler that restricts data loading to a subset of the dataset. 2025-09-07T07:36:50.8376574Z 2025-09-07T07:36:50.8376706Z It is especially useful in conjunction with 2025-09-07T07:36:50.8376947Z :class:`torch.nn.parallel.DistributedDataParallel`. In such a case, each 2025-09-07T07:36:50.8377216Z process can pass a :class:`~torch.utils.data.DistributedSampler` instance as a 2025-09-07T07:36:50.8377443Z :class:`~torch.utils.data.DataLoader` sampler, and load a subset of the 2025-09-07T07:36:50.8377611Z original dataset that is exclusive to it. 2025-09-07T07:36:50.8377690Z 2025-09-07T07:36:50.8377775Z .. note:: 2025-09-07T07:36:50.8378017Z Dataset is assumed to be of constant size and that any instance of it always 2025-09-07T07:36:50.8378154Z returns the same elements in the same order. 2025-09-07T07:36:50.8378244Z 2025-09-07T07:36:50.8378325Z Args: 2025-09-07T07:36:50.8378442Z dataset: Dataset used for sampling. 2025-09-07T07:36:50.8378666Z num_replicas (int, optional): Number of processes participating in 2025-09-07T07:36:50.8378913Z distributed training. By default, :attr:`world_size` is retrieved from the 2025-09-07T07:36:50.8379037Z current distributed group. 2025-09-07T07:36:50.8379275Z rank (int, optional): Rank of the current process within :attr:`num_replicas`. 2025-09-07T07:36:50.8379477Z By default, :attr:`rank` is retrieved from the current distributed 2025-09-07T07:36:50.8379576Z group. 2025-09-07T07:36:50.8379794Z shuffle (bool, optional): If ``True`` (default), sampler will shuffle the 2025-09-07T07:36:50.8379890Z indices. 2025-09-07T07:36:50.8380079Z seed (int, optional): random seed used to shuffle the sampler if 2025-09-07T07:36:50.8380273Z :attr:`shuffle=True`. This number should be identical across all 2025-09-07T07:36:50.8380448Z processes in the distributed group. Default: ``0``. 2025-09-07T07:36:50.8384742Z drop_last (bool, optional): if ``True``, then the sampler will drop the 2025-09-07T07:36:50.8385044Z tail of the data to make it evenly divisible across the number of 2025-09-07T07:36:50.8385263Z replicas. If ``False``, the sampler will add extra indices to make 2025-09-07T07:36:50.8385474Z the data evenly divisible across the replicas. Default: ``False``. 2025-09-07T07:36:50.8385597Z 2025-09-07T07:36:50.8385690Z .. warning:: 2025-09-07T07:36:50.8385877Z In distributed mode, calling the :meth:`set_epoch` method at 2025-09-07T07:36:50.8386142Z the beginning of each epoch **before** creating the :class:`DataLoader` iterator 2025-09-07T07:36:50.8386402Z is necessary to make shuffling work properly across multiple epochs. Otherwise, 2025-09-07T07:36:50.8386539Z the same ordering will be always used. 2025-09-07T07:36:50.8386619Z 2025-09-07T07:36:50.8386705Z Example:: 2025-09-07T07:36:50.8386801Z 2025-09-07T07:36:50.8386902Z >>> # xdoctest: +SKIP 2025-09-07T07:36:50.8387136Z >>> sampler = DistributedSampler(dataset) if is_distributed else None 2025-09-07T07:36:50.8387304Z >>> loader = DataLoader(dataset, shuffle=(sampler is None), 2025-09-07T07:36:50.8387452Z ... sampler=sampler) 2025-09-07T07:36:50.8387603Z >>> for epoch in range(start_epoch, n_epochs): 2025-09-07T07:36:50.8387729Z ... if is_distributed: 2025-09-07T07:36:50.8387857Z ... sampler.set_epoch(epoch) 2025-09-07T07:36:50.8387948Z ... train(loader) 2025-09-07T07:36:50.8388029Z 2025-09-07T07:36:50.8388294Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.8388371Z 2025-09-07T07:36:50.8388484Z warnings.warn(msg) 2025-09-07T07:36:50.8388563Z 2025-09-07T07:36:50.8388796Z --- Parse Warning: 139 / 146 --- 2025-09-07T07:36:50.8389783Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=WeightedRandomSampler in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/sampler.py line=227. 2025-09-07T07:36:50.8390044Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.8390352Z Samples elements from ``[0,..,len(weights)-1]`` with given probabilities (weights). 2025-09-07T07:36:50.8390432Z 2025-09-07T07:36:50.8390514Z Args: 2025-09-07T07:36:50.8390772Z weights (sequence) : a sequence of weights, not necessary summing up to one 2025-09-07T07:36:50.8390909Z num_samples (int): number of samples to draw 2025-09-07T07:36:50.8391131Z replacement (bool): if ``True``, samples are drawn with replacement. 2025-09-07T07:36:50.8391333Z If not, they are drawn without replacement, which means that when a 2025-09-07T07:36:50.8391551Z sample index is drawn for a row, it cannot be drawn again for that row. 2025-09-07T07:36:50.8391724Z generator (Generator): Generator used in sampling. 2025-09-07T07:36:50.8391804Z 2025-09-07T07:36:50.8391902Z Example: 2025-09-07T07:36:50.8392043Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2025-09-07T07:36:50.8392128Z >>> list( 2025-09-07T07:36:50.8392260Z ... WeightedRandomSampler( 2025-09-07T07:36:50.8392397Z ... [0.1, 0.9, 0.4, 0.7, 3.0, 0.6], 5, replacement=True 2025-09-07T07:36:50.8392493Z ... ) 2025-09-07T07:36:50.8392571Z ... ) 2025-09-07T07:36:50.8392654Z [4, 4, 1, 4, 5] 2025-09-07T07:36:50.8392750Z >>> list( 2025-09-07T07:36:50.8392859Z ... WeightedRandomSampler( 2025-09-07T07:36:50.8393009Z ... [0.9, 0.4, 0.05, 0.2, 0.3, 0.1], 5, replacement=False 2025-09-07T07:36:50.8393088Z ... ) 2025-09-07T07:36:50.8393168Z ... ) 2025-09-07T07:36:50.8393289Z [0, 1, 4, 3, 2] 2025-09-07T07:36:50.8393367Z 2025-09-07T07:36:50.8393628Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.8393704Z 2025-09-07T07:36:50.8393799Z warnings.warn(msg) 2025-09-07T07:36:50.8393887Z 2025-09-07T07:36:50.8394076Z --- Parse Warning: 140 / 146 --- 2025-09-07T07:36:50.8395117Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=BatchSampler in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/sampler.py line=300. 2025-09-07T07:36:50.8395394Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.8395562Z Wraps another sampler to yield a mini-batch of indices. 2025-09-07T07:36:50.8395652Z 2025-09-07T07:36:50.8395733Z Args: 2025-09-07T07:36:50.8395972Z sampler (Sampler or Iterable): Base sampler. Can be any iterable object 2025-09-07T07:36:50.8396097Z batch_size (int): Size of mini-batch. 2025-09-07T07:36:50.8396297Z drop_last (bool): If ``True``, the sampler will drop the last batch if 2025-09-07T07:36:50.8396473Z its size would be less than ``batch_size`` 2025-09-07T07:36:50.8396551Z 2025-09-07T07:36:50.8396638Z Example: 2025-09-07T07:36:50.8396737Z >>> list( 2025-09-07T07:36:50.8396834Z ... BatchSampler( 2025-09-07T07:36:50.8397051Z ... SequentialSampler(range(10)), batch_size=3, drop_last=False 2025-09-07T07:36:50.8397135Z ... ) 2025-09-07T07:36:50.8397216Z ... ) 2025-09-07T07:36:50.8397332Z [[0, 1, 2], [3, 4, 5], [6, 7, 8], [9]] 2025-09-07T07:36:50.8397417Z >>> list( 2025-09-07T07:36:50.8397688Z ... BatchSampler(SequentialSampler(range(10)), batch_size=3, drop_last=True) 2025-09-07T07:36:50.8397768Z ... ) 2025-09-07T07:36:50.8397865Z [[0, 1, 2], [3, 4, 5], [6, 7, 8]] 2025-09-07T07:36:50.8397959Z 2025-09-07T07:36:50.8398209Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.8398301Z 2025-09-07T07:36:50.8398399Z warnings.warn(msg) 2025-09-07T07:36:50.8398475Z 2025-09-07T07:36:50.8398715Z --- Parse Warning: 141 / 146 --- 2025-09-07T07:36:50.8399673Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=IterDataPipe in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/datapipe.py line=56. 2025-09-07T07:36:50.8399940Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.8400017Z 2025-09-07T07:36:50.8400121Z Iterable-style DataPipe. 2025-09-07T07:36:50.8400210Z 2025-09-07T07:36:50.8400466Z All DataPipes that represent an iterable of data samples should subclass this. 2025-09-07T07:36:50.8400730Z This style of DataPipes is particularly useful when data come from a stream, or 2025-09-07T07:36:50.8401089Z when the number of samples is too large to fit them all in memory. ``IterDataPipe`` is lazily initialized and its 2025-09-07T07:36:50.8401385Z elements are computed only when ``next()`` is called on the iterator of an ``IterDataPipe``. 2025-09-07T07:36:50.8401480Z 2025-09-07T07:36:50.8401705Z All subclasses should overwrite :meth:`__iter__`, which would return an 2025-09-07T07:36:50.8402062Z iterator of samples in this DataPipe. Calling ``__iter__`` of an ``IterDataPipe`` automatically invokes its 2025-09-07T07:36:50.8402431Z method ``reset()``, which by default performs no operation. When writing a custom ``IterDataPipe``, users should 2025-09-07T07:36:50.8402722Z override ``reset()`` if necessary. The common usages include resetting buffers, pointers, 2025-09-07T07:36:50.8402920Z and various state variables within the custom ``IterDataPipe``. 2025-09-07T07:36:50.8403022Z 2025-09-07T07:36:50.8403117Z Note: 2025-09-07T07:36:50.8403326Z Only `one` iterator can be valid for each ``IterDataPipe`` at a time, 2025-09-07T07:36:50.8403688Z and the creation a second iterator will invalidate the first one. This constraint is necessary because 2025-09-07T07:36:50.8404107Z some ``IterDataPipe`` have internal buffers, whose states can become invalid if there are multiple iterators. 2025-09-07T07:36:50.8404371Z The code example below presents details on how this constraint looks in practice. 2025-09-07T07:36:50.8404748Z If you have any feedback related to this constraint, please see `GitHub IterDataPipe Single Iterator Issue`_. 2025-09-07T07:36:50.8404827Z 2025-09-07T07:36:50.8405117Z These DataPipes can be invoked in two ways, using the class constructor or applying their 2025-09-07T07:36:50.8405479Z functional form onto an existing ``IterDataPipe`` (recommended, available to most but not all DataPipes). 2025-09-07T07:36:50.8405793Z You can chain multiple `IterDataPipe` together to form a pipeline that will perform multiple 2025-09-07T07:36:50.8405909Z operations in succession. 2025-09-07T07:36:50.8405989Z 2025-09-07T07:36:50.8406177Z .. _GitHub IterDataPipe Single Iterator Issue: 2025-09-07T07:36:50.8406317Z https://github.com/pytorch/data/issues/45 2025-09-07T07:36:50.8406398Z 2025-09-07T07:36:50.8406492Z Note: 2025-09-07T07:36:50.8406719Z When a subclass is used with :class:`~torch.utils.data.DataLoader`, each 2025-09-07T07:36:50.8406993Z item in the DataPipe will be yielded from the :class:`~torch.utils.data.DataLoader` 2025-09-07T07:36:50.8407212Z iterator. When :attr:`num_workers > 0`, each worker process will have a 2025-09-07T07:36:50.8407447Z different copy of the DataPipe object, so it is often desired to configure 2025-09-07T07:36:50.8407695Z each copy independently to avoid having duplicate data returned from the 2025-09-07T07:36:50.8407933Z workers. :func:`~torch.utils.data.get_worker_info`, when called in a worker 2025-09-07T07:36:50.8408194Z process, returns information about the worker. It can be used in either the 2025-09-07T07:36:50.8408439Z dataset's :meth:`__iter__` method or the :class:`~torch.utils.data.DataLoader` 's 2025-09-07T07:36:50.8408649Z :attr:`worker_init_fn` option to modify each copy's behavior. 2025-09-07T07:36:50.8408742Z 2025-09-07T07:36:50.8408827Z Examples: 2025-09-07T07:36:50.8408930Z General Usage: 2025-09-07T07:36:50.8409026Z >>> # xdoctest: +SKIP 2025-09-07T07:36:50.8409234Z >>> from torchdata.datapipes.iter import IterableWrapper, Mapper 2025-09-07T07:36:50.8409366Z >>> dp = IterableWrapper(range(10)) 2025-09-07T07:36:50.8409550Z >>> map_dp_1 = Mapper(dp, lambda x: x + 1) # Using class constructor 2025-09-07T07:36:50.8409659Z >>> map_dp_2 = dp.map( 2025-09-07T07:36:50.8409762Z ... lambda x: x + 1 2025-09-07T07:36:50.8409892Z ... ) # Using functional form (recommended) 2025-09-07T07:36:50.8409996Z >>> list(map_dp_1) 2025-09-07T07:36:50.8410091Z [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] 2025-09-07T07:36:50.8410198Z >>> list(map_dp_2) 2025-09-07T07:36:50.8410292Z [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] 2025-09-07T07:36:50.8410440Z >>> filter_dp = map_dp_1.filter(lambda x: x % 2 == 0) 2025-09-07T07:36:50.8410543Z >>> list(filter_dp) 2025-09-07T07:36:50.8410631Z [2, 4, 6, 8, 10] 2025-09-07T07:36:50.8410757Z Single Iterator Constraint Example: 2025-09-07T07:36:50.8410962Z >>> from torchdata.datapipes.iter import IterableWrapper, Mapper 2025-09-07T07:36:50.8411088Z >>> source_dp = IterableWrapper(range(10)) 2025-09-07T07:36:50.8411196Z >>> it1 = iter(source_dp) 2025-09-07T07:36:50.8411284Z >>> list(it1) 2025-09-07T07:36:50.8411413Z [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] 2025-09-07T07:36:50.8411514Z >>> it1 = iter(source_dp) 2025-09-07T07:36:50.8411603Z >>> it2 = iter( 2025-09-07T07:36:50.8411704Z ... source_dp 2025-09-07T07:36:50.8411863Z ... ) # The creation of a new iterator invalidates `it1` 2025-09-07T07:36:50.8411951Z >>> next(it2) 2025-09-07T07:36:50.8412074Z 0 2025-09-07T07:36:50.8412259Z >>> next(it1) # Further usage of `it1` will raise a `RunTimeError` 2025-09-07T07:36:50.8412347Z 2025-09-07T07:36:50.8412598Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.8412676Z 2025-09-07T07:36:50.8412786Z warnings.warn(msg) 2025-09-07T07:36:50.8412863Z 2025-09-07T07:36:50.8413071Z --- Parse Warning: 142 / 146 --- 2025-09-07T07:36:50.8414152Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=DemultiplexerIterDataPipe in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/combining.py line=375. 2025-09-07T07:36:50.8414412Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.8414533Z 2025-09-07T07:36:50.8414988Z Splits the input DataPipe into multiple child DataPipes, using the given classification function (functional name: ``demux``). 2025-09-07T07:36:50.8415083Z 2025-09-07T07:36:50.8415268Z A list of the child DataPipes is returned from this operation. 2025-09-07T07:36:50.8415344Z 2025-09-07T07:36:50.8415439Z Args: 2025-09-07T07:36:50.8415574Z datapipe: Iterable DataPipe being filtered 2025-09-07T07:36:50.8415774Z num_instances: number of instances of the DataPipe to create 2025-09-07T07:36:50.8416147Z classifier_fn: a function that maps values to an integer within the range ``[0, num_instances - 1]`` or ``None`` 2025-09-07T07:36:50.8416474Z drop_none: defaults to ``False``, if ``True``, the function will skip over elements classified as ``None`` 2025-09-07T07:36:50.8416803Z buffer_size: this defines the maximum number of inputs that the buffer can hold across all child 2025-09-07T07:36:50.8416984Z DataPipes while waiting for their values to be yielded. 2025-09-07T07:36:50.8417193Z Defaults to ``1000``. Use ``-1`` for the unlimited buffer. 2025-09-07T07:36:50.8417272Z 2025-09-07T07:36:50.8417367Z Examples: 2025-09-07T07:36:50.8417490Z >>> # xdoctest: +REQUIRES(module:torchdata) 2025-09-07T07:36:50.8417665Z >>> from torchdata.datapipes.iter import IterableWrapper 2025-09-07T07:36:50.8417772Z >>> def odd_or_even(n): 2025-09-07T07:36:50.8417865Z ... return n % 2 2025-09-07T07:36:50.8417988Z >>> source_dp = IterableWrapper(range(5)) 2025-09-07T07:36:50.8418207Z >>> dp1, dp2 = source_dp.demux(num_instances=2, classifier_fn=odd_or_even) 2025-09-07T07:36:50.8418293Z >>> list(dp1) 2025-09-07T07:36:50.8418388Z [0, 2, 4] 2025-09-07T07:36:50.8418473Z >>> list(dp2) 2025-09-07T07:36:50.8418553Z [1, 3] 2025-09-07T07:36:50.8418802Z >>> # It can also filter out any element that gets `None` from the `classifier_fn` 2025-09-07T07:36:50.8418909Z >>> def odd_or_even_no_zero(n): 2025-09-07T07:36:50.8419037Z ... return n % 2 if n != 0 else None 2025-09-07T07:36:50.8419140Z >>> dp1, dp2 = source_dp.demux( 2025-09-07T07:36:50.8419354Z ... num_instances=2, classifier_fn=odd_or_even_no_zero, drop_none=True 2025-09-07T07:36:50.8419445Z ... ) 2025-09-07T07:36:50.8419530Z >>> list(dp1) 2025-09-07T07:36:50.8419622Z [2, 4] 2025-09-07T07:36:50.8419706Z >>> list(dp2) 2025-09-07T07:36:50.8419785Z [1, 3] 2025-09-07T07:36:50.8419877Z 2025-09-07T07:36:50.8420131Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.8420223Z 2025-09-07T07:36:50.8420344Z warnings.warn(msg) 2025-09-07T07:36:50.8420423Z 2025-09-07T07:36:50.8420626Z --- Parse Warning: 143 / 146 --- 2025-09-07T07:36:50.8421684Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=MultiplexerIterDataPipe in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/combining.py line=594. 2025-09-07T07:36:50.8421985Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.8422063Z 2025-09-07T07:36:50.8422370Z Yields one element at a time from each of the input Iterable DataPipes (functional name: ``mux``). 2025-09-07T07:36:50.8422458Z 2025-09-07T07:36:50.8422806Z As in, one element from the 1st input DataPipe, then one element from the 2nd DataPipe in the next iteration, 2025-09-07T07:36:50.8423002Z and so on. It ends when the shortest input DataPipe is exhausted. 2025-09-07T07:36:50.8423082Z 2025-09-07T07:36:50.8423165Z Args: 2025-09-07T07:36:50.8423586Z datapipes: Iterable DataPipes that will take turn to yield their elements, until the shortest DataPipe is exhausted 2025-09-07T07:36:50.8423870Z 2025-09-07T07:36:50.8424054Z Example: 2025-09-07T07:36:50.8424179Z >>> # xdoctest: +REQUIRES(module:torchdata) 2025-09-07T07:36:50.8424356Z >>> from torchdata.datapipes.iter import IterableWrapper 2025-09-07T07:36:50.8424466Z >>> dp1, dp2, dp3 = ( 2025-09-07T07:36:50.8424578Z ... IterableWrapper(range(3)), 2025-09-07T07:36:50.8424706Z ... IterableWrapper(range(10, 15)), 2025-09-07T07:36:50.8424822Z ... IterableWrapper(range(20, 25)), 2025-09-07T07:36:50.8424903Z ... ) 2025-09-07T07:36:50.8425018Z >>> list(dp1.mux(dp2, dp3)) 2025-09-07T07:36:50.8425110Z [0, 10, 20, 1, 11, 21, 2, 12, 22] 2025-09-07T07:36:50.8425187Z 2025-09-07T07:36:50.8425453Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.8425532Z 2025-09-07T07:36:50.8425638Z warnings.warn(msg) 2025-09-07T07:36:50.8425715Z 2025-09-07T07:36:50.8425907Z --- Parse Warning: 144 / 146 --- 2025-09-07T07:36:50.8426995Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ZipperIterDataPipe in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/combining.py line=665. 2025-09-07T07:36:50.8427257Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.8427342Z 2025-09-07T07:36:50.8427700Z Aggregates elements into a tuple from each of the input DataPipes (functional name: ``zip``). 2025-09-07T07:36:50.8427778Z 2025-09-07T07:36:50.8428014Z The output is stopped as soon as the shortest input DataPipe is exhausted. 2025-09-07T07:36:50.8428092Z 2025-09-07T07:36:50.8428184Z Args: 2025-09-07T07:36:50.8428334Z *datapipes: Iterable DataPipes being aggregated 2025-09-07T07:36:50.8428410Z 2025-09-07T07:36:50.8428505Z Example: 2025-09-07T07:36:50.8428622Z >>> # xdoctest: +REQUIRES(module:torchdata) 2025-09-07T07:36:50.8428808Z >>> from torchdata.datapipes.iter import IterableWrapper 2025-09-07T07:36:50.8428904Z >>> dp1, dp2, dp3 = ( 2025-09-07T07:36:50.8429018Z ... IterableWrapper(range(5)), 2025-09-07T07:36:50.8429147Z ... IterableWrapper(range(10, 15)), 2025-09-07T07:36:50.8429257Z ... IterableWrapper(range(20, 25)), 2025-09-07T07:36:50.8429347Z ... ) 2025-09-07T07:36:50.8429449Z >>> list(dp1.zip(dp2, dp3)) 2025-09-07T07:36:50.8429579Z [(0, 10, 20), (1, 11, 21), (2, 12, 22), (3, 13, 23), (4, 14, 24)] 2025-09-07T07:36:50.8429670Z 2025-09-07T07:36:50.8429921Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.8430011Z 2025-09-07T07:36:50.8430105Z warnings.warn(msg) 2025-09-07T07:36:50.8430220Z 2025-09-07T07:36:50.8430420Z --- Parse Warning: 145 / 146 --- 2025-09-07T07:36:50.8431474Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=FileOpenerIterDataPipe in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/fileopener.py line=18. 2025-09-07T07:36:50.8431791Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.8431870Z 2025-09-07T07:36:50.8432244Z Given pathnames, opens files and yield pathname and file stream in a tuple (functional name: ``open_files``). 2025-09-07T07:36:50.8432334Z 2025-09-07T07:36:50.8432415Z Args: 2025-09-07T07:36:50.8432589Z datapipe: Iterable datapipe that provides pathnames 2025-09-07T07:36:50.8432761Z mode: An optional string that specifies the mode in which 2025-09-07T07:36:50.8432970Z the file is opened by ``open()``. It defaults to ``r``, other options are 2025-09-07T07:36:50.8433145Z ``b`` for reading in binary mode and ``t`` for text mode. 2025-09-07T07:36:50.8433341Z encoding: An optional string that specifies the encoding of the 2025-09-07T07:36:50.8433643Z underlying file. It defaults to ``None`` to match the default encoding of ``open``. 2025-09-07T07:36:50.8433767Z length: Nominal length of the datapipe 2025-09-07T07:36:50.8433844Z 2025-09-07T07:36:50.8433937Z Note: 2025-09-07T07:36:50.8434205Z The opened file handles will be closed by Python's GC periodically. Users can choose 2025-09-07T07:36:50.8434321Z to close them explicitly. 2025-09-07T07:36:50.8434472Z 2025-09-07T07:36:50.8434559Z Example: 2025-09-07T07:36:50.8434669Z >>> # xdoctest: +SKIP 2025-09-07T07:36:50.8434801Z >>> from torchdata.datapipes.iter import ( 2025-09-07T07:36:50.8434893Z ... FileLister, 2025-09-07T07:36:50.8434996Z ... FileOpener, 2025-09-07T07:36:50.8435090Z ... StreamReader, 2025-09-07T07:36:50.8435183Z ... ) 2025-09-07T07:36:50.8435395Z >>> dp = FileLister(root=".").filter(lambda fname: fname.endswith(".txt")) 2025-09-07T07:36:50.8435496Z >>> dp = FileOpener(dp) 2025-09-07T07:36:50.8435609Z >>> dp = StreamReader(dp) 2025-09-07T07:36:50.8435732Z >>> list(dp) 2025-09-07T07:36:50.8435836Z [('./abc.txt', 'abc')] 2025-09-07T07:36:50.8435914Z 2025-09-07T07:36:50.8436165Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.8436254Z 2025-09-07T07:36:50.8436348Z warnings.warn(msg) 2025-09-07T07:36:50.8436437Z 2025-09-07T07:36:50.8436630Z --- Parse Warning: 146 / 146 --- 2025-09-07T07:36:50.8437656Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=GrouperIterDataPipe in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/grouping.py line=155. 2025-09-07T07:36:50.8437932Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-09-07T07:36:50.8438011Z 2025-09-07T07:36:50.8438440Z Groups data from IterDataPipe by keys from ``group_key_fn``, yielding a ``DataChunk`` with batch size up to ``group_size``. 2025-09-07T07:36:50.8438521Z 2025-09-07T07:36:50.8438627Z (functional name: ``groupby``). 2025-09-07T07:36:50.8438717Z 2025-09-07T07:36:50.8439101Z The samples are read sequentially from the source ``datapipe``, and a batch of samples belonging to the same group 2025-09-07T07:36:50.8439410Z will be yielded as soon as the size of the batch reaches ``group_size``. When the buffer is full, 2025-09-07T07:36:50.8439713Z the DataPipe will yield the largest batch with the same key, provided that its size is larger 2025-09-07T07:36:50.8440031Z than ``guaranteed_group_size``. If its size is smaller, it will be dropped if ``drop_remaining=True``. 2025-09-07T07:36:50.8440157Z 2025-09-07T07:36:50.8440540Z After iterating through the entirety of source ``datapipe``, everything not dropped due to the buffer capacity 2025-09-07T07:36:50.8440869Z will be yielded from the buffer, even if the group sizes are smaller than ``guaranteed_group_size``. 2025-09-07T07:36:50.8440973Z 2025-09-07T07:36:50.8441066Z Args: 2025-09-07T07:36:50.8441195Z datapipe: Iterable datapipe to be grouped 2025-09-07T07:36:50.8441470Z group_key_fn: Function used to generate group key from the data of the source datapipe 2025-09-07T07:36:50.8441720Z keep_key: Option to yield the matching key along with the items in a tuple, 2025-09-07T07:36:50.8441895Z resulting in `(key, [items])` otherwise returning [items] 2025-09-07T07:36:50.8442120Z buffer_size: The size of buffer for ungrouped data 2025-09-07T07:36:50.8442397Z group_size: The max size of each group, a batch is yielded as soon as it reaches this size 2025-09-07T07:36:50.8442729Z guaranteed_group_size: The guaranteed minimum group size to be yielded in case the buffer is full 2025-09-07T07:36:50.8443094Z drop_remaining: Specifies if the group smaller than ``guaranteed_group_size`` will be dropped from buffer 2025-09-07T07:36:50.8443222Z when the buffer is full 2025-09-07T07:36:50.8443316Z 2025-09-07T07:36:50.8443398Z Example: 2025-09-07T07:36:50.8443484Z >>> import os 2025-09-07T07:36:50.8443589Z >>> # xdoctest: +SKIP 2025-09-07T07:36:50.8443761Z >>> from torchdata.datapipes.iter import IterableWrapper 2025-09-07T07:36:50.8443868Z >>> def group_fn(file): 2025-09-07T07:36:50.8444006Z ... return os.path.basename(file).split(".")[0] 2025-09-07T07:36:50.8444116Z >>> source_dp = IterableWrapper( 2025-09-07T07:36:50.8444285Z ... ["a.png", "b.png", "a.json", "b.json", "a.jpg", "c.json"] 2025-09-07T07:36:50.8444363Z ... ) 2025-09-07T07:36:50.8444513Z >>> dp0 = source_dp.groupby(group_key_fn=group_fn) 2025-09-07T07:36:50.8444598Z >>> list(dp0) 2025-09-07T07:36:50.8444755Z [['a.png', 'a.json', 'a.jpg'], ['b.png', 'b.json'], ['c.json']] 2025-09-07T07:36:50.8444953Z >>> # A group is yielded as soon as its size equals to `group_size` 2025-09-07T07:36:50.8445157Z >>> dp1 = source_dp.groupby(group_key_fn=group_fn, group_size=2) 2025-09-07T07:36:50.8445253Z >>> list(dp1) 2025-09-07T07:36:50.8445409Z [['a.png', 'a.json'], ['b.png', 'b.json'], ['a.jpg'], ['c.json']] 2025-09-07T07:36:50.8445756Z >>> # Scenario where `buffer` is full, and group 'a' needs to be yielded since its size > `guaranteed_group_size` 2025-09-07T07:36:50.8445870Z >>> dp2 = source_dp.groupby( 2025-09-07T07:36:50.8445973Z ... group_key_fn=group_fn, 2025-09-07T07:36:50.8446077Z ... buffer_size=3, 2025-09-07T07:36:50.8446166Z ... group_size=3, 2025-09-07T07:36:50.8446268Z ... guaranteed_group_size=2, 2025-09-07T07:36:50.8446359Z ... ) 2025-09-07T07:36:50.8446442Z >>> list(dp2) 2025-09-07T07:36:50.8446600Z [['a.png', 'a.json'], ['b.png', 'b.json'], ['a.jpg'], ['c.json']] 2025-09-07T07:36:50.8446687Z 2025-09-07T07:36:50.8446939Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-09-07T07:36:50.8447031Z 2025-09-07T07:36:50.8447126Z warnings.warn(msg) 2025-09-07T07:36:50.8447204Z 2025-09-07T07:36:50.8447327Z  2025-09-07T07:36:50.8447499Z === Found 8 run-time warnings === 2025-09-07T07:36:50.8447688Z --- Runtime Warning: 1 / 8 --- 2025-09-07T07:36:50.8447947Z example = 2025-09-07T07:36:50.8449322Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py:1351: 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:1974.) 2025-09-07T07:36:50.8449448Z return super().refine_names(names) 2025-09-07T07:36:50.8449531Z 2025-09-07T07:36:50.8449719Z --- Runtime Warning: 2 / 8 --- 2025-09-07T07:36:50.8450072Z example = 2025-09-07T07:36:50.8450714Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py:282: UserWarning: Warning only once for all operators, other operators may also be overridden. 2025-09-07T07:36:50.8451029Z Overriding a previously registered kernel for the same operator and the same dispatch key 2025-09-07T07:36:50.8451233Z operator: aten::div.Tensor(Tensor self, Tensor other) -> Tensor 2025-09-07T07:36:50.8451538Z registered at /var/lib/jenkins/workspace/build/aten/src/ATen/RegisterSchema.cpp:6 2025-09-07T07:36:50.8451642Z dispatch key: CPU 2025-09-07T07:36:50.8452080Z previous kernel: registered at /var/lib/jenkins/workspace/aten/src/ATen/LegacyBatchingRegistrations.cpp:1079 2025-09-07T07:36:50.8453079Z new kernel: registered at :1 (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/core/dispatch/OperatorEntry.cpp:225.) 2025-09-07T07:36:50.8453273Z impl_fn(self.ns, name.split("::")[-1], dispatch_key) 2025-09-07T07:36:50.8453352Z 2025-09-07T07:36:50.8453530Z --- Runtime Warning: 3 / 8 --- 2025-09-07T07:36:50.8453778Z example = 2025-09-07T07:36:50.8455627Z /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.) 2025-09-07T07:36:50.8455881Z return torch._nested_tensor_from_tensor_list(ts, dtype, None, device, None) 2025-09-07T07:36:50.8455959Z 2025-09-07T07:36:50.8456170Z --- Runtime Warning: 4 / 8 --- 2025-09-07T07:36:50.8456473Z example = 2025-09-07T07:36:50.8457976Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/const_fold.py:278: 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 2025-09-07T07:36:50.8458152Z new_node = root_const_gm.graph.get_attr(in_node.target) 2025-09-07T07:36:50.8458230Z 2025-09-07T07:36:50.8458417Z --- Runtime Warning: 5 / 8 --- 2025-09-07T07:36:50.8458698Z example = 2025-09-07T07:36:50.8459811Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py:392: 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) 2025-09-07T07:36:50.8459905Z warnings.warn( 2025-09-07T07:36:50.8459981Z 2025-09-07T07:36:50.8460165Z --- Runtime Warning: 6 / 8 --- 2025-09-07T07:36:50.8460487Z example = 2025-09-07T07:36:50.8461609Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py:392: 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) 2025-09-07T07:36:50.8461700Z warnings.warn( 2025-09-07T07:36:50.8461777Z 2025-09-07T07:36:50.8461963Z --- Runtime Warning: 7 / 8 --- 2025-09-07T07:36:50.8462235Z example = 2025-09-07T07:36:50.8463077Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/weight_norm.py:144: FutureWarning: `torch.nn.utils.weight_norm` is deprecated in favor of `torch.nn.utils.parametrizations.weight_norm`. 2025-09-07T07:36:50.8463194Z WeightNorm.apply(module, name, dim) 2025-09-07T07:36:50.8463285Z 2025-09-07T07:36:50.8463458Z --- Runtime Warning: 8 / 8 --- 2025-09-07T07:36:50.8463759Z example = 2025-09-07T07:36:50.8464575Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/weight_norm.py:144: FutureWarning: `torch.nn.utils.weight_norm` is deprecated in favor of `torch.nn.utils.parametrizations.weight_norm`. 2025-09-07T07:36:50.8464691Z WeightNorm.apply(module, name, dim) 2025-09-07T07:36:50.8464801Z 2025-09-07T07:36:50.8465109Z === 338 passed, 394 skipped, 154 warnings in 13.11 seconds === 2025-09-07T07:36:50.8465306Z Running test_autoload_disable 1/1 ... [2025-09-07 07:36:50.655117] 2025-09-07T07:36:50.9745239Z Processing /var/lib/jenkins/workspace/test/cpp_extensions 2025-09-07T07:36:53.5961522Z Preparing metadata (setup.py) ... [?25l- done 2025-09-07T07:36:53.5991040Z [?25hBuilding wheels for collected packages: torch_test_cpp_extension 2025-09-07T07:36:53.5999715Z  DEPRECATION: Building 'torch_test_cpp_extension' using the legacy setup.py bdist_wheel mechanism, which will be removed in a future version. pip 25.3 will enforce this behaviour change. A possible replacement is to use the standardized build interface by setting the `--use-pep517` option, (possibly combined with `--no-build-isolation`), or adding a `pyproject.toml` file to the source tree of 'torch_test_cpp_extension'. Discussion can be found at https://github.com/pypa/pip/issues/6334 2025-09-07T07:37:03.4183168Z  Building wheel for torch_test_cpp_extension (setup.py) ... [?25l- \ | / - \ | / - \ done 2025-09-07T07:37:03.4432579Z [?25h Created wheel for torch_test_cpp_extension: filename=torch_test_cpp_extension-0.0.0-cp313-cp313-linux_x86_64.whl size=10046815 sha256=f6ce1940210d1c76d8474ceb5ebb55cbb3a8ddecc8299ab501fa88bf2bad4945 2025-09-07T07:37:03.4433967Z Stored in directory: /tmp/pip-ephem-wheel-cache-cyoyj87g/wheels/55/0c/23/e830c2c3ad8ea6f122c5bd80ddd259405d4a52266f20ee6037 2025-09-07T07:37:03.4453297Z Successfully built torch_test_cpp_extension 2025-09-07T07:37:03.6013618Z Installing collected packages: torch_test_cpp_extension 2025-09-07T07:37:03.7660176Z Successfully installed torch_test_cpp_extension-0.0.0 2025-09-07T07:37:05.8261715Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T07:37:05.8263307Z import pkg_resources 2025-09-07T07:37:05.8554180Z 2025-09-07T07:37:05.8554396Z Running tests... 2025-09-07T07:37:05.8554755Z ---------------------------------------------------------------------- 2025-09-07T07:37:05.8687251Z . 2025-09-07T07:37:05.8687555Z ---------------------------------------------------------------------- 2025-09-07T07:37:05.8687930Z Ran 1 test in 0.013s 2025-09-07T07:37:05.8688101Z 2025-09-07T07:37:05.8688183Z OK 2025-09-07T07:37:05.8688290Z 2025-09-07T07:37:05.8688406Z Generating XML reports... 2025-09-07T07:37:06.4143574Z Running test_autoload_enable 1/1 ... [2025-09-07 07:37:06.414043] 2025-09-07T07:37:06.7345206Z Processing /var/lib/jenkins/workspace/test/cpp_extensions 2025-09-07T07:37:09.3483354Z Preparing metadata (setup.py) ... [?25l- done 2025-09-07T07:37:09.3512060Z [?25hBuilding wheels for collected packages: torch_test_cpp_extension 2025-09-07T07:37:09.3521179Z  DEPRECATION: Building 'torch_test_cpp_extension' using the legacy setup.py bdist_wheel mechanism, which will be removed in a future version. pip 25.3 will enforce this behaviour change. A possible replacement is to use the standardized build interface by setting the `--use-pep517` option, (possibly combined with `--no-build-isolation`), or adding a `pyproject.toml` file to the source tree of 'torch_test_cpp_extension'. Discussion can be found at https://github.com/pypa/pip/issues/6334 2025-09-07T07:37:18.9762737Z  Building wheel for torch_test_cpp_extension (setup.py) ... [?25l- \ | / - \ | / - \ done 2025-09-07T07:37:19.0012197Z [?25h Created wheel for torch_test_cpp_extension: filename=torch_test_cpp_extension-0.0.0-cp313-cp313-linux_x86_64.whl size=10046815 sha256=54314a20b4375d1b73f92218724526c3600e0d4faa4e608feb86ec8a49ce3c93 2025-09-07T07:37:19.0013585Z Stored in directory: /tmp/pip-ephem-wheel-cache-r5h3994y/wheels/55/0c/23/e830c2c3ad8ea6f122c5bd80ddd259405d4a52266f20ee6037 2025-09-07T07:37:19.0033315Z Successfully built torch_test_cpp_extension 2025-09-07T07:37:19.1584536Z Installing collected packages: torch_test_cpp_extension 2025-09-07T07:37:19.3229737Z Successfully installed torch_test_cpp_extension-0.0.0 2025-09-07T07:37:21.3887785Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T07:37:21.3889355Z import pkg_resources 2025-09-07T07:37:21.4146546Z 2025-09-07T07:37:21.4146988Z Running tests... 2025-09-07T07:37:21.4147370Z ---------------------------------------------------------------------- 2025-09-07T07:37:21.4259542Z . 2025-09-07T07:37:21.4259917Z ---------------------------------------------------------------------- 2025-09-07T07:37:21.4260330Z Ran 1 test in 0.011s 2025-09-07T07:37:21.4260491Z 2025-09-07T07:37:21.4260584Z OK 2025-09-07T07:37:21.4260707Z 2025-09-07T07:37:21.4260997Z Generating XML reports... 2025-09-07T07:37:21.9512373Z Running test_cpp_extensions_aot_ninja 1/1 ... [2025-09-07 07:37:21.950911] 2025-09-07T07:37:22.3048855Z Processing /var/lib/jenkins/workspace/test/cpp_extensions 2025-09-07T07:37:24.9551061Z Preparing metadata (setup.py) ... [?25l- \ done 2025-09-07T07:37:24.9580614Z [?25hBuilding wheels for collected packages: torch_test_cpp_extension 2025-09-07T07:37:24.9589320Z  DEPRECATION: Building 'torch_test_cpp_extension' using the legacy setup.py bdist_wheel mechanism, which will be removed in a future version. pip 25.3 will enforce this behaviour change. A possible replacement is to use the standardized build interface by setting the `--use-pep517` option, (possibly combined with `--no-build-isolation`), or adding a `pyproject.toml` file to the source tree of 'torch_test_cpp_extension'. Discussion can be found at https://github.com/pypa/pip/issues/6334 2025-09-07T07:37:32.1988777Z  Building wheel for torch_test_cpp_extension (setup.py) ... [?25l- \ | / - \ | / - \ | done 2025-09-07T07:37:32.2079528Z [?25h Created wheel for torch_test_cpp_extension: filename=torch_test_cpp_extension-0.0.0-cp313-cp313-linux_x86_64.whl size=3235172 sha256=49fe45845b6fa22291b736b6607637fc18ee0413c4cdd1903373373e5606f484 2025-09-07T07:37:32.2080899Z Stored in directory: /tmp/pip-ephem-wheel-cache-e27od_l9/wheels/55/0c/23/e830c2c3ad8ea6f122c5bd80ddd259405d4a52266f20ee6037 2025-09-07T07:37:32.2100879Z Successfully built torch_test_cpp_extension 2025-09-07T07:37:32.3664903Z Installing collected packages: torch_test_cpp_extension 2025-09-07T07:37:32.4405336Z Successfully installed torch_test_cpp_extension-0.0.0 2025-09-07T07:37:32.8160805Z Processing /var/lib/jenkins/workspace/test/cpp_extensions/no_python_abi_suffix_test 2025-09-07T07:37:34.3353891Z Preparing metadata (setup.py) ... [?25l- done 2025-09-07T07:37:34.3383577Z [?25hBuilding wheels for collected packages: no_python_abi_suffix_test 2025-09-07T07:37:34.3392387Z  DEPRECATION: Building 'no_python_abi_suffix_test' using the legacy setup.py bdist_wheel mechanism, which will be removed in a future version. pip 25.3 will enforce this behaviour change. A possible replacement is to use the standardized build interface by setting the `--use-pep517` option, (possibly combined with `--no-build-isolation`), or adding a `pyproject.toml` file to the source tree of 'no_python_abi_suffix_test'. Discussion can be found at https://github.com/pypa/pip/issues/6334 2025-09-07T07:37:36.1699058Z  Building wheel for no_python_abi_suffix_test (setup.py) ... [?25l- \ | done 2025-09-07T07:37:36.1703552Z [?25h Created wheel for no_python_abi_suffix_test: filename=no_python_abi_suffix_test-0.0.0-cp313-cp313-linux_x86_64.whl size=2971 sha256=2fa6e2bc6310c122bc894917c50d54d13330ab7cbe5c04c7591855ccec435ca3 2025-09-07T07:37:36.1705223Z Stored in directory: /tmp/pip-ephem-wheel-cache-x6wxrhm6/wheels/c7/be/1e/a693498df0ac11a249ac34fc70a78f5f1b2d84d49e78482cb4 2025-09-07T07:37:36.1724160Z Successfully built no_python_abi_suffix_test 2025-09-07T07:37:36.3281750Z Installing collected packages: no_python_abi_suffix_test 2025-09-07T07:37:36.3386337Z Successfully installed no_python_abi_suffix_test-0.0.0 2025-09-07T07:37:36.3996728Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:37:36.4000456Z 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'] ... [2025-09-07 07:37:36.399821] 2025-09-07T07:37:39.1264202Z 2025-09-07T07:37:39.1265298Z 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_905713632aaa3367_.log 2025-09-07T07:37:39.1274163Z Running 21 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_sycl_extension, 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_autocast_apis_for_maia_device, test/test_cpp_extensions_aot_ninja.py::TestMAIATensor::test_conv_backend_override, test/test_cpp_extensions_aot_ninja.py::TestMAIATensor::test_matmul_autocast_default_precision, test/test_cpp_extensions_aot_ninja.py::TestMAIATensor::test_matmul_autocast_float16_precision, 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 2025-09-07T07:37:39.1282393Z 2025-09-07T07:37:39.1282730Z Running test_cpp_extensions_aot_no_ninja 1/1 ... [2025-09-07 07:37:39.126842] 2025-09-07T07:37:39.4486827Z Processing /var/lib/jenkins/workspace/test/cpp_extensions 2025-09-07T07:37:42.0643185Z Preparing metadata (setup.py) ... [?25l- done 2025-09-07T07:37:42.0672329Z [?25hBuilding wheels for collected packages: torch_test_cpp_extension 2025-09-07T07:37:42.0681398Z  DEPRECATION: Building 'torch_test_cpp_extension' using the legacy setup.py bdist_wheel mechanism, which will be removed in a future version. pip 25.3 will enforce this behaviour change. A possible replacement is to use the standardized build interface by setting the `--use-pep517` option, (possibly combined with `--no-build-isolation`), or adding a `pyproject.toml` file to the source tree of 'torch_test_cpp_extension'. Discussion can be found at https://github.com/pypa/pip/issues/6334 2025-09-07T07:37:51.6691607Z  Building wheel for torch_test_cpp_extension (setup.py) ... [?25l- \ | / - \ | / - \ done 2025-09-07T07:37:51.6942191Z [?25h Created wheel for torch_test_cpp_extension: filename=torch_test_cpp_extension-0.0.0-cp313-cp313-linux_x86_64.whl size=10046815 sha256=772c49086d067c36c969f708aebae693bec958dcf9c9c9a54bbb216d036a363d 2025-09-07T07:37:51.6943827Z Stored in directory: /tmp/pip-ephem-wheel-cache-nta3lbto/wheels/55/0c/23/e830c2c3ad8ea6f122c5bd80ddd259405d4a52266f20ee6037 2025-09-07T07:37:51.6963247Z Successfully built torch_test_cpp_extension 2025-09-07T07:37:51.8526244Z Installing collected packages: torch_test_cpp_extension 2025-09-07T07:37:52.0148950Z Successfully installed torch_test_cpp_extension-0.0.0 2025-09-07T07:37:52.4039519Z Processing /var/lib/jenkins/workspace/test/cpp_extensions/no_python_abi_suffix_test 2025-09-07T07:37:53.9229135Z Preparing metadata (setup.py) ... [?25l- done 2025-09-07T07:37:53.9258949Z [?25hBuilding wheels for collected packages: no_python_abi_suffix_test 2025-09-07T07:37:53.9266965Z  DEPRECATION: Building 'no_python_abi_suffix_test' using the legacy setup.py bdist_wheel mechanism, which will be removed in a future version. pip 25.3 will enforce this behaviour change. A possible replacement is to use the standardized build interface by setting the `--use-pep517` option, (possibly combined with `--no-build-isolation`), or adding a `pyproject.toml` file to the source tree of 'no_python_abi_suffix_test'. Discussion can be found at https://github.com/pypa/pip/issues/6334 2025-09-07T07:37:55.6924449Z  Building wheel for no_python_abi_suffix_test (setup.py) ... [?25l- \ | done 2025-09-07T07:37:55.6929194Z [?25h Created wheel for no_python_abi_suffix_test: filename=no_python_abi_suffix_test-0.0.0-cp313-cp313-linux_x86_64.whl size=2971 sha256=9762985df8c7cef9e839b7c71b724e6cea1006cc3f319b2cae7df2d3893af696 2025-09-07T07:37:55.6930560Z Stored in directory: /tmp/pip-ephem-wheel-cache-qb37tlp8/wheels/c7/be/1e/a693498df0ac11a249ac34fc70a78f5f1b2d84d49e78482cb4 2025-09-07T07:37:55.6949514Z Successfully built no_python_abi_suffix_test 2025-09-07T07:37:55.8510146Z Installing collected packages: no_python_abi_suffix_test 2025-09-07T07:37:55.8615871Z Successfully installed no_python_abi_suffix_test-0.0.0 2025-09-07T07:37:55.9222464Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:37:55.9225323Z 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'] ... [2025-09-07 07:37:55.922300] 2025-09-07T07:37:58.6485464Z 2025-09-07T07:37:58.6486432Z 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_4f956189465bff68_.log 2025-09-07T07:37:58.6494959Z Running 21 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_sycl_extension, 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_autocast_apis_for_maia_device, test/test_cpp_extensions_aot_no_ninja.py::TestMAIATensor::test_conv_backend_override, test/test_cpp_extensions_aot_no_ninja.py::TestMAIATensor::test_matmul_autocast_default_precision, test/test_cpp_extensions_aot_no_ninja.py::TestMAIATensor::test_matmul_autocast_float16_precision, 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 2025-09-07T07:37:58.6502822Z 2025-09-07T07:37:58.6503027Z Running inductor/test_memory 1/1 ... [2025-09-07 07:37:58.648839] 2025-09-07T07:37:58.6503457Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:37:58.6504571Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_memory.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'] ... [2025-09-07 07:37:58.649136] 2025-09-07T07:38:05.2450481Z 2025-09-07T07:38:05.2451441Z inductor/test_memory 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_memory_1.1_82c2efb3447a39ed_.log 2025-09-07T07:38:05.2452149Z 2025-09-07T07:38:05.2454694Z Running inductor/test_inductor_utils 1/1 ... [2025-09-07 07:38:05.245290] 2025-09-07T07:38:05.2455175Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:38:05.2458289Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_inductor_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'] ... [2025-09-07 07:38:05.245619] 2025-09-07T07:38:07.7912478Z 2025-09-07T07:38:07.7913486Z inductor/test_inductor_utils 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_inductor_utils_1.1_1869bf28ec1b2ca0_.log 2025-09-07T07:38:07.7914239Z 2025-09-07T07:38:07.7916399Z Running inductor/test_best_config 1/1 ... [2025-09-07 07:38:07.791470] 2025-09-07T07:38:07.7916858Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:38:07.7920198Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_best_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'] ... [2025-09-07 07:38:07.791813] 2025-09-07T07:38:14.4012490Z 2025-09-07T07:38:14.4013493Z inductor/test_best_config 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_best_config_1.1_e13ecd56e44e83e6_.log 2025-09-07T07:38:14.4014218Z 2025-09-07T07:38:14.4016252Z Running inductor/test_cudagraph_trees 1/1 ... [2025-09-07 07:38:14.401467] 2025-09-07T07:38:14.4016724Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:38:14.4020146Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_cudagraph_trees.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 07:38:14.401799] 2025-09-07T07:38:21.0209505Z 2025-09-07T07:38:21.0210667Z inductor/test_cudagraph_trees 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_cudagraph_trees_1.1_4fcbd507104037b0_.log 2025-09-07T07:38:21.0211758Z 2025-09-07T07:38:21.0213117Z Running inductor/test_metrics 1/1 ... [2025-09-07 07:38:21.021138] 2025-09-07T07:38:21.0213539Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:38:21.0216663Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_metrics.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'] ... [2025-09-07 07:38:21.021476] 2025-09-07T07:38:27.6239209Z 2025-09-07T07:38:27.6240152Z inductor/test_metrics 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_metrics_1.1_5d08cdc510470e9d_.log 2025-09-07T07:38:27.6241046Z 2025-09-07T07:38:27.6243074Z Running inductor/test_cuda_repro 1/1 ... [2025-09-07 07:38:27.624150] 2025-09-07T07:38:27.6243528Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:38:27.6246771Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_cuda_repro.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 07:38:27.624485] 2025-09-07T07:38:34.2166839Z 2025-09-07T07:38:34.2167810Z inductor/test_cuda_repro 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_cuda_repro_1.1_db856c23d8f4a0d0_.log 2025-09-07T07:38:34.2168549Z 2025-09-07T07:38:34.2170625Z Running inductor/test_profiler 1/1 ... [2025-09-07 07:38:34.216895] 2025-09-07T07:38:34.2171070Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:38:34.2174868Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_profiler.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 07:38:34.217215] 2025-09-07T07:38:40.8288785Z 2025-09-07T07:38:40.8289678Z inductor/test_profiler 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_profiler_1.1_f0ae22f5398bdbf9_.log 2025-09-07T07:38:40.8290392Z 2025-09-07T07:38:40.8292212Z Running inductor/test_cooperative_reductions 1/1 ... [2025-09-07 07:38:40.829066] 2025-09-07T07:38:40.8292720Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:38:40.8296026Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_cooperative_reductions.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 07:38:40.829374] 2025-09-07T07:38:47.3573816Z 2025-09-07T07:38:47.3574962Z inductor/test_cooperative_reductions 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_cooperative_reductions_1.1_dca1e77c891c4854_.log 2025-09-07T07:38:47.3575809Z 2025-09-07T07:38:47.3577364Z Running higher_order_ops/test_invoke_quant 1/1 ... [2025-09-07 07:38:47.357594] 2025-09-07T07:38:47.3577854Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:38:47.3581045Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'higher_order_ops/test_invoke_quant.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'] ... [2025-09-07 07:38:47.357900] 2025-09-07T07:38:54.1326178Z 2025-09-07T07:38:54.1327430Z higher_order_ops/test_invoke_quant 1/1 was successful, full logs can be found in artifacts with path test/test-reports/higher_order_ops.test_invoke_quant_1.1_d8839f0ad92df4ea_.log 2025-09-07T07:38:54.1328353Z Running 0 items in this shard: 2025-09-07T07:38:54.1328548Z 2025-09-07T07:38:54.1330007Z Running inductor/test_online_softmax 1/1 ... [2025-09-07 07:38:54.132841] 2025-09-07T07:38:54.1330551Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:38:54.1333621Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_online_softmax.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'] ... [2025-09-07 07:38:54.133167] 2025-09-07T07:39:00.7311153Z 2025-09-07T07:39:00.7312177Z inductor/test_online_softmax 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_online_softmax_1.1_4804946730bead78_.log 2025-09-07T07:39:00.7312930Z 2025-09-07T07:39:00.7314817Z Running dynamo/test_unittest 1/1 ... [2025-09-07 07:39:00.731318] 2025-09-07T07:39:00.7315254Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:39:00.7318576Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_unittest.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'] ... [2025-09-07 07:39:00.731653] 2025-09-07T07:39:03.6498186Z 2025-09-07T07:39:03.6499060Z dynamo/test_unittest 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_unittest_1.1_155a29f2f033ae65_.log 2025-09-07T07:39:03.6499862Z Running 0 items in this shard: 2025-09-07T07:39:03.6500050Z 2025-09-07T07:39:03.6502147Z Running dynamo/test_guard_serialization 1/1 ... [2025-09-07 07:39:03.650054] 2025-09-07T07:39:03.6502616Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:39:03.6505698Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_guard_serialization.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'] ... [2025-09-07 07:39:03.650369] 2025-09-07T07:39:10.4237305Z 2025-09-07T07:39:10.4238595Z dynamo/test_guard_serialization 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_guard_serialization_1.1_c3b7d2eef121cfed_.log 2025-09-07T07:39:10.4239537Z Running 0 items in this shard: 2025-09-07T07:39:10.4239727Z 2025-09-07T07:39:10.4242380Z Running functorch/test_minifier 1/1 ... [2025-09-07 07:39:10.424054] 2025-09-07T07:39:10.4242823Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:39:10.4247016Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'functorch/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'] ... [2025-09-07 07:39:10.424494] 2025-09-07T07:39:13.2925145Z 2025-09-07T07:39:13.2925982Z functorch/test_minifier 1/1 was successful, full logs can be found in artifacts with path test/test-reports/functorch.test_minifier_1.1_66462f2669bb0aee_.log 2025-09-07T07:39:13.2926818Z Running 0 items in this shard: 2025-09-07T07:39:13.2927023Z 2025-09-07T07:39:13.2928663Z Running test_legacy_vmap 1/1 ... [2025-09-07 07:39:13.292731] 2025-09-07T07:39:13.2929076Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:39:13.2932146Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_legacy_vmap.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 07:39:13.293032] 2025-09-07T07:39:16.1110060Z 2025-09-07T07:39:16.1114144Z test_legacy_vmap 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_legacy_vmap_1.1_6fa30e9529a8e96a_.log 2025-09-07T07:39:16.1115162Z Running 0 items in this shard: 2025-09-07T07:39:16.1115357Z 2025-09-07T07:39:16.1115633Z Running torch_np/numpy_tests/core/test_einsum 1/1 ... [2025-09-07 07:39:16.111210] 2025-09-07T07:39:16.1116118Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:39:16.1117331Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'torch_np/numpy_tests/core/test_einsum.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 07:39:16.111512] 2025-09-07T07:39:18.8293850Z 2025-09-07T07:39:18.8297003Z torch_np/numpy_tests/core/test_einsum 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.core.test_einsum_1.1_68731c26ec7c3279_.log 2025-09-07T07:39:18.8298088Z Running 0 items in this shard: 2025-09-07T07:39:18.8298284Z 2025-09-07T07:39:18.8298525Z Running inductor/test_benchmarking 1/1 ... [2025-09-07 07:39:18.829571] 2025-09-07T07:39:18.8299007Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:39:18.8300798Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_benchmarking.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'] ... [2025-09-07 07:39:18.829871] 2025-09-07T07:39:25.7034310Z 2025-09-07T07:39:25.7035526Z inductor/test_benchmarking 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_benchmarking_1.1_ddc8c38a8ac8cf68_.log 2025-09-07T07:39:25.7036395Z Running 0 items in this shard: 2025-09-07T07:39:25.7036586Z 2025-09-07T07:39:25.7038620Z Running dynamo/test_model_output 1/1 ... [2025-09-07 07:39:25.703683] 2025-09-07T07:39:25.7039066Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:39:25.7042390Z 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'] ... [2025-09-07 07:39:25.704041] 2025-09-07T07:39:28.6223867Z 2025-09-07T07:39:28.6225630Z 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_1fca90ceebe469a7_.log 2025-09-07T07:39:28.6227050Z Running 0 items in this shard: 2025-09-07T07:39:28.6227331Z 2025-09-07T07:39:28.6229094Z Running torch_np/test_basic 1/1 ... [2025-09-07 07:39:28.622705] 2025-09-07T07:39:28.6229727Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:39:28.6234755Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'torch_np/test_basic.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 07:39:28.623142] 2025-09-07T07:39:31.3412836Z 2025-09-07T07:39:31.3414226Z torch_np/test_basic 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.test_basic_1.1_c90483a7353c3771_.log 2025-09-07T07:39:31.3415619Z Running 0 items in this shard: 2025-09-07T07:39:31.3415991Z 2025-09-07T07:39:31.3418485Z Running test_segment_reductions 1/1 ... [2025-09-07 07:39:31.341655] 2025-09-07T07:39:31.3419192Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:39:31.3423938Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_segment_reductions.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 07:39:31.342058] 2025-09-07T07:39:34.1102174Z 2025-09-07T07:39:34.1103249Z test_segment_reductions 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_segment_reductions_1.1_5e055efed0598a97_.log 2025-09-07T07:39:34.1104091Z Running 0 items in this shard: 2025-09-07T07:39:34.1104285Z 2025-09-07T07:39:34.1106973Z Running test_ops_fwd_gradients 1/2 ... [2025-09-07 07:39:34.110511] 2025-09-07T07:39:34.1107418Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:39:34.1110617Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_ops_fwd_gradients.py', '-m', 'serial', '--shard-id=1', '--num-shards=2', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 07:39:34.110853] 2025-09-07T07:39:38.2306867Z 2025-09-07T07:39:38.2307791Z test_ops_fwd_gradients 1/2 was successful, full logs can be found in artifacts with path test/test-reports/test_ops_fwd_gradients_1.2_9d25eb4d1ea54eea_.log 2025-09-07T07:39:38.2308607Z Running 0 items in this shard: 2025-09-07T07:39:38.2308801Z 2025-09-07T07:39:38.2310694Z Running inductor/test_minifier_isolate 1/1 ... [2025-09-07 07:39:38.230907] 2025-09-07T07:39:38.2311163Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:39:38.2314442Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_minifier_isolate.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'] ... [2025-09-07 07:39:38.231224] 2025-09-07T07:39:44.8360765Z 2025-09-07T07:39:44.8361824Z inductor/test_minifier_isolate 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_minifier_isolate_1.1_51c7b842fc82b6fa_.log 2025-09-07T07:39:44.8362612Z 2025-09-07T07:39:44.8364866Z Running xpu/test_conv 1/1 ... [2025-09-07 07:39:44.836304] 2025-09-07T07:39:44.8365342Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:39:44.8368711Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'xpu/test_conv.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 07:39:44.836652] 2025-09-07T07:39:48.1016005Z 2025-09-07T07:39:48.1016897Z xpu/test_conv 1/1 was successful, full logs can be found in artifacts with path test/test-reports/xpu.test_conv_1.1_84426e479369f0c8_.log 2025-09-07T07:39:48.1017628Z Running 0 items in this shard: 2025-09-07T07:39:48.1017829Z 2025-09-07T07:39:48.1019990Z Running dynamo/test_flat_apply 1/1 ... [2025-09-07 07:39:48.101844] 2025-09-07T07:39:48.1020656Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:39:48.1024296Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_flat_apply.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'] ... [2025-09-07 07:39:48.102201] 2025-09-07T07:39:51.0203964Z 2025-09-07T07:39:51.0204919Z dynamo/test_flat_apply 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_flat_apply_1.1_87f3d2b947e040d0_.log 2025-09-07T07:39:51.0205738Z Running 0 items in this shard: 2025-09-07T07:39:51.0207520Z 2025-09-07T07:39:51.0207769Z Running export/test_unflatten 1/1 ... [2025-09-07 07:39:51.020622] 2025-09-07T07:39:51.0208259Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:39:51.0211627Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'export/test_unflatten.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'] ... [2025-09-07 07:39:51.020960] 2025-09-07T07:39:53.6888273Z 2025-09-07T07:39:53.6889207Z export/test_unflatten 1/1 was successful, full logs can be found in artifacts with path test/test-reports/export.test_unflatten_1.1_1a921c762e6d6c33_.log 2025-09-07T07:39:53.6890016Z Running 0 items in this shard: 2025-09-07T07:39:53.6890239Z 2025-09-07T07:39:53.6892331Z Running lazy/test_step_closures 1/1 ... [2025-09-07 07:39:53.689088] 2025-09-07T07:39:53.6892803Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:39:53.6896521Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'lazy/test_step_closures.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'] ... [2025-09-07 07:39:53.689429] 2025-09-07T07:39:56.3572226Z 2025-09-07T07:39:56.3573212Z lazy/test_step_closures 1/1 was successful, full logs can be found in artifacts with path test/test-reports/lazy.test_step_closures_1.1_fd91728d1cb1c3a6_.log 2025-09-07T07:39:56.3574250Z Running 0 items in this shard: 2025-09-07T07:39:56.3574443Z 2025-09-07T07:39:56.3576335Z Running test_monitor 1/1 ... [2025-09-07 07:39:56.357495] 2025-09-07T07:39:56.3576787Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:39:56.3580167Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_monitor.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 07:39:56.357826] 2025-09-07T07:39:59.0256420Z 2025-09-07T07:39:59.0257295Z test_monitor 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_monitor_1.1_9ca4a0a67946229a_.log 2025-09-07T07:39:59.0258251Z Running 0 items in this shard: 2025-09-07T07:39:59.0258442Z 2025-09-07T07:39:59.0260298Z Running functorch/test_logging 1/1 ... [2025-09-07 07:39:59.025882] 2025-09-07T07:39:59.0260754Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:39:59.0264067Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'functorch/test_logging.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 07:39:59.026201] 2025-09-07T07:40:01.8943197Z 2025-09-07T07:40:01.8944188Z functorch/test_logging 1/1 was successful, full logs can be found in artifacts with path test/test-reports/functorch.test_logging_1.1_ec21638435366299_.log 2025-09-07T07:40:01.8945018Z Running 0 items in this shard: 2025-09-07T07:40:01.8945227Z 2025-09-07T07:40:01.8947773Z Running test_stateless 1/1 ... [2025-09-07 07:40:01.894576] 2025-09-07T07:40:01.8948209Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:40:01.8951632Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_stateless.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'] ... [2025-09-07 07:40:01.894941] 2025-09-07T07:40:04.5628696Z 2025-09-07T07:40:04.5630007Z test_stateless 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_stateless_1.1_c219d45a7456ef30_.log 2025-09-07T07:40:04.5631322Z Running 0 items in this shard: 2025-09-07T07:40:04.5631677Z 2025-09-07T07:40:04.5634799Z Running torch_np/numpy_tests/core/test_numeric 1/1 ... [2025-09-07 07:40:04.563269] 2025-09-07T07:40:04.5635586Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:40:04.5640124Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'torch_np/numpy_tests/core/test_numeric.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 07:40:04.563693] 2025-09-07T07:40:07.3818701Z 2025-09-07T07:40:07.3819809Z torch_np/numpy_tests/core/test_numeric 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.core.test_numeric_1.1_523aba28fe1f49bd_.log 2025-09-07T07:40:07.3820769Z Running 0 items in this shard: 2025-09-07T07:40:07.3820958Z 2025-09-07T07:40:07.3823021Z Running test_weak 1/1 ... [2025-09-07 07:40:07.382132] 2025-09-07T07:40:07.3823410Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:40:07.3827421Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_weak.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 07:40:07.382514] 2025-09-07T07:40:10.0504803Z 2025-09-07T07:40:10.0505615Z test_weak 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_weak_1.1_2c83fbcb3af82eda_.log 2025-09-07T07:40:10.0506334Z Running 0 items in this shard: 2025-09-07T07:40:10.0506771Z 2025-09-07T07:40:10.0509279Z Running inductor/test_mkldnn_pattern_matcher 1/3 ... [2025-09-07 07:40:10.050761] 2025-09-07T07:40:10.0509786Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:40:10.0513212Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_mkldnn_pattern_matcher.py', '-m', 'serial', '--shard-id=1', '--num-shards=3', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 07:40:10.051119] 2025-09-07T07:40:17.1750743Z 2025-09-07T07:40:17.1752424Z inductor/test_mkldnn_pattern_matcher 1/3 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_mkldnn_pattern_matcher_1.3_9b39c6f3b6bd1b91_.log 2025-09-07T07:40:17.1754106Z Running 0 items in this shard: 2025-09-07T07:40:17.1754475Z 2025-09-07T07:40:17.1756125Z Running functorch/test_ac 1/1 ... [2025-09-07 07:40:17.175422] 2025-09-07T07:40:17.1757068Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:40:17.1761774Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'functorch/test_ac.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 07:40:17.175866] 2025-09-07T07:40:23.8213320Z 2025-09-07T07:40:23.8214825Z functorch/test_ac 1/1 was successful, full logs can be found in artifacts with path test/test-reports/functorch.test_ac_1.1_5c91fc4fed173b99_.log 2025-09-07T07:40:23.8216055Z 2025-09-07T07:40:23.8218347Z Running dynamo/test_cudagraphs 1/1 ... [2025-09-07 07:40:23.821672] 2025-09-07T07:40:23.8219051Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:40:23.8223938Z 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'] ... [2025-09-07 07:40:23.822071] 2025-09-07T07:40:26.5909381Z 2025-09-07T07:40:26.5910697Z dynamo/test_cudagraphs 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_cudagraphs_1.1_5ee84fcd1d23eddb_.log 2025-09-07T07:40:26.5911440Z 2025-09-07T07:40:26.5913382Z Running test_decomp 2/12 ... [2025-09-07 07:40:26.591179] 2025-09-07T07:40:26.5913834Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:40:26.5917254Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_decomp.py', '-m', 'serial', '--shard-id=2', '--num-shards=12', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 07:40:26.591533] 2025-09-07T07:40:31.9128833Z 2025-09-07T07:40:31.9129682Z test_decomp 2/12 was successful, full logs can be found in artifacts with path test/test-reports/test_decomp_2.12_e6882d5301161e2c_.log 2025-09-07T07:40:31.9130399Z Running 0 items in this shard: 2025-09-07T07:40:31.9130608Z 2025-09-07T07:40:31.9132363Z Running test_decomp 5/12 ... [2025-09-07 07:40:31.913091] 2025-09-07T07:40:31.9132795Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:40:31.9136127Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_decomp.py', '-m', 'serial', '--shard-id=5', '--num-shards=12', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 07:40:31.913418] 2025-09-07T07:40:37.1847970Z 2025-09-07T07:40:37.1848792Z test_decomp 5/12 was successful, full logs can be found in artifacts with path test/test-reports/test_decomp_5.12_ffafffcf65ab2980_.log 2025-09-07T07:40:37.1849497Z Running 0 items in this shard: 2025-09-07T07:40:37.1849915Z 2025-09-07T07:40:37.1851445Z Running test_decomp 12/12 ... [2025-09-07 07:40:37.184998] 2025-09-07T07:40:37.1851837Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:40:37.1854887Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_decomp.py', '-m', 'serial', '--shard-id=12', '--num-shards=12', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 07:40:37.185303] 2025-09-07T07:40:42.4565576Z 2025-09-07T07:40:42.4566525Z test_decomp 12/12 was successful, full logs can be found in artifacts with path test/test-reports/test_decomp_12.12_e9f84d705eeb6a7e_.log 2025-09-07T07:40:42.4567264Z Running 0 items in this shard: 2025-09-07T07:40:42.4567474Z 2025-09-07T07:40:42.4569062Z Running inductor/test_fxir_backend 1/1 ... [2025-09-07 07:40:42.456766] 2025-09-07T07:40:42.4569577Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:40:42.4573178Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_fxir_backend.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'] ... [2025-09-07 07:40:42.457097] 2025-09-07T07:40:49.0020557Z 2025-09-07T07:40:49.0021552Z inductor/test_fxir_backend 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_fxir_backend_1.1_41474295c0cf2fda_.log 2025-09-07T07:40:49.0022290Z 2025-09-07T07:40:49.0024065Z Running test_accelerator 1/1 ... [2025-09-07 07:40:49.002251] 2025-09-07T07:40:49.0024540Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:40:49.0027748Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_accelerator.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'] ... [2025-09-07 07:40:49.002577] 2025-09-07T07:40:51.5245720Z 2025-09-07T07:40:51.5246639Z test_accelerator 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_accelerator_1.1_2f3f76164df69ea2_.log 2025-09-07T07:40:51.5247304Z 2025-09-07T07:40:51.5249106Z Running test_ops 3/6 ... [2025-09-07 07:40:51.524778] 2025-09-07T07:40:51.5249494Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:40:51.5252817Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_ops.py', '-m', 'serial', '--shard-id=3', '--num-shards=6', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 07:40:51.525074] 2025-09-07T07:41:05.4077663Z 2025-09-07T07:41:05.4078460Z test_ops 3/6 was successful, full logs can be found in artifacts with path test/test-reports/test_ops_3.6_f6129d50a947b8f2_.log 2025-09-07T07:41:05.4079145Z Running 0 items in this shard: 2025-09-07T07:41:05.4079340Z 2025-09-07T07:41:05.4081478Z Running test_ops 6/6 ... [2025-09-07 07:41:05.407999] 2025-09-07T07:41:05.4081904Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:41:05.4085377Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_ops.py', '-m', 'serial', '--shard-id=6', '--num-shards=6', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 07:41:05.408331] 2025-09-07T07:41:19.3409908Z 2025-09-07T07:41:19.3411022Z test_ops 6/6 was successful, full logs can be found in artifacts with path test/test-reports/test_ops_6.6_ea719f729fadaff6_.log 2025-09-07T07:41:19.3412088Z Running 0 items in this shard: 2025-09-07T07:41:19.3412391Z 2025-09-07T07:41:21.4576716Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T07:41:21.4578532Z import pkg_resources 2025-09-07T07:41:21.5246680Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T07:41:21.5248488Z import pkg_resources 2025-09-07T07:41:21.5249935Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T07:41:21.5251423Z import pkg_resources 2025-09-07T07:41:21.5803162Z Running inductor/test_memory 1/1 ... [2025-09-07 07:41:21.580022] 2025-09-07T07:41:21.5803608Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:41:21.5806458Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_memory.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'] ... [2025-09-07 07:41:21.580404] 2025-09-07T07:41:21.6460987Z Running inductor/test_inductor_utils 1/1 ... [2025-09-07 07:41:21.645810] 2025-09-07T07:41:21.6461468Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:41:21.6462994Z Running inductor/test_best_config 1/1 ... [2025-09-07 07:41:21.646126] 2025-09-07T07:41:21.6464303Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_inductor_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'] ... [2025-09-07 07:41:21.646134] 2025-09-07T07:41:21.6465626Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:41:21.6466802Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_best_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'] ... [2025-09-07 07:41:21.646426] 2025-09-07T07:41:24.4535390Z 2025-09-07T07:41:24.4536706Z inductor/test_inductor_utils 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_inductor_utils_1.1_ab4675a51fdc11c2_.log 2025-09-07T07:41:24.4537499Z 2025-09-07T07:41:27.2321697Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T07:41:27.2324957Z import pkg_resources 2025-09-07T07:41:27.3562424Z Running inductor/test_cudagraph_trees 1/1 ... [2025-09-07 07:41:27.355844] 2025-09-07T07:41:27.3563331Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:41:27.3565395Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_cudagraph_trees.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 07:41:27.356211] 2025-09-07T07:41:28.8193864Z 2025-09-07T07:41:28.8195169Z inductor/test_memory 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_memory_1.1_40dae7de287e3bb0_.log 2025-09-07T07:41:28.8195885Z 2025-09-07T07:41:30.3752213Z 2025-09-07T07:41:30.3753282Z inductor/test_best_config 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_best_config_1.1_9f0260c3e440a129_.log 2025-09-07T07:41:30.3754083Z 2025-09-07T07:41:31.6461027Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T07:41:31.6462634Z import pkg_resources 2025-09-07T07:41:31.7681411Z Running inductor/test_metrics 1/1 ... [2025-09-07 07:41:31.767865] 2025-09-07T07:41:31.7682058Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:41:31.7684193Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_metrics.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'] ... [2025-09-07 07:41:31.768204] 2025-09-07T07:41:33.2516373Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T07:41:33.2517925Z import pkg_resources 2025-09-07T07:41:33.3763689Z Running inductor/test_cuda_repro 1/1 ... [2025-09-07 07:41:33.376048] 2025-09-07T07:41:33.3764488Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:41:33.3766518Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_cuda_repro.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 07:41:33.376393] 2025-09-07T07:41:34.5923548Z 2025-09-07T07:41:34.5924713Z inductor/test_cudagraph_trees 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_cudagraph_trees_1.1_c14536a621895c51_.log 2025-09-07T07:41:34.5925478Z 2025-09-07T07:41:37.4517964Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T07:41:37.4519544Z import pkg_resources 2025-09-07T07:41:37.5744434Z Running inductor/test_profiler 1/1 ... [2025-09-07 07:41:37.574077] 2025-09-07T07:41:37.5744923Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:41:37.5746951Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_profiler.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 07:41:37.574435] 2025-09-07T07:41:39.0569124Z 2025-09-07T07:41:39.0570099Z inductor/test_metrics 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_metrics_1.1_a212e0c9a70ebb61_.log 2025-09-07T07:41:39.0570791Z 2025-09-07T07:41:39.4890115Z Uploading artifacts took 0.43 seconds 2025-09-07T07:41:40.6347146Z 2025-09-07T07:41:40.6348685Z inductor/test_cuda_repro 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_cuda_repro_1.1_52a69bab795660bf_.log 2025-09-07T07:41:40.6350040Z 2025-09-07T07:41:41.9506063Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T07:41:41.9508974Z import pkg_resources 2025-09-07T07:41:42.0735958Z Running inductor/test_cooperative_reductions 1/1 ... [2025-09-07 07:41:42.073139] 2025-09-07T07:41:42.0736903Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:41:42.0739333Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_cooperative_reductions.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 07:41:42.073489] 2025-09-07T07:41:43.4416958Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T07:41:43.4418726Z import pkg_resources 2025-09-07T07:41:43.5636042Z Running higher_order_ops/test_invoke_quant 1/1 ... [2025-09-07 07:41:43.563288] 2025-09-07T07:41:43.5636552Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:41:43.5638850Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'higher_order_ops/test_invoke_quant.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'] ... [2025-09-07 07:41:43.563640] 2025-09-07T07:41:45.0011487Z 2025-09-07T07:41:45.0012747Z inductor/test_profiler 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_profiler_1.1_3cc0a9ad00619e77_.log 2025-09-07T07:41:45.0013904Z 2025-09-07T07:41:47.8071764Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T07:41:47.8073348Z import pkg_resources 2025-09-07T07:41:47.9296222Z Running inductor/test_online_softmax 1/1 ... [2025-09-07 07:41:47.929308] 2025-09-07T07:41:47.9296710Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:41:47.9299068Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_online_softmax.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'] ... [2025-09-07 07:41:47.929651] 2025-09-07T07:41:49.2654775Z 2025-09-07T07:41:49.2656391Z inductor/test_cooperative_reductions 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_cooperative_reductions_1.1_df7f504b01a4c7d2_.log 2025-09-07T07:41:49.2657257Z 2025-09-07T07:41:52.0778550Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T07:41:52.0780116Z import pkg_resources 2025-09-07T07:41:52.2008996Z Running dynamo/test_unittest 1/1 ... [2025-09-07 07:41:52.200520] 2025-09-07T07:41:52.2009480Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:41:52.2011577Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_unittest.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'] ... [2025-09-07 07:41:52.200886] 2025-09-07T07:41:55.1969078Z 2025-09-07T07:41:55.1970161Z inductor/test_online_softmax 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_online_softmax_1.1_615f35cbf487d0ed_.log 2025-09-07T07:41:55.1971009Z 2025-09-07T07:41:55.2447676Z 2025-09-07T07:41:55.2449009Z higher_order_ops/test_invoke_quant 1/1 was successful, full logs can be found in artifacts with path test/test-reports/higher_order_ops.test_invoke_quant_1.1_787a6c9bf74b25ac_.log 2025-09-07T07:41:55.2455199Z Running 14 items in this shard: test/higher_order_ops/test_invoke_quant.py::TestInvokeQuantEager::test_construct_inline, test/higher_order_ops/test_invoke_quant.py::TestInvokeQuantEager::test_inline, test/higher_order_ops/test_invoke_quant.py::TestInvokeQuantEager::test_multiple, test/higher_order_ops/test_invoke_quant.py::TestInvokeQuantEager::test_simple, test/higher_order_ops/test_invoke_quant.py::TestInvokeQuantAotEager::test_construct_inline, test/higher_order_ops/test_invoke_quant.py::TestInvokeQuantAotEager::test_inline, test/higher_order_ops/test_invoke_quant.py::TestInvokeQuantAotEager::test_multiple, test/higher_order_ops/test_invoke_quant.py::TestInvokeQuantAotEager::test_simple, test/higher_order_ops/test_invoke_quant.py::TestInvokeQuantInductor::test_construct_inline, test/higher_order_ops/test_invoke_quant.py::TestInvokeQuantInductor::test_inline, test/higher_order_ops/test_invoke_quant.py::TestInvokeQuantInductor::test_multiple, test/higher_order_ops/test_invoke_quant.py::TestInvokeQuantInductor::test_pattern_matching, test/higher_order_ops/test_invoke_quant.py::TestInvokeQuantInductor::test_prologue, test/higher_order_ops/test_invoke_quant.py::TestInvokeQuantInductor::test_simple 2025-09-07T07:41:55.2460824Z 2025-09-07T07:41:55.6703581Z 2025-09-07T07:41:55.6704706Z dynamo/test_unittest 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_unittest_1.1_9f3606c3e38f6838_.log 2025-09-07T07:41:55.6706194Z Running 1 items in this shard: test/dynamo/test_unittest.py::TestUnittest::test_SkipTest 2025-09-07T07:41:55.6706627Z 2025-09-07T07:41:58.0715734Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T07:41:58.0717308Z import pkg_resources 2025-09-07T07:41:58.0718781Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T07:41:58.0720270Z import pkg_resources 2025-09-07T07:41:58.1937192Z Running dynamo/test_guard_serialization 1/1 ... [2025-09-07 07:41:58.193383] 2025-09-07T07:41:58.1938081Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:41:58.1938694Z Running functorch/test_minifier 1/1 ... [2025-09-07 07:41:58.193594] 2025-09-07T07:41:58.1939122Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:41:58.1940667Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_guard_serialization.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'] ... [2025-09-07 07:41:58.193735] 2025-09-07T07:41:58.1942843Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'functorch/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'] ... [2025-09-07 07:41:58.193912] 2025-09-07T07:41:58.5001522Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T07:41:58.5003082Z import pkg_resources 2025-09-07T07:41:58.6587620Z Running test_legacy_vmap 1/1 ... [2025-09-07 07:41:58.658372] 2025-09-07T07:41:58.6588409Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:41:58.6592980Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_legacy_vmap.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 07:41:58.658928] 2025-09-07T07:42:01.7134753Z 2025-09-07T07:42:01.7136273Z functorch/test_minifier 1/1 was successful, full logs can be found in artifacts with path test/test-reports/functorch.test_minifier_1.1_ce144ab3321c4b91_.log 2025-09-07T07:42:01.7140581Z Running 5 items in this shard: test/functorch/test_minifier.py::TestMinifier::test_has_add_mul, test/functorch/test_minifier.py::TestMinifier::test_has_mul_minifier, test/functorch/test_minifier.py::TestMinifier::test_input_returned, test/functorch/test_minifier.py::TestMinifier::test_module, test/functorch/test_minifier.py::TestMinifier::test_tup_use 2025-09-07T07:42:01.7143914Z 2025-09-07T07:42:03.8454885Z 2025-09-07T07:42:03.8456016Z test_legacy_vmap 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_legacy_vmap_1.1_82888cacbc32e9b0_.log 2025-09-07T07:42:03.8495521Z Running 124 items in this shard: test/test_legacy_vmap.py::TestVmapAPILegacy::test_accepts_nested_inputs, test/test_legacy_vmap.py::TestVmapAPILegacy::test_backward_unsupported_interaction, test/test_legacy_vmap.py::TestVmapAPILegacy::test_batched_gradient_basic, test/test_legacy_vmap.py::TestVmapAPILegacy::test_constant_function, test/test_legacy_vmap.py::TestVmapAPILegacy::test_different_map_dim_size_raises, test/test_legacy_vmap.py::TestVmapAPILegacy::test_fallback_atan2, test/test_legacy_vmap.py::TestVmapAPILegacy::test_fallback_does_not_warn_by_default, test/test_legacy_vmap.py::TestVmapAPILegacy::test_fallback_masked_fill, test/test_legacy_vmap.py::TestVmapAPILegacy::test_fallback_multiple_returns, test/test_legacy_vmap.py::TestVmapAPILegacy::test_fallback_warns_when_warnings_are_enabled, test/test_legacy_vmap.py::TestVmapAPILegacy::test_fallback_with_undefined_grad, test/test_legacy_vmap.py::TestVmapAPILegacy::test_fallback_zero_dim, test/test_legacy_vmap.py::TestVmapAPILegacy::test_func_with_no_inputs, test/test_legacy_vmap.py::TestVmapAPILegacy::test_functools_partial, test/test_legacy_vmap.py::TestVmapAPILegacy::test_grad_unsupported_interaction, test/test_legacy_vmap.py::TestVmapAPILegacy::test_in_dim_not_in_tensor_err_msg, test/test_legacy_vmap.py::TestVmapAPILegacy::test_in_dims_wrong_type_err_msg, test/test_legacy_vmap.py::TestVmapAPILegacy::test_inplace_fallback_nary_different_levels, test/test_legacy_vmap.py::TestVmapAPILegacy::test_inplace_fallback_nary_same_levels, test/test_legacy_vmap.py::TestVmapAPILegacy::test_inplace_fallback_unary, test/test_legacy_vmap.py::TestVmapAPILegacy::test_integer_in_dim_but_not_tensor_input_err_msg, test/test_legacy_vmap.py::TestVmapAPILegacy::test_multiple_inputs, test/test_legacy_vmap.py::TestVmapAPILegacy::test_multiple_out_dims, test/test_legacy_vmap.py::TestVmapAPILegacy::test_multiple_outputs, test/test_legacy_vmap.py::TestVmapAPILegacy::test_multiple_outputs_error_cases, test/test_legacy_vmap.py::TestVmapAPILegacy::test_nested_non_default_in_dims, test/test_legacy_vmap.py::TestVmapAPILegacy::test_nested_out_dims, test/test_legacy_vmap.py::TestVmapAPILegacy::test_nested_with_different_map_dim, test/test_legacy_vmap.py::TestVmapAPILegacy::test_nested_with_same_map_dim, test/test_legacy_vmap.py::TestVmapAPILegacy::test_nn_module, test/test_legacy_vmap.py::TestVmapAPILegacy::test_non_default_in_dims_out_dims, test/test_legacy_vmap.py::TestVmapAPILegacy::test_non_tensor_output_raises, test/test_legacy_vmap.py::TestVmapAPILegacy::test_non_zero_in_dims, test/test_legacy_vmap.py::TestVmapAPILegacy::test_none_in_dims, test/test_legacy_vmap.py::TestVmapAPILegacy::test_nonzero_out_dims, test/test_legacy_vmap.py::TestVmapAPILegacy::test_noop_in_inner_vmap, test/test_legacy_vmap.py::TestVmapAPILegacy::test_not_enough_in_dims_err_msg, test/test_legacy_vmap.py::TestVmapAPILegacy::test_out_dim_out_of_bounds_err_msg, test/test_legacy_vmap.py::TestVmapAPILegacy::test_out_dims_and_num_outputs_mismatch_err_msg, test/test_legacy_vmap.py::TestVmapAPILegacy::test_out_dims_edge_case, test/test_legacy_vmap.py::TestVmapAPILegacy::test_out_dims_must_be_int_or_tuple_of_int_err_msg, test/test_legacy_vmap.py::TestVmapAPILegacy::test_single_input, test/test_legacy_vmap.py::TestVmapAPILegacy::test_unsupported_op_err_msg, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_T_numpy, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_as_strided, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_binary_pointwise_ops, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_bmm, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_cat, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_chunk, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_clamp, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_clone, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_comparison_ops, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_conj, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_contiguous, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_diagonal, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_dot, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_expand_as, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_fill_and_zero_inplace, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_imag, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_is_complex, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_is_contiguous, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_is_floating_point, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_mm, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_movedim, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_mv, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_narrow, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_new_empty, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_new_empty_strided, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_new_zeros, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_no_random_op_support, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_real, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_reshape, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_reshape_as, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_result_type, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_select, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_slice, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_split, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_squeeze, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_stack, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_stride, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_sum_dim, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_t, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_tensor_split, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_to, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_trace, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_transpose, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_unary_pointwise_ops, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_unbind, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_unfold, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_view, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_view_as, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_view_as_complex, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_view_as_real, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_vmap_fallback_check, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_vmap_fallback_check_ok, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_add_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_binary_cross_entropy_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_diagonal_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_div_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_expand_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_index_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_inplace_manyview_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_inplace_on_view_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_lgamma_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_log1p_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_log_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_logsumexp_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_max_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_median_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_min_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_mul_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_permute_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_reshape_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_select_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_sigmoid_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_slice_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_stack_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_sub_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_threshold_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_trace_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_unrelated_output_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_unrelated_output_multiple_grad_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_vmap_fallback_check, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_vmap_fallback_check_ok 2025-09-07T07:42:03.8534439Z 2025-09-07T07:42:04.5004835Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T07:42:04.5006400Z import pkg_resources 2025-09-07T07:42:04.6253509Z Running torch_np/numpy_tests/core/test_einsum 1/1 ... [2025-09-07 07:42:04.624968] 2025-09-07T07:42:04.6254419Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:42:04.6256131Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'torch_np/numpy_tests/core/test_einsum.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 07:42:04.625313] 2025-09-07T07:42:06.6344491Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T07:42:06.6346047Z import pkg_resources 2025-09-07T07:42:06.7573941Z Running inductor/test_benchmarking 1/1 ... [2025-09-07 07:42:06.757006] 2025-09-07T07:42:06.7574460Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:42:06.7576061Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_benchmarking.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'] ... [2025-09-07 07:42:06.757328] 2025-09-07T07:42:13.0805565Z 2025-09-07T07:42:13.0806818Z dynamo/test_guard_serialization 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_guard_serialization_1.1_6cf8123f9c96d634_.log 2025-09-07T07:42:13.0822769Z Running 38 items in this shard: test/dynamo/test_guard_serialization.py::TestGuardSerialization::test_bool_match, test/dynamo/test_guard_serialization.py::TestGuardSerialization::test_builtin_match, test/dynamo/test_guard_serialization.py::TestGuardSerialization::test_closure_match, test/dynamo/test_guard_serialization.py::TestGuardSerialization::test_constant_match, test/dynamo/test_guard_serialization.py::TestGuardSerialization::test_default_device, test/dynamo/test_guard_serialization.py::TestGuardSerialization::test_deterministic_algorithms, test/dynamo/test_guard_serialization.py::TestGuardSerialization::test_dict_contains, test/dynamo/test_guard_serialization.py::TestGuardSerialization::test_dict_keys_match, test/dynamo/test_guard_serialization.py::TestGuardSerialization::test_dict_version, test/dynamo/test_guard_serialization.py::TestGuardSerialization::test_dispatch_key_set_match, test/dynamo/test_guard_serialization.py::TestGuardSerialization::test_dual_level, test/dynamo/test_guard_serialization.py::TestGuardSerialization::test_duplicate_input, test/dynamo/test_guard_serialization.py::TestGuardSerialization::test_empty_nn_module_hooks_dict, test/dynamo/test_guard_serialization.py::TestGuardSerialization::test_equals_match, test/dynamo/test_guard_serialization.py::TestGuardSerialization::test_fsdp_training_state, test/dynamo/test_guard_serialization.py::TestGuardSerialization::test_function_locals, test/dynamo/test_guard_serialization.py::TestGuardSerialization::test_function_match, test/dynamo/test_guard_serialization.py::TestGuardSerialization::test_functorch_stack_match, test/dynamo/test_guard_serialization.py::TestGuardSerialization::test_grad_mode, test/dynamo/test_guard_serialization.py::TestGuardSerialization::test_grad_mode_loading, test/dynamo/test_guard_serialization.py::TestGuardSerialization::test_hasattr_serialization, test/dynamo/test_guard_serialization.py::TestGuardSerialization::test_id_match, test/dynamo/test_guard_serialization.py::TestGuardSerialization::test_id_match_with_config, test/dynamo/test_guard_serialization.py::TestGuardSerialization::test_mapping_keys_check, test/dynamo/test_guard_serialization.py::TestGuardSerialization::test_name_match, test/dynamo/test_guard_serialization.py::TestGuardSerialization::test_nn_module, test/dynamo/test_guard_serialization.py::TestGuardSerialization::test_none_match, test/dynamo/test_guard_serialization.py::TestGuardSerialization::test_not_present_in_generic_dict, test/dynamo/test_guard_serialization.py::TestGuardSerialization::test_range_iterator_match, test/dynamo/test_guard_serialization.py::TestGuardSerialization::test_sequence_length, test/dynamo/test_guard_serialization.py::TestGuardSerialization::test_shape_env, test/dynamo/test_guard_serialization.py::TestGuardSerialization::test_skipped_objects, test/dynamo/test_guard_serialization.py::TestGuardSerialization::test_tensor_match, test/dynamo/test_guard_serialization.py::TestGuardSerialization::test_tensor_subclass_metadata_match, test/dynamo/test_guard_serialization.py::TestGuardSerialization::test_torch_function_state, test/dynamo/test_guard_serialization.py::TestGuardSerialization::test_tuple_iterator_len, test/dynamo/test_guard_serialization.py::TestGuardSerialization::test_type_match, test/dynamo/test_guard_serialization.py::TestGuardSerialization::test_weakref_alive 2025-09-07T07:42:13.0845862Z 2025-09-07T07:42:14.5330576Z 2025-09-07T07:42:14.5331556Z inductor/test_benchmarking 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_benchmarking_1.1_7897117210f8db43_.log 2025-09-07T07:42:14.5338081Z Running 12 items in this shard: test/inductor/test_benchmarking.py::TestBenchmarker::test_benchmark_cpu_smoke_benchmarker_cls0, test/inductor/test_benchmarking.py::TestBenchmarker::test_benchmark_cpu_smoke_benchmarker_cls1, test/inductor/test_benchmarking.py::TestBenchmarker::test_benchmark_gpu_smoke_benchmarker_cls0, test/inductor/test_benchmarking.py::TestBenchmarker::test_benchmark_gpu_smoke_benchmarker_cls1, test/inductor/test_benchmarking.py::TestBenchmarker::test_benchmark_safely_infers_device_many_devices_benchmarker_cls0, test/inductor/test_benchmarking.py::TestBenchmarker::test_benchmark_safely_infers_device_many_devices_benchmarker_cls1, test/inductor/test_benchmarking.py::TestBenchmarker::test_benchmark_safely_infers_device_no_devices_benchmarker_cls0, test/inductor/test_benchmarking.py::TestBenchmarker::test_benchmark_safely_infers_device_no_devices_benchmarker_cls1, test/inductor/test_benchmarking.py::TestBenchmarker::test_benchmark_smoke_benchmarker_cls0_device_cpu, test/inductor/test_benchmarking.py::TestBenchmarker::test_benchmark_smoke_benchmarker_cls0_device_cuda, test/inductor/test_benchmarking.py::TestBenchmarker::test_benchmark_smoke_benchmarker_cls1_device_cpu, test/inductor/test_benchmarking.py::TestBenchmarker::test_benchmark_smoke_benchmarker_cls1_device_cuda 2025-09-07T07:42:14.5343994Z 2025-09-07T07:42:15.8908722Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T07:42:15.8910284Z import pkg_resources 2025-09-07T07:42:16.0130516Z Running dynamo/test_model_output 1/1 ... [2025-09-07 07:42:16.012756] 2025-09-07T07:42:16.0131225Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:42:16.0133490Z 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'] ... [2025-09-07 07:42:16.013100] 2025-09-07T07:42:17.2729751Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T07:42:17.2731327Z import pkg_resources 2025-09-07T07:42:17.3960679Z Running torch_np/test_basic 1/1 ... [2025-09-07 07:42:17.395770] 2025-09-07T07:42:17.3961152Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:42:17.3964796Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'torch_np/test_basic.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 07:42:17.396166] 2025-09-07T07:42:19.2328837Z 2025-09-07T07:42:19.2330403Z 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_d012bbd059faee66_.log 2025-09-07T07:42:19.2342910Z Running 18 items in this shard: test/dynamo/test_model_output.py::TestHFPretrained::test_pretrained, test/dynamo/test_model_output.py::TestHFPretrained::test_pretrained_non_const_attr, test/dynamo/test_model_output.py::TestModelOutput::test_mo_assign, test/dynamo/test_model_output.py::TestModelOutput::test_mo_create, test/dynamo/test_model_output.py::TestModelOutput::test_mo_from_outside, test/dynamo/test_model_output.py::TestModelOutput::test_mo_getattr, test/dynamo/test_model_output.py::TestModelOutput::test_mo_getattr_missing, test/dynamo/test_model_output.py::TestModelOutput::test_mo_getitem, test/dynamo/test_model_output.py::TestModelOutput::test_mo_index, test/dynamo/test_model_output.py::TestModelOutput::test_mo_init, test/dynamo/test_model_output.py::TestModelOutput::test_mo_init2, test/dynamo/test_model_output.py::TestModelOutput::test_mo_init_with_disable, test/dynamo/test_model_output.py::TestModelOutput::test_mo_newkey, test/dynamo/test_model_output.py::TestModelOutput::test_mo_reconstruct_bytecode, test/dynamo/test_model_output.py::TestModelOutput::test_mo_tuple, test/dynamo/test_model_output.py::TestModelOutput::test_none, test/dynamo/test_model_output.py::TestModelOutput::test_reconstruction, test/dynamo/test_model_output.py::TestModelOutputBertCPU::test_HF_bert_model_output_cpu 2025-09-07T07:42:19.2354686Z 2025-09-07T07:42:19.3608669Z 2025-09-07T07:42:19.3610294Z torch_np/numpy_tests/core/test_einsum 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.core.test_einsum_1.1_03b420e8413f3b98_.log 2025-09-07T07:42:19.3645395Z Running 50 items in this shard: test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_broadcasting_dot_cases, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_collapse, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_combined_views_mapping, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_complex, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_different_paths_dtype_B, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_different_paths_dtype_D, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_different_paths_dtype_F, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_different_paths_dtype_b, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_different_paths_dtype_d, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_different_paths_dtype_e, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_different_paths_dtype_f, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_different_paths_dtype_h, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_different_paths_dtype_i, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_different_paths_dtype_l, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_edge_cases, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_einsum_all_contig_non_contig_output, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_einsum_broadcast, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_einsum_errors, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_einsum_failed_on_p9_and_s390x, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_einsum_fixed_collapsingbug, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_einsum_fixedstridebug, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_einsum_misc, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_einsum_sums_cfloat128, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_einsum_sums_cfloat64, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_einsum_sums_float16, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_einsum_sums_float32, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_einsum_sums_float64, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_einsum_sums_int16, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_einsum_sums_int32, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_einsum_sums_int64, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_einsum_sums_int8, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_einsum_sums_uint8, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_einsum_views, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_expand, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_hadamard_like_products, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_index_transformations, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_inner_product, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_out_is_res, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_output_order, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_random_cases, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_small_boolean_arrays, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsum::test_subscript_range, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsumPath::test_edge_paths, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsumPath::test_long_paths, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsumPath::test_memory_contraints, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsumPath::test_path_type_input, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsumPath::test_path_type_input_internal_trace, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsumPath::test_path_type_input_invalid, test/torch_np/numpy_tests/core/test_einsum.py::TestEinsumPath::test_spaces, test/torch_np/numpy_tests/core/test_einsum.py::TestMisc::test_overlap 2025-09-07T07:42:19.3677736Z 2025-09-07T07:42:21.8668427Z 2025-09-07T07:42:21.8669805Z torch_np/test_basic 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.test_basic_1.1_02b3aa9b4fb15ba1_.log 2025-09-07T07:42:21.8936630Z Running 453 items in this shard: test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func0, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func1, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func10, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func11, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func12, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func13, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func14, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func15, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func16, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func17, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func18, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func19, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func2, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func20, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func21, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func22, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func23, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func24, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func25, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func26, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func27, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func28, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func29, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func3, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func30, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func31, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func32, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func33, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func34, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func35, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func36, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func37, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func38, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func39, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func4, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func40, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func41, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func42, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func43, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func44, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func45, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func46, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func47, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func48, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func49, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func5, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func50, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func51, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func52, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func53, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func54, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func55, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func56, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func57, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func58, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func59, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func6, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func60, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func61, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func62, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func63, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func64, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func65, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func66, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func67, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func68, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func69, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func7, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func70, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func71, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func72, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func73, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func74, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func8, test/torch_np/test_basic.py::TestOneArr::test_asarray_array_func9, test/torch_np/test_basic.py::TestOneArr::test_asarray_list_func0, test/torch_np/test_basic.py::TestOneArr::test_asarray_list_func1, test/torch_np/test_basic.py::TestOneArr::test_asarray_list_func10, test/torch_np/test_basic.py::TestOneArr::test_asarray_list_func11, test/torch_np/test_basic.py::TestOneArr::test_asarray_list_func12, test/torch_np/test_basic.py::TestOneArr::test_asarray_list_func13, test/torch_np/test_basic.py::TestOneArr::test_asarray_list_func14, test/torch_np/test_basic.py::TestOneArr::test_asarray_list_func15, test/torch_np/test_basic.py::TestOneArr::test_asarray_list_func16, test/torch_np/test_basic.py::TestOneArr::test_asarray_list_func17, test/torch_np/test_basic.py::TestOneArr::test_asarray_list_func18, test/torch_np/test_basic.py::TestOneArr::test_asarray_list_func19, test/torch_np/test_basic.py::TestOneArr::test_asarray_list_func2, test/torch_np/test_basic.py::TestOneArr::test_asarray_list_func20, 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test/torch_np/test_basic.py::TestOneArrAndAxis::test_andaxis_tensor_func4_axis_1, test/torch_np/test_basic.py::TestOneArrAndAxis::test_andaxis_tensor_func5_axis3, test/torch_np/test_basic.py::TestOneArrAndAxis::test_andaxis_tensor_func5_axis_-1, test/torch_np/test_basic.py::TestOneArrAndAxis::test_andaxis_tensor_func5_axis_0, test/torch_np/test_basic.py::TestOneArrAndAxis::test_andaxis_tensor_func5_axis_1, test/torch_np/test_basic.py::TestOneArrAndAxis::test_andaxis_tensor_func6_axis3, test/torch_np/test_basic.py::TestOneArrAndAxis::test_andaxis_tensor_func6_axis_-1, test/torch_np/test_basic.py::TestOneArrAndAxis::test_andaxis_tensor_func6_axis_0, test/torch_np/test_basic.py::TestOneArrAndAxis::test_andaxis_tensor_func6_axis_1, test/torch_np/test_basic.py::TestOneArrAndAxis::test_andaxis_tensor_func7_axis3, test/torch_np/test_basic.py::TestOneArrAndAxis::test_andaxis_tensor_func7_axis_-1, test/torch_np/test_basic.py::TestOneArrAndAxis::test_andaxis_tensor_func7_axis_0, test/torch_np/test_basic.py::TestOneArrAndAxis::test_andaxis_tensor_func7_axis_1, test/torch_np/test_basic.py::TestOneArrAndAxis::test_andaxis_tensor_func8_axis3, test/torch_np/test_basic.py::TestOneArrAndAxis::test_andaxis_tensor_func8_axis_-1, test/torch_np/test_basic.py::TestOneArrAndAxis::test_andaxis_tensor_func8_axis_0, test/torch_np/test_basic.py::TestOneArrAndAxis::test_andaxis_tensor_func8_axis_1, test/torch_np/test_basic.py::TestOneArrAndAxis::test_andaxis_tensor_func9_axis3, test/torch_np/test_basic.py::TestOneArrAndAxis::test_andaxis_tensor_func9_axis_-1, test/torch_np/test_basic.py::TestOneArrAndAxis::test_andaxis_tensor_func9_axis_0, test/torch_np/test_basic.py::TestOneArrAndAxis::test_andaxis_tensor_func9_axis_1, test/torch_np/test_basic.py::TestOneArrAndAxesTuple::test_andtuple_array_func0_axes0, test/torch_np/test_basic.py::TestOneArrAndAxesTuple::test_andtuple_array_func0_axes1, test/torch_np/test_basic.py::TestOneArrAndAxesTuple::test_andtuple_array_func0_axes2, test/torch_np/test_basic.py::TestOneArrAndAxesTuple::test_andtuple_list_func0_axes0, test/torch_np/test_basic.py::TestOneArrAndAxesTuple::test_andtuple_list_func0_axes1, test/torch_np/test_basic.py::TestOneArrAndAxesTuple::test_andtuple_list_func0_axes2, test/torch_np/test_basic.py::TestOneArrAndAxesTuple::test_andtuple_tensor_func0_axes0, test/torch_np/test_basic.py::TestOneArrAndAxesTuple::test_andtuple_tensor_func0_axes1, test/torch_np/test_basic.py::TestOneArrAndAxesTuple::test_andtuple_tensor_func0_axes2, test/torch_np/test_basic.py::TestOneArrAndShape::test_andshape_array_func0, test/torch_np/test_basic.py::TestOneArrAndShape::test_andshape_array_func1, test/torch_np/test_basic.py::TestOneArrAndShape::test_andshape_array_func2, test/torch_np/test_basic.py::TestOneArrAndShape::test_andshape_array_func3, test/torch_np/test_basic.py::TestOneArrAndShape::test_andshape_array_func4, test/torch_np/test_basic.py::TestOneArrAndShape::test_andshape_list_func0, test/torch_np/test_basic.py::TestOneArrAndShape::test_andshape_list_func1, test/torch_np/test_basic.py::TestOneArrAndShape::test_andshape_list_func2, test/torch_np/test_basic.py::TestOneArrAndShape::test_andshape_list_func3, test/torch_np/test_basic.py::TestOneArrAndShape::test_andshape_list_func4, test/torch_np/test_basic.py::TestOneArrAndShape::test_andshape_tensor_func0, test/torch_np/test_basic.py::TestOneArrAndShape::test_andshape_tensor_func1, test/torch_np/test_basic.py::TestOneArrAndShape::test_andshape_tensor_func2, test/torch_np/test_basic.py::TestOneArrAndShape::test_andshape_tensor_func3, test/torch_np/test_basic.py::TestOneArrAndShape::test_andshape_tensor_func4, test/torch_np/test_basic.py::TestOneArrToScalar::test_toscalar_array_func0_np_func0, test/torch_np/test_basic.py::TestOneArrToScalar::test_toscalar_array_func1_np_func1, test/torch_np/test_basic.py::TestOneArrToScalar::test_toscalar_array_func2_np_func2, test/torch_np/test_basic.py::TestOneArrToScalar::test_toscalar_list_func0_np_func0, test/torch_np/test_basic.py::TestOneArrToScalar::test_toscalar_list_func1_np_func1, test/torch_np/test_basic.py::TestOneArrToScalar::test_toscalar_list_func2_np_func2, test/torch_np/test_basic.py::TestOneArrToScalar::test_toscalar_tensor_func0_np_func0, test/torch_np/test_basic.py::TestOneArrToScalar::test_toscalar_tensor_func1_np_func1, test/torch_np/test_basic.py::TestOneArrToScalar::test_toscalar_tensor_func2_np_func2, test/torch_np/test_basic.py::TestShapeLikeToArray::test_shape_func0, test/torch_np/test_basic.py::TestShapeLikeToArray::test_shape_func1, test/torch_np/test_basic.py::TestShapeLikeToArray::test_shape_func2, test/torch_np/test_basic.py::TestShapeLikeToArray::test_shape_func3, test/torch_np/test_basic.py::TestSequenceOfArrays::test_several_func0, test/torch_np/test_basic.py::TestSequenceOfArrays::test_several_func1, test/torch_np/test_basic.py::TestSequenceOfArrays::test_several_func2, test/torch_np/test_basic.py::TestSequenceOfArrays::test_several_func3, test/torch_np/test_basic.py::TestSequenceOfArrays::test_single_array_func0, test/torch_np/test_basic.py::TestSequenceOfArrays::test_single_array_func1, test/torch_np/test_basic.py::TestSequenceOfArrays::test_single_array_func2, test/torch_np/test_basic.py::TestSequenceOfArrays::test_single_array_func3, test/torch_np/test_basic.py::TestSequenceOfArrays::test_single_list_func0, test/torch_np/test_basic.py::TestSequenceOfArrays::test_single_list_func1, test/torch_np/test_basic.py::TestSequenceOfArrays::test_single_list_func2, test/torch_np/test_basic.py::TestSequenceOfArrays::test_single_list_func3, test/torch_np/test_basic.py::TestSequenceOfArrays::test_single_tensor_func0, test/torch_np/test_basic.py::TestSequenceOfArrays::test_single_tensor_func1, test/torch_np/test_basic.py::TestSequenceOfArrays::test_single_tensor_func2, test/torch_np/test_basic.py::TestSequenceOfArrays::test_single_tensor_func3, test/torch_np/test_basic.py::TestSequenceOfArraysToSingle::test_several_func0, test/torch_np/test_basic.py::TestSequenceOfArraysToSingle::test_several_func1, test/torch_np/test_basic.py::TestSequenceOfArraysToSingle::test_several_func2, test/torch_np/test_basic.py::TestSequenceOfArraysToSingle::test_several_func3, test/torch_np/test_basic.py::TestSequenceOfArraysToSingle::test_several_func4, test/torch_np/test_basic.py::TestSequenceOfArraysToSingle::test_several_func5, test/torch_np/test_basic.py::TestSequenceOfArraysToSingle::test_several_func6, test/torch_np/test_basic.py::TestArrayToSequence::test_asarray_array_func0, test/torch_np/test_basic.py::TestArrayToSequence::test_asarray_array_func1, test/torch_np/test_basic.py::TestArrayToSequence::test_asarray_list_func0, test/torch_np/test_basic.py::TestArrayToSequence::test_asarray_list_func1, test/torch_np/test_basic.py::TestArrayToSequence::test_asarray_tensor_func0, test/torch_np/test_basic.py::TestArrayToSequence::test_asarray_tensor_func1, test/torch_np/test_basic.py::TestPythonArgsToArray::test_argstoarray_simple_func0_args0, test/torch_np/test_basic.py::TestPythonArgsToArray::test_argstoarray_simple_func1_args1, test/torch_np/test_basic.py::TestPythonArgsToArray::test_argstoarray_simple_func2_args2, test/torch_np/test_basic.py::TestPythonArgsToArray::test_argstoarray_simple_func3_args3, test/torch_np/test_basic.py::TestPythonArgsToArray::test_argstoarray_simple_func4_args4, test/torch_np/test_basic.py::TestPythonArgsToArray::test_argstoarray_simple_func5_args5, test/torch_np/test_basic.py::TestPythonArgsToArray::test_argstoarray_simple_func6_args6, test/torch_np/test_basic.py::TestPythonArgsToArray::test_argstoarray_simple_func7_args7, test/torch_np/test_basic.py::TestPythonArgsToArray::test_argstoarray_simple_func8_args8, test/torch_np/test_basic.py::TestPythonArgsToArray::test_argstoarray_simple_func9_args9, test/torch_np/test_basic.py::TestNormalizations::test_too_few_args_positional, test/torch_np/test_basic.py::TestNormalizations::test_unknown_args, test/torch_np/test_basic.py::TestNormalizations::test_unknown_args_with_defaults, test/torch_np/test_basic.py::TestCopyTo::test_copyto_basic, test/torch_np/test_basic.py::TestCopyTo::test_copyto_typecast, test/torch_np/test_basic.py::TestCopyTo::test_copytobcast, test/torch_np/test_basic.py::TestDivmod::test_divmod_no_out, test/torch_np/test_basic.py::TestDivmod::test_divmod_out, test/torch_np/test_basic.py::TestDivmod::test_divmod_out_both_pos_and_kw, test/torch_np/test_basic.py::TestDivmod::test_divmod_out_list, test/torch_np/test_basic.py::TestDivmod::test_divmod_pos_only, test/torch_np/test_basic.py::TestSmokeNotImpl::test_nimpl_basic, test/torch_np/test_basic.py::TestDefaultDtype::test_defaultdtype_defaults, test/torch_np/test_basic.py::TestDefaultDtype::test_set_default_float_dt_float32, test/torch_np/test_basic.py::TestDefaultDtype::test_set_default_float_dt_pytorch, test/torch_np/test_basic.py::TestDefaultDtype::test_set_default_float_float32, test/torch_np/test_basic.py::TestExport::test_exported_objects, test/torch_np/test_basic.py::TestCtorNested::test_arrays_in_lists, test/torch_np/test_basic.py::TestMisc::test_f16_on_cuda, test/torch_np/test_basic.py::TestMisc::test_ndarrays_to_tensors 2025-09-07T07:42:21.9143088Z 2025-09-07T07:42:21.9958515Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T07:42:21.9960082Z import pkg_resources 2025-09-07T07:42:22.1191726Z Running test_segment_reductions 1/1 ... [2025-09-07 07:42:22.118879] 2025-09-07T07:42:22.1192368Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:42:22.1195781Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_segment_reductions.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 07:42:22.119226] 2025-09-07T07:42:22.1672046Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T07:42:22.1675126Z import pkg_resources 2025-09-07T07:42:22.3084893Z Running test_ops_fwd_gradients 1/2 ... [2025-09-07 07:42:22.308103] 2025-09-07T07:42:22.3085370Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:42:22.3087141Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_ops_fwd_gradients.py', '-m', 'not serial', '--shard-id=1', '--num-shards=2', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 07:42:22.308459] 2025-09-07T07:42:24.7740850Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T07:42:24.7742430Z import pkg_resources 2025-09-07T07:42:24.8964634Z Running inductor/test_minifier_isolate 1/1 ... [2025-09-07 07:42:24.896113] 2025-09-07T07:42:24.8965334Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:42:24.8966819Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_minifier_isolate.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'] ... [2025-09-07 07:42:24.896429] 2025-09-07T07:42:26.2895169Z 2025-09-07T07:42:26.2896384Z test_segment_reductions 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_segment_reductions_1.1_b694a67b4212d830_.log 2025-09-07T07:42:26.2931779Z Running 74 items in this shard: test/test_segment_reductions.py::TestSegmentReductionsCPU::test_multi_d_cpu_bfloat16_int32, test/test_segment_reductions.py::TestSegmentReductionsCPU::test_multi_d_cpu_bfloat16_int64, test/test_segment_reductions.py::TestSegmentReductionsCPU::test_multi_d_cpu_float16_int32, test/test_segment_reductions.py::TestSegmentReductionsCPU::test_multi_d_cpu_float16_int64, test/test_segment_reductions.py::TestSegmentReductionsCPU::test_multi_d_cpu_float32_int32, test/test_segment_reductions.py::TestSegmentReductionsCPU::test_multi_d_cpu_float32_int64, test/test_segment_reductions.py::TestSegmentReductionsCPU::test_multi_d_cpu_float64_int32, test/test_segment_reductions.py::TestSegmentReductionsCPU::test_multi_d_cpu_float64_int64, test/test_segment_reductions.py::TestSegmentReductionsCPU::test_multi_d_simple_cpu_bfloat16_int32, test/test_segment_reductions.py::TestSegmentReductionsCPU::test_multi_d_simple_cpu_bfloat16_int64, test/test_segment_reductions.py::TestSegmentReductionsCPU::test_multi_d_simple_cpu_float16_int32, test/test_segment_reductions.py::TestSegmentReductionsCPU::test_multi_d_simple_cpu_float16_int64, test/test_segment_reductions.py::TestSegmentReductionsCPU::test_multi_d_simple_cpu_float32_int32, test/test_segment_reductions.py::TestSegmentReductionsCPU::test_multi_d_simple_cpu_float32_int64, test/test_segment_reductions.py::TestSegmentReductionsCPU::test_multi_d_simple_cpu_float64_int32, test/test_segment_reductions.py::TestSegmentReductionsCPU::test_multi_d_simple_cpu_float64_int64, test/test_segment_reductions.py::TestSegmentReductionsCPU::test_pytorch_scatter_test_cases_reduce_max_cpu_bfloat16_int32, test/test_segment_reductions.py::TestSegmentReductionsCPU::test_pytorch_scatter_test_cases_reduce_max_cpu_bfloat16_int64, test/test_segment_reductions.py::TestSegmentReductionsCPU::test_pytorch_scatter_test_cases_reduce_max_cpu_float16_int32, test/test_segment_reductions.py::TestSegmentReductionsCPU::test_pytorch_scatter_test_cases_reduce_max_cpu_float16_int64, test/test_segment_reductions.py::TestSegmentReductionsCPU::test_pytorch_scatter_test_cases_reduce_max_cpu_float32_int32, test/test_segment_reductions.py::TestSegmentReductionsCPU::test_pytorch_scatter_test_cases_reduce_max_cpu_float32_int64, test/test_segment_reductions.py::TestSegmentReductionsCPU::test_pytorch_scatter_test_cases_reduce_max_cpu_float64_int32, test/test_segment_reductions.py::TestSegmentReductionsCPU::test_pytorch_scatter_test_cases_reduce_max_cpu_float64_int64, test/test_segment_reductions.py::TestSegmentReductionsCPU::test_pytorch_scatter_test_cases_reduce_mean_cpu_bfloat16_int32, test/test_segment_reductions.py::TestSegmentReductionsCPU::test_pytorch_scatter_test_cases_reduce_mean_cpu_bfloat16_int64, test/test_segment_reductions.py::TestSegmentReductionsCPU::test_pytorch_scatter_test_cases_reduce_mean_cpu_float16_int32, test/test_segment_reductions.py::TestSegmentReductionsCPU::test_pytorch_scatter_test_cases_reduce_mean_cpu_float16_int64, test/test_segment_reductions.py::TestSegmentReductionsCPU::test_pytorch_scatter_test_cases_reduce_mean_cpu_float32_int32, test/test_segment_reductions.py::TestSegmentReductionsCPU::test_pytorch_scatter_test_cases_reduce_mean_cpu_float32_int64, test/test_segment_reductions.py::TestSegmentReductionsCPU::test_pytorch_scatter_test_cases_reduce_mean_cpu_float64_int32, test/test_segment_reductions.py::TestSegmentReductionsCPU::test_pytorch_scatter_test_cases_reduce_mean_cpu_float64_int64, test/test_segment_reductions.py::TestSegmentReductionsCPU::test_pytorch_scatter_test_cases_reduce_min_cpu_bfloat16_int32, test/test_segment_reductions.py::TestSegmentReductionsCPU::test_pytorch_scatter_test_cases_reduce_min_cpu_bfloat16_int64, test/test_segment_reductions.py::TestSegmentReductionsCPU::test_pytorch_scatter_test_cases_reduce_min_cpu_float16_int32, test/test_segment_reductions.py::TestSegmentReductionsCPU::test_pytorch_scatter_test_cases_reduce_min_cpu_float16_int64, test/test_segment_reductions.py::TestSegmentReductionsCPU::test_pytorch_scatter_test_cases_reduce_min_cpu_float32_int32, test/test_segment_reductions.py::TestSegmentReductionsCPU::test_pytorch_scatter_test_cases_reduce_min_cpu_float32_int64, test/test_segment_reductions.py::TestSegmentReductionsCPU::test_pytorch_scatter_test_cases_reduce_min_cpu_float64_int32, test/test_segment_reductions.py::TestSegmentReductionsCPU::test_pytorch_scatter_test_cases_reduce_min_cpu_float64_int64, test/test_segment_reductions.py::TestSegmentReductionsCPU::test_pytorch_scatter_test_cases_reduce_prod_cpu_bfloat16_int32, test/test_segment_reductions.py::TestSegmentReductionsCPU::test_pytorch_scatter_test_cases_reduce_prod_cpu_bfloat16_int64, test/test_segment_reductions.py::TestSegmentReductionsCPU::test_pytorch_scatter_test_cases_reduce_prod_cpu_float16_int32, test/test_segment_reductions.py::TestSegmentReductionsCPU::test_pytorch_scatter_test_cases_reduce_prod_cpu_float16_int64, test/test_segment_reductions.py::TestSegmentReductionsCPU::test_pytorch_scatter_test_cases_reduce_prod_cpu_float32_int32, test/test_segment_reductions.py::TestSegmentReductionsCPU::test_pytorch_scatter_test_cases_reduce_prod_cpu_float32_int64, test/test_segment_reductions.py::TestSegmentReductionsCPU::test_pytorch_scatter_test_cases_reduce_prod_cpu_float64_int32, test/test_segment_reductions.py::TestSegmentReductionsCPU::test_pytorch_scatter_test_cases_reduce_prod_cpu_float64_int64, test/test_segment_reductions.py::TestSegmentReductionsCPU::test_pytorch_scatter_test_cases_reduce_sum_cpu_bfloat16_int32, test/test_segment_reductions.py::TestSegmentReductionsCPU::test_pytorch_scatter_test_cases_reduce_sum_cpu_bfloat16_int64, test/test_segment_reductions.py::TestSegmentReductionsCPU::test_pytorch_scatter_test_cases_reduce_sum_cpu_float16_int32, test/test_segment_reductions.py::TestSegmentReductionsCPU::test_pytorch_scatter_test_cases_reduce_sum_cpu_float16_int64, test/test_segment_reductions.py::TestSegmentReductionsCPU::test_pytorch_scatter_test_cases_reduce_sum_cpu_float32_int32, test/test_segment_reductions.py::TestSegmentReductionsCPU::test_pytorch_scatter_test_cases_reduce_sum_cpu_float32_int64, test/test_segment_reductions.py::TestSegmentReductionsCPU::test_pytorch_scatter_test_cases_reduce_sum_cpu_float64_int32, test/test_segment_reductions.py::TestSegmentReductionsCPU::test_pytorch_scatter_test_cases_reduce_sum_cpu_float64_int64, test/test_segment_reductions.py::TestSegmentReductionsCPU::test_simple_1d_cpu_bfloat16_int32, test/test_segment_reductions.py::TestSegmentReductionsCPU::test_simple_1d_cpu_bfloat16_int64, test/test_segment_reductions.py::TestSegmentReductionsCPU::test_simple_1d_cpu_float16_int32, test/test_segment_reductions.py::TestSegmentReductionsCPU::test_simple_1d_cpu_float16_int64, test/test_segment_reductions.py::TestSegmentReductionsCPU::test_simple_1d_cpu_float32_int32, test/test_segment_reductions.py::TestSegmentReductionsCPU::test_simple_1d_cpu_float32_int64, test/test_segment_reductions.py::TestSegmentReductionsCPU::test_simple_1d_cpu_float64_int32, test/test_segment_reductions.py::TestSegmentReductionsCPU::test_simple_1d_cpu_float64_int64, test/test_segment_reductions.py::TestSegmentReductionsCPU::test_simple_zero_length_cpu_bfloat16_int32, test/test_segment_reductions.py::TestSegmentReductionsCPU::test_simple_zero_length_cpu_bfloat16_int64, test/test_segment_reductions.py::TestSegmentReductionsCPU::test_simple_zero_length_cpu_float16_int32, test/test_segment_reductions.py::TestSegmentReductionsCPU::test_simple_zero_length_cpu_float16_int64, test/test_segment_reductions.py::TestSegmentReductionsCPU::test_simple_zero_length_cpu_float32_int32, test/test_segment_reductions.py::TestSegmentReductionsCPU::test_simple_zero_length_cpu_float32_int64, test/test_segment_reductions.py::TestSegmentReductionsCPU::test_simple_zero_length_cpu_float64_int32, test/test_segment_reductions.py::TestSegmentReductionsCPU::test_simple_zero_length_cpu_float64_int64, test/test_segment_reductions.py::TestSegmentReductionsCPU::test_unsafe_flag_cpu_int32, test/test_segment_reductions.py::TestSegmentReductionsCPU::test_unsafe_flag_cpu_int64 2025-09-07T07:42:26.2966339Z 2025-09-07T07:42:29.0153129Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T07:42:29.0156527Z import pkg_resources 2025-09-07T07:42:29.1382623Z Running xpu/test_conv 1/1 ... [2025-09-07 07:42:29.137913] 2025-09-07T07:42:29.1383050Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:42:29.1385376Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'xpu/test_conv.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 07:42:29.138287] 2025-09-07T07:42:32.1546185Z 2025-09-07T07:42:32.1547194Z inductor/test_minifier_isolate 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_minifier_isolate_1.1_795de9fc81710923_.log 2025-09-07T07:42:32.1548182Z 2025-09-07T07:42:32.7123298Z 2025-09-07T07:42:32.7124515Z xpu/test_conv 1/1 was successful, full logs can be found in artifacts with path test/test-reports/xpu.test_conv_1.1_5da1d68b9d0abf6b_.log 2025-09-07T07:42:32.7125259Z Running 0 items in this shard: 2025-09-07T07:42:32.7125453Z 2025-09-07T07:42:34.9695540Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T07:42:34.9698485Z import pkg_resources 2025-09-07T07:42:35.0917141Z Running dynamo/test_flat_apply 1/1 ... [2025-09-07 07:42:35.091347] 2025-09-07T07:42:35.0917922Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:42:35.0920738Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_flat_apply.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'] ... [2025-09-07 07:42:35.091711] 2025-09-07T07:42:35.5318396Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T07:42:35.5321432Z import pkg_resources 2025-09-07T07:42:35.6644866Z Running export/test_unflatten 1/1 ... [2025-09-07 07:42:35.664157] 2025-09-07T07:42:35.6645334Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:42:35.6648073Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'export/test_unflatten.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'] ... [2025-09-07 07:42:35.664557] 2025-09-07T07:42:38.8132600Z 2025-09-07T07:42:38.8134096Z dynamo/test_flat_apply 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_flat_apply_1.1_c4c3189eeaeb4ba6_.log 2025-09-07T07:42:38.8330639Z Running 4 items in this shard: test/dynamo/test_flat_apply.py::FlatApplyTests::test_non_tensor_output, test/dynamo/test_flat_apply.py::FlatApplyTests::test_nonstrict_trace_captured_tensor_post_aot_graph, test/dynamo/test_flat_apply.py::FlatApplyTests::test_nonstrict_trace_dynamo_graph, test/dynamo/test_flat_apply.py::FlatApplyTests::test_simple 2025-09-07T07:42:38.8333685Z 2025-09-07T07:42:41.5992874Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T07:42:41.5996088Z import pkg_resources 2025-09-07T07:42:41.7218728Z Running lazy/test_step_closures 1/1 ... [2025-09-07 07:42:41.721452] 2025-09-07T07:42:41.7219570Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:42:41.7222068Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'lazy/test_step_closures.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'] ... [2025-09-07 07:42:41.721861] 2025-09-07T07:42:46.6948116Z 2025-09-07T07:42:46.6949106Z lazy/test_step_closures 1/1 was successful, full logs can be found in artifacts with path test/test-reports/lazy.test_step_closures_1.1_5b584738ad6fe6e9_.log 2025-09-07T07:42:46.6951091Z Running 4 items in this shard: test/lazy/test_step_closures.py::ClosuresTest::test_asynchronous, test/lazy/test_step_closures.py::ClosuresTest::test_asynchronous_exception, test/lazy/test_step_closures.py::ClosuresTest::test_synchronous, test/lazy/test_step_closures.py::ClosuresTest::test_synchronous_exception 2025-09-07T07:42:46.6952706Z 2025-09-07T07:42:49.4765336Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T07:42:49.5986895Z import pkg_resources 2025-09-07T07:42:49.5987419Z Running test_monitor 1/1 ... [2025-09-07 07:42:49.598355] 2025-09-07T07:42:49.5987849Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:42:49.5989532Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_monitor.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 07:42:49.598691] 2025-09-07T07:42:52.4668645Z 2025-09-07T07:42:52.4669751Z test_monitor 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_monitor_1.1_0e5e59c149b17f0a_.log 2025-09-07T07:42:52.4671836Z Running 6 items in this shard: test/test_monitor.py::TestMonitor::test_event_handler, test/test_monitor.py::TestMonitor::test_fixed_count_stat, test/test_monitor.py::TestMonitor::test_interval_stat, test/test_monitor.py::TestMonitor::test_log_event, test/test_monitor.py::TestMonitor::test_wait_counter, test/test_monitor.py::TestMonitorTensorboard::test_event_handler 2025-09-07T07:42:52.4673373Z 2025-09-07T07:42:55.2391588Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T07:42:55.2393167Z import pkg_resources 2025-09-07T07:42:55.3616860Z Running functorch/test_logging 1/1 ... [2025-09-07 07:42:55.361364] 2025-09-07T07:42:55.3617362Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:42:55.3619214Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'functorch/test_logging.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 07:42:55.361684] 2025-09-07T07:42:57.0104322Z 2025-09-07T07:42:57.0105932Z export/test_unflatten 1/1 was successful, full logs can be found in artifacts with path test/test-reports/export.test_unflatten_1.1_da3f4c58ac98883f_.log 2025-09-07T07:42:57.0116902Z Running 28 items in this shard: test/export/test_unflatten.py::TestUnflatten::test_assert_tensor_metadata_stack, test/export/test_unflatten.py::TestUnflatten::test_attr_as_submod_input, test/export/test_unflatten.py::TestUnflatten::test_dedup_sym_size, test/export/test_unflatten.py::TestUnflatten::test_double_nested_submodule, test/export/test_unflatten.py::TestUnflatten::test_duplicate_placeholder, test/export/test_unflatten.py::TestUnflatten::test_fx_trace, test/export/test_unflatten.py::TestUnflatten::test_nested_leaf_non_strict, test/export/test_unflatten.py::TestUnflatten::test_placeholder_and_get_attr_ordering_after_unflattened, test/export/test_unflatten.py::TestUnflatten::test_simple_alias, test/export/test_unflatten.py::TestUnflatten::test_unflatten_buffer_mutation, test/export/test_unflatten.py::TestUnflatten::test_unflatten_constant_obj, test/export/test_unflatten.py::TestUnflatten::test_unflatten_constant_tensor, test/export/test_unflatten.py::TestUnflatten::test_unflatten_container_type, test/export/test_unflatten.py::TestUnflatten::test_unflatten_eager, test/export/test_unflatten.py::TestUnflatten::test_unflatten_empty_branch, test/export/test_unflatten.py::TestUnflatten::test_unflatten_nested, test/export/test_unflatten.py::TestUnflatten::test_unflatten_nested_access, test/export/test_unflatten.py::TestUnflatten::test_unflatten_none, test/export/test_unflatten.py::TestUnflatten::test_unflatten_param_list_dict, test/export/test_unflatten.py::TestUnflatten::test_unflatten_preserve_signature, test/export/test_unflatten.py::TestUnflatten::test_unflatten_preserve_with_unused_input, test/export/test_unflatten.py::TestUnflatten::test_unflatten_requires_grad_param, test/export/test_unflatten.py::TestUnflatten::test_unflatten_shared_submodule, test/export/test_unflatten.py::TestUnflatten::test_unflatten_skipped_call_module, test/export/test_unflatten.py::TestUnflatten::test_unflatten_submodule_ordering, test/export/test_unflatten.py::TestUnflatten::test_unflatten_with_inplace_compile, test/export/test_unflatten.py::TestUnflatten::test_unflatten_wrong_input, test/export/test_unflatten.py::TestUnflatten::test_unflattened_module_nodes_has_meta_val 2025-09-07T07:42:57.0127496Z 2025-09-07T07:42:58.5303776Z 2025-09-07T07:42:58.5305406Z functorch/test_logging 1/1 was successful, full logs can be found in artifacts with path test/test-reports/functorch.test_logging_1.1_e26a9141144bfc4f_.log 2025-09-07T07:42:58.5306485Z Running 1 items in this shard: test/functorch/test_logging.py::TestAOTLogging::test_logging 2025-09-07T07:42:58.5306917Z 2025-09-07T07:42:59.7427662Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T07:42:59.7429215Z import pkg_resources 2025-09-07T07:42:59.8652672Z Running test_stateless 1/1 ... [2025-09-07 07:42:59.864942] 2025-09-07T07:42:59.8653159Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:42:59.8654989Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_stateless.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'] ... [2025-09-07 07:42:59.865274] 2025-09-07T07:43:01.3252479Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T07:43:01.3254017Z import pkg_resources 2025-09-07T07:43:01.4473023Z Running torch_np/numpy_tests/core/test_numeric 1/1 ... [2025-09-07 07:43:01.446951] 2025-09-07T07:43:01.4473545Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:43:01.4476206Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'torch_np/numpy_tests/core/test_numeric.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 07:43:01.447332] 2025-09-07T07:43:09.2735255Z 2025-09-07T07:43:09.2736821Z torch_np/numpy_tests/core/test_numeric 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.core.test_numeric_1.1_30ef391ee837e942_.log 2025-09-07T07:43:09.2836833Z Running 273 items in this shard: test/torch_np/numpy_tests/core/test_numeric.py::TestResize::test_copies, test/torch_np/numpy_tests/core/test_numeric.py::TestResize::test_negative_resize, test/torch_np/numpy_tests/core/test_numeric.py::TestResize::test_repeats, test/torch_np/numpy_tests/core/test_numeric.py::TestResize::test_reshape_from_zero, test/torch_np/numpy_tests/core/test_numeric.py::TestResize::test_zeroresize, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_choose, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_clip, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_compress, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_count_nonzero, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_cumproduct, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_diagonal, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_dunder_round_accuracy, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_dunder_round_dtype0, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_dunder_round_dtype1, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_dunder_round_dtype2, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_dunder_round_dtype3, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_dunder_round_dtype4, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_dunder_round_dtype5, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_dunder_round_dtype6, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_dunder_round_dtype7, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_dunder_round_edgecases_val_2147483647_ndigits_-1, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_dunder_round_edgecases_val_2147483647_ndigits_-10, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_dunder_round_edgecases_val_2147483647_ndigits_-9, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_mean, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_prod, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_ptp, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_ravel, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_repeat, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_reshape, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_round, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_round_2, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_round_py_consistency, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_searchsorted, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_size, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_squeeze, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_std, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_sum, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_swapaxes, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_take, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_trace, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_transpose, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_var, test/torch_np/numpy_tests/core/test_numeric.py::TestIsscalar::test_isscalar, test/torch_np/numpy_tests/core/test_numeric.py::TestBoolScalar::test_bitwise_and_eq, test/torch_np/numpy_tests/core/test_numeric.py::TestBoolScalar::test_bitwise_and_is, test/torch_np/numpy_tests/core/test_numeric.py::TestBoolScalar::test_bitwise_or_eq, test/torch_np/numpy_tests/core/test_numeric.py::TestBoolScalar::test_bitwise_or_is, test/torch_np/numpy_tests/core/test_numeric.py::TestBoolScalar::test_bitwise_xor_eq, test/torch_np/numpy_tests/core/test_numeric.py::TestBoolScalar::test_bitwise_xor_is, test/torch_np/numpy_tests/core/test_numeric.py::TestBoolScalar::test_logical, test/torch_np/numpy_tests/core/test_numeric.py::TestBoolArray::test_all_any, test/torch_np/numpy_tests/core/test_numeric.py::TestBoolArray::test_logical_and_or_xor, test/torch_np/numpy_tests/core/test_numeric.py::TestBoolArray::test_logical_not_abs, test/torch_np/numpy_tests/core/test_numeric.py::TestBoolCmp::test_double, test/torch_np/numpy_tests/core/test_numeric.py::TestBoolCmp::test_float, test/torch_np/numpy_tests/core/test_numeric.py::TestSeterr::test_default, test/torch_np/numpy_tests/core/test_numeric.py::TestSeterr::test_divide_err, test/torch_np/numpy_tests/core/test_numeric.py::TestSeterr::test_errobj, test/torch_np/numpy_tests/core/test_numeric.py::TestSeterr::test_set, test/torch_np/numpy_tests/core/test_numeric.py::TestFloatExceptions::test_floating_exceptions_typecode_D, test/torch_np/numpy_tests/core/test_numeric.py::TestFloatExceptions::test_floating_exceptions_typecode_F, test/torch_np/numpy_tests/core/test_numeric.py::TestFloatExceptions::test_floating_exceptions_typecode_d, test/torch_np/numpy_tests/core/test_numeric.py::TestFloatExceptions::test_floating_exceptions_typecode_e, test/torch_np/numpy_tests/core/test_numeric.py::TestFloatExceptions::test_floating_exceptions_typecode_f, test/torch_np/numpy_tests/core/test_numeric.py::TestFloatExceptions::test_warnings, test/torch_np/numpy_tests/core/test_numeric.py::TestTypes::test_can_cast, test/torch_np/numpy_tests/core/test_numeric.py::TestTypes::test_can_cast_2, test/torch_np/numpy_tests/core/test_numeric.py::TestTypes::test_can_cast_values, test/torch_np/numpy_tests/core/test_numeric.py::TestTypes::test_coercion, test/torch_np/numpy_tests/core/test_numeric.py::TestTypes::test_coercion_2, test/torch_np/numpy_tests/core/test_numeric.py::TestTypes::test_promote_types_endian, test/torch_np/numpy_tests/core/test_numeric.py::TestTypes::test_result_type, test/torch_np/numpy_tests/core/test_numeric.py::TestTypes::test_tesult_type_2, test/torch_np/numpy_tests/core/test_numeric.py::TestFromiter::test_2592_dtype0_count_10_error_index_5, test/torch_np/numpy_tests/core/test_numeric.py::TestFromiter::test_2592_dtype0_count_10_error_index_9, test/torch_np/numpy_tests/core/test_numeric.py::TestFromiter::test_empty_result, test/torch_np/numpy_tests/core/test_numeric.py::TestFromiter::test_failed_itemsetting, test/torch_np/numpy_tests/core/test_numeric.py::TestFromiter::test_lengths, test/torch_np/numpy_tests/core/test_numeric.py::TestFromiter::test_too_few_items, test/torch_np/numpy_tests/core/test_numeric.py::TestFromiter::test_types, test/torch_np/numpy_tests/core/test_numeric.py::TestFromiter::test_values, test/torch_np/numpy_tests/core/test_numeric.py::TestNonzeroAndCountNonzero::test_count_nonzero_axis, test/torch_np/numpy_tests/core/test_numeric.py::TestNonzeroAndCountNonzero::test_count_nonzero_axis_all_dtypes_typecode_?, test/torch_np/numpy_tests/core/test_numeric.py::TestNonzeroAndCountNonzero::test_count_nonzero_axis_all_dtypes_typecode_B, test/torch_np/numpy_tests/core/test_numeric.py::TestNonzeroAndCountNonzero::test_count_nonzero_axis_all_dtypes_typecode_D, test/torch_np/numpy_tests/core/test_numeric.py::TestNonzeroAndCountNonzero::test_count_nonzero_axis_all_dtypes_typecode_F, test/torch_np/numpy_tests/core/test_numeric.py::TestNonzeroAndCountNonzero::test_count_nonzero_axis_all_dtypes_typecode_b, test/torch_np/numpy_tests/core/test_numeric.py::TestNonzeroAndCountNonzero::test_count_nonzero_axis_all_dtypes_typecode_d, test/torch_np/numpy_tests/core/test_numeric.py::TestNonzeroAndCountNonzero::test_count_nonzero_axis_all_dtypes_typecode_e, test/torch_np/numpy_tests/core/test_numeric.py::TestNonzeroAndCountNonzero::test_count_nonzero_axis_all_dtypes_typecode_f, test/torch_np/numpy_tests/core/test_numeric.py::TestNonzeroAndCountNonzero::test_count_nonzero_axis_all_dtypes_typecode_h, test/torch_np/numpy_tests/core/test_numeric.py::TestNonzeroAndCountNonzero::test_count_nonzero_axis_all_dtypes_typecode_i, test/torch_np/numpy_tests/core/test_numeric.py::TestNonzeroAndCountNonzero::test_count_nonzero_axis_all_dtypes_typecode_l, test/torch_np/numpy_tests/core/test_numeric.py::TestNonzeroAndCountNonzero::test_count_nonzero_list, test/torch_np/numpy_tests/core/test_numeric.py::TestNonzeroAndCountNonzero::test_countnonzero_axis_empty, test/torch_np/numpy_tests/core/test_numeric.py::TestNonzeroAndCountNonzero::test_countnonzero_keepdims, test/torch_np/numpy_tests/core/test_numeric.py::TestNonzeroAndCountNonzero::test_nonzero_onedim, test/torch_np/numpy_tests/core/test_numeric.py::TestNonzeroAndCountNonzero::test_nonzero_onedim_differs, test/torch_np/numpy_tests/core/test_numeric.py::TestNonzeroAndCountNonzero::test_nonzero_trivial, test/torch_np/numpy_tests/core/test_numeric.py::TestNonzeroAndCountNonzero::test_nonzero_trivial_differs, test/torch_np/numpy_tests/core/test_numeric.py::TestNonzeroAndCountNonzero::test_nonzero_twodim, test/torch_np/numpy_tests/core/test_numeric.py::TestNonzeroAndCountNonzero::test_nonzero_zerod, test/torch_np/numpy_tests/core/test_numeric.py::TestNonzeroAndCountNonzero::test_nonzero_zerod_differs, test/torch_np/numpy_tests/core/test_numeric.py::TestNonzeroAndCountNonzero::test_sparse, test/torch_np/numpy_tests/core/test_numeric.py::TestIndex::test_boolean, test/torch_np/numpy_tests/core/test_numeric.py::TestIndex::test_boolean_edgecase, test/torch_np/numpy_tests/core/test_numeric.py::TestBinaryRepr::test_large_neg_int64, test/torch_np/numpy_tests/core/test_numeric.py::TestBinaryRepr::test_neg_width_boundaries, test/torch_np/numpy_tests/core/test_numeric.py::TestBinaryRepr::test_negative, test/torch_np/numpy_tests/core/test_numeric.py::TestBinaryRepr::test_positive, test/torch_np/numpy_tests/core/test_numeric.py::TestBinaryRepr::test_sufficient_width, test/torch_np/numpy_tests/core/test_numeric.py::TestBinaryRepr::test_zero, test/torch_np/numpy_tests/core/test_numeric.py::TestBaseRepr::test_base3, test/torch_np/numpy_tests/core/test_numeric.py::TestBaseRepr::test_base_range, test/torch_np/numpy_tests/core/test_numeric.py::TestBaseRepr::test_negative, test/torch_np/numpy_tests/core/test_numeric.py::TestBaseRepr::test_positive, test/torch_np/numpy_tests/core/test_numeric.py::TestArrayComparisons::test_array_equal, test/torch_np/numpy_tests/core/test_numeric.py::TestArrayComparisons::test_array_equal_equal_nan, test/torch_np/numpy_tests/core/test_numeric.py::TestArrayComparisons::test_array_equiv, test/torch_np/numpy_tests/core/test_numeric.py::TestArrayComparisons::test_none_compares_elementwise, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_array_double, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_clip_complex, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_clip_func_takes_out, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_clip_inplace_array, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_clip_inplace_simple, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_clip_nan, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_clip_non_contig, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_clip_property, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_clip_scalar_nan_propagation_arr0_amin0_amax0, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_clip_value_min_max_flip_amin2_amax2, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_clip_value_min_max_flip_amin_1_amax1, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_clip_value_min_max_flip_amin_1_amax_0, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_clip_with_out_array_int32, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_clip_with_out_array_outint32, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_clip_with_out_memory_overlap, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_clip_with_out_simple, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_clip_with_out_simple2, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_clip_with_out_simple_int32, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_clip_with_out_transposed, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_noncontig_inplace, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_ones_pathological_2_dtype_D, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_ones_pathological_2_dtype_F, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_ones_pathological_2_dtype_e, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_ones_pathological_dtype_?, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_ones_pathological_dtype_B, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_ones_pathological_dtype_b, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_ones_pathological_dtype_d, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_ones_pathological_dtype_f, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_ones_pathological_dtype_h, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_ones_pathological_dtype_i, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_ones_pathological_dtype_l, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_simple_complex, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_simple_double, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_simple_inplace_01, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_simple_inplace_02, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_simple_int, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_simple_int32_inout_casting0, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_simple_int32_inout_casting_unsafe, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_simple_int32_out, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_simple_int64_inout, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_simple_int64_out, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_simple_nonnative, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_simple_out, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_type_cast_01, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_type_cast_02, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_type_cast_03, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_type_cast_04, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_type_cast_05, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_type_cast_06, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_type_cast_07, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_type_cast_08, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_type_cast_09, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_type_cast_10, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_type_cast_11, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_type_cast_12, test/torch_np/numpy_tests/core/test_numeric.py::TestAllclose::test_equalnan, test/torch_np/numpy_tests/core/test_numeric.py::TestAllclose::test_ip_allclose, test/torch_np/numpy_tests/core/test_numeric.py::TestAllclose::test_ip_not_allclose, test/torch_np/numpy_tests/core/test_numeric.py::TestAllclose::test_min_int, test/torch_np/numpy_tests/core/test_numeric.py::TestAllclose::test_no_parameter_modification, test/torch_np/numpy_tests/core/test_numeric.py::TestIsclose::test_equal_nan, test/torch_np/numpy_tests/core/test_numeric.py::TestIsclose::test_ip_all_isclose, test/torch_np/numpy_tests/core/test_numeric.py::TestIsclose::test_ip_isclose, test/torch_np/numpy_tests/core/test_numeric.py::TestIsclose::test_ip_isclose_allclose, test/torch_np/numpy_tests/core/test_numeric.py::TestIsclose::test_ip_none_isclose, test/torch_np/numpy_tests/core/test_numeric.py::TestIsclose::test_no_parameter_modification, test/torch_np/numpy_tests/core/test_numeric.py::TestIsclose::test_non_finite_scalar, test/torch_np/numpy_tests/core/test_numeric.py::TestIsclose::test_scalar_return, test/torch_np/numpy_tests/core/test_numeric.py::TestStdVar::test_basic, test/torch_np/numpy_tests/core/test_numeric.py::TestStdVar::test_ddof1, test/torch_np/numpy_tests/core/test_numeric.py::TestStdVar::test_ddof2, test/torch_np/numpy_tests/core/test_numeric.py::TestStdVar::test_out_scalar, test/torch_np/numpy_tests/core/test_numeric.py::TestStdVar::test_scalars, test/torch_np/numpy_tests/core/test_numeric.py::TestStdVarComplex::test_basic, test/torch_np/numpy_tests/core/test_numeric.py::TestStdVarComplex::test_scalars, test/torch_np/numpy_tests/core/test_numeric.py::TestCreationFuncs::test_empty, test/torch_np/numpy_tests/core/test_numeric.py::TestCreationFuncs::test_for_reference_leak, test/torch_np/numpy_tests/core/test_numeric.py::TestCreationFuncs::test_full, test/torch_np/numpy_tests/core/test_numeric.py::TestCreationFuncs::test_ones, test/torch_np/numpy_tests/core/test_numeric.py::TestCreationFuncs::test_zeros, test/torch_np/numpy_tests/core/test_numeric.py::TestLikeFuncs::test_dtype_str_bytes_likefunc0_dtype0, test/torch_np/numpy_tests/core/test_numeric.py::TestLikeFuncs::test_dtype_str_bytes_likefunc0_dtype1, test/torch_np/numpy_tests/core/test_numeric.py::TestLikeFuncs::test_dtype_str_bytes_likefunc1_dtype0, test/torch_np/numpy_tests/core/test_numeric.py::TestLikeFuncs::test_dtype_str_bytes_likefunc1_dtype1, test/torch_np/numpy_tests/core/test_numeric.py::TestLikeFuncs::test_dtype_str_bytes_likefunc2_dtype0, test/torch_np/numpy_tests/core/test_numeric.py::TestLikeFuncs::test_dtype_str_bytes_likefunc2_dtype1, test/torch_np/numpy_tests/core/test_numeric.py::TestLikeFuncs::test_dtype_str_bytes_likefunc3_dtype0, test/torch_np/numpy_tests/core/test_numeric.py::TestLikeFuncs::test_dtype_str_bytes_likefunc3_dtype1, test/torch_np/numpy_tests/core/test_numeric.py::TestLikeFuncs::test_empty_like, test/torch_np/numpy_tests/core/test_numeric.py::TestLikeFuncs::test_filled_like, test/torch_np/numpy_tests/core/test_numeric.py::TestLikeFuncs::test_ones_like, test/torch_np/numpy_tests/core/test_numeric.py::TestLikeFuncs::test_zeros_like, test/torch_np/numpy_tests/core/test_numeric.py::TestCorrelate::test_complex, test/torch_np/numpy_tests/core/test_numeric.py::TestCorrelate::test_float, test/torch_np/numpy_tests/core/test_numeric.py::TestCorrelate::test_mode, test/torch_np/numpy_tests/core/test_numeric.py::TestCorrelate::test_no_overwrite, test/torch_np/numpy_tests/core/test_numeric.py::TestCorrelate::test_zero_size, test/torch_np/numpy_tests/core/test_numeric.py::TestConvolve::test_mode, test/torch_np/numpy_tests/core/test_numeric.py::TestConvolve::test_no_overwrite, test/torch_np/numpy_tests/core/test_numeric.py::TestConvolve::test_numpy_doc_examples, test/torch_np/numpy_tests/core/test_numeric.py::TestConvolve::test_object, test/torch_np/numpy_tests/core/test_numeric.py::TestDtypePositional::test_dtype_positional, test/torch_np/numpy_tests/core/test_numeric.py::TestArgwhere::test_2D, test/torch_np/numpy_tests/core/test_numeric.py::TestArgwhere::test_list, test/torch_np/numpy_tests/core/test_numeric.py::TestArgwhere::test_nd_nd_0, test/torch_np/numpy_tests/core/test_numeric.py::TestArgwhere::test_nd_nd_1, test/torch_np/numpy_tests/core/test_numeric.py::TestArgwhere::test_nd_nd_2, test/torch_np/numpy_tests/core/test_numeric.py::TestStringFunction::test_set_string_function, test/torch_np/numpy_tests/core/test_numeric.py::TestRoll::test_roll1d, test/torch_np/numpy_tests/core/test_numeric.py::TestRoll::test_roll2d, test/torch_np/numpy_tests/core/test_numeric.py::TestRoll::test_roll_empty, test/torch_np/numpy_tests/core/test_numeric.py::TestRollaxis::test_exceptions, test/torch_np/numpy_tests/core/test_numeric.py::TestRollaxis::test_results, test/torch_np/numpy_tests/core/test_numeric.py::TestMoveaxis::test_errors, test/torch_np/numpy_tests/core/test_numeric.py::TestMoveaxis::test_move_multiples, test/torch_np/numpy_tests/core/test_numeric.py::TestMoveaxis::test_move_new_position, test/torch_np/numpy_tests/core/test_numeric.py::TestMoveaxis::test_move_to_end, test/torch_np/numpy_tests/core/test_numeric.py::TestMoveaxis::test_preserve_order, test/torch_np/numpy_tests/core/test_numeric.py::TestCross::test_2x2, test/torch_np/numpy_tests/core/test_numeric.py::TestCross::test_2x3, test/torch_np/numpy_tests/core/test_numeric.py::TestCross::test_3x3, test/torch_np/numpy_tests/core/test_numeric.py::TestCross::test_broadcasting, test/torch_np/numpy_tests/core/test_numeric.py::TestCross::test_broadcasting_shapes, test/torch_np/numpy_tests/core/test_numeric.py::TestCross::test_uint8_int32_mixed_dtypes, test/torch_np/numpy_tests/core/test_numeric.py::TestOuterMisc::test_outer_out_param, test/torch_np/numpy_tests/core/test_numeric.py::TestIndices::test_return_type_dtype0_dims0, test/torch_np/numpy_tests/core/test_numeric.py::TestIndices::test_return_type_dtype0_dims1, test/torch_np/numpy_tests/core/test_numeric.py::TestIndices::test_return_type_dtype0_dims2, test/torch_np/numpy_tests/core/test_numeric.py::TestIndices::test_return_type_dtype1_dims0, test/torch_np/numpy_tests/core/test_numeric.py::TestIndices::test_return_type_dtype1_dims1, test/torch_np/numpy_tests/core/test_numeric.py::TestIndices::test_return_type_dtype1_dims2, test/torch_np/numpy_tests/core/test_numeric.py::TestIndices::test_return_type_dtype2_dims0, test/torch_np/numpy_tests/core/test_numeric.py::TestIndices::test_return_type_dtype2_dims1, test/torch_np/numpy_tests/core/test_numeric.py::TestIndices::test_return_type_dtype2_dims2, test/torch_np/numpy_tests/core/test_numeric.py::TestIndices::test_return_type_dtype3_dims0, test/torch_np/numpy_tests/core/test_numeric.py::TestIndices::test_return_type_dtype3_dims1, test/torch_np/numpy_tests/core/test_numeric.py::TestIndices::test_return_type_dtype3_dims2, test/torch_np/numpy_tests/core/test_numeric.py::TestIndices::test_scalar_input, test/torch_np/numpy_tests/core/test_numeric.py::TestIndices::test_simple, test/torch_np/numpy_tests/core/test_numeric.py::TestIndices::test_single_input, test/torch_np/numpy_tests/core/test_numeric.py::TestIndices::test_sparse, test/torch_np/numpy_tests/core/test_numeric.py::TestRequire::test_C_and_F_simul, test/torch_np/numpy_tests/core/test_numeric.py::TestRequire::test_non_array_input, test/torch_np/numpy_tests/core/test_numeric.py::TestRequire::test_require_each, test/torch_np/numpy_tests/core/test_numeric.py::TestRequire::test_unknown_requirement, test/torch_np/numpy_tests/core/test_numeric.py::TestBroadcast::test_broadcast_error_kwargs, test/torch_np/numpy_tests/core/test_numeric.py::TestBroadcast::test_broadcast_in_args, test/torch_np/numpy_tests/core/test_numeric.py::TestBroadcast::test_broadcast_single_arg, test/torch_np/numpy_tests/core/test_numeric.py::TestBroadcast::test_number_of_arguments, test/torch_np/numpy_tests/core/test_numeric.py::TestBroadcast::test_shape_mismatch_error_message, test/torch_np/numpy_tests/core/test_numeric.py::TestTensordot::test_zero_dimension, test/torch_np/numpy_tests/core/test_numeric.py::TestTensordot::test_zero_dimension_einsum, test/torch_np/numpy_tests/core/test_numeric.py::TestTensordot::test_zero_dimensional 2025-09-07T07:43:09.2935264Z 2025-09-07T07:43:09.9950386Z 2025-09-07T07:43:09.9951485Z test_stateless 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_stateless_1.1_e75f1e39a58532ec_.log 2025-09-07T07:43:09.9973135Z Running 50 items in this shard: test/test_stateless.py::TestStatelessFunctionalAPI::test_circular_references_stateless, test/test_stateless.py::TestStatelessFunctionalAPI::test_circular_references_torch_func, test/test_stateless.py::TestStatelessFunctionalAPI::test_functional_batch_norm_stateless, test/test_stateless.py::TestStatelessFunctionalAPI::test_functional_batch_norm_torch_func, test/test_stateless.py::TestStatelessFunctionalAPI::test_functional_call_member_reference_stateless, test/test_stateless.py::TestStatelessFunctionalAPI::test_functional_call_member_reference_torch_func, test/test_stateless.py::TestStatelessFunctionalAPI::test_functional_call_multiple_dicts_error, test/test_stateless.py::TestStatelessFunctionalAPI::test_functional_call_stateless, test/test_stateless.py::TestStatelessFunctionalAPI::test_functional_call_torch_func, test/test_stateless.py::TestStatelessFunctionalAPI::test_functional_call_tuple_dicts, test/test_stateless.py::TestStatelessFunctionalAPI::test_functional_call_with_data_parallel_error_stateless, test/test_stateless.py::TestStatelessFunctionalAPI::test_functional_call_with_data_parallel_error_torch_func, test/test_stateless.py::TestStatelessFunctionalAPI::test_functional_call_with_data_parallel_stateless, test/test_stateless.py::TestStatelessFunctionalAPI::test_functional_call_with_data_parallel_torch_func, test/test_stateless.py::TestStatelessFunctionalAPI::test_functional_call_with_gradient_stateless, test/test_stateless.py::TestStatelessFunctionalAPI::test_functional_call_with_gradient_torch_func, test/test_stateless.py::TestStatelessFunctionalAPI::test_functional_call_with_jit_stateless, test/test_stateless.py::TestStatelessFunctionalAPI::test_functional_call_with_jit_torch_func, test/test_stateless.py::TestStatelessFunctionalAPI::test_functional_call_with_kwargs_stateless, test/test_stateless.py::TestStatelessFunctionalAPI::test_functional_call_with_kwargs_torch_func, test/test_stateless.py::TestStatelessFunctionalAPI::test_in_place_operator_stateless, test/test_stateless.py::TestStatelessFunctionalAPI::test_in_place_operator_torch_func, test/test_stateless.py::TestStatelessFunctionalAPI::test_reparametrize_module_fail_reset_to_original_stateless, test/test_stateless.py::TestStatelessFunctionalAPI::test_reparametrize_module_fail_reset_to_original_torch_func, test/test_stateless.py::TestStatelessFunctionalAPI::test_reparametrize_some_weights_stateless, test/test_stateless.py::TestStatelessFunctionalAPI::test_reparametrize_some_weights_torch_func, test/test_stateless.py::TestStatelessFunctionalAPI::test_reparametrize_special_stateless, test/test_stateless.py::TestStatelessFunctionalAPI::test_reparametrize_special_torch_func, test/test_stateless.py::TestStatelessFunctionalAPI::test_reparametrize_strict_stateless, test/test_stateless.py::TestStatelessFunctionalAPI::test_reparametrize_strict_torch_func, test/test_stateless.py::TestStatelessFunctionalAPI::test_reparametrize_tie_some_weights_stateless, test/test_stateless.py::TestStatelessFunctionalAPI::test_reparametrize_tie_some_weights_torch_func, test/test_stateless.py::TestStatelessFunctionalAPI::test_reparametrize_tie_weights_stateless, test/test_stateless.py::TestStatelessFunctionalAPI::test_reparametrize_tie_weights_strict_stateless, test/test_stateless.py::TestStatelessFunctionalAPI::test_reparametrize_tie_weights_strict_torch_func, test/test_stateless.py::TestStatelessFunctionalAPI::test_reparametrize_tie_weights_torch_func, test/test_stateless.py::TestStatelessFunctionalAPI::test_reparametrized_module_change_parametrization_original_stateless, test/test_stateless.py::TestStatelessFunctionalAPI::test_reparametrized_module_change_parametrization_original_torch_func, test/test_stateless.py::TestStatelessFunctionalAPI::test_setattr_stateless, test/test_stateless.py::TestStatelessFunctionalAPI::test_setattr_strict_stateless, test/test_stateless.py::TestStatelessFunctionalAPI::test_setattr_strict_torch_func, test/test_stateless.py::TestStatelessFunctionalAPI::test_setattr_torch_func, test/test_stateless.py::TestStatelessFunctionalAPI::test_tied_weights_errors_stateless, test/test_stateless.py::TestStatelessFunctionalAPI::test_tied_weights_errors_torch_func, test/test_stateless.py::TestStatelessFunctionalAPI::test_tied_weights_no_error_without_flag, test/test_stateless.py::TestStatelessFunctionalAPI::test_tied_weights_warns_stateless, test/test_stateless.py::TestStatelessFunctionalAPI::test_tied_weights_warns_torch_func, test/test_stateless.py::TestStatelessDeprecation::test_private_stateless_warns, test/test_stateless.py::TestStatelessDeprecation::test_stateless_functional_call_warns, test/test_stateless.py::TestPythonOptimizeMode::test_runs_with_optimize_flag 2025-09-07T07:43:09.9994055Z 2025-09-07T07:43:12.0932325Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T07:43:12.0933886Z import pkg_resources 2025-09-07T07:43:12.2150410Z Running test_weak 1/1 ... [2025-09-07 07:43:12.214676] 2025-09-07T07:43:12.2150928Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:43:12.2152271Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_weak.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 07:43:12.215020] 2025-09-07T07:43:12.8601184Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T07:43:12.8604172Z import pkg_resources 2025-09-07T07:43:12.9822833Z Running inductor/test_mkldnn_pattern_matcher 1/3 ... [2025-09-07 07:43:12.981926] 2025-09-07T07:43:12.9823923Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:43:12.9826661Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_mkldnn_pattern_matcher.py', '-m', 'not serial', '--shard-id=1', '--num-shards=3', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 07:43:12.982297] 2025-09-07T07:43:18.4884533Z 2025-09-07T07:43:18.4885639Z test_weak 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_weak_1.1_570ad567ab56fcd2_.log 2025-09-07T07:43:18.4897183Z Running 39 items in this shard: test/test_weak.py::WeakTest::test_make_weak_keyed_dict_from_dict, test/test_weak.py::WeakTest::test_make_weak_keyed_dict_from_weak_keyed_dict, test/test_weak.py::WeakTest::test_make_weak_keyed_dict_repr, test/test_weak.py::WeakTest::test_threaded_weak_key_dict_copy, test/test_weak.py::WeakTest::test_threaded_weak_key_dict_deepcopy, test/test_weak.py::WeakTest::test_weak_keyed_bad_delitem, test/test_weak.py::WeakTest::test_weak_keyed_delitem, test/test_weak.py::WeakTest::test_weak_keyed_dict_popitem, test/test_weak.py::WeakTest::test_weak_keyed_dict_setdefault, test/test_weak.py::WeakTest::test_weak_keyed_dict_update, test/test_weak.py::WeakTest::test_weak_keyed_union_operators, test/test_weak.py::WeakKeyDictionaryTestCase::test_bool, test/test_weak.py::WeakKeyDictionaryTestCase::test_constructor, test/test_weak.py::WeakKeyDictionaryTestCase::test_get, test/test_weak.py::WeakKeyDictionaryTestCase::test_getitem, test/test_weak.py::WeakKeyDictionaryTestCase::test_items, test/test_weak.py::WeakKeyDictionaryTestCase::test_keys, test/test_weak.py::WeakKeyDictionaryTestCase::test_len, test/test_weak.py::WeakKeyDictionaryTestCase::test_pop, test/test_weak.py::WeakKeyDictionaryTestCase::test_popitem, test/test_weak.py::WeakKeyDictionaryTestCase::test_read, test/test_weak.py::WeakKeyDictionaryTestCase::test_setdefault, test/test_weak.py::WeakKeyDictionaryTestCase::test_update, test/test_weak.py::WeakKeyDictionaryTestCase::test_values, test/test_weak.py::WeakKeyDictionaryTestCase::test_write, test/test_weak.py::WeakKeyDictionaryScriptObjectTestCase::test_bool, test/test_weak.py::WeakKeyDictionaryScriptObjectTestCase::test_constructor, test/test_weak.py::WeakKeyDictionaryScriptObjectTestCase::test_get, test/test_weak.py::WeakKeyDictionaryScriptObjectTestCase::test_getitem, test/test_weak.py::WeakKeyDictionaryScriptObjectTestCase::test_items, test/test_weak.py::WeakKeyDictionaryScriptObjectTestCase::test_keys, test/test_weak.py::WeakKeyDictionaryScriptObjectTestCase::test_len, test/test_weak.py::WeakKeyDictionaryScriptObjectTestCase::test_pop, test/test_weak.py::WeakKeyDictionaryScriptObjectTestCase::test_popitem, test/test_weak.py::WeakKeyDictionaryScriptObjectTestCase::test_read, test/test_weak.py::WeakKeyDictionaryScriptObjectTestCase::test_setdefault, test/test_weak.py::WeakKeyDictionaryScriptObjectTestCase::test_update, test/test_weak.py::WeakKeyDictionaryScriptObjectTestCase::test_values, test/test_weak.py::WeakKeyDictionaryScriptObjectTestCase::test_write 2025-09-07T07:43:18.4908326Z 2025-09-07T07:43:21.4664538Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T07:43:21.4667856Z import pkg_resources 2025-09-07T07:43:21.5900406Z Running functorch/test_ac 1/1 ... [2025-09-07 07:43:21.589683] 2025-09-07T07:43:21.5901205Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:43:21.5903844Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'functorch/test_ac.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 07:43:21.590046] 2025-09-07T07:43:30.1651708Z 2025-09-07T07:43:30.1653100Z functorch/test_ac 1/1 was successful, full logs can be found in artifacts with path test/test-reports/functorch.test_ac_1.1_e88ff3f2ef950181_.log 2025-09-07T07:43:30.1654139Z 2025-09-07T07:43:33.1262213Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T07:43:33.1263901Z import pkg_resources 2025-09-07T07:43:33.3020546Z Running dynamo/test_cudagraphs 1/1 ... [2025-09-07 07:43:33.301631] 2025-09-07T07:43:33.3021287Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:43:33.3024059Z 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'] ... [2025-09-07 07:43:33.302010] 2025-09-07T07:43:36.4591845Z 2025-09-07T07:43:36.4593426Z dynamo/test_cudagraphs 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_cudagraphs_1.1_37cfbd62f93213a8_.log 2025-09-07T07:43:36.4594868Z 2025-09-07T07:43:39.3632763Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T07:43:39.3635855Z import pkg_resources 2025-09-07T07:43:39.4991643Z Running test_decomp 2/12 ... [2025-09-07 07:43:39.498732] 2025-09-07T07:43:39.4992423Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:43:39.4994809Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_decomp.py', '-m', 'not serial', '--shard-id=2', '--num-shards=12', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 07:43:39.499093] 2025-09-07T07:47:00.6174521Z 2025-09-07T07:47:00.6176004Z test_ops_fwd_gradients 1/2 was successful, full logs can be found in artifacts with path test/test-reports/test_ops_fwd_gradients_1.2_34af7c847864d5c2_.log 2025-09-07T07:47:00.7614583Z Running 1604 items in this shard: test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad_H_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad_T_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad___getitem___cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad___radd___cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad___radd___cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad___rdiv___cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad___rdiv___cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad___rmatmul___cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad___rmul___cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad___rsub___cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad__segment_reduce_offsets_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad__unsafe_masked_index_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad_abs_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad_acos_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad_acos_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad_acosh_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad_add_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad_add_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad_addbmm_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad_addbmm_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad_addcdiv_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad_addcmul_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad_addmm_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad_addmm_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad_addmm_decomposed_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad_addr_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad_alias_copy_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad_alias_copy_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad_allclose_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad_amin_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad_aminmax_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad_arange_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad_argsort_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad_argwhere_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad_argwhere_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad_as_strided_copy_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad_as_strided_partial_views_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad_as_strided_scatter_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad_asin_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad_asinh_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad_atan_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad_atanh_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad_atleast_1d_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad_atleast_3d_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad_atleast_3d_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad_baddbmm_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_fn_fwgrad_bwgrad_baddbmm_cpu_float64, 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test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_linalg_pinv_hermitian_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_linalg_pinv_singular_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_linalg_pinv_singular_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_linalg_qr_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_linalg_solve_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_linalg_solve_triangular_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_linalg_tensorsolve_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_linalg_vander_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_linalg_vander_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_linalg_vector_norm_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_linspace_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_linspace_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_linspace_tensor_overload_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_log10_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_log10_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_log1p_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_log_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_log_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_log_softmax_with_dtype_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_logaddexp2_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_logaddexp_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_logdet_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_logical_and_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_logical_not_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_logical_or_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_logical_or_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_logical_xor_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_logit_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_logspace_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_logsumexp_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_long_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_lt_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_lu_solve_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_lu_unpack_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_lu_unpack_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_mH_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_mT_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_masked_argmin_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_masked_cumprod_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_masked_cumprod_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_masked_cumsum_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_masked_cumsum_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_masked_fill_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_masked_log_softmax_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_masked_logsumexp_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_masked_logsumexp_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_masked_mean_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_masked_mean_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_masked_normalize_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_masked_prod_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_masked_prod_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_masked_select_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_masked_select_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_masked_softmin_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_masked_std_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_masked_var_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_matmul_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_matmul_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_matrix_exp_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_max_binary_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_max_pool2d_with_indices_backward_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_max_reduction_no_dim_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_max_reduction_with_dim_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_mean_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_mean_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_meshgrid_list_of_tensors_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_meshgrid_list_of_tensors_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_meshgrid_variadic_tensors_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_mm_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_mm_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_msort_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_mul_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_mul_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_multinomial_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_mv_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_mv_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_mvlgamma_mvlgamma_p_1_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_mvlgamma_mvlgamma_p_3_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_mvlgamma_mvlgamma_p_5_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nanmean_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nansum_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_narrow_copy_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_narrow_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_narrow_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_native_batch_norm_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_native_dropout_backward_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_native_layer_norm_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_ne_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_new_empty_strided_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_new_empty_strided_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_new_full_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_new_ones_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nextafter_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_adaptive_avg_pool3d_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_adaptive_max_pool1d_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_adaptive_max_pool2d_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_avg_pool2d_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_avg_pool3d_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_bilinear_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_binary_cross_entropy_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_binary_cross_entropy_with_logits_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_celu_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_channel_shuffle_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_channel_shuffle_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_conv1d_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_conv2d_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_conv2d_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_conv3d_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_conv_transpose1d_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_conv_transpose2d_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_conv_transpose3d_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_cosine_similarity_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_cross_entropy_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_dropout2d_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_dropout_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_elu_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_embedding_bag_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_feature_alpha_dropout_without_train_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_glu_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_hardshrink_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_hardtanh_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_huber_loss_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_instance_norm_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_interpolate_bilinear_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_interpolate_nearest-exact_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_interpolate_nearest_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_interpolate_trilinear_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_kl_div_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_l1_loss_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_l1_loss_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_layer_norm_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_leaky_relu_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_linear_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_local_response_norm_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_max_unpool1d_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_max_unpool1d_grad_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_max_unpool2d_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_mish_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_mse_loss_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_multi_margin_loss_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_multilabel_soft_margin_loss_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_nll_loss_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_normalize_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_normalize_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_pad_circular_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_pad_replicate_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_pad_replicate_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_pad_replicate_negative_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_pad_replicate_negative_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_pairwise_distance_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_pairwise_distance_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_pixel_shuffle_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_relu_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_rms_norm_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_rms_norm_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_silu_complex_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_silu_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_smooth_l1_loss_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_soft_margin_loss_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_softmin_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_softmin_with_dtype_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_softplus_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_softsign_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_triplet_margin_loss_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_triplet_margin_with_distance_loss_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nn_functional_upsample_nearest_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nonzero_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_nonzero_static_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_norm_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_norm_fro_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_normal_in_place_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_ones_like_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_ones_like_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_ormqr_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_outer_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_pca_lowrank_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_pca_lowrank_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_permute_copy_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_pinverse_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_pinverse_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_polar_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_polygamma_polygamma_n_2_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_polygamma_polygamma_n_3_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_pow_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_pow_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_qr_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_quantile_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_rand_like_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_randint_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_randint_like_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_randn_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_randn_like_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_ravel_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_real_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_reciprocal_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_reciprocal_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_remainder_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_repeat_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_repeat_interleave_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_repeat_interleave_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_reshape_as_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_reshape_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_resize__cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_resize__cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_resize_as__cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_resolve_conj_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_resolve_neg_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_roll_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_rot90_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_rot90_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_round_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_round_decimals_0_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_round_decimals_3_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_rsqrt_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_rsub_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_scalar_tensor_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_scalar_tensor_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_scatter_add_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_scatter_reduce_amin_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_searchsorted_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_select_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_select_scatter_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_sgn_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_sigmoid_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_sign_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_signal_windows_blackman_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_signal_windows_general_hamming_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_signal_windows_hamming_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_signal_windows_hann_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_signal_windows_nuttall_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_sin_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_sin_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_sinc_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_sinc_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_sinh_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_sinh_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_softmax_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_softmax_with_dtype_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_softmax_with_dtype_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_sort_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_sparse_sampled_addmm_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_special_airy_ai_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_special_bessel_j1_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_special_bessel_y0_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_special_bessel_y1_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_special_chebyshev_polynomial_w_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_special_erfcx_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_special_hermite_polynomial_he_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_special_i1e_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_special_laguerre_polynomial_l_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_special_legendre_polynomial_p_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_special_modified_bessel_i1_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_special_modified_bessel_k1_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_special_ndtr_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_special_ndtri_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_special_scaled_modified_bessel_k0_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_special_scaled_modified_bessel_k1_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_special_shifted_chebyshev_polynomial_w_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_special_xlog1py_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_split_list_args_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_split_with_sizes_copy_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_squeeze_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_squeeze_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_squeeze_multiple_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_std_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_std_mean_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_std_unbiased_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_sub_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_sub_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_sum_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_sum_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_sum_to_size_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_svd_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_svd_lowrank_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_t_copy_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_t_copy_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_t_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_t_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_take_along_dim_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_take_along_dim_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_take_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_tan_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_tanh_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_tensordot_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_tile_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_to_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_to_sparse_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_torch_ops_aten__safe_softmax_default_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_trace_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_transpose_copy_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_transpose_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_transpose_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_trapezoid_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_trapezoid_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_trapz_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_triangular_solve_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_triangular_solve_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_trunc_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_unbind_copy_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_unbind_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_unbind_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_unflatten_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_unfold_copy_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_unfold_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_uniform_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_unique_consecutive_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_unsafe_split_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_unsafe_split_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_unsqueeze_copy_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_var_mean_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_var_mean_unbiased_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_var_unbiased_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_var_unbiased_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_vdot_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_view_as_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_view_as_cpu_float64, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_view_as_real_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_view_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_vsplit_cpu_complex128, test/test_ops_fwd_gradients.py::TestFwdGradientsCPU::test_inplace_forward_mode_AD_vstack_cpu_complex128 2025-09-07T07:47:00.8457682Z 2025-09-07T07:47:03.5631989Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T07:47:03.5635125Z import pkg_resources 2025-09-07T07:47:03.7355990Z Running test_decomp 5/12 ... [2025-09-07 07:47:03.735242] 2025-09-07T07:47:03.7356416Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:47:03.7359660Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_decomp.py', '-m', 'not serial', '--shard-id=5', '--num-shards=12', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 07:47:03.735730] 2025-09-07T07:50:03.4547792Z 2025-09-07T07:50:03.4549066Z inductor/test_mkldnn_pattern_matcher 1/3 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_mkldnn_pattern_matcher_1.3_0af52990ccc5da0b_.log 2025-09-07T07:50:03.4605204Z Running 91 items in this shard: test/inductor/test_mkldnn_pattern_matcher.py::TestPatternMatcher::test_conv2d_add_scalar, test/inductor/test_mkldnn_pattern_matcher.py::TestPatternMatcher::test_conv2d_binary_fusion_failed, test/inductor/test_mkldnn_pattern_matcher.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_False_bfloat16_dynamic_False_reshape_a_True_M_1_inplace_add_False_expand_a_scale_False, test/inductor/test_mkldnn_pattern_matcher.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_False_bfloat16_dynamic_False_reshape_a_True_M_1_inplace_add_False_expand_a_scale_True, test/inductor/test_mkldnn_pattern_matcher.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_False_bfloat16_dynamic_False_reshape_a_True_M_1_inplace_add_True_expand_a_scale_True, test/inductor/test_mkldnn_pattern_matcher.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_False_bfloat16_dynamic_False_reshape_a_True_M_32_inplace_add_False_expand_a_scale_True, test/inductor/test_mkldnn_pattern_matcher.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_False_bfloat16_dynamic_False_reshape_a_True_M_32_inplace_add_True_expand_a_scale_False, test/inductor/test_mkldnn_pattern_matcher.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_False_bfloat16_dynamic_True_reshape_a_False_M_1_inplace_add_False_expand_a_scale_False, test/inductor/test_mkldnn_pattern_matcher.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_False_bfloat16_dynamic_True_reshape_a_False_M_32_inplace_add_False_expand_a_scale_True, test/inductor/test_mkldnn_pattern_matcher.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_False_bfloat16_dynamic_True_reshape_a_True_M_1_inplace_add_False_expand_a_scale_False, test/inductor/test_mkldnn_pattern_matcher.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_False_bfloat16_dynamic_True_reshape_a_True_M_1_inplace_add_True_expand_a_scale_True, test/inductor/test_mkldnn_pattern_matcher.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_False_bfloat16_dynamic_True_reshape_a_True_M_32_inplace_add_False_expand_a_scale_False, test/inductor/test_mkldnn_pattern_matcher.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_False_float32_dynamic_False_reshape_a_False_M_1_inplace_add_False_expand_a_scale_True, test/inductor/test_mkldnn_pattern_matcher.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_False_float32_dynamic_False_reshape_a_False_M_32_inplace_add_False_expand_a_scale_True, test/inductor/test_mkldnn_pattern_matcher.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_False_float32_dynamic_False_reshape_a_True_M_1_inplace_add_False_expand_a_scale_True, test/inductor/test_mkldnn_pattern_matcher.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_False_float32_dynamic_False_reshape_a_True_M_1_inplace_add_True_expand_a_scale_False, test/inductor/test_mkldnn_pattern_matcher.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_False_float32_dynamic_False_reshape_a_True_M_32_inplace_add_False_expand_a_scale_False, test/inductor/test_mkldnn_pattern_matcher.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_False_float32_dynamic_False_reshape_a_True_M_32_inplace_add_True_expand_a_scale_True, test/inductor/test_mkldnn_pattern_matcher.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_False_float32_dynamic_True_reshape_a_False_M_1_inplace_add_False_expand_a_scale_True, test/inductor/test_mkldnn_pattern_matcher.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_True_bfloat16_dynamic_False_reshape_a_False_M_1_inplace_add_True_expand_a_scale_True, test/inductor/test_mkldnn_pattern_matcher.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_True_bfloat16_dynamic_False_reshape_a_False_M_32_inplace_add_False_expand_a_scale_False, test/inductor/test_mkldnn_pattern_matcher.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_True_bfloat16_dynamic_False_reshape_a_False_M_32_inplace_add_False_expand_a_scale_True, test/inductor/test_mkldnn_pattern_matcher.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_True_bfloat16_dynamic_False_reshape_a_True_M_1_inplace_add_False_expand_a_scale_False, test/inductor/test_mkldnn_pattern_matcher.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_True_bfloat16_dynamic_True_reshape_a_False_M_1_inplace_add_False_expand_a_scale_False, test/inductor/test_mkldnn_pattern_matcher.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_True_bfloat16_dynamic_True_reshape_a_False_M_1_inplace_add_True_expand_a_scale_False, test/inductor/test_mkldnn_pattern_matcher.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_True_bfloat16_dynamic_True_reshape_a_False_M_32_inplace_add_True_expand_a_scale_False, test/inductor/test_mkldnn_pattern_matcher.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_True_bfloat16_dynamic_True_reshape_a_True_M_1_inplace_add_True_expand_a_scale_True, test/inductor/test_mkldnn_pattern_matcher.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_True_bfloat16_dynamic_True_reshape_a_True_M_32_inplace_add_True_expand_a_scale_True, test/inductor/test_mkldnn_pattern_matcher.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_True_float32_dynamic_False_reshape_a_False_M_1_inplace_add_True_expand_a_scale_False, test/inductor/test_mkldnn_pattern_matcher.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_True_float32_dynamic_False_reshape_a_True_M_32_inplace_add_False_expand_a_scale_False, test/inductor/test_mkldnn_pattern_matcher.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_True_float32_dynamic_True_reshape_a_False_M_1_inplace_add_False_expand_a_scale_True, test/inductor/test_mkldnn_pattern_matcher.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_True_float32_dynamic_True_reshape_a_False_M_32_inplace_add_True_expand_a_scale_False, test/inductor/test_mkldnn_pattern_matcher.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_True_float32_dynamic_True_reshape_a_False_M_32_inplace_add_True_expand_a_scale_True, test/inductor/test_mkldnn_pattern_matcher.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_True_float32_dynamic_True_reshape_a_True_M_1_inplace_add_False_expand_a_scale_False, test/inductor/test_mkldnn_pattern_matcher.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_True_float32_dynamic_True_reshape_a_True_M_1_inplace_add_True_expand_a_scale_False, test/inductor/test_mkldnn_pattern_matcher.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_True_float32_dynamic_True_reshape_a_True_M_1_inplace_add_True_expand_a_scale_True, test/inductor/test_mkldnn_pattern_matcher.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_True_float32_dynamic_True_reshape_a_True_M_32_inplace_add_False_expand_a_scale_False, test/inductor/test_mkldnn_pattern_matcher.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_True_float32_dynamic_True_reshape_a_True_M_32_inplace_add_True_expand_a_scale_True, test/inductor/test_mkldnn_pattern_matcher.py::TestPatternMatcher::test_linear_add_bias, test/inductor/test_mkldnn_pattern_matcher.py::TestPatternMatcher::test_linear_input_non_contiguous_3D_wo_bias, test/inductor/test_mkldnn_pattern_matcher.py::TestPatternMatcher::test_qat_qconv2d_hardswish, test/inductor/test_mkldnn_pattern_matcher.py::TestPatternMatcher::test_qat_qconv2d_relu, test/inductor/test_mkldnn_pattern_matcher.py::TestPatternMatcher::test_qcat, test/inductor/test_mkldnn_pattern_matcher.py::TestPatternMatcher::test_qconv2d_add_broadcast_shapes_cpu, test/inductor/test_mkldnn_pattern_matcher.py::TestPatternMatcher::test_qconv2d_add_xpu, test/inductor/test_mkldnn_pattern_matcher.py::TestPatternMatcher::test_qconv2d_dequant_promotion_xpu, test/inductor/test_mkldnn_pattern_matcher.py::TestPatternMatcher::test_qconv2d_hardswish_cpu, test/inductor/test_mkldnn_pattern_matcher.py::TestPatternMatcher::test_qconv2d_hardtanh_int8_mixed_bf16_xpu, test/inductor/test_mkldnn_pattern_matcher.py::TestPatternMatcher::test_qconv2d_int8_mixed_bf16, test/inductor/test_mkldnn_pattern_matcher.py::TestPatternMatcher::test_qconv2d_relu6_cpu, test/inductor/test_mkldnn_pattern_matcher.py::TestPatternMatcher::test_qconv2d_relu_cpu, test/inductor/test_mkldnn_pattern_matcher.py::TestPatternMatcher::test_qconv2d_relu_xpu, test/inductor/test_mkldnn_pattern_matcher.py::TestPatternMatcher::test_qconv2d_silu_cpu, test/inductor/test_mkldnn_pattern_matcher.py::TestPatternMatcher::test_qconv2d_silu_int8_mixed_bf16_cpu, test/inductor/test_mkldnn_pattern_matcher.py::TestPatternMatcher::test_qconv2d_with_concat_cpu, test/inductor/test_mkldnn_pattern_matcher.py::TestPatternMatcher::test_qflatten, test/inductor/test_mkldnn_pattern_matcher.py::TestPatternMatcher::test_qlinear_add_cpu_use_relu_False_is_qat_True_is_dynamic_False, test/inductor/test_mkldnn_pattern_matcher.py::TestPatternMatcher::test_qlinear_add_cpu_use_relu_True_is_qat_False_is_dynamic_True, test/inductor/test_mkldnn_pattern_matcher.py::TestPatternMatcher::test_qlinear_add_fp8_inductor_cpu, test/inductor/test_mkldnn_pattern_matcher.py::TestPatternMatcher::test_qlinear_add_int8_mixed_bf16_use_relu_False_is_qat_False_is_dynamic_True, test/inductor/test_mkldnn_pattern_matcher.py::TestPatternMatcher::test_qlinear_add_int8_mixed_bf16_use_relu_False_is_qat_True_is_dynamic_False, test/inductor/test_mkldnn_pattern_matcher.py::TestPatternMatcher::test_qlinear_add_int8_mixed_bf16_use_relu_True_is_qat_False_is_dynamic_False, test/inductor/test_mkldnn_pattern_matcher.py::TestPatternMatcher::test_qlinear_add_int8_mixed_bf16_use_relu_True_is_qat_False_is_dynamic_True, test/inductor/test_mkldnn_pattern_matcher.py::TestPatternMatcher::test_qlinear_add_int8_mixed_bf16_xpu_use_relu_False_is_qat_False_is_dynamic_False, test/inductor/test_mkldnn_pattern_matcher.py::TestPatternMatcher::test_qlinear_add_int8_mixed_bf16_xpu_use_relu_True_is_qat_False_is_dynamic_False, test/inductor/test_mkldnn_pattern_matcher.py::TestPatternMatcher::test_qlinear_dequant_promotion_dynamic_cpu, test/inductor/test_mkldnn_pattern_matcher.py::TestPatternMatcher::test_qlinear_dequant_promotion_int8_mixed_bf16_input_dim_exceeds_2_xpu, test/inductor/test_mkldnn_pattern_matcher.py::TestPatternMatcher::test_qlinear_gelu_cpu, test/inductor/test_mkldnn_pattern_matcher.py::TestPatternMatcher::test_qlinear_gelu_int8_mixed_bf16, test/inductor/test_mkldnn_pattern_matcher.py::TestPatternMatcher::test_qlinear_gelu_int8_mixed_bf16_xpu, test/inductor/test_mkldnn_pattern_matcher.py::TestPatternMatcher::test_qlinear_input_dim_exceeds_2_and_not_contiguous, test/inductor/test_mkldnn_pattern_matcher.py::TestPatternMatcher::test_qlinear_int8_mixed_bf16_input_dim_exceeds_2, test/inductor/test_mkldnn_pattern_matcher.py::TestPatternMatcher::test_qlinear_int8_mixed_bf16_input_dim_exceeds_2_and_not_contiguous_use_autocast, test/inductor/test_mkldnn_pattern_matcher.py::TestPatternMatcher::test_qlinear_int8_mixed_bf16_use_autocast, test/inductor/test_mkldnn_pattern_matcher.py::TestPatternMatcher::test_qlinear_mul_xpu, test/inductor/test_mkldnn_pattern_matcher.py::TestPatternMatcher::test_qlinear_relu_input_dim_exceeds_2, test/inductor/test_mkldnn_pattern_matcher.py::TestPatternMatcher::test_qlinear_relu_int8_mixed_bf16_xpu, test/inductor/test_mkldnn_pattern_matcher.py::TestPatternMatcher::test_reproduce_113440_issue_1, test/inductor/test_mkldnn_pattern_matcher.py::TestPatternMatcher::test_reproduce_113440_issue_2, test/inductor/test_mkldnn_pattern_matcher.py::TestPatternMatcher::test_reproduce_99842_issue, test/inductor/test_mkldnn_pattern_matcher.py::TestPatternMatcher::test_smooth_quant_with_int_mm_has_bias_False_bfloat16_per_channel_quant_False_dynamic_False, test/inductor/test_mkldnn_pattern_matcher.py::TestPatternMatcher::test_smooth_quant_with_int_mm_has_bias_False_bfloat16_per_channel_quant_False_dynamic_True, test/inductor/test_mkldnn_pattern_matcher.py::TestPatternMatcher::test_smooth_quant_with_int_mm_has_bias_False_bfloat16_per_channel_quant_True_dynamic_True, test/inductor/test_mkldnn_pattern_matcher.py::TestPatternMatcher::test_smooth_quant_with_int_mm_has_bias_False_float32_per_channel_quant_True_dynamic_False, test/inductor/test_mkldnn_pattern_matcher.py::TestPatternMatcher::test_smooth_quant_with_int_mm_has_bias_True_bfloat16_per_channel_quant_False_dynamic_True, test/inductor/test_mkldnn_pattern_matcher.py::TestPatternMatcher::test_smooth_quant_with_int_mm_has_bias_True_float32_per_channel_quant_False_dynamic_False, test/inductor/test_mkldnn_pattern_matcher.py::TestPatternMatcher::test_smooth_quant_with_int_mm_has_bias_True_float32_per_channel_quant_True_dynamic_False, test/inductor/test_mkldnn_pattern_matcher.py::TestPatternMatcherGenericCPU::test_conv3d_binary_broadcast_shapes_cpu, test/inductor/test_mkldnn_pattern_matcher.py::TestPatternMatcherGenericCPU::test_conv_transpose2d_unary_cpu, test/inductor/test_mkldnn_pattern_matcher.py::TestDynamicPatternMatcherGenericCPU::test_conv3d_binary_dynamic_shapes_cpu, test/inductor/test_mkldnn_pattern_matcher.py::TestDynamicPatternMatcherGenericCPU::test_conv3d_unary_dynamic_shapes_cpu 2025-09-07T07:50:03.4661513Z 2025-09-07T07:50:06.2801156Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T07:50:06.2803849Z import pkg_resources 2025-09-07T07:50:06.4049510Z Running test_decomp 12/12 ... [2025-09-07 07:50:06.404612] 2025-09-07T07:50:06.4049994Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:50:06.4052338Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_decomp.py', '-m', 'not serial', '--shard-id=12', '--num-shards=12', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 07:50:06.405023] 2025-09-07T07:51:33.9380047Z 2025-09-07T07:51:33.9384022Z test_decomp 2/12 was successful, full logs can be found in artifacts with path test/test-reports/test_decomp_2.12_d5f2b8c1611429fa_.log 2025-09-07T07:51:33.9855527Z Running 754 items in this shard: test/test_decomp.py::TestDecompCPU::test_cat_single_input_cpu, test/test_decomp.py::TestDecompCPU::test_comprehensive_T_cpu_int8, test/test_decomp.py::TestDecompCPU::test_comprehensive___getitem___cpu_complex128, test/test_decomp.py::TestDecompCPU::test_comprehensive___radd___cpu_complex64, test/test_decomp.py::TestDecompCPU::test_comprehensive___rdiv___cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive___rmod___cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive___rmul___cpu_int32, test/test_decomp.py::TestDecompCPU::test_comprehensive___ror___cpu_bool, test/test_decomp.py::TestDecompCPU::test_comprehensive___rpow___cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive___rpow___cpu_int64, test/test_decomp.py::TestDecompCPU::test_comprehensive___rsub___cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive___rsub___cpu_int16, test/test_decomp.py::TestDecompCPU::test_comprehensive___rxor___cpu_bool, test/test_decomp.py::TestDecompCPU::test_comprehensive___rxor___cpu_int16, test/test_decomp.py::TestDecompCPU::test_comprehensive__segment_reduce_lengths_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive__segment_reduce_offsets_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive__unsafe_masked_index_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_comprehensive__unsafe_masked_index_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive__unsafe_masked_index_put_accumulate_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_comprehensive_acos_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_acos_cpu_int16, test/test_decomp.py::TestDecompCPU::test_comprehensive_acosh_cpu_int16, test/test_decomp.py::TestDecompCPU::test_comprehensive_acosh_cpu_int64, test/test_decomp.py::TestDecompCPU::test_comprehensive_add_cpu_int64, test/test_decomp.py::TestDecompCPU::test_comprehensive_addbmm_cpu_int64, test/test_decomp.py::TestDecompCPU::test_comprehensive_addmm_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_addmm_decomposed_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_addmm_decomposed_cpu_int32, test/test_decomp.py::TestDecompCPU::test_comprehensive_addr_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_comprehensive_addr_cpu_int64, test/test_decomp.py::TestDecompCPU::test_comprehensive_addr_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_comprehensive_alias_copy_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive_alias_copy_cpu_int32, test/test_decomp.py::TestDecompCPU::test_comprehensive_all_cpu_int32, test/test_decomp.py::TestDecompCPU::test_comprehensive_allclose_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_comprehensive_amax_cpu_int32, test/test_decomp.py::TestDecompCPU::test_comprehensive_amin_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive_amin_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_comprehensive_aminmax_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive_aminmax_cpu_int16, test/test_decomp.py::TestDecompCPU::test_comprehensive_arange_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_argmin_cpu_int8, test/test_decomp.py::TestDecompCPU::test_comprehensive_argsort_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_comprehensive_as_strided_copy_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive_as_strided_copy_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_as_strided_copy_cpu_int32, test/test_decomp.py::TestDecompCPU::test_comprehensive_as_strided_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_as_strided_partial_views_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_as_strided_partial_views_cpu_int8, test/test_decomp.py::TestDecompCPU::test_comprehensive_asin_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_asinh_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_atan_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive_atan_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_atanh_cpu_bool, test/test_decomp.py::TestDecompCPU::test_comprehensive_atanh_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_atleast_2d_cpu_bool, test/test_decomp.py::TestDecompCPU::test_comprehensive_atleast_2d_cpu_int8, test/test_decomp.py::TestDecompCPU::test_comprehensive_atleast_2d_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_comprehensive_atleast_3d_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_comprehensive_baddbmm_cpu_int8, 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test/test_decomp.py::TestDecompCPU::test_comprehensive_linalg_multi_dot_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive_linalg_multi_dot_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_linalg_multi_dot_cpu_int64, test/test_decomp.py::TestDecompCPU::test_comprehensive_linalg_norm_subgradients_at_zero_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive_linalg_tensorinv_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_comprehensive_linspace_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_log10_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_comprehensive_log10_cpu_int16, test/test_decomp.py::TestDecompCPU::test_comprehensive_log2_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_comprehensive_log_normal_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_log_softmax_with_dtype_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_comprehensive_log_softmax_with_dtype_cpu_int8, test/test_decomp.py::TestDecompCPU::test_comprehensive_logdet_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_comprehensive_logical_not_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_logical_or_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive_logical_or_cpu_int32, test/test_decomp.py::TestDecompCPU::test_comprehensive_logical_xor_cpu_int32, test/test_decomp.py::TestDecompCPU::test_comprehensive_logit_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_comprehensive_logspace_cpu_int32, test/test_decomp.py::TestDecompCPU::test_comprehensive_logspace_tensor_overload_cpu_int8, test/test_decomp.py::TestDecompCPU::test_comprehensive_logsumexp_cpu_int64, test/test_decomp.py::TestDecompCPU::test_comprehensive_lt_cpu_int8, test/test_decomp.py::TestDecompCPU::test_comprehensive_mT_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive_mT_cpu_int32, test/test_decomp.py::TestDecompCPU::test_comprehensive_mT_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_comprehensive_masked_amin_cpu_int32, test/test_decomp.py::TestDecompCPU::test_comprehensive_masked_amin_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_comprehensive_masked_argmax_cpu_int16, test/test_decomp.py::TestDecompCPU::test_comprehensive_masked_argmin_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_masked_cumsum_cpu_int32, test/test_decomp.py::TestDecompCPU::test_comprehensive_masked_fill_cpu_complex32, test/test_decomp.py::TestDecompCPU::test_comprehensive_masked_fill_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_masked_logsumexp_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive_masked_logsumexp_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_comprehensive_masked_select_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_masked_softmin_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_masked_std_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_masked_var_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive_max_pool2d_with_indices_backward_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive_max_pool2d_with_indices_backward_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_max_reduction_no_dim_cpu_int64, test/test_decomp.py::TestDecompCPU::test_comprehensive_median_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_median_cpu_int64, test/test_decomp.py::TestDecompCPU::test_comprehensive_median_cpu_int8, test/test_decomp.py::TestDecompCPU::test_comprehensive_meshgrid_variadic_tensors_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_meshgrid_variadic_tensors_cpu_int32, 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test/test_decomp.py::TestDecompCPU::test_comprehensive_var_mean_unbiased_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive_view_cpu_int16, test/test_decomp.py::TestDecompCPU::test_comprehensive_where_cpu_int32, test/test_decomp.py::TestDecompCPU::test_comprehensive_where_cpu_int8, test/test_decomp.py::TestDecompCPU::test_comprehensive_zero__cpu_complex128, test/test_decomp.py::TestDecompCPU::test_comprehensive_zeros_like_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive_zeros_like_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick__chunk_cat_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick__softmax_backward_data_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick__unsafe_masked_index_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick__unsafe_masked_index_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick__unsafe_masked_index_put_accumulate_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick__unsafe_masked_index_put_accumulate_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick__upsample_bilinear2d_aa_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_acos_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_addcdiv_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_addmv_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_quick_addmv_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_amax_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_any_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_arange_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_as_strided_copy_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_quick_as_strided_copy_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_as_strided_scatter_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_asin_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_quick_asinh_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_asinh_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_atan2_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_atan_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_atan_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_atan_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_baddbmm_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_bernoulli_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_bitwise_not_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_bitwise_or_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_block_diag_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_quick_ceil_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_ceil_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_clamp_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_clamp_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_clamp_max_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_clamp_max_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_clone_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_quick_constant_pad_nd_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_copysign_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_core_backward__unsafe_masked_index_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_core_backward_deg2rad_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_core_backward_masked_fill_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_core_backward_nan_to_num_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_core_backward_nn_functional_max_unpool3d_grad_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_core_backward_norm_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_core_backward_roll_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_core_backward_sinc_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_core_backward_split_with_sizes_copy_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_core_backward_unfold_copy_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_cos_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_count_nonzero_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_quick_count_nonzero_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_cumprod_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_diag_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_diag_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_diag_embed_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_diagonal_copy_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_diagonal_copy_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_diagonal_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_div_trunc_rounding_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_empty_like_cpu_complex32, test/test_decomp.py::TestDecompCPU::test_quick_empty_strided_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_erfc_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_erfinv_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_exp2_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_exp_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_exp_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_expand_copy_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_expand_copy_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_quick_expm1_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_eye_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_eye_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_eye_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_eye_cpu_float8_e5m2, test/test_decomp.py::TestDecompCPU::test_quick_eye_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_fft_fft2_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_fft_fft2_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_fft_fft_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_fft_fftn_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_fft_fftn_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_fft_hfft_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_fft_ifft2_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_fft_ifftn_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_fft_ifftn_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_fft_ihfft2_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_fft_ihfft_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_fft_ihfftn_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_fft_ihfftn_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_fft_irfft2_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_fft_irfft_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_fft_rfft2_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_fft_rfft_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_fft_rfft_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_fft_rfft_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_fft_rfftn_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_fill_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_flip_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_quick_flip_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_flip_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_floor_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_floor_divide_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_fmax_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_fmax_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_fmin_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_frexp_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_full_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_quick_full_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_i0_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_index_add_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_index_copy_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_quick_index_fill_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_index_select_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_index_select_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_isinf_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_isnan_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_isneginf_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_item_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_item_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_quick_item_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_lcm_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_linalg_vector_norm_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_linspace_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_linspace_tensor_overload_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_linspace_tensor_overload_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_log10_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_log1p_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_log2_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_logspace_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_quick_logspace_tensor_overload_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_logsumexp_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_logsumexp_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_lt_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_masked_fill_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_masked_fill_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_mv_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_mvlgamma_mvlgamma_p_1_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_mvlgamma_mvlgamma_p_1_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_mvlgamma_mvlgamma_p_1_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_mvlgamma_mvlgamma_p_3_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_mvlgamma_mvlgamma_p_5_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_mvlgamma_mvlgamma_p_5_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_nansum_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_narrow_copy_cpu_complex32, test/test_decomp.py::TestDecompCPU::test_quick_neg_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_quick_neg_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_new_empty_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_new_full_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_new_full_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_new_zeros_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_new_zeros_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_quick_nn_functional_binary_cross_entropy_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_nn_functional_binary_cross_entropy_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_nn_functional_hardshrink_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_nn_functional_hardswish_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_nn_functional_hardswish_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_nn_functional_hardtanh_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_nn_functional_max_unpool2d_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_nn_functional_relu6_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_nn_functional_relu_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_nn_functional_relu_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_norm_inf_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_norm_inf_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_quick_norm_nuc_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_ones_like_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_ones_like_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_permute_copy_cpu_complex32, test/test_decomp.py::TestDecompCPU::test_quick_permute_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_pow_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_prod_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_rad2deg_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_randn_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_renorm_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_repeat_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_repeat_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_round_decimals_0_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_round_decimals_3_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_rsub_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_select_scatter_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_sgn_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_sigmoid_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_quick_sigmoid_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_sign_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_sign_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_signbit_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_sinc_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_sinc_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_sinh_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_quick_slice_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_slice_scatter_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_special_entr_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_special_erfcx_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_special_i0e_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_special_i1_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_special_i1e_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_special_ndtr_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_special_xlog1py_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_special_xlog1py_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_special_zeta_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_special_zeta_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_split_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_split_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_split_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_split_list_args_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_quick_split_list_args_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_split_with_sizes_copy_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_split_with_sizes_copy_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_split_with_sizes_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_quick_split_with_sizes_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_split_with_sizes_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_sqrt_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_sqrt_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_squeeze_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_squeeze_multiple_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_stack_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_quick_stack_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_std_mean_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_sub_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_sum_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_t_copy_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_t_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_take_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_take_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_tan_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_transpose_copy_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_transpose_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_quick_tril_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_tril_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_tril_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_triu_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_quick_unbind_cpu_complex32, test/test_decomp.py::TestDecompCPU::test_quick_unsafe_split_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_unsafe_split_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_unsqueeze_copy_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_unsqueeze_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_unsqueeze_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_var_mean_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_quick_where_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_quick_xlogy_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_xlogy_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_zero__cpu_complex128, test/test_decomp.py::TestDecompCPU::test_rnn_decomp_module_nn_GRU_train_mode_cpu_float64, test/test_decomp.py::HasDecompTest::test_has_decomposition 2025-09-07T07:51:34.0178199Z 2025-09-07T07:51:37.3853108Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T07:51:37.3856440Z import pkg_resources 2025-09-07T07:51:37.5077359Z Running inductor/test_fxir_backend 1/1 ... [2025-09-07 07:51:37.507253] 2025-09-07T07:51:37.5078220Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:51:37.5080140Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_fxir_backend.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'] ... [2025-09-07 07:51:37.507614] 2025-09-07T07:51:44.7401216Z 2025-09-07T07:51:44.7402878Z inductor/test_fxir_backend 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_fxir_backend_1.1_ae99cd93c7bdb84f_.log 2025-09-07T07:51:44.7404341Z 2025-09-07T07:51:47.4573193Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T07:51:47.4574771Z import pkg_resources 2025-09-07T07:51:47.5791355Z Running test_accelerator 1/1 ... [2025-09-07 07:51:47.578858] 2025-09-07T07:51:47.5791849Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:51:47.5793992Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_accelerator.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'] ... [2025-09-07 07:51:47.579180] 2025-09-07T07:51:50.8906322Z 2025-09-07T07:51:50.8907958Z test_accelerator 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_accelerator_1.1_548bbdc85abab761_.log 2025-09-07T07:51:50.8909264Z 2025-09-07T07:51:54.4971928Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T07:51:54.4974969Z import pkg_resources 2025-09-07T07:51:54.6180652Z Running test_ops 3/6 ... [2025-09-07 07:51:54.617722] 2025-09-07T07:51:54.6181395Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:51:54.6183889Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_ops.py', '-m', 'not serial', '--shard-id=3', '--num-shards=6', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 07:51:54.618058] 2025-09-07T07:54:02.0638907Z 2025-09-07T07:54:02.0639762Z test_decomp 5/12 was successful, full logs can be found in artifacts with path test/test-reports/test_decomp_5.12_bb793c969622273f_.log 2025-09-07T07:54:02.0864450Z Running 688 items in this shard: test/test_decomp.py::TestDecompCPU::test_comprehensive_H_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_comprehensive_T_cpu_int32, test/test_decomp.py::TestDecompCPU::test_comprehensive___getitem___cpu_complex32, test/test_decomp.py::TestDecompCPU::test_comprehensive___radd___cpu_int8, test/test_decomp.py::TestDecompCPU::test_comprehensive___rmatmul___cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive___rxor___cpu_uint8, test/test_decomp.py::TestDecompCPU::test_comprehensive__chunk_cat_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive__unsafe_masked_index_put_accumulate_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_acosh_cpu_bool, test/test_decomp.py::TestDecompCPU::test_comprehensive_acosh_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_comprehensive_add_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_comprehensive_addbmm_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_comprehensive_addcdiv_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_addcdiv_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_addmm_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_comprehensive_addmv_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_comprehensive_addmv_cpu_int8, test/test_decomp.py::TestDecompCPU::test_comprehensive_addmv_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_comprehensive_addr_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_addr_cpu_int16, test/test_decomp.py::TestDecompCPU::test_comprehensive_amax_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_amax_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_comprehensive_amin_cpu_int8, test/test_decomp.py::TestDecompCPU::test_comprehensive_angle_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_argmax_cpu_int32, test/test_decomp.py::TestDecompCPU::test_comprehensive_argmin_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive_argwhere_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_as_strided_copy_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_comprehensive_as_strided_copy_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_comprehensive_as_strided_scatter_cpu_int32, test/test_decomp.py::TestDecompCPU::test_comprehensive_asinh_cpu_bool, test/test_decomp.py::TestDecompCPU::test_comprehensive_atan_cpu_bool, test/test_decomp.py::TestDecompCPU::test_comprehensive_atanh_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive_atanh_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_atleast_1d_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_comprehensive_atleast_1d_cpu_complex32, test/test_decomp.py::TestDecompCPU::test_comprehensive_atleast_1d_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_atleast_1d_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_comprehensive_atleast_3d_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_comprehensive_baddbmm_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_baddbmm_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_bincount_cpu_int16, test/test_decomp.py::TestDecompCPU::test_comprehensive_bitwise_left_shift_cpu_int16, test/test_decomp.py::TestDecompCPU::test_comprehensive_bitwise_right_shift_cpu_int32, test/test_decomp.py::TestDecompCPU::test_comprehensive_bitwise_xor_cpu_int16, test/test_decomp.py::TestDecompCPU::test_comprehensive_broadcast_tensors_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_broadcast_to_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_byte_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_cartesian_prod_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_comprehensive_cartesian_prod_cpu_int64, test/test_decomp.py::TestDecompCPU::test_comprehensive_cat_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_comprehensive_cat_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_cdouble_cpu_int16, test/test_decomp.py::TestDecompCPU::test_comprehensive_cfloat_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_cfloat_cpu_int8, test/test_decomp.py::TestDecompCPU::test_comprehensive_cfloat_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_comprehensive_chalf_cpu_bool, test/test_decomp.py::TestDecompCPU::test_comprehensive_chalf_cpu_complex32, test/test_decomp.py::TestDecompCPU::test_comprehensive_char_cpu_complex32, test/test_decomp.py::TestDecompCPU::test_comprehensive_char_cpu_int32, test/test_decomp.py::TestDecompCPU::test_comprehensive_char_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_comprehensive_cholesky_inverse_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_comprehensive_cholesky_solve_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_chunk_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive_clamp_min_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_clamp_min_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_comprehensive_clone_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_comprehensive_clone_cpu_int32, test/test_decomp.py::TestDecompCPU::test_comprehensive_column_stack_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive_column_stack_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_column_stack_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_column_stack_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_comprehensive_combinations_cpu_int16, test/test_decomp.py::TestDecompCPU::test_comprehensive_complex_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_conj_physical_cpu_int32, test/test_decomp.py::TestDecompCPU::test_comprehensive_contiguous_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_count_nonzero_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_comprehensive_cummax_cpu_bool, test/test_decomp.py::TestDecompCPU::test_comprehensive_cummax_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_cumprod_cpu_int16, test/test_decomp.py::TestDecompCPU::test_comprehensive_cumprod_cpu_int64, test/test_decomp.py::TestDecompCPU::test_comprehensive_diagflat_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_diagonal_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_comprehensive_diagonal_scatter_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_comprehensive_diff_cpu_int16, test/test_decomp.py::TestDecompCPU::test_comprehensive_digamma_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_digamma_cpu_int16, test/test_decomp.py::TestDecompCPU::test_comprehensive_div_no_rounding_mode_cpu_int64, test/test_decomp.py::TestDecompCPU::test_comprehensive_dot_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_comprehensive_dot_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_comprehensive_dstack_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_empty_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_comprehensive_empty_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_empty_cpu_int64, test/test_decomp.py::TestDecompCPU::test_comprehensive_empty_like_cpu_int64, test/test_decomp.py::TestDecompCPU::test_comprehensive_empty_permuted_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_eq_cpu_int8, test/test_decomp.py::TestDecompCPU::test_comprehensive_erfc_cpu_int32, test/test_decomp.py::TestDecompCPU::test_comprehensive_exp2_cpu_int32, test/test_decomp.py::TestDecompCPU::test_comprehensive_expand_as_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_expm1_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_eye_cpu_int16, test/test_decomp.py::TestDecompCPU::test_comprehensive_fft_fft_cpu_int32, test/test_decomp.py::TestDecompCPU::test_comprehensive_fft_fftshift_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_comprehensive_fft_hfft2_cpu_int32, test/test_decomp.py::TestDecompCPU::test_comprehensive_fft_ifft2_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_fft_ifft2_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_comprehensive_fft_ifftshift_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_comprehensive_fft_ifftshift_cpu_int16, test/test_decomp.py::TestDecompCPU::test_comprehensive_fft_irfftn_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_comprehensive_fft_rfft2_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_fill_cpu_int8, test/test_decomp.py::TestDecompCPU::test_comprehensive_flip_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_flip_cpu_int8, test/test_decomp.py::TestDecompCPU::test_comprehensive_fliplr_cpu_int64, test/test_decomp.py::TestDecompCPU::test_comprehensive_flipud_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive_flipud_cpu_int32, test/test_decomp.py::TestDecompCPU::test_comprehensive_float_cpu_int32, test/test_decomp.py::TestDecompCPU::test_comprehensive_float_cpu_int64, test/test_decomp.py::TestDecompCPU::test_comprehensive_float_power_cpu_int64, test/test_decomp.py::TestDecompCPU::test_comprehensive_fmax_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_fmax_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_full_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive_full_cpu_int32, test/test_decomp.py::TestDecompCPU::test_comprehensive_gcd_cpu_int8, test/test_decomp.py::TestDecompCPU::test_comprehensive_geqrf_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_grid_sampler_3d_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_gt_cpu_int8, test/test_decomp.py::TestDecompCPU::test_comprehensive_half_cpu_int32, test/test_decomp.py::TestDecompCPU::test_comprehensive_hash_tensor_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_histogramdd_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_hsplit_cpu_int32, test/test_decomp.py::TestDecompCPU::test_comprehensive_hstack_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_index_add_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_comprehensive_index_fill_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_comprehensive_index_fill_cpu_int64, test/test_decomp.py::TestDecompCPU::test_comprehensive_index_put_cpu_int8, test/test_decomp.py::TestDecompCPU::test_comprehensive_index_reduce_amin_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_index_reduce_amin_cpu_int32, test/test_decomp.py::TestDecompCPU::test_comprehensive_index_reduce_mean_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_index_reduce_prod_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_index_reduce_prod_cpu_int64, test/test_decomp.py::TestDecompCPU::test_comprehensive_inner_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_inner_cpu_int64, test/test_decomp.py::TestDecompCPU::test_comprehensive_isfinite_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive_isin_cpu_int16, test/test_decomp.py::TestDecompCPU::test_comprehensive_isin_cpu_int32, test/test_decomp.py::TestDecompCPU::test_comprehensive_isnan_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_isreal_cpu_int32, test/test_decomp.py::TestDecompCPU::test_comprehensive_isreal_cpu_int64, test/test_decomp.py::TestDecompCPU::test_comprehensive_jiterator_4inputs_with_extra_args_cpu_int32, test/test_decomp.py::TestDecompCPU::test_comprehensive_jiterator_unary_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_kthvalue_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_ldexp_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_le_cpu_bool, test/test_decomp.py::TestDecompCPU::test_comprehensive_le_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_comprehensive_lgamma_cpu_int32, test/test_decomp.py::TestDecompCPU::test_comprehensive_lgamma_cpu_int64, test/test_decomp.py::TestDecompCPU::test_comprehensive_linalg_cross_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_linalg_cross_cpu_int8, test/test_decomp.py::TestDecompCPU::test_comprehensive_linalg_diagonal_cpu_complex32, test/test_decomp.py::TestDecompCPU::test_comprehensive_linalg_diagonal_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_linalg_ldl_factor_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_linalg_ldl_factor_ex_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_comprehensive_linalg_lstsq_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_comprehensive_linalg_matrix_power_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_comprehensive_linalg_matrix_power_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_linalg_pinv_hermitian_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_linalg_qr_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_comprehensive_linalg_slogdet_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_linalg_svdvals_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_comprehensive_linalg_svdvals_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_linalg_vecdot_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_linalg_vecdot_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_linspace_tensor_overload_cpu_int32, test/test_decomp.py::TestDecompCPU::test_comprehensive_log10_cpu_int64, test/test_decomp.py::TestDecompCPU::test_comprehensive_log10_cpu_int8, test/test_decomp.py::TestDecompCPU::test_comprehensive_log1p_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive_log1p_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_comprehensive_log2_cpu_int16, test/test_decomp.py::TestDecompCPU::test_comprehensive_log2_cpu_int64, test/test_decomp.py::TestDecompCPU::test_comprehensive_log_cpu_bool, test/test_decomp.py::TestDecompCPU::test_comprehensive_log_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_log_softmax_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_log_softmax_with_dtype_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_logaddexp_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_logaddexp_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_logcumsumexp_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_comprehensive_logcumsumexp_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_logical_and_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_logical_and_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_comprehensive_logical_not_cpu_int64, test/test_decomp.py::TestDecompCPU::test_comprehensive_logical_not_cpu_int8, test/test_decomp.py::TestDecompCPU::test_comprehensive_logical_or_cpu_bool, test/test_decomp.py::TestDecompCPU::test_comprehensive_logical_or_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_comprehensive_logical_or_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_logical_xor_cpu_bool, test/test_decomp.py::TestDecompCPU::test_comprehensive_logical_xor_cpu_int16, test/test_decomp.py::TestDecompCPU::test_comprehensive_logspace_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_logspace_tensor_overload_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_logsumexp_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_logsumexp_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_comprehensive_mH_cpu_int16, test/test_decomp.py::TestDecompCPU::test_comprehensive_mT_cpu_int8, test/test_decomp.py::TestDecompCPU::test_comprehensive_masked_argmin_cpu_int64, test/test_decomp.py::TestDecompCPU::test_comprehensive_masked_cumprod_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_comprehensive_masked_cumprod_cpu_int64, test/test_decomp.py::TestDecompCPU::test_comprehensive_masked_cumsum_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_comprehensive_masked_logaddexp_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_masked_logaddexp_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_masked_logsumexp_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_comprehensive_masked_normalize_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_masked_scatter_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_masked_scatter_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_masked_sum_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_comprehensive_masked_sum_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_masked_var_cpu_int32, test/test_decomp.py::TestDecompCPU::test_comprehensive_matmul_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_matmul_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_comprehensive_matrix_exp_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_comprehensive_max_reduction_no_dim_cpu_bool, test/test_decomp.py::TestDecompCPU::test_comprehensive_median_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive_median_cpu_int16, test/test_decomp.py::TestDecompCPU::test_comprehensive_meshgrid_list_of_tensors_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_comprehensive_meshgrid_list_of_tensors_cpu_int8, test/test_decomp.py::TestDecompCPU::test_comprehensive_meshgrid_variadic_tensors_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_min_binary_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive_min_binary_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_min_binary_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_comprehensive_min_reduction_no_dim_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive_min_reduction_with_dim_cpu_int8, test/test_decomp.py::TestDecompCPU::test_comprehensive_mm_cpu_complex64, 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test/test_decomp.py::TestDecompCPU::test_comprehensive_view_copy_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive_view_copy_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_comprehensive_view_copy_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_comprehensive_view_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_vsplit_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_comprehensive_vstack_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_vstack_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_where_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_comprehensive_zero__cpu_int32, test/test_decomp.py::TestDecompCPU::test_comprehensive_zero__cpu_int64, test/test_decomp.py::TestDecompCPU::test_comprehensive_zeros_cpu_bool, test/test_decomp.py::TestDecompCPU::test_comprehensive_zeros_cpu_complex32, test/test_decomp.py::TestDecompCPU::test_comprehensive_zeros_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_quick__batch_norm_with_update_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick__chunk_cat_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick__unsafe_masked_index_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_add_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_addmm_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_addmv_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_addr_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_alias_copy_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_alias_copy_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_all_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_amin_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_aminmax_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_as_strided_copy_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_asin_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_atan2_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_atan2_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_atanh_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_baddbmm_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_bitwise_and_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_bitwise_left_shift_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_block_diag_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_cat_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_cat_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_quick_cauchy_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_ceil_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_ceil_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_clamp_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_clamp_max_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_clamp_max_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_clamp_max_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_clone_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_clone_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_conj_physical_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_constant_pad_nd_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_constant_pad_nd_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_copysign_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_core_backward__softmax_backward_data_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_core_backward_diagonal_copy_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_core_backward_renorm_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_core_backward_stack_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_core_backward_unsqueeze_copy_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_cos_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_count_nonzero_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_cumprod_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_quick_cumsum_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_diag_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_quick_diag_embed_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_diagonal_copy_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_diagonal_cpu_complex32, test/test_decomp.py::TestDecompCPU::test_quick_diagonal_scatter_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_quick_dist_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_div_no_rounding_mode_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_dot_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_quick_dot_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_empty_like_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_empty_strided_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_eq_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_erf_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_erfc_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_erfc_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_erfc_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_erfinv_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_erfinv_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_exp2_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_exp_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_expand_copy_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_expand_copy_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_expand_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_expand_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_expm1_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_fft_fft_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_fft_fftn_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_fft_hfft2_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_fft_hfft_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_quick_fft_hfft_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_fft_hfftn_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_fft_hfftn_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_fft_ifft_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_fft_ifftn_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_quick_fft_irfft2_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_quick_fft_irfft_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_fft_rfft2_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_fft_rfft2_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_fft_rfftn_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_fill_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_flip_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_floor_divide_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_fmax_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_frexp_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_gcd_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_gcd_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_gt_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_heaviside_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_heaviside_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_i0_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_i0_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_igamma_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_igammac_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_index_copy_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_index_fill_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_index_select_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_isinf_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_isnan_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_quick_item_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_le_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_lerp_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_lgamma_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_lgamma_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_linspace_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_quick_linspace_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_logaddexp2_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_logaddexp_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_quick_logical_and_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_logical_and_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_logical_or_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_logical_or_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_logspace_tensor_overload_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_logsumexp_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_lt_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_lt_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_mul_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_mul_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_quick_mul_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_mv_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_mvlgamma_mvlgamma_p_3_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_mvlgamma_mvlgamma_p_3_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_mvlgamma_mvlgamma_p_5_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_mvlgamma_mvlgamma_p_5_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_nan_to_num_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_nan_to_num_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_nansum_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_native_dropout_backward_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_native_dropout_backward_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_native_layer_norm_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_neg_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_new_empty_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_new_full_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_new_zeros_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_nn_functional_binary_cross_entropy_with_logits_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_nn_functional_gelu_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_nn_functional_glu_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_nn_functional_max_unpool2d_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_nn_functional_max_unpool3d_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_nn_functional_pad_constant_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_nn_functional_relu6_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_nn_functional_relu6_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_nn_functional_silu_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_nn_functional_softplus_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_norm_cpu_complex32, test/test_decomp.py::TestDecompCPU::test_quick_normal_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_normal_in_place_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_ones_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_permute_copy_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_prod_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_quick_prod_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_prod_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_reciprocal_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_reciprocal_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_remainder_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_roll_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_rot90_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_rot90_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_round_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_rsqrt_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_rsqrt_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_rsub_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_quick_rsub_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_select_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_select_scatter_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_sign_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_sin_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_sin_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_quick_sin_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_sinc_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_sinc_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_sinh_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_special_entr_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_special_entr_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_special_i1e_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_special_ndtr_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_special_ndtr_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_special_xlog1py_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_special_xlog1py_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_split_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_split_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_split_with_sizes_copy_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_sqrt_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_quick_sqrt_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_sqrt_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_squeeze_copy_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_quick_squeeze_copy_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_squeeze_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_quick_squeeze_cpu_complex32, test/test_decomp.py::TestDecompCPU::test_quick_stack_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_std_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_std_mean_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_quick_sub_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_sub_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_quick_sum_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_t_copy_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_quick_t_copy_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_t_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_transpose_copy_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_tril_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_tril_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_unbind_copy_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_quick_unbind_copy_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_unbind_copy_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_unbind_copy_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_unbind_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_unfold_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_unsafe_split_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_quick_unsafe_split_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_unsqueeze_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_var_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_var_mean_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_var_mean_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_vdot_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_quick_vdot_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_view_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_where_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_xlogy_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_zero__cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_zero__cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_zeros_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_zeros_like_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_rnn_decomp_module_nn_LSTM_eval_mode_cpu_float64, test/test_decomp.py::DecompOneOffTestsCPU::test_amp_batch_norm_backward_cpu 2025-09-07T07:54:02.1083286Z 2025-09-07T07:54:04.9252808Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T07:54:04.9256124Z import pkg_resources 2025-09-07T07:54:05.0481713Z Running test_ops 6/6 ... [2025-09-07 07:54:05.047764] 2025-09-07T07:54:05.0482460Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T07:54:05.0484706Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_ops.py', '-m', 'not serial', '--shard-id=6', '--num-shards=6', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-09-07 07:54:05.048143] 2025-09-07T08:00:53.4568061Z 2025-09-07T08:00:53.4569066Z test_ops 3/6 was successful, full logs can be found in artifacts with path test/test-reports/test_ops_3.6_61e3e51763e65d05_.log 2025-09-07T08:00:53.6590767Z Running 5784 items in this shard: test/test_ops.py::TestCommonCPU::test_compare_cpu___getitem___cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu___ror___cpu_int64, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs__conversions_bfloat16_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs__conversions_int_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs__conversions_short_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_as_strided_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_chunk_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_div_no_rounding_mode_cpu_float32, 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test/test_ops.py::TestCommonCPU::test_errors___rand___cpu, test/test_ops.py::TestCommonCPU::test_errors_amin_cpu, test/test_ops.py::TestCommonCPU::test_errors_div_trunc_rounding_cpu, test/test_ops.py::TestCommonCPU::test_errors_fft_fft_cpu, test/test_ops.py::TestCommonCPU::test_errors_fft_hfft2_cpu, test/test_ops.py::TestCommonCPU::test_errors_fft_ifft_cpu, test/test_ops.py::TestCommonCPU::test_errors_fft_ifftn_cpu, test/test_ops.py::TestCommonCPU::test_errors_fft_irfft_cpu, test/test_ops.py::TestCommonCPU::test_errors_fft_rfftn_cpu, test/test_ops.py::TestCommonCPU::test_errors_gather_cpu, test/test_ops.py::TestCommonCPU::test_errors_geometric_cpu, test/test_ops.py::TestCommonCPU::test_errors_hstack_cpu, test/test_ops.py::TestCommonCPU::test_errors_index_select_cpu, test/test_ops.py::TestCommonCPU::test_errors_item_cpu, test/test_ops.py::TestCommonCPU::test_errors_jiterator_binary_return_by_ref_cpu, test/test_ops.py::TestCommonCPU::test_errors_linalg_diagonal_cpu, test/test_ops.py::TestCommonCPU::test_errors_logaddexp_cpu, test/test_ops.py::TestCommonCPU::test_errors_max_binary_cpu, test/test_ops.py::TestCommonCPU::test_errors_minimum_cpu, test/test_ops.py::TestCommonCPU::test_errors_multinomial_cpu, test/test_ops.py::TestCommonCPU::test_errors_neg_cpu, test/test_ops.py::TestCommonCPU::test_errors_nn_functional_adaptive_avg_pool3d_cpu, test/test_ops.py::TestCommonCPU::test_errors_nn_functional_conv1d_cpu, test/test_ops.py::TestCommonCPU::test_errors_nn_functional_group_norm_cpu, test/test_ops.py::TestCommonCPU::test_errors_nn_functional_max_pool3d_cpu, test/test_ops.py::TestCommonCPU::test_errors_nn_functional_poisson_nll_loss_cpu, test/test_ops.py::TestCommonCPU::test_errors_renorm_cpu, test/test_ops.py::TestCommonCPU::test_errors_reshape_cpu, test/test_ops.py::TestCommonCPU::test_errors_signal_windows_cosine_cpu, test/test_ops.py::TestCommonCPU::test_errors_sparse_mul_layout3_cpu, test/test_ops.py::TestCommonCPU::test_errors_sparse_sum_layout2_cpu, test/test_ops.py::TestCommonCPU::test_errors_special_shifted_chebyshev_polynomial_w_cpu, test/test_ops.py::TestCommonCPU::test_errors_unbind_copy_cpu, test/test_ops.py::TestCommonCPU::test_errors_unbind_cpu, test/test_ops.py::TestCommonCPU::test_errors_uniform_cpu, test/test_ops.py::TestCommonCPU::test_errors_view_copy_cpu, test/test_ops.py::TestCommonCPU::test_errors_view_cpu, test/test_ops.py::TestCommonCPU::test_errors_xlogy_cpu, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_acos_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_addcdiv_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_amax_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_as_strided_copy_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_atan_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_baddbmm_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_clamp_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_clamp_min_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_cumprod_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_deg2rad_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_diag_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_digamma_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_div_floor_rounding_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_div_no_rounding_mode_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_equal_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_expand_copy_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_fft_fft2_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_fft_fftn_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_fft_ifft_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_fft_ihfft_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_fft_rfft2_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_full_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_index_add_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_linalg_det_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_linalg_inv_ex_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_linalg_lu_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_linalg_solve_ex_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_linalg_svd_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_logit_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_minimum_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_nanmean_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_narrow_copy_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_ne_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_nn_functional_normalize_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_norm_nuc_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_permute_copy_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_polygamma_polygamma_n_0_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_quantile_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_remainder_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_scatter_reduce_prod_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_sinc_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_sinh_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_slice_scatter_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_special_bessel_y1_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_special_chebyshev_polynomial_u_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_special_entr_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_special_i1e_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_special_modified_bessel_k0_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_special_shifted_chebyshev_polynomial_v_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_special_zeta_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_std_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_transpose_copy_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_triangular_solve_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_tril_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_unfold_copy_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_var_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_view_copy_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_vstack_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_T_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices___getitem___cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices___rmatmul___cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices__softmax_backward_data_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices__unsafe_masked_index_put_accumulate_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_abs_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_addbmm_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_all_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_all_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_allclose_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_argmax_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_argwhere_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_bfloat16_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_cdist_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_clamp_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_clone_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_combinations_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_constant_pad_nd_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_contiguous_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_copysign_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_cross_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_cumprod_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_diag_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_diff_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_dist_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_einsum_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_equal_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_exp2_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_exp_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_expand_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_exponential_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_eye_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_fft_fft2_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_fft_fftn_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_fft_hfft_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_fft_hfftn_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_fft_ifft_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_fft_irfftn_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_fft_irfftn_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_fft_rfft2_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_fft_rfftn_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_fliplr_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_fmod_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_grid_sampler_2d_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_gt_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_half_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_histogramdd_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_hstack_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_igammac_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_index_add_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_index_reduce_mean_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_isclose_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_isinf_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_jiterator_4inputs_with_extra_args_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_jiterator_binary_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_kron_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_ldexp_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_ldexp_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_linalg_cholesky_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_linalg_cond_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_linalg_cross_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_linalg_det_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_linalg_diagonal_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_linalg_eigh_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_linalg_lu_factor_ex_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_linalg_matrix_power_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_linalg_multi_dot_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_linspace_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_log2_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_logical_and_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_logit_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_logspace_tensor_overload_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_masked_cumsum_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_masked_logaddexp_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_masked_prod_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_masked_scatter_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_masked_std_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_matmul_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_meshgrid_variadic_tensors_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_minimum_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_mode_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_mul_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_mvlgamma_mvlgamma_p_3_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_mvlgamma_mvlgamma_p_5_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nanmean_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_neg_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_new_full_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_new_zeros_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_new_zeros_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_adaptive_max_pool1d_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_adaptive_max_pool2d_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_avg_pool3d_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_channel_shuffle_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_conv2d_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_cosine_similarity_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_cross_entropy_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_dropout3d_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_feature_alpha_dropout_without_train_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_gaussian_nll_loss_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_glu_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_grid_sample_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_interpolate_area_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_logsigmoid_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_margin_ranking_loss_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_max_pool3d_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_max_unpool1d_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_max_unpool2d_grad_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_multi_head_attention_forward_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_normalize_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_pairwise_distance_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_softplus_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_softshrink_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_softsign_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_triplet_margin_with_distance_loss_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_nonzero_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_norm_inf_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_polygamma_polygamma_n_2_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_randint_like_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_ravel_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_real_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_real_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_reciprocal_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_remainder_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_resize__cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_resize__cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_rot90_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_rot90_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_rsqrt_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_scalar_tensor_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_select_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_slice_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_slice_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_special_bessel_y0_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_special_erfcx_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_special_hermite_polynomial_h_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_special_hermite_polynomial_h_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_special_i0e_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_special_i1e_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_special_laguerre_polynomial_l_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_special_legendre_polynomial_p_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_special_log_ndtr_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_special_shifted_chebyshev_polynomial_t_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_special_shifted_chebyshev_polynomial_v_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_special_spherical_bessel_j0_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_special_spherical_bessel_j0_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_special_zeta_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_sqrt_cpu_int64, 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test/test_ops.py::TestMathBitsCPU::test_conj_view_isreal_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_jiterator_4inputs_with_extra_args_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_linalg_cholesky_ex_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_linalg_eigvals_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_linalg_eigvalsh_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_linalg_inv_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_linalg_lu_factor_ex_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_linalg_norm_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_linalg_pinv_singular_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_linalg_slogdet_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_linalg_solve_ex_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_linalg_svdvals_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_linalg_tensorsolve_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_linalg_vector_norm_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_log10_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_log2_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_logspace_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_masked_mean_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_masked_select_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_masked_sum_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_mean_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_meshgrid_variadic_tensors_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_mul_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_narrow_copy_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_narrow_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_new_empty_strided_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_new_full_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_nn_functional_conv2d_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_nn_functional_conv3d_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_nn_functional_pixel_shuffle_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_nonzero_static_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_ones_like_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_permute_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_qr_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_randn_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_repeat_interleave_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_rsub_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_short_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_split_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_sqrt_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_tanh_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_trace_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_transpose_copy_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_transpose_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_triangular_solve_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_unsqueeze_copy_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_unsqueeze_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_var_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_var_mean_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_var_mean_unbiased_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_where_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_zeros_like_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view___getitem___cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__chunk_cat_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs__conversions_byte_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs__conversions_short_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_acos_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_acosh_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_addcdiv_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_addcmul_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_all_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_atleast_2d_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_cosh_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_div_no_rounding_mode_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_dot_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_expand_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_eye_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_fft_hfft2_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_flatten_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_flip_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_index_select_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_linalg_svd_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_linalg_svdvals_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_log2_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_logical_not_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_meshgrid_list_of_tensors_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_new_empty_strided_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_new_zeros_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_ones_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_permute_copy_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_reshape_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_roll_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_rsub_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_sinh_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_square_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_squeeze_multiple_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_sum_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_tanh_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_tensor_split_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_transpose_copy_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_true_divide_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_unbind_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_unsqueeze_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__unsafe_masked_index_put_accumulate_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_acos_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_addmv_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_all_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_as_strided_partial_views_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_asin_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_block_diag_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_broadcast_tensors_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_byte_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_chalf_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_cholesky_solve_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_cos_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_cov_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_cross_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_div_no_rounding_mode_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_dsplit_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_dstack_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_einsum_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_empty_strided_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_expand_copy_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_expand_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_flipud_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_gather_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_hsplit_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_inner_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_int_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_jiterator_binary_return_by_ref_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_kron_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_lerp_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_linalg_cond_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_linalg_diagonal_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_linalg_inv_ex_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_linalg_lstsq_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_linalg_lstsq_grad_oriented_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_linalg_lu_solve_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_linalg_norm_subgradients_at_zero_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_linalg_slogdet_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_linalg_vander_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_logspace_tensor_overload_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_lu_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_masked_logsumexp_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_masked_normalize_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_masked_std_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_mul_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_nn_functional_conv2d_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_nn_functional_conv_transpose1d_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_nn_functional_l1_loss_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_nn_functional_pad_replicate_negative_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_nn_functional_triplet_margin_with_distance_loss_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_norm_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_real_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_repeat_interleave_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_reshape_as_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_sin_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_sqrt_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_square_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_svd_lowrank_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_tensor_split_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_trapz_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_var_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_vdot_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_view_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_vsplit_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_view_T_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__chunk_cat_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__native_batch_norm_legit_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs__conversions_char_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_as_strided_partial_views_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_asinh_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_atan2_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_atleast_2d_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_block_diag_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_cauchy_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_constant_pad_nd_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_cumprod_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_diagonal_scatter_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_dsplit_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_eq_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_fft_fft_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_fft_ifft_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_fft_ifftshift_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_fft_ihfft_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_fft_irfft_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_fft_irfftn_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_fft_rfft2_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_frac_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_index_select_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_linalg_svdvals_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_linalg_vecdot_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_logaddexp2_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_meshgrid_list_of_tensors_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_native_layer_norm_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_neg_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_new_empty_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_new_ones_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_nn_functional_celu_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_nn_functional_pixel_shuffle_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_nn_functional_prelu_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_round_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_signbit_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_sinc_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_special_bessel_j0_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_special_i1e_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_special_log_softmax_with_dtype_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_special_logit_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_special_multigammaln_mvlgamma_p_5_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_special_ndtr_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_squeeze_multiple_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_stack_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_stft_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_sum_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_tan_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_true_divide_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_view_as_complex_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_vsplit_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__unsafe_masked_index_put_accumulate_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_addmm_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_block_diag_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_bmm_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_bucketize_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_cauchy_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_ceil_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_chalf_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_cholesky_solve_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_complex_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_count_nonzero_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_cummin_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_cumsum_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_diagonal_scatter_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_empty_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_expm1_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_fft_fft2_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_fft_fftshift_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_fft_hfft_cpu_float64, 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test/test_ops.py::TestMathBitsCPU::test_neg_view_linalg_det_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_linalg_diagonal_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_linalg_eig_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_linalg_lu_solve_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_linalg_matrix_power_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_linalg_matrix_rank_hermitian_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_linalg_norm_subgradients_at_zero_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_linalg_solve_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_linalg_vecdot_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_logspace_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_lu_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_masked_amax_cpu_float64, 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test/test_ops.py::TestRefsOpsInfoCPU::test_refs_are_in_python_ref_db_op__refs_positive_cpu, test/test_ops.py::TestRefsOpsInfoCPU::test_refs_are_in_python_ref_db_op__refs_rad2deg_cpu, test/test_ops.py::TestRefsOpsInfoCPU::test_refs_are_in_python_ref_db_op__refs_reshape_cpu, test/test_ops.py::TestRefsOpsInfoCPU::test_refs_are_in_python_ref_db_op__refs_rfloordiv_cpu, test/test_ops.py::TestRefsOpsInfoCPU::test_refs_are_in_python_ref_db_op__refs_rot90_cpu, test/test_ops.py::TestRefsOpsInfoCPU::test_refs_are_in_python_ref_db_op__refs_rsub_cpu, test/test_ops.py::TestRefsOpsInfoCPU::test_refs_are_in_python_ref_db_op__refs_sigmoid__cpu, test/test_ops.py::TestRefsOpsInfoCPU::test_refs_are_in_python_ref_db_op__refs_sign_cpu, test/test_ops.py::TestRefsOpsInfoCPU::test_refs_are_in_python_ref_db_op__refs_special_bessel_j1_cpu, test/test_ops.py::TestRefsOpsInfoCPU::test_refs_are_in_python_ref_db_op__refs_special_i1_cpu, test/test_ops.py::TestRefsOpsInfoCPU::test_refs_are_in_python_ref_db_op__refs_special_multigammaln_cpu, test/test_ops.py::TestRefsOpsInfoCPU::test_refs_are_in_python_ref_db_op__refs_special_xlog1py_cpu, test/test_ops.py::TestRefsOpsInfoCPU::test_refs_are_in_python_ref_db_op__refs_sqrt__cpu, test/test_ops.py::TestRefsOpsInfoCPU::test_refs_are_in_python_ref_db_op__refs_square_cpu, test/test_ops.py::TestRefsOpsInfoCPU::test_refs_are_in_python_ref_db_op__refs_std_cpu, test/test_ops.py::TestRefsOpsInfoCPU::test_refs_are_in_python_ref_db_op__refs_t_cpu, test/test_ops.py::TestRefsOpsInfoCPU::test_refs_are_in_python_ref_db_op__refs_trace_cpu, test/test_ops.py::TestRefsOpsInfoCPU::test_refs_are_in_python_ref_db_op__refs_transpose_copy_cpu, test/test_ops.py::TestRefsOpsInfoCPU::test_refs_are_in_python_ref_db_op__refs_tril_cpu, test/test_ops.py::TestRefsOpsInfoCPU::test_refs_are_in_python_ref_db_op__refs_true_divide_cpu, 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test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_binary_cross_entropy_with_logits_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_conv3d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_cosine_embedding_loss_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_dropout_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_fractional_max_pool3d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_glu_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_group_norm_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_hardtanh_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_instance_norm_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_l1_loss_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_max_unpool1d_grad_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_multi_margin_loss_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_pad_reflect_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_poisson_nll_loss_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_rms_norm_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_softmin_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_upsample_bilinear_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_norm_fro_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_reshape_as_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_round_decimals_3_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_scatter_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_scatter_reduce_amin_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_scatter_reduce_prod_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_sgn_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_special_i1e_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_special_ndtr_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_special_ndtri_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_square_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_sum_to_size_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_t_copy_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_to_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_torch_ops_aten__safe_softmax_default_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_trapz_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_tril_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_unbind_copy_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_unfold_copy_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_unsafe_split_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_var_mean_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp___rdiv___cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp___rmod___cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp__softmax_backward_data_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp__unsafe_masked_index_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp__upsample_bilinear2d_aa_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_add_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_addbmm_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_addmv_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_addr_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_amin_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_as_strided_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_as_strided_partial_views_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_asinh_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_atleast_2d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_atleast_3d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_clamp_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_clamp_min_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_conj_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_copysign_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_diag_embed_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_diagonal_copy_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_diagonal_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_expand_as_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_expand_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_fft_irfftn_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_fft_rfft2_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_fmax_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_frexp_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_hsplit_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_hypot_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_index_add_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_index_select_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_linalg_cond_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_linalg_cross_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_linalg_eigvalsh_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_linalg_lu_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_linalg_lu_factor_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_linalg_matrix_norm_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_linalg_norm_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_linalg_pinv_hermitian_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_linalg_solve_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_linalg_svdvals_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_mean_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_minimum_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_mvlgamma_mvlgamma_p_5_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_adaptive_avg_pool2d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_adaptive_avg_pool3d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_adaptive_max_pool2d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_batch_norm_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_channel_shuffle_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_conv_transpose2d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_cosine_embedding_loss_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_dropout3d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_fractional_max_pool2d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_hardsigmoid_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_instance_norm_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_interpolate_bilinear_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_interpolate_nearest_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_interpolate_trilinear_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_multi_margin_loss_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_multilabel_soft_margin_loss_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_pad_circular_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_pad_replicate_negative_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_relu_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_silu_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_softmin_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_triplet_margin_with_distance_loss_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_upsample_nearest_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_norm_inf_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_norm_nuc_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_permute_copy_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_permute_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_polygamma_polygamma_n_3_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_prod_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_quantile_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_repeat_interleave_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_round_decimals_0_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_round_decimals_neg_3_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_rsub_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_scatter_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_scatter_reduce_amax_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_scatter_reduce_sum_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_sgn_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_sinc_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_slice_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_sort_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_sparse_sampled_addmm_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_special_ndtr_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_squeeze_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_t_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_trace_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_trapz_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_trunc_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_unflatten_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_unfold_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_var_mean_unbiased_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_cummax_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_diagflat_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_dot_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_dstack_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_empty_permuted_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_eq_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_erfinv_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_exp2_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_expand_as_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_fft_fft_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_fft_ifft_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_fft_ifftn_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_fft_ifftshift_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_fft_rfft2_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_fft_rfft_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_fill_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_float_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_full_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_gather_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_gcd_cpu_int64, test/test_ops.py::TestFakeTensorCPU::test_fake_geometric_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_geqrf_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_gradient_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_hypot_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_i0_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_imag_cpu_complex64, test/test_ops.py::TestFakeTensorCPU::test_fake_index_put_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_isinf_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_jiterator_2inputs_2outputs_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_jiterator_4inputs_with_extra_args_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_jiterator_unary_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_le_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_linalg_diagonal_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_linalg_eig_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_linalg_eigvals_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_linalg_inv_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_linalg_ldl_factor_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_linalg_ldl_factor_ex_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_linalg_matrix_rank_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_linalg_slogdet_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_linalg_solve_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_linalg_solve_ex_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_log10_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_logdet_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_logical_and_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_logical_xor_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_logit_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_logspace_tensor_overload_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_masked_median_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_masked_prod_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_max_pool2d_with_indices_backward_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_max_reduction_with_dim_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_mm_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_mode_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_msort_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_multinomial_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_mv_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nan_to_num_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_narrow_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_neg_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_new_zeros_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_alpha_dropout_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_conv3d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_conv_transpose2d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_embedding_bag_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_embedding_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_feature_alpha_dropout_with_train_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_gelu_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_hardshrink_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_interpolate_area_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_kl_div_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_leaky_relu_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_linear_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_max_pool1d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_max_unpool3d_grad_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_mish_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_multilabel_margin_loss_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_one_hot_cpu_int64, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_pad_replicate_negative_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_pixel_shuffle_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_pixel_unshuffle_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_poisson_nll_loss_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_prelu_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_relu_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_rrelu_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_selu_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_softplus_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_softsign_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_norm_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_norm_fro_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_norm_inf_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_normal_in_place_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_ones_like_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_pca_lowrank_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_positive_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_prod_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_quantile_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_randn_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_repeat_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_resize__cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_resolve_neg_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_rsqrt_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_scatter_reduce_amax_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_signal_windows_general_cosine_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_signal_windows_hamming_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_signal_windows_kaiser_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_signbit_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_sinh_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_slice_scatter_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_sort_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_sparse_sampled_addmm_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_special_bessel_j1_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_std_mean_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_sub_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_take_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_to_sparse_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_topk_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_trace_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_tril_indices_cpu_int64, test/test_ops.py::TestFakeTensorCPU::test_fake_triu_indices_cpu_int64, test/test_ops.py::TestFakeTensorCPU::test_fake_unbind_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_unflatten_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_view_as_real_cpu_complex64, test/test_ops.py::TestFakeTensorCPU::test_fake_view_copy_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_where_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_zero__cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_zeros_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_T_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops___getitem___cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops___rmatmul___cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops__softmax_backward_data_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops__unsafe_masked_index_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops__unsafe_masked_index_put_accumulate_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_addmv_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_angle_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_any_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_as_strided_copy_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_atleast_3d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_bernoulli_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_bfloat16_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_bincount_cpu_int64, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_cat_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_char_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_clamp_max_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_conj_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_constant_pad_nd_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_cos_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_cummax_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_cumulative_trapezoid_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_digamma_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_div_trunc_rounding_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_dot_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_dstack_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_erf_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_expm1_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_fft_fftn_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_fft_ifft2_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_fft_ifftn_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_fft_ihfft2_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_fft_irfft_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_fft_irfftn_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_fft_rfft2_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_fft_rfftn_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_flipud_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_floor_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_fmod_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_gt_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_hash_tensor_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_igamma_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_index_copy_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_item_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_jiterator_4inputs_with_extra_args_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_jiterator_binary_return_by_ref_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_lerp_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_lgamma_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_linalg_cholesky_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_linalg_lu_factor_ex_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_linalg_lu_solve_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_linalg_solve_ex_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_linalg_tensorinv_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_linspace_tensor_overload_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_logaddexp2_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_logaddexp_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_logit_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_logspace_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_masked_argmax_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_masked_cumsum_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_masked_fill_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_masked_prod_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_masked_sum_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_masked_var_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_max_pool2d_with_indices_backward_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_median_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_meshgrid_variadic_tensors_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nansum_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_narrow_copy_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_native_layer_norm_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_adaptive_max_pool1d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_avg_pool2d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_avg_pool3d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_batch_norm_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_fractional_max_pool3d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_hardtanh_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_interpolate_bilinear_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_interpolate_nearest-exact_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_interpolate_nearest_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_layer_norm_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_logsigmoid_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_margin_ranking_loss_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_max_pool2d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_max_unpool1d_grad_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_multi_head_attention_forward_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_one_hot_cpu_int64, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_pad_reflect_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_scaled_dot_product_attention_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_smooth_l1_loss_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_norm_nuc_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_permute_copy_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_polygamma_polygamma_n_0_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_polygamma_polygamma_n_2_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_polygamma_polygamma_n_3_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_renorm_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_reshape_as_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_resolve_neg_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_rsub_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_scalar_tensor_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_signal_windows_gaussian_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_signal_windows_general_hamming_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_special_bessel_y0_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_special_chebyshev_polynomial_u_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_special_chebyshev_polynomial_v_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_special_laguerre_polynomial_l_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_special_log_ndtr_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_special_modified_bessel_i0_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_special_modified_bessel_k1_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_special_ndtri_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_special_shifted_chebyshev_polynomial_v_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_squeeze_copy_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_std_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_true_divide_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_unsafe_chunk_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_unsqueeze_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_var_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_xlogy_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_strided_layout__refs_arange_cpu_int16, test/test_ops.py::TestFakeTensorCPU::test_strided_layout__refs_linspace_cpu_complex64, test/test_ops.py::TestFakeTensorCPU::test_strided_layout__refs_linspace_cpu_float16, test/test_ops.py::TestFakeTensorCPU::test_strided_layout__refs_linspace_tensor_overload_cpu_float64, test/test_ops.py::TestFakeTensorCPU::test_strided_layout__refs_linspace_tensor_overload_cpu_int8, test/test_ops.py::TestFakeTensorCPU::test_strided_layout__refs_logspace_cpu_bfloat16, test/test_ops.py::TestFakeTensorCPU::test_strided_layout__refs_logspace_cpu_float16, test/test_ops.py::TestFakeTensorCPU::test_strided_layout__refs_logspace_tensor_overload_cpu_int64, test/test_ops.py::TestFakeTensorCPU::test_strided_layout__refs_logspace_tensor_overload_cpu_int8, test/test_ops.py::TestFakeTensorCPU::test_strided_layout__refs_ones_cpu_int64, test/test_ops.py::TestFakeTensorCPU::test_strided_layout__refs_zeros_cpu_complex32, test/test_ops.py::TestFakeTensorCPU::test_strided_layout_arange_cpu_int32, test/test_ops.py::TestFakeTensorCPU::test_strided_layout_full_cpu_bool, test/test_ops.py::TestFakeTensorCPU::test_strided_layout_full_cpu_int64, test/test_ops.py::TestFakeTensorCPU::test_strided_layout_linspace_cpu_complex64, test/test_ops.py::TestFakeTensorCPU::test_strided_layout_linspace_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_strided_layout_linspace_cpu_uint8, test/test_ops.py::TestFakeTensorCPU::test_strided_layout_linspace_tensor_overload_cpu_float64, test/test_ops.py::TestFakeTensorCPU::test_strided_layout_linspace_tensor_overload_cpu_uint8, test/test_ops.py::TestFakeTensorCPU::test_strided_layout_logspace_tensor_overload_cpu_uint8, test/test_ops.py::TestFakeTensorCPU::test_strided_layout_ones_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_strided_layout_ones_cpu_uint8, test/test_ops.py::TestFakeTensorCPU::test_strided_layout_zeros_cpu_bfloat16, test/test_ops.py::TestFakeTensorCPU::test_strided_layout_zeros_cpu_int64, test/test_ops.py::TestTagsCPU::test_tags___rsub___cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__batch_norm_with_update_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs__conversions_bool_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs__conversions_chalf_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs__conversions_float_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs__conversions_int_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs__conversions_polar_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_addcmul_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_amax_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_broadcast_tensors_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_clamp_min_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_constant_pad_nd_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_copysign_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_cos_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_cosh_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_diag_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_diagonal_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_dsplit_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_expand_copy_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_fft_fftshift_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_fft_hfft_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_fft_ifftn_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_floor_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_fmax_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_hsplit_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_isnan_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_linalg_diagonal_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_log10_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_masked_fill_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_minimum_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_narrow_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_ne_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_neg_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_nn_functional_elu_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_nn_functional_group_norm_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_nn_functional_pixel_shuffle_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_nn_functional_softplus_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_nn_functional_tanhshrink_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_nn_functional_triplet_margin_loss_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_pow_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_rad2deg_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_remainder_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_renorm_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_repeat_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_reshape_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_rsub_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_sigmoid_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_signbit_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_special_i1_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_special_log_ndtr_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_special_multigammaln_mvlgamma_p_5_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_split_with_sizes_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_square_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_squeeze_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_squeeze_multiple_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_std_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_triu_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_triu_indices_cpu_int64, test/test_ops.py::TestTagsCPU::test_tags__refs_unfold_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_unsqueeze_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_vdot_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__segment_reduce_lengths_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__unsafe_masked_index_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_abs_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_addr_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_any_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_as_strided_scatter_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_bitwise_xor_cpu_int64, test/test_ops.py::TestTagsCPU::test_tags_broadcast_shapes_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_cauchy_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_clamp_min_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_clone_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_column_stack_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_conj_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_count_nonzero_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_cummax_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_diag_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_diagonal_scatter_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_empty_permuted_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_erfinv_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_exp_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_expand_as_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_eye_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_fft_fftn_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_fft_hfftn_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_fft_irfft_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_fft_rfftn_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_fliplr_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_float_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_floor_divide_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_full_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_geometric_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_gradient_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_grid_sampler_3d_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_histogram_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_i0_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_igammac_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_isclose_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_ldexp_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_lerp_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_linalg_cholesky_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_linalg_cond_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_linalg_cross_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_linalg_det_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_linalg_ldl_factor_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_linalg_slogdet_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_linalg_svdvals_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_linalg_tensorinv_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_linalg_tensorsolve_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_log10_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_log1p_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_log_normal_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_log_softmax_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_log_softmax_with_dtype_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_logspace_tensor_overload_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_mH_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_masked_argmin_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_masked_cumsum_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_masked_median_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_masked_select_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_masked_sum_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_meshgrid_variadic_tensors_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_min_reduction_with_dim_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_minimum_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_mul_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_adaptive_avg_pool2d_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_binary_cross_entropy_with_logits_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_feature_alpha_dropout_without_train_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_gelu_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_hardtanh_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_interpolate_bilinear_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_kl_div_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_max_pool3d_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_max_unpool1d_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_mse_loss_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_multi_head_attention_forward_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_nll_loss_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_pad_circular_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_pixel_unshuffle_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_soft_margin_loss_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_norm_nuc_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_normal_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_ones_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_ones_like_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_pca_lowrank_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_pinverse_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_polar_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_rand_like_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_real_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_reshape_as_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_reshape_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_resolve_neg_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_scatter_reduce_amin_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_scatter_reduce_prod_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_select_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_signal_windows_blackman_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_signal_windows_cosine_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_signal_windows_general_cosine_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_signal_windows_hann_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_signal_windows_nuttall_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_sinh_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_special_bessel_y1_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_special_log_ndtr_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_special_modified_bessel_k1_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_special_scaled_modified_bessel_k1_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_special_shifted_chebyshev_polynomial_u_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_special_shifted_chebyshev_polynomial_w_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_special_spherical_bessel_j0_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_special_zeta_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_sqrt_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_squeeze_multiple_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_stack_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_std_mean_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_sub_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_tanh_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_tensor_split_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_torch__scaled_mm_cpu_float8_e4m3fn, test/test_ops.py::TestTagsCPU::test_tags_trace_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_transpose_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_true_divide_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_unbind_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_unsqueeze_copy_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_unsqueeze_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_view_as_complex_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_view_as_real_cpu_complex64, test/test_ops.py::TestTagsCPU::test_tags_view_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_zeros_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_zeros_like_cpu_float32 2025-09-07T08:00:53.8577143Z 2025-09-07T08:00:56.4508473Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T08:00:56.4510041Z import pkg_resources 2025-09-07T08:02:53.6328027Z 2025-09-07T08:02:53.6331323Z test_decomp 12/12 was successful, full logs can be found in artifacts with path test/test-reports/test_decomp_12.12_425de9ca3de161c6_.log 2025-09-07T08:02:53.6818231Z Running 737 items in this shard: test/test_decomp.py::TestDecompCPU::test_comprehensive___getitem___cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive___rand___cpu_int64, test/test_decomp.py::TestDecompCPU::test_comprehensive___rdiv___cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive___rpow___cpu_int8, test/test_decomp.py::TestDecompCPU::test_comprehensive___rsub___cpu_complex64, test/test_decomp.py::TestDecompCPU::test_comprehensive___rxor___cpu_int64, test/test_decomp.py::TestDecompCPU::test_comprehensive__batch_norm_with_update_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive__chunk_cat_cpu_bool, test/test_decomp.py::TestDecompCPU::test_comprehensive__chunk_cat_cpu_int16, test/test_decomp.py::TestDecompCPU::test_comprehensive__native_batch_norm_legit_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive__segment_reduce_lengths_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive__unsafe_masked_index_put_accumulate_cpu_int8, test/test_decomp.py::TestDecompCPU::test_comprehensive_abs_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive_acosh_cpu_int32, test/test_decomp.py::TestDecompCPU::test_comprehensive_acosh_cpu_int8, test/test_decomp.py::TestDecompCPU::test_comprehensive_addbmm_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_addbmm_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_addbmm_cpu_int32, test/test_decomp.py::TestDecompCPU::test_comprehensive_addcmul_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_comprehensive_addmv_cpu_int16, 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test/test_decomp.py::TestDecompCPU::test_comprehensive_atanh_cpu_int32, test/test_decomp.py::TestDecompCPU::test_comprehensive_atanh_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_comprehensive_atleast_2d_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_comprehensive_atleast_2d_cpu_int32, test/test_decomp.py::TestDecompCPU::test_comprehensive_atleast_3d_cpu_int8, test/test_decomp.py::TestDecompCPU::test_comprehensive_baddbmm_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_comprehensive_bfloat16_cpu_complex32, test/test_decomp.py::TestDecompCPU::test_comprehensive_bitwise_and_cpu_int8, test/test_decomp.py::TestDecompCPU::test_comprehensive_bitwise_xor_cpu_bool, test/test_decomp.py::TestDecompCPU::test_comprehensive_bmm_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive_bmm_cpu_int8, test/test_decomp.py::TestDecompCPU::test_comprehensive_cartesian_prod_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_comprehensive_cartesian_prod_cpu_float32, 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test/test_decomp.py::TestDecompCPU::test_comprehensive_nn_functional_rrelu_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_nn_functional_smooth_l1_loss_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive_nn_functional_smooth_l1_loss_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_nn_functional_softmin_with_dtype_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_nn_functional_softsign_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_comprehensive_nn_functional_tanhshrink_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_nn_functional_triplet_margin_loss_cpu_int32, test/test_decomp.py::TestDecompCPU::test_comprehensive_nn_functional_triplet_margin_with_distance_loss_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_nn_functional_triplet_margin_with_distance_loss_cpu_int16, 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test/test_decomp.py::TestDecompCPU::test_comprehensive_pinverse_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_polygamma_polygamma_n_2_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_polygamma_polygamma_n_2_cpu_int64, test/test_decomp.py::TestDecompCPU::test_comprehensive_polygamma_polygamma_n_4_cpu_int16, test/test_decomp.py::TestDecompCPU::test_comprehensive_pow_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_put_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_comprehensive_put_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_rad2deg_cpu_int8, test/test_decomp.py::TestDecompCPU::test_comprehensive_randint_like_cpu_int16, test/test_decomp.py::TestDecompCPU::test_comprehensive_randn_like_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_comprehensive_reciprocal_cpu_int8, test/test_decomp.py::TestDecompCPU::test_comprehensive_repeat_cpu_complex64, 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test/test_decomp.py::TestDecompCPU::test_comprehensive_sinh_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_sinh_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_comprehensive_slice_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive_softmax_with_dtype_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_softmax_with_dtype_cpu_int8, test/test_decomp.py::TestDecompCPU::test_comprehensive_sort_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_special_bessel_j0_cpu_bool, test/test_decomp.py::TestDecompCPU::test_comprehensive_special_bessel_y1_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_special_chebyshev_polynomial_t_cpu_int32, test/test_decomp.py::TestDecompCPU::test_comprehensive_special_chebyshev_polynomial_u_cpu_int8, test/test_decomp.py::TestDecompCPU::test_comprehensive_special_chebyshev_polynomial_v_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_special_chebyshev_polynomial_w_cpu_int8, test/test_decomp.py::TestDecompCPU::test_comprehensive_special_entr_cpu_int64, test/test_decomp.py::TestDecompCPU::test_comprehensive_special_erfcx_cpu_int64, test/test_decomp.py::TestDecompCPU::test_comprehensive_special_i0e_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive_special_i1_cpu_bool, test/test_decomp.py::TestDecompCPU::test_comprehensive_special_i1_cpu_int64, test/test_decomp.py::TestDecompCPU::test_comprehensive_special_i1e_cpu_bool, test/test_decomp.py::TestDecompCPU::test_comprehensive_special_i1e_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_comprehensive_special_legendre_polynomial_p_cpu_bool, test/test_decomp.py::TestDecompCPU::test_comprehensive_special_modified_bessel_i1_cpu_int64, test/test_decomp.py::TestDecompCPU::test_comprehensive_special_modified_bessel_k0_cpu_bool, 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test/test_decomp.py::TestDecompCPU::test_comprehensive_take_along_dim_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive_take_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_tile_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_comprehensive_to_sparse_cpu_int16, test/test_decomp.py::TestDecompCPU::test_comprehensive_to_sparse_cpu_int32, test/test_decomp.py::TestDecompCPU::test_comprehensive_torch_ops_aten__safe_softmax_default_cpu_int64, test/test_decomp.py::TestDecompCPU::test_comprehensive_trace_cpu_int8, test/test_decomp.py::TestDecompCPU::test_comprehensive_trapezoid_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_comprehensive_trapz_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive_trapz_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_comprehensive_tril_cpu_int64, test/test_decomp.py::TestDecompCPU::test_comprehensive_true_divide_cpu_int16, 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test/test_decomp.py::TestDecompCPU::test_comprehensive_unsqueeze_copy_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_comprehensive_vdot_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_vdot_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_comprehensive_view_as_complex_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_view_copy_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_view_copy_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_view_cpu_bool, test/test_decomp.py::TestDecompCPU::test_comprehensive_view_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_comprehensive_vsplit_cpu_bool, test/test_decomp.py::TestDecompCPU::test_comprehensive_vsplit_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_where_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_comprehensive_where_cpu_int64, test/test_decomp.py::TestDecompCPU::test_comprehensive_zeros_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick__chunk_cat_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick__chunk_cat_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick__native_batch_norm_legit_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick__unsafe_masked_index_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick__unsafe_masked_index_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick__unsafe_masked_index_put_accumulate_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_acos_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_acos_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_quick_acos_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_acosh_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_addcmul_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_addmm_decomposed_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_quick_addmm_decomposed_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_alias_copy_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_amin_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_any_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_as_strided_scatter_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_as_strided_scatter_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_atan_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_quick_bitwise_not_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_bitwise_xor_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_bitwise_xor_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_block_diag_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_cat_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_cauchy_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_clamp_min_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_clamp_min_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_constant_pad_nd_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_copysign_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_core_backward_block_diag_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_core_backward_clamp_max_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_core_backward_clamp_min_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_core_backward_dot_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_core_backward_expand_copy_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_core_backward_linalg_cross_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_core_backward_mvlgamma_mvlgamma_p_1_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_core_backward_mvlgamma_mvlgamma_p_5_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_core_backward_nn_functional_max_unpool2d_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_core_backward_nn_functional_unfold_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_core_backward_rad2deg_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_core_backward_rsub_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_core_backward_std_unbiased_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_core_backward_transpose_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_count_nonzero_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_count_nonzero_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_count_nonzero_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_cumprod_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_deg2rad_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_diag_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_diag_embed_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_diagonal_copy_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_diagonal_scatter_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_quick_diagonal_scatter_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_digamma_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_dist_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_div_floor_rounding_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_div_no_rounding_mode_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_quick_div_trunc_rounding_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_dot_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_empty_like_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_empty_like_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_empty_like_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_eq_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_erf_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_erf_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_exp2_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_quick_exp2_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_exp_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_expm1_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_quick_eye_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_quick_fft_hfft2_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_quick_fft_hfft_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_fft_hfft_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_fft_hfftn_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_fft_ifft2_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_quick_fft_ifftn_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_quick_fft_ifftn_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_fft_ifftn_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_fft_ihfft_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_fft_ihfft_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_fft_ihfft_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_fft_ihfftn_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_fft_ihfftn_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_fft_irfft2_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_fft_irfft2_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_fft_irfft_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_fill_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_flip_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_floor_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_fmin_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_fmin_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_fmod_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_geometric_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_hypot_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_igamma_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_igammac_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_index_add_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_index_copy_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_index_fill_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_index_select_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_index_select_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_quick_index_select_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_isnan_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_isnan_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_isneginf_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_isposinf_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_lcm_cpu_int64, 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test/test_decomp.py::TestDecompCPU::test_quick_sgn_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_sgn_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_sgn_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_sigmoid_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_sigmoid_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_sigmoid_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_signbit_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_sinc_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_quick_sinc_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_sinh_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_slice_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_slice_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_slice_scatter_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_slice_scatter_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_special_entr_cpu_float64, 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test/test_decomp.py::TestDecompCPU::test_quick_stack_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_std_unbiased_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_std_unbiased_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_sum_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_sum_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_t_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_tanh_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_tanh_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_transpose_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_transpose_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_tril_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_tril_indices_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_triu_indices_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_trunc_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_trunc_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_unbind_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_unfold_cpu_complex32, test/test_decomp.py::TestDecompCPU::test_quick_uniform_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_uniform_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_unsafe_split_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_unsqueeze_copy_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_unsqueeze_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_quick_unsqueeze_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_var_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_quick_var_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_var_unbiased_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_view_copy_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_view_copy_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_where_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_where_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_zeros_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_zeros_like_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_rnn_decomp_module_nn_RNN_train_mode_cpu_float32, test/test_decomp.py::DecompOneOffTestsCPU::test_contiguous_softmax_cpu, test/test_decomp.py::DecompOneOffTestsCPU::test_rms_norm_decomp_cuda_cpu, test/test_decomp.py::DecompOneOffTestsCPU::test_sdpa_nn_functional_scaled_dot_product_attention_cpu_float64 2025-09-07T08:02:53.7299451Z 2025-09-07T08:02:54.1863533Z Uploading artifacts took 0.55 seconds 2025-09-07T08:02:56.8626119Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T08:02:56.8627776Z import pkg_resources 2025-09-07T08:03:26.3101063Z 2025-09-07T08:03:26.3103627Z test_ops 6/6 was successful, full logs can be found in artifacts with path test/test-reports/test_ops_6.6_afc85a981f8d59d0_.log 2025-09-07T08:03:26.5118256Z Running 5797 items in this shard: test/test_ops.py::TestCommonCPU::test_compare_cpu___rand___cpu_int64, test/test_ops.py::TestCommonCPU::test_compare_cpu___rdiv___cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu___rsub___cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__chunk_cat_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__native_batch_norm_legit_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs__conversions_chalf_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs__conversions_half_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_addr_cpu_float32, 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test/test_ops.py::TestCommonCPU::test_compare_cpu__unsafe_masked_index_put_accumulate_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_addcdiv_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_addmv_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_arange_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_cfloat_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_chunk_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_combinations_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_complex_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_cross_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_cumulative_trapezoid_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_dist_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_empty_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_empty_like_cpu_float32, 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test/test_ops.py::TestCommonCPU::test_complex_half_reference_testing_movedim_cpu_complex32, test/test_ops.py::TestCommonCPU::test_complex_half_reference_testing_new_empty_strided_cpu_complex32, test/test_ops.py::TestCommonCPU::test_complex_half_reference_testing_permute_copy_cpu_complex32, test/test_ops.py::TestCommonCPU::test_complex_half_reference_testing_resolve_neg_cpu_complex32, test/test_ops.py::TestCommonCPU::test_complex_half_reference_testing_scalar_tensor_cpu_complex32, test/test_ops.py::TestCommonCPU::test_complex_half_reference_testing_squeeze_multiple_cpu_complex32, test/test_ops.py::TestCommonCPU::test_complex_half_reference_testing_triu_cpu_complex32, test/test_ops.py::TestCommonCPU::test_complex_half_reference_testing_unfold_cpu_complex32, test/test_ops.py::TestCommonCPU::test_complex_half_reference_testing_view_as_cpu_complex32, test/test_ops.py::TestCommonCPU::test_complex_half_reference_testing_view_cpu_complex32, test/test_ops.py::TestCommonCPU::test_dtypes_T_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__chunk_cat_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs__conversions_byte_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs__conversions_double_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_allclose_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_as_strided_partial_views_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_atanh_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_bitwise_not_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_bitwise_or_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_bitwise_right_shift_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_bucketize_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_cat_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_clamp_min_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_contiguous_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_copysign_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_count_nonzero_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_diagonal_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_diagonal_scatter_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_div_floor_rounding_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_eq_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_exponential_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_fft_fftn_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_fft_rfftn_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_flatten_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_flip_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_flipud_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_floor_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_gt_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_hstack_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_index_select_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_isneginf_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_isreal_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_lerp_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_linalg_cross_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_logaddexp_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_logspace_tensor_overload_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_narrow_copy_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_native_layer_norm_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_nn_functional_celu_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_nn_functional_glu_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_nn_functional_huber_loss_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_nn_functional_log_softmax_with_dtype_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_nn_functional_mse_loss_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_nn_functional_poisson_nll_loss_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_nn_functional_softshrink_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_permute_copy_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_permute_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_remainder_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_renorm_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_select_scatter_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_sgn_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_special_log_ndtr_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_special_logit_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_sqrt_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_squeeze_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_std_mean_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_stft_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_sum_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_sum_to_size_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_t_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_var_mean_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_vdot_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_view_copy_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_where_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__segment_reduce_lengths_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__segment_reduce_offsets_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__softmax_backward_data_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__unsafe_masked_index_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_addmm_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_amin_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_angle_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_any_cpu, 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test/test_ops.py::TestCommonCPU::test_dtypes_nn_functional_margin_ranking_loss_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_nn_functional_pixel_unshuffle_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_nn_functional_relu_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_nn_functional_silu_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_nn_functional_triplet_margin_with_distance_loss_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_nonzero_static_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_norm_inf_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_pinverse_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_polygamma_polygamma_n_0_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_polygamma_polygamma_n_4_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_positive_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_pow_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_qr_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_quantile_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_randn_like_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_real_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_repeat_interleave_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_reshape_as_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_resolve_neg_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_round_decimals_neg_3_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_sgn_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_short_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_sign_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_signal_windows_cosine_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_signal_windows_hamming_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_sinc_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_special_bessel_j1_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_special_entr_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_special_hermite_polynomial_he_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_special_legendre_polynomial_p_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_special_log_ndtr_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_special_modified_bessel_i1_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_split_list_args_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_split_with_sizes_copy_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_sub_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_take_along_dim_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_torch_ops_aten__efficient_attention_forward_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_torch_ops_aten__flash_attention_forward_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_tril_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_tril_indices_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_var_mean_unbiased_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_view_as_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_zeros_like_cpu, test/test_ops.py::TestCommonCPU::test_errors_T_cpu, test/test_ops.py::TestCommonCPU::test_errors___rxor___cpu, test/test_ops.py::TestCommonCPU::test_errors_add_cpu, test/test_ops.py::TestCommonCPU::test_errors_arange_cpu, test/test_ops.py::TestCommonCPU::test_errors_bitwise_or_cpu, test/test_ops.py::TestCommonCPU::test_errors_bitwise_right_shift_cpu, test/test_ops.py::TestCommonCPU::test_errors_clamp_max_cpu, test/test_ops.py::TestCommonCPU::test_errors_diag_embed_cpu, test/test_ops.py::TestCommonCPU::test_errors_diagonal_copy_cpu, test/test_ops.py::TestCommonCPU::test_errors_dsplit_cpu, test/test_ops.py::TestCommonCPU::test_errors_fft_fftn_cpu, test/test_ops.py::TestCommonCPU::test_errors_fft_ihfft2_cpu, test/test_ops.py::TestCommonCPU::test_errors_fft_rfft2_cpu, test/test_ops.py::TestCommonCPU::test_errors_flipud_cpu, test/test_ops.py::TestCommonCPU::test_errors_ge_cpu, test/test_ops.py::TestCommonCPU::test_errors_jiterator_binary_cpu, 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test/test_ops.py::TestCommonCPU::test_errors_signal_windows_blackman_cpu, test/test_ops.py::TestCommonCPU::test_errors_signal_windows_gaussian_cpu, test/test_ops.py::TestCommonCPU::test_errors_sparse_mul_layout1_cpu, test/test_ops.py::TestCommonCPU::test_errors_sparse_mul_layout2_cpu, test/test_ops.py::TestCommonCPU::test_errors_sparse_randn_like_layout1_cpu, test/test_ops.py::TestCommonCPU::test_errors_sparse_randn_like_layout4_cpu, test/test_ops.py::TestCommonCPU::test_errors_sparse_sum_layout3_cpu, test/test_ops.py::TestCommonCPU::test_errors_special_chebyshev_polynomial_t_cpu, test/test_ops.py::TestCommonCPU::test_errors_special_hermite_polynomial_h_cpu, test/test_ops.py::TestCommonCPU::test_errors_special_hermite_polynomial_he_cpu, test/test_ops.py::TestCommonCPU::test_errors_special_laguerre_polynomial_l_cpu, test/test_ops.py::TestCommonCPU::test_errors_special_shifted_chebyshev_polynomial_v_cpu, test/test_ops.py::TestCommonCPU::test_errors_t_copy_cpu, test/test_ops.py::TestCommonCPU::test_errors_trace_cpu, test/test_ops.py::TestCommonCPU::test_errors_view_as_cpu, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_arange_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_argmax_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_argmin_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_column_stack_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_cummax_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_empty_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_erfc_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_floor_divide_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_fmax_cpu_float32, 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test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_linalg_lu_solve_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_linalg_matrix_power_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_linalg_matrix_rank_hermitian_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_linalg_pinv_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_linalg_slogdet_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_linalg_tensorinv_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_linspace_tensor_overload_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_log_softmax_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_log_softmax_with_dtype_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_masked_select_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_matmul_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_max_reduction_with_dim_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_mv_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_mvlgamma_mvlgamma_p_1_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_nanquantile_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_native_batch_norm_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_nn_functional_avg_pool3d_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_nn_functional_gelu_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_nn_functional_hardshrink_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_nn_functional_softshrink_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_ormqr_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_polygamma_polygamma_n_4_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_rad2deg_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_rsqrt_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_scatter_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_searchsorted_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_signbit_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_special_chebyshev_polynomial_v_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_special_chebyshev_polynomial_w_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_special_erfcx_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_special_laguerre_polynomial_l_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_special_legendre_polynomial_p_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_special_modified_bessel_i0_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_special_scaled_modified_bessel_k1_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_special_spherical_bessel_j0_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_squeeze_copy_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_sub_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_svd_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_true_divide_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_unbind_copy_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices___rmatmul___cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_abs_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_acosh_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_acosh_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_add_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_alias_copy_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_aminmax_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_any_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_argsort_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_argsort_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_as_strided_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_as_strided_partial_views_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_asinh_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_atanh_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_atleast_1d_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_bernoulli_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_bitwise_and_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_bitwise_left_shift_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_bool_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_broadcast_tensors_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_broadcast_to_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_bucketize_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_ceil_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_char_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_char_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_cholesky_solve_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_chunk_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_clamp_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_clamp_max_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_column_stack_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_combinations_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_complex_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_conj_physical_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_constant_pad_nd_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_cos_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_count_nonzero_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_cummax_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_cumprod_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_cumulative_trapezoid_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_diag_embed_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_digamma_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_einsum_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_empty_like_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_empty_strided_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_eq_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_erfc_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_exp2_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_expand_as_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_expand_copy_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_eye_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_fft_fftshift_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_fft_hfft2_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_fft_ifft2_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_fft_ihfft2_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_fft_irfft_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_fft_rfftn_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_fliplr_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_float_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_float_power_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_floor_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_fmax_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_fmin_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_frexp_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_full_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_gather_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_gradient_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_grid_sampler_3d_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_hash_tensor_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_histogram_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_igamma_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_index_fill_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_index_put_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_index_reduce_amax_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_index_reduce_amin_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_index_reduce_prod_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_inner_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_isposinf_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_item_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_jiterator_2inputs_2outputs_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_jiterator_unary_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_kron_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_le_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_linalg_cholesky_ex_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_linalg_eig_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_linalg_eigvals_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_linalg_inv_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_linalg_lstsq_grad_oriented_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_linalg_vector_norm_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_log1p_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_log1p_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_log_softmax_with_dtype_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_logdet_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_logspace_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_lu_unpack_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_mH_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_masked_argmax_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_masked_cumprod_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_masked_mean_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_masked_normalize_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_masked_select_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_masked_std_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_max_reduction_no_dim_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_max_reduction_with_dim_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_mean_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_min_reduction_no_dim_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_min_reduction_with_dim_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_mm_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_multinomial_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_mv_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nanmedian_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nanquantile_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_narrow_copy_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_native_dropout_backward_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_neg_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_new_ones_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nextafter_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_cosine_embedding_loss_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_elu_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_feature_alpha_dropout_without_train_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_hardsigmoid_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_hardtanh_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_interpolate_bilinear_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_interpolate_trilinear_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_l1_loss_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_leaky_relu_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_max_pool1d_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_max_pool2d_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_max_pool3d_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_pad_constant_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_pad_reflect_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_relu6_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_softmin_with_dtype_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_upsample_bilinear_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nonzero_static_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_norm_nuc_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_normal_number_mean_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_ones_like_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_permute_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_polygamma_polygamma_n_0_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_polygamma_polygamma_n_4_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_polygamma_polygamma_n_4_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_qr_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_quantile_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_repeat_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_resize_as__cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_resolve_conj_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_resolve_conj_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_resolve_neg_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_round_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_scatter_add_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_scatter_reduce_amax_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_scatter_reduce_mean_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_searchsorted_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_select_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_select_scatter_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_short_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_signal_windows_cosine_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_signal_windows_gaussian_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_signal_windows_general_cosine_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_signal_windows_hamming_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_signbit_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_sin_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_slice_scatter_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_softmax_with_dtype_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_softmax_with_dtype_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_special_bessel_j1_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_special_chebyshev_polynomial_u_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_special_chebyshev_polynomial_v_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_special_chebyshev_polynomial_w_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_special_erfcx_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_special_i1_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_special_ndtri_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_special_scaled_modified_bessel_k1_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_special_shifted_chebyshev_polynomial_t_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_special_shifted_chebyshev_polynomial_u_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_split_list_args_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_split_with_sizes_copy_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_split_with_sizes_copy_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_square_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_squeeze_multiple_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_squeeze_multiple_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_std_mean_unbiased_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_sub_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_t_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_tan_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_tensor_split_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_to_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_to_sparse_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_to_sparse_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_transpose_copy_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_trapezoid_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_unflatten_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_unfold_copy_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_unique_consecutive_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_unravel_index_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_unsqueeze_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_var_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_var_unbiased_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_view_copy_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_xlogy_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_zeros_like_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_zeros_like_cpu_int64, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_acos_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_acosh_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_add_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_argsort_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_bitwise_and_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_bitwise_not_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_bitwise_or_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_bool_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_broadcast_to_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_chunk_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_cosh_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_count_nonzero_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_digamma_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_erfinv_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_isposinf_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_jiterator_binary_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_lgamma_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_log_softmax_with_dtype_cpu_bool, 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test/test_ops.py::TestMathBitsCPU::test_conj_view_reciprocal_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_resize__cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_sinh_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_std_unbiased_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_stft_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_sum_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_svd_lowrank_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_t_copy_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_tensor_split_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_tensordot_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_true_divide_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_uniform_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_vdot_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_vsplit_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_zero__cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view___radd___cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view___rmatmul___cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs__conversions_float_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs__conversions_long_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_abs_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_any_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_as_strided_partial_views_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_asin_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_chunk_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_clone_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_conj_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_cos_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_empty_like_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_expand_copy_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_fft_fft2_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_fft_hfft_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_fft_ifftshift_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_item_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_linalg_diagonal_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_linalg_matrix_norm_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_linalg_vecdot_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_log_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_logical_or_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_meshgrid_variadic_tensors_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_nn_functional_pairwise_distance_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_normal__in_place_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_pow_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_prod_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_real_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_reciprocal_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_reshape_as_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_rot90_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_softmax_with_dtype_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_split_with_sizes_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_squeeze_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_sub_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_to_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_transpose_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__unsafe_masked_index_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_addbmm_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_addr_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_allclose_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_as_strided_scatter_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_asinh_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_atleast_3d_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_baddbmm_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_bool_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_broadcast_to_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_cdouble_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_cfloat_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_cholesky_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_chunk_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_contiguous_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_cumprod_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_cumsum_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_diag_embed_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_exp_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_fft_fft2_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_fft_hfftn_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_fft_ifftn_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_flip_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_geqrf_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_gradient_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_index_put_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_isclose_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_isreal_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_item_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_linalg_cholesky_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_linalg_eigh_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_linalg_eigvalsh_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_linalg_lu_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_linalg_solve_ex_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_linalg_svd_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_linalg_vector_norm_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_log1p_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_logaddexp_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_logcumsumexp_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_mH_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_masked_cumprod_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_masked_select_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_masked_sum_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_matmul_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_meshgrid_list_of_tensors_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_meshgrid_variadic_tensors_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_mv_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_narrow_copy_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_neg_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_new_empty_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_new_ones_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_new_zeros_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_nn_functional_conv1d_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_nonzero_static_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_ones_like_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_pow_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_prod_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_rand_like_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_randn_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_rsqrt_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_rsub_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_scatter_add_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_select_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_sigmoid_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_split_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_split_list_args_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_squeeze_multiple_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_sum_to_size_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_take_along_dim_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_to_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_transpose_copy_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_unbind_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_var_mean_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_zeros_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_view___getitem___cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view___rdiv___cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view___rmatmul___cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs__conversions_byte_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs__conversions_cfloat_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs__conversions_float_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_addr_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_amin_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_broadcast_tensors_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_clamp_max_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_contiguous_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_cos_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_diag_embed_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_digamma_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_div_no_rounding_mode_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_erfc_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_expm1_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_fft_fftshift_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_fft_ifft2_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_fft_irfft2_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_fill_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_flip_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_floor_divide_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_frexp_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_ge_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_gt_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_heaviside_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_hsplit_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_igamma_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_index_add_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_lerp_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_lgamma_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_linalg_matrix_norm_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_linalg_svd_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_log2_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_log_normal_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_meshgrid_variadic_tensors_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_movedim_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_nan_to_num_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_ne_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_new_empty_strided_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_nn_functional_dropout_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_nn_functional_hardtanh_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_nn_functional_layer_norm_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_nn_functional_margin_ranking_loss_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_nn_functional_nll_loss_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_nn_functional_pairwise_distance_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_nn_functional_poisson_nll_loss_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_nn_functional_softmin_with_dtype_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_nn_functional_softshrink_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_normal__in_place_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_normal_number_mean_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_positive_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_pow_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_prod_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_rad2deg_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_reshape_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_rsub_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_sigmoid_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_sign_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_special_i0e_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_std_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_sub_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_trace_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_tril_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_triu_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_unbind_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_unflatten_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_view_as_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_view_copy_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_zeros_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__softmax_backward_data_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__unsafe_masked_index_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_acosh_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_addmv_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_angle_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_any_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_argsort_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_atleast_3d_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_bfloat16_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_broadcast_to_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_cdist_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_clamp_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_column_stack_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_combinations_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_conj_physical_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_cross_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_cumprod_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_diagflat_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_dsplit_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_einsum_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_empty_like_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_eq_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_erfinv_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_expand_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_fft_ihfftn_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_fft_irfft_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_flatten_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_geqrf_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_gradient_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_grid_sampler_3d_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_gt_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_igamma_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_igammac_cpu_float64, 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test/test_ops.py::TestFakeTensorCPU::test_fake_addbmm_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_alias_copy_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_allclose_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_angle_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_any_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_as_strided_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_as_strided_scatter_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_T_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast___rpow___cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast__batch_norm_with_update_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast__segment_reduce_lengths_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_addmm_decomposed_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_addr_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_argmin_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_bitwise_or_cpu_int64, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_bool_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_byte_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_cholesky_solve_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_clamp_max_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_clone_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_cov_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_cross_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_cumulative_trapezoid_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_diagflat_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_diagonal_scatter_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_div_trunc_rounding_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_equal_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_fft_hfft2_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_fft_ihfft_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_fft_ihfftn_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_fft_rfftn_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_flipud_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_frac_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_gcd_cpu_int64, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_hstack_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_igamma_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_index_copy_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_int_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_isnan_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_jiterator_binary_return_by_ref_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_linalg_diagonal_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_linalg_lstsq_grad_oriented_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_linalg_matrix_norm_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_linalg_tensorinv_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_linalg_tensorsolve_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_linalg_vecdot_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_log2_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_logical_or_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_lt_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_masked_cumprod_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_masked_cumsum_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_masked_mean_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_masked_scatter_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_min_binary_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_mul_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_narrow_copy_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_new_empty_strided_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_nextafter_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_nn_functional_adaptive_max_pool3d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_nn_functional_avg_pool3d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_nn_functional_batch_norm_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_nn_functional_conv2d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_nn_functional_feature_alpha_dropout_without_train_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_nn_functional_fractional_max_pool2d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_nn_functional_fractional_max_pool3d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_nn_functional_grid_sample_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_nn_functional_group_norm_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_nn_functional_hardswish_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_nn_functional_interpolate_nearest_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_nn_functional_l1_loss_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_nn_functional_linear_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_nn_functional_max_pool2d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_nn_functional_max_unpool3d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_nn_functional_mish_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_nn_functional_multilabel_margin_loss_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_nn_functional_pad_reflect_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_nn_functional_pad_replicate_negative_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_nn_functional_pixel_shuffle_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_nn_functional_scaled_dot_product_attention_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_nn_functional_triplet_margin_loss_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_nn_functional_unfold_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_nonzero_static_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_norm_inf_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_pca_lowrank_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_permute_copy_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_pinverse_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_prod_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_qr_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_randint_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_randn_like_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_resize__cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_scatter_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_searchsorted_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_signal_windows_general_hamming_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_sinc_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_sort_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_special_chebyshev_polynomial_t_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_special_hermite_polynomial_he_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_special_laguerre_polynomial_l_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_special_modified_bessel_k0_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_special_ndtri_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_special_scaled_modified_bessel_k0_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_special_shifted_chebyshev_polynomial_w_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_sqrt_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_svd_lowrank_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_take_along_dim_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_tensor_split_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_to_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_trace_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_triu_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_triu_indices_cpu_int64, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_true_divide_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_unbind_copy_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_unfold_copy_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_unravel_index_cpu_int64, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_zero__cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_zeros_like_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_bitwise_right_shift_cpu_int64, test/test_ops.py::TestFakeTensorCPU::test_fake_broadcast_to_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_cat_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_cholesky_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_chunk_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_contiguous_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_cov_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_T_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp___rsub___cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_abs_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_addcmul_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_addr_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_as_strided_partial_views_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_as_strided_scatter_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_atleast_1d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_cholesky_inverse_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_clamp_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_double_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_dsplit_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_dstack_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_erf_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_expand_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_expm1_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_fft_hfft2_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_fft_irfftn_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_fft_rfftn_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_float_power_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_frac_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_hsplit_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_index_reduce_amax_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_index_reduce_prod_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_ldexp_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_lerp_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_linalg_cross_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_linalg_eigvalsh_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_linalg_lstsq_grad_oriented_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_log1p_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_masked_std_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_masked_sum_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_max_reduction_no_dim_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_mode_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nanquantile_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nansum_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_neg_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_adaptive_avg_pool2d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_adaptive_max_pool1d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_conv1d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_conv_transpose3d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_hardswish_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_interpolate_area_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_interpolate_bicubic_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_interpolate_linear_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_multilabel_margin_loss_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_multilabel_soft_margin_loss_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_nll_loss_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_normalize_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_pad_circular_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_pad_replicate_negative_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_pdist_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_rrelu_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_selu_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_softplus_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_softshrink_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_norm_inf_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_normal_number_mean_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_ormqr_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_pca_lowrank_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_polygamma_polygamma_n_0_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_polygamma_polygamma_n_4_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_put_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_qr_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_quantile_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_rad2deg_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_ravel_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_remainder_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_renorm_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_repeat_interleave_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_reshape_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_resolve_neg_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_roll_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_round_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_scatter_reduce_sum_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_sign_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_sparse_mm_reduce_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_special_log_ndtr_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_sqrt_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_topk_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_trace_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_triu_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_var_unbiased_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_vdot_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_view_as_complex_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_vstack_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_where_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp___radd___cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp___rmul___cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp___rpow___cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp__batch_norm_with_update_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_addmm_decomposed_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_amax_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_block_diag_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_cartesian_prod_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_cdouble_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_ceil_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_cholesky_solve_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_combinations_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_complex_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_constant_pad_nd_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_contiguous_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_cosh_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_cummin_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_diag_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_diagflat_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_digamma_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_erfc_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_fft_fft2_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_fft_rfftn_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_fmin_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_half_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_ldexp_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_lgamma_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_linalg_cholesky_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_linalg_det_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_linalg_eig_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_linalg_eigvals_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_linalg_householder_product_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_linalg_lu_factor_ex_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_linalg_solve_triangular_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_linalg_svd_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_linalg_tensorinv_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_linalg_vecdot_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_linalg_vector_norm_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_log_softmax_with_dtype_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_logaddexp2_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_logit_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_logsumexp_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_lu_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_lu_unpack_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_masked_amax_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_masked_cumprod_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_masked_median_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_masked_select_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_masked_std_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_masked_sum_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_masked_var_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_max_reduction_with_dim_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_meshgrid_variadic_tensors_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nanquantile_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nansum_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_narrow_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_adaptive_max_pool1d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_avg_pool2d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_celu_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_dropout2d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_embedding_bag_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_glu_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_hardswish_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_interpolate_bicubic_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_interpolate_linear_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_leaky_relu_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_margin_ranking_loss_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_max_pool2d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_max_unpool2d_grad_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_mish_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_mse_loss_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_pixel_unshuffle_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_smooth_l1_loss_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_soft_margin_loss_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_polar_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_polygamma_polygamma_n_1_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_pow_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_qr_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_ravel_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_reshape_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_slice_scatter_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_sparse_mm_reduce_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_special_entr_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_special_i1_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_squeeze_copy_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_svd_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_take_along_dim_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_tile_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_trapezoid_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_triangular_solve_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_unbind_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_var_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_view_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_vstack_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_cummin_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_diag_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_diagonal_copy_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_diagonal_scatter_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_dsplit_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_empty_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_expm1_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_fft_fft2_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_fft_fftn_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_fft_ihfft_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_fft_irfft2_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_floor_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_fmin_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_frexp_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_full_like_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_histc_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_index_copy_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_index_reduce_prod_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_istft_cpu_complex64, test/test_ops.py::TestFakeTensorCPU::test_fake_kron_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_lgamma_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_linalg_det_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_linalg_lstsq_grad_oriented_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_linalg_lu_factor_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_linalg_pinv_singular_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_linspace_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_logaddexp2_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_logsumexp_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_masked_argmin_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_masked_softmin_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_min_reduction_with_dim_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_mvlgamma_mvlgamma_p_5_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_native_layer_norm_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_adaptive_avg_pool3d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_conv2d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_cross_entropy_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_fractional_max_pool2d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_gaussian_nll_loss_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_hardtanh_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_hinge_embedding_loss_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_l1_loss_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_local_response_norm_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_margin_ranking_loss_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_max_pool2d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_max_pool3d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_nll_loss_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_pad_replicate_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_pairwise_distance_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_pdist_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_silu_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_triplet_margin_loss_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_ormqr_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_polygamma_polygamma_n_0_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_rand_like_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_randint_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_randint_like_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_round_decimals_0_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_round_decimals_neg_3_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_rsub_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_sgn_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_signal_windows_nuttall_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_softmax_with_dtype_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_special_chebyshev_polynomial_v_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_special_hermite_polynomial_h_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_special_laguerre_polynomial_l_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_special_ndtr_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_special_scaled_modified_bessel_k0_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_special_shifted_chebyshev_polynomial_w_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_special_xlog1py_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_squeeze_copy_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_squeeze_multiple_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_sum_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_t_copy_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_t_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_tan_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_transpose_copy_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_transpose_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_trapz_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_unsafe_split_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_unsqueeze_copy_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_var_mean_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_view_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_vsplit_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_vstack_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_xlogy_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops___rmod___cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops__chunk_cat_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_addbmm_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_addr_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_all_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_amin_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_as_strided_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_atan2_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_atanh_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_baddbmm_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_bitwise_or_cpu_int64, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_bitwise_right_shift_cpu_int64, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_cartesian_prod_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_cauchy_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_cdist_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_chalf_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_column_stack_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_conj_physical_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_einsum_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_erfc_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_expand_copy_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_expand_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_exponential_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_fft_fftshift_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_fft_hfftn_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_fft_rfft_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_fill_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_flatten_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_flip_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_frac_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_grid_sampler_3d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_half_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_hypot_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_isclose_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_isin_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_jiterator_unary_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_linalg_det_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_linalg_householder_product_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_linalg_matrix_norm_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_linalg_matrix_rank_hermitian_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_logspace_tensor_overload_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_masked_argmin_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_masked_median_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_masked_norm_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_mean_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_mvlgamma_mvlgamma_p_3_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nan_to_num_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nanmean_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_native_dropout_backward_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_new_empty_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_new_ones_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nextafter_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_dropout2d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_embedding_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_feature_alpha_dropout_with_train_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_huber_loss_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_instance_norm_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_interpolate_trilinear_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_l1_loss_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_max_unpool2d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_max_unpool3d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_relu_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_silu_complex_cpu_complex64, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_soft_margin_loss_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_softplus_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nonzero_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_ones_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_ormqr_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_permute_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_polygamma_polygamma_n_1_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_pow_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_repeat_interleave_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_resize__cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_resolve_conj_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_scatter_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_signal_windows_bartlett_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_slice_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_softmax_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_softmax_with_dtype_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_special_chebyshev_polynomial_w_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_special_hermite_polynomial_he_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_special_i1e_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_special_spherical_bessel_j0_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_split_with_sizes_copy_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_split_with_sizes_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_sqrt_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_squeeze_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_squeeze_multiple_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_torch__scaled_mm_cpu_float8_e4m3fn, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_tril_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_triu_indices_cpu_int64, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_unfold_copy_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_view_as_complex_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_view_as_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_vsplit_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_strided_layout__refs_arange_cpu_bfloat16, test/test_ops.py::TestFakeTensorCPU::test_strided_layout__refs_linspace_cpu_complex128, test/test_ops.py::TestFakeTensorCPU::test_strided_layout__refs_linspace_cpu_int64, test/test_ops.py::TestFakeTensorCPU::test_strided_layout__refs_linspace_tensor_overload_cpu_float16, test/test_ops.py::TestFakeTensorCPU::test_strided_layout__refs_linspace_tensor_overload_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_strided_layout__refs_logspace_tensor_overload_cpu_float16, test/test_ops.py::TestFakeTensorCPU::test_strided_layout__refs_ones_cpu_bfloat16, test/test_ops.py::TestFakeTensorCPU::test_strided_layout__refs_ones_cpu_complex128, test/test_ops.py::TestFakeTensorCPU::test_strided_layout__refs_ones_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_strided_layout__refs_ones_cpu_int32, test/test_ops.py::TestFakeTensorCPU::test_strided_layout__refs_zeros_cpu_bfloat16, test/test_ops.py::TestFakeTensorCPU::test_strided_layout__refs_zeros_cpu_bool, test/test_ops.py::TestFakeTensorCPU::test_strided_layout__refs_zeros_cpu_complex64, test/test_ops.py::TestFakeTensorCPU::test_strided_layout__refs_zeros_cpu_float64, test/test_ops.py::TestFakeTensorCPU::test_strided_layout_full_cpu_int16, test/test_ops.py::TestFakeTensorCPU::test_strided_layout_full_cpu_uint8, test/test_ops.py::TestFakeTensorCPU::test_strided_layout_linspace_cpu_int32, test/test_ops.py::TestFakeTensorCPU::test_strided_layout_linspace_tensor_overload_cpu_complex64, test/test_ops.py::TestFakeTensorCPU::test_strided_layout_linspace_tensor_overload_cpu_int32, test/test_ops.py::TestFakeTensorCPU::test_strided_layout_linspace_tensor_overload_cpu_int8, test/test_ops.py::TestFakeTensorCPU::test_strided_layout_logspace_cpu_float16, test/test_ops.py::TestFakeTensorCPU::test_strided_layout_logspace_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_strided_layout_logspace_cpu_int8, test/test_ops.py::TestFakeTensorCPU::test_strided_layout_ones_cpu_bool, test/test_ops.py::TestFakeTensorCPU::test_strided_layout_ones_cpu_complex128, test/test_ops.py::TestFakeTensorCPU::test_strided_layout_zeros_cpu_bool, test/test_ops.py::TestFakeTensorCPU::test_strided_layout_zeros_cpu_complex128, test/test_ops.py::TestTagsCPU::test_tags___rdiv___cpu_float32, test/test_ops.py::TestTagsCPU::test_tags___rmatmul___cpu_float32, test/test_ops.py::TestTagsCPU::test_tags___rmul___cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs__conversions_bfloat16_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs__conversions_cdouble_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs__conversions_cfloat_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs__conversions_char_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs__conversions_long_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_asin_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_atan2_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_atan_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_atanh_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_atleast_2d_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_bitwise_not_cpu_int64, test/test_ops.py::TestTagsCPU::test_tags__refs_ceil_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_diagonal_copy_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_div_trunc_rounding_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_empty_strided_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_eq_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_equal_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_erfinv_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_fft_hfft2_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_fft_ifft2_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_fft_rfft_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_gcd_cpu_int64, test/test_ops.py::TestTagsCPU::test_tags__refs_ge_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_hstack_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_i0_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_igammac_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_index_select_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_isposinf_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_istft_cpu_complex64, test/test_ops.py::TestTagsCPU::test_tags__refs_linalg_svd_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_linspace_tensor_overload_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_logical_not_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_logical_or_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_logsumexp_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_lt_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_mean_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_new_ones_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_nn_functional_gelu_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_nn_functional_l1_loss_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_nn_functional_pairwise_distance_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_sinc_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_softmax_with_dtype_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_special_multigammaln_mvlgamma_p_1_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_special_spherical_bessel_j0_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_special_zeta_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_std_mean_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_tan_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_transpose_copy_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_var_mean_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_view_copy_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_view_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_vstack_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_acos_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_add_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_addmm_decomposed_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_aminmax_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_angle_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_arange_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_argmax_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_argwhere_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_as_strided_copy_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_atanh_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_baddbmm_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_bernoulli_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_bitwise_not_cpu_int64, test/test_ops.py::TestTagsCPU::test_tags_bucketize_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_cholesky_inverse_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_contiguous_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_cov_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_cummin_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_div_trunc_rounding_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_einsum_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_empty_strided_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_expm1_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_exponential_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_fft_hfft_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_fft_ifft_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_fft_ifftn_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_fft_ihfft2_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_fmod_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_full_like_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_gcd_cpu_int64, test/test_ops.py::TestTagsCPU::test_tags_grid_sampler_2d_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_igamma_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_index_copy_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_index_put_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_isfinite_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_isinf_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_isnan_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_isreal_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_lcm_cpu_int64, test/test_ops.py::TestTagsCPU::test_tags_linalg_eigvalsh_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_linalg_lu_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_linalg_multi_dot_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_linalg_norm_subgradients_at_zero_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_linalg_solve_ex_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_linalg_solve_triangular_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_linalg_vector_norm_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_linspace_tensor_overload_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_log_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_logaddexp2_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_logdet_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_logical_xor_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_logit_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_mT_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_masked_fill_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_masked_softmin_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_mm_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_mvlgamma_mvlgamma_p_1_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_mvlgamma_mvlgamma_p_5_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nanmean_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nanmedian_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_native_layer_norm_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_new_full_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_adaptive_max_pool2d_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_avg_pool1d_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_bilinear_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_celu_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_channel_shuffle_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_conv_transpose3d_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_dropout3d_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_embedding_bag_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_hinge_embedding_loss_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_interpolate_area_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_interpolate_linear_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_logsigmoid_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_multilabel_soft_margin_loss_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_pairwise_distance_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_pixel_shuffle_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_poisson_nll_loss_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_rms_norm_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_tanhshrink_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_triplet_margin_with_distance_loss_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_normal_in_place_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_permute_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_positive_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_rad2deg_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_randn_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_reciprocal_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_remainder_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_repeat_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_round_decimals_0_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_rsqrt_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_short_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_signbit_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_softmax_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_sparse_sampled_addmm_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_special_airy_ai_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_special_i1_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_special_i1e_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_special_ndtr_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_special_polygamma_special_polygamma_n_0_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_squeeze_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_std_mean_unbiased_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_t_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_to_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_torch_ops_aten__safe_softmax_default_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_tril_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_triu_indices_cpu_int64, test/test_ops.py::TestTagsCPU::test_tags_var_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_vdot_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_where_cpu_float32 2025-09-07T08:03:26.7086508Z 2025-09-07T08:03:26.9614945Z Running test batch 'tests to run' cost 5516.51 seconds 2025-09-07T08:03:27.8993028Z 2025-09-07T08:03:27.8995381Z real 92m0.979s 2025-09-07T08:03:27.8995687Z user 315m59.668s 2025-09-07T08:03:27.8995927Z sys 21m5.613s 2025-09-07T08:03:27.8996157Z + assert_git_not_dirty 2025-09-07T08:03:27.8996458Z + [[ linux-jammy-py3.13-clang12 != *rocm* ]] 2025-09-07T08:03:27.8996827Z + [[ linux-jammy-py3.13-clang12 != *xla* ]] 2025-09-07T08:03:27.8999222Z ++ git status --porcelain 2025-09-07T08:03:27.8999713Z ++ grep -v '?? third_party' 2025-09-07T08:03:57.5018621Z ++ true 2025-09-07T08:03:57.5019203Z + git_status= 2025-09-07T08:03:57.5021664Z + [[ -n '' ]] 2025-09-07T08:03:57.5021987Z + test_aten 2025-09-07T08:03:57.5022249Z + echo 'Running ATen tests with pytorch lib' 2025-09-07T08:03:57.5022632Z Running ATen tests with pytorch lib 2025-09-07T08:03:57.5022936Z + [[ -n '' ]] 2025-09-07T08:03:57.5023199Z + echo 'Running test with the build folder' 2025-09-07T08:03:57.5023531Z Running test with the build folder 2025-09-07T08:03:57.5023993Z + TEST_BASE_DIR=build/bin 2025-09-07T08:03:57.5024478Z + ln -sf /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib/libc10.so build/bin 2025-09-07T08:03:57.5055716Z + ln -sf '/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib/libcaffe2*' build/bin 2025-09-07T08:03:57.5078500Z + ln -sf '/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib/libmkldnn*' build/bin 2025-09-07T08:03:57.5089949Z + ln -sf '/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib/libnccl*' build/bin 2025-09-07T08:03:57.5102253Z + 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 2025-09-07T08:03:57.5111251Z + ls build/bin 2025-09-07T08:03:57.5152975Z BackoffTest c10_typeid_test 2025-09-07T08:03:57.5153435Z CMakeFiles cmake_install.cmake 2025-09-07T08:03:57.5154047Z CTestTestfile.cmake cpu_allocator_test 2025-09-07T08:03:57.5154820Z CppSignature_test cpu_generator_test 2025-09-07T08:03:57.5155554Z Dict_test cpu_profiling_allocator_test 2025-09-07T08:03:57.5155938Z Dimname_test cpu_rng_test 2025-09-07T08:03:57.5156405Z FileStoreTest dlconvertor_test 2025-09-07T08:03:57.5156817Z HashStoreTest example_allreduce 2025-09-07T08:03:57.5157178Z IListRef_test extension_backend_test 2025-09-07T08:03:57.5157598Z KernelFunction_test half_test 2025-09-07T08:03:57.5157920Z List_test inline_container_test 2025-09-07T08:03:57.5158258Z MaybeOwned_test ivalue_test 2025-09-07T08:03:57.5158628Z NamedTensor_test kernel_function_legacy_test 2025-09-07T08:03:57.5159059Z ProcessGroupGlooTest kernel_function_test 2025-09-07T08:03:57.5159475Z StorageUtils_test kernel_lambda_legacy_test 2025-09-07T08:03:57.5159882Z TCPStoreTest kernel_lambda_test 2025-09-07T08:03:57.5160248Z apply_utils_test kernel_stackbased_test 2025-09-07T08:03:57.5160592Z atest lazy_tensor_test 2025-09-07T08:03:57.5160927Z backend_fallback_test legacy_vmap_test 2025-09-07T08:03:57.5161276Z basic libc10.so 2025-09-07T08:03:57.5161560Z broadcast_test 'libcaffe2*' 2025-09-07T08:03:57.5161896Z c10_AllocatorConfig_test 'libmkldnn*' 2025-09-07T08:03:57.5162225Z c10_ArrayRef_test 'libnccl*' 2025-09-07T08:03:57.5162594Z c10_Bitset_test libtorch.so 2025-09-07T08:03:57.5162966Z c10_CompileTimeFunctionPointer_test libtorch_cpu.so 2025-09-07T08:03:57.5163402Z c10_ConstexprCrc_test libtorch_global_deps.so 2025-09-07T08:03:57.5163811Z c10_DeadlockDetection_test libtorch_python.so 2025-09-07T08:03:57.5164218Z c10_DeviceGuard_test libtorchbind_test.so 2025-09-07T08:03:57.5164646Z c10_Device_test make_boxed_from_unboxed_functor_test 2025-09-07T08:03:57.5165072Z c10_DispatchKeySet_test math_kernel_test 2025-09-07T08:03:57.5165432Z c10_Enumerate_test memory_format_test 2025-09-07T08:03:57.5165801Z c10_Half_test memory_overlapping_test 2025-09-07T08:03:57.5166210Z c10_InlineDeviceGuard_test mobile_memory_cleanup 2025-09-07T08:03:57.5166621Z c10_InlineStreamGuard_test native_test 2025-09-07T08:03:57.5166990Z c10_IntrusiveList_test op_allowlist_test 2025-09-07T08:03:57.5167382Z c10_LeftRight_test op_registration_test 2025-09-07T08:03:57.5167780Z c10_Metaprogramming_test operator_name_test 2025-09-07T08:03:57.5168166Z c10_NetworkFlow_test operators_test 2025-09-07T08:03:57.5168531Z c10_Scalar_test packedtensoraccessor_test 2025-09-07T08:03:57.5168917Z c10_Semaphore_test parallel_benchmark 2025-09-07T08:03:57.5169276Z c10_SizesAndStrides_test pow_test 2025-09-07T08:03:57.5169612Z c10_StreamGuard_test protoc 2025-09-07T08:03:57.5169920Z c10_SymInt_test protoc-3.13.0.0 2025-09-07T08:03:57.5170274Z c10_Synchronized_test quantized_test 2025-09-07T08:03:57.5170638Z c10_ThreadLocal_test reduce_ops_test 2025-09-07T08:03:57.5171018Z c10_TypeIndex_test reportMemoryUsage_test 2025-09-07T08:03:57.5171398Z c10_TypeList_test scalar_tensor_test 2025-09-07T08:03:57.5171731Z c10_TypeTraits_test scalar_test 2025-09-07T08:03:57.5172090Z c10_accumulate_test static_runtime_bench 2025-09-07T08:03:57.5172463Z c10_bfloat16_test static_runtime_test 2025-09-07T08:03:57.5172905Z c10_bit_cast_test stride_properties_test 2025-09-07T08:03:57.5173286Z c10_complex_math_test tensor_iterator_test 2025-09-07T08:03:57.5173649Z c10_complex_test test_api 2025-09-07T08:03:57.5173960Z c10_cow_test test_cpp_rpc 2025-09-07T08:03:57.5174281Z c10_error_test test_dist_autograd 2025-09-07T08:03:57.5174599Z c10_exception_test test_jit 2025-09-07T08:03:57.5174908Z c10_flags_test test_lazy 2025-09-07T08:03:57.5175228Z c10_generic_math_test test_nativert 2025-09-07T08:03:57.5175595Z c10_intrusive_ptr_benchmark test_parallel 2025-09-07T08:03:57.5175962Z c10_intrusive_ptr_test thread_init_test 2025-09-07T08:03:57.5176325Z c10_irange_test torch_shm_manager 2025-09-07T08:03:57.5176697Z c10_lazy_test type_ptr_test 2025-09-07T08:03:57.5177051Z c10_logging_test type_test 2025-09-07T08:03:57.5177376Z c10_optional_test undefined_tensor_test 2025-09-07T08:03:57.5177818Z c10_ordered_preserving_dict_test vec_test_all_types_AVX2 2025-09-07T08:03:57.5178272Z c10_registry_test vec_test_all_types_AVX512 2025-09-07T08:03:57.5178697Z c10_small_vector_test vec_test_all_types_DEFAULT 2025-09-07T08:03:57.5179089Z c10_ssize_test verify_api_visibility 2025-09-07T08:03:57.5179444Z c10_string_util_test weakref_test 2025-09-07T08:03:57.5179786Z c10_string_view_test wrapdim_test 2025-09-07T08:03:57.5180126Z c10_tempfile_test xla_tensor_test 2025-09-07T08:03:57.5180501Z + aten/tools/run_tests.sh build/bin 2025-09-07T08:03:57.5180813Z + set -e 2025-09-07T08:03:57.5183480Z ++ dirname aten/tools/run_tests.sh 2025-09-07T08:03:57.5192915Z + VALGRIND_SUP=/var/lib/jenkins/workspace/aten/tools/valgrind.sup 2025-09-07T08:03:57.5193440Z + export CPP_TESTS_DIR=build/bin 2025-09-07T08:03:57.5193776Z + CPP_TESTS_DIR=build/bin 2025-09-07T08:03:57.5194037Z + VALGRIND=ON 2025-09-07T08:03:57.5195984Z + 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 2025-09-07T08:03:59.5117202Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T08:03:59.5118751Z import pkg_resources 2025-09-07T08:04:00.9374712Z Downloading https://ossci-metrics.s3.amazonaws.com/disabled-tests-condensed.json to /var/lib/jenkins/workspace/test/.pytorch-disabled-tests.json 2025-09-07T08:04:00.9510587Z Found test times from artifacts 2025-09-07T08:04:01.0061623Z Found test times from artifacts 2025-09-07T08:04:01.0079651Z Running all tests 2025-09-07T08:04:01.0084416Z Running parallel tests on 3 processes 2025-09-07T08:04:01.0086068Z Name: tests to run (est. time: 0.0min) 2025-09-07T08:04:01.0086390Z Serial tests (0): 2025-09-07T08:04:01.0086698Z Parallel tests (19): 2025-09-07T08:04:01.0086991Z cpp/Dict_test 1/1 2025-09-07T08:04:01.0087256Z cpp/Dimname_test 1/1 2025-09-07T08:04:01.0087601Z cpp/NamedTensor_test 1/1 2025-09-07T08:04:01.0087897Z cpp/apply_utils_test 1/1 2025-09-07T08:04:01.0088200Z cpp/atest 1/1 2025-09-07T08:04:01.0088466Z cpp/basic 1/1 2025-09-07T08:04:01.0088711Z cpp/broadcast_test 1/1 2025-09-07T08:04:01.0089061Z cpp/cpu_generator_test 1/1 2025-09-07T08:04:01.0089356Z cpp/dlconvertor_test 1/1 2025-09-07T08:04:01.0089655Z cpp/extension_backend_test 1/1 2025-09-07T08:04:01.0090044Z cpp/lazy_tensor_test 1/1 2025-09-07T08:04:01.0090335Z cpp/legacy_vmap_test 1/1 2025-09-07T08:04:01.0090857Z cpp/native_test 1/1 2025-09-07T08:04:01.0091140Z cpp/operators_test 1/1 2025-09-07T08:04:01.0091514Z cpp/scalar_tensor_test 1/1 2025-09-07T08:04:01.0091803Z cpp/scalar_test 1/1 2025-09-07T08:04:01.0092063Z cpp/tensor_iterator_test 1/1 2025-09-07T08:04:01.0092368Z cpp/undefined_tensor_test 1/1 2025-09-07T08:04:01.0092668Z cpp/wrapdim_test 1/1 2025-09-07T08:04:01.0092933Z Name: excluded (est. time: 0.0min) 2025-09-07T08:04:01.0093231Z Serial tests (0): 2025-09-07T08:04:01.0093482Z Parallel tests (0): 2025-09-07T08:04:01.0093818Z Running cpp/Dict_test 1/1 ... [2025-09-07 08:04:01.009014] 2025-09-07T08:04:01.0094217Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:04:01.0097749Z 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-26b17c8732fd0acb.xml', '-x', '--reruns=2'] ... [2025-09-07 08:04:01.009520] 2025-09-07T08:04:02.4266648Z 2025-09-07T08:04:02.4267523Z cpp/Dict_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.Dict_test_1.1_3a4ecc67491a5bf2_.log 2025-09-07T08:04:02.4268150Z 2025-09-07T08:04:03.1916429Z Uploading artifacts took 0.76 seconds 2025-09-07T08:04:03.1916865Z Running cpp/Dimname_test 1/1 ... [2025-09-07 08:04:03.191442] 2025-09-07T08:04:03.1917315Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:04:03.1921680Z 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-9b950317e06da8c6.xml', '-x', '--reruns=2'] ... [2025-09-07 08:04:03.191914] 2025-09-07T08:04:04.6084837Z 2025-09-07T08:04:04.6085751Z cpp/Dimname_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.Dimname_test_1.1_ed88b811c13117c3_.log 2025-09-07T08:04:04.6086426Z 2025-09-07T08:04:04.6086834Z Running cpp/NamedTensor_test 1/1 ... [2025-09-07 08:04:04.608399] 2025-09-07T08:04:04.6087296Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:04:04.6090495Z 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-a27215edb4b3f419.xml', '-x', '--reruns=2'] ... [2025-09-07 08:04:04.608809] 2025-09-07T08:04:06.0255874Z 2025-09-07T08:04:06.0256972Z cpp/NamedTensor_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.NamedTensor_test_1.1_5e9426198d19fe21_.log 2025-09-07T08:04:06.0257667Z 2025-09-07T08:04:06.0257888Z Running cpp/apply_utils_test 1/1 ... [2025-09-07 08:04:06.025326] 2025-09-07T08:04:06.0258303Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:04:06.0259567Z 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-e42d862238ecbc1f.xml', '-x', '--reruns=2'] ... [2025-09-07 08:04:06.025660] 2025-09-07T08:04:07.4422661Z 2025-09-07T08:04:07.4423916Z 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_d6d7c88d7374ab83_.log 2025-09-07T08:04:07.4424596Z 2025-09-07T08:04:07.4424777Z Running cpp/atest 1/1 ... [2025-09-07 08:04:07.442175] 2025-09-07T08:04:07.4425155Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:04:07.4427507Z 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-707eb081519d7e31.xml', '-x', '--reruns=2'] ... [2025-09-07 08:04:07.442534] 2025-09-07T08:04:08.8593651Z 2025-09-07T08:04:08.8594633Z cpp/atest 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.atest_1.1_3f28373c67339f86_.log 2025-09-07T08:04:08.8595544Z 2025-09-07T08:04:08.8595719Z Running cpp/basic 1/1 ... [2025-09-07 08:04:08.859147] 2025-09-07T08:04:08.8596111Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:04:08.8597397Z 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-94a9cfe55178009d.xml', '-x', '--reruns=2'] ... [2025-09-07 08:04:08.859488] 2025-09-07T08:04:10.3262027Z 2025-09-07T08:04:10.3262896Z cpp/basic 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.basic_1.1_165d0c48541eadee_.log 2025-09-07T08:04:10.3263485Z 2025-09-07T08:04:10.3263697Z Running cpp/broadcast_test 1/1 ... [2025-09-07 08:04:10.326081] 2025-09-07T08:04:10.3264110Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:04:10.3266595Z 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-2701f49e8cdd30b2.xml', '-x', '--reruns=2'] ... [2025-09-07 08:04:10.326406] 2025-09-07T08:04:11.7430587Z 2025-09-07T08:04:11.7431569Z cpp/broadcast_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.broadcast_test_1.1_3841be58eaa665f6_.log 2025-09-07T08:04:11.7432294Z 2025-09-07T08:04:11.7432579Z Running cpp/cpu_generator_test 1/1 ... [2025-09-07 08:04:11.742953] 2025-09-07T08:04:11.7433006Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:04:11.7435983Z 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-d3614b6975be70d9.xml', '-x', '--reruns=2'] ... [2025-09-07 08:04:11.743363] 2025-09-07T08:04:13.1599536Z 2025-09-07T08:04:13.1600576Z 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_4fe631821afe6f4e_.log 2025-09-07T08:04:13.1601345Z 2025-09-07T08:04:13.1601792Z Running cpp/dlconvertor_test 1/1 ... [2025-09-07 08:04:13.159846] 2025-09-07T08:04:13.1602303Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:04:13.1604584Z 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-ec37ece5a436e3d3.xml', '-x', '--reruns=2'] ... [2025-09-07 08:04:13.160207] 2025-09-07T08:04:14.5769133Z 2025-09-07T08:04:14.5770069Z cpp/dlconvertor_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.dlconvertor_test_1.1_ac3f2638ac674695_.log 2025-09-07T08:04:14.5770774Z 2025-09-07T08:04:14.5771025Z Running cpp/extension_backend_test 1/1 ... [2025-09-07 08:04:14.576710] 2025-09-07T08:04:14.5771585Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:04:14.5772938Z 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-d3c19ea8445b9707.xml', '-x', '--reruns=2'] ... [2025-09-07 08:04:14.577062] 2025-09-07T08:04:15.9939764Z 2025-09-07T08:04:15.9940758Z 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_9cb505932bf01389_.log 2025-09-07T08:04:15.9941584Z 2025-09-07T08:04:15.9941795Z Running cpp/lazy_tensor_test 1/1 ... [2025-09-07 08:04:15.993710] 2025-09-07T08:04:15.9942241Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:04:15.9943519Z 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-b67afc3ce3d4bfff.xml', '-x', '--reruns=2'] ... [2025-09-07 08:04:15.994071] 2025-09-07T08:04:17.4105172Z 2025-09-07T08:04:17.4106108Z 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_9e927be4058b040b_.log 2025-09-07T08:04:17.4107668Z 2025-09-07T08:04:17.4107894Z Running cpp/legacy_vmap_test 1/1 ... [2025-09-07 08:04:17.410397] 2025-09-07T08:04:17.4108411Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:04:17.4110044Z 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-4024f9d28338f4cf.xml', '-x', '--reruns=2'] ... [2025-09-07 08:04:17.410781] 2025-09-07T08:04:18.8273942Z 2025-09-07T08:04:18.8274921Z 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_ea4821a80d6e03d0_.log 2025-09-07T08:04:18.8275848Z 2025-09-07T08:04:18.8276057Z Running cpp/native_test 1/1 ... [2025-09-07 08:04:18.827271] 2025-09-07T08:04:18.8276540Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:04:18.8278738Z 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-7e8dd203ee58d16d.xml', '-x', '--reruns=2'] ... [2025-09-07 08:04:18.827649] 2025-09-07T08:04:20.2443878Z 2025-09-07T08:04:20.2444775Z cpp/native_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.native_test_1.1_65c698005f6a4fef_.log 2025-09-07T08:04:20.2445433Z 2025-09-07T08:04:20.2445623Z Running cpp/operators_test 1/1 ... [2025-09-07 08:04:20.244134] 2025-09-07T08:04:20.2446095Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:04:20.2447541Z 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-3920ef9b68d273b8.xml', '-x', '--reruns=2'] ... [2025-09-07 08:04:20.244515] 2025-09-07T08:04:21.6613286Z 2025-09-07T08:04:21.6614457Z cpp/operators_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.operators_test_1.1_69c8d26c10cd6a38_.log 2025-09-07T08:04:21.6615143Z 2025-09-07T08:04:21.6615352Z Running cpp/scalar_tensor_test 1/1 ... [2025-09-07 08:04:21.661128] 2025-09-07T08:04:21.6615921Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:04:21.6617539Z 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-0671953657d2ef8a.xml', '-x', '--reruns=2'] ... [2025-09-07 08:04:21.661499] 2025-09-07T08:04:23.0780977Z 2025-09-07T08:04:23.0782044Z 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_4482e15ade2ff953_.log 2025-09-07T08:04:23.0782773Z 2025-09-07T08:04:23.0782960Z Running cpp/scalar_test 1/1 ... [2025-09-07 08:04:23.077998] 2025-09-07T08:04:23.0783381Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:04:23.0786024Z 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-faeed5544d993e42.xml', '-x', '--reruns=2'] ... [2025-09-07 08:04:23.078363] 2025-09-07T08:04:24.5450457Z 2025-09-07T08:04:24.5451350Z cpp/scalar_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.scalar_test_1.1_1992686cce71e827_.log 2025-09-07T08:04:24.5452031Z 2025-09-07T08:04:24.5452347Z Running cpp/tensor_iterator_test 1/1 ... [2025-09-07 08:04:24.544808] 2025-09-07T08:04:24.5452800Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:04:24.5454046Z 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-1f7b0e5e5cb685c7.xml', '-x', '--reruns=2'] ... [2025-09-07 08:04:24.545131] 2025-09-07T08:04:26.0118714Z 2025-09-07T08:04:26.0119717Z 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_8a0f3c0b096c6ab7_.log 2025-09-07T08:04:26.0120436Z 2025-09-07T08:04:26.0120678Z Running cpp/undefined_tensor_test 1/1 ... [2025-09-07 08:04:26.011730] 2025-09-07T08:04:26.0121142Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:04:26.0122726Z 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-e24955dde79646de.xml', '-x', '--reruns=2'] ... [2025-09-07 08:04:26.012052] 2025-09-07T08:04:27.4788374Z 2025-09-07T08:04:27.4790805Z 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_d66ebbd1aa3b1061_.log 2025-09-07T08:04:27.4792336Z 2025-09-07T08:04:27.4792739Z Running cpp/wrapdim_test 1/1 ... [2025-09-07 08:04:27.478716] 2025-09-07T08:04:27.4793502Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:04:27.4795404Z 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-83cd2500f1390be7.xml', '-x', '--reruns=2'] ... [2025-09-07 08:04:27.479121] 2025-09-07T08:04:28.8957195Z 2025-09-07T08:04:28.8958403Z cpp/wrapdim_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.wrapdim_test_1.1_9fa81e8044c03441_.log 2025-09-07T08:04:28.8959064Z 2025-09-07T08:04:31.0710681Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T08:04:31.0712470Z import pkg_resources 2025-09-07T08:04:31.0740353Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T08:04:31.0741868Z import pkg_resources 2025-09-07T08:04:31.0762664Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T08:04:31.0764180Z import pkg_resources 2025-09-07T08:04:31.1939633Z Running cpp/Dict_test 1/1 ... [2025-09-07 08:04:31.193611] 2025-09-07T08:04:31.1940240Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:04:31.1943460Z 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-8023a3d8b9f3dc45.xml', '-x', '--reruns=2'] ... [2025-09-07 08:04:31.194044] 2025-09-07T08:04:31.1956505Z Running cpp/Dimname_test 1/1 ... [2025-09-07 08:04:31.195439] 2025-09-07T08:04:31.1957209Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:04:31.1961310Z 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-0fba66cad771e3ae.xml', '-x', '--reruns=2'] ... [2025-09-07 08:04:31.195845] 2025-09-07T08:04:31.1985491Z Running cpp/NamedTensor_test 1/1 ... [2025-09-07 08:04:31.198335] 2025-09-07T08:04:31.1986178Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:04:31.1990321Z 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-7651a3e580b1dfe6.xml', '-x', '--reruns=2'] ... [2025-09-07 08:04:31.198741] 2025-09-07T08:04:33.5647658Z 2025-09-07T08:04:33.5649059Z cpp/Dimname_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.Dimname_test_1.1_e208065504129833_.log 2025-09-07T08:04:33.5650269Z 2025-09-07T08:04:33.8683726Z 2025-09-07T08:04:33.8684879Z cpp/NamedTensor_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.NamedTensor_test_1.1_1b1014358a5e506f_.log 2025-09-07T08:04:33.8685853Z 2025-09-07T08:04:36.5246408Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T08:04:36.5248068Z import pkg_resources 2025-09-07T08:04:36.6506838Z Running cpp/apply_utils_test 1/1 ... [2025-09-07 08:04:36.650291] 2025-09-07T08:04:36.6507373Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:04:36.6510175Z 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-8d377173906b40d6.xml', '-x', '--reruns=2'] ... [2025-09-07 08:04:36.650733] 2025-09-07T08:04:36.7795888Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T08:04:36.7797441Z import pkg_resources 2025-09-07T08:04:36.9103702Z Running cpp/atest 1/1 ... [2025-09-07 08:04:36.909933] 2025-09-07T08:04:36.9104749Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:04:36.9107418Z 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-4df35de280bcb1fb.xml', '-x', '--reruns=2'] ... [2025-09-07 08:04:36.910399] 2025-09-07T08:04:37.3692587Z 2025-09-07T08:04:37.3693539Z cpp/Dict_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.Dict_test_1.1_c1afc84018f0322e_.log 2025-09-07T08:04:37.3694391Z 2025-09-07T08:04:39.1198332Z 2025-09-07T08:04:39.1199806Z 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_44f36c7c11fb9bd6_.log 2025-09-07T08:04:39.1200989Z 2025-09-07T08:04:40.1810675Z 2025-09-07T08:04:40.1811970Z cpp/atest 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.atest_1.1_f527566bb9461f5a_.log 2025-09-07T08:04:40.1813116Z 2025-09-07T08:04:40.4814194Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T08:04:40.4815750Z import pkg_resources 2025-09-07T08:04:40.6084307Z Running cpp/basic 1/1 ... [2025-09-07 08:04:40.608133] 2025-09-07T08:04:40.6084738Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:04:40.6088453Z 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-bd6d4184f088e0c5.xml', '-x', '--reruns=2'] ... [2025-09-07 08:04:40.608581] 2025-09-07T08:04:41.9448755Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T08:04:41.9450621Z import pkg_resources 2025-09-07T08:04:42.0692454Z Running cpp/broadcast_test 1/1 ... [2025-09-07 08:04:42.068876] 2025-09-07T08:04:42.0692911Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:04:42.0696301Z 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-fd1e07b34b387f12.xml', '-x', '--reruns=2'] ... [2025-09-07 08:04:42.069360] 2025-09-07T08:04:42.9770887Z 2025-09-07T08:04:42.9772318Z cpp/basic 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.basic_1.1_ca84ef9bba3c8b2a_.log 2025-09-07T08:04:42.9773044Z 2025-09-07T08:04:43.1488497Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T08:04:43.1491336Z import pkg_resources 2025-09-07T08:04:43.2767443Z Running cpp/cpu_generator_test 1/1 ... [2025-09-07 08:04:43.276357] 2025-09-07T08:04:43.2768287Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:04:43.2771639Z 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-6873bf5ee2811fb3.xml', '-x', '--reruns=2'] ... [2025-09-07 08:04:43.276827] 2025-09-07T08:04:43.8872088Z 2025-09-07T08:04:43.8873063Z cpp/broadcast_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.broadcast_test_1.1_1db4093d5263e76d_.log 2025-09-07T08:04:43.8874057Z 2025-09-07T08:04:45.9196209Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T08:04:45.9198976Z import pkg_resources 2025-09-07T08:04:46.0491417Z Running cpp/dlconvertor_test 1/1 ... [2025-09-07 08:04:46.048775] 2025-09-07T08:04:46.0491892Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:04:46.0494721Z 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-59637b0bbd51d8c8.xml', '-x', '--reruns=2'] ... [2025-09-07 08:04:46.049206] 2025-09-07T08:04:46.4965729Z 2025-09-07T08:04:46.4967116Z 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_4771adfc923c76be_.log 2025-09-07T08:04:46.4968217Z 2025-09-07T08:04:46.7475757Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T08:04:46.7478659Z import pkg_resources 2025-09-07T08:04:46.8741717Z Running cpp/extension_backend_test 1/1 ... [2025-09-07 08:04:46.873709] 2025-09-07T08:04:46.8742570Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:04:46.8744966Z 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-f181777d2546998d.xml', '-x', '--reruns=2'] ... [2025-09-07 08:04:46.874156] 2025-09-07T08:04:48.1174712Z 2025-09-07T08:04:48.1175822Z cpp/dlconvertor_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.dlconvertor_test_1.1_90bfbf3385e58a6c_.log 2025-09-07T08:04:48.1176535Z 2025-09-07T08:04:48.6917019Z 2025-09-07T08:04:48.6918337Z 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_d8c0a6637c208f7f_.log 2025-09-07T08:04:48.6919105Z 2025-09-07T08:04:49.3809551Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T08:04:49.3811194Z import pkg_resources 2025-09-07T08:04:49.5076188Z Running cpp/lazy_tensor_test 1/1 ... [2025-09-07 08:04:49.507294] 2025-09-07T08:04:49.5076706Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:04:49.5081213Z 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-eb9a83e529bb7871.xml', '-x', '--reruns=2'] ... [2025-09-07 08:04:49.507737] 2025-09-07T08:04:50.9606630Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T08:04:50.9608207Z import pkg_resources 2025-09-07T08:04:51.0858417Z Running cpp/legacy_vmap_test 1/1 ... [2025-09-07 08:04:51.085472] 2025-09-07T08:04:51.0858897Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:04:51.0862445Z 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-ac9529dd7d15fb19.xml', '-x', '--reruns=2'] ... [2025-09-07 08:04:51.085923] 2025-09-07T08:04:51.2750407Z 2025-09-07T08:04:51.2751917Z 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_579513526771d31a_.log 2025-09-07T08:04:51.2752797Z 2025-09-07T08:04:51.5268264Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T08:04:51.5269848Z import pkg_resources 2025-09-07T08:04:51.6516528Z Running cpp/native_test 1/1 ... [2025-09-07 08:04:51.651266] 2025-09-07T08:04:51.6516977Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:04:51.6520719Z 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-e5c38c19b8158abb.xml', '-x', '--reruns=2'] ... [2025-09-07 08:04:51.651759] 2025-09-07T08:04:53.5698835Z 2025-09-07T08:04:53.5700001Z cpp/native_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.native_test_1.1_df254d41502183d9_.log 2025-09-07T08:04:53.5700652Z 2025-09-07T08:04:54.1494132Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T08:04:54.1495707Z import pkg_resources 2025-09-07T08:04:54.2739259Z Running cpp/operators_test 1/1 ... [2025-09-07 08:04:54.273596] 2025-09-07T08:04:54.2739921Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:04:54.2742620Z 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-e2ee2da842085462.xml', '-x', '--reruns=2'] ... [2025-09-07 08:04:54.274033] 2025-09-07T08:04:54.9072213Z 2025-09-07T08:04:54.9073193Z 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_711f7b64c3e2e794_.log 2025-09-07T08:04:54.9073883Z 2025-09-07T08:04:56.4424185Z 2025-09-07T08:04:56.4425357Z cpp/operators_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.operators_test_1.1_e2ceccd212ecb4e7_.log 2025-09-07T08:04:56.4426057Z 2025-09-07T08:04:56.4777505Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T08:04:56.4779056Z import pkg_resources 2025-09-07T08:04:56.6020730Z Running cpp/scalar_tensor_test 1/1 ... [2025-09-07 08:04:56.601805] 2025-09-07T08:04:56.6021203Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:04:56.6024828Z 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-bd3788a73f4acf27.xml', '-x', '--reruns=2'] ... [2025-09-07 08:04:56.602240] 2025-09-07T08:04:57.8864869Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T08:04:57.8867916Z import pkg_resources 2025-09-07T08:04:58.0113412Z Running cpp/scalar_test 1/1 ... [2025-09-07 08:04:58.010949] 2025-09-07T08:04:58.0114096Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:04:58.0117260Z 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-45cf3207f6cf35fb.xml', '-x', '--reruns=2'] ... [2025-09-07 08:04:58.011417] 2025-09-07T08:04:58.5197456Z 2025-09-07T08:04:58.5199010Z 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_adac998c6c935dd5_.log 2025-09-07T08:04:58.5200411Z 2025-09-07T08:04:59.3047448Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T08:04:59.3050479Z import pkg_resources 2025-09-07T08:04:59.4299599Z Running cpp/tensor_iterator_test 1/1 ... [2025-09-07 08:04:59.429554] 2025-09-07T08:04:59.4300456Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:04:59.4303011Z 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-1dcc36ea0d7be31b.xml', '-x', '--reruns=2'] ... [2025-09-07 08:04:59.429988] 2025-09-07T08:05:00.1794043Z 2025-09-07T08:05:00.1795494Z cpp/scalar_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.scalar_test_1.1_3a11bd0f23ebf57f_.log 2025-09-07T08:05:00.1796686Z 2025-09-07T08:05:01.4116259Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T08:05:01.4119354Z import pkg_resources 2025-09-07T08:05:01.5364024Z Running cpp/undefined_tensor_test 1/1 ... [2025-09-07 08:05:01.535985] 2025-09-07T08:05:01.5364875Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:05:01.5367869Z 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-5781b788a40aba43.xml', '-x', '--reruns=2'] ... [2025-09-07 08:05:01.536443] 2025-09-07T08:05:03.0015043Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T08:05:03.0016685Z import pkg_resources 2025-09-07T08:05:03.1276667Z Running cpp/wrapdim_test 1/1 ... [2025-09-07 08:05:03.127274] 2025-09-07T08:05:03.1277115Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:05:03.1280193Z 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-b12e378e1b0ea43e.xml', '-x', '--reruns=2'] ... [2025-09-07 08:05:03.127758] 2025-09-07T08:05:03.3540587Z 2025-09-07T08:05:03.3541626Z 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_7d67805f22f589aa_.log 2025-09-07T08:05:03.3542361Z 2025-09-07T08:05:04.9957255Z 2025-09-07T08:05:04.9958761Z cpp/wrapdim_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.wrapdim_test_1.1_f262c5b907f04fa9_.log 2025-09-07T08:05:04.9959919Z 2025-09-07T08:05:06.3899057Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T08:05:06.3902050Z import pkg_resources 2025-09-07T08:05:07.4085735Z 2025-09-07T08:05:07.4086735Z 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_711526c2e22e9bb9_.log 2025-09-07T08:05:07.4087487Z 2025-09-07T08:05:07.8306589Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T08:05:07.8309446Z import pkg_resources 2025-09-07T08:05:08.5074017Z Running test batch 'tests to run' cost 67.5 seconds 2025-09-07T08:05:09.1218268Z + run_if_exists tensor_interop_test 2025-09-07T08:05:09.1218677Z + local test_name=tensor_interop_test 2025-09-07T08:05:09.1219189Z + [[ -x build/bin/tensor_interop_test ]] 2025-09-07T08:05:09.1219727Z + echo 'Warning: tensor_interop_test does not exist.' 2025-09-07T08:05:09.1220240Z Warning: tensor_interop_test does not exist. 2025-09-07T08:05:09.1220659Z + run_if_exists cudnn_test 2025-09-07T08:05:09.1220990Z + local test_name=cudnn_test 2025-09-07T08:05:09.1221343Z + [[ -x build/bin/cudnn_test ]] 2025-09-07T08:05:09.1221814Z + echo 'Warning: cudnn_test does not exist.' 2025-09-07T08:05:09.1222315Z Warning: cudnn_test does not exist. 2025-09-07T08:05:09.1222722Z + run_if_exists cuda_generator_test 2025-09-07T08:05:09.1223130Z + local test_name=cuda_generator_test 2025-09-07T08:05:09.1223856Z + [[ -x build/bin/cuda_generator_test ]] 2025-09-07T08:05:09.1224241Z + echo 'Warning: cuda_generator_test does not exist.' 2025-09-07T08:05:09.1224626Z Warning: cuda_generator_test does not exist. 2025-09-07T08:05:09.1224973Z + run_if_exists apply_test 2025-09-07T08:05:09.1225285Z + local test_name=apply_test 2025-09-07T08:05:09.1225582Z + [[ -x build/bin/apply_test ]] 2025-09-07T08:05:09.1225902Z + echo 'Warning: apply_test does not exist.' 2025-09-07T08:05:09.1226235Z Warning: apply_test does not exist. 2025-09-07T08:05:09.1226552Z + run_if_exists stream_test 2025-09-07T08:05:09.1226847Z + local test_name=stream_test 2025-09-07T08:05:09.1227136Z + [[ -x build/bin/stream_test ]] 2025-09-07T08:05:09.1227441Z + echo 'Warning: stream_test does not exist.' 2025-09-07T08:05:09.1227868Z Warning: stream_test does not exist. 2025-09-07T08:05:09.1228187Z + run_if_exists cuda_half_test 2025-09-07T08:05:09.1228534Z + local test_name=cuda_half_test 2025-09-07T08:05:09.1228820Z + [[ -x build/bin/cuda_half_test ]] 2025-09-07T08:05:09.1229169Z + echo 'Warning: cuda_half_test does not exist.' 2025-09-07T08:05:09.1229535Z Warning: cuda_half_test does not exist. 2025-09-07T08:05:09.1229867Z + run_if_exists cuda_vectorized_test 2025-09-07T08:05:09.1230178Z + local test_name=cuda_vectorized_test 2025-09-07T08:05:09.1230509Z + [[ -x build/bin/cuda_vectorized_test ]] 2025-09-07T08:05:09.1230887Z + echo 'Warning: cuda_vectorized_test does not exist.' 2025-09-07T08:05:09.1231287Z Warning: cuda_vectorized_test does not exist. 2025-09-07T08:05:09.1231633Z + run_if_exists cuda_distributions_test 2025-09-07T08:05:09.1231972Z + local test_name=cuda_distributions_test 2025-09-07T08:05:09.1232320Z + [[ -x build/bin/cuda_distributions_test ]] 2025-09-07T08:05:09.1232721Z + echo 'Warning: cuda_distributions_test does not exist.' 2025-09-07T08:05:09.1233130Z Warning: cuda_distributions_test does not exist. 2025-09-07T08:05:09.1233500Z + run_if_exists cuda_optional_test 2025-09-07T08:05:09.1233820Z + local test_name=cuda_optional_test 2025-09-07T08:05:09.1234198Z + [[ -x build/bin/cuda_optional_test ]] 2025-09-07T08:05:09.1234631Z + echo 'Warning: cuda_optional_test does not exist.' 2025-09-07T08:05:09.1235019Z Warning: cuda_optional_test does not exist. 2025-09-07T08:05:09.1235375Z + run_if_exists cuda_tensor_interop_test 2025-09-07T08:05:09.1235720Z + local test_name=cuda_tensor_interop_test 2025-09-07T08:05:09.1236059Z + [[ -x build/bin/cuda_tensor_interop_test ]] 2025-09-07T08:05:09.1236457Z + echo 'Warning: cuda_tensor_interop_test does not exist.' 2025-09-07T08:05:09.1236883Z Warning: cuda_tensor_interop_test does not exist. 2025-09-07T08:05:09.1237245Z + run_if_exists cuda_complex_test 2025-09-07T08:05:09.1237542Z + local test_name=cuda_complex_test 2025-09-07T08:05:09.1237859Z + [[ -x build/bin/cuda_complex_test ]] 2025-09-07T08:05:09.1238218Z + echo 'Warning: cuda_complex_test does not exist.' 2025-09-07T08:05:09.1238597Z Warning: cuda_complex_test does not exist. 2025-09-07T08:05:09.1238941Z + run_if_exists cuda_complex_math_test 2025-09-07T08:05:09.1239276Z + local test_name=cuda_complex_math_test 2025-09-07T08:05:09.1239616Z + [[ -x build/bin/cuda_complex_math_test ]] 2025-09-07T08:05:09.1240007Z + echo 'Warning: cuda_complex_math_test does not exist.' 2025-09-07T08:05:09.1240421Z Warning: cuda_complex_math_test does not exist. 2025-09-07T08:05:09.1240765Z + run_if_exists cuda_cub_test 2025-09-07T08:05:09.1241057Z + local test_name=cuda_cub_test 2025-09-07T08:05:09.1241357Z + [[ -x build/bin/cuda_cub_test ]] 2025-09-07T08:05:09.1241697Z + echo 'Warning: cuda_cub_test does not exist.' 2025-09-07T08:05:09.1242049Z Warning: cuda_cub_test does not exist. 2025-09-07T08:05:09.1242380Z + run_if_exists cuda_atomic_ops_test 2025-09-07T08:05:09.1242708Z + local test_name=cuda_atomic_ops_test 2025-09-07T08:05:09.1243046Z + [[ -x build/bin/cuda_atomic_ops_test ]] 2025-09-07T08:05:09.1243411Z + echo 'Warning: cuda_atomic_ops_test does not exist.' 2025-09-07T08:05:09.1243813Z Warning: cuda_atomic_ops_test does not exist. 2025-09-07T08:05:09.1244204Z + '[' ON == ON ']' 2025-09-07T08:05:09.1244916Z + valgrind --suppressions=/var/lib/jenkins/workspace/aten/tools/valgrind.sup --error-exitcode=1 build/bin/basic '--gtest_filter=-*CUDA' 2025-09-07T08:05:09.1476977Z ==47146== Memcheck, a memory error detector 2025-09-07T08:05:09.1477487Z ==47146== Copyright (C) 2002-2022, and GNU GPL'd, by Julian Seward et al. 2025-09-07T08:05:09.1478080Z ==47146== Using Valgrind-3.20.0 and LibVEX; rerun with -h for copyright info 2025-09-07T08:05:09.1478603Z ==47146== Command: build/bin/basic --gtest_filter=-*CUDA 2025-09-07T08:05:09.1478965Z ==47146== 2025-09-07T08:05:09.6836953Z ==47146== Warning: set address range perms: large range [0x4a4c000, 0x1a643000) (defined) 2025-09-07T08:05:09.6837991Z ==47146== Warning: set address range perms: large range [0x5c30000, 0x1740f000) (defined) 2025-09-07T08:05:32.9054935Z Running main() from /var/lib/jenkins/workspace/third_party/googletest/googletest/src/gtest_main.cc 2025-09-07T08:05:32.9347610Z Note: Google Test filter = -*CUDA 2025-09-07T08:05:32.9397166Z [==========] Running 6 tests from 1 test suite. 2025-09-07T08:05:32.9423792Z [----------] Global test environment set-up. 2025-09-07T08:05:32.9493815Z [----------] 6 tests from BasicTest 2025-09-07T08:05:32.9517421Z [ RUN ] BasicTest.BasicTestCPU 2025-09-07T08:05:33.5076379Z hwloc x86 backend cannot work under Valgrind, disabling. 2025-09-07T08:05:33.5076884Z May be reenabled by dumping CPUIDs with hwloc-gather-cpuid 2025-09-07T08:05:33.5077788Z and reloading them under Valgrind with HWLOC_CPUID_PATH. 2025-09-07T08:05:33.5662848Z hwloc x86 backend cannot work under Valgrind, disabling. 2025-09-07T08:05:33.5663368Z May be reenabled by dumping CPUIDs with hwloc-gather-cpuid 2025-09-07T08:05:33.5663838Z and reloading them under Valgrind with HWLOC_CPUID_PATH. 2025-09-07T08:05:33.6456029Z hwloc x86 backend cannot work under Valgrind, disabling. 2025-09-07T08:05:33.6456546Z May be reenabled by dumping CPUIDs with hwloc-gather-cpuid 2025-09-07T08:05:33.6457252Z and reloading them under Valgrind with HWLOC_CPUID_PATH. 2025-09-07T08:05:34.9963861Z 916 ms 2025-09-07T08:05:35.0832965Z 55 ms 2025-09-07T08:05:35.1695701Z 68 ms 2025-09-07T08:05:36.1679324Z [ OK ] BasicTest.BasicTestCPU (3214 ms) 2025-09-07T08:05:36.1690794Z [ RUN ] BasicTest.BasicTestHalfCPU 2025-09-07T08:05:36.3360508Z 120 ms 2025-09-07T08:05:36.3891478Z 47 ms 2025-09-07T08:05:36.4571403Z 65 ms 2025-09-07T08:05:36.5115230Z [ OK ] BasicTest.BasicTestHalfCPU (341 ms) 2025-09-07T08:05:36.5115676Z [ RUN ] BasicTest.FactoryMethodsTest 2025-09-07T08:05:36.5445210Z [ OK ] BasicTest.FactoryMethodsTest (32 ms) 2025-09-07T08:05:36.5445598Z [ RUN ] BasicTest.BasicStdTestCPU 2025-09-07T08:05:36.6228238Z Simple example: called once 2025-09-07T08:05:36.7466948Z throw: call_once will retry 2025-09-07T08:05:36.7480280Z throw: call_once will retry 2025-09-07T08:05:36.7484691Z Didn't throw, call_once will not attempt again 2025-09-07T08:05:36.7504696Z [ OK ] BasicTest.BasicStdTestCPU (205 ms) 2025-09-07T08:05:36.7505122Z [ RUN ] BasicTest.TestForBlobResizeCPU 2025-09-07T08:05:36.7678678Z [ OK ] BasicTest.TestForBlobResizeCPU (17 ms) 2025-09-07T08:05:36.7679102Z [ RUN ] BasicTest.TestForBlobStridesResizeCPU 2025-09-07T08:05:36.7711767Z [ OK ] BasicTest.TestForBlobStridesResizeCPU (3 ms) 2025-09-07T08:05:36.7733352Z [----------] 6 tests from BasicTest (3820 ms total) 2025-09-07T08:05:36.7733637Z 2025-09-07T08:05:36.7745470Z [----------] Global test environment tear-down 2025-09-07T08:05:36.7773567Z [==========] 6 tests from 1 test suite ran. (3846 ms total) 2025-09-07T08:05:36.7786041Z [ PASSED ] 6 tests. 2025-09-07T08:05:38.7564051Z ==47146== 2025-09-07T08:05:38.7567941Z ==47146== HEAP SUMMARY: 2025-09-07T08:05:38.7568304Z ==47146== in use at exit: 433,012 bytes in 6,391 blocks 2025-09-07T08:05:38.7568815Z ==47146== total heap usage: 651,832 allocs, 645,441 frees, 199,638,430 bytes allocated 2025-09-07T08:05:38.7569275Z ==47146== 2025-09-07T08:05:38.7930635Z ==47146== LEAK SUMMARY: 2025-09-07T08:05:38.7930988Z ==47146== definitely lost: 0 bytes in 0 blocks 2025-09-07T08:05:38.7931363Z ==47146== indirectly lost: 0 bytes in 0 blocks 2025-09-07T08:05:38.7931748Z ==47146== possibly lost: 69,920 bytes in 2 blocks 2025-09-07T08:05:38.7932146Z ==47146== still reachable: 363,092 bytes in 6,389 blocks 2025-09-07T08:05:38.7932543Z ==47146== suppressed: 0 bytes in 0 blocks 2025-09-07T08:05:38.7932999Z ==47146== Rerun with --leak-check=full to see details of leaked memory 2025-09-07T08:05:38.7933412Z ==47146== 2025-09-07T08:05:38.7933737Z ==47146== For lists of detected and suppressed errors, rerun with: -s 2025-09-07T08:05:38.7934255Z ==47146== ERROR SUMMARY: 0 errors from 0 contexts (suppressed: 0 from 0) 2025-09-07T08:05:38.8360002Z + [[ -x build/bin/tensor_interop_test ]] 2025-09-07T08:05:38.8361491Z + [[ -n '' ]] 2025-09-07T08:05:38.8361894Z + assert_git_not_dirty 2025-09-07T08:05:38.8362174Z + [[ linux-jammy-py3.13-clang12 != *rocm* ]] 2025-09-07T08:05:38.8362543Z + [[ linux-jammy-py3.13-clang12 != *xla* ]] 2025-09-07T08:05:38.8368979Z ++ git status --porcelain 2025-09-07T08:05:38.8369650Z ++ grep -v '?? third_party' 2025-09-07T08:05:39.1079195Z ++ true 2025-09-07T08:05:39.1079929Z + git_status= 2025-09-07T08:05:39.1080301Z + [[ -n '' ]] 2025-09-07T08:05:39.1080541Z + test_libtorch 1 2025-09-07T08:05:39.1080791Z + local SHARD=1 2025-09-07T08:05:39.1081032Z + [[ default != \s\l\o\w ]] 2025-09-07T08:05:39.1081314Z + echo 'Testing libtorch' 2025-09-07T08:05:39.1081583Z Testing libtorch 2025-09-07T08:05:39.1082382Z + ln -sf /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib/libbackend_with_compiler.so /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/bin 2025-09-07T08:05:39.1094019Z + ln -sf /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib/libjitbackend_test.so /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/bin 2025-09-07T08:05:39.1104931Z + ln -sf /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib/libcaffe2_nvrtc.so /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/bin 2025-09-07T08:05:39.1117500Z + ln -sf /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib/libc10.so /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/bin 2025-09-07T08:05:39.1129411Z + ln -sf /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib/libshm /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib/libshm.so /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib/libshm_windows /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/bin 2025-09-07T08:05:39.1141965Z + 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 /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/bin 2025-09-07T08:05:39.1151896Z + ln -sf '/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib/libnvfuser*' /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/bin 2025-09-07T08:05:39.1162582Z + export CPP_TESTS_DIR=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/bin 2025-09-07T08:05:39.1163264Z + CPP_TESTS_DIR=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/bin 2025-09-07T08:05:39.1163723Z + [[ -z 1 ]] 2025-09-07T08:05:39.1163942Z + [[ 1 == \1 ]] 2025-09-07T08:05:39.1164171Z + test_libtorch_api 2025-09-07T08:05:39.1164482Z + MNIST_DIR=/var/lib/jenkins/workspace/test/cpp/api/mnist 2025-09-07T08:05:39.1165064Z + python tools/download_mnist.py --quiet -d /var/lib/jenkins/workspace/test/cpp/api/mnist 2025-09-07T08:05:39.1642337Z Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz ... 2025-09-07T08:05:39.1913581Z Failed to download (trying next): 2025-09-07T08:05:39.1914080Z HTTP Error 404: Not Found 2025-09-07T08:05:39.1914650Z Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-images-idx3-ubyte.gz ... 2025-09-07T08:05:39.5230778Z Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz ... 2025-09-07T08:05:39.5286358Z Failed to download (trying next): 2025-09-07T08:05:39.5286696Z HTTP Error 404: Not Found 2025-09-07T08:05:39.5287205Z Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-labels-idx1-ubyte.gz ... 2025-09-07T08:05:39.5695179Z Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz ... 2025-09-07T08:05:39.5750287Z Failed to download (trying next): 2025-09-07T08:05:39.5750620Z HTTP Error 404: Not Found 2025-09-07T08:05:39.5751120Z Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-images-idx3-ubyte.gz ... 2025-09-07T08:05:39.6559772Z Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz ... 2025-09-07T08:05:39.6616741Z Failed to download (trying next): 2025-09-07T08:05:39.6617069Z HTTP Error 404: Not Found 2025-09-07T08:05:39.6617579Z Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-labels-idx1-ubyte.gz ... 2025-09-07T08:05:39.7034781Z + [[ linux-jammy-py3.13-clang12 == *asan* ]] 2025-09-07T08:05:39.7035193Z + [[ linux-jammy-py3.13-clang12 == *slow-gradcheck* ]] 2025-09-07T08:05:39.7035561Z + OMP_NUM_THREADS=2 2025-09-07T08:05:39.7035932Z + TORCH_CPP_TEST_MNIST_PATH=/var/lib/jenkins/workspace/test/cpp/api/mnist 2025-09-07T08:05:39.7036539Z + python test/run_test.py --cpp --verbose -i cpp/test_api -k 'not IMethodTest' 2025-09-07T08:05:41.6750029Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T08:05:41.6751592Z import pkg_resources 2025-09-07T08:05:43.0973636Z Downloading https://ossci-metrics.s3.amazonaws.com/disabled-tests-condensed.json to /var/lib/jenkins/workspace/test/.pytorch-disabled-tests.json 2025-09-07T08:05:43.1109310Z Found test times from artifacts 2025-09-07T08:05:43.1662618Z Found test times from artifacts 2025-09-07T08:05:43.1682037Z Running all tests 2025-09-07T08:05:43.1685307Z Running parallel tests on 3 processes 2025-09-07T08:05:43.1685987Z Name: tests to run (est. time: 0.0min) 2025-09-07T08:05:43.1686350Z Serial tests (0): 2025-09-07T08:05:43.1686604Z Parallel tests (1): 2025-09-07T08:05:43.1686851Z cpp/test_api 1/1 2025-09-07T08:05:43.1687118Z Name: excluded (est. time: 0.0min) 2025-09-07T08:05:43.1687413Z Serial tests (0): 2025-09-07T08:05:43.1687655Z Parallel tests (0): 2025-09-07T08:05:43.1690788Z Running cpp/test_api 1/1 ... [2025-09-07 08:05:43.168931] 2025-09-07T08:05:43.1691209Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:05:43.1696017Z Executing ['pytest', '/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/bin/test_api', '-m', 'serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-4aefea7ce7febdef.xml', '-k', 'not IMethodTest', '-x', '--reruns=2'] ... [2025-09-07 08:05:43.169343] 2025-09-07T08:05:45.2368283Z 2025-09-07T08:05:45.2369278Z cpp/test_api 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.test_api_1.1_be5a29205a7a63e0_.log 2025-09-07T08:05:45.2369910Z 2025-09-07T08:05:45.9619397Z Uploading artifacts took 0.72 seconds 2025-09-07T08:05:48.0392800Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T08:05:48.0394401Z import pkg_resources 2025-09-07T08:05:48.0532621Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T08:05:48.0534359Z import pkg_resources 2025-09-07T08:05:48.0870895Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T08:05:48.0872412Z import pkg_resources 2025-09-07T08:05:48.1597476Z Running cpp/test_api 1/1 ... [2025-09-07 08:05:48.159425] 2025-09-07T08:05:48.1598046Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:05:48.1601337Z Executing ['pytest', '/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/bin/test_api', '-m', 'not serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-9875f65f81f7441f.xml', '-k', 'not IMethodTest', '-x', '--reruns=2'] ... [2025-09-07 08:05:48.159867] 2025-09-07T08:07:47.4636098Z 2025-09-07T08:07:47.4637386Z cpp/test_api 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.test_api_1.1_84a0fcc45d951c28_.log 2025-09-07T08:07:47.4649979Z 2025-09-07T08:07:48.0969912Z Running test batch 'tests to run' cost 124.93 seconds 2025-09-07T08:07:48.7195075Z + [[ linux-jammy-py3.13-clang12 != *android* ]] 2025-09-07T08:07:48.7195562Z + [[ linux-jammy-py3.13-clang12 != *cuda* ]] 2025-09-07T08:07:48.7195918Z + [[ linux-jammy-py3.13-clang12 != *asan* ]] 2025-09-07T08:07:48.7196262Z + [[ linux-jammy-py3.13-clang12 != *s390x* ]] 2025-09-07T08:07:48.7196628Z + export CPP_TESTS_DIR=build/bin 2025-09-07T08:07:48.7196930Z + CPP_TESTS_DIR=build/bin 2025-09-07T08:07:48.7197335Z + python test/run_test.py --cpp --verbose -i cpp/static_runtime_test 2025-09-07T08:07:50.7166189Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T08:07:50.7167773Z import pkg_resources 2025-09-07T08:07:52.1416447Z Downloading https://ossci-metrics.s3.amazonaws.com/disabled-tests-condensed.json to /var/lib/jenkins/workspace/test/.pytorch-disabled-tests.json 2025-09-07T08:07:52.1552243Z Found test times from artifacts 2025-09-07T08:07:52.2101726Z Found test times from artifacts 2025-09-07T08:07:52.2121009Z Running all tests 2025-09-07T08:07:52.2124623Z Running parallel tests on 3 processes 2025-09-07T08:07:52.2124985Z Name: tests to run (est. time: 0.0min) 2025-09-07T08:07:52.2125320Z Serial tests (0): 2025-09-07T08:07:52.2125566Z Parallel tests (1): 2025-09-07T08:07:52.2125848Z cpp/static_runtime_test 1/1 2025-09-07T08:07:52.2126235Z Name: excluded (est. time: 0.0min) 2025-09-07T08:07:52.2126578Z Serial tests (0): 2025-09-07T08:07:52.2126824Z Parallel tests (0): 2025-09-07T08:07:52.2127183Z Running cpp/static_runtime_test 1/1 ... [2025-09-07 08:07:52.212560] 2025-09-07T08:07:52.2127620Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:07:52.2132037Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/static_runtime_test', '-m', 'serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-606d9766f0e9a60e.xml', '-x', '--reruns=2'] ... [2025-09-07 08:07:52.212975] 2025-09-07T08:07:53.6797054Z 2025-09-07T08:07:53.6798227Z cpp/static_runtime_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.static_runtime_test_1.1_f6d79da0e7e91cb7_.log 2025-09-07T08:07:53.6798994Z 2025-09-07T08:07:54.4376190Z Uploading artifacts took 0.76 seconds 2025-09-07T08:07:56.5904848Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T08:07:56.5906874Z import pkg_resources 2025-09-07T08:07:56.6204053Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T08:07:56.6205676Z import pkg_resources 2025-09-07T08:07:56.6326422Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-09-07T08:07:56.6328028Z import pkg_resources 2025-09-07T08:07:56.7135313Z Running cpp/static_runtime_test 1/1 ... [2025-09-07 08:07:56.713199] 2025-09-07T08:07:56.7135793Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-09-07T08:07:56.7138871Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/static_runtime_test', '-m', 'not serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-972d9791961baf18.xml', '-x', '--reruns=2'] ... [2025-09-07 08:07:56.713621] 2025-09-07T08:14:33.0085943Z 2025-09-07T08:14:33.0086767Z cpp/static_runtime_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.static_runtime_test_1.1_10cab88594276004_.log 2025-09-07T08:14:33.0088046Z 2025-09-07T08:14:33.6437044Z Running test batch 'tests to run' cost 401.43 seconds 2025-09-07T08:14:34.2791608Z + [[ -z 1 ]] 2025-09-07T08:14:34.2791883Z + [[ 1 == \2 ]] 2025-09-07T08:14:34.2792477Z + assert_git_not_dirty 2025-09-07T08:14:34.2792793Z + [[ linux-jammy-py3.13-clang12 != *rocm* ]] 2025-09-07T08:14:34.2793154Z + [[ linux-jammy-py3.13-clang12 != *xla* ]] 2025-09-07T08:14:34.2797587Z ++ git status --porcelain 2025-09-07T08:14:34.2798155Z ++ grep -v '?? third_party' 2025-09-07T08:14:34.5523868Z ++ true 2025-09-07T08:14:34.5524672Z + git_status= 2025-09-07T08:14:34.5524982Z + [[ -n '' ]] 2025-09-07T08:14:34.5525318Z + [[ linux-jammy-py3.13-clang12 == *xpu* ]] 2025-09-07T08:14:34.5525896Z + sccache_epilogue 2025-09-07T08:14:34.5526248Z + echo '::group::Sccache Compilation Log' 2025-09-07T08:14:34.5527023Z ##[group]Sccache Compilation Log 2025-09-07T08:14:34.5527390Z + echo '=================== sccache compilation log ===================' 2025-09-07T08:14:34.5527840Z =================== sccache compilation log =================== 2025-09-07T08:14:34.5528476Z + python /var/lib/jenkins/workspace/.ci/pytorch/print_sccache_log.py /var/lib/jenkins/sccache_error.log 2025-09-07T08:14:34.5664300Z + echo '=========== If your build fails, please take a look at the log above for possible reasons ===========' 2025-09-07T08:14:34.5665025Z =========== If your build fails, please take a look at the log above for possible reasons =========== 2025-09-07T08:14:34.5665539Z + sccache --show-stats 2025-09-07T08:14:34.5687348Z Compile requests 1595 2025-09-07T08:14:34.5687683Z Compile requests executed 137 2025-09-07T08:14:34.5688013Z Cache hits 99 2025-09-07T08:14:34.5688330Z Cache hits (C/C++) 99 2025-09-07T08:14:34.5688667Z Cache misses 37 2025-09-07T08:14:34.5688990Z Cache misses (C/C++) 37 2025-09-07T08:14:34.5689301Z Cache hits rate 72.79 % 2025-09-07T08:14:34.5689641Z Cache hits rate (C/C++) 72.79 % 2025-09-07T08:14:34.5690012Z Cache timeouts 0 2025-09-07T08:14:34.5690334Z Cache read errors 0 2025-09-07T08:14:34.5691022Z Forced recaches 0 2025-09-07T08:14:34.5691351Z Cache write errors 0 2025-09-07T08:14:34.5691675Z Cache errors 0 2025-09-07T08:14:34.5691998Z Compilations 37 2025-09-07T08:14:34.5692313Z Compilation failures 1 2025-09-07T08:14:34.5692651Z Non-cacheable compilations 0 2025-09-07T08:14:34.5692986Z Non-cacheable calls 12 2025-09-07T08:14:34.5693318Z Non-compilation calls 1446 2025-09-07T08:14:34.5693641Z Unsupported compiler calls 0 2025-09-07T08:14:34.5694040Z Average cache write 0.043 s 2025-09-07T08:14:34.5694374Z Average compiler 12.913 s 2025-09-07T08:14:34.5694807Z Average cache read hit 0.038 s 2025-09-07T08:14:34.5695143Z Failed distributed compilations 0 2025-09-07T08:14:34.5695467Z 2025-09-07T08:14:34.5695564Z Non-cacheable reasons: 2025-09-07T08:14:34.5695841Z -E 12 2025-09-07T08:14:34.5696050Z 2025-09-07T08:14:34.5696298Z Cache location s3, name: ossci-compiler-cache-circleci-v2, prefix: / 2025-09-07T08:14:34.5696765Z Version (client) 0.10.0 2025-09-07T08:14:34.5697067Z + sccache --stop-server 2025-09-07T08:14:34.5710028Z Stopping sccache server... 2025-09-07T08:14:34.5713171Z Compile requests 1595 2025-09-07T08:14:34.5713557Z Compile requests executed 137 2025-09-07T08:14:34.5713877Z Cache hits 99 2025-09-07T08:14:34.5714269Z Cache hits (C/C++) 99 2025-09-07T08:14:34.5714608Z Cache misses 37 2025-09-07T08:14:34.5714929Z Cache misses (C/C++) 37 2025-09-07T08:14:34.5715267Z Cache hits rate 72.79 % 2025-09-07T08:14:34.5715586Z Cache hits rate (C/C++) 72.79 % 2025-09-07T08:14:34.5715915Z Cache timeouts 0 2025-09-07T08:14:34.5716387Z Cache read errors 0 2025-09-07T08:14:34.5716710Z Forced recaches 0 2025-09-07T08:14:34.5717017Z Cache write errors 0 2025-09-07T08:14:34.5717333Z Cache errors 0 2025-09-07T08:14:34.5717652Z Compilations 37 2025-09-07T08:14:34.5717976Z Compilation failures 1 2025-09-07T08:14:34.5718300Z Non-cacheable compilations 0 2025-09-07T08:14:34.5718643Z Non-cacheable calls 12 2025-09-07T08:14:34.5718980Z Non-compilation calls 1446 2025-09-07T08:14:34.5719317Z Unsupported compiler calls 0 2025-09-07T08:14:34.5719647Z Average cache write 0.043 s 2025-09-07T08:14:34.5719980Z Average compiler 12.913 s 2025-09-07T08:14:34.5720318Z Average cache read hit 0.038 s 2025-09-07T08:14:34.5720658Z Failed distributed compilations 0 2025-09-07T08:14:34.5720892Z 2025-09-07T08:14:34.5720990Z Non-cacheable reasons: 2025-09-07T08:14:34.5721378Z -E 12 2025-09-07T08:14:34.5721664Z 2025-09-07T08:14:34.5721934Z Cache location s3, name: ossci-compiler-cache-circleci-v2, prefix: / 2025-09-07T08:14:34.5729554Z Version (client) 0.10.0 2025-09-07T08:14:34.5730043Z + echo ::endgroup:: 2025-09-07T08:14:34.5730550Z ##[endgroup] 2025-09-07T08:14:34.5730784Z + cleanup_workspace 2025-09-07T08:14:34.5731299Z + echo 'sudo may print the following warning message that can be ignored. The chown command will still run.' 2025-09-07T08:14:34.5732116Z sudo may print the following warning message that can be ignored. The chown command will still run. 2025-09-07T08:14:34.5732848Z + echo ' sudo: setrlimit(RLIMIT_STACK): Operation not permitted' 2025-09-07T08:14:34.5733405Z sudo: setrlimit(RLIMIT_STACK): Operation not permitted 2025-09-07T08:14:34.5734102Z + echo 'For more details refer to https://github.com/sudo-project/sudo/issues/42' 2025-09-07T08:14:34.5735030Z For more details refer to https://github.com/sudo-project/sudo/issues/42 2025-09-07T08:14:34.5735526Z + sudo chown -R 1000 /var/lib/jenkins/workspace 2025-09-07T08:14:35.2656499Z ##[group]Run pytorch/test-infra/.github/actions/upload-benchmark-results@main 2025-09-07T08:14:35.2656992Z with: 2025-09-07T08:14:35.2657253Z benchmark-results-dir: test/test-reports 2025-09-07T08:14:35.2657593Z dry-run: false 2025-09-07T08:14:35.2657839Z schema-version: v3 2025-09-07T08:14:35.2658303Z github-token: *** 2025-09-07T08:14:35.2658528Z env: 2025-09-07T08:14:35.2658756Z GIT_DEFAULT_BRANCH: main 2025-09-07T08:14:35.2659227Z DOCKER_CONTAINER_ID: 2a1e06725f9987eb4a7a91656c15a654e902febdeb1d85d79899892d5c9f5783 2025-09-07T08:14:35.2659736Z ##[endgroup] 2025-09-07T08:14:35.2688643Z ##[group]Run set -eux 2025-09-07T08:14:35.2688957Z set -eux 2025-09-07T08:14:35.2689275Z  2025-09-07T08:14:35.2689495Z if [[ -n "" ]]; then 2025-09-07T08:14:35.2689774Z  source "" 2025-09-07T08:14:35.2690031Z fi 2025-09-07T08:14:35.2690394Z python3 -mpip install boto3==1.35.33 psutil==7.0.0 pynvml==12.0.0 2025-09-07T08:14:35.2690929Z  2025-09-07T08:14:35.2691155Z DEVICE_NAME="" 2025-09-07T08:14:35.2691414Z DEVICE_TYPE="" 2025-09-07T08:14:35.2691673Z  2025-09-07T08:14:35.2691914Z if command -v nvidia-smi; then 2025-09-07T08:14:35.2692384Z  # NB: I'm using PyTorch here to get the device name, however, it needs to 2025-09-07T08:14:35.2692977Z  # install the correct version of PyTorch manually for now. Any PyTorch 2025-09-07T08:14:35.2693540Z  # version is fine, I just use 2.7.1 to satify PYPIDEP linter 2025-09-07T08:14:35.2693985Z  python3 -mpip install torch==2.7.1 2025-09-07T08:14:35.2694346Z elif command -v rocminfo; then 2025-09-07T08:14:35.2694793Z  # NB: Installing torch on ROCm runner with pip here causes CI to fail 2025-09-07T08:14:35.2695375Z  # with a memoryview is too large error only on MI300 runners. Is pip 2025-09-07T08:14:35.2695967Z  # version on ROCm runner there too old? As a workaround, let's use the 2025-09-07T08:14:35.2696479Z  # GPU device name coming from rocminfo instead 2025-09-07T08:14:35.2696856Z  DEVICE_NAME=rocm 2025-09-07T08:14:35.2697341Z  DEVICE_TYPE=$(rocminfo | grep "Marketing Name" | tail -n1 | awk -F':' '{print $2}' | xargs) 2025-09-07T08:14:35.2697859Z fi 2025-09-07T08:14:35.2698074Z  2025-09-07T08:14:35.2698348Z echo "DEVICE_NAME=$DEVICE_NAME" >> $GITHUB_ENV 2025-09-07T08:14:35.2698774Z echo "DEVICE_TYPE=$DEVICE_TYPE" >> $GITHUB_ENV 2025-09-07T08:14:35.2708415Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-07T08:14:35.2708810Z env: 2025-09-07T08:14:35.2709032Z GIT_DEFAULT_BRANCH: main 2025-09-07T08:14:35.2709516Z DOCKER_CONTAINER_ID: 2a1e06725f9987eb4a7a91656c15a654e902febdeb1d85d79899892d5c9f5783 2025-09-07T08:14:35.2710017Z ##[endgroup] 2025-09-07T08:14:35.2741353Z + [[ -n '' ]] 2025-09-07T08:14:35.2741727Z + python3 -mpip install boto3==1.35.33 psutil==7.0.0 pynvml==12.0.0 2025-09-07T08:14:35.5159514Z Defaulting to user installation because normal site-packages is not writeable 2025-09-07T08:14:36.6027838Z Collecting boto3==1.35.33 2025-09-07T08:14:36.6198568Z Downloading boto3-1.35.33-py3-none-any.whl (139 kB) 2025-09-07T08:14:36.9344871Z Collecting psutil==7.0.0 2025-09-07T08:14:36.9384188Z Downloading psutil-7.0.0-cp36-abi3-manylinux_2_12_x86_64.manylinux2010_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (277 kB) 2025-09-07T08:14:36.9662909Z Collecting pynvml==12.0.0 2025-09-07T08:14:36.9697959Z Downloading pynvml-12.0.0-py3-none-any.whl (26 kB) 2025-09-07T08:14:38.1251673Z Collecting botocore<1.36.0,>=1.35.33 2025-09-07T08:14:38.1311004Z Downloading botocore-1.35.99-py3-none-any.whl (13.3 MB) 2025-09-07T08:14:38.2768145Z 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) 2025-09-07T08:14:38.3152615Z Collecting s3transfer<0.11.0,>=0.10.0 2025-09-07T08:14:38.3190746Z Downloading s3transfer-0.10.4-py3-none-any.whl (83 kB) 2025-09-07T08:14:38.3674358Z Collecting nvidia-ml-py<13.0.0a0,>=12.0.0 2025-09-07T08:14:38.3710689Z Downloading nvidia_ml_py-12.575.51-py3-none-any.whl (47 kB) 2025-09-07T08:14:38.3806773Z 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) 2025-09-07T08:14:38.3822501Z 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) 2025-09-07T08:14:38.5742920Z 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) 2025-09-07T08:14:38.7118236Z Installing collected packages: botocore, s3transfer, nvidia-ml-py, pynvml, psutil, boto3 2025-09-07T08:14:39.2261570Z Attempting uninstall: nvidia-ml-py 2025-09-07T08:14:39.2262892Z Found existing installation: nvidia-ml-py 11.525.84 2025-09-07T08:14:39.2276830Z Uninstalling nvidia-ml-py-11.525.84: 2025-09-07T08:14:39.2468731Z Successfully uninstalled nvidia-ml-py-11.525.84 2025-09-07T08:14:39.3109988Z Attempting uninstall: psutil 2025-09-07T08:14:39.3110879Z Found existing installation: psutil 5.9.8 2025-09-07T08:14:39.3179399Z Uninstalling psutil-5.9.8: 2025-09-07T08:14:39.3185823Z Successfully uninstalled psutil-5.9.8 2025-09-07T08:14:39.4894171Z Successfully installed boto3-1.35.33 botocore-1.35.99 nvidia-ml-py-12.575.51 psutil-7.0.0 pynvml-12.0.0 s3transfer-0.10.4 2025-09-07T08:14:39.6097293Z + DEVICE_NAME= 2025-09-07T08:14:39.6097660Z + DEVICE_TYPE= 2025-09-07T08:14:39.6097914Z + command -v nvidia-smi 2025-09-07T08:14:39.6098214Z + command -v rocminfo 2025-09-07T08:14:39.6098578Z + echo DEVICE_NAME= 2025-09-07T08:14:39.6098829Z + echo DEVICE_TYPE= 2025-09-07T08:14:39.6131606Z ##[group]Run set -eux 2025-09-07T08:14:39.6131898Z set -eux 2025-09-07T08:14:39.6132157Z  2025-09-07T08:14:39.6132396Z if [[ -z "${GITHUB_TOKEN}" ]]; then 2025-09-07T08:14:39.6132771Z  echo "Missing github-token input" 2025-09-07T08:14:39.6133109Z  exit 1 2025-09-07T08:14:39.6133341Z fi 2025-09-07T08:14:39.6139666Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-07T08:14:39.6140084Z env: 2025-09-07T08:14:39.6140312Z GIT_DEFAULT_BRANCH: main 2025-09-07T08:14:39.6140782Z DOCKER_CONTAINER_ID: 2a1e06725f9987eb4a7a91656c15a654e902febdeb1d85d79899892d5c9f5783 2025-09-07T08:14:39.6141281Z DEVICE_NAME: 2025-09-07T08:14:39.6141513Z DEVICE_TYPE: 2025-09-07T08:14:39.6141987Z GITHUB_TOKEN: *** 2025-09-07T08:14:39.6142228Z ##[endgroup] 2025-09-07T08:14:39.6167919Z + [[ -z *** ]] 2025-09-07T08:14:39.6219782Z ##[group]Run pytorch/test-infra/.github/actions/get-workflow-job-id@main 2025-09-07T08:14:39.6220245Z with: 2025-09-07T08:14:39.6220620Z github-token: *** 2025-09-07T08:14:39.6220856Z env: 2025-09-07T08:14:39.6221086Z GIT_DEFAULT_BRANCH: main 2025-09-07T08:14:39.6221543Z DOCKER_CONTAINER_ID: 2a1e06725f9987eb4a7a91656c15a654e902febdeb1d85d79899892d5c9f5783 2025-09-07T08:14:39.6222055Z DEVICE_NAME: 2025-09-07T08:14:39.6222291Z DEVICE_TYPE: 2025-09-07T08:14:39.6222517Z ##[endgroup] 2025-09-07T08:14:39.6247537Z ##[group]Run set -eux 2025-09-07T08:14:39.6247819Z set -eux 2025-09-07T08:14:39.6248054Z  2025-09-07T08:14:39.6248538Z python3 "${GITHUB_ACTION_PATH}/../../scripts/get_workflow_job_id.py" "${GITHUB_RUN_ID}" "${RUNNER_NAME}" 2025-09-07T08:14:39.6254473Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-07T08:14:39.6254859Z env: 2025-09-07T08:14:39.6255083Z GIT_DEFAULT_BRANCH: main 2025-09-07T08:14:39.6255563Z DOCKER_CONTAINER_ID: 2a1e06725f9987eb4a7a91656c15a654e902febdeb1d85d79899892d5c9f5783 2025-09-07T08:14:39.6256156Z DEVICE_NAME: 2025-09-07T08:14:39.6256391Z DEVICE_TYPE: 2025-09-07T08:14:39.6256783Z GITHUB_TOKEN: *** 2025-09-07T08:14:39.6257162Z ##[endgroup] 2025-09-07T08:14:39.6281723Z + python3 /home/ec2-user/actions-runner/_work/_actions/pytorch/test-infra/main/.github/actions/get-workflow-job-id/../../scripts/get_workflow_job_id.py 17524754568 i-09e16a84d31719099 2025-09-07T08:14:41.9368125Z setting job-id=49774041675 2025-09-07T08:14:41.9368665Z setting job-name=linux-jammy-py3.13-clang12 / test (default, 1, 5, linux.4xlarge) 2025-09-07T08:14:41.9492318Z ##[group]Run set -eux 2025-09-07T08:14:41.9492607Z set -eux 2025-09-07T08:14:41.9492839Z  2025-09-07T08:14:41.9493060Z if [[ -n "" ]]; then 2025-09-07T08:14:41.9493328Z  source "" 2025-09-07T08:14:41.9493572Z fi 2025-09-07T08:14:41.9493785Z  2025-09-07T08:14:41.9494286Z python3 "${GITHUB_ACTION_PATH}/../../scripts/benchmarks/gather_metadata.py" \ 2025-09-07T08:14:41.9494819Z  --schema-version "${SCHEMA_VERSION}" \ 2025-09-07T08:14:41.9495186Z  --repo "${REPO}" \ 2025-09-07T08:14:41.9495513Z  --head-branch "${HEAD_BRANCH}" \ 2025-09-07T08:14:41.9495860Z  --head-sha "${HEAD_SHA}" \ 2025-09-07T08:14:41.9496196Z  --workflow-id "${WORKFLOW_RUN_ID}" \ 2025-09-07T08:14:41.9496564Z  --run-attempt "${RUN_ATTEMPT}" \ 2025-09-07T08:14:41.9496902Z  --job-id "${JOB_ID}" \ 2025-09-07T08:14:41.9497215Z  --job-name "${JOB_NAME}" 2025-09-07T08:14:41.9503155Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-07T08:14:41.9503550Z env: 2025-09-07T08:14:41.9503777Z GIT_DEFAULT_BRANCH: main 2025-09-07T08:14:41.9504248Z DOCKER_CONTAINER_ID: 2a1e06725f9987eb4a7a91656c15a654e902febdeb1d85d79899892d5c9f5783 2025-09-07T08:14:41.9504750Z DEVICE_NAME: 2025-09-07T08:14:41.9504988Z DEVICE_TYPE: 2025-09-07T08:14:41.9505263Z SCHEMA_VERSION: v3 2025-09-07T08:14:41.9505521Z REPO: pytorch/pytorch 2025-09-07T08:14:41.9505884Z HEAD_BRANCH: refs/heads/main 2025-09-07T08:14:41.9506249Z HEAD_SHA: 93fb23d6fae7c4e82c4239a1033e522088742634 2025-09-07T08:14:41.9506614Z WORKFLOW_RUN_ID: 17524754568 2025-09-07T08:14:41.9506890Z RUN_ATTEMPT: 1 2025-09-07T08:14:41.9507107Z JOB_ID: 49774041675 2025-09-07T08:14:41.9507497Z JOB_NAME: linux-jammy-py3.13-clang12 / test (default, 1, 5, linux.4xlarge) 2025-09-07T08:14:41.9507947Z ##[endgroup] 2025-09-07T08:14:41.9536169Z + [[ -n '' ]] 2025-09-07T08:14:41.9538516Z + 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 refs/heads/main --head-sha 93fb23d6fae7c4e82c4239a1033e522088742634 --workflow-id 17524754568 --run-attempt 1 --job-id 49774041675 --job-name 'linux-jammy-py3.13-clang12 / test (default, 1, 5, linux.4xlarge)' 2025-09-07T08:14:41.9862774Z ##[group]Run set -eux 2025-09-07T08:14:41.9863079Z set -eux 2025-09-07T08:14:41.9863295Z  2025-09-07T08:14:41.9863520Z if [[ -n "" ]]; then 2025-09-07T08:14:41.9863814Z  source "" 2025-09-07T08:14:41.9864058Z fi 2025-09-07T08:14:41.9864257Z  2025-09-07T08:14:41.9864655Z python3 "${GITHUB_ACTION_PATH}/../../scripts/benchmarks/gather_runners_info.py" 2025-09-07T08:14:41.9870583Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-07T08:14:41.9870972Z env: 2025-09-07T08:14:41.9871179Z GIT_DEFAULT_BRANCH: main 2025-09-07T08:14:41.9871649Z DOCKER_CONTAINER_ID: 2a1e06725f9987eb4a7a91656c15a654e902febdeb1d85d79899892d5c9f5783 2025-09-07T08:14:41.9872156Z DEVICE_NAME: 2025-09-07T08:14:41.9872384Z DEVICE_TYPE: 2025-09-07T08:14:41.9872596Z ##[endgroup] 2025-09-07T08:14:41.9896329Z + [[ -n '' ]] 2025-09-07T08:14:41.9897174Z + python3 /home/ec2-user/actions-runner/_work/_actions/pytorch/test-infra/main/.github/actions/upload-benchmark-results/../../scripts/benchmarks/gather_runners_info.py 2025-09-07T08:14:42.0298125Z INFO:root:Fail to import torch to get the device name 2025-09-07T08:14:42.0411100Z ##[group]Run set -eux 2025-09-07T08:14:42.0411387Z set -eux 2025-09-07T08:14:42.0411619Z  2025-09-07T08:14:42.0411862Z # TODO (huydhn): Implement this part 2025-09-07T08:14:42.0412273Z echo "dependencies={}" >> "${GITHUB_OUTPUT}" 2025-09-07T08:14:42.0418266Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-07T08:14:42.0418660Z env: 2025-09-07T08:14:42.0418924Z GIT_DEFAULT_BRANCH: main 2025-09-07T08:14:42.0419389Z DOCKER_CONTAINER_ID: 2a1e06725f9987eb4a7a91656c15a654e902febdeb1d85d79899892d5c9f5783 2025-09-07T08:14:42.0419906Z DEVICE_NAME: 2025-09-07T08:14:42.0420142Z DEVICE_TYPE: 2025-09-07T08:14:42.0420411Z ##[endgroup] 2025-09-07T08:14:42.0443649Z + echo 'dependencies={}' 2025-09-07T08:14:42.0474342Z ##[group]Run set -eux 2025-09-07T08:14:42.0474685Z set -eux 2025-09-07T08:14:42.0474928Z  2025-09-07T08:14:42.0475149Z if [[ -n "" ]]; then 2025-09-07T08:14:42.0475483Z  source "" 2025-09-07T08:14:42.0475746Z fi 2025-09-07T08:14:42.0475968Z  2025-09-07T08:14:42.0476235Z if [[ ! -d "${BENCHMARK_RESULTS_DIR}" ]]; then 2025-09-07T08:14:42.0476672Z  echo "${BENCHMARK_RESULTS_DIR} does not exist, skipping" 2025-09-07T08:14:42.0477171Z  # We don't want the job to fail if the directory doesn't exist 2025-09-07T08:14:42.0477573Z  exit 0 2025-09-07T08:14:42.0477801Z fi 2025-09-07T08:14:42.0477999Z  2025-09-07T08:14:42.0478236Z if [[ "${DRY_RUN}" == "true" ]]; then 2025-09-07T08:14:42.0478725Z  python3 "${GITHUB_ACTION_PATH}/../../scripts/upload_benchmark_results.py" \ 2025-09-07T08:14:42.0479297Z  --benchmark-results-dir "${BENCHMARK_RESULTS_DIR}" \ 2025-09-07T08:14:42.0479740Z  --metadata "${BENCHMARK_METADATA}" \ 2025-09-07T08:14:42.0480095Z  --runners "${RUNNER_INFO}" \ 2025-09-07T08:14:42.0480452Z  --dependencies "${DEPENDENCIES}" \ 2025-09-07T08:14:42.0480794Z  --dry-run 2025-09-07T08:14:42.0481052Z else 2025-09-07T08:14:42.0481433Z  python3 "${GITHUB_ACTION_PATH}/../../scripts/upload_benchmark_results.py" \ 2025-09-07T08:14:42.0482005Z  --benchmark-results-dir "${BENCHMARK_RESULTS_DIR}" \ 2025-09-07T08:14:42.0482445Z  --metadata "${BENCHMARK_METADATA}" \ 2025-09-07T08:14:42.0496533Z  --runners "${RUNNER_INFO}" \ 2025-09-07T08:14:42.0497108Z  --dependencies "${DEPENDENCIES}" 2025-09-07T08:14:42.0497468Z fi 2025-09-07T08:14:42.0502994Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-07T08:14:42.0503388Z env: 2025-09-07T08:14:42.0503619Z GIT_DEFAULT_BRANCH: main 2025-09-07T08:14:42.0504102Z DOCKER_CONTAINER_ID: 2a1e06725f9987eb4a7a91656c15a654e902febdeb1d85d79899892d5c9f5783 2025-09-07T08:14:42.0504612Z DEVICE_NAME: 2025-09-07T08:14:42.0504849Z DEVICE_TYPE: 2025-09-07T08:14:42.0505140Z BENCHMARK_RESULTS_DIR: test/test-reports 2025-09-07T08:14:42.0505468Z DRY_RUN: false 2025-09-07T08:14:42.0506780Z BENCHMARK_METADATA: {"timestamp": 1757232881, "schema_version": "v3", "name": "linux-jammy-py3.13-clang12 / test (default, 1, 5, linux.4xlarge)", "repo": "pytorch/pytorch", "head_branch": "refs/heads/main", "head_sha": "93fb23d6fae7c4e82c4239a1033e522088742634", "workflow_id": 17524754568, "run_attempt": 1, "job_id": 49774041675} 2025-09-07T08:14:42.0508506Z RUNNER_INFO: [{"cpu_info": "x86_64", "cpu_count": 16, "avail_mem_in_gb": 30, "extra_info": {"hostname": "ip-10-0-6-53.ec2.internal"}, "name": "", "type": ""}] 2025-09-07T08:14:42.0509151Z DEPENDENCIES: {} 2025-09-07T08:14:42.0509386Z ##[endgroup] 2025-09-07T08:14:42.0532856Z + [[ -n '' ]] 2025-09-07T08:14:42.0533162Z + [[ ! -d test/test-reports ]] 2025-09-07T08:14:42.0533507Z + [[ false == \t\r\u\e ]] 2025-09-07T08:14:42.0536514Z + 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": 1757232881, "schema_version": "v3", "name": "linux-jammy-py3.13-clang12 / test (default, 1, 5, linux.4xlarge)", "repo": "pytorch/pytorch", "head_branch": "refs/heads/main", "head_sha": "93fb23d6fae7c4e82c4239a1033e522088742634", "workflow_id": 17524754568, "run_attempt": 1, "job_id": 49774041675}' --runners '[{"cpu_info": "x86_64", "cpu_count": 16, "avail_mem_in_gb": 30, "extra_info": {"hostname": "ip-10-0-6-53.ec2.internal"}, "name": "", "type": ""}]' --dependencies '{}' 2025-09-07T08:14:42.1943117Z /home/ec2-user/actions-runner/_work/_actions/pytorch/test-infra/main/.github/actions/upload-benchmark-results/../../scripts/upload_benchmark_results.py:236: UserWarning: {'included': [{'test_file': 'lazy/test_ts_opinfo'}], 'excluded': []} from test/test-reports/td_exclusions-e2c77fbdefcfb8f57eb9.json is not a benchmark record, skipping 2025-09-07T08:14:42.1945017Z warn(f"{result} from {filepath} is not a benchmark record, skipping") 2025-09-07T08:14:42.2027619Z /home/ec2-user/actions-runner/_work/_actions/pytorch/test-infra/main/.github/actions/upload-benchmark-results/../../scripts/upload_benchmark_results.py:236: UserWarning: {'included': [{'test_file': 'test_reductions'}, {'test_file': 'dynamo/cpython/3_13/test_sort'}, {'test_file': 'inductor/test_extension_backend'}, {'test_file': 'test_ci_sanity_check_fail'}, {'test_file': 'inductor/test_aot_inductor_arrayref'}, {'test_file': 'inductor/test_compiled_autograd'}, {'test_file': 'inductor/test_triton_kernels'}, {'test_file': 'inductor/test_torchinductor'}, {'test_file': 'higher_order_ops/test_with_effects'}, {'test_file': 'dynamo/test_functions'}, {'test_file': 'test_openreg'}, {'test_file': 'dynamo/test_dynamic_shapes'}, {'test_file': 'inductor/test_compile_subprocess'}, {'test_file': 'dynamo/test_utils'}, {'test_file': 'inductor/test_cpu_cpp_wrapper'}, {'test_file': 'inductor/test_minifier'}, {'test_file': 'inductor/test_torchinductor_codegen_dynamic_shapes'}, {'test_file': 'dynamo/test_repros'}, {'test_file': 'inductor/test_fuzzer'}, {'test_file': 'functorch/test_aotdispatch'}, {'test_file': 'test_jit_fuser_te'}, {'test_file': 'test_type_hints'}, {'test_file': 'profiler/test_profiler'}, {'test_file': 'inductor/test_split_cat_fx_aten_passes'}, {'test_file': 'inductor/test_torchinductor_dynamic_shapes'}, {'test_file': 'export/test_torchbind'}, {'test_file': 'test_numpy_interop'}, {'test_file': 'test_nestedtensor'}, {'test_file': 'test_jit'}, {'test_file': 'dynamo/test_modes'}, {'test_file': 'inductor/test_memory'}, {'test_file': 'test_cpp_extensions_mtia_backend'}, {'test_file': 'test_content_store'}, {'test_file': 'inductor/test_inductor_freezing'}, {'test_file': 'dynamo/test_structured_trace'}, {'test_file': 'inductor/test_torchbind'}, {'test_file': 'test_cpp_extensions_stream_and_event'}, {'test_file': 'functorch/test_eager_transforms'}, {'test_file': 'dynamo/test_aot_autograd_cache'}, {'test_file': 'test_python_dispatch'}, {'test_file': 'test_tensor_creation_ops'}, {'test_file': 'inductor/test_cpu_repro'}, {'test_file': 'dynamo/test_trace_rules'}, {'test_file': 'dynamo/test_package'}, {'test_file': 'torch_np/numpy_tests/core/test_indexing'}, {'test_file': 'nn/test_parametrization'}, {'test_file': 'test_autograd'}, {'test_file': 'functorch/test_control_flow'}, {'test_file': 'test_testing'}, {'test_file': 'test_indexing'}, {'test_file': 'dynamo/test_subclasses'}, {'test_file': 'test_cpp_extensions_jit'}, {'test_file': 'export/test_draft_export'}, {'test_file': 'test_nn'}, {'test_file': 'test_overrides'}, {'test_file': 'test_type_promotion'}, {'test_file': 'test_quantization'}, {'test_file': 'inductor/test_aot_inductor'}, {'test_file': 'inductor/test_kernel_benchmark'}, {'test_file': 'dynamo/cpython/3_13/test_generator_stop'}, {'test_file': 'dynamo/cpython/3_13/test_exception_variations'}, {'test_file': 'dynamo/cpython/3_13/test_int_literal'}, {'test_file': 'dynamo/cpython/3_13/test_with'}, {'test_file': 'dynamo/cpython/3_13/test_contextlib'}, {'test_file': 'dynamo/cpython/3_13/test_raise'}, {'test_file': 'dynamo/cpython/3_13/test_ordered_dict'}, {'test_file': 'dynamo/cpython/3_13/test_numeric_tower'}, {'test_file': 'dynamo/cpython/3_13/test_collections'}, {'test_file': 'dynamo/cpython/3_13/test_heapq'}, {'test_file': 'dynamo/cpython/3_13/test_exceptions'}, {'test_file': 'dynamo/cpython/3_13/test_itertools'}, {'test_file': 'dynamo/cpython/3_13/test_int'}, {'test_file': 'dynamo/cpython/3_13/test_baseexception'}, {'test_file': 'dynamo/cpython/3_13/test_operator'}, {'test_file': 'dynamo/cpython/3_13/test_userlist'}, {'test_file': 'dynamo/cpython/3_13/test_sys'}, {'test_file': 'dynamo/cpython/3_13/test_userdict'}, {'test_file': 'dynamo/cpython/3_13/test_generators'}, {'test_file': 'dynamo/cpython/3_13/test_math'}, {'test_file': 'dynamo/cpython/3_13/test_range'}, {'test_file': 'dynamo/cpython/3_13/test_defaultdict'}, {'test_file': 'dynamo/cpython/3_13/test_iter'}, {'test_file': 'dynamo/cpython/3_13/test_set'}, {'test_file': 'dynamo/cpython/3_13/test_bool'}, {'test_file': 'dynamo/cpython/3_13/test_cmath'}, {'test_file': 'dynamo/cpython/3_13/test_dict'}, {'test_file': 'dynamo/cpython/3_13/test_float'}, {'test_file': 'dynamo/cpython/3_13/test_list'}, {'test_file': 'dynamo/cpython/3_13/test_complex'}, {'test_file': 'dynamo/cpython/3_13/test_tuple'}, {'test_file': 'test_package'}, {'test_file': 'export/test_retraceability'}, {'test_file': 'export/test_export_strict'}, {'test_file': 'inductor/test_triton_extension_backend'}, {'test_file': 'inductor/test_triton_syntax'}, {'test_file': 'test_autoload'}, {'test_file': 'dynamo/test_deque_reconstruct'}, {'test_file': 'test_utils_config_module'}, {'test_file': 'test_mkl_verbose'}, {'test_file': 'export/test_unflatten_training_ir'}, {'test_file': 'inductor/test_cpp_wrapper_hipify'}, {'test_file': 'inductor/test_external_callables'}, {'test_file': 'inductor/test_remote_cache'}, {'test_file': 'export/test_export_training_ir_to_run_decomp'}, {'test_file': 'inductor/test_segmented_tree'}, {'test_file': 'export/test_serdes'}, {'test_file': 'dynamo/cpython/3_13/test_unittest/test_assertions'}, {'test_file': 'test_comparison_utils'}, {'test_file': 'inductor/test_provenance_tracing'}, {'test_file': 'export/test_functionalized_assertions'}, {'test_file': 'test_license'}, {'test_file': 'dynamo/test_base_output'}, {'test_file': 'test_mkldnn_verbose'}, {'test_file': 'inductor/test_inductor_utils'}, {'test_file': 'inductor/test_flex_decoding'}, {'test_file': 'cpp_extensions/torch_stable_test_extension/torch_stable_test/test_torch_stable'}, {'test_file': 'inductor/test_analysis'}, {'test_file': 'test_extension_utils'}, {'test_file': 'test_rename_privateuse1_to_existing_device'}, {'test_file': 'inductor/test_cutedsl_template'}, {'test_file': 'inductor/test_ck_backend'}, {'test_file': 'inductor/test_memory_planning'}, {'test_file': 'export/test_export_with_inline_and_install'}, {'test_file': 'dynamo/test_skip_guard_eval_unsafe'}, {'test_file': 'inductor/test_inplace_padding'}, {'test_file': 'dynamo/test_buffers_override'}, {'test_file': 'test_custom_ops'}, {'test_file': 'inductor/test_flex_attention'}, {'test_file': 'inductor/test_b2b_gemm'}, {'test_file': 'functorch/test_ac_logging'}, {'test_file': 'inductor/test_inductor_annotations'}, {'test_file': 'dynamo/test_resume'}, {'test_file': 'inductor/test_template_heuristics_registry'}, {'test_file': 'inductor/test_debug_trace'}, {'test_file': 'test_ao_sparsity'}, {'test_file': 'inductor/test_cutlass_backend'}, {'test_file': 'test_cpp_api_parity'}, {'test_file': 'inductor/test_async_compile'}, {'test_file': 'dynamo/test_nops'}, {'test_file': 'torch_np/test_nep50_examples'}, {'test_file': 'torch_np/test_binary_ufuncs'}, {'test_file': 'inductor/test_best_config'}, {'test_file': 'test_hop_infra'}, {'test_file': 'torch_np/test_unary_ufuncs'}, {'test_file': 'inductor/test_aot_inductor_package'}, {'test_file': 'inductor/test_triton_cpu_backend'}, {'test_file': 'inductor/test_pad_mm'}, {'test_file': 'typing/test_python_operators'}, {'test_file': 'inductor/test_aot_inductor_custom_ops'}, {'test_file': 'inductor/test_cudagraph_trees'}, {'test_file': 'inductor/test_compile_worker'}, {'test_file': 'dynamo/test_modules'}, {'test_file': 'test_transformers'}, {'test_file': 'dynamo/test_global'}, {'test_file': 'export/test_export'}, {'test_file': 'test_foreach'}, {'test_file': 'test_appending_byte_serializer'}, {'test_file': 'test_fx_experimental'}, {'test_file': 'inductor/test_triton_wrapper'}, {'test_file': 'inductor/test_torchinductor_strided_blocks'}, {'test_file': 'test_file_check'}, {'test_file': 'dynamo/test_interop'}, {'test_file': 'dynamo/test_metrics_context'}, {'test_file': 'test_functionalization'}, {'test_file': 'dynamo/test_inline_and_install'}, {'test_file': 'inductor/test_smoke'}, {'test_file': 'torch_np/test_ufuncs_basic'}, {'test_file': 'test_proxy_tensor'}, {'test_file': 'inductor/test_fx_fusion'}, {'test_file': 'inductor/test_move_constructors_to_cuda'}, {'test_file': 'dynamo/test_skip_non_tensor'}, {'test_file': 'export/test_tree_utils'}, {'test_file': 'dynamo/test_frame_init'}, {'test_file': 'test_fx'}, {'test_file': 'torch_np/test_dtype'}, {'test_file': 'inductor/test_indexing'}, {'test_file': 'inductor/test_minifier_utils'}, {'test_file': 'test_typing'}, {'test_file': 'test_transformers_privateuse1'}, {'test_file': 'functorch/test_aot_joint_with_descriptors'}, {'test_file': 'test_utils_filelock'}, {'test_file': 'inductor/test_metrics'}, {'test_file': 'inductor/test_coordinate_descent_tuner'}, {'test_file': 'inductor/test_foreach'}, {'test_file': 'backends/xeon/test_launch'}, {'test_file': 'inductor/test_torchinductor_opinfo'}, {'test_file': 'dynamo/test_dicts'}, {'test_file': 'dynamo/test_sdpa'}, {'test_file': 'dynamo/test_list'}, {'test_file': 'inductor/test_autoheuristic'}, {'test_file': 'test_flop_counter'}, {'test_file': 'xpu/test_fusion'}, {'test_file': 'dynamo/test_fx_graph_runnable'}, {'test_file': 'inductor/test_ordered_set'}, {'test_file': 'dynamo/test_recompiles'}, {'test_file': 'test_per_overload_api'}, {'test_file': 'inductor/test_xpu_basic'}, {'test_file': 'export/test_cpp_serdes'}, {'test_file': 'inductor/test_utils'}, {'test_file': 'inductor/test_cuda_repro'}, {'test_file': 'test_pytree'}, {'test_file': 'inductor/test_fp8'}, {'test_file': 'dynamo/test_nested_graph_breaks'}, {'test_file': 'dynamo/test_pre_dispatch'}, {'test_file': 'dynamo/test_fx_passes_pre_grad'}, {'test_file': 'inductor/test_combo_kernels'}, {'test_file': 'inductor/test_gpu_cpp_wrapper'}, {'test_file': 'inductor/test_device_assert'}, {'test_file': 'inductor/test_op_completeness'}, {'test_file': 'export/test_tools'}, {'test_file': 'export/test_export_opinfo'}, {'test_file': 'dynamo/test_subgraphs'}, {'test_file': 'profiler/test_kineto'}, {'test_file': 'inductor/test_subgraph_choice'}, {'test_file': 'inductor/test_codecache'}, {'test_file': 'test_logging'}, {'test_file': 'test_expanded_weights'}, {'test_file': 'inductor/test_static_cuda_launcher'}, {'test_file': 'torch_np/test_random'}, {'test_file': 'inductor/test_triton_heuristics'}, {'test_file': 'export/test_schema'}, {'test_file': 'dynamo/test_reconstruct'}, {'test_file': 'inductor/test_helion_kernels'}, {'test_file': 'test_compile_benchmark_util'}, {'test_file': 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'cpp_extensions/python_agnostic_extension/test/test_python_agnostic'}, {'test_file': 'torch_np/numpy_tests/core/test_scalarinherit'}, {'test_file': 'inductor/test_graph_transform_observer'}, {'test_file': 'test_show_pickle'}, {'test_file': 'inductor/test_quantization'}, {'test_file': 'test_native_functions'}, {'test_file': 'test_utils'}, {'test_file': 'inductor/test_compiled_optimizers'}, {'test_file': 'dynamo/test_install_free_tensors'}, {'test_file': 'dynamo/test_graph_region_tracker'}, {'test_file': 'inductor/test_cooperative_reductions'}, {'test_file': 'inductor/test_inplacing_pass'}, {'test_file': 'dynamo/test_pgo'}, {'test_file': 'inductor/test_inductor_scheduler'}, {'test_file': 'inductor/test_cpu_select_algorithm'}, {'test_file': 'inductor/test_codegen_triton'}, {'test_file': 'export/test_package'}, {'test_file': 'inductor/test_cudacodecache'}, {'test_file': 'dynamo/test_export'}, {'test_file': 'inductor/test_custom_post_grad_passes'}, {'test_file': 'test_hub'}, {'test_file': 'dynamo/test_view'}, {'test_file': 'test_module_tracker'}, {'test_file': 'dynamo/test_after_aot'}, {'test_file': 'test_complex'}, {'test_file': 'test_meta'}, {'test_file': 'xpu/test_gemm'}, {'test_file': 'test_tensorexpr'}, {'test_file': 'inductor/test_halide'}, {'test_file': 'higher_order_ops/test_invoke_quant'}, {'test_file': 'inductor/test_online_softmax'}, {'test_file': 'inductor/test_split_cat_fx_passes'}, {'test_file': 'test_cuda_expandable_segments'}, {'test_file': 'dynamo/test_unittest'}, {'test_file': 'inductor/test_max_autotune'}, {'test_file': 'dynamo/test_guard_serialization'}, {'test_file': 'functorch/test_minifier'}, {'test_file': 'test_legacy_vmap'}, {'test_file': 'dynamo/test_cudagraphs_expandable_segments'}, {'test_file': 'test_multiprocessing'}, {'test_file': 'torch_np/numpy_tests/core/test_einsum'}, {'test_file': 'inductor/test_benchmarking'}, {'test_file': 'dynamo/test_model_output'}, {'test_file': 'torch_np/test_basic'}, {'test_file': 'test_segment_reductions'}, {'test_file': 'test_ops_fwd_gradients'}, {'test_file': 'inductor/test_compile'}, {'test_file': 'test_dispatch'}, {'test_file': 'test_pruning_op'}, {'test_file': 'inductor/test_multi_kernel'}, {'test_file': 'inductor/test_decompose_mem_bound_mm'}, {'test_file': 'inductor/test_block_analysis'}, {'test_file': 'inductor/test_minifier_isolate'}, {'test_file': 'export/test_swap'}, {'test_file': 'inductor/test_op_dtype_prop'}, {'test_file': 'test_tensorexpr_pybind'}, {'test_file': 'dynamo/test_misc'}, {'test_file': 'inductor/test_loop_ordering'}, {'test_file': 'inductor/test_cutlass_evt'}, {'test_file': 'dynamo/test_sets'}, {'test_file': 'inductor/test_cudagraph_trees_expandable_segments'}, {'test_file': 'dynamo/test_backward_higher_order_ops'}, {'test_file': 'inductor/test_torchinductor_codegen_config_overrides'}, {'test_file': 'dynamo/test_export_mutations'}, {'test_file': 'inductor/test_scatter_optimization'}, {'test_file': 'xpu/test_conv'}, {'test_file': 'test_ops_jit'}, {'test_file': 'inductor/test_perf'}, {'test_file': 'inductor/test_layout_optim'}, {'test_file': 'nn/test_multihead_attention'}, {'test_file': 'inductor/test_binary_folding'}, {'test_file': 'inductor/test_snode_runtime'}, {'test_file': 'distributions/test_constraints'}, {'test_file': 'functorch/test_ac_knapsack'}, {'test_file': 'profiler/test_record_function'}, {'test_file': 'export/test_serialize'}, {'test_file': 'test_ops_gradients'}, {'test_file': 'functorch/test_vmap'}, {'test_file': 'dynamo/test_flat_apply'}, {'test_file': 'export/test_unflatten'}, {'test_file': 'test_jiterator'}, {'test_file': 'lazy/test_step_closures'}, {'test_file': 'test_namedtuple_return_api'}, {'test_file': 'test_monitor'}, {'test_file': 'functorch/test_logging'}, {'test_file': 'test_stateless'}, {'test_file': 'torch_np/numpy_tests/core/test_numeric'}, {'test_file': 'test_weak'}, {'test_file': 'inductor/test_mkldnn_pattern_matcher'}, {'test_file': 'test_jit_disabled'}, {'test_file': 'dynamo/test_optimizers'}, {'test_file': 'functorch/test_ac'}, {'test_file': 'inductor/test_dependencies'}, {'test_file': 'inductor/test_custom_lowering'}, {'test_file': 'inductor/test_control_flow'}, {'test_file': 'dynamo/test_profiler'}, {'test_file': 'optim/test_lrscheduler'}, {'test_file': 'test_fake_tensor'}, {'test_file': 'inductor/test_needs_exact_strides'}, {'test_file': 'inductor/test_config'}, {'test_file': 'dynamo/test_sources'}, {'test_file': 'test_cuda_trace'}, {'test_file': 'dynamo/test_base_hop'}, {'test_file': 'inductor/test_fused_attention'}, {'test_file': 'export/test_nativert'}, {'test_file': 'inductor/test_padding'}, {'test_file': 'dynamo/test_backends'}, {'test_file': 'dynamo/test_verify_correctness'}, {'test_file': 'dynamo/test_python_dispatcher'}, {'test_file': 'test_set_default_mobile_cpu_allocator'}, {'test_file': 'torch_np/test_indexing'}, {'test_file': 'torch_np/test_scalars_0D_arrays'}, {'test_file': 'test_numba_integration'}, {'test_file': 'dynamo/test_cudagraphs'}, {'test_file': 'dynamo/test_deviceguard'}, {'test_file': 'torch_np/numpy_tests/lib/test_function_base'}, {'test_file': 'test_tensorboard'}, {'test_file': 'dynamo/test_higher_order_ops'}, {'test_file': 'dynamo/test_comptime'}, {'test_file': 'test_datapipe'}, {'test_file': 'dynamo/test_logging'}, {'test_file': 'dynamo/test_debug_utils'}, {'test_file': 'test_out_dtype_op'}, {'test_file': 'export/test_hop'}, {'test_file': 'profiler/test_cpp_thread'}, {'test_file': 'inductor/test_auto_functionalize'}, {'test_file': 'torch_np/test_function_base'}, {'test_file': 'dynamo/test_activation_checkpointing'}, {'test_file': 'cpp_extensions/libtorch_agnostic_extension/test/test_libtorch_agnostic'}, {'test_file': 'dynamo/test_aot_autograd'}, {'test_file': 'dynamo/test_graph_deduplication'}, {'test_file': 'test_model_exports_to_core_aten'}, {'test_file': 'test_itt'}, {'test_file': 'test_modules'}, {'test_file': 'test_cuda_sanitizer'}, {'test_file': 'inductor/test_efficient_conv_bn_eval'}, {'test_file': 'dynamo/test_error_messages'}, {'test_file': 'test_cuda'}, {'test_file': 'inductor/test_unbacked_symints'}, {'test_file': 'test_cuda_nvml_based_avail'}, {'test_file': 'inductor/test_mps_basic'}, {'test_file': 'test_autograd_fallback'}, {'test_file': 'dynamo/test_decorators'}, {'test_file': 'nn/test_lazy_modules'}, {'test_file': 'test_fx_reinplace_pass'}, {'test_file': 'torch_np/numpy_tests/lib/test_type_check'}, {'test_file': 'dynamo/test_compiler_bisector'}, {'test_file': 'inductor/test_custom_partitioner_fn'}, {'test_file': 'test_type_info'}, {'test_file': 'dynamo/test_unspec'}, {'test_file': 'lazy/test_functionalization'}, {'test_file': 'dynamo/test_aot_compile'}, {'test_file': 'test_functionalization_of_rng_ops'}, {'test_file': 'test_subclass'}, {'test_file': 'test_decomp'}, {'test_file': 'dynamo/test_einops'}, {'test_file': 'dynamo/test_callback'}, {'test_file': 'test_masked'}, {'test_file': 'export/test_experimental'}, {'test_file': 'nn/test_pruning'}, {'test_file': 'export/test_converter'}, {'test_file': 'test_bundled_inputs'}, {'test_file': 'inductor/test_fxir_backend'}, {'test_file': 'torch_np/numpy_tests/lib/test_histograms'}, {'test_file': 'test_maskedtensor'}, {'test_file': 'dynamo/test_reorder_logs'}, {'test_file': 'dynamo/test_exceptions'}, {'test_file': 'export/test_lift_unlift'}, {'test_file': 'dynamo/test_torchrec'}, {'test_file': 'test_public_bindings'}, {'test_file': 'dynamo/test_exc'}, {'test_file': 'test_sparse_semi_structured'}, {'test_file': 'dynamo/test_input_attr_tracking'}, {'test_file': 'test_matmul_cuda'}, {'test_file': 'test_dataloader'}, {'test_file': 'test_sympy_utils'}, {'test_file': 'inductor/test_mmdecomp'}, {'test_file': 'test_schema_check'}, {'test_file': 'export/test_pass_infra'}, {'test_file': 'dynamo/test_minifier'}, {'test_file': 'profiler/test_execution_trace'}, {'test_file': 'torch_np/numpy_tests/core/test_scalarmath'}, {'test_file': 'benchmark_utils/test_benchmark_utils'}, {'test_file': 'optim/test_swa_utils'}, {'test_file': 'dynamo/test_ctx_manager'}, {'test_file': 'dynamo/test_guard_manager'}, {'test_file': 'optim/test_optim'}, {'test_file': 'lazy/test_ts_opinfo'}, {'test_file': 'dynamo/test_recompile_ux'}, {'test_file': 'test_futures'}, {'test_file': 'dynamo/test_bytecode_utils'}, {'test_file': 'test_dynamic_shapes'}, {'test_file': 'functorch/test_vmap_registrations'}, {'test_file': 'dynamo/test_precompile_context'}, {'test_file': 'torch_np/numpy_tests/core/test_dtype'}, {'test_file': 'dynamo/test_fake_distributed'}, {'test_file': 'inductor/test_distributed_patterns'}, {'test_file': 'test_autocast'}, {'test_file': 'torch_np/numpy_tests/core/test_shape_base'}, {'test_file': 'dynamo/test_hooks'}, {'test_file': 'nn/test_packed_sequence'}, {'test_file': 'export/test_verifier'}, {'test_file': 'export/test_sparse'}, {'test_file': 'dynamo/test_generator'}, {'test_file': 'test_torch'}, {'test_file': 'test_serialization'}, {'test_file': 'test_shape_ops'}, {'test_file': 'lazy/test_generator'}, {'test_file': 'test_numa_binding'}, {'test_file': 'torch_np/numpy_tests/lib/test_twodim_base'}, {'test_file': 'torch_np/numpy_tests/lib/test_arraypad'}, {'test_file': 'test_accelerator'}, {'test_file': 'torch_np/numpy_tests/core/test_getlimits'}, {'test_file': 'nn/test_embedding'}, {'test_file': 'torch_np/numpy_tests/fft/test_helper'}, {'test_file': 'nn/test_dropout'}, {'test_file': 'test_functional_optim'}, {'test_file': 'torch_np/numpy_tests/fft/test_pocketfft'}, {'test_file': 'torch_np/test_ndarray_methods'}, {'test_file': 'test_sort_and_select'}, {'test_file': 'torch_np/numpy_tests/lib/test_index_tricks'}, {'test_file': 'torch_np/numpy_tests/lib/test_shape_base_'}, {'test_file': 'test_vulkan'}, {'test_file': 'torch_np/numpy_tests/linalg/test_linalg'}, {'test_file': 'nn/test_load_state_dict'}, {'test_file': 'test_ops'}, {'test_file': 'test_jit_llga_fuser'}, {'test_file': 'test_native_mha'}, {'test_file': 'test_cuda_primary_ctx'}, {'test_file': 'nn/test_module_hooks'}, {'test_file': 'test_view_ops'}, {'test_file': 'test_xnnpack_integration'}, {'test_file': 'test_mkldnn'}, {'test_file': 'torch_np/numpy_tests/core/test_dlpack'}, {'test_file': 'test_linalg'}, {'test_file': 'test_mkldnn_fusion'}, {'test_file': 'test_sparse_csr'}, {'test_file': 'test_scatter_gather_ops'}, {'test_file': 'dynamo/test_python_autograd'}, {'test_file': 'torch_np/numpy_tests/core/test_scalar_methods'}, {'test_file': 'torch_np/numpy_tests/core/test_numerictypes'}, {'test_file': 'profiler/test_memory_profiler'}, {'test_file': 'nn/test_pooling'}, {'test_file': 'test_unary_ufuncs'}, {'test_file': 'lazy/test_debug_util'}, {'test_file': 'test_multiprocessing_spawn'}, {'test_file': 'nn/test_convolution'}, {'test_file': 'nn/test_init'}, {'test_file': 'torch_np/numpy_tests/lib/test_arraysetops'}, {'test_file': 'test_functional_autograd_benchmark'}, {'test_file': 'test_function_schema'}, {'test_file': 'test_cuda_multigpu'}, {'test_file': 'test_sparse'}, {'test_file': 'test_mobile_optimizer'}, {'test_file': 'torch_np/test_reductions'}, {'test_file': 'test_dlpack'}, {'test_file': 'torch_np/numpy_tests/core/test_scalar_ctors'}, {'test_file': 'profiler/test_profiler_tree'}, {'test_file': 'test_spectral_ops'}, {'test_file': 'test_prims'}, {'test_file': 'test_jit_autocast'}, {'test_file': 'profiler/test_torch_tidy'}, {'test_file': 'profiler/test_python_tracer'}, {'test_file': 'lazy/test_reuse_ir'}, {'test_file': 'distributions/test_distributions'}, {'test_file': 'doctests'}, {'test_file': 'test_autoload_disable'}, {'test_file': 'test_autoload_enable'}, {'test_file': 'test_cpp_extensions_aot_ninja'}, {'test_file': 'test_cpp_extensions_aot_no_ninja'}], 'excluded': []} from test/test-reports/td_exclusions-f88779ec0f12c174dfbb.json is not a benchmark record, skipping 2025-09-07T08:14:42.2110044Z warn(f"{result} from {filepath} is not a benchmark record, skipping") 2025-09-07T08:14:42.2111774Z /home/ec2-user/actions-runner/_work/_actions/pytorch/test-infra/main/.github/actions/upload-benchmark-results/../../scripts/upload_benchmark_results.py:236: UserWarning: {'included': [{'test_file': 'cpp/static_runtime_test'}], 'excluded': []} from test/test-reports/td_exclusions-23d8701a428f14b89625.json is not a benchmark record, skipping 2025-09-07T08:14:42.2113456Z warn(f"{result} from {filepath} is not a benchmark record, skipping") 2025-09-07T08:14:42.2115327Z /home/ec2-user/actions-runner/_work/_actions/pytorch/test-infra/main/.github/actions/upload-benchmark-results/../../scripts/upload_benchmark_results.py:236: UserWarning: {'included': [{'test_file': 'cpp/test_api'}], 'excluded': []} from test/test-reports/td_exclusions-794f3162fe94f3abfe01.json is not a benchmark record, skipping 2025-09-07T08:14:42.2116959Z warn(f"{result} from {filepath} is not a benchmark record, skipping") 2025-09-07T08:14:42.2120883Z /home/ec2-user/actions-runner/_work/_actions/pytorch/test-infra/main/.github/actions/upload-benchmark-results/../../scripts/upload_benchmark_results.py:236: UserWarning: {'included': [{'test_file': 'cpp/Dict_test'}, {'test_file': 'cpp/Dimname_test'}, {'test_file': 'cpp/NamedTensor_test'}, {'test_file': 'cpp/apply_utils_test'}, {'test_file': 'cpp/atest'}, {'test_file': 'cpp/basic'}, {'test_file': 'cpp/broadcast_test'}, {'test_file': 'cpp/cpu_generator_test'}, {'test_file': 'cpp/dlconvertor_test'}, {'test_file': 'cpp/extension_backend_test'}, {'test_file': 'cpp/lazy_tensor_test'}, {'test_file': 'cpp/legacy_vmap_test'}, {'test_file': 'cpp/native_test'}, {'test_file': 'cpp/operators_test'}, {'test_file': 'cpp/scalar_tensor_test'}, {'test_file': 'cpp/scalar_test'}, {'test_file': 'cpp/tensor_iterator_test'}, {'test_file': 'cpp/undefined_tensor_test'}, {'test_file': 'cpp/wrapdim_test'}], 'excluded': []} from test/test-reports/td_exclusions-37bb05bf75249c7c93da.json is not a benchmark record, skipping 2025-09-07T08:14:42.2124984Z warn(f"{result} from {filepath} is not a benchmark record, skipping") 2025-09-07T08:14:42.2229208Z ##[group]Run cat test/**/*_toprint.log || true 2025-09-07T08:14:42.2229637Z cat test/**/*_toprint.log || true 2025-09-07T08:14:42.2236236Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-07T08:14:42.2236619Z env: 2025-09-07T08:14:42.2236849Z GIT_DEFAULT_BRANCH: main 2025-09-07T08:14:42.2237324Z DOCKER_CONTAINER_ID: 2a1e06725f9987eb4a7a91656c15a654e902febdeb1d85d79899892d5c9f5783 2025-09-07T08:14:42.2237838Z DEVICE_NAME: 2025-09-07T08:14:42.2238063Z DEVICE_TYPE: 2025-09-07T08:14:42.2238288Z ##[endgroup] 2025-09-07T08:14:42.2323104Z cat: 'test/**/*_toprint.log': No such file or directory 2025-09-07T08:14:42.2359660Z ##[group]Run kill "$MONITOR_SCRIPT_PID" 2025-09-07T08:14:42.2360045Z kill "$MONITOR_SCRIPT_PID" 2025-09-07T08:14:42.2365531Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-07T08:14:42.2365913Z env: 2025-09-07T08:14:42.2366151Z GIT_DEFAULT_BRANCH: main 2025-09-07T08:14:42.2366628Z DOCKER_CONTAINER_ID: 2a1e06725f9987eb4a7a91656c15a654e902febdeb1d85d79899892d5c9f5783 2025-09-07T08:14:42.2367144Z DEVICE_NAME: 2025-09-07T08:14:42.2367383Z DEVICE_TYPE: 2025-09-07T08:14:42.2367621Z MONITOR_SCRIPT_PID: 44213 2025-09-07T08:14:42.2367902Z ##[endgroup] 2025-09-07T08:14:42.2514691Z Prepare all required actions 2025-09-07T08:14:42.2515228Z Getting action download info 2025-09-07T08:14:42.3642108Z Download action repository 'seemethere/upload-artifact-s3@v5' (SHA:baba72d0712b404f646cebe0730933554ebce96a) 2025-09-07T08:14:42.6384834Z Download action repository 'actions/upload-artifact@v4' (SHA:ea165f8d65b6e75b540449e92b4886f43607fa02) 2025-09-07T08:14:43.0822644Z ##[group]Run ./.github/actions/upload-test-artifacts 2025-09-07T08:14:43.0823018Z with: 2025-09-07T08:14:43.0823314Z file-suffix: test-default-1-5-linux.4xlarge_49774041675 2025-09-07T08:14:43.0823928Z s3-bucket: gha-artifacts 2025-09-07T08:14:43.0824324Z env: 2025-09-07T08:14:43.0824542Z GIT_DEFAULT_BRANCH: main 2025-09-07T08:14:43.0825001Z DOCKER_CONTAINER_ID: 2a1e06725f9987eb4a7a91656c15a654e902febdeb1d85d79899892d5c9f5783 2025-09-07T08:14:43.0825521Z DEVICE_NAME: 2025-09-07T08:14:43.0825755Z DEVICE_TYPE: 2025-09-07T08:14:43.0825995Z ##[endgroup] 2025-09-07T08:14:43.0869349Z ##[group]Run # Remove any previous test jsons if they exist 2025-09-07T08:14:43.0869828Z # Remove any previous test jsons if they exist 2025-09-07T08:14:43.0870217Z rm -f test-jsons-*.zip 2025-09-07T08:14:43.0870660Z zip -r "test-jsons-${FILE_SUFFIX}.zip" test/test-reports -i '*.json' 2025-09-07T08:14:43.0876741Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-07T08:14:43.0877111Z env: 2025-09-07T08:14:43.0877330Z GIT_DEFAULT_BRANCH: main 2025-09-07T08:14:43.0877799Z DOCKER_CONTAINER_ID: 2a1e06725f9987eb4a7a91656c15a654e902febdeb1d85d79899892d5c9f5783 2025-09-07T08:14:43.0878308Z DEVICE_NAME: 2025-09-07T08:14:43.0878543Z DEVICE_TYPE: 2025-09-07T08:14:43.0878838Z FILE_SUFFIX: test-default-1-5-linux.4xlarge_49774041675 2025-09-07T08:14:43.0879206Z ##[endgroup] 2025-09-07T08:14:43.1015634Z adding: test/test-reports/td_exclusions-e2c77fbdefcfb8f57eb9.json (deflated 16%) 2025-09-07T08:14:43.1020347Z adding: test/test-reports/td_exclusions-f88779ec0f12c174dfbb.json (deflated 82%) 2025-09-07T08:14:43.1021072Z adding: test/test-reports/td_exclusions-23d8701a428f14b89625.json (deflated 14%) 2025-09-07T08:14:43.1021730Z adding: test/test-reports/td_exclusions-794f3162fe94f3abfe01.json (deflated 18%) 2025-09-07T08:14:43.1022374Z adding: test/test-reports/td_exclusions-37bb05bf75249c7c93da.json (deflated 73%) 2025-09-07T08:14:43.1048363Z ##[group]Run # Remove any previous test reports if they exist 2025-09-07T08:14:43.1048855Z # Remove any previous test reports if they exist 2025-09-07T08:14:43.1049246Z rm -f test-reports-*.zip 2025-09-07T08:14:43.1049726Z zip -r 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test/test-reports/cpp.legacy_vmap_test_1.1_ea4821a80d6e03d0_.log (deflated 73%) 2025-09-07T08:14:43.2982469Z adding: test/test-reports/cpp.native_test_1.1_65c698005f6a4fef_.log (deflated 73%) 2025-09-07T08:14:43.2983258Z adding: test/test-reports/cpp.operators_test_1.1_69c8d26c10cd6a38_.log (deflated 73%) 2025-09-07T08:14:43.2983947Z adding: test/test-reports/cpp.apply_utils_test_1.1_44f36c7c11fb9bd6_.log (deflated 75%) 2025-09-07T08:14:43.3024199Z ##[group]Run # Remove any previous debugging artifacts if they exist 2025-09-07T08:14:43.3025100Z # Remove any previous debugging artifacts if they exist 2025-09-07T08:14:43.3025739Z rm -f debug-*.zip 2025-09-07T08:14:43.3026209Z if [ -d 'test/debug' ]; then 2025-09-07T08:14:43.3026786Z  zip -r "debug-${FILE_SUFFIX}.zip" test/debug 2025-09-07T08:14:43.3027351Z fi 2025-09-07T08:14:43.3036029Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-07T08:14:43.3036715Z env: 2025-09-07T08:14:43.3037113Z GIT_DEFAULT_BRANCH: main 2025-09-07T08:14:43.3037965Z DOCKER_CONTAINER_ID: 2a1e06725f9987eb4a7a91656c15a654e902febdeb1d85d79899892d5c9f5783 2025-09-07T08:14:43.3038860Z DEVICE_NAME: 2025-09-07T08:14:43.3039215Z DEVICE_TYPE: 2025-09-07T08:14:43.3039694Z FILE_SUFFIX: test-default-1-5-linux.4xlarge_49774041675 2025-09-07T08:14:43.3040380Z ##[endgroup] 2025-09-07T08:14:43.3160300Z ##[group]Run seemethere/upload-artifact-s3@v5 2025-09-07T08:14:43.3160644Z with: 2025-09-07T08:14:43.3160855Z s3-bucket: gha-artifacts 2025-09-07T08:14:43.3161187Z s3-prefix: pytorch/pytorch/17524754568/1/artifact 2025-09-07T08:14:43.3161544Z retention-days: 14 2025-09-07T08:14:43.3161800Z if-no-files-found: warn 2025-09-07T08:14:43.3162067Z path: test-jsons-*.zip 2025-09-07T08:14:43.3162329Z name: artifact 2025-09-07T08:14:43.3162568Z region: us-east-1 2025-09-07T08:14:43.3162796Z env: 2025-09-07T08:14:43.3163007Z GIT_DEFAULT_BRANCH: main 2025-09-07T08:14:43.3163478Z DOCKER_CONTAINER_ID: 2a1e06725f9987eb4a7a91656c15a654e902febdeb1d85d79899892d5c9f5783 2025-09-07T08:14:43.3163984Z DEVICE_NAME: 2025-09-07T08:14:43.3164210Z DEVICE_TYPE: 2025-09-07T08:14:43.3164508Z ##[endgroup] 2025-09-07T08:14:43.6878585Z NOTE: s3-prefix specified, ignoring name parameter 2025-09-07T08:14:43.6879061Z With the provided path, there will be 1 file uploaded 2025-09-07T08:14:43.6879529Z Uploading to s3 prefix: pytorch/pytorch/17524754568/1/artifact 2025-09-07T08:14:43.6927508Z Starting upload of test-jsons-test-default-1-5-linux.4xlarge_49774041675.zip 2025-09-07T08:14:43.8150693Z Finished upload of test-jsons-test-default-1-5-linux.4xlarge_49774041675.zip 2025-09-07T08:14:43.8333867Z ##[group]Run seemethere/upload-artifact-s3@v5 2025-09-07T08:14:43.8334218Z with: 2025-09-07T08:14:43.8334433Z s3-bucket: gha-artifacts 2025-09-07T08:14:43.8334761Z s3-prefix: pytorch/pytorch/17524754568/1/artifact 2025-09-07T08:14:43.8335129Z retention-days: 14 2025-09-07T08:14:43.8335389Z if-no-files-found: error 2025-09-07T08:14:43.8335661Z path: test-reports-*.zip 2025-09-07T08:14:43.8335928Z name: artifact 2025-09-07T08:14:43.8336160Z region: us-east-1 2025-09-07T08:14:43.8336382Z env: 2025-09-07T08:14:43.8336599Z GIT_DEFAULT_BRANCH: main 2025-09-07T08:14:43.8337079Z DOCKER_CONTAINER_ID: 2a1e06725f9987eb4a7a91656c15a654e902febdeb1d85d79899892d5c9f5783 2025-09-07T08:14:43.8337584Z DEVICE_NAME: 2025-09-07T08:14:43.8337813Z DEVICE_TYPE: 2025-09-07T08:14:43.8338031Z ##[endgroup] 2025-09-07T08:14:44.1661712Z NOTE: s3-prefix specified, ignoring name parameter 2025-09-07T08:14:44.1662392Z With the provided path, there will be 1 file uploaded 2025-09-07T08:14:44.1662861Z Uploading to s3 prefix: pytorch/pytorch/17524754568/1/artifact 2025-09-07T08:14:44.1708210Z Starting upload of test-reports-test-default-1-5-linux.4xlarge_49774041675.zip 2025-09-07T08:14:44.3223076Z Finished upload of test-reports-test-default-1-5-linux.4xlarge_49774041675.zip 2025-09-07T08:14:44.3407490Z ##[group]Run seemethere/upload-artifact-s3@v5 2025-09-07T08:14:44.3407841Z with: 2025-09-07T08:14:44.3408084Z s3-bucket: gha-artifacts 2025-09-07T08:14:44.3408418Z s3-prefix: pytorch/pytorch/17524754568/1/artifact 2025-09-07T08:14:44.3408798Z retention-days: 14 2025-09-07T08:14:44.3409233Z if-no-files-found: ignore 2025-09-07T08:14:44.3409501Z path: logs-*.zip 2025-09-07T08:14:44.3409739Z name: artifact 2025-09-07T08:14:44.3410004Z region: us-east-1 2025-09-07T08:14:44.3410238Z env: 2025-09-07T08:14:44.3410443Z GIT_DEFAULT_BRANCH: main 2025-09-07T08:14:44.3410919Z DOCKER_CONTAINER_ID: 2a1e06725f9987eb4a7a91656c15a654e902febdeb1d85d79899892d5c9f5783 2025-09-07T08:14:44.3411427Z DEVICE_NAME: 2025-09-07T08:14:44.3411659Z DEVICE_TYPE: 2025-09-07T08:14:44.3411873Z ##[endgroup] 2025-09-07T08:14:44.6738081Z NOTE: s3-prefix specified, ignoring name parameter 2025-09-07T08:14:44.6738572Z With the provided path, there will be 1 file uploaded 2025-09-07T08:14:44.6739279Z Uploading to s3 prefix: pytorch/pytorch/17524754568/1/artifact 2025-09-07T08:14:44.6785319Z Starting upload of logs-test-default-1-5-linux.4xlarge_49774041675.zip 2025-09-07T08:14:44.8399681Z Finished upload of logs-test-default-1-5-linux.4xlarge_49774041675.zip 2025-09-07T08:14:44.8589370Z ##[group]Run seemethere/upload-artifact-s3@v5 2025-09-07T08:14:44.8589733Z with: 2025-09-07T08:14:44.8589997Z s3-bucket: gha-artifacts 2025-09-07T08:14:44.8590318Z s3-prefix: pytorch/pytorch/17524754568/1/artifact 2025-09-07T08:14:44.8590723Z retention-days: 14 2025-09-07T08:14:44.8590993Z if-no-files-found: ignore 2025-09-07T08:14:44.8591277Z path: debug-*.zip 2025-09-07T08:14:44.8591507Z name: artifact 2025-09-07T08:14:44.8591753Z region: us-east-1 2025-09-07T08:14:44.8591993Z env: 2025-09-07T08:14:44.8592215Z GIT_DEFAULT_BRANCH: main 2025-09-07T08:14:44.8592680Z DOCKER_CONTAINER_ID: 2a1e06725f9987eb4a7a91656c15a654e902febdeb1d85d79899892d5c9f5783 2025-09-07T08:14:44.8593193Z DEVICE_NAME: 2025-09-07T08:14:44.8593425Z DEVICE_TYPE: 2025-09-07T08:14:44.8593662Z ##[endgroup] 2025-09-07T08:14:45.1858413Z No files were found with the provided path: debug-*.zip. No artifacts will be uploaded. 2025-09-07T08:14:45.2051082Z ##[group]Run # shellcheck disable=SC2156 2025-09-07T08:14:45.2051468Z # shellcheck disable=SC2156 2025-09-07T08:14:45.2052062Z find . -iname "core.[1-9]*" -exec docker exec "${DOCKER_CONTAINER_ID}" sh -c "gdb python {} -ex 'bt' -ex 'q'" \; 2025-09-07T08:14:45.2058723Z shell: /usr/bin/bash -e {0} 2025-09-07T08:14:45.2059008Z env: 2025-09-07T08:14:45.2059231Z GIT_DEFAULT_BRANCH: main 2025-09-07T08:14:45.2059709Z DOCKER_CONTAINER_ID: 2a1e06725f9987eb4a7a91656c15a654e902febdeb1d85d79899892d5c9f5783 2025-09-07T08:14:45.2060211Z DEVICE_NAME: 2025-09-07T08:14:45.2060448Z DEVICE_TYPE: 2025-09-07T08:14:45.2060689Z ##[endgroup] 2025-09-07T08:14:45.5175050Z Prepare all required actions 2025-09-07T08:14:45.5175733Z Getting action download info 2025-09-07T08:14:45.6184614Z ##[group]Run ./.github/actions/upload-utilization-stats 2025-09-07T08:14:45.6184985Z with: 2025-09-07T08:14:45.6185203Z job_id: 49774041675 2025-09-07T08:14:45.6185603Z job_name: linux-jammy-py3.13-clang12 / test (default, 1, 5, linux.4xlarge) 2025-09-07T08:14:45.6186075Z workflow_name: pull 2025-09-07T08:14:45.6186324Z workflow_run_id: 17524754568 2025-09-07T08:14:45.6186606Z workflow_attempt: 1 2025-09-07T08:14:45.6186849Z env: 2025-09-07T08:14:45.6187071Z GIT_DEFAULT_BRANCH: main 2025-09-07T08:14:45.6187526Z DOCKER_CONTAINER_ID: 2a1e06725f9987eb4a7a91656c15a654e902febdeb1d85d79899892d5c9f5783 2025-09-07T08:14:45.6188034Z DEVICE_NAME: 2025-09-07T08:14:45.6188262Z DEVICE_TYPE: 2025-09-07T08:14:45.6188489Z ##[endgroup] 2025-09-07T08:14:45.6219894Z ##[group]Run echo "workflow_id: 17524754568" 2025-09-07T08:14:45.6220336Z echo "workflow_id: 17524754568" 2025-09-07T08:14:45.6220672Z echo "workflow_attempt: 1" 2025-09-07T08:14:45.6220995Z echo "workflow_Name: pull" 2025-09-07T08:14:45.6221307Z echo "job_id: 49774041675" 2025-09-07T08:14:45.6221800Z echo "job_name: linux-jammy-py3.13-clang12 / test (default, 1, 5, linux.4xlarge)" 2025-09-07T08:14:45.6222327Z echo "artifact_prefix: " 2025-09-07T08:14:45.6222630Z python3 --version 2025-09-07T08:14:45.6229336Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-07T08:14:45.6229726Z env: 2025-09-07T08:14:45.6229986Z GIT_DEFAULT_BRANCH: main 2025-09-07T08:14:45.6230465Z DOCKER_CONTAINER_ID: 2a1e06725f9987eb4a7a91656c15a654e902febdeb1d85d79899892d5c9f5783 2025-09-07T08:14:45.6230982Z DEVICE_NAME: 2025-09-07T08:14:45.6231218Z DEVICE_TYPE: 2025-09-07T08:14:45.6231435Z ##[endgroup] 2025-09-07T08:14:45.6256113Z workflow_id: 17524754568 2025-09-07T08:14:45.6256437Z workflow_attempt: 1 2025-09-07T08:14:45.6256734Z workflow_Name: pull 2025-09-07T08:14:45.6256985Z job_id: 49774041675 2025-09-07T08:14:45.6257381Z job_name: linux-jammy-py3.13-clang12 / test (default, 1, 5, linux.4xlarge) 2025-09-07T08:14:45.6257986Z artifact_prefix: 2025-09-07T08:14:45.6268586Z Python 3.9.23 2025-09-07T08:14:45.6315913Z ##[group]Run nick-fields/retry@v3.0.0 2025-09-07T08:14:45.6316217Z with: 2025-09-07T08:14:45.6316441Z shell: bash 2025-09-07T08:14:45.6316669Z timeout_minutes: 5 2025-09-07T08:14:45.6316901Z max_attempts: 5 2025-09-07T08:14:45.6317142Z retry_wait_seconds: 30 2025-09-07T08:14:45.6317706Z command: set -eu python3 -m pip install python-dateutil==2.8.2 boto3==1.35.42 pandas==2.1.3 dataclasses_json==0.6.7 2025-09-07T08:14:45.6318317Z polling_interval_seconds: 1 2025-09-07T08:14:45.6318612Z warning_on_retry: true 2025-09-07T08:14:45.6318868Z continue_on_error: false 2025-09-07T08:14:45.6319129Z env: 2025-09-07T08:14:45.6319339Z GIT_DEFAULT_BRANCH: main 2025-09-07T08:14:45.6319817Z DOCKER_CONTAINER_ID: 2a1e06725f9987eb4a7a91656c15a654e902febdeb1d85d79899892d5c9f5783 2025-09-07T08:14:45.6320316Z DEVICE_NAME: 2025-09-07T08:14:45.6320551Z DEVICE_TYPE: 2025-09-07T08:14:45.6320776Z ##[endgroup] 2025-09-07T08:14:45.9642726Z Defaulting to user installation because normal site-packages is not writeable 2025-09-07T08:14:46.0892154Z Collecting python-dateutil==2.8.2 2025-09-07T08:14:46.1054688Z Downloading python_dateutil-2.8.2-py2.py3-none-any.whl (247 kB) 2025-09-07T08:14:47.0994914Z Collecting boto3==1.35.42 2025-09-07T08:14:47.1033634Z Downloading boto3-1.35.42-py3-none-any.whl (139 kB) 2025-09-07T08:14:47.6451639Z Collecting pandas==2.1.3 2025-09-07T08:14:47.6492259Z Downloading pandas-2.1.3-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (12.3 MB) 2025-09-07T08:14:47.7886624Z Requirement already satisfied: dataclasses_json==0.6.7 in /home/ec2-user/.local/lib/python3.9/site-packages (0.6.7) 2025-09-07T08:14:47.7904334Z Requirement already satisfied: six>=1.5 in /usr/lib/python3.9/site-packages (from python-dateutil==2.8.2) (1.15.0) 2025-09-07T08:14:47.7950040Z Requirement already satisfied: jmespath<2.0.0,>=0.7.1 in /usr/lib/python3.9/site-packages (from boto3==1.35.42) (0.10.0) 2025-09-07T08:14:47.7955813Z Requirement already satisfied: s3transfer<0.11.0,>=0.10.0 in /home/ec2-user/.local/lib/python3.9/site-packages (from boto3==1.35.42) (0.10.4) 2025-09-07T08:14:47.7959858Z Requirement already satisfied: botocore<1.36.0,>=1.35.42 in /home/ec2-user/.local/lib/python3.9/site-packages (from boto3==1.35.42) (1.35.99) 2025-09-07T08:14:47.8947417Z Collecting tzdata>=2022.1 2025-09-07T08:14:47.8984963Z Downloading tzdata-2025.2-py2.py3-none-any.whl (347 kB) 2025-09-07T08:14:48.7525760Z Collecting numpy<2,>=1.22.4 2025-09-07T08:14:48.7566200Z Downloading numpy-1.26.4-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (18.2 MB) 2025-09-07T08:14:48.9509031Z Requirement already satisfied: pytz>=2020.1 in /usr/lib/python3.9/site-packages (from pandas==2.1.3) (2022.7.1) 2025-09-07T08:14:48.9541979Z Requirement already satisfied: marshmallow<4.0.0,>=3.18.0 in /home/ec2-user/.local/lib/python3.9/site-packages (from dataclasses_json==0.6.7) (3.26.1) 2025-09-07T08:14:48.9546434Z Requirement already satisfied: typing-inspect<1,>=0.4.0 in /home/ec2-user/.local/lib/python3.9/site-packages (from dataclasses_json==0.6.7) (0.9.0) 2025-09-07T08:14:48.9628678Z Requirement already satisfied: urllib3<1.27,>=1.25.4 in /usr/lib/python3.9/site-packages (from botocore<1.36.0,>=1.35.42->boto3==1.35.42) (1.25.10) 2025-09-07T08:14:48.9720732Z Requirement already satisfied: packaging>=17.0 in /home/ec2-user/.local/lib/python3.9/site-packages (from marshmallow<4.0.0,>=3.18.0->dataclasses_json==0.6.7) (25.0) 2025-09-07T08:14:48.9825227Z Requirement already satisfied: typing-extensions>=3.7.4 in /home/ec2-user/.local/lib/python3.9/site-packages (from typing-inspect<1,>=0.4.0->dataclasses_json==0.6.7) (4.15.0) 2025-09-07T08:14:48.9829144Z Requirement already satisfied: mypy-extensions>=0.3.0 in /home/ec2-user/.local/lib/python3.9/site-packages (from typing-inspect<1,>=0.4.0->dataclasses_json==0.6.7) (1.1.0) 2025-09-07T08:14:49.1821138Z Installing collected packages: python-dateutil, tzdata, numpy, pandas, boto3 2025-09-07T08:14:54.1679336Z Attempting uninstall: boto3 2025-09-07T08:14:54.1679849Z Found existing installation: boto3 1.35.33 2025-09-07T08:14:54.1778323Z Uninstalling boto3-1.35.33: 2025-09-07T08:14:54.1791764Z Successfully uninstalled boto3-1.35.33 2025-09-07T08:14:54.2367703Z Successfully installed boto3-1.35.42 numpy-1.26.4 pandas-2.1.3 python-dateutil-2.8.2 tzdata-2025.2 2025-09-07T08:14:54.7143800Z Command completed after 1 attempt(s). 2025-09-07T08:14:54.7201677Z ##[group]Run python3 -m tools.stats.upload_utilization_stats.upload_utilization_stats \ 2025-09-07T08:14:54.7202403Z python3 -m tools.stats.upload_utilization_stats.upload_utilization_stats \ 2025-09-07T08:14:54.7202919Z  --workflow-run-id "17524754568" \ 2025-09-07T08:14:54.7203271Z  --workflow-name "pull" \ 2025-09-07T08:14:54.7203626Z  --workflow-run-attempt "1" \ 2025-09-07T08:14:54.7203961Z  --job-id "49774041675" \ 2025-09-07T08:14:54.7204424Z  --job-name "linux-jammy-py3.13-clang12 / test (default, 1, 5, linux.4xlarge)" \ 2025-09-07T08:14:54.7204933Z  --local-path "" \ 2025-09-07T08:14:54.7205229Z  --artifact-prefix "" 2025-09-07T08:14:54.7211118Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-07T08:14:54.7211510Z env: 2025-09-07T08:14:54.7211721Z GIT_DEFAULT_BRANCH: main 2025-09-07T08:14:54.7212190Z DOCKER_CONTAINER_ID: 2a1e06725f9987eb4a7a91656c15a654e902febdeb1d85d79899892d5c9f5783 2025-09-07T08:14:54.7212703Z DEVICE_NAME: 2025-09-07T08:14:54.7212932Z DEVICE_TYPE: 2025-09-07T08:14:54.7213149Z ##[endgroup] 2025-09-07T08:14:56.6387215Z repo: pytorch/pytorch 2025-09-07T08:14:56.6387600Z Search for test log in s3 bucket: ossci-utilization 2025-09-07T08:14:56.6388419Z Downloading logs-test-default-1-5-linux.4xlarge_49774041675.zip 2025-09-07T08:14:56.6389062Z extracting usage_log.txt from zip file logs-test-default-1-5-linux.4xlarge_49774041675.zip 2025-09-07T08:14:56.6389628Z Converted Log Model: UtilizationMetadata: 2025-09-07T08:14:56.6390986Z UtilizationMetadata(level='metadata', workflow_id='17524754568', job_id='49774041675', workflow_name='pull', job_name='linux-jammy-py3.13-clang12 / test (default, 1, 5, linux.4xlarge)', usage_collect_interval=1.0, data_model_version=1.5, start_at=1757226543, gpu_count=0, cpu_count=16, gpu_type=None, error=None) 2025-09-07T08:14:56.6392432Z [Db Segments] detected pytest cmd: 18, generated segments: 18 2025-09-07T08:14:56.6393152Z [db model] Peek db timeseries 2025-09-07T08:14:56.6393535Z :{ 2025-09-07T08:14:56.6393798Z "created_at": 1757232896, 2025-09-07T08:14:56.6394308Z "type": "utilization", 2025-09-07T08:14:56.6394675Z "tags": [ 2025-09-07T08:14:56.6406937Z "record" 2025-09-07T08:14:56.6407319Z ], 2025-09-07T08:14:56.6407572Z "time_stamp": 1757226543, 2025-09-07T08:14:56.6407885Z "repo": "pytorch/pytorch", 2025-09-07T08:14:56.6408180Z "workflow_id": 17524754568, 2025-09-07T08:14:56.6408464Z "run_attempt": 1, 2025-09-07T08:14:56.6408722Z "job_id": 49774041675, 2025-09-07T08:14:56.6408993Z "workflow_name": "pull", 2025-09-07T08:14:56.6409431Z "job_name": "linux-jammy-py3.13-clang12 / test (default, 1, 5, linux.4xlarge)", 2025-09-07T08:14:56.6409896Z "json_data": "{}" 2025-09-07T08:14:56.6410141Z } 2025-09-07T08:14:56.6410659Z Writing 1 documents to S3 ossci-utilization/util_metadata/v_1.5/pytorch/pytorch/17524754568/1/49774041675/metadata 2025-09-07T08:14:56.6411620Z Done! Finish writing document to S3 ossci-utilization/util_metadata/v_1.5/pytorch/pytorch/17524754568/1/49774041675/metadata 2025-09-07T08:14:56.6412623Z Writing 1263 documents to S3 ossci-utilization/util_timeseries/v_1.5/pytorch/pytorch/17524754568/1/49774041675/time_series 2025-09-07T08:14:56.6413682Z Done! Finish writing document to S3 ossci-utilization/util_timeseries/v_1.5/pytorch/pytorch/17524754568/1/49774041675/time_series 2025-09-07T08:14:56.7535059Z ##[group]Run pytorch/test-infra/.github/actions/teardown-linux@main 2025-09-07T08:14:56.7535520Z with: 2025-09-07T08:14:56.7535716Z env: 2025-09-07T08:14:56.7535935Z GIT_DEFAULT_BRANCH: main 2025-09-07T08:14:56.7536409Z DOCKER_CONTAINER_ID: 2a1e06725f9987eb4a7a91656c15a654e902febdeb1d85d79899892d5c9f5783 2025-09-07T08:14:56.7536927Z DEVICE_NAME: 2025-09-07T08:14:56.7537147Z DEVICE_TYPE: 2025-09-07T08:14:56.7537370Z ##[endgroup] 2025-09-07T08:14:56.7564655Z ##[group]Run set -eou pipefail 2025-09-07T08:14:56.7565096Z set -eou pipefail 2025-09-07T08:14:56.7565377Z  2025-09-07T08:14:56.7565756Z echo "Holding runner for 2 hours until all ssh sessions have logged out" 2025-09-07T08:14:56.7566272Z for _ in $(seq 1440); do 2025-09-07T08:14:56.7566623Z  # Break if no ssh session exists anymore 2025-09-07T08:14:56.7566987Z  if [ "$(who)" = "" ]; then 2025-09-07T08:14:56.7567289Z  break 2025-09-07T08:14:56.7567565Z  fi 2025-09-07T08:14:56.7567808Z  echo "." 2025-09-07T08:14:56.7568040Z  sleep 5 2025-09-07T08:14:56.7568281Z done 2025-09-07T08:14:56.7574002Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-07T08:14:56.7574495Z env: 2025-09-07T08:14:56.7574713Z GIT_DEFAULT_BRANCH: main 2025-09-07T08:14:56.7575182Z DOCKER_CONTAINER_ID: 2a1e06725f9987eb4a7a91656c15a654e902febdeb1d85d79899892d5c9f5783 2025-09-07T08:14:56.7575694Z DEVICE_NAME: 2025-09-07T08:14:56.7575925Z DEVICE_TYPE: 2025-09-07T08:14:56.7576141Z ##[endgroup] 2025-09-07T08:14:56.7600076Z Holding runner for 2 hours until all ssh sessions have logged out 2025-09-07T08:14:56.7676652Z ##[group]Run # ignore expansion of "docker ps -q" since it could be empty 2025-09-07T08:14:56.7677233Z # ignore expansion of "docker ps -q" since it could be empty 2025-09-07T08:14:56.7677678Z # shellcheck disable=SC2046 2025-09-07T08:14:56.7678042Z docker stop $(docker ps -q) || true 2025-09-07T08:14:56.7678393Z # Prune all of the docker images 2025-09-07T08:14:56.7678728Z docker system prune -af 2025-09-07T08:14:56.7684097Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-07T08:14:56.7684490Z env: 2025-09-07T08:14:56.7684709Z GIT_DEFAULT_BRANCH: main 2025-09-07T08:14:56.7685180Z DOCKER_CONTAINER_ID: 2a1e06725f9987eb4a7a91656c15a654e902febdeb1d85d79899892d5c9f5783 2025-09-07T08:14:56.7685686Z DEVICE_NAME: 2025-09-07T08:14:56.7685913Z DEVICE_TYPE: 2025-09-07T08:14:56.7686144Z ##[endgroup] 2025-09-07T08:15:07.8602551Z 2a1e06725f99 2025-09-07T08:15:08.2831511Z Deleted Containers: 2025-09-07T08:15:08.2831966Z 2a1e06725f9987eb4a7a91656c15a654e902febdeb1d85d79899892d5c9f5783 2025-09-07T08:15:08.2832328Z 2025-09-07T08:15:10.9336449Z Deleted Images: 2025-09-07T08:15:10.9337319Z untagged: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-jammy-py3.13-clang12-ae53c6842aa4c2407d0ad976491ca941c2635c77 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2025-09-07T08:15:11.0607999Z git version 2.47.1 2025-09-07T08:15:11.0642949Z Copying '/home/ec2-user/.gitconfig' to '/home/ec2-user/actions-runner/_work/_temp/72e6ff91-28bf-4f45-912f-18af04ea663d/.gitconfig' 2025-09-07T08:15:11.0651324Z Temporarily overriding HOME='/home/ec2-user/actions-runner/_work/_temp/72e6ff91-28bf-4f45-912f-18af04ea663d' before making global git config changes 2025-09-07T08:15:11.0652429Z Adding repository directory to the temporary git global config as a safe directory 2025-09-07T08:15:11.0656497Z [command]/usr/bin/git config --global --add safe.directory /home/ec2-user/actions-runner/_work/pytorch/pytorch 2025-09-07T08:15:11.0691987Z [command]/usr/bin/git config --local --name-only --get-regexp core\.sshCommand 2025-09-07T08:15:11.0730600Z [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' || :" 2025-09-07T08:15:11.1039265Z Entering 'android/libs/fbjni' 2025-09-07T08:15:11.1095384Z Entering 'third_party/FP16' 2025-09-07T08:15:11.1151702Z Entering 'third_party/FXdiv' 2025-09-07T08:15:11.1214124Z Entering 'third_party/NNPACK' 2025-09-07T08:15:11.1271801Z Entering 'third_party/NVTX' 2025-09-07T08:15:11.1330126Z Entering 'third_party/VulkanMemoryAllocator' 2025-09-07T08:15:11.1386221Z Entering 'third_party/XNNPACK' 2025-09-07T08:15:11.1468074Z Entering 'third_party/aiter' 2025-09-07T08:15:11.1525233Z Entering 'third_party/aiter/3rdparty/composable_kernel' 2025-09-07T08:15:11.1589294Z Entering 'third_party/benchmark' 2025-09-07T08:15:11.1646862Z Entering 'third_party/composable_kernel' 2025-09-07T08:15:11.1715472Z Entering 'third_party/cpp-httplib' 2025-09-07T08:15:11.1772011Z Entering 'third_party/cpuinfo' 2025-09-07T08:15:11.1831625Z Entering 'third_party/cudnn_frontend' 2025-09-07T08:15:11.1888009Z Entering 'third_party/cutlass' 2025-09-07T08:15:11.1953889Z Entering 'third_party/fbgemm' 2025-09-07T08:15:11.2013642Z Entering 'third_party/fbgemm/external/asmjit' 2025-09-07T08:15:11.2071929Z Entering 'third_party/fbgemm/external/composable_kernel' 2025-09-07T08:15:11.2138281Z Entering 'third_party/fbgemm/external/cpuinfo' 2025-09-07T08:15:11.2194978Z Entering 'third_party/fbgemm/external/cutlass' 2025-09-07T08:15:11.2265058Z Entering 'third_party/fbgemm/external/googletest' 2025-09-07T08:15:11.2320901Z Entering 'third_party/fbgemm/external/hipify_torch' 2025-09-07T08:15:11.2375119Z Entering 'third_party/fbgemm/external/json' 2025-09-07T08:15:11.2432836Z Entering 'third_party/flash-attention' 2025-09-07T08:15:11.2490130Z Entering 'third_party/flash-attention/csrc/composable_kernel' 2025-09-07T08:15:11.2552658Z Entering 'third_party/flash-attention/csrc/cutlass' 2025-09-07T08:15:11.2618577Z Entering 'third_party/flatbuffers' 2025-09-07T08:15:11.2677578Z Entering 'third_party/fmt' 2025-09-07T08:15:11.2733587Z Entering 'third_party/gemmlowp/gemmlowp' 2025-09-07T08:15:11.2789728Z Entering 'third_party/gloo' 2025-09-07T08:15:11.2847199Z Entering 'third_party/googletest' 2025-09-07T08:15:11.2903851Z Entering 'third_party/ideep' 2025-09-07T08:15:11.2958419Z Entering 'third_party/ideep/mkl-dnn' 2025-09-07T08:15:11.3025666Z Entering 'third_party/ittapi' 2025-09-07T08:15:11.3082492Z Entering 'third_party/kineto' 2025-09-07T08:15:11.3139090Z Entering 'third_party/kineto/libkineto/third_party/dynolog' 2025-09-07T08:15:11.3194009Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM' 2025-09-07T08:15:11.3251293Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr' 2025-09-07T08:15:11.3307341Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt' 2025-09-07T08:15:11.3363507Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags' 2025-09-07T08:15:11.3419477Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc' 2025-09-07T08:15:11.3476809Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog' 2025-09-07T08:15:11.3532873Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest' 2025-09-07T08:15:11.3589525Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/json' 2025-09-07T08:15:11.3646409Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs' 2025-09-07T08:15:11.3703114Z Entering 'third_party/kineto/libkineto/third_party/fmt' 2025-09-07T08:15:11.3759749Z Entering 'third_party/kineto/libkineto/third_party/googletest' 2025-09-07T08:15:11.3815903Z Entering 'third_party/kleidiai' 2025-09-07T08:15:11.3876364Z Entering 'third_party/mimalloc' 2025-09-07T08:15:11.3933097Z Entering 'third_party/nlohmann' 2025-09-07T08:15:11.3991458Z Entering 'third_party/onnx' 2025-09-07T08:15:11.4066083Z Entering 'third_party/onnx/third_party/pybind11' 2025-09-07T08:15:11.4126374Z Entering 'third_party/opentelemetry-cpp' 2025-09-07T08:15:11.4185428Z Entering 'third_party/opentelemetry-cpp/third_party/benchmark' 2025-09-07T08:15:11.4240615Z Entering 'third_party/opentelemetry-cpp/third_party/googletest' 2025-09-07T08:15:11.4294916Z Entering 'third_party/opentelemetry-cpp/third_party/ms-gsl' 2025-09-07T08:15:11.4348791Z Entering 'third_party/opentelemetry-cpp/third_party/nlohmann-json' 2025-09-07T08:15:11.4405579Z Entering 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto' 2025-09-07T08:15:11.4459534Z Entering 'third_party/opentelemetry-cpp/third_party/opentracing-cpp' 2025-09-07T08:15:11.4514818Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp' 2025-09-07T08:15:11.4569922Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb' 2025-09-07T08:15:11.4627514Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest' 2025-09-07T08:15:11.4684242Z Entering 'third_party/opentelemetry-cpp/tools/vcpkg' 2025-09-07T08:15:11.4760813Z Entering 'third_party/pocketfft' 2025-09-07T08:15:11.4817521Z Entering 'third_party/protobuf' 2025-09-07T08:15:11.4877259Z Entering 'third_party/protobuf/third_party/benchmark' 2025-09-07T08:15:11.4932764Z Entering 'third_party/protobuf/third_party/googletest' 2025-09-07T08:15:11.4989624Z Entering 'third_party/psimd' 2025-09-07T08:15:11.5045797Z Entering 'third_party/pthreadpool' 2025-09-07T08:15:11.5103131Z Entering 'third_party/pybind11' 2025-09-07T08:15:11.5160400Z Entering 'third_party/python-peachpy' 2025-09-07T08:15:11.5217458Z Entering 'third_party/sleef' 2025-09-07T08:15:11.5272724Z Entering 'third_party/tensorpipe' 2025-09-07T08:15:11.5329169Z Entering 'third_party/tensorpipe/third_party/googletest' 2025-09-07T08:15:11.5382732Z Entering 'third_party/tensorpipe/third_party/libnop' 2025-09-07T08:15:11.5439183Z Entering 'third_party/tensorpipe/third_party/libuv' 2025-09-07T08:15:11.5494066Z Entering 'third_party/tensorpipe/third_party/pybind11' 2025-09-07T08:15:11.5547289Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang' 2025-09-07T08:15:11.5622619Z [command]/usr/bin/git config --local --name-only --get-regexp http\.https\:\/\/github\.com\/\.extraheader 2025-09-07T08:15:11.5643408Z http.https://github.com/.extraheader 2025-09-07T08:15:11.5652356Z [command]/usr/bin/git config --local --unset-all http.https://github.com/.extraheader 2025-09-07T08:15:11.5683051Z [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' || :" 2025-09-07T08:15:11.5980902Z Entering 'android/libs/fbjni' 2025-09-07T08:15:11.6019079Z http.https://github.com/.extraheader 2025-09-07T08:15:11.6054395Z Entering 'third_party/FP16' 2025-09-07T08:15:11.6092609Z http.https://github.com/.extraheader 2025-09-07T08:15:11.6127915Z Entering 'third_party/FXdiv' 2025-09-07T08:15:11.6165615Z http.https://github.com/.extraheader 2025-09-07T08:15:11.6199960Z Entering 'third_party/NNPACK' 2025-09-07T08:15:11.6238169Z http.https://github.com/.extraheader 2025-09-07T08:15:11.6271530Z Entering 'third_party/NVTX' 2025-09-07T08:15:11.6312161Z http.https://github.com/.extraheader 2025-09-07T08:15:11.6347127Z Entering 'third_party/VulkanMemoryAllocator' 2025-09-07T08:15:11.6385600Z http.https://github.com/.extraheader 2025-09-07T08:15:11.6419983Z Entering 'third_party/XNNPACK' 2025-09-07T08:15:11.6458632Z http.https://github.com/.extraheader 2025-09-07T08:15:11.6508025Z Entering 'third_party/aiter' 2025-09-07T08:15:11.6546583Z http.https://github.com/.extraheader 2025-09-07T08:15:11.6581707Z Entering 'third_party/aiter/3rdparty/composable_kernel' 2025-09-07T08:15:11.6619472Z http.https://github.com/.extraheader 2025-09-07T08:15:11.6664204Z Entering 'third_party/benchmark' 2025-09-07T08:15:11.6701546Z http.https://github.com/.extraheader 2025-09-07T08:15:11.6735712Z Entering 'third_party/composable_kernel' 2025-09-07T08:15:11.6773303Z http.https://github.com/.extraheader 2025-09-07T08:15:11.6817950Z Entering 'third_party/cpp-httplib' 2025-09-07T08:15:11.6855999Z http.https://github.com/.extraheader 2025-09-07T08:15:11.6890076Z Entering 'third_party/cpuinfo' 2025-09-07T08:15:11.6927699Z http.https://github.com/.extraheader 2025-09-07T08:15:11.6960876Z Entering 'third_party/cudnn_frontend' 2025-09-07T08:15:11.6998770Z http.https://github.com/.extraheader 2025-09-07T08:15:11.7033982Z Entering 'third_party/cutlass' 2025-09-07T08:15:11.7071109Z http.https://github.com/.extraheader 2025-09-07T08:15:11.7115702Z Entering 'third_party/fbgemm' 2025-09-07T08:15:11.7153802Z http.https://github.com/.extraheader 2025-09-07T08:15:11.7190091Z Entering 'third_party/fbgemm/external/asmjit' 2025-09-07T08:15:11.7227645Z http.https://github.com/.extraheader 2025-09-07T08:15:11.7260838Z Entering 'third_party/fbgemm/external/composable_kernel' 2025-09-07T08:15:11.7298384Z http.https://github.com/.extraheader 2025-09-07T08:15:11.7339403Z Entering 'third_party/fbgemm/external/cpuinfo' 2025-09-07T08:15:11.7376159Z http.https://github.com/.extraheader 2025-09-07T08:15:11.7410173Z Entering 'third_party/fbgemm/external/cutlass' 2025-09-07T08:15:11.7446850Z http.https://github.com/.extraheader 2025-09-07T08:15:11.7488813Z Entering 'third_party/fbgemm/external/googletest' 2025-09-07T08:15:11.7527099Z http.https://github.com/.extraheader 2025-09-07T08:15:11.7560680Z Entering 'third_party/fbgemm/external/hipify_torch' 2025-09-07T08:15:11.7599297Z http.https://github.com/.extraheader 2025-09-07T08:15:11.7633558Z Entering 'third_party/fbgemm/external/json' 2025-09-07T08:15:11.7670825Z http.https://github.com/.extraheader 2025-09-07T08:15:11.7707685Z Entering 'third_party/flash-attention' 2025-09-07T08:15:11.7747300Z http.https://github.com/.extraheader 2025-09-07T08:15:11.7781947Z Entering 'third_party/flash-attention/csrc/composable_kernel' 2025-09-07T08:15:11.7819718Z http.https://github.com/.extraheader 2025-09-07T08:15:11.7861333Z Entering 'third_party/flash-attention/csrc/cutlass' 2025-09-07T08:15:11.7898096Z http.https://github.com/.extraheader 2025-09-07T08:15:11.7943171Z Entering 'third_party/flatbuffers' 2025-09-07T08:15:11.7981855Z http.https://github.com/.extraheader 2025-09-07T08:15:11.8018999Z Entering 'third_party/fmt' 2025-09-07T08:15:11.8057326Z http.https://github.com/.extraheader 2025-09-07T08:15:11.8092329Z Entering 'third_party/gemmlowp/gemmlowp' 2025-09-07T08:15:11.8131763Z http.https://github.com/.extraheader 2025-09-07T08:15:11.8165483Z Entering 'third_party/gloo' 2025-09-07T08:15:11.8204983Z http.https://github.com/.extraheader 2025-09-07T08:15:11.8239893Z Entering 'third_party/googletest' 2025-09-07T08:15:11.8278275Z http.https://github.com/.extraheader 2025-09-07T08:15:11.8312578Z Entering 'third_party/ideep' 2025-09-07T08:15:11.8350282Z http.https://github.com/.extraheader 2025-09-07T08:15:11.8382941Z Entering 'third_party/ideep/mkl-dnn' 2025-09-07T08:15:11.8419969Z http.https://github.com/.extraheader 2025-09-07T08:15:11.8461961Z Entering 'third_party/ittapi' 2025-09-07T08:15:11.8499670Z http.https://github.com/.extraheader 2025-09-07T08:15:11.8533757Z Entering 'third_party/kineto' 2025-09-07T08:15:11.8571819Z http.https://github.com/.extraheader 2025-09-07T08:15:11.8605936Z Entering 'third_party/kineto/libkineto/third_party/dynolog' 2025-09-07T08:15:11.8644174Z http.https://github.com/.extraheader 2025-09-07T08:15:11.8677817Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM' 2025-09-07T08:15:11.8717424Z http.https://github.com/.extraheader 2025-09-07T08:15:11.8754635Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr' 2025-09-07T08:15:11.8792247Z http.https://github.com/.extraheader 2025-09-07T08:15:11.8826592Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt' 2025-09-07T08:15:11.8863671Z http.https://github.com/.extraheader 2025-09-07T08:15:11.8897851Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags' 2025-09-07T08:15:11.8936543Z http.https://github.com/.extraheader 2025-09-07T08:15:11.8970001Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc' 2025-09-07T08:15:11.9008384Z http.https://github.com/.extraheader 2025-09-07T08:15:11.9044259Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog' 2025-09-07T08:15:11.9081960Z http.https://github.com/.extraheader 2025-09-07T08:15:11.9117603Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest' 2025-09-07T08:15:11.9155281Z http.https://github.com/.extraheader 2025-09-07T08:15:11.9189869Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/json' 2025-09-07T08:15:11.9230416Z http.https://github.com/.extraheader 2025-09-07T08:15:11.9265663Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs' 2025-09-07T08:15:11.9302957Z http.https://github.com/.extraheader 2025-09-07T08:15:11.9339235Z Entering 'third_party/kineto/libkineto/third_party/fmt' 2025-09-07T08:15:11.9376367Z http.https://github.com/.extraheader 2025-09-07T08:15:11.9410184Z Entering 'third_party/kineto/libkineto/third_party/googletest' 2025-09-07T08:15:11.9446704Z http.https://github.com/.extraheader 2025-09-07T08:15:11.9482614Z Entering 'third_party/kleidiai' 2025-09-07T08:15:11.9520508Z http.https://github.com/.extraheader 2025-09-07T08:15:11.9554580Z Entering 'third_party/mimalloc' 2025-09-07T08:15:11.9592301Z http.https://github.com/.extraheader 2025-09-07T08:15:11.9627405Z Entering 'third_party/nlohmann' 2025-09-07T08:15:11.9665902Z http.https://github.com/.extraheader 2025-09-07T08:15:11.9701014Z Entering 'third_party/onnx' 2025-09-07T08:15:11.9738996Z http.https://github.com/.extraheader 2025-09-07T08:15:11.9791021Z Entering 'third_party/onnx/third_party/pybind11' 2025-09-07T08:15:11.9829913Z http.https://github.com/.extraheader 2025-09-07T08:15:11.9865487Z Entering 'third_party/opentelemetry-cpp' 2025-09-07T08:15:11.9904098Z http.https://github.com/.extraheader 2025-09-07T08:15:11.9940471Z Entering 'third_party/opentelemetry-cpp/third_party/benchmark' 2025-09-07T08:15:11.9976936Z http.https://github.com/.extraheader 2025-09-07T08:15:12.0009885Z Entering 'third_party/opentelemetry-cpp/third_party/googletest' 2025-09-07T08:15:12.0047081Z http.https://github.com/.extraheader 2025-09-07T08:15:12.0080621Z Entering 'third_party/opentelemetry-cpp/third_party/ms-gsl' 2025-09-07T08:15:12.0118285Z http.https://github.com/.extraheader 2025-09-07T08:15:12.0151389Z Entering 'third_party/opentelemetry-cpp/third_party/nlohmann-json' 2025-09-07T08:15:12.0188031Z http.https://github.com/.extraheader 2025-09-07T08:15:12.0223092Z Entering 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto' 2025-09-07T08:15:12.0260642Z http.https://github.com/.extraheader 2025-09-07T08:15:12.0294478Z Entering 'third_party/opentelemetry-cpp/third_party/opentracing-cpp' 2025-09-07T08:15:12.0332796Z http.https://github.com/.extraheader 2025-09-07T08:15:12.0367752Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp' 2025-09-07T08:15:12.0405607Z http.https://github.com/.extraheader 2025-09-07T08:15:12.0438865Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb' 2025-09-07T08:15:12.0474799Z http.https://github.com/.extraheader 2025-09-07T08:15:12.0510110Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest' 2025-09-07T08:15:12.0546998Z http.https://github.com/.extraheader 2025-09-07T08:15:12.0582205Z Entering 'third_party/opentelemetry-cpp/tools/vcpkg' 2025-09-07T08:15:12.0620895Z http.https://github.com/.extraheader 2025-09-07T08:15:12.0675850Z Entering 'third_party/pocketfft' 2025-09-07T08:15:12.0715046Z http.https://github.com/.extraheader 2025-09-07T08:15:12.0748839Z Entering 'third_party/protobuf' 2025-09-07T08:15:12.0786951Z http.https://github.com/.extraheader 2025-09-07T08:15:12.0825393Z Entering 'third_party/protobuf/third_party/benchmark' 2025-09-07T08:15:12.0863978Z http.https://github.com/.extraheader 2025-09-07T08:15:12.0899112Z Entering 'third_party/protobuf/third_party/googletest' 2025-09-07T08:15:12.0936571Z http.https://github.com/.extraheader 2025-09-07T08:15:12.0972676Z Entering 'third_party/psimd' 2025-09-07T08:15:12.1011656Z http.https://github.com/.extraheader 2025-09-07T08:15:12.1046281Z Entering 'third_party/pthreadpool' 2025-09-07T08:15:12.1084249Z http.https://github.com/.extraheader 2025-09-07T08:15:12.1119479Z Entering 'third_party/pybind11' 2025-09-07T08:15:12.1157544Z http.https://github.com/.extraheader 2025-09-07T08:15:12.1191868Z Entering 'third_party/python-peachpy' 2025-09-07T08:15:12.1230329Z http.https://github.com/.extraheader 2025-09-07T08:15:12.1263775Z Entering 'third_party/sleef' 2025-09-07T08:15:12.1302160Z http.https://github.com/.extraheader 2025-09-07T08:15:12.1336981Z Entering 'third_party/tensorpipe' 2025-09-07T08:15:12.1376207Z http.https://github.com/.extraheader 2025-09-07T08:15:12.1411131Z Entering 'third_party/tensorpipe/third_party/googletest' 2025-09-07T08:15:12.1448982Z http.https://github.com/.extraheader 2025-09-07T08:15:12.1482384Z Entering 'third_party/tensorpipe/third_party/libnop' 2025-09-07T08:15:12.1520778Z http.https://github.com/.extraheader 2025-09-07T08:15:12.1553713Z Entering 'third_party/tensorpipe/third_party/libuv' 2025-09-07T08:15:12.1590488Z http.https://github.com/.extraheader 2025-09-07T08:15:12.1637749Z Entering 'third_party/tensorpipe/third_party/pybind11' 2025-09-07T08:15:12.1663265Z http.https://github.com/.extraheader 2025-09-07T08:15:12.1696679Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang' 2025-09-07T08:15:12.1735421Z http.https://github.com/.extraheader 2025-09-07T08:15:12.1856001Z A job completed hook has been configured by the self-hosted runner administrator 2025-09-07T08:15:12.1881592Z ##[group]Run '/home/ec2-user/runner-scripts/after_job.sh' 2025-09-07T08:15:12.1886859Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-07T08:15:12.1887261Z ##[endgroup] 2025-09-07T08:15:12.1999851Z [!ALERT!] Swap in detected! [!ALERT!] 2025-09-07T08:15:23.4315794Z [!ALERT!] Swap out detected [!ALERT!] 2025-09-07T08:15:42.7329410Z Cleaning up orphan processes